Sutter Surgical Hospital - North Valley

Transcription

A Community Health Needs Assessment
of the
Hospital Service Area
Conducted on behalf of:
455 Plumas Blvd.
Yuba City, CA 95991
Conducted by:
Valley Vision
July 2013
2
Table of Contents
Introduction .................................................................................................................................... 5
“Health Need” and Objectives of the Assessment ......................................................................... 5
Organization of the Report ............................................................................................................. 6
Methodology Overview .................................................................................................................. 6
Defining the HSA ......................................................................................................................... 6
Data and Indicators ..................................................................................................................... 7
Key Informant Interviews ....................................................................................................... 7
Health Outcomes .................................................................................................................... 8
Health Drivers ......................................................................................................................... 9
Analysis ....................................................................................................................................... 9
Primary Data ........................................................................................................................... 9
Secondary Data ....................................................................................................................... 9
Findings ......................................................................................................................................... 10
Description of the HSA .............................................................................................................. 10
Racial and Ethnic Makeup ..................................................................................................... 10
Community Vulnerability ...................................................................................................... 10
Health Outcomes ...................................................................................................................... 11
Diabetes, Hearth Disease, Hypertension, and Stroke ....................................................... 13
Mental Health, Substance Abuse and Self-Inflicted Injury ............................................... 14
Respiratory: Chronic Obstructive Pulmonary Disease (COPD) and Asthma ..................... 16
Health Drivers ........................................................................................................................... 16
Behavioral Data ..................................................................................................................... 17
Obesity, Overweight, and Vegetable Consumption ......................................................... 17
Physical Activity ................................................................................................................ 17
Environmental Data .............................................................................................................. 17
Safety Profile ..................................................................................................................... 17
Crime Rates ................................................................................................................... 17
Assault, Accidents, and Unintentional Injury................................................................ 18
Motor Vehicle Crash Death Rate .................................................................................. 20
Food Environment ............................................................................................................ 20
mRFEI ............................................................................................................................ 20
Food Deserts ................................................................................................................. 21
Health Assets..................................................................................................................... 22
Socio-economic Indicators .................................................................................................... 24
Health Needs and Health Improvement ................................................................................... 24
What Would Help? ................................................................................................................ 26
Limits ............................................................................................................................................. 26
Conclusion ..................................................................................................................................... 27
Appendices.................................................................................................................................... 28
3
Lists of Tables
Table 1: Health outcome data used in the CHNA reported as ED visits, hospitalization, and
mortality.................................................................................................................................. 8
Table 2: Socio-demographic, behavioral, and environmental data profiles used in the CHNA ..... 9
Table 3: Total population (and percent of total) with racial and ethnic make-up of the SSHNV
HSA compared to state totals ............................................................................................... 10
Table 4: Age-adjusted all-cause mortality rate, infant mortality, and life expectancy for SSHNV
HSA, compared to county and state benchmarks ................................................................ 12
Table 5: Mortality rates for SSHNV HSA compared to county and state benchmarks ................. 13
Table 6: Emergency department visitation for various causes for SSHNV HSA compared to
county and state benchmarks ............................................................................................... 14
Table 7: Hospitalizations for various causes for SSHNV HSA compared to county and state
benchmarks ........................................................................................................................... 14
Table 8: Emergency department visits due to mental health, substance abuse, and self-inflicted
injury compared to county and state benchmarks ............................................................... 15
Table 9: Hospitalizations due to mental health, substance abuse, and self-inflicted injury
compared to county and state benchmarks ......................................................................... 15
Table 10: Emergency department visits and hospitalizations due to COPD and asthma compared
to county and state benchmarks .......................................................................................... 16
Table 11: Percent overweight, obese, and not eating at least five servings of fruits and
vegetables daily for SSHNV HSA ........................................................................................... 17
Table 12: Emergency department visit rates for assault, accidents, and unintentional injury
compared to county and state benchmarks ......................................................................... 19
Table 13: Hospitalization rates for assault, accidents, and unintentional injury compared to
county and state benchmarks............................................................................................... 19
Table 14: Socio-demographic characteristics of populations in the SSHNV HSA and Yuba and
Sutter Counties ..................................................................................................................... 24
Table 15: Health needs and health drivers for the SSHNV HSA ................................................... 25
Table 16: Summary of suggestions to improve community health taken from key informant
interviews .............................................................................................................................. 26
4
List of Figures
Figure 1: Sutter Surgical Hospital - North Valley hospital service area .......................................... 7
Figure 2: Sutter Surgical Hospital - North Valley service area map of vulnerability .................... 11
Figure 3: Major crimes by municipality as reported by the California Attorney General’s Office,
2010 ...................................................................................................................................... 18
Figure 4: mRFEI by census tract in the SSHNV HSA ...................................................................... 21
Figure 5: USDA Defined Food Deserts Tracts: Kaiser Permanente CHNA Data Platform/US
Department of Agriculture, 2011.......................................................................................... 22
Figure 6: Federal defined primary medical care health professional shortage areas as designated
by the Bureau of Health Professionals, 2011........................................................................ 23
5
Introduction
In early 2010, the Patient Protection and Affordable Care Act was enacted. The
legislation requires that nonprofit hospitals conduct a community health needs assessment
(CHNA) every three years. Based on the results of this assessment hospitals must develop a
strategic implementation plan detailing how they will address the needs identified in the CHNA.
Nonprofit hospitals are required to submit these plans annually as part of their Internal
Revenue Service Form 990. The federal law extends the requirements to virtually all hospitals
operating in the US, defining a “hospital organization” as “an organization that operates a
facility required by a State to be licensed, registered, or similarly recognized as a hospital,” and
“any other organization that the Secretary determines has the provision of hospital care as its
principal function or purpose constituting the basis for its exemption under section 501(c)(3).”1
In accordance with these legislative requirements, Sutter Surgical Hospital - North Valley
(SSHNV) of Yuba City, California conducted a CHNA of the hospital service area (HSA). Valley
Vision, Inc. conducted the CHNA over a period of months. Valley Vision (www.valleyvision.org)
is a nonprofit 501(c)(3) consulting firm serving a broad range of communities across Northern
California. The organization’s mission is to improve quality of life through the delivery research
on important topics such as healthcare, economic development, and sustainable environmental
practices. Valley Vision has conducted multiple CHNAs across an array of communities for over
seven years. As the lead consultant, Valley Vision assembled a team of experts from multiple
sectors to conduct the assessment that included: 1) a public health expert with over a decade
of experience in conducting CHNAs, 2) a geographer with expertise in using GIS technology to
map health-related characteristics of populations across large geographic areas, and 3)
additional public health practitioners and consultants to collect and analyze data.
“Health Need” and Objectives of the Assessment
The CHNA was anchored and guided by the following objective:
To provide necessary information for Sutter Surgical Hospital - North Valley’s community
health improvement plan, identify the health needs of the hospital’s defined service area,
and contributing factors that create both barriers and opportunities for these
populations to live healthier lives.
The World Health Organization defines health needs as “objectively determined
deficiencies in health that require health care, from promotion to palliation.” 2 Building on this
1
Notice 2011-52, Notice and Request for Comments Regarding the Community Health Needs Assessment
Requirements for Tax-exempt Hospitals; retrieved from: http://www.irs.gov/pub/irs-drop/n-11-52.pdf
2
Expert Committee on Health Statistics. Fourteenth Report. Geneva, World Health Organization, 1971. WHO
Technical Report Series No. 472, pp 21‐22.
6
and the definitions compiled by Kaiser Permanente3, the CHNA used the following definitions
for health need and health driver:
Health Need: A poor health outcome and its associated driver.
Health Driver: A behavioral, environmental, and/or clinical factor, as well as more
upstream social economic factors, that impact health.
Organization of the Report
The following pages contain the results of the needs assessment. The report is organized
accordingly: first, the methodology used to conduct the needs assessment is described. Here,
the study area, or HSA, is identified and described, data and variables used in the study are
outlined, and the analytical framework used to interpret these data is articulated. Further a
detailed description of the methodology is contained the appendices.
Next, the study findings are provided beginning with an examination of health outcomes
for the HSA, followed by health drivers. These include health behavior indicators as well as
indicators that examine the physical environment of the HSA. The report identifies specific
health needs of the HSA, and closes with recommendations to improve community health.
Methodology Overview
Defining the HSA
The HSA was determined by analyzing patient discharge data where it was determined
that approximately 74% of all patients resided in four ZIP codes that were located in two
counties. ZIP codes 95901 (Marysville and Linda areas) and 95961 (Olivehurst area) are both
located in Yuba County. ZIP codes 95991 (south central Yuba City area) and 95933 (western
Yuba City area) are both located in Sutter County. A map of the service area is shown in Figure 1.
ZIP code boundaries in rural communities are sometimes not representative of population
distributions across a ZIP code. To display where populations reside among the ZIP code
boundaries note above, population density within each ZIP code is also shown in Figure 1.
3
Community Health Needs Assessment Toolkit – Part 2. (September, 2012). Kaiser Permanente Community Benefit
Programs.
7
Figure 1: Sutter Surgical Hospital - North Valley hospital service area
Data and Indicators
Health data used in the CHNA were both primary and secondary data. Primary data
included key informant interviews with local area experts. Secondary data included quantitative
data that addressed both health outcomes and behavioral and environmental variables
described as health drivers.
