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Is there an association between disease ignorance and selfrated health? The HUNT Study, a cross-sectional survey
Journal:
BMJ Open
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Manuscript ID:
Article Type:
Date Submitted by the Author:
Complete List of Authors:
bmjopen-2014-004962
Research
29-Jan-2014
Secondary Subject Heading:
Epidemiology, Public health, Diabetes and endocrinology
Hypertension < CARDIOLOGY, General diabetes < DIABETES &
ENDOCRINOLOGY, Thyroid disease < DIABETES & ENDOCRINOLOGY,
PRIMARY CARE
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Keywords:
General practice / Family practice
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<b>Primary Subject
Heading</b>:
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Jørgensen, Pål; Norwegian University of Science and Technology,
Department of Public Health and General Practice
Langhammer, Arnulf; Norwegian University of Science and Technology,
HUNT Research Centre
Krokstad, Steinar; Norwegian University of Science and Technology, HUNT
Research Centre
Forsmo, Siri; Norwegian University of Science and Technology, Department
of Public Health and General Practice
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Disease ignorance and self-rated health, any relationship? The HUNT
Study, a cross-sectional survey
Pål Jørgensen, Arnulf Langhammer, Steinar Krokstad, Siri Forsmo
Department of Public Health and General Practice, Norwegian University of Science and
Technology, 7489 Trondheim, Norway Pål Jørgensen
PhD Candidate
HUNT Research Centre, Department of Public Health and General Practice, Norwegian
University of Science and Technology, 7600 Levanger, Norway Arnulf Langhammer
Professor, Head of HUNT Databank
HUNT Research Centre, Department of Public Health and General Practice, Norwegian
University of Science and Technology, Steinar Krokstad
Professor, Head of HUNT Research Center
Department of Public Health and General Practice, Norwegian University of Science and
Technology, Siri Forsmo
Professor, Head of Department
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Correspondence to: P Jørgensen pal_jorgensen@ntnu.no
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Keywords: Self-rated health, awareness, hypothyroidism, diabetes, hypertension.
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ABSTRACT
Objective To explore if awareness versus unawareness of thyroid dysfunction, diabetes
mellitus or hypertension is associated with self-rated health.
Design Large-scale, cross sectional population based study. The association between thyroid
function, diabetes mellitus, and blood pressure and self-rated health was explored by multiple
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logistic regression analysis.
Setting The second survey of the Nord-Trøndelag Health Study, HUNT2, 1995-97.
Participants 33,734 persons aged 40-70 years.
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Primary outcome measures Logistic regression was used to estimate odds ratios for good
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self-rated health as a function of thyroid status, diabetes mellitus status and blood pressure
status.
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Results Persons aware of their hypothyroidism, diabetes mellitus, or hypertension reported
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poorer self-rated health than individuals without such conditions. Women with unknown and
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subclinical hypothyroidism reported better self-rated health than women with normal thyroid
status. Both in women and men, unknown and probable diabetes, as well as unknown
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mild/moderate hypertension was not associated with poorer health. Further, persons with
unknown severe hypertension reported better health than normotensive persons.
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Conclusions People with undiagnosed, but prevalent hypothyroidism, diabetes mellitus, and
hypertension often have good self-rated health, whilst when aware of their diagnoses, they
report reduced self-rated health. Use of screening, more sensitive tests, and widened
diagnostic criteria might have a negative effect on perceived health in the population.
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STRENGTHS AND LIMITATIONS OF THIS STUDY
Strengths
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Sample from a large-scale, general population
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High-prevalent diseases under study; ensuring statistical power in subgroup analyses
Limitations
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Study mainly based on self-reported data
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Cross-sectional study; susceptibility to confounding and impossibility to assume
causal relationships
INTRODUCTION
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Guidelines for prevention and treatment have been developed for most high prevalent diseases
in western countries aiming for reduction of morbidity and mortality by interventions mainly
in primary health care (PHC).
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In the society there seems to be an increasing conviction of achievable zero-vision regarding
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risks and diseases. Part of the strategy is to detect risk factors and pre-diseases in even earlier
stages. From a secondary or tertiary health care level, this might seem reasonable since
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intervention on many individuals with specific risk factors presumably can prevent or delay
disease or progression of disease. Further, health authorities and hospital clinicians regularly
raise concern of the lack of detection of risk factors, of subclinical conditions and of
achieving treatment goals.[1-7] Norwegian studies have shown that guidelines are often
difficult to implement and adhere to in PHC.[8 9] According to guidelines, most individuals
would be defined as at risk and resources needed to handle this appropriately could destabilize
the entire health care system.[10-12] An American review pointed out knowledge, attitude,
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and behaviour as barriers of physician’s adherence to clinical guidelines.[13] In an already
complex and busy PHC-setting, one might expect that resources used for disease prevention
and case finding have to compete with resources for handling acknowledged disease. Also,
physicians might want to avoid increasing disease related burden for patients, in line with the
old wisdom; “primum non nocere”.[14] Thus the risk and disease zero-visions in society and
among politicians are seldom shared by PHC professionals.
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When guidelines, mainly based on research from high risk hospital populations, are applied
on low risk populations in PHC, more healthy individuals are identified as being at risk or are
given diagnoses. Also the widened inclusion criteria for diagnoses in general and use of more
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sensitive tests contribute to define more individuals at risk or as unhealthy.[15] Possible
undesirable outcomes of such strategies remain unclear.
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Self-rated health (SRH) is a valid and widely used measure of general health in epidemiologic
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research.[16-19] It is associated with several clinical conditions often seen in PHC, with
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recovery [20-23] and is found to predict morbidity, sick leave, and disability pension,[24 25]
as well as mortality.[26 27] The majority of studies describing association between labelling
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of disease per se and SRH have focused on arterial hypertension.[28-31] However, one study
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has indicated reduced SRH among individuals labelled with thyroid dysfunctions.[32]
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The aim of this study was to investigate whether persons’ awareness versus unawareness of
thyroid dysfunction, diabetes mellitus, or hypertension was associated with their SRH, as
reported in a population-based health study in Norway.
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METHODS
Study population
The data sample in this study stem from the second wave of the Nord-Trøndelag Health Study
(HUNT2) conducted in 1995-97 in the county of Nord-Trøndelag, Norway. All individuals
aged 20 years and older, living in the county, were invited (94,194 individuals). In all, 66.7%
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of men (n=30,860) and 75.5% of women (n=35,280) participated. The survey consisted of
both questionnaires and measurements, and is described previously in detail.[33 34] In our
study we included answers from the main questionnaire and the baseline measurements for
persons aged 40-70 years. The age span was chosen because thyroid stimulating hormone
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(TSH) was analysed in all women and in 50% of men at this age, of a rather low disease
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burden in people younger than 40 years, and of a lower attendance rate under and above this
age span. A total of 24,950 individuals had TSH measurements and answered thyroid
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questions, thus were eligible for analysis on thyroid dysfunction, whilst in the analysis of
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diabetes mellitus and blood pressure, 33,734 individuals were included in all.
Self-rated health
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The first question in the main questionnaire in HUNT2, answered before attending the
examination stations, was “How is your health at the moment?”. The four answer alternatives
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were dichotomized into poor or good (reference); good covering the answers “good” and
“very good”, poor covering “poor” and “not so good”.
Thyroid function
The participants answered questions on history of hypo- and hyperthyroidism, goitre, other
thyroid diseases, and treatment with thyroxin, radio-iodine, surgery, or thyreostatic
medication.
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Serum TSH and free T4 were analysed at the Hormone laboratory, Aker University Hospital,
Norway. The laboratory reference value for TSH, as defined prior to the survey, was 0.2-4.5
mU/L and for free T4 8.0-20.0 pmol/L. If TSH was <0.2 mU/L or >4.0 mU/L, and/or if the
participant reported any thyroid disease, serum free T4 was also measured.[35]
Individuals reporting no previous thyroid disease and having TSH within reference range
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were categorized as “no thyroid disease” and chosen as reference category. No previous
thyroid disease combined with TSH >4.5 mU/L and free T4 <8.0 pmol/L was defined as
unknown hypothyroidism. No previous thyroid disease combined with TSH >4.5 mU/L and
free T4 8.0 – 20.0 pmol/L was defined as subclinical hypothyroidism. Individuals reporting
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hypothyroidism and use of thyroxin were classified as having known hypothyroidism,
regardless of the TSH and T4 levels. Affirmative answers to other thyroid related questions or
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measures outside reference range in the remainders were classified as other thyroid
dysfunction.
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Diabetes mellitus
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Diabetes mellitus was assessed through self-report and blood samples. Serum glucose was
analysed at Levanger Hospital, Norway. Those reporting no diabetes and having normal
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glucose levels (<5.5 mmol/L) were classified as “no diabetes” and were chosen as reference
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category. No self-reported diabetes and non-fasting glucose >11.0 mmol/L was categorized as
unknown diabetes, whereas no diabetes and non-fasting glucose 5.5-11.0 mmol/L was
categorized as probable diabetes. Self-reported diabetes was classified as known diabetes
regardless of the glucose level.
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Blood pressure
In the questionnaire participants were asked about the doctor’s advice after the latest blood
pressure measurement prior to participation in HUNT. The answer categories were: “no
follow-up and no medication necessary”, “recommended follow-up examination but not to
take medicine”, “start or continue taking medicine for high blood pressure”, or “never
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measured”. At HUNT2, mean systolic and mean diastolic arterial blood pressure (BP) of
measurement 2 and 3 was categorized into normal (systolic (s) BP < 140 mmHg and diastolic
(d) BP < 90 mmHg), mild hypertension (sBP 140-159 mmHg and dBP <100 mmHg or sBP
<160 mmHg and dBP 90-99 mmHg), moderate hypertension (sBP 160-179 mmHg and dBP
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<109 mmHg or sBP <180 mmHg and dBP 100-109 mmHg), and severe hypertension
(sBP>180 mmHg or dBP>110 mmHg). We constructed a new variable to define
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normotensive (reference), unknown mild- and moderate hypertensive, unknown severe
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hypertensive, and known hypertensive persons on the basis of self-report and measures.
Statistical analysis
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The descriptive analyses of the study population were stratified by gender, and we used chisquare tests to examine any difference in proportions of SRH between the independent
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variables. Gender-stratified multiple logistic regression were used to estimate odds ratio (OR)
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with 95% confidence intervals (CI) for good SRH, as a function of thyroid status, diabetes
mellitus status and blood pressure status, in separate univariate and multivariate analyses for
each condition.
Age, smoking, alcohol consumption, body mass index (BMI), working- and educational
status, and self-reported limiting long-term illness or injury are associated with SRH[36] and
the diseases under study, but likely not affected by SRH or the diseases. Hence, these
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variables were included, a priori, as confounders in the models. Age was categorized into age
groups; 40-49 years, 50-59 years, and 60-70 years. Smoking status was categorized into never
smoked daily, previous daily smoker, and current daily smoker. Alcohol units (AU) were
defined as number of glasses of wine, beer or liquor. Those reporting to be teetotalers or to
have alcohol intake less than four times a month or less than seven AU per two weeks were
categorized as low consumers, those reporting drinking five to eight times a month or 8-14
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AU per two weeks as moderate consumers, and those drinking more often than eight times a
month or more than 14 AU as heavy consumers. BMI (kg/m2) was calculated of measured
height and weight and categorized according to The World Health Organization’s (WHO)
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definition; underweight (18.5 kg/m2), normal weight (18.5-24.9 kg/m2), overweight (25.029.9 kg/m2) and obese (>30.0 kg/m2). People reporting paid- or self-employed work were
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classified as working, otherwise as not working. Educational level was categorized into <10
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years, 10-12 years and >12 years. We chose affirmative answer to the question “Do you suffer
from any long-term illness or injury (at least one year) of a physical or psychological nature
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that impairs your functioning in your everyday life?” to represent all relevant chronic medical
conditions that could confound the results.
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To examine whether the association of the three disease statuses with SRH differed by
categories of the other independent variables, we used likelihood ratio-tests with p-value for
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statistical interaction. We tested for multicollinearity between the independent variables, by
linear regression.
In an additional analysis, the association between SRH and having had one or more medical
consultations during the last year was investigated by logistic regression models, stratified by
gender, both in the total study population and after exclusion of persons with diagnoses under
study.
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All analyses were preformed with IBM SPSS Statistics version 20 for windows.
The study was approved by the Regional Committee for Medical Research. All participants
signed written informed consent.
RESULTS
In all age categories, a higher proportion of men than women reported good SRH (p<0.001),
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and in both sexes, the proportion reporting good SRH declined by age (p<0.002) (Table 1).
The proportion reporting good SRH was lower in overweight, obese, and underweight
women, than in normal weight women (p<0.001). In men the proportion reporting good SRH
declined between the normal weight group to overweight-, obese-, and underweight group
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(p<0.001). In previous and current female smokers, a lower proportion reported good SRH
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compared to non smokers (p<0.001). In men, the proportion reporting good SRH was higher
among non smokers than among previous and current smokers (p<0.001), whereas there was
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no difference between previous and current smokers. The proportion reporting good SRH was
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higher in moderate and heavy alcohol consumers than in low consumers, increased with
education and was higher for participants in paid work (p<0.001). Underweight men and
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persons with “any long-term impairment” had the lowest proportion reporting good SRH of
all groups.
There was no multicollinearity between the independent variables.
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Thyroid function
Thyroid dysfunctions, both known and unknown were more often observed among women
than among men (p<0.001) (Table 1). Women with known hypothyroidism had lower odds of
reporting good SRH (OR 0.49 (95% CI 0.41 to 0.59) compared to women without thyroid
disease in the adjusted analyses, but women with unknown or subclinical hypothyroidism had
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84% and 48% higher odds, respectively, of reporting good SRH compared to the odds of
women without thyroid disease (Table 2). A corresponding, but non-significant pattern was
found among men.
Diabetes mellitus
The prevalence of unknown, probable, and known diabetes was slightly higher in men than in
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women (p<0.001) (Table 1). Women with known diabetes mellitus had lower odds of good
SRH than those without diabetes in the adjusted analyses; OR 0.53 (95% CI 0.41 to 0.69),
whereas in women with unknown or possible diabetes mellitus, the odds of good SRH were
similar to the odds among persons without diabetes, in the adjusted analyses (Table 2). In
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men, the association of diabetes status with SRH differed by levels of education. Among men
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without higher education (12 years or less), the odds of good SRH were as in the main-effect
model (Table 2). However in men with higher education, the ORs of good SRH were barely
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significantly lower among men with unknown diabetes (OR 0.29 (95% CI 0.09-1.00)) and
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among men with possible diabetes (OR 0.79 (95% CI 0.63-1.00)) compared to men without
diabetes. Among men with known diabetes in this stratum, the OR of good SRH was 0.31
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(95% CI 0.17-0.54) compared to men without diabetes.
Blood pressure
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The prevalence of unknown mild and moderate hypertension was higher in men than in
women (p<0.001). Unknown severe hypertension and known hypertension were equally
distributed between women and men (Table 1). Women with known hypertension had lower
odds of reporting good SRH than normotensive women, in the adjusted analyses. The figures
were similar in men (Table 2). In contrast; compared to normotensive women, those with
unknown severe hypertension had 52 % higher odds of reporting good SRH, with similar
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figures in men. Persons with unknown mild and moderate hypertension reported good SRH
similar to the normotensive ones.
Additional analyses
Women with poor SRH had more than six times the odds of those with good SRH to have had
a medical consultation during the last year; OR 6.29 (95% CI 5.47 to 7.22). For men the
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corresponding OR was 5.53 (95% CI 4.86 to 6.29). After exclusion of persons with diagnosed
thyroid disease, diabetes mellitus, known hypertension, and “any long-term disease”, the
corresponding OR was 3.71 (95% CI 2.90 to 4.73), with similar figures in men.
DISCUSSION
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This large population-based study showed that persons with known thyroid dysfunction,
diabetes mellitus, and hypertension were less likely to report good SRH than those without
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such conditions. Less expected, persons with unknown and subclinical hypothyroidism were
more likely to report good SRH than those without thyroid disease. Similarly, those with
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unknown severe hypertension were more likely to report good SRH, compared to persons
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with normal blood pressure. In general, persons with unknown diabetes and unknown
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mild/moderate hypertension, reported good health, just like the reference group.
Although both a qualitative and quantitative measure of association, the estimated ORs in our
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study will, if interpreted as relative risks, overestimate the case, because the prevalence of
good SRH is high in all groups.[37]
The main strengths of this study were the numbers of participants, that the diseases we studied
were high-prevalent, and that we could assume representativeness to the general population
regarding the variables included. It is known that individuals with high burden of symptoms
are less likely to attend surveys, but so far there has been little evidence that non-participation
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on this basis introduce substantial bias in associational studies.[38] We studied diseases with
generally low symptom-burden, and expect selection bias to be negligible.
The main limitation was possibly that we relied on self-reported data on both dependent and
independent variables. The validity of self reported measures relevant for this study is
questionable. However, if any, there should be a non-differential misclassification, that only
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should underestimate the associations found.
There is always a possibility of residual confounding in non-randomized study designs, and
due to the observational, cross-sectional design we neither can assume a causal relationship.
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Bias due to differential detection of disease in the study population could lead to a type 1
error. Hypothyroidism and diabetes mellitus are often associated with vague symptoms such
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as tiredness and weakness. These symptoms are strongly associated with reduced SRH.[39] It
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is likely that presenting such symptoms for the GP would result in measurements of TSH, free
T4, and serum glucose, thus reveal any related dysfunction. Low self-perceived health
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increases the probability to visit a GP.[40] The social security covers most costs related to
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clinical measurements and blood sampling in Norway, thus we expect GPs to have a low
threshold to measure thyroid function, blood pressure or glucose levels. People who do not
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consult their GP will not have diseases with mild or no symptoms diagnosed, and their
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personality could be characterized by less worrying and more optimistic attitude to health
being reflected in better SRH.[41]
On the other hand, the association between known disease and poor SRH could in fact be
explained physiologically due to the pathological effect of disease. Lack of corresponding
association between ignored, but prevalent disease and poor SRH is not in line with this
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hypothesis, rising question of a possible adverse effect of disease labelling. However,
confounding by severity of disease cannot be ruled out.
Due to low numbers of persons having unknown hypothyroidism and diabetes mellitus, the
results should be interpreted with caution. On the other hand, there were high numbers in the
subclinical and probable groups, and analyses of these groups did not show any associations
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with poor SRH, also rising question of a possible disease labelling effect.
Consistent findings regarding unknown, subclinical, and probable disease, versus known
disease, could indicate a potential adverse effect of disease labelling. Although early detection
of disease is protective on morbidity and mortality for many diseases, low SRH is also shown
ee
to be associated morbidity and mortality.[25-27] This is an important aspect in the debate of
presymptomatic case-finding.
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The BMJ's Too Much Medicine campaign aims to highlight the threat to human health posed
by overdiagnosis and the waste of resources on unnecessary care (http://www.bmj.com/too-
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much-medicine). American data have shown that the majority of all health care includes
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preference-sensitive and supply-sensitive services. The extent of these services varies greatly
without necessarily leading to better health.[42 43] A great deal of activity in such services is
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based on identifying subclinical disease. The negative health effect this might have,
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modulated through reduced SRH, can explain why population mortality is not reduced in
areas with high frequency of diagnoses.
Both public and academic debates are often characterised by the conviction that all medical
treatment is efficient. Wennberg showed that a relatively small proportion of all medical
treatment is indisputably good for health.[44] The concern for unrevealed risk factors and
subclinical conditions might lead to unnecessary costly health care interventions, increase
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supply-sensitive services without positive health effect and have a negative influence on
people’s general and self-perceived health causing more harm than good.[15]
Of ethical reasons, the possible causal effect of disease labelling on SRH is impossible to
assess in a randomized controlled trial. Our study emphasizes the need for more prospective
research to investigate potential health effects of disease labelling and early diagnosis.
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CONCLUSION
Our data suggest that early identification of disease may imply a negative effect on SRH and
thereby eventually cause more harm than good. On a population level, such efforts might
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increase the costs in health care and the proportion of supply-sensitive services without any
documented positive health effect. More research is needed concerning the possible adverse
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effects of disease labelling on SRH and mortality.
