A Community Health Needs Assessment of the Sutter Coast Hospital Service Area Conducted on behalf of Sutter Coast Hospital 800 E. Washington Blvd. Crescent City, CA 95531 Conducted by: Valley Vision August 2013 2
Acknowledgements The community health needs assessment team would like to thank all those who contributed to the community health assessment described herein. First, we are deeply grateful for the key informants who gave their time and expertise to inform both the direction and outcomes of the study. Additionally, many community residents volunteered their time as focus group participants to give our research team a first‐hand perspective of living in the most vulnerable communities in the Sutter Coast Hospital service area. Finally, we are very grateful to the individuals and organizations in the community who volunteer their time and expertise to improve the health and well‐being of the area’s most vulnerable residents. 3
Executive Summary Every three years nonprofit hospitals are required to conduct community health needs assessments (CHNA) and use the results to develop implementation plans. These requirements are imposed on virtually all nonprofit hospitals by both state and federal regulations. Between January and August 2013, Valley Vision, Inc., conducted an assessment of the health needs of residents living in the Sutter Coast Hospital Service Area (HSA). For the purposes of the assessment, a health need was defined as “a poor health outcome and its associated driver.” A health driver was defined as “a behavioral, environmental, and/or clinical factor, as well as more upstream social economic factors, that impacts health.” The objective of the CHNA was as follows: To provide necessary information for Sutter Coast Hospital’s community benefit plan, to identify communities and specific groups within these communities experiencing health disparities, especially as these disparities relate to chronic disease, and to further identify contributing factors that contribute to the creation of both barriers and pathways to living healthier lives within these communities. A community‐based participatory research orientation was used to conduct the assessment, which included both primary and secondary data. Primary data collection involved gathering input from more than 65 members of the HSA, including expert interviews with 11 key informants and focus group interviews with 54 community members. In addition, a community health assets assessment collected information about 68 assets in the greater hospital service area. Secondary data used included health outcome data, sociodemographic data, and behavioral and environmental data at the ZIP code or census tract level. Health outcome data included Emergency Department (ED) visits, hospitalization, and mortality rates related to heart disease, diabetes, stroke, hypertension, COPD, asthma, safety and mental health conditions. Sociodemographic data included data on race and ethnicity, poverty (female‐headed households, families with children, people over 65 years of age), educational attainment, health insurance status, and housing arrangement (own or rent). Further, behavioral and environmental data helped describe general living conditions of the HSA, such as crime rates, access to parks, availability of healthy food, and leading causes of death. The Sutter Coast Hospital service area (HSA) was identified through the collection and analysis of ZIP codes associated with patients discharged from the hospital over a six‐month period. Through this analysis, it was determined that approximately 90% of all patients resided in five ZIP codes split between two counties and two states. With the exception of ZIP code 97415 (Brookings, OR) all of the ZIP codes in the HSA are located in Del Norte County. A map of the hospital service area is shown in Figure 1. 4
Figure 1: Sutter Coast Hospital service area Health Outcome Indicators Age‐adjusted rates of ED visits and hospitalization due to heart disease, diabetes, stroke and hypertension were consistently above state and county benchmarks for most of the ZIP codes in the HSA. In general, Whites and Native Americans had the highest rates for these conditions compared to other racial and ethnic groups. Mortality data for these conditions also showed high rates across the HSA. Environmental and Behavioral Indicators Analysis of environmental indicators showed that many of these communities had conditions that were barriers to active lifestyles, such as elevated crime rates and a traffic climate that is unfriendly to bicyclists and pedestrians. Furthermore, these communities frequently had higher percentages of residents that were obese or overweight. Access to healthy food outlets was limited in rural areas, while the concentration of fast food outlets and 5
convenience stores was high in urban areas. Analysis of the health behaviors of these residents also revealed many behaviors that correlate to poor health, such as having a diet that is low in fruit and vegetable consumption and having limited physical activity. A list of priority health needs, which were identified through careful analysis of both quantitative and qualitative data, is included below. All needs are noted as a “health driver,” or a condition or situation that contributed to poor health outcomes. The complete priority health needs table can be found in Appendix E. Priority health needs for the Sutter Coast HSA 1. Lack of access to primary and preventative services 2. Limited access to mental health services 3. Inability to fulfill basic needs, including food and shelter 4. Limited access to reliable transportation 5. Limited access to safe and affordable places to exercise 6. Lack of access to dental care 7. Limited health literacy and health education opportunities 6
Table of Contents Executive Summary ................................................................................................................. 3 Introduction ............................................................................................................................ 9 “Health Need” and Objectives of the Assessment ................................................................... 9 Organization of the Report .................................................................................................... 10 Methodology ......................................................................................................................... 10 Community‐Based Participatory Research (CBPR) Approach .......................................................... 10 Unit of Analysis and Study Area ..................................................................................................... 11 Identifying the Hospital Service Area ............................................................................................. 11 Primary Data: The Community Voice .............................................................................................. 13 Key Informant Interviews ................................................................................................................... 14 Focus Groups ...................................................................................................................................... 14 Community Health Assets .............................................................................................................. 14 Selection of Data Criteria ............................................................................................................... 14 Health Outcomes ........................................................................................................................... 15 Health Drivers ................................................................................................................................ 15 Data Analysis ................................................................................................................................. 16 Primary Data ....................................................................................................................................... 16 Secondary Data .................................................................................................................................. 16 Findings ................................................................................................................................. 