Key Informant Interviews
Key informants were health and community experts familiar with populations and
geographic areas residing within the HSA. To gain a deeper understanding of the health issues
pertaining to chronic disease and the populations living in these vulnerable communities input
from six key informant interviews was collected and analyzed using a theoretically grounded
interview guide (see interview protocol in Appendix A). Analysis was conducted on each
interview to identify key themes. A list of all key informants interviewed, including name,
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professional title, date of interview, and a description of their knowledge and experience is
detailed in Appendix B.
Health Outcomes
Data were included in the study only if they met the following standards:
1. All data were to be sourced from credible and reputable sources.
2. Data must be consistently collected and organized in the same way to allow for
future trending.
All indicators listed below were examined at the ZIP code level unless noted otherwise.
National, county, state, and Healthy People 2020 targets (when available) were used as
benchmarks to determine severity. Rates above any benchmark are denoted by bold text in the
tables. All rates are reported as per 10,000 of population unless noted otherwise. Health
outcome indicator data were adjusted using Empirical Bayes Smoothing, where possible, to
increase the stability of estimates by reducing the impact of the small number problem. To
provide relative comparison across ZIP codes, rates of ED visits and hospitalization rates for
heart disease, diabetes, hypertension, and stroke were age-adjusted to reduce the influence of
age. (Appendix C contains a detailed methodology of all data processing and data sources).
Secondary quantitative data used in the assessment include those listed in Tables 1 and 2. Table
1 lists health outcome indicators, and Table 2 lists health drivers.
Table 1: Health outcome data used in the CHNA reported as ED visits, hospitalization, and
mortality
ED and Hospitalization4
Accidents
Hypertension*
Asthma
Assault
Mental Health
Substance Abuse
Cancer
Stroke*
Chronic Obstructive
Pulmonary Disease
Unintentional Injuries
Diabetes*
Self-Inflicted Injury
Heart Disease*
*Age-adjusted by 2010 California standard population
4
5
Mortality5
All-Cause Mortality*
Infant Mortality
Alzheimer’s Disease
Cancer
Chronic Lower
Respiratory Disease
Injuries
Life Expectancy
Diabetes
Renal Disease
Heart Disease
Stroke
Hypertension
Suicide
Office of Statewide Health Planning and Development, ED Visits and Hospitalization, 2011
California Department of Public Health, Deaths by Cause, 2010
Liver Disease
9
Health Drivers
Health drivers are behavioral, environmental, and/or clinical factors, as well as more
upstream social economic factors, that impact health. Health driver indicators used in the
report are noted in Table 2.
Table 2: Socio-demographic, behavioral, and environmental data profiles used in the CHNA







Socio-Demographic Data
Total Population
High School Graduation
Family Makeup
Percent Uninsured
Poverty Level
Unemployment
Age
Race/Ethnicity
Behavioral and Environmental Profiles
Safety Profile
Food Environment Profile
 Food Deserts
Major Crimes
 modified Retail Food Environment Index
Assault
(mRFEI)
Unintentional Injury
 Fruit and vegetable consumption
Accidents
Motor vehicle crash death rate
Active Living Profile
Physical Wellbeing Profile
 Age-adjusted overall mortality
Physical activity
 Life expectancy
Overweight & obese
 Infant mortality
 Health Professional Shortage Areas
(primary care, dental, etc.)
 Health assets
 Excessive drinking
Analysis
Primary Data
A standard interview protocol was used in all key informant interviews (see Appendix A).
Notes were taken during each interview highlighting comments that addressed questions in the
protocol. These individual interview notes were analyzed with all others to identify recurring
themes.
Secondary Data
The primary method of analyzing all data was comparisons to benchmarks. These
included national, county, state, and Healthy People 2020 benchmarks when available, as well
as intra HSA comparisons among the four ZIP codes included in the study.
10
Findings
Description of the HSA
Racial and Ethnic Makeup
The HSA is home to approximately 135,000 residents. The racial and ethnic make up of
the community is show in Table 3.
Table 3: Total population (and percent of total) with racial and ethnic make-up of the SSHNV
HSA compared to state totals
ZIP Code
95901
Marysville
95961
Olivehurst
95991
Yuba City
95993
Yuba City
HSA Totals
Total
Pop.
32,266
25,552
40,719
35,798
134,335
Asian
(%)
Native
Hawaiian/
Pacific
Islander
(%)
2 or
More
races
(%)
Other
race
(%)
Hispanic
(%)
White
(%)
Black
(%)
American
Indian
(%)
8,343
(25.9)
7,225
(28.3)
14,306
(35.1)
6,493
(18.1)
18,105
(56.1)
14,135
(55.3)
20,131
(49.4)
18,088
(50.5)
890
(2.8)
625
(2.4)
944
(2.3)
637
(1.8)
255
(0.8)
549
(2.1)
226
(0.6)
353
(1.0)
2,893
(9.0)
1,582
(6.2)
3,401
(8.4)
8,629
(24.1)
21
(0.1)
192
(0.8)
86
(0.2)
138
(0.4)
1,71
(5.3)
989
(3.9)
1,58
(3.9)
1,35
(3.8)
45
(0.1)
254
(1.0)
39
(0.1)
107
(0.3)
36,367
(27.1)
70,459
(52.5)
3,096
(2.3)
1,383
(1.0)
16,505
(12.3)
437
(0.3)
5,642
(4.2)
445
(0.3)
(40.7)
(5.8)
(0.4)
(12.9)
(0.4)
(2.4)
(0.3)
CA (Percentages
(37.2)
Only)
(Table Source: US Census, 2010)
Whites accounted for over half of the HSA’s residents (52.5% compared to all of CA
40.7%). Other groups that comprised the bulk of the HSA’s population included Hispanics
(27.1%), Asians (12.3%), Multi-race (2.4%), Blacks (2.3%), and American Indians (1.0%).
Community Vulnerability
A key to understanding community health is understanding community vulnerability to
poor or unwanted health outcomes. This is accomplished by examining certain sociodemographics, often referred to as the social determinants of health, in order to identify areas
of the HSA with high vulnerability to unwanted health outcomes. To accomplish this, race and
ethnicity, household makeup, income, and age variables were combined into a vulnerability
index that described the level of vulnerability of each census tract. This index was then mapped
for the entire HSA. A tract was considered more vulnerable, or more likely to have higher
unwanted health outcomes than others in the HSA, if it had higher: 1) percent non-White or
Hispanic population; 2) percent single female parent headed households; 3) percent below
125% of the poverty level; 4) percent under five years old; and 5) percent 65 years of age or
11
older living in the census tract. Figure 2 shows the vulnerability index for each census tract
located all or in part of the SSHNV HSA. In the map, darker gradient colors indicate higher
vulnerability.
Figure 2: Sutter Surgical Hospital - North Valley service area map of vulnerability
Central Yuba City, the Linda area, and portions of Olivehurst were areas identified with
the highest vulnerability in the HSA.
Health Outcomes
By some standards, both Yuba and Sutter Counties are not as healthy as other California
counties. The Robert Wood Johnson Foundation (RWJF) produces County Health Rankings and
Roadmaps for nearly every county in the United States. This report produces county-level
estimates that are based on data collected by the Centers for Disease Control and Prevention’s
12
(CDC) Behavioral Risk Factor Surveillance System (BRFSS)6. This report for 2013 uses data
collection over a seven-year period (2005-2011).
The report combines both health outcome indicators (morbidity and mortality) and
other determinant of health (behavioral, socio-economic, etc.) into an overall ranking for each
county within any given state. Counties are assigned a ranked from best to worst, where a
ranking of one is the best ranking, two is second best, and so on.
The 2013 county health rankings indicated that Yuba County ranked 50 out of 57
counties in California for overall health. Sutter County ranked 33. Mortality rankings showed
Yuba County ranked 48 of 57, while Sutter County ranked 36. As to overall morbidity, Yuba
ranked 46 of 57, while Sutter ranked 30. Data indicated that 22% and 23% of Yuba and Sutter
County residents, respectively, self-reported their health as “poor or fair,” compared to a
California benchmark of 19%, and national benchmark of 10%. Further, Yuba and Sutter County
residents reported that that on average they experienced 4.1 and 4.3 days, respectively, over a
30-day period that their physical health was “not good.” This compares to the state benchmark
of 3.7 and national benchmark of 2.6 days.
Mortality
Mortality within the HSA is examined below. Table 4 displays the age-adjusted all-cause
mortality rate, infant mortality, and life expectancy for the HSA. Table 5 examines mortality
rates due to various causes and compares these with both county and state benchmarks. Rates
that appear in boldface type exceed one or more benchmarks. ZIP codes are compared to both
county and state benchmarks.