FOOTNOTES
ev
Copyright The Corresponding Author has the right to grant on behalf of all authors and does
grant on behalf of all authors, a worldwide licence to the Publishers and its licensees in
perpetuity, in all forms, formats and media (whether known now or created in the future), to i)
publish, reproduce, distribute, display and store the Contribution, ii) translate the Contribution
into other languages, create adaptations, reprints, include within collections and create
summaries, extracts and/or, abstracts of the Contribution, iii) create any other derivative
work(s) based on the Contribution, iv) to exploit all subsidiary rights in the Contribution, v)
the inclusion of electronic links from the Contribution to third party material where-ever it
may be located; and, vi) licence any third party to do any or all of the above.
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Competing interests All authors have completed the ICMJE uniform disclosure form at
www.icmje.org/coi_disclosure.pdf and declare: no support from any organisation for the
submitted work; no financial relationships with any organisations that might have an interest
in the submitted work in the previous three years; no other relationships or activities that
could appear to have influenced the submitted work.
Contributors All authors meet the four criteria for authorship recommended by the
International Committee of Medical Journal Editors. SF, AL, and SK have been active
supervisors in study conception, design, conduct, interpretation, and reporting. PJ analyzed
the data and drafted the manuscript. Critical revisions were done by all supervisors and all
authors approved the final version of the manuscript. SF, AL, SK and PJ are joint guarantors.
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Acknowledgements The Nord-Trøndelag Health Study (The HUNT Study) is a collaboration
between HUNT Research Centre (Faculty of Medicine, Norwegian University of Science and
Technology NTNU), Nord-Trøndelag County Council, Central Norway Health Authority, and
the Norwegian Institute of Public Health. The authors would like to thank Bjørn Olav Åsvold
for his contribution in planning of the thyroid-part of the study.
Transparency declaration PJ affirms that this manuscript is an honest, accurate, and
transparent account of the study being reported; that no important aspects of the study have
been omitted; and that there were no discrepancies from the study as planned.
Ethical approval The study was approved by the Regional Committee for Medical Research.
All participants signed written informed consent.
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Funding No external funding. PJ received a PhD-grant, funded by the Norwegian University
of Science and Technology, NTNU.
Data sharing statement No additional data available.
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University Press, 2010.
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BMJ Open
Table 1. Good self-rated health (SRH good) by sex and characteristics of the study population
Study population
(n= 33,734)
Men (n=16,220)
Women (n=17,514)
%
SRH good
(%)
n
%
SRH good
(%)
7058
5709
4747
40.3
32.6
27.1
77.6
64.4
56.6
6517
5328
4375
40.2
32.8
27.0
81.7
72.1
59.6
6821
7026
3489
104
0.6
39.1
40.3
20.0
58.4
72.8
68.5
56.5
4804
8694
2635
35
0.2
29.7
53.8
16.3
42.9
75.3
73.6
64.8
6973
4651
5763
40.1
26.7
33.1
70.3
68.0
64.3
4849
6105
5172
30.1
37.9
32.0
80.0
69.8
69.1
88.7
8.8
2.6
66.7
76.8
76.3
11488
2839
1375
73.2
18.1
8.8
71.0
78.7
78.3
ee
n
14888
1471
430
rr
48.5
33.9
17.7
60.5
72.8
80.0
5711
6615
3339
36.5
42.2
21.3
63.1
76.0
84.7
11539
5627
67.2
32.8
77.1
49.0
12309
3665
77.1
22.9
79.9
49.2
10348
6275
59.1
35.8
9912
5829
63.0
37.0
88.7
46.0
14373
107
466
858
872
86.2
0.6
2.8
5.1
5.2
68.7
78.5
75.0
48.9
61.4
7804
16
150
124
180
94.3
0.2
1.8
1.5
2.2
72.1
68.8
66.4
58.1
56.2
11279
46
5728
395
64.6
0.3
32.8
2.3
69.4
52.2
65.9
43.1
9196
88
6392
482
56.9
0.5
39.6
3.0
74.6
67.8
71.5
49.5
9135
4473
403
3327
52.6
25.8
2.4
19.2
72.4
67.9
70.7
54.0
6858
5343
349
3498
42.7
33.3
2.2
21.8
77.5
74.7
73.8
59.6
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8133
5682
2962
85.9
39.8
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Age group
40-50 years
51-60 years
61-70 years
BMI (kg/m2)
<18,5
18,5-24,9
25,0-29,9
>30
(0.4% missing)
Smoking status
Never smoked daily
Previous daily smoker
Daily smoker
(0.7% missing)
Alcohol use
None to low intake
Moderate intake
High intake
(3.7% missing)
Educational level
< 10 years
10 – 12 years
> 12 years
(3.8% missing)
Employed
Yes
No
(1.8% missing)
Any long-term impairment
No
Yes
(4.1% missing)
Thyroid function
No thyroid disease
Unknown hypothyroidism
Subclinical hypothyroidism
Known hypothyroidism
Other thyroid dysfunction
(1.3% missing)
Diabetes mellitus
No diabetes
Unknown diabetes
Probable diabetes
Known diabetes
(0.4% missing)
Blood pressure status
Normotensive
Unknown mild/moderate hypertension
Unknown severe hypertension
Known hypertension
(0.9% missing)
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Table 2. The association between self-rated health and thyroid function, diabetes mellitus and blood pressure. Odds ratio (OR) of good self-rated health and
95% confidence intervals (95% CI), crude and adjusted for age, other long-term illness or injury that impairs function in everyday life, smoking habits,
alcohol use, educational level, work status and body mass index. Cases with missing data were excluded from the analyses.
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Women
Thyroid function
No thyroid dysfunction
Unknown hypothyroidism
Subclinical hypothyroidism
Known hypothyroidism
Other thyroid dysfunction
Diabetes mellitus
No diabetes
Unknown diabetes
Probable diabetes
Known diabetes
Blood pressure
No hypertension
Unknown mild/moderate hypertension
Unknown severe hypertension
Known hypertension
OR (95% CI)
Crude
n
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12476
92
394
718
729
9946
39
4797
318
8180
3787
325
2745
Adjusted
1.00
1.66 (1.05-2.64)
1.37 (1.10-1.69)
0.44 (0.38-0.50)
0.72 (0.63-0.83)
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1.00
0.48 (0.27-0.86)
0.85 (0.80-0.91)
0.33 (0.27-0.41)
1.00
0.81 (0.75-0.87)
0.92 (0.74-1.15)
0.45 (0.41-0.49)
Men
OR (95% CI)
Crude
n
Adjusted
1.00
1.84 (1.02-3.33)
1.48 (1.13-1.94)
0.49 (0.41-0.59)
0.77 (0.65-0.93)
7045
14
128
107
163
1.00
0.85 (0.30-2.45)
0.77 (0.54-1.08)
0.54 (0.37-0.77)
0.50 (0.37-0.67)
1.00
1.28 (0.35-4.65)
1.13 (0.72-1.76)
0.69 (0.44-1.09)
0.58 (0.41-0.84)
1.00
0.66 (0.32-1.37)
1.01 (0.92-1.10)
0.53 (0.41-0.69)
8464
82
5759
395
1.00
0.72 (0.46-1.13)
0.86 (0.80-0.92)
0.33 (0.28-0.40)
1.00
1.03 (0.59-1.81)
0.99 (0.91-1.08)
0.55 (0.43-0.70)
1.00
1.10 (0.99-1.22)
1.52 (1.14-2.02)
0.69 (0.61-0.77)
6378
4837
308
3118
1.00
0.86 (0.79-0.93)
0.85 (0.77-1.08)
0.43 (0.39-0.47)
1.00
1.01 (0.92-1.12)
1.48 (1.09-2.02)
0.64 (0.57-0.72)
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What is already known on this topic
Awareness of thyroid dysfunction, diabetes mellitus, and
hypertension is associated with reduced self-rated health
Screening, increasingly sensitive diagnostic tests, and
widened diagnostic criteria will define more healthy people as
sick
What this study adds
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Unawareness of hypothyroidism, diabetes mellitus, or
hypertension is not associated with reduced self-rated health
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STROBE 2007 (v4) Statement—Checklist of items that should be included in reports of cross-sectional studies
Section/Topic
Title and abstract
Item
#
1
Introduction
Recommendation
Reported on page #
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(a) Indicate the study’s design with a commonly used term in the title or the abstract
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(b) Provide in the abstract an informative and balanced summary of what was done and what was found
2
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Background/rationale
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Explain the scientific background and rationale for the investigation being reported
Objectives
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State specific objectives, including any prespecified hypotheses
Study design
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Present key elements of study design early in the paper
Setting
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Describe the setting, locations, and relevant dates, including periods of recruitment, exposure, follow-up, and data
collection
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Participants
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(a) Give the eligibility criteria, and the sources and methods of selection of participants
5
Variables
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Clearly define all outcomes, exposures, predictors, potential confounders, and effect modifiers. Give diagnostic criteria, if
Data sources/
8*
For each variable of interest, give sources of data and details of methods of assessment (measurement). Describe
Bias
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Describe any efforts to address potential sources of bias
Study size
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Explain how the study size was arrived at
Quantitative variables
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Methods
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applicable
measurement
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Statistical methods
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3-4
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(b) Describe any methods used to examine subgroups and interactions
(c) Explain how missing data were addressed
5-8
5-8
7-8
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5-8
7-8
8
Table 2
(d) If applicable, describe analytical methods taking account of sampling strategy
-
(e) Describe any sensitivity analyses
-
Results
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Participants
13*
(a) Report numbers of individuals at each stage of study—eg numbers potentially eligible, examined for eligibility,
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confirmed eligible, included in the study, completing follow-up, and analysed
Descriptive data
14*
(b) Give reasons for non-participation at each stage
5
(c) Consider use of a flow diagram
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(a) Give characteristics of study participants (eg demographic, clinical, social) and information on exposures and potential
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Table 1
(b) Indicate number of participants with missing data for each variable of interest
Table 1
Outcome data
15*
Report numbers of outcome events or summary measures
Main results
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(a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates and their precision (eg, 95% confidence
interval). Make clear which confounders were adjusted for and why they were included
Table 1
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Table 2
Page 9-11
(b) Report category boundaries when continuous variables were categorized
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(c) If relevant, consider translating estimates of relative risk into absolute risk for a meaningful time period
Other analyses
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Discussion
-
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Key results
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Summarise key results with reference to study objectives
Limitations
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Discuss limitations of the study, taking into account sources of potential bias or imprecision. Discuss both direction and
magnitude of any potential bias
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Interpretation
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Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results from
similar studies, and other relevant evidence
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Generalisability
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Discuss the generalisability (external validity) of the study results
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Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on
Other information
Funding
which the present article is based
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*Give information separately for cases and controls in case-control studies and, if applicable, for exposed and unexposed groups in cohort and cross-sectional studies.
Note: An Explanation and Elaboration article discusses each checklist item and gives methodological background and published examples of transparent reporting. The STROBE
checklist is best used in conjunction with this article (freely available on the Web sites of PLoS Medicine at http://www.plosmedicine.org/, Annals of Internal Medicine at
http://www.annals.org/, and Epidemiology at http://www.epidem.com/). Information on the STROBE Initiative is available at www.strobe-statement.org.
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Is there an association between disease ignorance and selfrated health? The HUNT Study, a cross-sectional survey
Journal:
BMJ Open
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Manuscript ID:
Article Type:
Date Submitted by the Author:
Complete List of Authors:
bmjopen-2014-004962.R1
Research
28-Apr-2014
Secondary Subject Heading:
Epidemiology, Public health, Diabetes and endocrinology
Hypertension < CARDIOLOGY, General diabetes < DIABETES &
ENDOCRINOLOGY, Thyroid disease < DIABETES & ENDOCRINOLOGY,
PRIMARY CARE
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Keywords:
General practice / Family practice
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<b>Primary Subject
Heading</b>:
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Jørgensen, Pål; Norwegian University of Science and Technology,
Department of Public Health and General Practice
Langhammer, Arnulf; Norwegian University of Science and Technology,
HUNT Research Centre
Krokstad, Steinar; Norwegian University of Science and Technology, HUNT
Research Centre
Forsmo, Siri; Norwegian University of Science and Technology, Department
of Public Health and General Practice
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Is there an association between disease ignorance and self-rated health? The HUNT Study, a crosssectional survey
Pål Jørgensen, Arnulf Langhammer, Steinar Krokstad, Siri Forsmo
Department of Public Health and General Practice, Norwegian University of Science and
Technology, 7489 Trondheim, Norway Pål Jørgensen
PhD Candidate
HUNT Research Centre, Department of Public Health and General Practice, Norwegian
University of Science and Technology, 7600 Levanger, Norway Arnulf Langhammer
Professor, Head of HUNT Databank
HUNT Research Centre, Department of Public Health and General Practice, Norwegian
University of Science and Technology, Steinar Krokstad
Professor, Head of HUNT Research Center
Department of Public Health and General Practice, Norwegian University of Science and
Technology, Siri Forsmo
Professor, Head of Department
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Correspondence to: P Jørgensen pal_jorgensen@ntnu.no
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Keywords: Self-rated health, awareness, hypothyroidism, diabetes, hypertension.
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ABSTRACT
Objective To explore if awareness versus unawareness of thyroid dysfunction, diabetes
mellitus or hypertension is associated with self-rated health.
Design Large-scale, cross sectional population based study. The association between thyroid
function, diabetes mellitus, and blood pressure and self-rated health was explored by multiple
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logistic regression analysis.
Setting The second survey of the Nord-Trøndelag Health Study, HUNT2, 1995-97.
Participants 33,734 persons aged 40-70 years.
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Primary outcome measures Logistic regression was used to estimate odds ratios for good
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self-rated health as a function of thyroid status, diabetes mellitus status and blood pressure
status.
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Results Persons aware of their hypothyroidism, diabetes mellitus, or hypertension reported
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poorer self-rated health than individuals without such conditions. Women with unknown and
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subclinical hypothyroidism reported better self-rated health than women with normal thyroid
status. Both in women and men, unknown and probable diabetes, as well as unknown
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mild/moderate hypertension was not associated with poorer health. Further, persons with
unknown severe hypertension reported better health than normotensive persons.
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Conclusions People with undiagnosed, but prevalent hypothyroidism, diabetes mellitus, and
hypertension often have good self-rated health, whilst when aware of their diagnoses, they
report reduced self-rated health. Use of screening, more sensitive tests, and widened
diagnostic criteria might have a negative effect on perceived health in the population.
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STRENGTHS AND LIMITATIONS OF THIS STUDY
Strengths
•
Sample from a large-scale, general population
•
High-prevalent diseases under study; ensuring statistical power in subgroup analyses
Limitations
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Study mainly based on self-reported data
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Cross-sectional study; susceptibility to confounding and impossibility to assume
causal relationships
INTRODUCTION
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Guidelines for prevention and treatment have been developed for most high prevalent diseases
in western countries aiming for reduction of morbidity and mortality by interventions mainly
in primary health care (PHC).
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In the society there seems to be an increasing conviction of achievable zero-vision regarding
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risks and diseases. Part of the strategy is to detect risk factors and pre-diseases in even earlier
stages. From a secondary or tertiary health care level, this might seem reasonable since
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intervention on many individuals with specific risk factors presumably can prevent or delay
disease or progression of disease. Further, health authorities and hospital clinicians regularly
raise concern of the lack of detection of risk factors, of subclinical conditions and of
achieving treatment goals.[1-7] Norwegian studies have shown that guidelines are often
difficult to implement and adhere to in PHC.[8 9] According to guidelines, most individuals
would be defined as at risk and resources needed to handle this appropriately could destabilize
the entire health care system.[10-12] An American review pointed out knowledge, attitude,
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and behaviour as barriers of physician’s adherence to clinical guidelines.[13] In an already
complex and busy PHC-setting, one might expect that resources used for disease prevention
and case finding have to compete with resources for handling acknowledged disease. Also,
physicians might want to avoid increasing disease related burden for patients, in line with the
old wisdom; “primum non nocere”.[14] Thus the risk and disease zero-visions in society and
among politicians are seldom shared by PHC professionals.
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When guidelines, mainly based on research from high risk hospital populations, are applied
on low risk populations in PHC, more healthy individuals are identified as being at risk or are
given diagnoses. Also the widened inclusion criteria for diagnoses in general and use of more
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sensitive tests contribute to define more individuals at risk or as unhealthy.[15] Possible
undesirable outcomes of such strategies remain unclear.
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Self-rated health (SRH) is a valid and widely used measure of general health in epidemiologic
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research.[16-19] It is associated with several clinical conditions often seen in PHC, with
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recovery [20-23] and is found to predict morbidity, sick leave, and disability pension,[24 25]
as well as mortality.[26-28] The majority of studies describing association between labelling
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of disease per se and SRH have focused on arterial hypertension.[29-32] However, one study
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has indicated reduced SRH among individuals labelled with thyroid dysfunctions.[33]
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The aim of this study was to investigate whether persons’ awareness versus unawareness of
thyroid dysfunction, diabetes mellitus, or hypertension was associated with their SRH, as
reported in a population-based health study in Norway.
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METHODS
Study population
The data sample in this study stem from the second wave of the Nord-Trøndelag Health Study
(HUNT2) conducted in 1995-97 in the county of Nord-Trøndelag, Norway. All individuals
aged 20 years and older, living in the county, were invited (94,194 individuals). In all, 66.7%
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of men (n=30,860) and 75.5% of women (n=35,280) participated. The survey consisted of
both questionnaires and measurements, and is described previously in detail.[34 35] In our
study we included answers from the main questionnaire and the baseline measurements for
persons aged 40-70 years. The age span was chosen because thyroid stimulating hormone
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(TSH) was analysed in all women and in 50% of men at this age, of a rather low disease
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burden in people younger than 40 years, and of a lower attendance rate under and above this
age span. A total of 24,950 individuals had TSH measurements and answered thyroid
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questions, thus were eligible for analysis on thyroid dysfunction, whilst in the analysis of
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diabetes mellitus and blood pressure, 33,734 individuals were included in all.
Self-rated health
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The first question in the main questionnaire in HUNT2, answered before attending the
examination stations, was “How is your health at the moment?”, with answer alternatives;
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“very good”, “good”, “not so good”, and “poor”. This short version of SRH measure is shown
to be a valid predictor of mortality. [18 28 36 37] We dichotomized the answers into “good”
(very good, good) and “poor” (not so good, poor). Dichotomization of multinomial SRH is
commonly performed, and has been validated by Manor et. al.[38]
Thyroid function
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The participants answered questions on history of hypo- and hyperthyroidism, goitre, other
thyroid diseases, and treatment with thyroxin, radio-iodine, surgery, or thyreostatic
medication.
Serum TSH and free T4 were analysed at the Hormone laboratory, Aker University Hospital,
Norway. The laboratory reference value for TSH, as defined prior to the survey, was 0.2-4.5
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mU/L and for free T4 8.0-20.0 pmol/L. If TSH was <0.2 mU/L or >4.0 mU/L, and/or if the
participant reported any thyroid disease, serum free T4 was also measured.[39]
Individuals reporting no previous thyroid disease and having TSH within reference range
were categorized as “no thyroid disease” and chosen as reference category. No previous
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thyroid disease combined with TSH >4.5 mU/L and free T4 <8.0 pmol/L was defined as
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unknown hypothyroidism. No previous thyroid disease combined with TSH >4.5 mU/L and
free T4 8.0 – 20.0 pmol/L was defined as subclinical hypothyroidism. Individuals reporting
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hypothyroidism and use of thyroxin were classified as having known hypothyroidism,
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regardless of the TSH and T4 levels. Affirmative answers to other thyroid related questions or
measures outside reference range in the remainders were classified as other thyroid
dysfunction.
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Diabetes mellitus
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Diabetes mellitus was assessed through self-report and blood samples. Serum glucose was
analysed at Levanger Hospital, Norway. Those reporting no diabetes and having normal
glucose levels (<5.5 mmol/L) were classified as “no diabetes” and were chosen as reference
category. No self-reported diabetes and non-fasting glucose >11.0 mmol/L was categorized as
unknown diabetes, whereas no diabetes and non-fasting glucose 5.5-11.0 mmol/L was
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categorized as probable diabetes. Self-reported diabetes was classified as known diabetes
regardless of the glucose level.