16 Description of the HSA ................................................................................................................... 16 Racial and Ethnic Makeup .............................................................................................................. 17 Health Outcomes ........................................................................................................................... 17 Diabetes, Heart Disease, Hypertension, and Stroke ...................................................................... 20 Mental Health, Substance Abuse, and Self‐Inflicted Injury ........................................................... 21 Respiratory Illness: Chronic Obstructive Pulmonary Disease (COPD) and Asthma ....................... 23 Behavioral and Environmental Indicators—Health Drivers ............................................................. 23 Safety Profile ...................................................................................................................................... 24 Crime Rates .................................................................................................................................... 24 Assault, Accidents, and Unintentional Injury ................................................................................. 26 Fatal Traffic Accidents .................................................................................................................... 27 Food Environment Profile .................................................................................................................. 28 Obesity, Overweight, and Fruit and Vegetable Consumption ....................................................... 28 Retail Food ..................................................................................................................................... 29 Active Living Profile ............................................................................................................................ 31 Park Access ..................................................................................................................................... 31 Access to Healthcare and Related Services ........................................................................................ 32 Community Vulnerability ............................................................................................................... 34 Socio‐demographic Indicators ........................................................................................................ 35 Improving Community Health ........................................................................................................ 37 Limitations ............................................................................................................................ 39 Conclusion ............................................................................................................................. 40 Appendices ............................................................................................................................ 41 7
Lists of Tables Table 1: Health outcome data used in the CHNA reported as ED visits, hospitalization, and mortality ................................................................................................................................ 15 Table 2: Sociodemographic, behavioral, and environmental data profiles used in the CHNA .... 15 Table 3: Total population and percent of residents by race and ethnicity of the SCH HSA ......... 17 Table 4: Age‐adjusted all‐cause mortality rate, infant mortality, and life expectancy for SCH HSA, compared to county and state benchmarks ......................................................................... 18 Table 5: Mortality rates for SCH HSA compared to county and state benchmarks ..................... 19 Table 6: Emergency department visitation for various causes for SCH HSA compared to county and state benchmarks ........................................................................................................... 20 Table 7: Hospitalizations for various causes for SCH HSA compared to county and state benchmarks ........................................................................................................................... 21 Table 8: Emergency department visits due to mental health, substance abuse, and self‐inflicted injury compared to county and state benchmarks ............................................................... 22 Table 9: Hospitalizations due to mental health, substance abuse, and self‐inflicted injury compared to county and state benchmarks ......................................................................... 22 Table 10: ED visit and hospitalization due to COPD (including asthma and bronchitis) and asthma specifically compared to county and state benchmarks (rates per 10,000 population) ............................................................................................................................ 23 Table 11: ED visit rates for assault, accidents, and unintentional injury compared to county and state benchmarks .................................................................................................................. 26 Table 12: Hospitalization rates for assault, accidents, and unintentional injury compared to county and state benchmarks ............................................................................................... 26 Table 13: Percent obese, percent overweight, percent eating at least five fruits and vegetables daily, presence (Y) or absence (N) of federally defined food deserts, number of certified farmers’ markets by ZIP code ............................................................................................... 28 Table 14: Sociodemographic characteristics of populations in the SCH HSA ............................... 36 Table 15: Summary of qualitative data for the SCH HSA .............................................................. 38 8
List of Figures Figure 1: Sutter Coast Hospital service area ................................................................................... 4 Figure 2: Sutter Coast Hospital service area ................................................................................. 12 Figure 3: Sutter Coast Hospital HSA population density by census tract ..................................... 13 Figure 4: Major crimes by municipality or jurisdiction as reported by FBI Uniform Crime Reports, 2011 ...................................................................................................................................... 25 Figure 5: Motor vehicle accidents with fatalities as reported by National Highway Traffic Safety Administration, 2010 ............................................................................................................ 27 Figure 6: modified Retail Food Environment Index (mRFEI) by census tract in the SCH HS ...... 30 Figure 7: Percent population living in census tract within ½ mile of park space (per 10,000) .... 31 Figure 8: Federal defined primary medical care health professional shortage areas as designated by the Bureau of Health Professionals, 2011 ....................................................................... 33 Figure 9: Sutter Coast Hospital service area map of vulnerability ............................................... 35 9
Introduction The Patient Protection and Affordable Care Act was enacted in early 2010. The legislation requires that 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 Coast Hospital (SCH) of Crescent City, California, conducted a CHNA of the hospital service area (HSA). Valley Vision, Inc., conducted the CHNA over a period of eight 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 of high‐
quality 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 Coast Hospital community health improvement plan and to identify the health needs of the hospital’s defined service area, along with 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. 10