Table 4: Age-adjusted all-cause mortality rate, infant mortality, and life expectancy for SSHNV
HSA, compared to county and state benchmarks
ZIP Code
95901
95961
Community
Marysville
Olivehurst
Yuba County
95991
Yuba City
95993
Yuba City
Sutter County
CA State
National
(Table Source: CDPH, 2010)
6
All-cause
Mortality
81.2
74.9
77.6
79.9
63.1
73.0
63.3
n/a
Infant Mortality
Life Expectancy
4.2
5.9
5.2
5.5
4.0
5.2
5.2
n/a
74.8
79.7
n/a
77.2
82.0
n/a
80.47
78.68
See: http://www.countyhealthrankings.org
Henry J. Kaiser Family Foundation State Health Facts, 2007. Retrieved from:
http://www.statehealthfacts.org/profileind.jsp?ind=784&cat=2&rgn=6
7-8
13
Both Sutter and Yuba Counties had all-cause mortality rates that exceeded the state
benchmark. Within the HSA, the Marysville/Linda area had the highest all-cause mortality rate,
which was nearly 125% of the state benchmark. This same ZIP code (95901) had the lowest life
expectancy among all others at 74.8 years, while western Yuba City (95933) had the highest at
82.0 year. Central/southern Yuba City (95991) had the second highest all-cause mortality rate at
79.9/10,000. Olivehurst (95961) had an infant mortality rate that exceeded both county and
state benchmarks.
Table 5: Mortality rates for SSHNV HSA compared to county and state benchmarks
Mortality due to:
ZIP Code
Community
95901
Marysville
95961
Olivehurst
Yuba County
95991
Yuba City
95993
Yuba City
Sutter County
CA State
(Table Source: CDPH, 2010)
Heart
Disease
Stroke
Cancer
Injury
Diabetes
21.4
12.8
12.4
22.1
14.2
13.6
11.5
3.1
3.0
2.5
4.2
5.0
4.0
3.5
19.8
13.1
15.2
15.2
17.4
15.3
14.3
5.4
3.4
5.0
3.3
3.6
3.7
2.7
1.5
2.5
1.4
1.5
2.1
1.6
1.8
Chronic
Lower
Resp.
Disease
6.1
3.1
4.2
7.8
3.6
4.9
3.6
All four ZIP codes in the HSA exceeded both county and state benchmarks for mortality
due to heart disease, and all four exceeded county benchmarks for mortality due to stroke.
Both Marysville/Linda area (95901) and central/southern Yuba City area (95991) were nearly or
over twice the state rate for heart disease and chronic lower respiratory disease. Mortality due
to injury for Yuba County was nearly twice that of the state rate, and both Yuba County ZIP
codes exceeded the county benchmark.
Morbidity
Below ED visits and hospitalization are examined for chronic diseases (diabetes, heart
disease, hypertension, and stroke), mental health, including substance abuse and suicide, and
respiratory conditions including asthma and chronic obstructive pulmonary disease (COPD). In
the tables that follow all rates were age-adjusted to remove the effects of age. Also, each rate is
stated per 10,000 of population.
Diabetes, Hearth Disease, Hypertension, and Stroke
Key informants consistently pointed to chronic diseases as a primary health issue in the
HSA. Tables 6 and 7 examine both ED visitation rates, as well as hospitalizations for the HSA for
diabetes, heart disease, hypertension, and stroke.
14
Table 6: Emergency department visitation for various causes for SSHNV HSA compared to
county and state benchmarks
ZIP Code
Community
95901
Marysville
95961
Olivehurst
Yuba County
95991
Yuba City
95993
Yuba City
Sutter County
CA State
(Table Source: OSHPD, 2011)
Diabetes
316.4
313.5
262.9
234.1
154.0
185.7
188.4
Emergency Department Visits due to:
Heart Disease
Hypertension
195.2
577.2
198.4
514.7
162.0
469.4
137.3
408.3
88.3
250.3
107.9
314.8
93.1
365.6
Stroke
21.0
19.6
18.5
21.9
10.2
16.2
16.2
Both Yuba County ZIP codes (95901 and 95961) consistently exceeded both county and
state benchmarks for ED visits due the four chronic diseases examined. Both ZIP codes had
rates for heart disease that was over twice the state rate. Sutter County ZIP code 95991
(central/southern Yuba City) also exceeded both county and state benchmarks for all ED visit
dues to all four chronic diseases as well, while ZIP code 95993 (western Yuba City) exceeded
neither the county or state benchmark.
Table 7: Hospitalizations for various causes for SSHNV HSA compared to county and state
benchmarks
ZIP Code
Community
95901
Marysville
95961
Olivehurst
Yuba County
95991
Yuba City
95993
Yuba City
Sutter County
CA State
Diabetes
298.7
345.6
274.8
255.1
171.6
208.1
190.9
Hospitalizations due to:
Heart Disease
Hypertension
376.8
550.9
372.5
578.6
339.9
514.2
298.2
508.0
204.6
343.1
249.7
418.7
218.4
380.9
Stroke
66.9
66.6
65.8
65.0
42.9
51.9
51.8
When examining hospitalization rates a pattern similar to ED visits appears. Both Yuba
County ZIP codes and one Sutter County ZIP code exceeded both county and state benchmarks
for hospitalizations due to the four chronic diseases examined. Further, both Sutter and Yuba
County rates were higher than the statewide benchmark, in some instances notably so, while
Yuba County rates were consistently higher than Sutter County.
Mental Health, Substance Abuse and Self-Inflicted Injury
Key informants identified mental health and substance abuse as key health issues in the
HSA. Tables 8 and 9 examine rates of ED visits within the HSA due to mental health, substance
abuse, and self-inflicted injury, and compare these to county and state benchmarks.
15
Table 8: Emergency department visits due to mental health, substance abuse, and self-inflicted
injury compared to county and state benchmarks
ZIP Code
Community
95901
95961
Marysville
Olivehurst
Yuba County
95991
Yuba City
95993
Yuba City
Sutter County
CA State
Mental Health
Substance Abuse
264.4
818.3
17.5
141.0
479.8
7.3
184.1
589.0
11.7
178.2
510.1
12.0
95.8
214.9
5.7
127.4
340.1
8.6
130.9
232.0
7.9
Rates for ZIP codes 95901 (Marysville/Linda area), 95961 (Oliverhurst), and 95991
(central/southern Yuba City) exceeded county and state benchmarks for ED visits due to mental
health, substance abuse, and self-inflicted injury. Standout ZIP code 95901 (Marysville/Linda)
had rates for mental health that was over twice the state rate, rates for substance abuse that
were over three times the state rate, and rates for self-inflicted injury that were over double
the state rate. Further, Yuba County rates were notably higher than state rates as well, with a
county rate for substance abuse over two and a half times the state rate.
Table 9: Hospitalizations due to mental health, substance abuse, and self-inflicted injury
compared to county and state benchmarks
ZIP Code
95901
95961
Community
Marysville
Olivehurst
Yuba County
95991
Yuba City
95993
Yuba City
Sutter County
CA State
Mental Health
256.0
173.2
212.3
238.5
136.5
184.0
182.1
Substance Abuse
324.7
4.7
225.6
5.1
271.6
4.8
237.7
4.0
118.3
2.6
178.2
3.3
143.8
4.3
Yuba County ZIP code 95901 (Marysville/Linda area) rates for hospitalizations due to
mental health, substance abuse, and self-inflicted injury exceeded both county and state
benchmarks, as did Sutter County ZIP code 95991 (central/southern Yuba City). Similar to ED
visits, Yuba County as a whole had rates that were higher that state rates.
These findings were confirmed by the RWJF County Health Rankings & Roadmap report,
that indicated that 16% of all Yuba County residents and 13% of Sutter County residents
16
engaged in excessive or binge drinking8. These numbers compared to a state rate of 17% and a
national benchmark of 7%. Further, key informants identified substance abuse among both
youth and adults as a primary health issue in the HSA. Key informants spoke of anxiety and
depression among community residents brought on by the stress of living in poverty conditions
and sometimes living in isolation, and how many abused both legal and illegal substances as a
form of self-medication.
Respiratory: Chronic Obstructive Pulmonary Disease (COPD) and Asthma
Key informants pointed to respiratory issues as priority health issues in the HSA,
especially those brought on by tobacco use. Table 10 examines ED visits and hospitalizations
due to COPD and asthma for the HSA.
Table 10: Emergency department visits and hospitalizations due to COPD and asthma compared
to county and state benchmarks
ZIP Code
Community
95901
Marysville
95961
Olivehurst
Yuba County
95991
Yuba City
95993
Yuba City
Sutter County
CA State
ED Visits due to:
COPD
Asthma
449.0
221.5
330.2
186.4
356.0
187.3
271.5
153.9
136.4
79.3
207.0
117.8
202.3
134.9
COPD
Asthma
351.9
131.4
274.4
105.6
306.3
115.6
251.3
102.5
167.6
76.6
213.3
91.6
156.8
70.4
All ZIP codes in the HSA exceeded county and/or state benchmarks for respiratory
related illness, with one exception (95993 rates for ED visits due to asthma). Standout ZIP code
95901 (Marysville/Linda area) had rates notably higher that state benchmarks and somewhat
higher than county. As with other indicators, Yuba County rates as a whole were higher than
statewide rates.
Health Drivers
Health drivers are behavioral, environmental, and/or clinical factors, as well as more
upstream social economic factors, that impact health. For this report health drivers included
healthy behavior indicators such as active living and obesity rates, environmental indicators
such as crime rates, the food environment, and similar, and socio-economic indicators such as
poverty, education attainment, and more. These are examined in greater detail below.
8
The report defined excessive drinking as “consuming more than 4 (women) or 5 (men) alcoholic beverages on a
single occasion in the past 30 days, or heavy drinking, defined as drinking more than one (women) or 2 (men)
drinks per day on average.”