Blood pressure
In the questionnaire participants were asked about the doctor’s advice after the latest blood
pressure measurement prior to participation in HUNT. The answer categories were: “no
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follow-up and no medication necessary”, “recommended follow-up examination but not to
take medicine”, “start or continue taking medicine for high blood pressure”, or “never
measured”. At HUNT2, mean systolic and mean diastolic arterial blood pressure (BP) of
measurement 2 and 3 was categorized into normal (systolic (s) BP < 140 mmHg and diastolic
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(d) BP < 90 mmHg), mild hypertension (sBP 140-159 mmHg and dBP <100 mmHg or sBP
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<160 mmHg and dBP 90-99 mmHg), moderate hypertension (sBP 160-179 mmHg and dBP
<109 mmHg or sBP <180 mmHg and dBP 100-109 mmHg), and severe hypertension
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(sBP>180 mmHg or dBP>110 mmHg). We constructed a new variable to define
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normotensive (reference), unknown mild- and moderate hypertensive, unknown severe
hypertensive, and known hypertensive persons on the basis of self-report and measures.
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Statistical analysis
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square tests to examine any difference in proportions of SRH between the independent
variables. Gender-stratified multiple logistic regression were used to estimate odds ratio (OR)
with 95% confidence intervals (CI) for good SRH, as a function of thyroid status, diabetes
mellitus status and blood pressure status, in separate unadjusted, age adjusted, and multi
adjusted analyses for each condition.
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Age, smoking, alcohol consumption, body mass index (BMI), working- and educational
status, and self-reported limiting long-term illness or injury are associated with SRH[40] and
the diseases under study, but likely not affected by SRH or the diseases. Hence, these
variables were included, a priori, as confounders in the models. Age was categorized into age
groups; 40-49 years, 50-59 years, and 60-70 years. Smoking status was categorized into never
smoked daily, previous daily smoker, and current daily smoker. Alcohol units (AU) were
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defined as number of glasses of wine, beer or liquor. Those reporting to be teetotalers or to
have alcohol intake less than four times a month or less than seven AU per two weeks were
categorized as low consumers, those reporting drinking five to eight times a month or 8-14
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AU per two weeks as moderate consumers, and those drinking more often than eight times a
month or more than 14 AU as heavy consumers. BMI (kg/m2) was calculated of measured
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height and weight and categorized according to The World Health Organization’s (WHO)
definition; underweight (18.5 kg/m2), normal weight (18.5-24.9 kg/m2), overweight (25.0-
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29.9 kg/m2) and obese (>30.0 kg/m2). People reporting paid- or self-employed work were
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classified as working, otherwise as not working. Educational level was categorized into <10
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years, 10-12 years and >12 years. We chose affirmative answer to the question “Do you suffer
from any long-term illness or injury (at least one year) of a physical or psychological nature
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that impairs your functioning in your everyday life?” to represent all relevant chronic medical
conditions that could confound the results.
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To examine whether the association of the three disease statuses with SRH differed by
categories of the other independent variables, we used likelihood ratio-tests with p-value for
statistical interaction. We tested for multicollinearity between the independent variables, by
linear regression.
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In an additional analysis, the association between SRH and having had one or more medical
consultations during the last year was investigated by logistic regression models, stratified by
gender, both in the total study population and after exclusion of persons with diagnoses under
study.
All analyses were preformed with IBM SPSS Statistics version 20 for windows.
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The study was approved by the Regional Committee for Medical Research. All participants
signed written informed consent.
RESULTS
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In all age categories, a higher proportion of men than women reported good SRH (p<0.001),
and in both sexes the proportion reporting good SRH declined by age (p<0.002) (Table 1).
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The proportion reporting good SRH was lower in overweight, obese, and underweight
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women, than in normal weight women (p<0.001). In men the proportion reporting good SRH
declined between the normal weight group to overweight-, obese-, and underweight group
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(p<0.001). In previous and current female smokers, a lower proportion reported good SRH
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compared to non smokers (p<0.001). In men, the proportion reporting good SRH was higher
among non smokers than among previous and current smokers (p<0.001), whereas there was
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no difference between previous and current smokers. The proportion reporting good SRH was
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higher in moderate and heavy alcohol consumers than in low consumers, increased with
education and was higher for participants in paid work (p<0.001). Underweight men and
persons with “any long-term impairment” had the lowest proportion reporting good SRH of
all groups. The proportion reporting good SRH in the overall HUNT 2 study population
differed from the proportions reported in persons without thyroid disease and normotensive
persons (p<0.01), but not from persons without diabetes mellitus. However, the absolute
differences were small.
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There was no multicollinearity between the independent variables.
Thyroid function
Thyroid dysfunctions, both known and unknown were more often observed among women
than among men (p<0.001) (Table 1). Women with known hypothyroidism had lower odds of
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reporting good SRH (OR 0.49 (95% CI 0.41 to 0.59) compared to women without thyroid
disease in the adjusted analyses, but women with unknown or subclinical hypothyroidism had
84% and 48% higher odds, respectively, of reporting good SRH compared to the odds of
women without thyroid disease (Table 2). The association between thyroid function and SRH
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was basically unchanged after inclusion of confounder variables. Corresponding, but nonsignificant associations were found among men.
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Diabetes mellitus
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The prevalence of unknown, probable, and known diabetes was slightly higher in men than in
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women (p<0.001) (Table 1). Women with known diabetes mellitus had lower odds of good
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SRH than those without diabetes in the adjusted analyses; OR 0.53 (95% CI 0.41 to 0.69),
whereas in women with unknown or possible diabetes mellitus, the odds of good SRH were
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similar to the odds among persons without diabetes, in the adjusted analyses (Table 2). In the
adjusted analyses, the association between unknown and probable diabetes mellitus with poor
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SRH, found in the crude analyses, disappeared when age was included in the model in women
and men, but also by inclusion of working status alone in women. In men, the association of
diabetes status with SRH differed by levels of education. Among men without higher
education (12 years or less), the odds of good SRH were as in the main-effect model (Table
2). However in men with higher education, the ORs of good SRH were barely significantly
lower among men with unknown diabetes (OR 0.29 (95% CI 0.09-1.00)) and among men with
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possible diabetes (OR 0.79 (95% CI 0.63-1.00)) compared to men without diabetes. Among
men with known diabetes in this stratum, the OR of good SRH was 0.31 (95% CI 0.17-0.54)
compared to men without diabetes.
Blood pressure
The prevalence of unknown mild and moderate hypertension was higher in men than in
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women (p<0.001). Unknown severe hypertension and known hypertension were equally
distributed between women and men (Table 1). Women with known hypertension had lower
odds of reporting good SRH than normotensive women, in the adjusted analyses. The figures
were similar in men (Table 2). In contrast; compared to normotensive women, those with
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unknown severe hypertension had 52 % higher odds of reporting good SRH, with similar
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figures in men. Persons with unknown mild and moderate hypertension reported good SRH
similar to the normotensive ones. Adjusted for age, the association between unknown mild
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and moderate hypertension and poor SRH disappeared, simultaneously; unknown severe
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hypertension became associated with good SRH in women. In men, age had to be added along
with either education- or working status to achieve the latter association. Additional analyses
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Women with poor SRH had more than six times the odds of those with good SRH to have had
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a medical consultation during the last year; OR 6.29 (95% CI 5.47 to 7.22). For men the
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corresponding OR was 5.53 (95% CI 4.86 to 6.29). After exclusion of persons with diagnosed
thyroid disease, diabetes mellitus, known hypertension, and “any long-term disease”, the
corresponding OR was 3.71 (95% CI 2.90 to 4.73), with similar figures in men.
DISCUSSION
This large population-based study showed that persons with known thyroid dysfunction,
diabetes mellitus, and hypertension were less likely to report good SRH than those without
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such conditions. Less expected, persons with unknown and subclinical hypothyroidism were
more likely to report good SRH than those without thyroid disease. Similarly, those with
unknown severe hypertension were more likely to report good SRH, compared to persons
with normal blood pressure. In general, persons with unknown diabetes and unknown
mild/moderate hypertension, reported good health, just like the reference group.
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In general, of the confounders, age seemed to influence the association between disease
statuses and SRH most when adjusted for. Age was found to explain the association of poor
SRH with unknown and probable diabetes, and with unknown mild and moderate
hypertension. In women, age even contributed to an association between unknown severe
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hypertension and good SRH. There seemed to be a linear decrease by age categories in the
association with good SRH. The way age is known to be related to both disease and SRH
makes these findings reasonable.
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Although both a qualitative and quantitative measure of association, the estimated ORs in our
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study will, if interpreted as relative risks, overestimate the case, because the prevalence of
good SRH is high in all groups.[41]
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The main strengths of this study were the numbers of participants, that the diseases we studied
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were high-prevalent, and that we could assume representativeness to the general population
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regarding the variables included. It is known that individuals with high burden of symptoms
are less likely to attend surveys, but so far there has been little evidence that non-participation
on this basis introduce substantial bias in associational studies.[42] We studied diseases with
generally low symptom-burden, and expect selection bias to be negligible.
The main limitation was possibly that we relied on self-reported data on both dependent and
independent variables. The validity of self reported measures relevant for this study is
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questionable. However, if any, there should be a non-differential misclassification, that only
should underestimate the associations found.
There is always a possibility of residual confounding in non-randomized study designs, and
due to the observational, cross-sectional design we neither can assume a causal relationship.
Bias due to differential detection of disease in the study population could lead to a type 1
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error. Hypothyroidism and diabetes mellitus are often associated with vague symptoms such
as tiredness and weakness. These symptoms are strongly associated with reduced SRH.[43] It
is likely that presenting such symptoms for the GP would result in measurements of TSH, free
T4, and serum glucose, thus reveal any related dysfunction. Low self-perceived health
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increases the probability to visit a GP.[44] The social security covers most costs related to
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clinical measurements and blood sampling in Norway, thus we expect GPs to have a low
threshold to measure thyroid function, blood pressure or glucose levels. People who do not
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consult their GP will not have diseases with mild or no symptoms diagnosed, and their
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personality could be characterized by less worrying and more optimistic attitude to health
being reflected in better SRH.[45]
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On the other hand, the association between known disease and poor SRH could in fact be
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explained physiologically due to the pathological effect of disease. Lack of corresponding
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association between ignored, but prevalent disease and poor SRH is not in line with this
hypothesis, rising question of a possible adverse effect of disease labelling. However,
confounding by severity of disease cannot be ruled out.
Due to low numbers of persons having unknown hypothyroidism and diabetes mellitus, the
results should be interpreted with caution. On the other hand, there were high numbers in the
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subclinical and probable groups, and analyses of these groups did not show any associations
with poor SRH, also rising question of a possible disease labelling effect.
The fact that the HUNT2 survey was carried out nearly 20 years ago raises a question of
generalisability to today’s population. Stability of SRH over time has not been investigated in
our study population, although investigated among adolescents.[46] We do not expect the
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association between low burden- or subclinical disease with SRH to be time dependent to the
extent that it would change our results considerably. Neither do we expect the changes in
prevalence of most explanatory variables to influence the associations found.
Consistent findings regarding unknown, subclinical, and probable disease, versus known
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disease, could indicate a potential adverse effect of disease labelling. Although early detection
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of disease is protective on morbidity and mortality for many diseases, low SRH is also shown
to be associated morbidity and mortality.[25-27] This is an important aspect in the debate of
presymptomatic case-finding.
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The BMJ's Too Much Medicine campaign aims to highlight the threat to human health posed
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by overdiagnosis and the waste of resources on unnecessary care (http://www.bmj.com/toomuch-medicine). American data have shown that the majority of all health care includes
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preference-sensitive and supply-sensitive services. The extent of these services varies greatly
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without necessarily leading to better health.[47 48] A great deal of activity in such services is
based on identifying subclinical disease. The negative health effect this might have,
modulated through reduced SRH, can explain why population mortality is not reduced in
areas with high frequency of diagnoses.
Both public and academic debates are often characterised by the conviction that all medical
treatment is efficient. Wennberg showed that a relatively small proportion of all medical
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treatment is indisputably good for health.[49] The concern for unrevealed risk factors and
subclinical conditions might lead to unnecessary costly health care interventions, increase
supply-sensitive services without positive health effect and have a negative influence on
people’s general and self-perceived health causing more harm than good.[15]
Of ethical reasons, the possible causal effect of disease labelling on SRH is impossible to
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assess in a randomized controlled trial. Our study emphasizes the need for more prospective
research to investigate potential health effects of disease labelling and early diagnosis.
CONCLUSION
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Our data suggest that early identification of disease may imply a negative effect on SRH, and
to the extent that SRH has been associated with greater mortality, this may lead to harm.
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However, as it is also known that diseases such as diabetes and hypertension also lead to
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increased mortality when detected after cardiovascular and metabolic complications have
developed, it remains to be seen if an early identification or a late detection strategy would
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provide optimal health for the population. FOOTNOTES
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Copyright The Corresponding Author has the right to grant on behalf of all authors and does
grant on behalf of all authors, a worldwide licence to the Publishers and its licensees in
perpetuity, in all forms, formats and media (whether known now or created in the future), to i)
publish, reproduce, distribute, display and store the Contribution, ii) translate the Contribution
into other languages, create adaptations, reprints, include within collections and create
summaries, extracts and/or, abstracts of the Contribution, iii) create any other derivative
work(s) based on the Contribution, iv) to exploit all subsidiary rights in the Contribution, v)
the inclusion of electronic links from the Contribution to third party material where-ever it
may be located; and, vi) licence any third party to do any or all of the above.
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Competing interests All authors have completed the ICMJE uniform disclosure form at
www.icmje.org/coi_disclosure.pdf and declare: no support from any organisation for the
submitted work; no financial relationships with any organisations that might have an interest
in the submitted work in the previous three years; no other relationships or activities that
could appear to have influenced the submitted work.
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Contributors All authors meet the four criteria for authorship recommended by the
International Committee of Medical Journal Editors. SF, AL, and SK have been active
supervisors in study conception, design, conduct, interpretation, and reporting. PJ analyzed
the data and drafted the manuscript. Critical revisions were done by all supervisors and all
authors approved the final version of the manuscript. SF, AL, SK and PJ are joint guarantors.
Acknowledgements The Nord-Trøndelag Health Study (The HUNT Study) is a collaboration
between HUNT Research Centre (Faculty of Medicine, Norwegian University of Science and
Technology NTNU), Nord-Trøndelag County Council, Central Norway Health Authority, and
the Norwegian Institute of Public Health. The authors would like to thank Bjørn Olav Åsvold
for his contribution in planning of the thyroid-part of the study.
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Transparency declaration PJ affirms that this manuscript is an honest, accurate, and
transparent account of the study being reported; that no important aspects of the study have
been omitted; and that there were no discrepancies from the study as planned.
Ethical approval The study was approved by the Regional Committee for Medical Research.
All participants signed written informed consent.
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Funding No external funding. PJ received a PhD-grant, funded by the Norwegian University
of Science and Technology, NTNU.
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Data sharing statement No additional data available.
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20. Bopp M, Braun J, Gutzwiller F, et al. Health risk or resource? Gradual and independent
association between self-rated health and mortality persists over 30 years. PLoS One
2012;7(2):e30795 doi: 10.1371/journal.pone.0030795 PONE-D-11-20499[pii][published
Online First: Epub Date]|.
21. Moller L, Kristensen TS, Hollnagel H. Self rated health as a predictor of coronary heart disease in
Copenhagen, Denmark. J Epidemiol Community Health 1996;50(4):423-8
22. Kaplan GA, Goldberg DE, Everson SA, et al. Perceived health status and morbidity and mortality:
Evidence from the Kuopio Ischaemic Heart Disease Risk Factor Study. Int J Epidemiol
1996;25(2):259-65 doi: Doi 10.1093/Ije/25.2.259[published Online First: Epub Date]|.
23. Krokstad S, Johnsen R, Westin S. Social determinants of disability pension: a 10-year follow-up of
62 000 people in a Norwegian county population. Int J Epidemiol 2002;31(6):1183-91 doi:
10.1093/ije/31.6.1183[published Online First: Epub Date]|.
24. Halford C, Wallman T, Welin L, et al. Effects of self-rated health on sick leave, disability pension,
hospital admissions and mortality. A population-based longitudinal study of nearly 15,000
observations among Swedish women and men. BMC Public Health 2012;12:1103 doi:
10.1186/1471-2458-12-1103[published Online First: Epub Date]|.
25. Latham K, Peek CW. Self-Rated Health and Morbidity Onset Among Late Midlife US Adults. J
Gerontol B-Psychol 2013;68(1):107-16 doi: DOI 10.1093/geronb/gbs104[published Online
First: Epub Date]|.
26. Benyamini Y, Idler EL. Community studies reporting association between self-rated health and
mortality - Additional studies, 1995 to 1998. Res Aging 1999;21(3):392-401 doi: Doi
10.1177/0164027599213002[published Online First: Epub Date]|.
27. Nielsen AB, Siersma V, Hiort LC, et al. Self-rated general health among 40-year-old Danes and its
association with all-cause mortality at 10-, 20-, and 29 years' follow-up. Scand J Public Health
2008;36(1):3-11 doi: 10.1177/1403494807085242[published Online First: Epub Date]|.
28. Schou MB, Krokstad S, Westin S. How is self-rated health associated with mortality? Tidsskr Nor
Laegeforen 2006;126(20):2644-7
29. Barger SD, Muldoon MF. Hypertension labelling was associated with poorer self-rated health in
the Third US National Health and Nutrition Examination Survey. J Hum Hypertens
2006;20(2):117-23 doi: 10.1038/sj.jhh.1001950[published Online First: Epub Date]|.
30. Bloom JR, Monterossa S. Hypertension Labeling and Sense of Well-Being. Am J Public Health
1981;71(11):1228-32 doi: Doi 10.2105/Ajph.71.11.1228[published Online First: Epub Date]|.
31. Moum T, Naess S, Sorensen T, et al. Hypertension Labeling, Life Events and Psychological WellBeing. Psychol Med 1990;20(3):635-46
32. Macdonald LA, Sackett DL, Haynes RB, et al. Labelling in hypertension: A review of the
behavioural and psychological consequences. J Chronic Dis 1984;37(12):933-42 doi:
http://dx.doi.org/10.1016/0021-9681(84)90070-5[published Online First: Epub Date]|.
33. Bianchi GP, Zaccheroni V, Solaroli E, et al. Health-related quality of life in patients with thyroid
disorders - A study based on Short-Form 36 and Nottingham Health Profile Questionnaires.
Qual Life Res 2004;13(1):45-54 doi: Doi 10.1023/B:Qure.0000015315.35184.66[published
Online First: Epub Date]|.
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Study population
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Men (n=16,220)
Women (n=17,514)
34. Holmen J, Midthjell K, Krüger Ø, et al. The Nord-Trøndelag Health Study 1995-97 (HUNT 2):
objectives, methods, and participation. Norsk Epidemiol 2003 13 (1 ):19-32
35. Krokstad S, Langhammer A, Hveem K, et al. Cohort Profile: The HUNT Study, Norway. Int J
Epidemiol 2012 doi: dys095 [pii] 10.1093/ije/dys095[published Online First: Epub Date]|.
36. Schnittker J, Bacak V. The Increasing Predictive Validity of Self-Rated Health. PLoS One
2014;9(1):e84933 doi: 10.1371/journal.pone.0084933[published Online First: Epub Date]|.
37. Idler EL, Benyamini Y. Self-rated health and mortality: A review of twenty-seven community
studies. J Health Soc Behav 1997;38(1):21-37 doi: Doi 10.2307/2955359[published Online
First: Epub Date]|.
38. Manor O, Matthews S, Power C. Dichotomous or categorical response? Analysing self-rated
health and lifetime social class. Int J Epidemiol 2000;29(1):149-57 doi: Doi
10.1093/Ije/29.1.149[published Online First: Epub Date]|.