and the definitions compiled by Kaiser Permanente3, the CHNA used the following definition 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 impacts 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 hospital service area (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 description of the methodology, including descriptions and definitions, is contained in the appendices. Next, the study findings are provided, beginning with an examination of health outcomes for the HSA, and 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 The assessment used a mixed‐method data collection approach that included primary data such as key informant interviews, community focus groups, and a community assets assessment. Secondary data included health outcomes, demographic data, behavioral data, and environmental data (the complete data dictionary available in Appendix G). Community‐Based Participatory Research (CBPR) Approach The assessment followed a community‐based participatory research approach for identification and verification of results at every stage of the assessment. This orientation aims at building capacity and enabling beneficial change within the community for which the assessment was conducted. Including participants in the process allows for a deeper understanding of the results.4 3
Community Health Needs Assessment Toolkit – Part 2. (September, 2012). Kaiser Permanente Community Benefit Programs. 4
Minkler, M., & Wallerstein, N. (2008). Introduction to community‐based participatory research. In M. Minkler & N. Wallerstein (Eds.), Community‐based participatory research for health: From process to outcomes, (pp. 5‐23). San Francisco: John Wiley & Sons; Peterson, D. J., & Alexander, G. R. (2001). Needs assessment in public health. New York: Kluwer Academic/Plenum Publishers; Summers, G. F. (1987). Democratic governance. In D. E. Johnson, L. R. Meiller, L. C. Miller, & G. F. Summers (Eds.), Needs assessment (pp. 3‐19). Ames, IA: Iowa State University Press. 11
Unit of Analysis and Study Area A key focus of this study was to examine specific geographically‐defined communities within the HSA and disparities related to chronic disease and mental health. To this end, ZIP code boundaries were selected as the unit of analysis for most indicators. This level of analysis allowed for examination of health outcomes at the community level that are often hidden when data are aggregated at the county level. Some indicators (demographic, behavioral, and environmental in nature) were included in the assessment at the census tract, census block, or point prevalence level, which allowed for deeper community‐level examination. Identifying the Hospital Service Area The Sutter Coast Hospital service area (HSA) was identified through the collection and analysis of ZIP codes associated with patients discharged from the hospital over a six‐month period. Through this analysis, it was determined that approximately 90% of all patients resided in five ZIP codes split between two counties and two states. With the exception of ZIP code 97415 (Brookings, OR), all of the ZIP codes in the HSA are located in Del Norte County. A map of the service area is shown in Figure 2. 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 shown in Figure 3. Figure 2: Sutter Coast Hospital service area 12
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Figure 3: Sutter Coast Hospital HSA population density by census tract Figure 3 above is included to show how population is distributed in the HSA. In most cases, ZIP codes in the HSA have populations that are concentrated within certain census tracts. These same ZIP codes also contain census tracts with very low population densities. Some ZIP codes, like 95543 (Gasquet) and 95548 (Klamath) have populations concentrated in a relatively small area compared to the overall size of the ZIP code. In general, the HSA is comprised of cities and towns that are set apart from one another and surrounded by large tracts of coastal and forested land. Primary Data: The Community Voice Health indicators used in the CHNA included both primary and secondary data. Primary data included key informant interviews with local area experts and focus groups with community members. Secondary data included quantitative data that addressed both health outcomes and behavioral and environmental variables described as health drivers. 14
Key Informant Interviews Key informants are 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 11 key informant interviews was collected using a theoretically grounded interview guide (see interview protocol in Appendix A). Content analysis was conducted on each interview to identify key themes and important points. A list of all key informants interviewed is included in Appendix B, detailing names, professional titles, interview dates, and descriptions of knowledge and experience. Focus Groups Selection of locations for focus groups was determined by feedback from key informants and analysis of health outcome indicators (ED visits, hospitalization, and mortality rates) that pointed to disease severity. Key informants were asked to identify populations that were most at risk for chronic health disparities and mental health issues. In addition, analysis of health outcome indicators by ZIP code, race, ethnicity, age, and sex revealed communities with high rates that exceeded established state, county, and HSA benchmarks. The focus group interview protocol is included in Appendix C. Location, date and demographic information for each focus group is included in Appendix D. Community Health Assets Data were collected on community health assets (health programs and support services) within the HSA. First, a list of assets was compiled from existing resource directories, key informant interviews and online searches. Next, detailed information for each asset was gathered though scans of the organizations’ web sites and, when possible, direct contact with staff via phone. The assets are organized by ZIP code with brief discussion in the body of the report; a detailed list appears in Appendix F. Selection of Data Criteria Criteria were established to help identify and determine all secondary data to be included for the study. Data were included only if they met the following standards: 1. All data must be sourced from credible and reputable sources. 2. Data must be consistently collected and organized in the same way to allow for future trending. 3. Data must be available at the ZIP code level or smaller. All indicators listed below were examined at the ZIP code level unless noted otherwise. County, state, and national 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 15
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 G contains a detailed methodology of all data processing and data sources. Secondary 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. Health Outcomes Table 1: Health outcome data used in the CHNA reported as ED visits, hospitalization, and mortality ED and Hospitalization5 Mortality6 Accidents Hypertension* Alcohol‐Induced** Infant Mortality Asthma Mental Health All‐Cause Mortality* Injuries Assault Substance Abuse Alzheimer’s Disease Life Expectancy Cancer Stroke* Arteriosclerosis** Liver Disease*** Chronic Obstructive Unintentional Injuries Cancer Parkinson’s** Pulmonary Disease Self‐Inflicted Injury Chronic Lower Diabetes* Renal Disease Respiratory Disease Heart Disease* Diabetes Stroke Heart Disease Suicide Homicide** Viral Hepatitis** Hypertension *Age‐adjusted by 2010 California standard population **Oregon only ***California only 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: Sociodemographic, behavioral, and environmental data profiles used in the CHNA Sociodemographic Data Total Population Family Makeup Poverty Level Age High School Graduation Percent Uninsured Unemployment Race/Ethnicity 5
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Office of Statewide Health Planning and Development, ED Visits and Hospitalization, 2011 California Department of Public Health, Deaths by Cause, 2010; Oregon Health Authority, Deaths by Cause, 2010 16