17
Behavioral Data
Obesity, Overweight, and Vegetable Consumption
The RWJF County Health Rankings & Roadmaps indicated that 31% of all adults in Yuba
County and 27% of adults in Sutter County were obese. These numbers compared to a state
rate of 24% and a national benchmark of 25%9. Key informants pointed to obesity as a
contributor to chronic diseases found in the HSA. Table 11 examines overweight and obese
rates, as well as the percent of residents in each ZIP code in the HSA not consuming at least five
servings of fruits and vegetables daily (no 5-a-day).
Table 11: Percent overweight, obese, and not eating at least five servings of fruits and
vegetables daily for SSHNV HSA
Percent
Overweight
95901
Marysville
33.6
95961
Olivehurst
34.4
95991
Yuba City
34.5
95993
Yuba City
33.7
(Table Source: Healthy City (www.healthycity.org))
ZIP Code
Community
Percent Obese
Percent no 5-a-day
25.7
25.5
26.5
22.9
52.4
52.2
53.5
53.6
Table 11 indicates that in each ZIP code in the HSA over one-third of all residents were
overweight, and, in all but one ZIP code (95993) one in four residents were obese. Last
Physical Activity
Drawing from data collected by the CDC’s BRFSS, the RWJF County Health Rankings &
Roadmaps report estimated that 25% of Yuba County residents and 22% of Sutter County
residents over the age of 20 reported no leisure time physical activity.
Further, key informants often described physical inactivity, due to a number of factors
such as limited parks and recreation opportunities, the lack of sidewalks in many rural areas,
and similar as a contributor to many of the poor health outcomes that existed in the
community.
Environmental Data
Safety Profile
Crime Rates
9
See: www.countyhealthrankings.org
18
Crime rates in a community are considered health drivers for a number of reasons. In
communities where crime rates are high, residents often feel unsafe out-of-doors and will avoid
physical activity for themselves and children. Figure 3 shows major crimes by municipality as
reported by various jurisdictions. Darker colored areas denote higher rates of major crime,
including homicide, forcible rape, robbery, aggravated assault, burglary, motor vehicle theft,
larceny, and arson.
Figure 3: Major crimes by municipality as reported by the California Attorney General’s Office,
2010
The municipalities located with ZIP code 95901 (Marysville/Linda area) reported the
highest major crime rates. The Yuba City area that included both ZIP codes 95991 and 95993
had the second highest rates. The Olivehurst area (95961) had the lowest crime rates in the
HSA.
Assault, Accidents, and Unintentional Injury
19
ED visits and hospitalizations due to assault, accidents, and unintentional injury for
residents that lived in the HSA are examined below. (Accidents included those that involved a
pedestrian or bicyclist, and may or may not have involved an automobile). Table 11 examines
ED visits and Table hospitalization.
Table 12: Emergency department visit rates for assault, accidents, and unintentional injury
compared to county and state benchmarks
ZIP Code
Community
95901
95961
Marysville
Olivehurst
Yuba County
95991
Yuba City
95993
Yuba City
Sutter County
CA State
Unintentional
Assault
Accidents
Injury
46.9
20.0
1,045.6
28.3
17.2
846.3
34.5
16.5
910.5
32.2
13.0
736.1
13.0
8.1
423.3
22.5
10.5
595.3
29.4
15.6
651.8
Marysville/Linda area ZIP code 95901 had rates that exceeded both county and state
benchmarks for assault, accidents, and unintentional injury. Yuba City ZIP code 95991
(central/southern area) had rates that exceeded both county and state benchmarks in all but
one instance—ED rates for accidents. Yuba County rates exceeded both Sutter County and state
rates as well, a consistent trend in ED visit and hospitalization rates.
Table 13: Hospitalization rates for assault, accidents, and unintentional injury compared to
county and state benchmarks
Hospitalization due to:
ZIP Code
95901
95961
Community
Marysville
Olivehurst
Yuba County
95991
Yuba City
95993
Yuba City
Sutter County
CA State
Assault
Accidents
5.3
5.5
4.8
4.4
1.8
3.2
3.9
3.0
2.4
2.4
2.5
1.6
2.0
2.0
Unintentional
Injury
223.3
168.0
205.5
201.9
140.8
175.7
154.6
Both Yuba County ZIP codes exceeded both county and state benchmarks, and only one
Sutter County ZIP code (95991, central/southern Yuba City) exceeded both county and state
benchmarks for assault, accidents, and unintentional injury.
20
Motor Vehicle Crash Death Rate
The RWJF County Health Rankings & Roadmaps report details motor vehicle crash rates
for counties. This is a measure of deaths (the crude mortality rate per 100,000 of population)
due to traffic accidents involving a motor vehicle, including buses, motorcycles, ATVs, industrial,
agricultural, and construction vehicles (or bicyclists or pedestrians colliding with any motor
vehicle). The rate for Yuba and Sutter Counties is 19 (per 100,000 of population). This compares
to both state and national benchmarks of 10.
Food Environment
The general food environment of the HSA is examined below using two indicators: the
modified Retail Food Environmental Index (mRFEI) and food deserts.
mRFEI
The data displayed below provides information about the mRFEI developed by the CDC.
The mRFEI indicates the availability of healthy foods by census tract by comparing the
proportion of healthy food outlets to all available food outlets. Darker colors indicate low
proportions of healthy food outlets compared to all outlets in the tract, and lighter colors
indicate areas in which more fresh food outlets are available to community residents.
21
Figure 4: mRFEI by census tract in the SSHNV HSA
Large portions of 95961 (Olivehurst area) have no healthy retail food outlets. Also, the
more densely populated portion of ZIP code 95991 (central/southern Yuba City) also was
designated as having no healthy retail outlets, while some portions of 95993 (western Yuba
City) had both no and poor access. Also, portions of Marysville (located in ZIP code 95901) had
no or poor healthy food access.
Food Deserts
Figure 5 displays USDA defined food desert census tracts within or near the SSHNV HSA.
Such tracts are designated by the federal government as census tracts in which at least 500
people or 33% of the population or live more than one mile from a supermarket or large
grocery store.
22
Figure 5: USDA Defined Food Deserts Tracts: Kaiser Permanente CHNA Data Platform/US
Department of Agriculture, 2011
The northern portion of ZIP code 95991 (central Yuba City) contains food deserts, as
does portions of western Yuba City in ZIP code 95993. A sizeable portion of the Olivehurst area
ZIP code (95961) contains food deserts while the Marysville/Linda area has two tracts that are
food deserts. This pattern, though not identical, has some similarities to the mRFEI as described
above.
Health Assets
Communities require resources in order to maintain and improve the health of the
residents. These include health related assets such as access to health care professionals and
related community-based organizations.
Health Professional Shortage Areas (HPSAs) are designated by the US Government
Health Resources and Services Administration (HRSA) as having shortage of primary medical
23
care, dental, or mental health providers and may be geographic (a county or service area),
demographic (low income population) or institutional (comprehensive health center, federally
qualified health center, or other public facility). Figure 6 shows federally designated primary
medical care HPSAs within the SSHNV HSA.
Figure 6: Federal defined primary medical care health professional shortage areas as designated
by the Bureau of Health Professionals, 2011
Figure 6 shows that virtually all of ZIP codes 95901 (Marysville/Linda area) and all of
95961 (Olivehurst area) are HPSAs. Further, analysis of data indicated that 46 distinct assets are
located in the HSA area (including adjacent ZIP codes beyond those of the HSA). These include
health care providers (primary and specialty), substance abuse treatment, family resource
centers, Yuba and Sutter County services, dental care, food bank, senior center, women’s
shelter, tribal health centers, and faith-based organizations. A complete list of these services is
available in Appendix D. The presence of these organizations presents SSHNV an opportunity to
improve community health through increased collaboration and coordination of services.
24
Socio-economic Indicators
There is an established relationship between health outcomes and socio-demographic
characteristics of populations. These characteristics are displayed below in Table 14.
Table 14: Socio-demographic characteristics of populations in the SSHNV HSA and Yuba and
Sutter Counties
ZIP
Code
Community
Median
Age
Percent
with a HS
Diploma
or Higher
77.8
72.4
78.3
75.0
82.4
78.2
80.8
Median
Income
Percent
Individuals
Below
Poverty
21.1
20.7
20.3
19.5
9.3
15.2
14.4
Percent
Percent
Uninsured
Unemployed10
(County
(County Only)
Only)
95901
Marysville
32.3
$43,513
95961
Olivehurst
29.5
$44,960
Yuba County
32.1
$46,617
19
18.2
95991
Yuba City
31.7
$42,009
95993
Yuba City
37.4
$61,368
Sutter County
34.5
$50,010
22
18.8
California
35.2
$61,632
21
11.7
United States
11
5.0
(Table Sources: % uninsured and unemployed: Robert Wood Johnson Foundation County Health
Rankings & Roadmap; all other data: US Census, 2010)
The relationship between health outcomes and social characteristics can be seen when
examining data in the SSHNV HSA and Yuba and Sutter Counties. For example, throughout this
report Yuba City ZIP code 95993 (western Yuba City area) consistently demonstrated some of
the best health outcomes of all ZIP codes in the HSA. This same ZIP code also had the highest
life expectancy (82 years), highest median income, highest education attainment, and lowest
percent of individuals living in poverty. The inverse of this trend is also apparent when
considering 95901, the Marysville/Linda area. This ZIP code consistently had the worst health
outcomes in many of the measures noted above, while it had the highest percent of individuals
living in poverty, the second lowest median income in the HSA (and 30% lower than the state
median income), and the lowest life expectancy in the HSA (74.8 years).