39. Bjoro T, Holmen J, Kruger O, et al. Prevalence of thyroid disease, thyroid dysfunction and thyroid
peroxidase antibodies in a large, unselected population. The Health Study of Nord-Trondelag
(HUNT). Eur J Endocrinol 2000;143(5):639-47 doi: 1430639 [pii][published Online First: Epub
Date]|.
40. Giron P. Determinants of self-rated health in Spain: differences by age groups for adults. Eur J
Public Health 2012;22(1):36-40 doi: 10.1093/eurpub/ckq133[published Online First: Epub
Date]|.
41. Davies HTO, Crombie IK, Tavakoli M. When can odds ratios mislead? Br Med J
1998;316(7136):989-91
42. Langhammer A, Krokstad S, Romundstad P, et al. The HUNT study: participation is associated with
survival and depends on socioeconomic status, diseases and symptoms. BMC Med Res
Methodol 2012;12:143 doi: 10.1186/1471-2288-12-143[published Online First: Epub Date]|.
43. Molarius A, Janson S. Self-rated health, chronic diseases, and symptoms among middle-aged and
elderly men and women. J Clin Epidemiol 2002;55(4):364-70 doi:
http://dx.doi.org/10.1016/S0895-4356(01)00491-7[published Online First: Epub Date]|.
44. Hansen AH, Halvorsen PA, Ringberg U, et al. Socio-economic inequalities in health care utilisation
in Norway: a population based cross-sectional survey. BMC Health Serv Res 2012;12:336 doi:
10.1186/1472-6963-12-336[published Online First: Epub Date]|.
45. Vingilis E, Wade T, Seeley J. Predictors of adolescent health care utilization. J Adolesc
2007;30(5):773-800 doi: http://dx.doi.org/10.1016/j.adolescence.2006.10.001[published
Online First: Epub Date]|.
46. Breidablik H-J, Meland E, Lydersen S. Self-rated health during adolescence: stability and
predictors of change (Young-HUNT study, Norway). The European Journal of Public Health
2009;19(1):73-78 doi: 10.1093/eurpub/ckn111[published Online First: Epub Date]|.
47. Welch HG, Sharp SM, Gottlieb DJ, et al. Geographic variation in diagnosis frequency and risk of
death among Medicare beneficiaries. JAMA 2011;305(11):1113-8 doi:
10.1001/jama.2011.307[published Online First: Epub Date]|.
48. Fisher ES, Wennberg DE, Stukel TA, et al. The implications of regional variations in Medicare
spending. Part 2: health outcomes and satisfaction with care. Ann Intern Med
2003;138(4):288-98
49. Wennberg JE. Tracking medicine. A researcher’s quest to understand health care. Oxford: Oxford
University Press, 2010.
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Page 20 of 43
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n
%
SRH good
(%)
n
%
SRH good
(%)
40-50 years
7058
40.3
77.6
6517
40.2
81.7
51-60 years
5709
32.6
64.4
5328
32.8
72.1
61-70 years
4747
27.1
56.6
4375
27.0
59.6
6821
0.6
58.4
4804
0.2
42.9
7026
39.1
72.8
8694
29.7
75.3
3489
40.3
68.5
2635
53.8
73.6
104
20.0
56.5
35
16.3
64.8
6973
40.1
70.3
4849
30.1
80.0
4651
26.7
68.0
6105
37.9
69.8
5763
33.1
64.3
5172
32.0
69.1
14888
88.7
11488
73.2
71.0
1471
8.8
76.8
2839
18.1
78.7
430
2.6
76.3
1375
8.8
78.3
< 10 years
8133
48.5
60.5
5711
36.5
63.1
10 – 12 years
5682
33.9
72.8
6615
42.2
76.0
> 12 years
2962
17.7
80.0
3339
21.3
84.7
Yes
11539
67.2
77.1
12309
77.1
79.9
No
5627
32.8
49.0
3665
22.9
49.2
Age group
BMI (kg/m2)
<18,5
18,5-24,9
25,0-29,9
>30
(0.4% missing)
Smoking status
Previous daily smoker
(0.7% missing)
None to low intake
High intake
66.7
w
Moderate intake
ie
Alcohol use
ev
Daily smoker
rr
Never smoked daily
ee
rp
Fo
(3.7% missing)
Educational level
ly
on
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
(3.8% missing)
Employed
(1.8% missing)
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Page 21 of 22
Any long-term impairment
No
10348
59.1
85.9
9912
63.0
88.7
Yes
6275
35.8
39.8
5829
37.0
46.0
14373
86.2
68.7
7804
94.3
72.1
Unknown hypothyroidism
107
0.6
78.5
16
0.2
68.8
Subclinical hypothyroidism
466
2.8
75.0
150
1.8
66.4
Known hypothyroidism
858
5.1
48.9
124
1.5
58.1
Other thyroid dysfunction
872
5.2
61.4
180
2.2
56.2
64.6
69.4
9196
56.9
74.6
(4.1% missing)
Thyroid function
No thyroid disease
rp
Fo
(1.3% missing)
Diabetes mellitus
No diabetes
ee
11279
rr
0.3
395
2.3
Normotensive
9135
52.6
ie
6858
Unknown mild/moderate hypertension
4473
25.8
5343
403
2.4
70.7
349
2.2
73.8
3327
19.2
54.0
3498
21.8
59.6
34332
97.3
70.3
Unknown diabetes
Probable diabetes
Known diabetes
(0.4% missing)
46
52.2
88
0.5
67.8
5728
32.8
65.9
6392
39.6
71.5
43.1
482
3.0
49.5
72.4
42.7
77.5
67.9
33.3
74.7
ev
Blood pressure status
Known hypertension
Overall study population, HUNT2
ly
(0.9% missing)
on
Unknown severe hypertension
w
1
2
3
4
5
6
7
8
9
10
11
12
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16
17
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23
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25
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29
30
31
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33
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35
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40
41
42
43
44
45
46
47
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49
50
51
52
53
54
55
56
57
58
59
60
30378
98.4
Table 1. Good self-rated health (SRH) by sex and characteristics of the study population.
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Table 2. The association between self-rated health and thyroid function, diabetes mellitus and blood
pressure. Odds ratio (OR) of good self-rated health and 95% confidence intervals (95% CI), crude,
age adjusted, and adjusted for age, other long-term illness or injury that impairs function in
everyday life, smoking habits, alcohol use, educational level, work status and body mass index. Cases
with missing data were excluded from the analyses.
Women
OR (95% CI)
Crude
n
Age
adjusted
Men
Multiple
adjusted
n
OR (95% CI)
Crude
Age
adjusted
Multiple
adjusted
Thyroid function
rp
Fo
No thyroid dysfunction
Unknown
hypothyroidism
Subclinical
hypothyroidism
1247
6
92
394
718
Known hypothyroidism
729
Other thyroid
dysfunction
704 1.00
1.00
1.00
5
0.85 (0.30- 0.92 (0.31- 1.28 (0.351.66 (1.05- 2.00 (1.25- 1.84 (1.024.65)
14 2.45)
3.19)
3.33)
2.64)
2.72)
9946
39
Known diabetes
318
1.00
1.00
0.85 (0.80- 0.94 (0.88- 1.01 (0.92- 575 0.86 (0.80- 0.94 (0.88- 0.99 (0.919 0.92)
1.08)
1.01)
0.91)
1.10)
1.01)
w
4797
846 1.00
1.00
1.00
4
0.72 (0.46- 0.88 (0.55- 1.03 (0.590.48 (0.27- 0.61 (0.34- 0.66 (0.3282 1.13)
1.10)
1.81)
0.86)
1.37)
1.39)
1.00
ie
Probable diabetes
ev
rr
Unknown diabetes
1.00
1.13 (0.721.37 (1.10- 1.49 (1.20- 1.48 (1.13- 128 0.77 (0.540.88 (0.62- 1.76)
1.08)
1.85)
1.94)
1.69)
1.24)
107
0.69 (0.440.54 (0.370.44 (0.38- 0.48 (0.41- 0.49 (0.410.63 (0.43- 1.09)
163 0.77)
0.55)
0.59)
0.50)
0.91)
0.58 (0.410.50 (0.370.72 (0.63- 0.77 (0.67- 0.77 (0.650.52 (0.38- 0.84)
0.67)
0.89)
0.93)
0.83)
0.71)
Diabetes mellitus
No diabetes
1.00
1.00
ee
0.33 (0.27- 0.42 (0.34- 0.53 (0.41- 395 0.33 (0.28- 0.42 (0.35- 0.55 (0.430.70)
0.40)
0.41)
0.51)
0.69)
0.51)
Blood pressure
8180
Unknown
mild/moderate
hypertension
3787
Unknown severe
hypertension
2745
Known hypertension
325
637 1.00
8
0.81 (0.75- 1.01 (0.93- 1.10 (0.990.86 (0.79483 0.93)
1.09)
0.87)
1.22)
7
0.92 (0.74- 1.34 (1.08- 1.52 (1.140.85 (0.77308 1.08)
1.15)
1.68)
2.02)
1.00
1.00
1.00
1.00
1.00
ly
No hypertension
on
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
1.01 (0.92- 1.01 (0.921.10)
1.12)
1.27 (0.99- 1.48 (1.091.62)
2.02)
0.45 (0.41- 0.59 (0.54- 0.69 (0.61- 311 0.43 (0.39- 0.54 (0.50- 0.64 (0.570.49)
0.60)
0.72)
8 0.47)
0.65)
0.77)
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Page 23 of 43
BMJ Open
Page 1 of 19
Disease ignorance and self-rated health, any relationship? The HUNT
Study, a cross-sectional survey
Pål Jørgensen, Arnulf Langhammer, Steinar Krokstad, Siri Forsmo
Department of Public Health and General Practice, Norwegian University of Science and
Technology, 7489 Trondheim, Norway Pål Jørgensen
PhD Candidate
HUNT Research Centre, Department of Public Health and General Practice, Norwegian
University of Science and Technology, 7600 Levanger, Norway Arnulf Langhammer
Professor, Head of HUNT Databank
HUNT Research Centre, Department of Public Health and General Practice, Norwegian
University of Science and Technology, Steinar Krokstad
Professor, Head of HUNT Research Center
Department of Public Health and General Practice, Norwegian University of Science and
Technology, Siri Forsmo
Professor, Head of Department
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Correspondence to: P Jørgensen pal_jorgensen@ntnu.no
Field Code Changed
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Keywords: Self-rated health, awareness, hypothyroidism, diabetes, hypertension.
Word count: 2995 3359
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Page 2 of 19
ABSTRACT
Objective To explore if awareness versus unawareness of thyroid dysfunction, diabetes
mellitus or hypertension is associated with self-rated health.
Design Large-scale, cross sectional population based study. The association between thyroid
rp
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function, diabetes mellitus, and blood pressure and self-rated health was explored by multiple
logistic regression analysis.
Setting The second survey of the Nord-Trøndelag Health Study, HUNT2, 1995-97.
Participants 33,734 persons aged 40-70 years.
ee
Primary outcome measures Logistic regression was used to estimate odds ratios for good
rr
self-rated health as a function of thyroid status, diabetes mellitus status and blood pressure
status.
ev
Results Persons aware of their hypothyroidism, diabetes mellitus, or hypertension reported
ie
poorer self-rated health than individuals without such conditions. Women with unknown and
subclinical hypothyroidism reported better self-rated health than women with normal thyroid
w
status. Both in women and men, unknown and probable diabetes, as well as unknown
mild/moderate hypertension was not associated with poorer health. Further, persons with
on
unknown severe hypertension reported better health than normotensive persons.
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Conclusions People with undiagnosed, but prevalent hypothyroidism, diabetes mellitus, and
hypertension often have good self-rated health, whilst when aware of their diagnoses, they
report reduced self-rated health. Use of screening, more sensitive tests, and widened
diagnostic criteria might have a negative effect on perceived health in the population.
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Page 3 of 19
STRENGTHS AND LIMITATIONS OF THIS STUDY
Strengths
•
Sample from a large-scale, general population
•
High-prevalent diseases under study; ensuring statistical power in subgroup analyses
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Limitations
•
Study mainly based on self-reported data
•
Cross-sectional study; susceptibility to confounding and impossibility to assume
causal relationships
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INTRODUCTION
ee
Guidelines for prevention and treatment have been developed for most high prevalent diseases
in western countries aiming for reduction of morbidity and mortality by interventions mainly
in primary health care (PHC).
w
ie
In the society there seems to be an increasing conviction of achievable zero-vision regarding
risks and diseases. Part of the strategy is to detect risk factors and pre-diseases in even earlier
on
stages. From a secondary or tertiary health care level, this might seem reasonable since
intervention on many individuals with specific risk factors presumably can prevent or delay
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BMJ Open
disease or progression of disease. Further, health authorities and hospital clinicians regularly
raise concern of the lack of detection of risk factors, of subclinical conditions and of
achieving treatment goals.[1-7] Norwegian studies have shown that guidelines are often
difficult to implement and adhere to in PHC.[8 9] According to guidelines, most individuals
would be defined as at risk and resources needed to handle this appropriately could destabilize
the entire health care system.[10-12] An American review pointed out knowledge, attitude,
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Page 4 of 19
and behaviour as barriers of physician’s adherence to clinical guidelines.[13] In an already
complex and busy PHC-setting, one might expect that resources used for disease prevention
and case finding have to compete with resources for handling acknowledged disease. Also,
physicians might want to avoid increasing disease related burden for patients, in line with the
old wisdom; “primum non nocere”.[14] Thus the risk and disease zero-visions in society and
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among politicians are seldom shared by PHC professionals.
When guidelines, mainly based on research from high risk hospital populations, are applied
on low risk populations in PHC, more healthy individuals are identified as being at risk or are
ee
given diagnoses. Also the widened inclusion criteria for diagnoses in general and use of more
sensitive tests contribute to define more individuals at risk or as unhealthy.[15] Possible
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undesirable outcomes of such strategies remain unclear.
Self-rated health (SRH) is a valid and widely used measure of general health in epidemiologic
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research.[16-19] It is associated with several clinical conditions often seen in PHC, with
recovery [20-23] and is found to predict morbidity, sick leave, and disability pension,[24 25]
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as well as mortality.[26-28] The majority of studies describing association between labelling
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of disease per se and SRH have focused on arterial hypertension.[29-32] However, one study
has indicated reduced SRH among individuals labelled with thyroid dysfunctions.[33]
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The aim of this study was to investigate whether persons’ awareness versus unawareness of
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thyroid dysfunction, diabetes mellitus, or hypertension was associated with their SRH, as
reported in a population-based health study in Norway.
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METHODS
Study population
The data sample in this study stem from the second wave of the Nord-Trøndelag Health Study
(HUNT2) conducted in 1995-97 in the county of Nord-Trøndelag, Norway. All individuals
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aged 20 years and older, living in the county, were invited (94,194 individuals). In all, 66.7%
of men (n=30,860) and 75.5% of women (n=35,280) participated. The survey consisted of
both questionnaires and measurements, and is described previously in detail.[34 35] In our
study we included answers from the main questionnaire and the baseline measurements for
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persons aged 40-70 years. The age span was chosen because thyroid stimulating hormone
(TSH) was analysed in all women and in 50% of men at this age, of a rather low disease
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burden in people younger than 40 years, and of a lower attendance rate under and above this
age span. A total of 24,950 individuals had TSH measurements and answered thyroid
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questions, thus were eligible for analysis on thyroid dysfunction, whilst in the analysis of
diabetes mellitus and blood pressure, 33,734 individuals were included in all.
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Self-rated health
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The first question in the main questionnaire in HUNT2, answered before attending the
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examination stations, was “How is your health at the moment?”, with answer alternatives;
“very good”, “good”, “not so good”, and “poor”. This short version of SRH measure is shown
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to be a valid predictor of mortality. [18 28 36 37] We dichotomized the answers into “good”
(very good, good) and “poor” (not so good, poor). Dichotomization of multinomial SRH is
commonly performed, and has been validated by Manor et. al.[38]
Thyroid function
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The participants answered questions on history of hypo- and hyperthyroidism, goitre, other
thyroid diseases, and treatment with thyroxin, radio-iodine, surgery, or thyreostatic
medication.
Serum TSH and free T4 were analysed at the Hormone laboratory, Aker University Hospital,
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Norway. The laboratory reference value for TSH, as defined prior to the survey, was 0.2-4.5
mU/L and for free T4 8.0-20.0 pmol/L. If TSH was <0.2 mU/L or >4.0 mU/L, and/or if the
participant reported any thyroid disease, serum free T4 was also measured.[39]
Individuals reporting no previous thyroid disease and having TSH within reference range
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were categorized as “no thyroid disease” and chosen as reference category. No previous
thyroid disease combined with TSH >4.5 mU/L and free T4 <8.0 pmol/L was defined as
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unknown hypothyroidism. No previous thyroid disease combined with TSH >4.5 mU/L and
free T4 8.0 – 20.0 pmol/L was defined as subclinical hypothyroidism. Individuals reporting
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hypothyroidism and use of thyroxin were classified as having known hypothyroidism,
regardless of the TSH and T4 levels. Affirmative answers to other thyroid related questions or
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measures outside reference range in the remainders were classified as other thyroid
dysfunction.
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Diabetes mellitus
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Diabetes mellitus was assessed through self-report and blood samples. Serum glucose was
analysed at Levanger Hospital, Norway. Those reporting no diabetes and having normal
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glucose levels (<5.5 mmol/L) were classified as “no diabetes” and were chosen as reference
category. No self-reported diabetes and non-fasting glucose >11.0 mmol/L was categorized as
unknown diabetes, whereas no diabetes and non-fasting glucose 5.5-11.0 mmol/L was
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categorized as probable diabetes. Self-reported diabetes was classified as known diabetes
regardless of the glucose level.
Blood pressure
In the questionnaire participants were asked about the doctor’s advice after the latest blood
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pressure measurement prior to participation in HUNT. The answer categories were: “no
follow-up and no medication necessary”, “recommended follow-up examination but not to
take medicine”, “start or continue taking medicine for high blood pressure”, or “never
measured”. At HUNT2, mean systolic and mean diastolic arterial blood pressure (BP) of
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measurement 2 and 3 was categorized into normal (systolic (s) BP < 140 mmHg and diastolic
(d) BP < 90 mmHg), mild hypertension (sBP 140-159 mmHg and dBP <100 mmHg or sBP
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<160 mmHg and dBP 90-99 mmHg), moderate hypertension (sBP 160-179 mmHg and dBP
<109 mmHg or sBP <180 mmHg and dBP 100-109 mmHg), and severe hypertension
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(sBP>180 mmHg or dBP>110 mmHg). We constructed a new variable to define
normotensive (reference), unknown mild- and moderate hypertensive, unknown severe
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hypertensive, and known hypertensive persons on the basis of self-report and measures.
Statistical analysis
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The descriptive analyses of the study population were stratified by gender, and we used chisquare tests to examine any difference in proportions of SRH between the independent
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variables. Gender-stratified multiple logistic regression were used to estimate odds ratio (OR)
with 95% confidence intervals (CI) for good SRH, as a function of thyroid status, diabetes
mellitus status and blood pressure status, in separate unadjusted, age adjusted, and multi
adjusted analyses for each condition.
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Age, smoking, alcohol consumption, body mass index (BMI), working- and educational
status, and self-reported limiting long-term illness or injury are associated with SRH[40] and
the diseases under study, but likely not affected by SRH or the diseases. Hence, these
variables were included, a priori, as confounders in the models. Age was categorized into age
groups; 40-49 years, 50-59 years, and 60-70 years. Smoking status was categorized into never
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smoked daily, previous daily smoker, and current daily smoker. Alcohol units (AU) were
defined as number of glasses of wine, beer or liquor. Those reporting to be teetotalers or to
have alcohol intake less than four times a month or less than seven AU per two weeks were
categorized as low consumers, those reporting drinking five to eight times a month or 8-14
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AU per two weeks as moderate consumers, and those drinking more often than eight times a
month or more than 14 AU as heavy consumers. BMI (kg/m2) was calculated of measured
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height and weight and categorized according to The World Health Organization’s (WHO)
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definition; underweight (18.5 kg/m2), normal weight (18.5-24.9 kg/m2), overweight (25.029.9 kg/m2) and obese (>30.0 kg/m2). People reporting paid- or self-employed work were
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classified as working, otherwise as not working. Educational level was categorized into <10
years, 10-12 years and >12 years. We chose affirmative answer to the question “Do you suffer
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from any long-term illness or injury (at least one year) of a physical or psychological nature
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that impairs your functioning in your everyday life?” to represent all relevant chronic medical
conditions that could confound the results.