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Behavioral and Environmental Profiles Safety Profile Food Environment Profile Major Crime  Overweight and obese Assault  Fruit and vegetable consumption Unintentional Injury  Food deserts Accidents  Farmer’s markets Motor vehicle crash death rate  modified Retail Food Environment Index (mRFEI) Active Living Profile Physical Well‐being Profile Park access  Age‐adjusted overall mortality  Life expectancy  Infant mortality  Health Professional Shortage Areas (primary care, dental, etc.)  Health assets Data Analysis Primary Data A standard interview protocol was used in all key informant interviews and focus groups (see Appendices A and C). Notes were taken during each interview and focus group, highlighting comments that addressed specific questions in the protocol. These individual interview and focus group notes were analyzed with all other data to identify recurring themes. Secondary Data Comparison to benchmarks was the main method of data analysis. These included County, State, and national benchmarks when available, as well as intra HSA comparisons among the five ZIP codes included in the study. Further, rates where examined by subgroups (groups with unique attributes such as race and ethnicity, age, sex, culture, lifestyle, or location within the HSA) to identify particular subgroups that may be experiencing disparities. Findings Description of the HSA Sutter Coast Hospital is located in Del Norte County, along the Northern Coast of California. The HSA is home to more than 42,000 residents. Crescent City (95531) and Brookings (97415) are the most populated areas in the HSA, while Gasquet (95543), Klamath (95548) and Smith River (95567) are smaller, more rural communities. Highway 101 links most of the communities in the HSA and serves as its major transportation corridor. 17
The Sutter Coast HSA is bounded by redwood forests to the east and the Pacific Ocean to the west, and also contains large sections of tribal land. National parks and beaches attract tourists during the summer months, although the area is prone to fog and rain during several months of the year. Racial and Ethnic Makeup Whites accounted for nearly three quarters of the HSA’s residents (72.2%). Other groups that comprised the bulk of the HSA’s population included Hispanics (14.1%), and American Indians (5.2%). The complete racial and ethnic make up of communities in the HSA is shown in Table 3. Table 3: Total population and percent of residents by race and ethnicity of the SCH HSA ZIP Code 95531 Crescent City 95543 Gasquet 95548 Klamath 95567 Smith River 97415 Brookings Total Pop. White (%) Black (%) 65.2 3.8 (15,992) (944) 84.1 0.9 765 (643) (7) 52.0 0.9 1,373 (714) (12) 59.6 0.2 1,930 (1,150) (4) 87.5 0.3 14,051 (12,288) (48) 72.2% 2.9% HSA Totals 42,647 (30,787) (1,015) (Table Source: US Census, 2010) 24,528 Hispanic (%) American Indian (%) 17.8 (4,369) 8.4 (64) 11.4 (156) 26.1 (504) 6.4 (901) 14.1% (5,994) 5.4 (1,319) 3.1 (24) 28.7 (394) 10.3 (198) 1.9 (266) 5.2% (2,201) Asian (%) 3.7 (918) 0.1 (1) 0.2 (3) 0.8 (16) 0.7 (101) 2.4% (1,039) Native Hawaiian
/ Pacific Islander (%) 2 or More races (%) Other race (%) 0.1 (24) 0.1 (1) 0.0 (0) 0.1 (1) 0.1 (16) 0.1% (42) 3.3 (805) 3.1 (24) 6.6 (91) 2.4 (46) 3.0 (423) 3.3% (1,389) 0.6 (157) 0.1 (1) 0.2 (3) 0.6 (11) 0.1 (8) 0.4% (180) Health Outcomes In some categories, Del Norte and Curry Counties demonstrate poor health outcomes when compared to other California and Oregon counties. The Robert Wood Johnson Foundation produces County Health Rankings and Roadmaps for nearly every county in the United States. The report includes county‐level estimates that are based on data collected by the Centers for Disease Control and Prevention’s (CDC) Behavioral Risk Factor Surveillance System (BRFSS)7. The 2013 report uses data collected over a seven‐year period (2005‐2011). The 2013 county health rankings indicated that Del Norte County was among the worst counties in California for overall health outcomes with a ranking of 53 out of 57. In Oregon, Curry County fared slightly better with a ranking of 26 out of 33 counties. Mortality rankings 7
See: http://www.countyhealthrankings.org 18
showed Del Norte County ranked 56 of 57, while Curry County ranked 31 of 33. Data indicated that 15% of Curry County residents self‐reported their health as “poor or fair,” compared to an Oregon benchmark of 14% and a national benchmark of 10%. Finally, Curry County residents reported that on average they experienced 3.6 days over a 30‐day period that their physical health was “not good.” This is below the Oregon benchmark of 3.7 days and the above the national benchmark of 2.6 days. Self‐reported health data were not available for Del Norte County. Most of the ZIP codes in the Sutter Coast Hospital HSA had notably higher rates of mortality, ED visits, and hospitalization due to chronic diseases, substance abuse, and mental health conditions compared to county and state benchmarks. The health outcomes included in the tables below were also consistently mentioned in the qualitative data as priority health concerns for residents in the HSA. Mortality Mortality within the HSA is examined below. Table 4 displays the age‐adjusted all‐cause mortality rate, infant mortality rate, 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 when available. Table 4: Age‐adjusted all‐cause mortality rate, infant mortality rate, and life expectancy for SCH HSA, compared to county and state benchmarks All‐cause Life Expectancy Mortality 95531 Crescent City 77.0 78.0 95543 Gasquet 0.0 82.7 95548 Klamath 66.7 77.2 95567 Smith River 68.9 78.4 Del Norte County 82.0 ‐‐ CA State 63.3 80.48 97415* Brookings ‐‐ Curry County 165.9 ‐‐ OR State 83.0 79.5 National ‐‐ 78.69 Healthy People 2020 ‐‐ ‐‐ (Table Source: CDPH, 2010; Oregon Health Authority, 2010) *ZIP code level mortality data was not available ZIP Code Community 8
Henry J. Kaiser Family Foundation State Health Facts, 2007. Retrieved from: http:// www.statehealthfacts.org/profileind.jsp?ind=784&cat=2&rgn=6 9
ibid Infant Mortality 5.5 4.8 4.8 4.6 8.0 5.2 ‐‐ 0.0 4.9 ‐‐ 6.0 19
Both Curry County and Del Norte County reported all‐cause mortality rates that exceeded their respective state benchmarks. In Del Norte County, three ZIP codes had all‐cause mortality rates above the state benchmark. These same ZIP codes (95531, 95548 and 95567) had life expectancy below the state benchmark of 80.4 years, while Gasquet (95543) had the highest life expectancy at 82.7 years. At 5.5 deaths per 10,000, Crescent City (95531) had an infant mortality rate that exceeded the state benchmark of 5.2 but fell below the Healthy People 2020 benchmark of 6.0. Curry County’s all‐cause mortality rate was twice the Oregon state benchmark. Table 5: Mortality rates for SCH HSA compared to county and state benchmarks Mortality due to: Heart Liver Suicide
Stroke Cancer Injury Diabetes
Disease
Disease* 95531 Crescent City 17.6 4.5 23.7 3.6 1.5 1.4 1.6 95543 Gasquet 0.0 0.0 0.0 0.0 0.0 0.0 0.0 95548 Klamath 21.6 4.0 14.9 6.2 0.0 0.0 1.5 95567 Smith River 30.8 5.2 23.1 3.8 0.0 1.2 0.0 Del Norte County 13.2 4.7 22.0 6.4 2.0 1.2 1.9 CA State 11.5 3.5 14.3 2.7 1.8 1.1 1.0 97415** Brookings ‐‐ ‐‐ ‐‐ ‐‐ ‐‐ ‐‐ ‐‐ Curry County 38.5 5.4 34.9 8.5 5.8 2.2 4.9 OR State 16.1 4.7 19.9 4.1 2.7 1.5 1.8 Healthy People 2020 10.1 3.4 16.1 3.6 6.6 0.8 1.0 (Table Source: CDPH, 2010; Oregon Health Authority, 201) * This variable is called Alcohol‐Induced in OR and contains ICD‐10 codes that are not included in the CA variable ** Oregon mortality data available at county level only. ZIP Code Community All Del Norte County ZIP codes except for Gasquet (95543) exceeded county, state, and Healthy People 2020 benchmarks for mortality due to heart disease and cancer. Klamath (95548) and Smith River (95567) were two and three times higher than the Healthy People 2020 benchmark for mortality due to heart disease. Compared to other ZIP codes in Del Norte County, Smith River (95567) had the highest rates of mortality due to heart disease, stroke, and chronic lower respiratory disease. Mortality due to injury for Klamath (95548) was more than twice the state rate at 6.2 but below the county rate of 6.4. All ZIP codes in Del Norte County fell below both state and county benchmarks for mortality due to diabetes. Curry County exceeded state benchmarks for all reported conditions, with mortality due to heart disease, cancer, injury, diabetes, and suicide at twice the state rate. Mortality due to heart disease was nearly four times higher than the Healthy People 2020 benchmark and mortality due to suicide was nearly five times higher. Mortality rates due to cancer and liver disease were more than twice the Healthy People 2020 benchmark. 20