Health Needs and Health Improvement
Earlier in this report a health need was defined as a poor health outcome and its
associated driver, where a health driver was defined as a behavioral, environmental, and/or
clinical factor, as well as more upstream social economic factors, that impact health. Table 15
articulates the SSHNV HSA health needs by identifying health issues (outcomes) that were
pointed out by community health experts, the specific populations in the community most
affected by these health issues, and, again, turning to communities health experts, many of the
drivers of specific health issues are identified.
10
Unemployment is the percent of the civilian labor force, age 16 and older, that is unemployed but seeking work
25
The table is not presented as all-inclusive; rather it contains a summary of key findings
that emerged from the key informant interviews that were conducted as a part of this health
assessment.
Table 15: Health needs and health drivers for the SSHNV HSA
Biggest Health Issues in the
Community
 Chronic diseases –
diabetes, hypertension
 Respiratory problems –
asthma
 Mental and emotion
health issues –
depression, low selfesteem, trauma
 Dental issues
Who is Most Affected



Low income (consistent
among all
races/ethnicities
Hispanic/Latino
Females (more
vulnerable)
Causes the Health Issues Identified










Tobacco use
o Ease of acquiring tobacco
o An abundance of tobacco
shops
Limited access to healthcare
o No health insurance
o Isolation in foothills
o Limited transportation
Poverty
o High unemployment
o Generational
Education
o Low attainment (formal)
o Limited health literacy
Poor diet
o Unaffordable fresh foods
o Unavailability of fresh foods
o High concentration o fast
food restaurants
Obesity/physical inactivity
o Limited parks and rec
opportunities
o Lack of walk-able
communities
Lack of preventative care
Substance Abuse
o Ease of access of alcohol
o Self-medication to treat
depression
Policies that do not support health
and wellbeing
o Smoking
o Nutrition
Agricultural Community
o Dust and other particulates
in air
26
What Would Help?
As a part of each interview, key informants were asked to identify practices, policies,
and related interventions they believed would help to improve overall community health.
Further, key informants were asked to suggest specific practices SSHNV could undertake as a
part of its community health improvement plan that would help improve community health. A
summary of these suggestions and ideas is presented below in Table 16.
Table 16: Summary of suggestions to improve community health taken from key informant
interviews
What would help improve community health?








Education
o Raise community awareness around
health issues
o More formal education
Enhance transportation services
Acculturation for immigrant communities
More collaboration among agencies
Economic development
o Reduce poverty
o Improve unemployment
Increase community safety
o Reduce gang activities
o Reduce illegal drug possession
Create more places for residents to be active
Increase number of community clinics
What can SSHNV do to improve community
health?
 Develop referral programs with FQHCs with
sliding scale
 Offer diagnostic services
 Open doors to Medi-Cal and low income
residents
 Offer pro-bono services to low income
patients
 Develop a holistic approach to healthcare
 Offer prevention education
 Offer nutrition education in schools
 Focus on injury prevention
 Engage groups experiencing health disparities
 Take undocumented patients that cannot
afford treatment
 Become more active in the healthcare
consortium
 Become more collaborative with other
members of the healthcare community
Limits
Study limitations included difficulties acquiring secondary data and assuring community
representation via primary data collection. ED visit and hospitalization data used in this
assessment are markers of prevalence, but do not fully represent the prevalence of a disease in
a given ZIP code. Currently there is no publicly available data set with prevalence markers at the
sub county level for the core health conditions examined in this assessment: heart disease,
diabetes, hypertension, stroke, and mental health. Similarly, behavioral data sets at the subcounty level were difficult to obtain and were not available by race and ethnicity. California
Health Interview Survey (CHIS) data was from years 2003-2005.
As is common, assuring that the community voice is thoroughly represented in primary
data collection was a challenge. Further, information on assets such as small community-based
organizations was difficult to find and catalog in a systemic manner. Lastly, it is important to
27
understand that services and resources provided by the listed health assets can change
frequently, and this directory serves only as a snapshot in time of their offerings.
Conclusion
Public health researchers have helped expand our understanding of community health
by demonstrating that health outcomes are the result of the interactions of multiple, interrelated variables such as socio-economic status, individual health behaviors, access to health
related resources, cultural and societal norms, the built environment, and neighborhood
characteristics such as crime rates. The results of this assessment help to shine a light on the
relationships of some of the variables that were collected and analyzed to describe the
Communities of Concern.
Hospital community benefit managers and personnel can use this expanded
understanding of community health, along with the results of these assessments, to target
specific interventions and improve health outcomes in some of the area’s more vulnerable
communities. By knowing where to focus community health improvement plans, i.e. the
identified Communities of Concern, and the specific conditions and health outcomes
experienced by their residents, community benefit programs can develop plans to address the
underlying contributors to negative health outcomes.
28
Appendices
Appendix A
Key Informant Interview Protocol
Question #1: What are the biggest health issues, conditions, and/or diseases the community or
those you serve struggle with?
Question #2: Who within your community appear(s) to struggle with these issues the most?
Question #3: What do you think is causing these health issues and health conditions you’ve
described?
Question #4: What does the community have that helps improve or maintain health?
Question #5: Of all those noted above, what is the biggest thing needed to improve the overall
health of the community?
Questions #6: What does Sutter North Surgical Hospital need to know about the community?
What ideas do you have for the hospital to improve the health of the community?
Question #7: What else does our team need to know about your community that hasn’t already
been addressed?
29
Appendix B
List of Key Informants
Name & Title
Amerjit Bhattal, Assistant Director
of Human Services
Monica Brokenbrough, Center
Manager
Dr. Lou Anne Cummings, Health
Officer
Mary Delong, Director of Student
Nutrition and Warehouse
Agency
Area of Expertise
Date
Sutter County Public Health
Public Health
6/5/13
Planned Parenthood Mar Monte
Community
Health
6/5/13
Sutter County Public Health
Public Health
6/5/13
Yuba City Unified School District
Nutrition
6/5/13
Rachael Farell, PA-C, CEO
Harmony Health
Community
Health
6/6/13
Katrina Henry, Director of Nurses
Yuba County, Public Health
Division
Public Health
6/6/13
Michelle Laughlin, Site Manager
Tribal Health
Tribal Health
6/6/13
Susana Ramirex, Center Manager
Planned Parenthood Mar Monte
Community
Health
6/5/13
Appendix C
Data Dictionary and Processing
Introduction
The secondary data supporting the 2013 Community Health Needs Assessment was collected
from a variety of sources, and processed in multiple stages before it was used for analysis. This
document details those stages. It begins with a description of the approaches used to define ZIP
code boundaries, and the approaches that were used to integrate records reported for PO
boxes into the analysis. General data sources are then listed, followed by a description of the
basic processing steps applied to most variables. It concludes by detailing additional specific
processing steps used to generate a subset of more complicated indicators.
ZIP Code Definitions
All health outcome variables collected in this analysis are reported by patient mailing ZIP codes.
ZIP codes are defined by the US Postal Service as a physical location (such as a PO Box), or a set
of roads along which addresses are located. The roads that comprise such a ZIP code may not
form contiguous areas. These definitions do not match the approach of the US Census Bureau,
which is the main source of population and demographic information in the US. Instead of
measuring the population along a collection of roads, the Census reports population figures for
distinct, contiguous areas. In an attempt to support the analysis of ZIP code data, the Census
Bureau created ZIP Code Tabulation Areas (ZCTAs). ZCTAs are created by identifying the
dominant ZIP code for addresses in a given block (the smallest unit of Census data available),
and then grouping blocks with the same dominant ZIP code into a corresponding ZCTA. The
creation of ZCTAs allows us to identify population figures that, in combination the health
outcome data reported at the ZIP code level, allow us to calculate rates for each ZCTA. But the
difference in the definition between mailing ZIP codes and ZCTAs has two important
implications for analyses of ZIP level data.
First, it should be understood that ZCTAs are approximate representations of ZIP codes, rather
than exact matches. While this is not ideal, it is nevertheless the nature of the data being
analyzed. Secondly, not all ZIP codes have corresponding ZCTAs. Some PO Box ZIP codes or
other unique ZIP codes (such as a ZIP code assigned to a single facility) may not have enough
addressees residing in a given census block to ever result in the creation of a ZCTA. But
residents whose mailing addresses correspond to these ZIP codes will still show up in reported
health outcome data. This means that rates cannot be calculated for these ZIP codes
individually because there are no matching ZCTA population figures.
In order to incorporate these patients into the analysis, the point location (latitude and
longitude) of all ZIP codes in California (Datasheer, L.L.C., 2012) were compared to the 2010
ZCTA boundaries. All ZIP codes (whether PO Box or unique ZIP code) that were not included in
the ZCTA dataset were identified. These ZIP codes were then assigned to either ZCTA that they
fell inside of, or in the case of rural areas that are not completely covered by ZCTAs, the ZCTA to
which they were closest. Health outcome information associated with these PO Box or unique
ZIP codes was then added to the ZCTAs to which they were assigned.