To examine whether the association of the three disease statuses with SRH differed by
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categories of the other independent variables, we used likelihood ratio-tests with p-value for
statistical interaction. We tested for multicollinearity between the independent variables, by
linear regression.
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In an additional analysis, the association between SRH and having had one or more medical
consultations during the last year was investigated by logistic regression models, stratified by
gender, both in the total study population and after exclusion of persons with diagnoses under
study.
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All analyses were preformed with IBM SPSS Statistics version 20 for windows.
The study was approved by the Regional Committee for Medical Research. All participants
signed written informed consent.
RESULTS
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In all age categories, a higher proportion of men than women reported good SRH (p<0.001),
and in both sexes the proportion reporting good SRH declined by age (p<0.002) (Table 1).
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The proportion reporting good SRH was lower in overweight, obese, and underweight
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women, than in normal weight women (p<0.001). In men the proportion reporting good SRH
declined between the normal weight group to overweight-, obese-, and underweight group
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(p<0.001). In previous and current female smokers, a lower proportion reported good SRH
compared to non smokers (p<0.001). In men, the proportion reporting good SRH was higher
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among non smokers than among previous and current smokers (p<0.001), whereas there was
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no difference between previous and current smokers. The proportion reporting good SRH was
higher in moderate and heavy alcohol consumers than in low consumers, increased with
education and was higher for participants in paid work (p<0.001). Underweight men and
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persons with “any long-term impairment” had the lowest proportion reporting good SRH of
all groups. The proportion reporting good SRH in the overall HUNT 2 study population
differed from the proportions reported in persons without thyroid disease and normotensive
persons (p<0.01), but not from persons without diabetes mellitus. However, the absolute
differences were small.
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There was no multicollinearity between the independent variables.
Thyroid function
Thyroid dysfunctions, both known and unknown were more often observed among women
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than among men (p<0.001) (Table 1). Women with known hypothyroidism had lower odds of
reporting good SRH (OR 0.49 (95% CI 0.41 to 0.59) compared to women without thyroid
disease in the adjusted analyses, but women with unknown or subclinical hypothyroidism had
84% and 48% higher odds, respectively, of reporting good SRH compared to the odds of
women without thyroid disease (Table 2). The association between thyroid function and SRH
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was basically unchanged after inclusion of confounder variables. A cCorresponding, but non-
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significant associations pattern wereas found among men.
Diabetes mellitus
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The prevalence of unknown, probable, and known diabetes was slightly higher in men than in
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women (p<0.001) (Table 1). Women with known diabetes mellitus had lower odds of good
SRH than those without diabetes in the adjusted analyses; OR 0.53 (95% CI 0.41 to 0.69),
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whereas in women with unknown or possible diabetes mellitus, the odds of good SRH were
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similar to the odds among persons without diabetes, in the adjusted analyses (Table 2). In the
adjusted analyses, the association between unknown and probable diabetes mellitus with poor
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SRH, found in the crude analyses, disappeared when age was included in the model in women
and men, but also by inclusion of working status alone in women. In men, the association of
diabetes status with SRH differed by levels of education. Among men without higher
education (12 years or less), the odds of good SRH were as in the main-effect model (Table
2). However in men with higher education, the ORs of good SRH were barely significantly
lower among men with unknown diabetes (OR 0.29 (95% CI 0.09-1.00)) and among men with
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possible diabetes (OR 0.79 (95% CI 0.63-1.00)) compared to men without diabetes. Among
men with known diabetes in this stratum, the OR of good SRH was 0.31 (95% CI 0.17-0.54)
compared to men without diabetes.
Blood pressure
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The prevalence of unknown mild and moderate hypertension was higher in men than in
women (p<0.001). Unknown severe hypertension and known hypertension were equally
distributed between women and men (Table 1). Women with known hypertension had lower
odds of reporting good SRH than normotensive women, in the adjusted analyses. The figures
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were similar in men (Table 2). In contrast; compared to normotensive women, those with
unknown severe hypertension had 52 % higher odds of reporting good SRH, with similar
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figures in men. Persons with unknown mild and moderate hypertension reported good SRH
similar to the normotensive ones. Adjusted for age, the association between unknown mild
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and moderate hypertension and poor SRH disappeared, simultaneously; unknown severe
hypertension became associated with good SRH in women. In men, age had to be added along
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with either education- or working status to achieve the latter association.
Additional analyses
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Women with poor SRH had more than six times the odds of those with good SRH to have had
a medical consultation during the last year; OR 6.29 (95% CI 5.47 to 7.22). For men the
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corresponding OR was 5.53 (95% CI 4.86 to 6.29). After exclusion of persons with diagnosed
thyroid disease, diabetes mellitus, known hypertension, and “any long-term disease”, the
corresponding OR was 3.71 (95% CI 2.90 to 4.73), with similar figures in men.
DISCUSSION
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This large population-based study showed that persons with known thyroid dysfunction,
diabetes mellitus, and hypertension were less likely to report good SRH than those without
such conditions. Less expected, persons with unknown and subclinical hypothyroidism were
more likely to report good SRH than those without thyroid disease. Similarly, those with
unknown severe hypertension were more likely to report good SRH, compared to persons
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with normal blood pressure. In general, persons with unknown diabetes and unknown
mild/moderate hypertension, reported good health, just like the reference group.
In general, of the confounders, age seemed to influence the association between disease
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statuses and SRH most when adjusted for. Age was found to explain the association of poor
SRH with unknown and probable diabetes, and with unknown mild and moderate
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hypertension. In women, age even contributed to an association between unknown severe
hypertension and good SRH. There seemed to be a linear decrease by age categories in the
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association with good SRH. The way age is known to be related to both disease and SRH
makes these findings reasonable.
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Although both a qualitative and quantitative measure of association, the estimated ORs in our
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study will, if interpreted as relative risks, overestimate the case, because the prevalence of
good SRH is high in all groups.[41]
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The main strengths of this study were the numbers of participants, that the diseases we studied
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were high-prevalent, and that we could assume representativeness to the general population
regarding the variables included. It is known that individuals with high burden of symptoms
are less likely to attend surveys, but so far there has been little evidence that non-participation
on this basis introduce substantial bias in associational studies.[42] We studied diseases with
generally low symptom-burden, and expect selection bias to be negligible.
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The main limitation was possibly that we relied on self-reported data on both dependent and
independent variables. The validity of self reported measures relevant for this study is
questionable. However, if any, there should be a non-differential misclassification, that only
should underestimate the associations found.
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There is always a possibility of residual confounding in non-randomized study designs, and
due to the observational, cross-sectional design we neither can assume a causal relationship.
Bias due to differential detection of disease in the study population could lead to a type 1
error. Hypothyroidism and diabetes mellitus are often associated with vague symptoms such
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as tiredness and weakness. These symptoms are strongly associated with reduced SRH.[43] It
is likely that presenting such symptoms for the GP would result in measurements of TSH, free
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T4, and serum glucose, thus reveal any related dysfunction. Low self-perceived health
increases the probability to visit a GP.[44] The social security covers most costs related to
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clinical measurements and blood sampling in Norway, thus we expect GPs to have a low
threshold to measure thyroid function, blood pressure or glucose levels. People who do not
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consult their GP will not have diseases with mild or no symptoms diagnosed, and their
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personality could be characterized by less worrying and more optimistic attitude to health
being reflected in better SRH.[45]
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On the other hand, the association between known disease and poor SRH could in fact be
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explained physiologically due to the pathological effect of disease. Lack of corresponding
association between ignored, but prevalent disease and poor SRH is not in line with this
hypothesis, rising question of a possible adverse effect of disease labelling. However,
confounding by severity of disease cannot be ruled out.
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Due to low numbers of persons having unknown hypothyroidism and diabetes mellitus, the
results should be interpreted with caution. On the other hand, there were high numbers in the
subclinical and probable groups, and analyses of these groups did not show any associations
with poor SRH, also rising question of a possible disease labelling effect.
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The fact that the HUNT2 survey was carried out nearly 20 years ago raises a question of
generalisability to today’s population. Stability of SRH over time has not been investigated in
our study population, although investigated among adolescents.[46] We do not expect the
association between low burden- or subclinical disease with SRH to be time dependent to the
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extent that it would change our results considerably. Neither do we expect the changes in
prevalence of most explanatory variables to influence the associations found.
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Consistent findings regarding unknown, subclinical, and probable disease, versus known
disease, could indicate a potential adverse effect of disease labelling. Although early detection
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of disease is protective on morbidity and mortality for many diseases, low SRH is also shown
to be associated morbidity and mortality.[25-27] This is an important aspect in the debate of
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presymptomatic case-finding.
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The BMJ's Too Much Medicine campaign aims to highlight the threat to human health posed
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by overdiagnosis and the waste of resources on unnecessary care (http://www.bmj.com/toomuch-medicine). American data have shown that the majority of all health care includes
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preference-sensitive and supply-sensitive services. The extent of these services varies greatly
without necessarily leading to better health.[47 48] A great deal of activity in such services is
based on identifying subclinical disease. The negative health effect this might have,
modulated through reduced SRH, can explain why population mortality is not reduced in
areas with high frequency of diagnoses.
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Both public and academic debates are often characterised by the conviction that all medical
treatment is efficient. Wennberg showed that a relatively small proportion of all medical
treatment is indisputably good for health.[49] The concern for unrevealed risk factors and
subclinical conditions might lead to unnecessary costly health care interventions, increase
supply-sensitive services without positive health effect and have a negative influence on
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people’s general and self-perceived health causing more harm than good.[15]
Of ethical reasons, the possible causal effect of disease labelling on SRH is impossible to
assess in a randomized controlled trial. Our study emphasizes the need for more prospective
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research to investigate potential health effects of disease labelling and early diagnosis.
CONCLUSION
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Our data suggest that early identification of disease may imply a negative effect on SRH, and
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to the extent that SRH has been associated with greater mortality, this may lead to harm.
However, as it is also known that diseases such as diabetes and hypertension also lead to
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increased mortality when detected after cardiovascular and metabolic complications have
developed, it remains to be seen if an early identification or a late detection strategy would
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provide optimal health for the population. early identification of disease may imply a negative
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effect on SRH and thereby eventually cause more harm than good. On a population level,
such efforts might increase the costs in health care and the proportion of supply-sensitive
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services without any documented positive health effect. More research is needed concerning
the possible adverse effects of disease labelling on SRH and mortality.
FOOTNOTES
Copyright The Corresponding Author has the right to grant on behalf of all authors and does
grant on behalf of all authors, a worldwide licence to the Publishers and its licensees in
perpetuity, in all forms, formats and media (whether known now or created in the future), to i)
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publish, reproduce, distribute, display and store the Contribution, ii) translate the Contribution
into other languages, create adaptations, reprints, include within collections and create
summaries, extracts and/or, abstracts of the Contribution, iii) create any other derivative
work(s) based on the Contribution, iv) to exploit all subsidiary rights in the Contribution, v)
the inclusion of electronic links from the Contribution to third party material where-ever it
may be located; and, vi) licence any third party to do any or all of the above.
Competing interests All authors have completed the ICMJE uniform disclosure form at
www.icmje.org/coi_disclosure.pdf and declare: no support from any organisation for the
submitted work; no financial relationships with any organisations that might have an interest
in the submitted work in the previous three years; no other relationships or activities that
could appear to have influenced the submitted work.
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Contributors All authors meet the four criteria for authorship recommended by the
International Committee of Medical Journal Editors. SF, AL, and SK have been active
supervisors in study conception, design, conduct, interpretation, and reporting. PJ analyzed
the data and drafted the manuscript. Critical revisions were done by all supervisors and all
authors approved the final version of the manuscript. SF, AL, SK and PJ are joint guarantors.
ee
Acknowledgements The Nord-Trøndelag Health Study (The HUNT Study) is a collaboration
between HUNT Research Centre (Faculty of Medicine, Norwegian University of Science and
Technology NTNU), Nord-Trøndelag County Council, Central Norway Health Authority, and
the Norwegian Institute of Public Health. The authors would like to thank Bjørn Olav Åsvold
for his contribution in planning of the thyroid-part of the study.
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Transparency declaration PJ affirms that this manuscript is an honest, accurate, and
transparent account of the study being reported; that no important aspects of the study have
been omitted; and that there were no discrepancies from the study as planned.
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Ethical approval The study was approved by the Regional Committee for Medical Research.
All participants signed written informed consent.
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Funding No external funding. PJ received a PhD-grant, funded by the Norwegian University
of Science and Technology, NTNU.
Data sharing statement No additional data available.
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STROBE 2007 (v4) Statement—Checklist of items that should be included in reports of cross-sectional studies
Section/Topic
Title and abstract
Item
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Introduction
Recommendation
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Objectives
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Setting
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collection
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Bias
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(a) Report numbers of individuals at each stage of study—eg numbers potentially eligible, examined for eligibility,
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Descriptive data
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(b) Give reasons for non-participation at each stage
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Main results
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Other information
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*Give information separately for cases and controls in case-control studies and, if applicable, for exposed and unexposed groups in cohort and cross-sectional studies.
Note: An Explanation and Elaboration article discusses each checklist item and gives methodological background and published examples of transparent reporting. The STROBE
checklist is best used in conjunction with this article (freely available on the Web sites of PLoS Medicine at http://www.plosmedicine.org/, Annals of Internal Medicine at
http://www.annals.org/, and Epidemiology at http://www.epidem.com/). Information on the STROBE Initiative is available at www.strobe-statement.org.
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Is there an association between disease ignorance and selfrated health? The HUNT Study, a cross-sectional survey
Journal:
BMJ Open
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Manuscript ID:
Article Type:
Date Submitted by the Author:
Complete List of Authors:
bmjopen-2014-004962.R2
Research
08-May-2014
Secondary Subject Heading:
Epidemiology, Public health, Diabetes and endocrinology
Hypertension < CARDIOLOGY, General diabetes < DIABETES &
ENDOCRINOLOGY, Thyroid disease < DIABETES & ENDOCRINOLOGY,
PRIMARY CARE
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Keywords:
General practice / Family practice
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<b>Primary Subject
Heading</b>:
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Jørgensen, Pål; Norwegian University of Science and Technology,
Department of Public Health and General Practice
Langhammer, Arnulf; Norwegian University of Science and Technology,
HUNT Research Centre
Krokstad, Steinar; Norwegian University of Science and Technology, HUNT
Research Centre
Forsmo, Siri; Norwegian University of Science and Technology, Department
of Public Health and General Practice
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Is there an association between disease ignorance and self-rated health? The HUNT Study, a crosssectional survey
Pål Jørgensen, Arnulf Langhammer, Steinar Krokstad, Siri Forsmo
Department of Public Health and General Practice, Norwegian University of Science and
Technology, 7489 Trondheim, Norway Pål Jørgensen
PhD Candidate
HUNT Research Centre, Department of Public Health and General Practice, Norwegian
University of Science and Technology, 7600 Levanger, Norway Arnulf Langhammer
Professor, Head of HUNT Databank
HUNT Research Centre, Department of Public Health and General Practice, Norwegian
University of Science and Technology, Steinar Krokstad
Professor, Head of HUNT Research Center
Department of Public Health and General Practice, Norwegian University of Science and
Technology, Siri Forsmo
Professor, Head of Department
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Correspondence to: P Jørgensen pal_jorgensen@ntnu.no
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Keywords: Self-rated health, awareness, hypothyroidism, diabetes, hypertension.
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ABSTRACT
Objective To explore if awareness versus unawareness of thyroid dysfunction, diabetes
mellitus or hypertension is associated with self-rated health.
Design Large-scale, cross sectional population based study. The association between thyroid
function, diabetes mellitus, and blood pressure and self-rated health was explored by multiple
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logistic regression analysis.
Setting The second survey of the Nord-Trøndelag Health Study, HUNT2, 1995-97.
Participants 33,734 persons aged 40-70 years.
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Primary outcome measures Logistic regression was used to estimate odds ratios for good
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self-rated health as a function of thyroid status, diabetes mellitus status and blood pressure
status.
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Results Persons aware of their hypothyroidism, diabetes mellitus, or hypertension reported
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poorer self-rated health than individuals without such conditions. Women with unknown and
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subclinical hypothyroidism reported better self-rated health than women with normal thyroid
status. Both in women and men, unknown and probable diabetes, as well as unknown
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mild/moderate hypertension was not associated with poorer health. Further, persons with
unknown severe hypertension reported better health than normotensive persons.
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Conclusions People with undiagnosed, but prevalent hypothyroidism, diabetes mellitus, and
hypertension often have good self-rated health, whilst when aware of their diagnoses, they
report reduced self-rated health. Use of screening, more sensitive tests, and widened
diagnostic criteria might have a negative effect on perceived health in the population.
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STRENGTHS AND LIMITATIONS OF THIS STUDY
Strengths
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Sample from a large-scale, general population
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High-prevalent diseases under study; ensuring statistical power in subgroup analyses
Limitations
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Study mainly based on self-reported data
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Cross-sectional study; susceptibility to confounding and impossibility to assume
causal relationships
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INTRODUCTION
Guidelines for prevention and treatment have been developed for most high prevalent diseases
in western countries aiming for reduction of morbidity and mortality by interventions mainly
in primary health care (PHC).
In the society there seems to be an increasing conviction of achievable zero-vision regarding
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risks and diseases. Part of the strategy is to detect risk factors and pre-diseases in even earlier
stages. From a secondary or tertiary health care level, this might seem reasonable since
intervention on many individuals with specific risk factors presumably can prevent or delay
disease or progression of disease. Further, health authorities and hospital clinicians regularly
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raise concern of the lack of detection of risk factors, of subclinical conditions and of
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achieving treatment goals.[1-7] Norwegian studies have shown that guidelines are often
difficult to implement and adhere to in PHC.[8 9] According to guidelines, most individuals
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would be defined as at risk and resources needed to handle this appropriately could destabilize
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the entire health care system.[10-12] An American review pointed out knowledge, attitude,
and behaviour as barriers of physician’s adherence to clinical guidelines.[13] In an already
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complex and busy PHC-setting, one might expect that resources used for disease prevention
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and case finding have to compete with resources for handling acknowledged disease. Also,
physicians might want to avoid increasing disease related burden for patients, in line with the
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old wisdom; “primum non nocere”.[14] Thus the risk and disease zero-visions in society and
among politicians are seldom shared by PHC professionals.
When guidelines, mainly based on research from high risk hospital populations, are applied
on low risk populations in PHC, more healthy individuals are identified as being at risk or are
given diagnoses. Also the widened inclusion criteria for diagnoses in general and use of more
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sensitive tests contribute to define more individuals at risk or as unhealthy.[15] Possible
undesirable outcomes of such strategies remain unclear.
Self-rated health (SRH) is a valid and widely used measure of general health in epidemiologic
research.[16-19] It is associated with several clinical conditions often seen in PHC, with
recovery [20-23] and is found to predict morbidity, sick leave, and disability pension,[24 25]
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as well as mortality.[26-28] The majority of studies describing association between labelling
of disease per se and SRH have focused on arterial hypertension.[29-32] However, one study
has indicated reduced SRH among individuals labelled with thyroid dysfunctions.[33]
The aim of this study was to investigate whether persons’ awareness versus unawareness of
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thyroid dysfunction, diabetes mellitus, or hypertension was associated with their SRH, as
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reported in a population-based health study in Norway.
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METHODS
Study population
The data sample in this study stem from the second wave of the Nord-Trøndelag Health Study
(HUNT2) conducted in 1995-97 in the county of Nord-Trøndelag, Norway. All individuals
aged 20 years and older, living in the county, were invited (94,194 individuals). In all, 66.7%
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of men (n=30,860) and 75.5% of women (n=35,280) participated. The survey consisted of
both questionnaires and measurements, and is described previously in detail.[34 35] In our
study we included answers from the main questionnaire and the baseline measurements for
persons aged 40-70 years. The age span was chosen because thyroid stimulating hormone
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(TSH) was analysed in all women and in 50% of men at this age, of a rather low disease
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burden in people younger than 40 years, and of a lower attendance rate under and above this
age span. A total of 24,950 individuals had TSH measurements and answered thyroid
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questions, thus were eligible for analysis on thyroid dysfunction, whilst in the analysis of
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diabetes mellitus and blood pressure, 33,734 individuals were included in all.