Morbidity Emergency department (ED) visitation and hospitalization rates for various causes can be used as an indicator of community health. In this section, ED visits and hospitalization for chronic diseases (diabetes, heart disease, hypertension, and stroke), mental health, substance abuse, and respiratory conditions (including chronic obstructive pulmonary disease and asthma) are examined. All rates were age‐adjusted to reduce the effects of age and each rate is stated per 10,000 of population. Data for all ZIP codes in the HSA, including 97415 (Brookings) were obtained from the California Office of Statewide Health Planning and Development (OSHPD). OSHPD data were available for residents of 97415 (Brookings) who utilized a hospital in California. Since ED and hospitalization rates for specific conditions are not reported at the county or ZIP code level for the state of Oregon, Brookings data were compared against Del Norte County and California state benchmarks. Diabetes, Heart Disease, Hypertension, and Stroke Key informants and focus groups consistently pointed to chronic diseases as a primary health issue in the HSA. Diabetes and heart disease were mentioned in nearly every key informant interview and focus group. Many individuals noted that they or a family were struggling with a chronic disease. Tables 6 and 7 examine both ED visitation rates and hospitalizations in the HSA for diabetes, heart disease, hypertension, and stroke. Table 6: Emergency department visitation for various causes for SCH HSA compared to county and state benchmarks ZIP Code Community 95531 Crescent City 95543 Gasquet 95548 Klamath 95567 Smith River 97415 Brookings Del Norte County CA State (Table Source: OSHPD, 2011) Diabetes 489.5 223.3 389.5 287.3 138.7 481.4 188.4 Emergency Department Visits due to: Heart Disease Hypertension 287.3 978.0 100.7 392.7 199.9 809.4 166.6 654.1 90.4 301.0 288.5 956.1 93.1 365.6 Stroke 19.6 12.1 14.8 10.5 10.3 23.1 16.2 Del Norte County’s benchmarks for ED visits due to diabetes, heart disease, hypertension, and stroke were appreciably higher than the state benchmarks. Four out of five ZIP codes in the HSA exceeded at least one of the established benchmarks for diabetes, heart disease, or hypertension. ED admissions for diabetes in 95531 (Crescent City) were two and a half times higher than the state rate, and rates in 95548 (Klamath) were twice as high. ED visits due to diabetes were also higher than the state benchmark in 95543 (Gasquet) and 95567 (Smith River). 21
The same pattern emerged for heart disease; heart‐disease‐related ED visits in 95531 (Crescent City) occurred at more than three times the state rate. ED visits due to heart disease in 95548 (Klamath) were twice the state rate, and 95567 (Smith River) was one and a half times higher than the state rate. ED visits due to heart disease in 95543 (Gasquet) also exceeded the state rate. At more than two and a half times the state rate, ZIP code 95531 (Crescent City) had the highest rates of emergency department visits due to hypertension in the HSA. Rates for 95548 (Klamath) were more than twice the state benchmark, and both 95567 (Smith River) and 95543 (Gasquet) were clearly above the state benchmark for ED visits due to hypertension. ZIP code 95531 (Crescent City) was the only HSA ZIP code that surpassed the state or county benchmarks for ED visits due to stroke. ZIP code 97415 (Brookings) did not exceed the specified benchmarks for any of the conditions examined in Table 6. Table 7: Hospitalizations for various causes for SCH HSA compared to county and state benchmarks ZIP Code Community 95531 Crescent City 95543 Gasquet 95548 Klamath 95567 Smith River 97415 Brookings Del Norte County CA State (Table Source: OSHPD, 2011) Diabetes 179.3 142.8 172.7 132.5 64.1 175.6 190.9 Hospitalizations due to: Heart Disease Hypertension 208.2 314.3 154.2 220.4 157.4 343.1 178.9 272.6 82.3 125.6 195.4 299.2 218.4 380.9 Stroke 44.2 40.7 44.6 35.6 21.2 42.7 51.8 Crescent City (95531) had the highest hospitalization rates in the HSA for diabetes, heart disease, hypertension, and stroke. Klamath (95548) exceeded county rates for hospitalization due to both hypertension and stroke. It is important to note that none of the ZIP codes in the HSA exceeded state rates for hospitalization for any of these conditions. Health professionals reported that community members frequently use the emergency department as a source for primary medical care, and that these individuals are often treated and released as opposed to presenting a condition that requires admission to the hospital. Mental Health, Substance Abuse, and Self‐Inflicted Injury Key informants and focus groups throughout the HSA identified mental health and substance abuse among both youth and adults as a serious health issue. Focus group participants spoke of anxiety and depression related to the stress of living in poverty or living in isolation, and noted that many community residents consumed both legal and illegal substances as a form of self‐medication. Stress and substance abuse were also identified as 22
contributors to both spousal and child abuse. Tables 8 and 9 examine rates of ED visits and hospitalization within the HSA due to mental health, substance abuse, and self‐inflicted injury, and compare these to county and state benchmarks. Table 8: Emergency department visits due to mental health, substance abuse, and self‐inflicted injury compared to county and state benchmarks ZIP Code Community 95531 Crescent City 95543 Gasquet 95548 Klamath 95567 Smith River 97415 Brookings Del Norte County CA State (Table Source: OSHPD, 2011) Emergency Department Visits due to: Mental Health Substance Abuse Self‐Inflicted Injury 545.2 1893.6 20.7 163.1 753.9 0.0 437.5 2476.4 16.1 464.4 1301.7 12.6 172.5 386.0 3.8 530.5 1859.7 22.4 130.9 232.0 7.9 Del Norte County benchmarks for ED visits due to mental health, substance abuse and self‐inflicted injury were also clearly higher than the state benchmarks. Three of the five ZIP codes in the HSA exceeded one or more benchmark for each of the conditions examined. Standout ZIP code 95548 (Klamath) had rates for mental‐health‐related ED visitation that were over three times the state rate, rates for substance abuse that were over ten times the state rate, and rates for self‐inflicted injury that were over double the state rate. While All ZIP codes in the HSA exceeded the state benchmark, ZIP codes 95531 (Crescent City), 95543 (Gasquet) and 95567 (Smith River) had rates for ED visits due to mental health and substance abuse that were particularly high. Rates for ED visits due to unintentional injury were highest in 95531 (Crescent City) at more than two and a half times the state benchmark. Table 9: Hospitalizations due to mental health, substance abuse, and self‐inflicted injury compared to county and state benchmarks ZIP Code Community 95531 Crescent City 95543 Gasquet 95548 Klamath 95567 Smith River 97415 Brookings Del Norte County CA State (Table Source: OSHPD, 2011) Mental Health 162.9 126.5 188.0 136.7 88.1 160.2 135.8 Hospitalizations due to: Substance Abuse Self‐Inflicted Injury 219.0 8.6 93.5 0.0 363.9 4.8 200.5 5.7 73.7 0.0 223.8 9.4 143.8 4.3 Del Norte County’s rate for hospitalizations due to mental health and substance abuse were also above the state benchmarks, with rates for self‐inflicted injury more than twice as high. ZIP codes 95531 (Crescent City), 95548 (Klamath), and 95567 (Smith River) exceeded state benchmarks for hospitalizations due to mental health, substance abuse, and self‐inflicted injury. 23
At twice the state rate, 95531 (Crescent City) was highest in the HSA for hospitalizations due to self‐inflicted injury. Respiratory Illness: Chronic Obstructive Pulmonary Disease (COPD) and Asthma Community residents and key informants mentioned respiratory issues such as Chronic Obstructive Pulmonary Disease (COPD) and asthma as conditions that affect many residents of the HSA. In most focus groups, tobacco use and exposure to mold in poorly maintained residential properties were identified as a contributor to respiratory illnesses. In an effort to understand the impacts of tobacco use and respiratory illness in the HSA, rates of ED visits and hospitalization related to COPD, asthma, and bronchitis were examined, along with rates of ED visits and hospitalization due specifically to asthma. These rates are displayed in Table 10. Table 10: ED visit and hospitalization due to COPD (including asthma and bronchitis) and asthma specifically compared to county and state benchmarks (rates per 10,000 population) ZIP Code Community 95531 Crescent City 95543 Gasquet 95548 Klamath 95567 Smith River 97415 Brookings Del Norte County CA State (Table Source: OSHPD, 2011) ED Visits due to: COPD Asthma 774.5 465.3 342.5 120.8 1003.2 571.5 513.1 310.7 209.2 105.0 764.4 456.4 202.3 134.9 Hospitalizations due to: COPD Asthma 202.9 65.8 138.3 50.1 248.6 58.7 191.3 71.9 103.0 25.6 204.2 64.3 156.8 70.4 All ZIP codes in the HSA exceeded county and/or state benchmarks for ED visits related to COPD. Both 95531 (Crescent City) and 95567 (Smith River) exceeded state benchmarks in all categories. Standout ZIP code 95548 (Klamath) had rates for ED visits due to COPD nearly ten times higher than the state benchmark and rates for hospitalizations more than four times higher. As with other indicators, Del Norte County rates as a whole were higher than statewide rates in most instances. Behavioral and Environmental Indicators—Health Drivers Health drivers are behavioral, environmental, and/or clinical factors, as well as more upstream social economic factors, that impact health. Health drivers for this report included healthy behavior indicators such as active living and obesity rates, environmental indicators such as crime rates, the food environment and socioeconomic indicators such as poverty and educational attainment. These are examined in greater detail below. 24