31
Data Sources
Secondary data were collected in three main categories: demographic information, health
outcome data, and behavioral and environmental data. Table B1 below lists demographic
variables collected from the US Census Bureau, and lists the geographic level at which they
were collected. These demographic variables were collected at the Census block, tract, ZCTA,
and state levels. Census blocks are roughly equivalent to city blocks in urban areas, and tracts
are roughly equivalent to neighborhoods.
Table B1. Demographic variables collected from the US Census Bureau
Variable Name
Asian Population
Definition
Hispanic or Latino and Race,
Not Hispanic or Latino, Asian
alone
Black Population
Non-Hispanic or Latino, Black
or African American alone
Hispanic Population Hispanic or Latino and Race,
Hispanic or Latino (of any race)
Native American
Population
Geographic Level
Tract
Tract
Tract
Tract
Total Households
Not Hispanic or Latino,
American Indian and Alaska
Native alone
Not Hispanic or Latino, Native
Hawaiian and Other Pacific
Islander alone
Not Hispanic or Latino, White
alone
Total Households
Married
Households
Married-couple family
household
Tract
Single Female
Headed
Households
Single Male Headed
Female householder, no
husband present, family
household
Male householder, no wife
present, family household
Tract
Non-Family
Households
Nonfamily household
Pacific Islander
Population
White Population
Source
2010 American
Community Survey 5 Year
Estimates Table DP05
2010 American
2010 American
2010 American
Tract
2010 American
Tract
2010 American
2010 American
Estimates Table S1101
2010 American
2010 American
2010 American
2010 American
Tract
Tract
Tract
32
Variable Name
Population in
Poverty (Under
100% Federal
Poverty Level)
Population in
Poverty (Under
125% Federal
Poverty Level)
Population in
Poverty (Under
200% Federal
Poverty Level)
Population by Age
Group:
0-4, 5-14, 15-24,
25-34,45-54, 55-64,
65-74, 75-84, and
85 and over
Total Population
Total Population
Definition
Total poverty under .50; .50 to
.99
Geographic Level
Tract
Source
2010 American
Estimates Table C17002
.99; 1.00 to 1.24
Tract
2010 American
.99; 1.00 to 1.24; 1.25 to 1.49;
1.50 to 1.84; 1.85 to 1.99
Tract
2010 American
Total Population by Age Group
Tract
2010 American
Total Population
Tract
Total Population
Block
2010 American
2010 Census Summary
File 1 Table P1
2010 Census Summary
File 1 Table QTP14
Asian/Pacific
Islander Population
ZCTA, State
Total Population, One Race,
Asian, Not Hispanic or Latino;
Native Hawaiian and Other
Pacific Islander, Not Hispanic or
Latino
Black Population
Black or African American, Not
Hispanic or Latino
Hispanic Population Total Population, Hispanic or
Latino (of any race)
Native American
Population
American Indian and Alaska
Native, Non Hispanic or Latino
White Population
Total Population, Once Race,
White, Not Hispanic or Latino
Male Population
Total Male Population
ZCTA, State
Female Population
Total Female Population
ZCTA, State
Population by Age
Group:
Under 1, 1-4, 5-14,
Total Male and Female
Population by Age Group
ZCTA, State
ZCTA, State
2010 Census Summary
File 1 Table QTP14
ZCTA, State
2010 Census Summary
File 1 Table QTP3
2010 Census Summary
File 1 Table QTP14
ZCTA, State
ZCTA, State
2010 Census Summary
File 1 Table QTP14
2010 Census Summary
File 1 Table PCT12
2010 Census Summary
File 1 Table PCT12
2010 Census Summary
File 1 Table PCT12
33
Variable Name
15-24, 25-34,45-54,
55-64, 65-74, 7584, and 85 and over
Total Population
Definition
Geographic Level
Source
Total Population
ZCTA, State
2010 Census Summary
File 1 Table PCT12
Collected health outcome data included the number of emergency department (ED) discharges,
hospital (H) discharges, and mortalities associated with a number of conditions. ED and H
discharge data for 2011 were obtained from the Office of Statewide Health Planning and
Development (OSHPD). Table B2 lists the specific variables collected by ZIP code. These values
report the total number of ED or H discharges that listed the corresponding ICD9 code as either
a primary or any secondary diagnosis, or a principal or other E-code, as the case may be. In
addition to reporting the total number of discharges associated with the specified codes per ZIP
code, this data was also broken down by sex (male and female), age (under 1 year, 1 to 4 years,
5 to 14 years, 15 to 24 years, 25 to 34 years, 35 to 44 years, 45 to 54 years, 55 to 64 years, 65 to
74 years, 75 to 84 years, and 85 years or older), and normalized race and ethnicity (Hispanic of
any race, non-Hispanic White, non-Hispanic Black, non-Hispanic Asian or Pacific Islander, nonHispanic Native American).
Table B2. 2011 OSHPD Hospitalization and Emergency Department Discharge Data by ZIP code
Category
Chronic Disease
Respiratory
Mental Health
Injuries11
Cancer
Other Indicators
11
Variable Name
Diabetes
Heart Disease
Hypertension
Stroke
Asthma
Chronic Obstructive Pulmonary Disease (COPD)
Mental Health
Mental Health, Substance Abuse
Unintentional Injury
Assault
Self Inflicted Injury
Accidents
Breast Cancer
Colorectal Cancer
Lung Cancer
Prostate Cancer
Hip Fractures
Tuberculosis
HIV
ICD9/E-Codes
250
410-417, 428, 440, 443, 444,
445, 452
401-405
430-436, 438
493-494
490-496
290, 293-298, 301-302, 310-311
291-292, 303-305
E800-E869, E880-E929
E960-E969, E999.1
E950-E959
E814, E826
174, 175
153, 154
162, 163
185
820
010-018, 137
042-044
ICD9 code definitions for the Unintentional Injury, Self Inflicted Injury, and Assault variables were based on
definitions given by the Centers for Disease Control and Prevention (CDC, 2011)
34
STDs
Oral cavity/dental
West Nile Virus
Acute Respiratory Infections
Urinary Tract Infections (UTI)
Complications Related to Pregnancy
042-044, 090-099, 054.1, 079.4
520-529
066.4
460-466
599.0
640-649
Mortality data, along with the total number of live births, for each ZIP code in 2010 were
collected from the California Department of Public Health (CDPH). The specific variables
collected are defined in Table B4. The majority of these variables were used to calculate
specific rates of mortality for 2010. A smaller number of them were used to calculate more
complex indicators of wellbeing. To increase the stability of these more complex measures,
rates were calculated using values from 2006 to 2010. These variables include the total number
of live births, total number of infant deaths (ages under 1 year), and all cause mortality by age.
Table B3 consequently also lists the years for which each variable was collected.
Table B3. CDPH birth and mortality data by ZIP code
Variable Name
Total Deaths
Male Deaths
Female Deaths
Population by Age Group:
Under 1, 1-4, 5-14, 15-24, 2534,45-54, 55-64, 65-74, 75-84,
and 85 and over
Diseases of the Heart
Malignant Neoplasms (Cancer)
Cerebrovascular Disease (Stroke)
Chronic Lower Respiratory
Disease
Alzheimer’s Disease
Unintentional Injuries
(Accidents)
Diabetes Mellitus
Influenza and Pneumonia
Chronic Liver Disease and
Cirrhosis
Intentional Self Harm (Suicide)
Essential Hypertension &
Hypertensive Renal Disease
Nephritis, Nephrotic Syndrome
and Nephrosis
All Other Causes
Total Births
Births with Infant Birthweight
ICD10 Code
Years Collected
2010
2010
2010
2006-2010
I00-I09, I11, I13, I20-I51
C00-C97
I60-I69
J40-J47
2010
2010
2010
2010
G30
V01-X59, Y85-Y86
2010
2010
E10-E14
J09-J18
K70, K73-K74
2010
2010
2010
U03, X60-X84, Y87.0
I10, I12, I15
2010
2010
N00-N07, N17-N19, N25-N27
2010
Residual Codes
2010
2006-2010
2006-2010
35
Under 1500 Grams, 1500-2499
Grams
Behavioral and environmental data were collected from a variety of sources, and at various
geographic levels. Table B4 lists the sources of these variables, and lists the geographic level at
which they were reported.
36
Table B4. Behavioral and environmental variable sources
Category
Variable
Year
Definition
Healthy Eating/
Active Living
Overweight and
Obese
20032005
Overweight and
Obese
2009
No 5 a day Fruit and
Vegetable
Consumption
Modified Retail
Food Environment
Index (mRFEI)
20032005
Percent of population 18 and over with
self-reported height and weight
corresponding to overweight or obese
BMIs (BMI greater than 25)
The percent of the adult population (age
20 and older) that has a body mass index
(BMI) greater than or equal to 30 kg/m2.