Self-rated health
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The first question in the main questionnaire in HUNT2, answered before attending the
examination stations, was “How is your health at the moment?”, with answer alternatives;
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“very good”, “good”, “not so good”, and “poor”. This short version of SRH measure is shown
to be a valid predictor of mortality. [18 28 36 37] We dichotomized the answers into “good”
(very good, good) and “poor” (not so good, poor). Dichotomization of multinomial SRH is
commonly performed, and has been validated by Manor et. al.[38]
Thyroid function
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The participants answered questions on history of hypo- and hyperthyroidism, goitre, other
thyroid diseases, and treatment with thyroxin, radio-iodine, surgery, or thyreostatic
medication.
Serum TSH and free T4 were analysed at the Hormone laboratory, Aker University Hospital,
Norway. The laboratory reference value for TSH, as defined prior to the survey, was 0.2-4.5
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mU/L and for free T4 8.0-20.0 pmol/L. If TSH was <0.2 mU/L or >4.0 mU/L, and/or if the
participant reported any thyroid disease, serum free T4 was also measured.[39]
Individuals reporting no previous thyroid disease and having TSH within reference range
were categorized as “no thyroid disease” and chosen as reference category. No previous
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thyroid disease combined with TSH >4.5 mU/L and free T4 <8.0 pmol/L was defined as
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unknown hypothyroidism. No previous thyroid disease combined with TSH >4.5 mU/L and
free T4 8.0 – 20.0 pmol/L was defined as subclinical hypothyroidism. Individuals reporting
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hypothyroidism and use of thyroxin were classified as having known hypothyroidism,
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regardless of the TSH and T4 levels. Affirmative answers to other thyroid related questions or
measures outside reference range in the remainders were classified as other thyroid
dysfunction.
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Diabetes mellitus
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Diabetes mellitus was assessed through self-report and blood samples. Serum glucose was
analysed at Levanger Hospital, Norway. Those reporting no diabetes and having normal
glucose levels (<5.5 mmol/L) were classified as “no diabetes” and were chosen as reference
category. No self-reported diabetes and non-fasting glucose >11.0 mmol/L was categorized as
unknown diabetes, whereas no diabetes and non-fasting glucose 5.5-11.0 mmol/L was
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categorized as probable diabetes. Self-reported diabetes was classified as known diabetes
regardless of the glucose level.
Blood pressure
In the questionnaire participants were asked about the doctor’s advice after the latest blood
pressure measurement prior to participation in HUNT. The answer categories were: “no
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follow-up and no medication necessary”, “recommended follow-up examination but not to
take medicine”, “start or continue taking medicine for high blood pressure”, or “never
measured”. At HUNT2, mean systolic and mean diastolic arterial blood pressure (BP) of
measurement 2 and 3 was categorized into normal (systolic (s) BP < 140 mmHg and diastolic
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(d) BP < 90 mmHg), mild hypertension (sBP 140-159 mmHg and dBP <100 mmHg or sBP
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<160 mmHg and dBP 90-99 mmHg), moderate hypertension (sBP 160-179 mmHg and dBP
<109 mmHg or sBP <180 mmHg and dBP 100-109 mmHg), and severe hypertension
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(sBP>180 mmHg or dBP>110 mmHg). We constructed a new variable to define
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normotensive (reference), unknown mild- and moderate hypertensive, unknown severe
hypertensive, and known hypertensive persons on the basis of self-report and measures.
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Statistical analysis
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The descriptive analyses of the study population were stratified by gender, and we used chi-
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square tests to examine any difference in proportions of SRH between the independent
variables. Gender-stratified multiple logistic regression were used to estimate odds ratio (OR)
with 95% confidence intervals (CI) for good SRH, as a function of thyroid status, diabetes
mellitus status and blood pressure status, in separate unadjusted, age adjusted, and multi
adjusted analyses for each condition.
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Age, smoking, alcohol consumption, body mass index (BMI), working- and educational
status, and self-reported limiting long-term illness or injury are associated with SRH[40] and
the diseases under study, but likely not affected by SRH or the diseases. Hence, these
variables were included, a priori, as confounders in the models. Age was categorized into age
groups; 40-49 years, 50-59 years, and 60-70 years. Smoking status was categorized into never
smoked daily, previous daily smoker, and current daily smoker. Alcohol units (AU) were
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defined as number of glasses of wine, beer or liquor. Those reporting to be teetotalers or to
have alcohol intake less than four times a month or less than seven AU per two weeks were
categorized as low consumers, those reporting drinking five to eight times a month or 8-14
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AU per two weeks as moderate consumers, and those drinking more often than eight times a
month or more than 14 AU as heavy consumers. BMI (kg/m2) was calculated of measured
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height and weight and categorized according to The World Health Organization’s (WHO)
definition; underweight (18.5 kg/m2), normal weight (18.5-24.9 kg/m2), overweight (25.0-
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29.9 kg/m2) and obese (>30.0 kg/m2). People reporting paid- or self-employed work were
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classified as working, otherwise as not working. Educational level was categorized into <10
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years, 10-12 years and >12 years. We chose affirmative answer to the question “Do you suffer
from any long-term illness or injury (at least one year) of a physical or psychological nature
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that impairs your functioning in your everyday life?” to represent all relevant chronic medical
conditions that could confound the results.
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To examine whether the association of the three disease statuses with SRH differed by
categories of the other independent variables, we used likelihood ratio-tests with p-value for
statistical interaction. We tested for multicollinearity between the independent variables, by
linear regression. Areas under the receiver operating characteristic curves (AUC) were
calculated to evaluate the performance of the logistic regression models.
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In an additional analysis, the association between SRH and having had one or more medical
consultations during the last year was investigated by logistic regression models, stratified by
gender, both in the total study population and after exclusion of persons with diagnoses under
study.
All analyses were preformed with IBM SPSS Statistics version 20 for windows.
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The study was approved by the Regional Committee for Medical Research. All participants
signed written informed consent.
RESULTS
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In all age categories, a higher proportion of men than women reported good SRH (p<0.001),
and in both sexes the proportion reporting good SRH declined by age (p<0.002) (Table 1).
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The proportion reporting good SRH was lower in overweight, obese, and underweight
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women, than in normal weight women (p<0.001). In men the proportion reporting good SRH
declined between the normal weight group to overweight-, obese-, and underweight group
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(p<0.001). In previous and current female smokers, a lower proportion reported good SRH
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compared to non smokers (p<0.001). In men, the proportion reporting good SRH was higher
among non smokers than among previous and current smokers (p<0.001), whereas there was
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no difference between previous and current smokers. The proportion reporting good SRH was
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higher in moderate and heavy alcohol consumers than in low consumers, increased with
education and was higher for participants in paid work (p<0.001). Underweight men and
persons with “any long-term impairment” had the lowest proportion reporting good SRH of
all groups. The proportion reporting good SRH in the overall HUNT 2 study population
differed from the proportions reported in persons without thyroid disease and normotensive
persons (p<0.01), but not from persons without diabetes mellitus. However, the absolute
differences were small.
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In all fully adjusted regression models, the AUC was 0.80. Unadjusted, the AUC ranged from
0.61 to 0.63.
There was no multicollinearity between the independent variables.
Thyroid function
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Thyroid dysfunctions, both known and unknown were more often observed among women
than among men (p<0.001) (Table 1). Women with known hypothyroidism had lower odds of
reporting good SRH than women without thyroid disease in the adjusted analyses.However,
women with unknown or subclinical hypothyroidism had 84% and 48% higher odds,
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respectively, of reporting good SRH compared to the odds of women without thyroid disease
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(Table 2). The association between thyroid function and SRH was basically unchanged after
inclusion of confounder variables. Corresponding, but non-significant associations were found
among men.
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Diabetes mellitus
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The prevalence of unknown, probable, and known diabetes was slightly higher in men than in
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women (p<0.001) (Table 1). Women with known diabetes mellitus had lower odds of good
SRH than those without diabetes in the adjusted analyses, whereas in women with unknown
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or possible diabetes mellitus, the odds of good SRH were similar to the odds among persons
without diabetes, in the adjusted analyses (Table 2). In the adjusted analyses, the association
between unknown and probable diabetes mellitus with poor SRH, found in the crude analyses,
disappeared when age was included in the model in women and men, but also by inclusion of
working status alone in women. In men, the association of diabetes status with SRH differed
by levels of education. Among men without higher education (12 years or less), the odds of
good SRH were as in the main-effect model (Table 2). However in men with higher
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education, the ORs of good SRH were barely significantly lower among men with unknown
diabetes (OR 0.29 (95% CI 0.09-1.00)) and among men with possible diabetes (OR 0.79 (95%
CI 0.63-1.00)) compared to men without diabetes. Among men with known diabetes in this
stratum, the OR of good SRH was 0.31 (95% CI 0.17-0.54) compared to men without
diabetes.
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Blood pressure
The prevalence of unknown mild and moderate hypertension was higher in men than in
women (p<0.001). Unknown severe hypertension and known hypertension were equally
distributed between women and men (Table 1). Women with known hypertension had lower
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odds of reporting good SRH than normotensive women, in the adjusted analyses. The figures
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were similar in men (Table 2). In contrast; compared to normotensive women, those with
unknown severe hypertension had 52 % higher odds of reporting good SRH, with similar
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figures in men. Persons with unknown mild and moderate hypertension reported good SRH
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similar to the normotensive ones. Adjusted for age, the association between unknown mild
and moderate hypertension and poor SRH disappeared, simultaneously; unknown severe
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hypertension became associated with good SRH in women. In men, age had to be added along
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Additional analyses
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Women with poor SRH had more than six times the odds of those with good SRH to have had
a medical consultation during the last year; OR 6.29 (95% CI 5.47 to 7.22). For men the
corresponding OR was 5.53 (95% CI 4.86 to 6.29). After exclusion of persons with diagnosed
thyroid disease, diabetes mellitus, known hypertension, and “any long-term disease”, the
corresponding OR was 3.71 (95% CI 2.90 to 4.73), with similar figures in men.
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DISCUSSION
This large population-based study showed that persons with known thyroid dysfunction,
diabetes mellitus, and hypertension were less likely to report good SRH than those without
such conditions. Less expected, persons with unknown and subclinical hypothyroidism were
more likely to report good SRH than those without thyroid disease. Similarly, those with
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unknown severe hypertension were more likely to report good SRH, compared to persons
with normal blood pressure. In general, persons with unknown diabetes and unknown
mild/moderate hypertension, reported good health, just like the reference group.
In general, of the confounders, age seemed to influence the association between disease
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statuses and SRH most when adjusted for. Age was found to explain the association of poor
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SRH with unknown and probable diabetes, and with unknown mild and moderate
hypertension. In women, age even contributed to an association between unknown severe
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hypertension and good SRH. There seemed to be a linear decrease by age categories in the
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association with good SRH. The way age is known to be related to both disease and SRH
makes these findings reasonable.
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Although both a qualitative and quantitative measure of association, the estimated ORs in our
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study will, if interpreted as relative risks, overestimate the case, because the prevalence of
good SRH is high in all groups.[41]
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The main strengths of this study were the numbers of participants, that the diseases we studied
were high-prevalent, and that we could assume representativeness to the general population
regarding the variables included. It is known that individuals with high burden of symptoms
are less likely to attend surveys, but so far there has been little evidence that non-participation
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on this basis introduce substantial bias in associational studies.[42] We studied diseases with
generally low symptom-burden, and expect selection bias to be negligible.
The main limitation was possibly that we relied on self-reported data on both dependent and
independent variables. The validity of self reported measures relevant for this study is
questionable. However, if any, there should be a non-differential misclassification, that only
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should underestimate the associations found.
There is always a possibility of residual confounding in non-randomized study designs, and
due to the observational, cross-sectional design we neither can assume a causal relationship.
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Bias due to differential detection of disease in the study population could lead to a type 1
error. Hypothyroidism and diabetes mellitus are often associated with vague symptoms such
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as tiredness and weakness. These symptoms are strongly associated with reduced SRH.[43] It
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is likely that presenting such symptoms for the GP would result in measurements of TSH, free
T4, and serum glucose, thus reveal any related dysfunction. Low self-perceived health
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increases the probability to visit a GP.[44] The social security covers most costs related to
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clinical measurements and blood sampling in Norway, thus we expect GPs to have a low
threshold to measure thyroid function, blood pressure or glucose levels. People who do not
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consult their GP will not have diseases with mild or no symptoms diagnosed, and their
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personality could be characterized by less worrying and more optimistic attitude to health
being reflected in better SRH.[45]
On the other hand, the association between known disease and poor SRH could in fact be
explained physiologically due to the pathological effect of disease. Lack of corresponding
association between ignored, but prevalent disease and poor SRH is not in line with this
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hypothesis, rising question of a possible adverse effect of disease labelling. However,
confounding by severity of disease cannot be ruled out.
Due to low numbers of persons having unknown hypothyroidism and diabetes mellitus, the
results should be interpreted with caution. On the other hand, there were high numbers in the
subclinical and probable groups, and analyses of these groups did not show any associations
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Fo
with poor SRH, also rising question of a possible disease labelling effect.
The fact that the HUNT2 survey was carried out nearly 20 years ago raises a question of
generalisability to today’s population. Stability of SRH over time has not been investigated in
our study population, although investigated among adolescents.[46] We do not expect the
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association between low burden- or subclinical disease with SRH to be time dependent to the
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extent that it would change our results considerably. Neither do we expect the changes in
prevalence of most explanatory variables to influence the associations found.
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Consistent findings regarding unknown, subclinical, and probable disease, versus known
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disease, could indicate a potential adverse effect of disease labelling. Although early detection
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of disease is protective on morbidity and mortality for many diseases, low SRH is also shown
to be associated morbidity and mortality.[25-27] This is an important aspect in the debate of
presymptomatic case-finding.
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The BMJ's Too Much Medicine campaign aims to highlight the threat to human health posed
by overdiagnosis and the waste of resources on unnecessary care (http://www.bmj.com/toomuch-medicine). American data have shown that the majority of all health care includes
preference-sensitive and supply-sensitive services. The extent of these services varies greatly
without necessarily leading to better health.[47 48] A great deal of activity in such services is
based on identifying subclinical disease. The negative health effect this might have,
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Page 16 of 24
modulated through reduced SRH, can explain why population mortality is not reduced in
areas with high frequency of diagnoses.
Both public and academic debates are often characterised by the conviction that all medical
treatment is efficient. Wennberg showed that a relatively small proportion of all medical
treatment is indisputably good for health.[49] The concern for unrevealed risk factors and
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Fo
subclinical conditions might lead to unnecessary costly health care interventions, increase
supply-sensitive services without positive health effect and have a negative influence on
people’s general and self-perceived health causing more harm than good.[15]
Of ethical reasons, the possible causal effect of disease labelling on SRH is impossible to
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assess in a randomized controlled trial. Our study emphasizes the need for more prospective
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research to investigate potential health effects of disease labelling and early diagnosis.
CONCLUSION
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Our data suggest that early identification of disease may imply a negative effect on SRH, and
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to the extent that SRH has been associated with greater mortality, this may lead to harm.
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However, as it is also known that diseases such as diabetes and hypertension also lead to
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increased mortality when detected after cardiovascular and metabolic complications have
developed, it remains to be seen if an early identification or a late detection strategy would
provide optimal health for the population.
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FOOTNOTES
Copyright The Corresponding Author has the right to grant on behalf of all authors and does
grant on behalf of all authors, a worldwide licence to the Publishers and its licensees in
perpetuity, in all forms, formats and media (whether known now or created in the future), to i)
publish, reproduce, distribute, display and store the Contribution, ii) translate the Contribution
into other languages, create adaptations, reprints, include within collections and create
summaries, extracts and/or, abstracts of the Contribution, iii) create any other derivative
work(s) based on the Contribution, iv) to exploit all subsidiary rights in the Contribution, v)
the inclusion of electronic links from the Contribution to third party material where-ever it
may be located; and, vi) licence any third party to do any or all of the above.
rp
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Competing interests All authors have completed the ICMJE uniform disclosure form at
www.icmje.org/coi_disclosure.pdf and declare: no support from any organisation for the
submitted work; no financial relationships with any organisations that might have an interest
in the submitted work in the previous three years; no other relationships or activities that
could appear to have influenced the submitted work.
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Contributors All authors meet the four criteria for authorship recommended by the
International Committee of Medical Journal Editors. SF, AL, and SK have been active
supervisors in study conception, design, conduct, interpretation, and reporting. PJ analyzed
the data and drafted the manuscript. Critical revisions were done by all supervisors and all
authors approved the final version of the manuscript. SF, AL, SK and PJ are joint guarantors.
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Acknowledgements The Nord-Trøndelag Health Study (The HUNT Study) is a collaboration
between HUNT Research Centre (Faculty of Medicine, Norwegian University of Science and
Technology NTNU), Nord-Trøndelag County Council, Central Norway Health Authority, and
the Norwegian Institute of Public Health. The authors would like to thank Bjørn Olav Åsvold
for his contribution in planning of the thyroid-part of the study.
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Transparency declaration PJ affirms that this manuscript is an honest, accurate, and
transparent account of the study being reported; that no important aspects of the study have
been omitted; and that there were no discrepancies from the study as planned.
on
Ethical approval The study was approved by the Regional Committee for Medical Research.
All participants signed written informed consent.
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Funding No external funding. PJ received a PhD-grant, funded by the Norwegian University
of Science and Technology, NTNU.
Data sharing statement No additional data available.
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Study population
Men (n=16,220)
Women (n=17,514)
n
%
SRH good
(%)
n
%
SRH good
(%)
40-50 years
7058
40.3
77.6
6517
40.2
81.7
51-60 years
5709
32.6
64.4
5328
32.8
72.1
61-70 years
4747
27.1
56.6
4375
27.0
59.6
0.6
58.4
4804
0.2
42.9
7026
39.1
72.8
8694
29.7
75.3
3489
40.3
68.5
2635
53.8
73.6
56.5
35
16.3
64.8
70.3
30.1
80.0
68.0
37.9
69.8
5172
32.0
69.1
(n= 33,734)
Age group
BMI (kg/m2)
<18,5
25,0-29,9
6821
20.0
Never smoked daily
6973
40.1
4849
Previous daily smoker
4651
26.7
6105
Daily smoker
5763
33.1
64.3
14888
88.7
66.7
1471
8.8
430
< 10 years
>30
(0.4% missing)
Smoking status
Alcohol use
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(0.7% missing)
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18,5-24,9
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23
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26
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31
32
33
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60
11488
73.2
71.0
76.8
2839
18.1
78.7
2.6
76.3
1375
8.8
78.3
8133
48.5
60.5
5711
36.5
63.1
10 – 12 years
5682
33.9
72.8
6615
42.2
76.0
> 12 years
2962
17.7
80.0
3339
21.3
84.7
None to low intake
Moderate intake
High intake
(3.7% missing)
Educational level
(3.8% missing)
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Yes
11539
67.2
77.1
12309
77.1
79.9
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5627
32.8
49.0
3665
22.9
49.2
No
10348
59.1
85.9
9912
63.0
88.7
Yes
6275
35.8
39.8
5829
37.0
46.0
14373
86.2
68.7
7804
94.3
72.1
Unknown hypothyroidism
107
0.6
78.5
16
0.2
68.8
Subclinical hypothyroidism
466
2.8
75.0
150
1.8
66.4
Known hypothyroidism
858
5.1
48.9
124
1.5
58.1
5.2
61.4
180
2.2
56.2
64.6
69.4
9196
56.9
74.6
No
(1.8% missing)
Any long-term impairment
(4.1% missing)
Thyroid function
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No thyroid disease
Other thyroid dysfunction
(1.3% missing)
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Diabetes mellitus
No diabetes
872
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11279
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46
0.3
52.2
88
0.5
67.8
Probable diabetes
5728
32.8
65.9
6392
39.6
71.5
Known diabetes
395
482
3.0
49.5
Unknown diabetes
2.3
43.1
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(0.4% missing)
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Blood pressure status
Normotensive
9135
52.6
72.4
6858
42.7
77.5
Unknown mild/moderate hypertension
4473
25.8
67.9
5343
33.3
74.7
403
2.4
70.7
349
2.2
73.8
3327
19.2
54.0
3498
21.8
59.6
34332
97.3
70.3
30378
98.4
74.9
Known hypertension
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Unknown severe hypertension
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(0.9% missing)
Overall study population, HUNT2
Table 1. Good self-rated health (SRH) by sex and characteristics of the study population.