Safety Profile Both key informants and focus group participants were asked for their opinions and views concerning health drivers that accounted for poor health outcomes within the HSA. Local health experts and community members stressed the connection between safety issues and the health of HSA residents. These issues include crime, which affects residents’ perception of safety outdoors, and pedestrian safety, which affects residents’ ability to navigate community streets on foot. Crime Rates For many reasons, crime rates in a community are considered a health driver. In communities where crime is perceived to be an issue, residents often feel unsafe outdoors and may avoid physical activity for themselves and children. Figure 4 shows major crimes as reported by various jurisdictions in the HSA. Darker‐colored areas denote higher rates of major crime, including homicide, forcible rape, robbery, aggravated assault, burglary, motor vehicle theft, larceny, and arson. 25
Figure 4: Major crimes by municipality or jurisdiction as reported by FBI Uniform Crime Reports, 2011 Major crime rates were collected for multiple jurisdictions within the HSA. The highest crime rates in the HSA were reported by the Yurok Tribe Police and Crescent City Police Departments, which are located within ZIP codes 95548 (Klamath) and 95531 (Crescent City) respectively. The lowest crime rate within the HSA was in the City of Brookings, located within 97415. All ZIP codes in the HSA also included areas within the jurisdictions of the Curry (97415) or Del Norte (all other ZIP codes) County Sheriffs; crime rates in those areas fell between those reported for Brookings and Crescent City. Key informants and focus groups frequently reported that drug‐related crime, usually theft, was an issue in many communities. Encountering open use of drugs or individuals under the influence in public places and on local beaches was also cited as a concern, especially for community members with young children. Finally, key informants and focus groups throughout the HSA stated that long response times when law enforcement is called contributed to a feeling that safety in the area is declining. 26
Assault, Accidents, and Unintentional Injury ED visits and hospitalizations due to assault, accidents, and unintentional injury for residents of the HSA are examined in Tables 11 and 12. Accidents include those that involved a pedestrian or bicyclist, and may or may not have involved an automobile. Table 11: ED visit rates for assault, accidents, and unintentional injury compared to county and state benchmarks ZIP Code Community 95531 Crescent City 95543 Gasquet 95548 Klamath 95567 Smith River 97415 Brookings Del Norte County CA State (Table Source: OSHPD, 2011) Emergency Department Visits due to: Unintentional Assault Accidents Injury 81.9 30.1 1438.5 27.6 15.1 664.6 90.3 20.0 1737.4 42.6 13.5 1314.9 15.5 9.2 393.7 80.8 29.7 1434.8 29.4 15.6 651.8 Del Norte County rates for ED visits due to assault and unintentional injury were more than twice the state benchmark. Four of the five ZIP codes in the HSA exceeded state benchmarks for ED visits due to unintentional injury, with 95531 (Crescent City), 95548 (Klamath) and 95567 (Smith River) at more than twice as high as the state. 95548 (Klamath) had the highest rate in the HSA for ED visits due to assault and unintentional injury. Table 12: Hospitalization rates for assault, accidents, and unintentional injury compared to county and state benchmarks Hospitalizations due to: ZIP Code Community 95531 Crescent City 95543 Gasquet 95548 Klamath 95567 Smith River 97415 Brookings Del Norte County CA State (Table Source: OSHPD, 2011) Assault Accidents 4.1 0.0 7.8 0.0 0.0 4.2 3.9 2.2 0.0 2.9 2.3 0.0 3.1 2.0 Unintentional Injury 167.3 140.7 252.1 147.2 75.7 171.0 154.6 ZIP codes 95531 (Crescent City) and 95548 (Klamath) exceeded state benchmarks for hospitalizations due to assault, accidents, and unintentional injury. In Klamath (95548), rates for hospitalizations due to assault were twice the state benchmark and the highest in the HSA. This ZIP code also had the highest rate for hospitalizations due to unintentional injury. 27
Fatal Traffic Accidents Motor vehicle accidents resulting in a fatality contribute to residents’ perception of safety when traveling through their community, especially for area residents who rely on public, pedestrian, and/or bicycle travel. Both key informants and focus group participants in the HSA stated that access to most services is largely dependent on adequate transportation. Most key informants reported that travel between cities in the HSA was largely car dependent, especially for trips to the grocery store or for medical appointments. Despite the associated safety concerns, many residents described getting around by either walking or biking. Community members noted that public transportation was available, but the length of time and associated costs were identified as barriers to use. Figure 5: Motor vehicle accidents with fatalities as reported by National Highway Traffic Safety Administration, 2010 28
In Figure 5, traffic accidents with fatalities in the Sutter Coast Hospital HSA are denoted with a green dot. Key informants and focus group participants in most areas noted that many roads in the HSA are not safe for walking or cycling due to the lack of sidewalks, bike lanes, and adequate lighting. Rain and fog were also cited as contributors to unsafe travel conditions, especially in the winter months. Focus group participants throughout the HSA reported that having US Highway 101 running through the center of both residential and commercial areas contributed to unsafe conditions for cyclists and pedestrians. Food Environment Profile Both community health experts and residents believed that nutrition‐related issues were among the primary health drivers leading to priority health issues within the HSA. Issues related to nutrition and food access, including limited access to healthy foods, consumption of and reliance on fast foods, and the cost of healthier foods, were consistently discussed among key informants and focus group participants. Obesity, Overweight, and Fruit and Vegetable Consumption Key informants and focus group participants pointed to obesity and poor diet as contributors to chronic diseases found in the HSA. Table 13 displays data that help illuminate the general food environment in the Sutter Coast Hospital HSA. The table displays the percentages of the total population in each ZIP code who were identified as obese or overweight, and who consumed at least five servings of fruit and vegetables per day. The table also shows whether any portion of a ZIP code contained a food desert, as well how many certified farmers’ markets were in operation within each ZIP code. Table 13: Percent obese, percent overweight, percent eating at least five fruits and vegetables daily, presence (Y) or absence (N) of federally defined food deserts, number of certified farmers’ markets by ZIP code # of % Food Farmers consuming ZIP Code Community Desert Markets 5‐a‐day 95531 Crescent City 24.1 35.7 48.8 Y 1 95543 Gasquet 24.1 33.9 46.5 N 0 Food 95548 Klamath 26.0 36.9 48.0 Y 0 Environment 95567 Smith River 25.1 36.4 47.8 N 0 97415 Brookings* ‐‐ ‐‐ ‐‐ N 2 CA State 24.810
‐‐ ‐‐ ‐‐ ‐‐ Curry County 24.4 37.3 20.4 ‐‐ ‐‐ OR State 24.1 36.6 26.6 ‐‐ ‐‐ (Table Sources: Obese/Overweight and Fruit/Veg Consumption: California Health Interview Survey, 2003‐2005 and Oregon BRFSS, 2004‐2007; Food deserts: Kaiser Permanente CHNA Data Platform/US % Obese % Overweight