Percent of population age 5 and over not
consuming five servings of fruit and
vegetables a day
Represents the percentage of all food
outlets in an area that are considered
healthy
Food Deserts
2011
USDA Defined food desert tracts
Tract
Physical Activity
2009
County
Crime
2010
Estimated percent of adults aged 20 and
over reporting no leisure time physical
activity
Major Crimes (Homicide, Forcible Rape,
Robbery, Aggravated Assault, Burglary,
Motor Vehicle theft, Larceny, Arson)
Motor Vehicle
Death Crash Rate
2013
Crude mortality rate per 100,000 of
population) due to traffic accidents
involving a motor vehicle
County
Safe Physical
Environments
2011
Reporting
Unit
ZIP Code
Data Source
County
Robert Wood Johnson
Foundation County Health
Rankings & Roadmaps
Healthy Cities/CHIS
ZIP Code
Tract
Municipality/
Jurisdiction
Healthy Cities/CHIS
Kaiser Permanente CHNA
Data Platform/ Centers for
Disease Control and
Prevention: Division of
Nutrition, Physical Activity,
and Obesity
Data Platform/ US
Department of Agriculture
Robert Wood Johnson
Rankings & Roadmaps
State of California
Department of Justice,
Office of the Attorney
General
(http://oag.ca.gov/crime/cjs
c-stats/2010/table11)
Robert Wood Johnson
Rankings & Roadmaps
37
Other
Indicators
Health Professional
Shortage Areas
(Primary Care)
2011
Unemployment
2011
Uninsured
2010
Federally designated primary care health
professional shortage areas, which may be
defined based on geographic areas or
distributions of people in specific
demographic groups
The percent of the civilian labor force, age
16 and older, that is unemployed but
seeking work
This measure represents the estimated
percent of the population under age 65
that has no health insurance coverage
Data Platform/ Bureau of
Health Professions
County
County
Robert Wood Johnson
Rankings & Roadmaps
Robert Wood Johnson
Rankings & Roadmaps
38
General Processing Steps
Rate Smoothing
All OSHPD and all single-year CDPH variables were collected for all ZIP codes in California. The
CDPH datasets included separate categories that included either patients who did not report
any ZIP code, or patients from ZIP codes in which the number of cases fell below a minimum
level. These patients were removed from the analysis. As described above, patient records in
ZIP codes not represented by ZCTAs were added to those ZIP codes corresponding to the ZCTAs
that they fell inside or were closest to. The next step in the analysis process was to calculate
rates for each of these variables. However, rather than calculating raw rates, empirical Bayes
smoothed rates (EBR) were created for all variables possible. Smoothed rates are considered
preferable to raw rates for two main reasons. First, the small population of many ZCTAs,
particularly those in rural areas, meant that the rates calculated for these areas would be
unstable; this is sometimes referred to as the small number problem. Empirical Bayes
smoothing seeks to address this issue by adjusting the calculated rate for areas with small
populations so that they more closely resemble the mean rate for the entire study area. The
amount of this adjustment is greater in areas with smaller populations, and less in areas with
larger populations.
Because the EBR were created for all ZCTAs in the state, ZCTAs with small populations that may
have unstable high rates had their rates “shrunk” to more closely match the overall variable
rate for ZCTAs in the entire state. This adjustment can be substantial for ZCTAs with very small
populations. The difference between raw rates and EBR in ZCTAs with very large populations,
on the other hand, is negligible. In this way, the stable rates in large population ZIP codes are
preserved, and the unstable rates in smaller population ZIP codes are shrunk to more closely
match the state norm. While this may not entirely resolve the small number problem in all
cases, it does make the comparison of the resulting rates more appropriate. Because the rate
for each ZCTA is adjusted to some degree by the EBR process, it also has a secondary benefit of
better preserving the privacy of patients within the ZCTAs.
EBR were calculated for each variable using the appropriate base population figure reported for
ZCTAs in the 2010 census: overall EBR for ZCTAs were calculated using total population, and sex,
age, and normalized race/ethnicity EBR were calculated using the appropriate corresponding
population stratification. EBR were calculated for every overall variable, but could not be
calculated for certain of the stratified variables. In these cases, raw rates were used instead.
The final rates in either case for H, ED, and the basic mortality variables were then multiplied by
10,000, so that the final rates represent H or ED discharges, or deaths, per 10,000 people.
Age Adjustment
The additional step of age adjustment was performed on the all-cause mortality variable as well
as four OSHPD reported ED and H conditions: diabetes, heart disease, hypertension, and stroke.
Because the occurrence of these conditions varies as a function of the age of the population,
differences in the age structure between ZCTAs could obscure the true nature of the variation
in their patterns. For example, it would not be unusual for a ZCTA with an older population to
39
have a higher rate of ED visits for stroke than a ZCTA with a younger population. In order to
accurately compare the experience of ED visits for stroke between these two populations, the
age profile of the ZCTA needs to be accounted for. Age adjusting the rates allows this to occur.
To age adjust these variables, we first calculated age stratified rates by dividing the number of
occurrences for each age category by the population for that category in each ZCTA. Age
stratified EBR were used whenever possible. Each age stratified rate was then multiplied by a
coefficient that gives the proportion of California’s total population for that age group as
reported in the 2010 Census. The resulting values are then summed and multiplied by 10,000
to create age adjusted rates per 10,000 people.
OSHPD Benchmark Rates
A final step was to obtain or generate benchmark rates to compare the ZCTA level rates to.
Benchmarks for all OSHPD variables were calculated at the HSA, county, and state levels by first,
assigning given ZIP codes to each level of analysis (HAS, county, or state); second, summing the
total number of cases and relevant population for all ZCTAs for each HSA, county, or the state;
and finally, dividing the total number of cases by the relevant population.
Benchmarks for CDPH variables were obtained from two sources. County and state rates were
found in the County Health Status Profiles 2010. Healthy People 2020 rates (U.S. Department of
Health and Human Services, 2012) were also used as benchmarks for mortality data.
Additional Well Being Variables
Further processing was also required for the two additional mortality based wellbeing variables,
infant mortality rate and life expectancy at birth. To develop more stable estimates of the true
value of these variables, their calculation was based on data reported by CDPH for the years
from 2006-2010. Because both ZIP code and ZCTAs can vary through time, the first step in this
analysis was to determine which ZIP codes and ZCTAs endured through the entire time period,
and which were either newly added or removed. This was done by first comparing ZIP code
boundaries from 2007 to 2010 ZCTA boundaries. The boundaries of ZIP codes/ZCTAs that
existed in both time periods were compared. While minor to more substantial changes in
boundaries did occur with some areas, values reported in various years for a given ZIP
code/ZCTA were taken as comparable. In a few instances, ZIP codes/ZCTAs that were included
in the 2010 ZCTA dataset were not included in the 2007 ZIP code list, or vice versa. The
creation date for these ZIP codes were confirmed using an online resource and if these were
created part way through the 2006 – 2010 time period, the ZIP code/ZCTA from which the new
ZIP codes were created were identified. The values for these newly created ZIP codes were
then added to the values of the ZIP code from which they were created. This meant that in the
end, rates were only calculated for those ZIP codes/ZCTAs that existed throughout the entire
time period, and that values reported for patients in newly created ZIP codes contributed to the
rates for the Zip Code/ZCTA from which their ZIP codes were created.
Processing for Specific Variables
40
Additional processing was needed to create the tract vulnerability index, the additional well
being variables, and some of the behavioral and environmental variables.
Tract Vulnerability Index
The tract vulnerability index was calculated using five tract level demographic variables
calculated from the 2010 American Community Survey 5 Year Estimates data: the percent nonWhite or Hispanic population, percent single parent households, percent of population below
125% of the Federal Poverty Level, the percent population younger than 5 years, and the
percent population 65 years or older.
These variables were selected because of their theoretical and observed relationships to
conditions related to poor health. The percent non-White or Hispanic population was included
because this group is traditionally considered to experience greater problems in accessing
health services, and experiences a disproportionate burden of negative health outcomes. The
percent of households headed by single parents was included as the structure of households in
this group leads to a greater risk of poverty and other health instability issues. The percent of
population below 125% of the federal poverty level was included because this is a standard
level used for qualification for many state and federally funded health and social support
programs. Age groups under 5 years old and 65 and older were included because these groups
are considered to be at a higher risk for varying negative health outcomes. The population
under 5 years group includes those at higher risk for infant mortality and unintentional injuries.
The 65 and over group experiences higher risk for conditions positively correlated with age,
most of which include the conditions examined in this assessment: heart disease, stroke,
diabetes, and hypertension, among others.
Each input variable was scaled so that it ranged from 0 to 1 (the tract with the lowest value on a
given variable received a value of 0, and the tract with the highest value received a 1; tracts
with values between the minimum and maximum received some corresponding value less than
1). The values for these variables were then added together to create the final index. This
meant that final index values could potentially range from 0 to 5, with higher index values
representing areas that had higher proportions of each population group.
Well Being Variables
Infant Mortality Rate
Infant mortality rate reports the number of infant deaths per 1,000 live births. It was calculated
by dividing the number of deaths for those with ages below 1 from 2006-2010 by the total
number of live births for the same time period (smoothed to EBR), and multiplying the result by
1,000.