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Table 2. The association between self-rated health and thyroid function, diabetes mellitus and blood
pressure. Odds ratio (OR) of good self-rated health and 95% confidence intervals (95% CI), crude,
age adjusted, and adjusted for age, other long-term illness or injury that impairs function in
everyday life, smoking habits, alcohol use, educational level, work status and body mass index. Cases
with missing data were excluded from the analyses.
Women
OR (95% CI)
Crude
n
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Age
adjusted
Men
Multiple
adjusted
n
OR (95% CI)
Crude
Age
adjusted
Multiple
adjusted
Thyroid function
No thyroid dysfunction
Unknown
hypothyroidism
Subclinical
hypothyroidism
1247
6
92
394
718
Known hypothyroidism
729
1.13 (0.721.37 (1.10- 1.49 (1.20- 1.48 (1.13- 128 0.77 (0.540.88 (0.62- 1.76)
1.08)
1.85)
1.94)
1.69)
1.24)
107
0.69 (0.440.54 (0.370.44 (0.38- 0.48 (0.41- 0.49 (0.410.63 (0.43- 1.09)
163 0.77)
0.55)
0.59)
0.50)
0.91)
0.58 (0.410.50 (0.370.72 (0.63- 0.77 (0.67- 0.77 (0.650.52 (0.38- 0.84)
0.67)
0.89)
0.93)
0.83)
0.71)
39
4797
Known diabetes
318
1.00
1.00
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Probable diabetes
846 1.00
1.00
1.00
4
0.72 (0.46- 0.88 (0.55- 1.03 (0.590.48 (0.27- 0.61 (0.34- 0.66 (0.3282 1.13)
1.10)
1.81)
0.86)
1.37)
1.39)
1.00
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Unknown diabetes
9946
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Diabetes mellitus
No diabetes
1.00
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Other thyroid
dysfunction
704 1.00
1.00
1.00
5
0.85 (0.30- 0.92 (0.31- 1.28 (0.351.66 (1.05- 2.00 (1.25- 1.84 (1.024.65)
14 2.45)
3.19)
3.33)
2.64)
2.72)
1.00
1.00
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0.85 (0.80- 0.94 (0.88- 1.01 (0.92- 575 0.86 (0.80- 0.94 (0.88- 0.99 (0.919 0.92)
1.08)
1.01)
0.91)
1.10)
1.01)
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0.33 (0.27- 0.42 (0.34- 0.53 (0.41- 395 0.33 (0.28- 0.42 (0.35- 0.55 (0.430.70)
0.40)
0.41)
0.51)
0.69)
0.51)
Blood pressure
No hypertension
8180
Unknown
mild/moderate
hypertension
3787
Unknown severe
hypertension
2745
Known hypertension
325
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637 1.00
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0.81 (0.75- 1.01 (0.93- 1.10 (0.990.86 (0.79483 0.93)
1.09)
0.87)
1.22)
7
0.92 (0.74- 1.34 (1.08- 1.52 (1.140.85 (0.77308 1.08)
1.68)
1.15)
2.02)
1.00
1.00
1.00
1.00
1.00
1.01 (0.92- 1.01 (0.921.10)
1.12)
1.27 (0.99- 1.48 (1.091.62)
2.02)
0.45 (0.41- 0.59 (0.54- 0.69 (0.61- 311 0.43 (0.39- 0.54 (0.50- 0.64 (0.570.49)
0.60)
0.72)
8 0.47)
0.65)
0.77)
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What is already known on this topic
Awareness of thyroid dysfunction, diabetes mellitus, and
hypertension is associated with reduced self-rated health
Screening, increasingly sensitive diagnostic tests, and
widened diagnostic criteria will define more healthy people as
sick
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What this study adds
Unawareness of hypothyroidism, diabetes mellitus, or
hypertension is not associated with reduced self-rated health
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Is there an association between disease ignorance and self-rated health? The HUNT Study, a crosssectional survey
Pål Jørgensen, Arnulf Langhammer, Steinar Krokstad, Siri Forsmo
Department of Public Health and General Practice, Norwegian University of Science and
Technology, 7489 Trondheim, Norway Pål Jørgensen
PhD Candidate
HUNT Research Centre, Department of Public Health and General Practice, Norwegian
University of Science and Technology, 7600 Levanger, Norway Arnulf Langhammer
Professor, Head of HUNT Databank
HUNT Research Centre, Department of Public Health and General Practice, Norwegian
University of Science and Technology, Steinar Krokstad
Professor, Head of HUNT Research Center
Department of Public Health and General Practice, Norwegian University of Science and
Technology, Siri Forsmo
Professor, Head of Department
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Correspondence to: P Jørgensen pal_jorgensen@ntnu.no
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Keywords: Self-rated health, awareness, hypothyroidism, diabetes, hypertension.
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ABSTRACT
Objective To explore if awareness versus unawareness of thyroid dysfunction, diabetes
mellitus or hypertension is associated with self-rated health.
Design Large-scale, cross sectional population based study. The association between thyroid
function, diabetes mellitus, and blood pressure and self-rated health was explored by multiple
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logistic regression analysis.
Setting The second survey of the Nord-Trøndelag Health Study, HUNT2, 1995-97.
Participants 33,734 persons aged 40-70 years.
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Primary outcome measures Logistic regression was used to estimate odds ratios for good
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self-rated health as a function of thyroid status, diabetes mellitus status and blood pressure
status.
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Results Persons aware of their hypothyroidism, diabetes mellitus, or hypertension reported
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poorer self-rated health than individuals without such conditions. Women with unknown and
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subclinical hypothyroidism reported better self-rated health than women with normal thyroid
status. Both in women and men, unknown and probable diabetes, as well as unknown
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mild/moderate hypertension was not associated with poorer health. Further, persons with
unknown severe hypertension reported better health than normotensive persons.
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Conclusions People with undiagnosed, but prevalent hypothyroidism, diabetes mellitus, and
hypertension often have good self-rated health, whilst when aware of their diagnoses, they
report reduced self-rated health. Use of screening, more sensitive tests, and widened
diagnostic criteria might have a negative effect on perceived health in the population.
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STRENGTHS AND LIMITATIONS OF THIS STUDY
Strengths
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Sample from a large-scale, general population
•
High-prevalent diseases under study; ensuring statistical power in subgroup analyses
Limitations
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Study mainly based on self-reported data
•
Cross-sectional study; susceptibility to confounding and impossibility to assume
causal relationships
INTRODUCTION
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Guidelines for prevention and treatment have been developed for most high prevalent diseases
in western countries aiming for reduction of morbidity and mortality by interventions mainly
in primary health care (PHC).
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In the society there seems to be an increasing conviction of achievable zero-vision regarding
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risks and diseases. Part of the strategy is to detect risk factors and pre-diseases in even earlier
stages. From a secondary or tertiary health care level, this might seem reasonable since
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intervention on many individuals with specific risk factors presumably can prevent or delay
disease or progression of disease. Further, health authorities and hospital clinicians regularly
raise concern of the lack of detection of risk factors, of subclinical conditions and of
achieving treatment goals.[1-7] Norwegian studies have shown that guidelines are often
difficult to implement and adhere to in PHC.[8 9] According to guidelines, most individuals
would be defined as at risk and resources needed to handle this appropriately could destabilize
the entire health care system.[10-12] An American review pointed out knowledge, attitude,
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and behaviour as barriers of physician’s adherence to clinical guidelines.[13] In an already
complex and busy PHC-setting, one might expect that resources used for disease prevention
and case finding have to compete with resources for handling acknowledged disease. Also,
physicians might want to avoid increasing disease related burden for patients, in line with the
old wisdom; “primum non nocere”.[14] Thus the risk and disease zero-visions in society and
among politicians are seldom shared by PHC professionals.
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When guidelines, mainly based on research from high risk hospital populations, are applied
on low risk populations in PHC, more healthy individuals are identified as being at risk or are
given diagnoses. Also the widened inclusion criteria for diagnoses in general and use of more
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sensitive tests contribute to define more individuals at risk or as unhealthy.[15] Possible
undesirable outcomes of such strategies remain unclear.
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Self-rated health (SRH) is a valid and widely used measure of general health in epidemiologic
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research.[16-19] It is associated with several clinical conditions often seen in PHC, with
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recovery [20-23] and is found to predict morbidity, sick leave, and disability pension,[24 25]
as well as mortality.[26-28] The majority of studies describing association between labelling
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of disease per se and SRH have focused on arterial hypertension.[29-32] However, one study
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has indicated reduced SRH among individuals labelled with thyroid dysfunctions.[33]
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The aim of this study was to investigate whether persons’ awareness versus unawareness of
thyroid dysfunction, diabetes mellitus, or hypertension was associated with their SRH, as
reported in a population-based health study in Norway.
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METHODS
Study population
The data sample in this study stem from the second wave of the Nord-Trøndelag Health Study
(HUNT2) conducted in 1995-97 in the county of Nord-Trøndelag, Norway. All individuals
aged 20 years and older, living in the county, were invited (94,194 individuals). In all, 66.7%
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of men (n=30,860) and 75.5% of women (n=35,280) participated. The survey consisted of
both questionnaires and measurements, and is described previously in detail.[34 35] In our
study we included answers from the main questionnaire and the baseline measurements for
persons aged 40-70 years. The age span was chosen because thyroid stimulating hormone
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(TSH) was analysed in all women and in 50% of men at this age, of a rather low disease
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burden in people younger than 40 years, and of a lower attendance rate under and above this
age span. A total of 24,950 individuals had TSH measurements and answered thyroid
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questions, thus were eligible for analysis on thyroid dysfunction, whilst in the analysis of
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diabetes mellitus and blood pressure, 33,734 individuals were included in all.
Self-rated health
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The first question in the main questionnaire in HUNT2, answered before attending the
examination stations, was “How is your health at the moment?”, with answer alternatives;
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“very good”, “good”, “not so good”, and “poor”. This short version of SRH measure is shown
to be a valid predictor of mortality. [18 28 36 37] We dichotomized the answers into “good”
(very good, good) and “poor” (not so good, poor). Dichotomization of multinomial SRH is
commonly performed, and has been validated by Manor et. al.[38]
Thyroid function
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The participants answered questions on history of hypo- and hyperthyroidism, goitre, other
thyroid diseases, and treatment with thyroxin, radio-iodine, surgery, or thyreostatic
medication.
Serum TSH and free T4 were analysed at the Hormone laboratory, Aker University Hospital,
Norway. The laboratory reference value for TSH, as defined prior to the survey, was 0.2-4.5
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mU/L and for free T4 8.0-20.0 pmol/L. If TSH was <0.2 mU/L or >4.0 mU/L, and/or if the
participant reported any thyroid disease, serum free T4 was also measured.[39]
Individuals reporting no previous thyroid disease and having TSH within reference range
were categorized as “no thyroid disease” and chosen as reference category. No previous
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thyroid disease combined with TSH >4.5 mU/L and free T4 <8.0 pmol/L was defined as
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unknown hypothyroidism. No previous thyroid disease combined with TSH >4.5 mU/L and
free T4 8.0 – 20.0 pmol/L was defined as subclinical hypothyroidism. Individuals reporting
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hypothyroidism and use of thyroxin were classified as having known hypothyroidism,
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regardless of the TSH and T4 levels. Affirmative answers to other thyroid related questions or
measures outside reference range in the remainders were classified as other thyroid
dysfunction.
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Diabetes mellitus
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Diabetes mellitus was assessed through self-report and blood samples. Serum glucose was
analysed at Levanger Hospital, Norway. Those reporting no diabetes and having normal
glucose levels (<5.5 mmol/L) were classified as “no diabetes” and were chosen as reference
category. No self-reported diabetes and non-fasting glucose >11.0 mmol/L was categorized as
unknown diabetes, whereas no diabetes and non-fasting glucose 5.5-11.0 mmol/L was
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categorized as probable diabetes. Self-reported diabetes was classified as known diabetes
regardless of the glucose level.
Blood pressure
In the questionnaire participants were asked about the doctor’s advice after the latest blood
pressure measurement prior to participation in HUNT. The answer categories were: “no
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follow-up and no medication necessary”, “recommended follow-up examination but not to
take medicine”, “start or continue taking medicine for high blood pressure”, or “never
measured”. At HUNT2, mean systolic and mean diastolic arterial blood pressure (BP) of
measurement 2 and 3 was categorized into normal (systolic (s) BP < 140 mmHg and diastolic
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(d) BP < 90 mmHg), mild hypertension (sBP 140-159 mmHg and dBP <100 mmHg or sBP
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<160 mmHg and dBP 90-99 mmHg), moderate hypertension (sBP 160-179 mmHg and dBP
<109 mmHg or sBP <180 mmHg and dBP 100-109 mmHg), and severe hypertension
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(sBP>180 mmHg or dBP>110 mmHg). We constructed a new variable to define
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normotensive (reference), unknown mild- and moderate hypertensive, unknown severe
hypertensive, and known hypertensive persons on the basis of self-report and measures.
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Statistical analysis
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The descriptive analyses of the study population were stratified by gender, and we used chi-
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square tests to examine any difference in proportions of SRH between the independent
variables. Gender-stratified multiple logistic regression were used to estimate odds ratio (OR)
with 95% confidence intervals (CI) for good SRH, as a function of thyroid status, diabetes
mellitus status and blood pressure status, in separate unadjusted, age adjusted, and multi
adjusted analyses for each condition.
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Age, smoking, alcohol consumption, body mass index (BMI), working- and educational
status, and self-reported limiting long-term illness or injury are associated with SRH[40] and
the diseases under study, but likely not affected by SRH or the diseases. Hence, these
variables were included, a priori, as confounders in the models. Age was categorized into age
groups; 40-49 years, 50-59 years, and 60-70 years. Smoking status was categorized into never
smoked daily, previous daily smoker, and current daily smoker. Alcohol units (AU) were
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defined as number of glasses of wine, beer or liquor. Those reporting to be teetotalers or to
have alcohol intake less than four times a month or less than seven AU per two weeks were
categorized as low consumers, those reporting drinking five to eight times a month or 8-14
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AU per two weeks as moderate consumers, and those drinking more often than eight times a
month or more than 14 AU as heavy consumers. BMI (kg/m2) was calculated of measured
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height and weight and categorized according to The World Health Organization’s (WHO)
definition; underweight (18.5 kg/m2), normal weight (18.5-24.9 kg/m2), overweight (25.0-
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29.9 kg/m2) and obese (>30.0 kg/m2). People reporting paid- or self-employed work were
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classified as working, otherwise as not working. Educational level was categorized into <10
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years, 10-12 years and >12 years. We chose affirmative answer to the question “Do you suffer
from any long-term illness or injury (at least one year) of a physical or psychological nature
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that impairs your functioning in your everyday life?” to represent all relevant chronic medical
conditions that could confound the results.
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To examine whether the association of the three disease statuses with SRH differed by
categories of the other independent variables, we used likelihood ratio-tests with p-value for
statistical interaction. We tested for multicollinearity between the independent variables, by
linear regression. Areas under the receiver operating characteristic curves (AUC) were
calculated to evaluate the performance of the logistic regression models.
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In an additional analysis, the association between SRH and having had one or more medical
consultations during the last year was investigated by logistic regression models, stratified by
gender, both in the total study population and after exclusion of persons with diagnoses under
study.
All analyses were preformed with IBM SPSS Statistics version 20 for windows.
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The study was approved by the Regional Committee for Medical Research. All participants
signed written informed consent.
RESULTS
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In all age categories, a higher proportion of men than women reported good SRH (p<0.001),
and in both sexes the proportion reporting good SRH declined by age (p<0.002) (Table 1).
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The proportion reporting good SRH was lower in overweight, obese, and underweight
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women, than in normal weight women (p<0.001). In men the proportion reporting good SRH
declined between the normal weight group to overweight-, obese-, and underweight group
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(p<0.001). In previous and current female smokers, a lower proportion reported good SRH
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compared to non smokers (p<0.001). In men, the proportion reporting good SRH was higher
among non smokers than among previous and current smokers (p<0.001), whereas there was
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no difference between previous and current smokers. The proportion reporting good SRH was
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higher in moderate and heavy alcohol consumers than in low consumers, increased with
education and was higher for participants in paid work (p<0.001). Underweight men and
persons with “any long-term impairment” had the lowest proportion reporting good SRH of
all groups. The proportion reporting good SRH in the overall HUNT 2 study population
differed from the proportions reported in persons without thyroid disease and normotensive
persons (p<0.01), but not from persons without diabetes mellitus. However, the absolute
differences were small.
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In all fully adjusted regression models, the AUC was 0.80. Unadjusted, the AUC ranged from
0.61 to 0.63.
There was no multicollinearity between the independent variables.
Thyroid function
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Thyroid dysfunctions, both known and unknown were more often observed among women
than among men (p<0.001) (Table 1). Women with known hypothyroidism had lower odds of
reporting good SRH (OR 0.49 (95% CI 0.41 to 0.59) compared tothan women without thyroid
disease in the adjusted analyses., butHowever, women with unknown or subclinical
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hypothyroidism had 84% and 48% higher odds, respectively, of reporting good SRH
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compared to the odds of women without thyroid disease (Table 2). The association between
thyroid function and SRH was basically unchanged after inclusion of confounder variables.
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Corresponding, but non-significant associations were found among men.
Diabetes mellitus
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The prevalence of unknown, probable, and known diabetes was slightly higher in men than in
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women (p<0.001) (Table 1). Women with known diabetes mellitus had lower odds of good
SRH than those without diabetes in the adjusted analyses; OR 0.53 (95% CI 0.41 to 0.69),
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whereas in women with unknown or possible diabetes mellitus, the odds of good SRH were
similar to the odds among persons without diabetes, in the adjusted analyses (Table 2). In the
adjusted analyses, the association between unknown and probable diabetes mellitus with poor
SRH, found in the crude analyses, disappeared when age was included in the model in women
and men, but also by inclusion of working status alone in women. In men, the association of
diabetes status with SRH differed by levels of education. Among men without higher
education (12 years or less), the odds of good SRH were as in the main-effect model (Table
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2). However in men with higher education, the ORs of good SRH were barely significantly
lower among men with unknown diabetes (OR 0.29 (95% CI 0.09-1.00)) and among men with
possible diabetes (OR 0.79 (95% CI 0.63-1.00)) compared to men without diabetes. Among
men with known diabetes in this stratum, the OR of good SRH was 0.31 (95% CI 0.17-0.54)
compared to men without diabetes.
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Blood pressure
The prevalence of unknown mild and moderate hypertension was higher in men than in
women (p<0.001). Unknown severe hypertension and known hypertension were equally
distributed between women and men (Table 1). Women with known hypertension had lower
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odds of reporting good SRH than normotensive women, in the adjusted analyses. The figures
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were similar in men (Table 2). In contrast; compared to normotensive women, those with
unknown severe hypertension had 52 % higher odds of reporting good SRH, with similar
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figures in men. Persons with unknown mild and moderate hypertension reported good SRH
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similar to the normotensive ones. Adjusted for age, the association between unknown mild
and moderate hypertension and poor SRH disappeared, simultaneously; unknown severe
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hypertension became associated with good SRH in women. In men, age had to be added along
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with either education- or working status to achieve the latter association.
Additional analyses
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Women with poor SRH had more than six times the odds of those with good SRH to have had
a medical consultation during the last year; OR 6.29 (95% CI 5.47 to 7.22). For men the
corresponding OR was 5.53 (95% CI 4.86 to 6.29). After exclusion of persons with diagnosed
thyroid disease, diabetes mellitus, known hypertension, and “any long-term disease”, the
corresponding OR was 3.71 (95% CI 2.90 to 4.73), with similar figures in men.