10
Levi, J. (2012). “F” as in Fat: How obesity threatens America’s future. Retrieved from: http://healthyamericans.org/assets/files/TFAH2012FasInFatFnlRv.pdf 29
Dept. of Agriculture, 2011; Farmers markets: California Federation of Certified Farmers Markets, 2012 and Oregon Farmers Market Association, 2013) * Oregon data only available at county level Table 13 indicates that in every Del Norte County ZIP code, over one‐third of all residents were overweight, and nearly one in four residents were obese. The same pattern appears in Curry County residents over the age of 18. Less than half of residents over the age of five in each Del Norte County ZIP code reported eating five servings of fruits and vegetables per day. In contrast, less than one quarter of Curry County residents over the age of 18 reported this level of consumption. The southern portion of ZIP code 95531 (Crescent City) contains census tracts designated as food deserts, as does all of ZIP code 95548 (Klamath). Residents of both 95531 (Crescent City) and 97415 (Brookings) had periodic access to farmers markets, although focus group participants reported that the prices were often unaffordable. Focus group participants in Crescent City noted that the ability to use Electronic Benefit Transfer (EBT) cards at the farmers market made it a more viable option for many families, although many indicated limited knowledge about how to prepare the available fruits and vegetables. Retail Food Figure 6 provides information about the modified Retail Food Environment Index (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. 30
Figure 6: modified Retail Food Environment Index (mRFEI) by census tract in the SCH HSA Large portions of 95548 (Klamath) have no healthy retail food outlets. Focus group participants reported that there is not a grocery store in this community, and many struggle to access healthy food. Some portions of the more densely populated ZIP code 95531 (Crescent City) were designated as having good access, while other portions had high access. Portions of Brookings (located in ZIP code 97415) also had good or high access to healthy food. Access to healthy food was identified as a major challenge for individuals and families throughout the HSA, especially those living in communities without grocery stores and where access to fresh produce is limited. Focus group participants in smaller communities reported relying on the local gas station or convenience store for daily grocery needs. Transportation was also a common topic in the qualitative data, and getting to the store is especially difficult for those who do not drive. Focus group participants frequently noted that it is easier and less expensive to feed a family with fast food than to shop for and prepare a healthy meal. 31
Active Living Profile Physical activity is a contributor to good health. Both health experts and focus group participants frequently mentioned the physical inactivity of HSA residents as a health driver resulting in many of the poor health outcomes experienced by HSA residents. Key informants and focus groups across the HSA consistently cited lack of time, lack of motivation, and seasonal depression as barriers to physical activity. Focus group participants noted that children who do not engage in physical activity at school would not have an opportunity to learn about the health benefits of exercise if their parents are not regularly active. Park Access One of the largest barriers to engagement in physical activity is access to a recreational area. Figure 7 depicts the percent of the population in census tracts within the Sutter Coast HSA who live within one‐half mile of a recreational park (federal, state, or city). Figure 7: Percent population living within one‐half mile of park space (per 10,000) 32
Most residents of the HSA live within close proximity to park space. Zip code 95531 (Crescent City), has multiple census tracts with a relatively low percentage of people living within one‐half mile of a federal, state, or city park compared to other ZIP codes. While the area’s natural beauty was mentioned frequently, key informants and focus groups throughout the HSA reported that many parks are too far away to access without a car and that routes to parks often involve traveling on roads that are not safe for bicycles or pedestrians. Focus group participants consistently cited illicit drug use, homeless camps, and a general perception that some parks and outdoor areas are unsafe as barriers to using these spaces for recreation and socializing. Key informants and focus groups also reported that for several months during the year, the weather is a barrier to outdoor activities. Access to Healthcare and Related Services Communities require resources in order to maintain and improve the health of their 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 a shortage of primary medical 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 8 shows federally designated primary medical care HPSAs within the SCH HSA. 33
Figure 8: Federally‐defined primary medical care health professional shortage areas as designated by the Bureau of Health Professionals, 2011 Figure 8 shows that all of the ZIP codes in the SCH HSA are HPSAs. Key informants and focus group participants consistently identified challenges related to accessing primary, specialty, and mental health care services. Both a lack of available providers and challenges associated with transportation were consistently identified as barriers to accessing medical services. Difficulty attracting and retaining medical providers was frequently cited as a significant challenge within the HSA, with key informants and focus groups reporting that having to find a new medical provider is a common experience. Further analysis of data indicated that 68 distinct assets are located in the HSA. These include health care providers, family resource centers, Del Norte and Curry County services, dental care, food banks, senior centers, tribal health centers, and faith‐based organizations. A complete list of these services is available in Appendix F. The presence of these organizations presents an opportunity for SCH to improve community health through increased collaboration and coordination of services. 34
Community Vulnerability A key to assessing community health is examining the community’s vulnerability to poor or unwanted health outcomes. This is accomplished by examining sociodemographic data, often referred to as the social determinants of health. 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) percentage of non‐White or Hispanic population; 2) percentage of single‐female‐headed households; 3) percentage below 125% of the poverty level; 4) percentage under five years old; and 5) percentage 65 years of age or older living in the census tract. Figure 9 shows the vulnerability index for each census tract located entirely or partially within the SCH HSA. On this map, darker gradient colors indicate higher vulnerability. 35
Figure 9: Sutter Coast Hospital service area map of vulnerability There is an established relationship between health outcomes and sociodemographic characteristics of populations that can be seen when examining these factors together. Based on the index described above and shown in Figure 9, Klamath and the surrounding area, along with a portion of Crescent City, were identified as having the highest vulnerability in the HSA. As seen throughout the report, these areas are also among the highest in the HSA for poor health outcomes. Sociodemographic Indicators Research demonstrates that social characteristics of populations are often related to health outcomes, such as economic status, race and ethnicity, education attainment, and family status. Likewise, when key informants and focus group participants were asked to identify key health drivers that contributed to poor health outcomes in the HSA, many pointed to social determinants such as poverty, education attainment, and culture. Sociodemographic characteristics of the HSA are displayed below in Table 14. 36