Life Expectancy at Birth
Life expectancy at birth values are reported in years, and were derived from period life tables
created in the statistical software program R using the Human Ecology, Evolution, and Health
41
Lab’s example period life table function. This function was modified to calculate life tables for
each ZCTA, and to allow the life table to be calculated from submitted age stratified mortality
rates. The age stratified mortality rates were calculated for each ZIP code by dividing the total
number of deaths in a given age category from 2006-2010 by five times the ZCTA population for
that age group in 2010 (smoothed to EBR). The age group population was multiplied by five to
match the five years of mortality data that were used to derive the rates. Multiple years were
used to increase the stability of the estimates. In contexts such as these, the population for the
central year (in this case, 2008) is usually used as the denominator. 2010 populations were
used because they were actual Census counts, as opposed to the estimates that were available
for 2008. It was felt that the dramatic changes in the housing market that occurred during this
time period reduced the reliability of 2008 population estimates, and so the 2010 population
figures were preferred.
Environmental and Behavioral Variables
The majority of environmental and behavioral variables were obtained from existing credible
sources. The reader is encouraged to review the documentation for those variables, available
from their sources, for their particulars. Two variables, however, were created specifically for
this analysis: alcohol availability, and park access.
References
Anselin, L. (2003). Rate Maps and Smoothing. Retrieved February 16, 2013, from
http://www.dpi.inpe.br/gilberto/tutorials/software/geoda/tutorials/w6_rates_slides.pdf
California Department of Public Health. (2012). Individual County Data Sheets. Retrieved
February 18, 2013, from County Health Status Profiles 2012:
http://www.cdph.ca.gov/programs/ohir/Pages/CHSPCountySheets.aspx
CDC. (2011). Matrix of E-code Groupings. Retrieved March 4, 2013, from Injury Prevention &
Control: Data & Statistics(WISQARS):
http://www.cdc.gov/injury/wisqars/ecode_matrix.html
Datasheer, L.L.C. (2012, March 3). ZIP Code Database STANDARD. Retrieved from ZipCodes.com: http://www.Zip-Codes.com
Datasheer, L.L.C. (2013). Zip-Codes.com. Retrieved February 16, 2013, from http://www.zipcodes.com/
Dignity Health. (2011). Community Need Index.
Esri. (2009, May 1). parks.sdc. Redlands, CA.
GeoLytics, Inc. (2008). Estimates of 2001 - 2007. E. Brunswick, NJ, USA.
Human Ecology, Evolution, and Health Lab. (2009, March 2). Life tables and R programming:
Period Life Table Construction. Retrieved February 16, 2013, from Formal Demogrpahy
Workshops, 2006 Workshop Labs: http://www.stanford.edu/group/heeh/cgibin/web/node/75
42
Klein, R. J., & Schoenborn, C. A. (2001). Age adjustment using the 2000 projected U.S.
population. Healthy People Statistical Notes, no. 20. Hyattsville, Maryland: National Center
for Health Statistics.
R Development Core Team. (2009). R: A language and environment for statistial computing.
Vienna, Austria: . R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-90005107-0, URL http://www.R-project.org.
U.S. Census Bureau. (2013a). 2010 American Community Survey 5-year estimates. Retrieved
February 14, 2013, from American Fact Finder:
http://factfinder2.census.gov/faces/nav/jsf/pages/searchresults.xhtml?refresh=t
U.S. Census Bureau. (2013b). 2010 Census Summary File 1. Retrieved February 14, 2013, from
American Fact Finder:
http://factfinder2.census.gov/faces/nav/jsf/pages/searchresults.xhtml?refresh=t
U.S. Census Bureau. (2011). 2010 TIGER/Line(R) Shapefiles. Retrieved August 31, 2011, from
http://www.census.gov/cgi-bin/geo/shapefiles2010/main
U.S. Deparment of Health and Human Services. (2012). Office of Disease Prevention and Health
Promotion. Healthy People 2020. Washington, DC. Retrieved February 18, 2013, from
http://www.healthypeople.gov/2020/topicsobjectives2020/pdfs/HP2020objectives.pdf
43
Rideout Health
Group
Pathways Alcohol
Treatment
Program
Cedar Lane FRC
Yuba County
Health & Human
Services, Public
Health Clinic
Yuba County
Tobacco Cessation
Yuba County Adult
Services Division
95901
P
P
E, P
P
S, M, E, I, CM,
P
95901
P
95901
I, P
95901
Specialty
Other
Non-profit
hospital
Cancer center,
neurosurgery
center, lab,
ancillary
services
Outpatient and
inpatient alcohol
and drug
treatment
Family Resource
Center
S, P
Testing and
vaccines
CM
Senior services
E, P
CM
P
Diapers,
clothes, father
involvement,
parenting
classes
Dental
Tobacco
P
Medical
Services
CM, C,
R, P
95901
95901
Substance Abuse
Nutrition
Mental Health
Hypertension
Diabetes
Asthma/
Lung Disease
Name
Zip Code
Appendix D
Health Assets Identified for SSHNV HSA
Yuba County
Children's Services
95901
C
Yuba-Sutter
Counties, Veteran
Service Office
95901
I, R
Maternal Child
and Adolescent
Health
95901
Peach Tree Clinic
95901
Medical
Services
P
I, R
Specialty
Other
Child welfare
Foster
parenting, ER,
family
reunification
Veterans
E, P
P
P
Children Health &
Disability
Prevention
Program (CDPH)
95901
R
Yuba County WIC
95901
P
Aegis Medical
System
95901
Child
Development
Center
95903
Brownsville Clinic
Sutter County WIC
95919
95953
E
Safety classes
S, P
Low income
families
S, P
Prevention,
periodic health
assessments for
children of low
income families
Pregnant mothers
and children
Childcare,
family
recreation
E, P
P
P
Yes
R
S, CM,
C, P
E
Dental
Tobacco
Substance Abuse
Nutrition
Mental Health
Hypertension
Diabetes
Asthma/
Lung Disease
Name
Zip Code
44
Lab, diagnostic
Olivehurst FRC
95961
Rideout Health
Group
95991
Buddy's House
95991
Pathways
Prevention
95991
Planned
Parenthood: Yuba
City Health Center
95991
Peach Tree Clinic
95991
Sutter Smiles
Dental Van
95991
Medical
Services
P
P
P
P
P
Other
Family Resource
Center
Diapers,
clothes, father
involvement,
parenting
classes
Surgery center,
pain
management,
occ health,
ortho, lab,
senior services,
E, P
R, P
E
Specialty
E, I, R
Dental
Tobacco
Substance Abuse
Nutrition
Mental Health
Hypertension
Diabetes
Asthma/
Lung Disease
Name
Zip Code
45
Sober living
E, I
S, P
Women's health
S, P
Low income
families
Dental care for
low income
families
Men's health,
birth control,
abortion
services
Yes
Sutter North
Medical Group
95991
Women's Circle
Nurse-Midwife
Services Inc.
95991
California Tribal
TANF
95991
CM
Bi-County Mental
Health
95991
S, M,
E, I,
CM, C,
R, P
Options for
Change
95991
P
P
Medical
Services
Specialty
Primary car,
surgical center,
neurology,
ophthalmology,
otolaryngology,
pain
management,
podiatry,
urology,
OBGYN, ortho,
physical
therapy,
gastro, derma,
occ health,
radiology,
urgent care
S, M, E, CM,
R, P
P
I, C, R,
P
Other
E, I, R, P
Midwifery
CM
Native American
Community
Cash assistance
Dental
Tobacco
Substance Abuse
Nutrition
Mental Health
Hypertension
Diabetes
Asthma/
Lung Disease
Name
Zip Code
46
Sutter County WIC
95991
P
Yuba-Sutter
Gleaners Food
Bank, Inc.
95991
P
Salvation Army
95991
Yuba City Senior
Center
95991
Ampla Health
95991
Twin Rivers Crisis
Center 560
Cooper Avenue,
Yuba City, CA
95991 (530) 7519511
95991
Casa De Esperanza
95992
Sutter Feather
Down Family
Practice
95993
Medical
Services
Specialty
Other
Food bank
Clothing
C
I, R, C
P
P
I, R
Shelter,
wellness/recovery,
family services
P
Internal,
pediatrics
S, P
Residential
program for
women
P
E, C, R
Sexual
assault/domestic
violence
prevention
P
P
Transportation,
shelter
Lab
Dental
95991
Tobacco
Co-Dependents
Anonymous for
Men & Women
Substance Abuse
Nutrition
Mental Health
Hypertension
Diabetes
Name
Zip Code
Asthma/
Lung Disease
47
95993
St. Andrew's
Presbyterian
Church
95993
National
Association of
Counties Prescription Drug
Discount Card
Program
95993
Nor-Cal Center on
Deafness
Child Care
Planning Council
of Yuba & Sutter
Counties
Harmony Health
Medical Clinic &
Family Resource
Center
95993
Specialty
P
Native American
Community
Other
P
M, E
E
E, P
Immunizations
P
C
95901
95901
Medical
Services
P
Prescriptions
I, R, A
Deaf residents
E
E, C, P
E
E, P
Childcare
Parenting
workshops
Cradle to the
grave primary care
FRC
Dental
Sutter County
Public Health
E
Tobacco
95993
P
Substance Abuse
Chronic Disease
Prevention
Program
Nutrition
95993
Mental Health
Sutter County WIC
E
Hypertension
95993
Diabetes
Feather river
Tribal Health
Asthma/
Lung Disease
Name
Zip Code
48

Sutter Surgical Hospital - North Valley

Transcription

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