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DISCUSSION
This large population-based study showed that persons with known thyroid dysfunction,
diabetes mellitus, and hypertension were less likely to report good SRH than those without
such conditions. Less expected, persons with unknown and subclinical hypothyroidism were
more likely to report good SRH than those without thyroid disease. Similarly, those with
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unknown severe hypertension were more likely to report good SRH, compared to persons
with normal blood pressure. In general, persons with unknown diabetes and unknown
mild/moderate hypertension, reported good health, just like the reference group.
In general, of the confounders, age seemed to influence the association between disease
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statuses and SRH most when adjusted for. Age was found to explain the association of poor
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SRH with unknown and probable diabetes, and with unknown mild and moderate
hypertension. In women, age even contributed to an association between unknown severe
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hypertension and good SRH. There seemed to be a linear decrease by age categories in the
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association with good SRH. The way age is known to be related to both disease and SRH
makes these findings reasonable.
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Although both a qualitative and quantitative measure of association, the estimated ORs in our
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study will, if interpreted as relative risks, overestimate the case, because the prevalence of
good SRH is high in all groups.[41]
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The main strengths of this study were the numbers of participants, that the diseases we studied
were high-prevalent, and that we could assume representativeness to the general population
regarding the variables included. It is known that individuals with high burden of symptoms
are less likely to attend surveys, but so far there has been little evidence that non-participation
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on this basis introduce substantial bias in associational studies.[42] We studied diseases with
generally low symptom-burden, and expect selection bias to be negligible.
The main limitation was possibly that we relied on self-reported data on both dependent and
independent variables. The validity of self reported measures relevant for this study is
questionable. However, if any, there should be a non-differential misclassification, that only
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should underestimate the associations found.
There is always a possibility of residual confounding in non-randomized study designs, and
due to the observational, cross-sectional design we neither can assume a causal relationship.
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Bias due to differential detection of disease in the study population could lead to a type 1
error. Hypothyroidism and diabetes mellitus are often associated with vague symptoms such
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as tiredness and weakness. These symptoms are strongly associated with reduced SRH.[43] It
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is likely that presenting such symptoms for the GP would result in measurements of TSH, free
T4, and serum glucose, thus reveal any related dysfunction. Low self-perceived health
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increases the probability to visit a GP.[44] The social security covers most costs related to
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clinical measurements and blood sampling in Norway, thus we expect GPs to have a low
threshold to measure thyroid function, blood pressure or glucose levels. People who do not
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consult their GP will not have diseases with mild or no symptoms diagnosed, and their
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personality could be characterized by less worrying and more optimistic attitude to health
being reflected in better SRH.[45]
On the other hand, the association between known disease and poor SRH could in fact be
explained physiologically due to the pathological effect of disease. Lack of corresponding
association between ignored, but prevalent disease and poor SRH is not in line with this
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hypothesis, rising question of a possible adverse effect of disease labelling. However,
confounding by severity of disease cannot be ruled out.
Due to low numbers of persons having unknown hypothyroidism and diabetes mellitus, the
results should be interpreted with caution. On the other hand, there were high numbers in the
subclinical and probable groups, and analyses of these groups did not show any associations
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with poor SRH, also rising question of a possible disease labelling effect.
The fact that the HUNT2 survey was carried out nearly 20 years ago raises a question of
generalisability to today’s population. Stability of SRH over time has not been investigated in
our study population, although investigated among adolescents.[46] We do not expect the
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association between low burden- or subclinical disease with SRH to be time dependent to the
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extent that it would change our results considerably. Neither do we expect the changes in
prevalence of most explanatory variables to influence the associations found.
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Consistent findings regarding unknown, subclinical, and probable disease, versus known
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disease, could indicate a potential adverse effect of disease labelling. Although early detection
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of disease is protective on morbidity and mortality for many diseases, low SRH is also shown
to be associated morbidity and mortality.[25-27] This is an important aspect in the debate of
presymptomatic case-finding.
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The BMJ's Too Much Medicine campaign aims to highlight the threat to human health posed
by overdiagnosis and the waste of resources on unnecessary care (http://www.bmj.com/toomuch-medicine). American data have shown that the majority of all health care includes
preference-sensitive and supply-sensitive services. The extent of these services varies greatly
without necessarily leading to better health.[47 48] A great deal of activity in such services is
based on identifying subclinical disease. The negative health effect this might have,
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modulated through reduced SRH, can explain why population mortality is not reduced in
areas with high frequency of diagnoses.
Both public and academic debates are often characterised by the conviction that all medical
treatment is efficient. Wennberg showed that a relatively small proportion of all medical
treatment is indisputably good for health.[49] The concern for unrevealed risk factors and
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subclinical conditions might lead to unnecessary costly health care interventions, increase
supply-sensitive services without positive health effect and have a negative influence on
people’s general and self-perceived health causing more harm than good.[15]
Of ethical reasons, the possible causal effect of disease labelling on SRH is impossible to
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assess in a randomized controlled trial. Our study emphasizes the need for more prospective
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research to investigate potential health effects of disease labelling and early diagnosis.
CONCLUSION
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Our data suggest that early identification of disease may imply a negative effect on SRH, and
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to the extent that SRH has been associated with greater mortality, this may lead to harm.
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However, as it is also known that diseases such as diabetes and hypertension also lead to
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increased mortality when detected after cardiovascular and metabolic complications have
developed, it remains to be seen if an early identification or a late detection strategy would
provide optimal health for the population.
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FOOTNOTES
Copyright The Corresponding Author has the right to grant on behalf of all authors and does
grant on behalf of all authors, a worldwide licence to the Publishers and its licensees in
perpetuity, in all forms, formats and media (whether known now or created in the future), to i)
publish, reproduce, distribute, display and store the Contribution, ii) translate the Contribution
into other languages, create adaptations, reprints, include within collections and create
summaries, extracts and/or, abstracts of the Contribution, iii) create any other derivative
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Page 16 of 22
work(s) based on the Contribution, iv) to exploit all subsidiary rights in the Contribution, v)
the inclusion of electronic links from the Contribution to third party material where-ever it
may be located; and, vi) licence any third party to do any or all of the above.
Competing interests All authors have completed the ICMJE uniform disclosure form at
www.icmje.org/coi_disclosure.pdf and declare: no support from any organisation for the
submitted work; no financial relationships with any organisations that might have an interest
in the submitted work in the previous three years; no other relationships or activities that
could appear to have influenced the submitted work.
Contributors All authors meet the four criteria for authorship recommended by the
International Committee of Medical Journal Editors. SF, AL, and SK have been active
supervisors in study conception, design, conduct, interpretation, and reporting. PJ analyzed
the data and drafted the manuscript. Critical revisions were done by all supervisors and all
authors approved the final version of the manuscript. SF, AL, SK and PJ are joint guarantors.
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Acknowledgements The Nord-Trøndelag Health Study (The HUNT Study) is a collaboration
between HUNT Research Centre (Faculty of Medicine, Norwegian University of Science and
Technology NTNU), Nord-Trøndelag County Council, Central Norway Health Authority, and
the Norwegian Institute of Public Health. The authors would like to thank Bjørn Olav Åsvold
for his contribution in planning of the thyroid-part of the study.
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Transparency declaration PJ affirms that this manuscript is an honest, accurate, and
transparent account of the study being reported; that no important aspects of the study have
been omitted; and that there were no discrepancies from the study as planned.
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Ethical approval The study was approved by the Regional Committee for Medical Research.
All participants signed written informed consent.
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Funding No external funding. PJ received a PhD-grant, funded by the Norwegian University
of Science and Technology, NTNU.
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Data sharing statement No additional data available.
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28. Schou MB, Krokstad S, Westin S. How is self-rated health associated with mortality? Tidsskr Nor
Laegeforen 2006;126(20):2644-7
29. Barger SD, Muldoon MF. Hypertension labelling was associated with poorer self-rated health in
the Third US National Health and Nutrition Examination Survey. J Hum Hypertens
2006;20(2):117-23 doi: 10.1038/sj.jhh.1001950[published Online First: Epub Date]|.
30. Bloom JR, Monterossa S. Hypertension Labeling and Sense of Well-Being. Am J Public Health
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31. Moum T, Naess S, Sorensen T, et al. Hypertension Labeling, Life Events and Psychological WellBeing. Psychol Med 1990;20(3):635-46
32. Macdonald LA, Sackett DL, Haynes RB, et al. Labelling in hypertension: A review of the
behavioural and psychological consequences. J Chronic Dis 1984;37(12):933-42 doi:
http://dx.doi.org/10.1016/0021-9681(84)90070-5[published Online First: Epub Date]|.
33. Bianchi GP, Zaccheroni V, Solaroli E, et al. Health-related quality of life in patients with thyroid
disorders - A study based on Short-Form 36 and Nottingham Health Profile Questionnaires.
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Women (n=17,514)
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objectives, methods, and participation. Norsk Epidemiol 2003 13 (1 ):19-32
35. Krokstad S, Langhammer A, Hveem K, et al. Cohort Profile: The HUNT Study, Norway. Int J
Epidemiol 2012 doi: dys095 [pii] 10.1093/ije/dys095[published Online First: Epub Date]|.
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37. Idler EL, Benyamini Y. Self-rated health and mortality: A review of twenty-seven community
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38. Manor O, Matthews S, Power C. Dichotomous or categorical response? Analysing self-rated
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40. Giron P. Determinants of self-rated health in Spain: differences by age groups for adults. Eur J
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University Press, 2010.
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Page 20 of 22
n
%
SRH good
(%)
n
%
SRH good
(%)
40-50 years
7058
40.3
77.6
6517
40.2
81.7
51-60 years
5709
32.6
64.4
5328
32.8
72.1
61-70 years
4747
27.1
56.6
4375
27.0
59.6
6821
0.6
58.4
4804
0.2
42.9
7026
39.1
72.8
8694
29.7
75.3
3489
40.3
68.5
2635
53.8
73.6
104
20.0
56.5
35
16.3
64.8
6973
40.1
70.3
4849
30.1
80.0
4651
26.7
68.0
6105
37.9
69.8
5763
33.1
64.3
5172
32.0
69.1
14888
88.7
11488
73.2
71.0
1471
8.8
76.8
2839
18.1
78.7
430
2.6
76.3
1375
8.8
78.3
< 10 years
8133
48.5
60.5
5711
36.5
63.1
10 – 12 years
5682
33.9
72.8
6615
42.2
76.0
> 12 years
2962
17.7
80.0
3339
21.3
84.7
Yes
11539
67.2
77.1
12309
77.1
79.9
No
5627
32.8
49.0
3665
22.9
49.2
Age group
BMI (kg/m2)
<18,5
18,5-24,9
25,0-29,9
>30
(0.4% missing)
Smoking status
Previous daily smoker
(0.7% missing)
None to low intake
High intake
66.7
w
Moderate intake
ie
Alcohol use
ev
Daily smoker
rr
Never smoked daily
ee
rp
Fo
(3.7% missing)
Educational level
ly
on
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
(3.8% missing)
Employed
(1.8% missing)
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Page 45 of 48
BMJ Open
Page 21 of 22
Any long-term impairment
No
10348
59.1
85.9
9912
63.0
88.7
Yes
6275
35.8
39.8
5829
37.0
46.0
14373
86.2
68.7
7804
94.3
72.1
Unknown hypothyroidism
107
0.6
78.5
16
0.2
68.8
Subclinical hypothyroidism
466
2.8
75.0
150
1.8
66.4
Known hypothyroidism
858
5.1
48.9
124
1.5
58.1
Other thyroid dysfunction
872
5.2
61.4
180
2.2
56.2
64.6
69.4
9196
56.9
74.6
(4.1% missing)
Thyroid function
No thyroid disease
rp
Fo
(1.3% missing)
Diabetes mellitus
No diabetes
ee
11279
rr
0.3
395
2.3
Normotensive
9135
52.6
ie
6858
Unknown mild/moderate hypertension
4473
25.8
5343
403
2.4
70.7
349
2.2
73.8
3327
19.2
54.0
3498
21.8
59.6
34332
97.3
70.3
Unknown diabetes
Probable diabetes
Known diabetes
(0.4% missing)
46
52.2
88
0.5
67.8
5728
32.8
65.9
6392
39.6
71.5
43.1
482
3.0
49.5
72.4
42.7
77.5
67.9
33.3
74.7
ev
Blood pressure status
Known hypertension
Overall study population, HUNT2
ly
(0.9% missing)
on
Unknown severe hypertension
w
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
30378
98.4
Table 1. Good self-rated health (SRH) by sex and characteristics of the study population.
For peer review only - http://bmjopen.bmj.com/site/about/guidelines.xhtml
74.9
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Page 22 of 22
Table 2. The association between self-rated health and thyroid function, diabetes mellitus and blood
pressure. Odds ratio (OR) of good self-rated health and 95% confidence intervals (95% CI), crude,
age adjusted, and adjusted for age, other long-term illness or injury that impairs function in
everyday life, smoking habits, alcohol use, educational level, work status and body mass index. Cases
with missing data were excluded from the analyses.
Women
OR (95% CI)
Crude
n
Age
adjusted
Men
Multiple
adjusted
n
OR (95% CI)
Crude
Age
adjusted
Multiple
adjusted
Thyroid function
rp
Fo
No thyroid dysfunction
Unknown
hypothyroidism
Subclinical
hypothyroidism
1247
6
92
394
718
Known hypothyroidism
729
Other thyroid
dysfunction
704 1.00
1.00
1.00
5
0.85 (0.30- 0.92 (0.31- 1.28 (0.351.66 (1.05- 2.00 (1.25- 1.84 (1.024.65)
14 2.45)
3.19)
3.33)
2.64)
2.72)
9946
39
Known diabetes
318
1.00
1.00
0.85 (0.80- 0.94 (0.88- 1.01 (0.92- 575 0.86 (0.80- 0.94 (0.88- 0.99 (0.919 0.92)
1.08)
1.01)
0.91)
1.10)
1.01)
w
4797
846 1.00
1.00
1.00
4
0.72 (0.46- 0.88 (0.55- 1.03 (0.590.48 (0.27- 0.61 (0.34- 0.66 (0.3282 1.13)
1.10)
1.81)
0.86)
1.37)
1.39)
1.00
ie
Probable diabetes
ev
rr
Unknown diabetes
1.00
1.13 (0.721.37 (1.10- 1.49 (1.20- 1.48 (1.13- 128 0.77 (0.540.88 (0.62- 1.76)
1.08)
1.85)
1.94)
1.69)
1.24)
107
0.69 (0.440.54 (0.370.44 (0.38- 0.48 (0.41- 0.49 (0.410.63 (0.43- 1.09)
163 0.77)
0.55)
0.59)
0.50)
0.91)
0.58 (0.410.50 (0.370.72 (0.63- 0.77 (0.67- 0.77 (0.650.52 (0.38- 0.84)
0.67)
0.89)
0.93)
0.83)
0.71)
Diabetes mellitus
No diabetes
1.00
1.00
ee
0.33 (0.27- 0.42 (0.34- 0.53 (0.41- 395 0.33 (0.28- 0.42 (0.35- 0.55 (0.430.70)
0.40)
0.41)
0.51)
0.69)
0.51)
Blood pressure
8180
Unknown
mild/moderate
hypertension
3787
Unknown severe
hypertension
2745
Known hypertension
325
637 1.00
8
0.81 (0.75- 1.01 (0.93- 1.10 (0.990.86 (0.79483 0.93)
1.09)
0.87)
1.22)
7
0.92 (0.74- 1.34 (1.08- 1.52 (1.140.85 (0.77308 1.08)
1.15)
1.68)
2.02)
1.00
1.00
1.00
1.00
1.00
ly
No hypertension
on
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
1.01 (0.92- 1.01 (0.921.10)
1.12)
1.27 (0.99- 1.48 (1.091.62)
2.02)
0.45 (0.41- 0.59 (0.54- 0.69 (0.61- 311 0.43 (0.39- 0.54 (0.50- 0.64 (0.570.49)
0.60)
0.72)
8 0.47)
0.65)
0.77)
For peer review only - http://bmjopen.bmj.com/site/about/guidelines.xhtml
Page 47 of 48
STROBE 2007 (v4) Statement—Checklist of items that should be included in reports of cross-sectional studies
Section/Topic
Title and abstract
Item
#
1
Introduction
Recommendation
Reported on page #
Fo
(a) Indicate the study’s design with a commonly used term in the title or the abstract
2
(b) Provide in the abstract an informative and balanced summary of what was done and what was found
2
rp
Background/rationale
2
Explain the scientific background and rationale for the investigation being reported
Objectives
3
State specific objectives, including any prespecified hypotheses
Study design
4
Present key elements of study design early in the paper
Setting
5
Describe the setting, locations, and relevant dates, including periods of recruitment, exposure, follow-up, and data
collection
5
Participants
6
(a) Give the eligibility criteria, and the sources and methods of selection of participants
5
Variables
7
Clearly define all outcomes, exposures, predictors, potential confounders, and effect modifiers. Give diagnostic criteria, if
Data sources/
8*
For each variable of interest, give sources of data and details of methods of assessment (measurement). Describe
Bias
9
Describe any efforts to address potential sources of bias
Study size
10
Explain how the study size was arrived at
Quantitative variables
11
Explain how quantitative variables were handled in the analyses. If applicable, describe which groupings were chosen and
ee
Methods
rr
4
2-3
ev
applicable
measurement
iew
comparability of assessment methods if there is more than one group
on
why
Statistical methods
12
3-4
(a) Describe all statistical methods, including those used to control for confounding
ly
(b) Describe any methods used to examine subgroups and interactions
(c) Explain how missing data were addressed
5-8
5-8
7-8
5
5-8
7-8
8
Table 2
(d) If applicable, describe analytical methods taking account of sampling strategy
-
(e) Describe any sensitivity analyses
-
Results
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Participants
13*
(a) Report numbers of individuals at each stage of study—eg numbers potentially eligible, examined for eligibility,
5
confirmed eligible, included in the study, completing follow-up, and analysed
Descriptive data
14*
(b) Give reasons for non-participation at each stage
5
(c) Consider use of a flow diagram
-
Fo
(a) Give characteristics of study participants (eg demographic, clinical, social) and information on exposures and potential
confounders
rp
Table 1
(b) Indicate number of participants with missing data for each variable of interest
Table 1
Outcome data
15*
Report numbers of outcome events or summary measures
Main results
16
(a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates and their precision (eg, 95% confidence
interval). Make clear which confounders were adjusted for and why they were included
Table 1
ee
Table 2
Page 9-11
(b) Report category boundaries when continuous variables were categorized
rr
5-7
(c) If relevant, consider translating estimates of relative risk into absolute risk for a meaningful time period
Other analyses
17
Report other analyses done—eg analyses of subgroups and interactions, and sensitivity analyses
ev
Discussion
-
10-11
Key results
18
Summarise key results with reference to study objectives
Limitations
19
Discuss limitations of the study, taking into account sources of potential bias or imprecision. Discuss both direction and
magnitude of any potential bias
11-13
Interpretation
20
Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results from
similar studies, and other relevant evidence
13-14
Generalisability
21
Discuss the generalisability (external validity) of the study results
22
Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on
Other information
Funding
which the present article is based
iew
11
on
11
ly
15
*Give information separately for cases and controls in case-control studies and, if applicable, for exposed and unexposed groups in cohort and cross-sectional studies.
Note: An Explanation and Elaboration article discusses each checklist item and gives methodological background and published examples of transparent reporting. The STROBE
checklist is best used in conjunction with this article (freely available on the Web sites of PLoS Medicine at http://www.plosmedicine.org/, Annals of Internal Medicine at
http://www.annals.org/, and Epidemiology at http://www.epidem.com/). Information on the STROBE Initiative is available at www.strobe-statement.org.
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Is there an association between disease
ignorance and self-rated health? The HUNT
Study, a cross-sectional survey
Pål Jørgensen, Arnulf Langhammer, Steinar Krokstad and Siri Forsmo
BMJ Open 2014 4:
doi: 10.1136/bmjopen-2014-004962
Updated information and services can be found at:
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