% Over 25 with no high school diploma % Non‐White or Hispanic % Residents Renting % No health insurance 55.6 42.4 23.8 8.0 52.2 34.1 53.8 54.9 40.3 21.9 1.2 23.0 27.0 21.7 19.2 7.3 8.3 11.2 33.8 18.7 58.9 27.0 34.5 59.3 12.3 11.4 21.2 2.2 0.6 2.4 2.2 2.2 11.1 0.4 0.2 3.6 10.1 2.7 15.3 9.2 10.2 9.811 13.9 9.1 9.8 39.2 20.4 41.3 35.5 38.5 43.3 29.1 28.6 36.9 ‐‐ ‐‐ ‐‐ ‐‐ 14.2 21.612 9.5 18.1 16.6 National 8.713 15.114 31.215 12.916 ‐‐ 8.717 7.918 ‐‐ 16.319 % Unemployed % Families in single female headed households in poverty 28.7 42.4 29.0 9.1 28.1 15.9 19.6 19.4 16.7 95531 Crescent City Gasquet 95543 Klamath 95548 Smith River 95567 Del Norte County California Brookings 97415 % Over age 5 with limited English % Families in poverty w/ children Curry County Oregon 2.0 0.0 3.0 3.9 2.2 2.0 3.5 4.4 2.0 % Over 65 headed Households in poverty Table 14: Sociodemographic characteristics of populations in the SCH HSA (Table Source: US Census, 2010) In many instances, the relationship between health outcomes and social characteristics is evident when examining data for the SCH HSA. Throughout this report Del Norte County ZIP code 95548 (Klamath) consistently demonstrated some of the worst health outcomes of all ZIP codes in the HSA. This same ZIP code also had the second‐lowest life expectancy at 78 years (see Table 4), highest unemployment rate, and second‐highest percentage of families with children living in poverty. The same trend also emerges when considering 95531, the Crescent City area. This ZIP code consistently had the worst health outcomes in many of the measures noted above, while it had the highest percentage of single‐female‐headed households living in 11
US Bureau of Labor Statistics (2012, December). Unemployment Rates for States Monthly Rankings, Seasonally Adjusted. Retrieved from: http://www.bls.gov/web/laus/laumstrk.htm 12
Fronstin, P. (2012, December). California’s Uninsured: Treading Water. California HealthCare Almanac. Retrieved from: http://www.chcf.org/~/media/MEDIA%20LIBRARY%20Files/PDF/C/PDF%20CaliforniaUninsured2012.pdf 13
2011 rate as reported by De Navas, Proctor, and Smith. (2012). Income, Poverty, and Health Insurance Coverage in the United States: 2011. US Department of Commerce‐ Economic and Statistics Administration‐ Census Bureau. 14
Ibid 15
Ibid 16
2010 Educational Attainment by Selected Characteristics. US Census Bureau, Unpublished Data. Retrieved from: http://www.census.gov/compendia/statab/cats/education/educational_attainment.html 17
Pandya, C., Batalova, J., and McHugh, M. (2011). Limited English Proficient Individuals in the United States: Number, Share, Growth, and Linguistic Diversity. Washington, DC: Migration Policy Institute. 18
US Bureau of Labor Statistics (2012, December). Unemployment Rates for States Monthly Rankings, Seasonally Adjusted. Retrieved from: http://www.bls.gov/web/laus/laumstrk.htm 19
2011 rate as reported by De Navas, Proctor, and Smith. (2012). Income, Poverty, and Health Insurance Coverage in the United States: 2011. US Department of Commerce‐ Economic and Statistics Administration‐ Census Bureau. 37
poverty, the second‐highest unemployment rate, and the lowest life expectancy in the HSA (77.2 years). While ZIP code 97415 (Brookings) generally reported better health outcomes, key informants and focus groups consistently mentioned that a significant number of community member live in poverty and experience challenges maintaining their health as a result. Brookings exceeded Curry County and Oregon state benchmarks for unemployment and percentage of families with children living in poverty. In most cases, Curry County exceeded Oregon state benchmarks for the sociodemographic characteristics examined. Key informants and community members who participated in focus groups consistently mentioned poverty as a significant contributor to poor health outcomes. Several key informants noted that multigenerational poverty is prevalent throughout the HSA, causing individuals and families to struggle with meeting basic needs like food and shelter. In such conditions, maintaining health is not a priority, and engaging in regular physical activity and eating fruits and vegetables is unlikely to happen. Key informants also discussed the feelings of helplessness and the unpredictability of daily life that often accompany living in extreme poverty and noted that many people turn to drugs and alcohol to relieve stress and anxiety. Focus group participants described living in substandard housing, often with more than one generation under the same roof. For many living in the HSA, the ability to visit a doctor, purchase groceries, or heat their homes is severely compromised. Improving Community Health 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 impacts health. Table 15 details the health outcomes that were identified within the SCH HSA during the qualitative data collection conducted as part of this health assessment. The table also identifies the populations in the community most affected by those issues and the drivers of specific health issues as articulated by key informants and community members. The summary of key qualitative findings in Table 15 is intended to provide additional detail about the health needs and health drivers that were uncovered for the HSA, along with recommendations from community members about what could be done to improve their health and the overall health of the region. Examining poor health outcomes, contributing factors, and suggested solutions offers a starting point for planning actions that could lead to positive change. A detailed health needs table is included in Appendix E. 38
Table 15: Summary of qualitative data for the SCH HSA What are the biggest health issues, conditions, and/or diseases the community struggles with?  Chronic diseases: diabetes, hypertension, heart disease  Mental health issues: depression, anxiety, “stress of being poor”  Substance abuse: alcohol, methamphetamine, prescription medications, marijuana  Obesity and malnutrition  Respiratory problems: asthma, allergies, chronic infection  Dental issues: tooth loss, chronic pain  Child maltreatment and neglect Who within your community appear(s) to struggle with these issues the most?  Low income and people living in poverty (consistent among all races/ethnicities)  Hispanic/Latino  Native Americans  The elderly  People living in rural and isolated areas  Homeless  Children What do you think is causing these health issues and health conditions you’ve described?  Poverty o High unemployment in the area o Limited economic opportunities o Multigenerational poverty is common  Limited access to healthcare o Lack of preventative and specialty care options o Limited number of providers o Cost of care prohibitive, even with insurance  Transportation o Many areas are car dependent o Public transportation takes a long time, is costly o Having to choose between gas, food, or medication  Substance Abuse o Self‐medication to treat depression, anxiety o Across all ages and ethnicities  Food access o Fresh foods unavailable or not affordable o High concentration of fast food restaurants o Native diet not supported in food distributions  Built environment o Lack of safe places to bike and walk  Education o Low attainment (formal) o Limited health literacy 39
What are some of the biggest things needed to improve the health of your community?  Increased access to primary, specialty and preventive care with a priority on offering services like dialysis and chemotherapy in the area  Increased access to mental health and substance abuse treatment services, especially for youth  More free and low‐cost transportation options, especially from rural areas to population centers and healthcare delivery points  Ongoing free and low‐cost health and nutrition education opportunities  More economic and educational opportunities for all area residents and clearly articulated career paths for high school and college students  Multiuse community and recreation facilities that are open to the public, affordable, and accessible year‐round  Increased access to socioeconomic and health‐related data for the region  Continue building partnerships and policies that promote community health  Improved access to information about available programs and services and increased outreach to communities and populations that are difficult to reach  A teen center and more low‐cost options for positive youth activities, including sports and physical activity  Increased safety for cyclists and pedestrians, such as bike lanes, lighted crosswalks and adequate shoulder on main roads  Inclusion of Native diet in food distribution meals and nutrition education programs  A grocery store and a pharmacy in every community Limitations 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 sub‐county level were difficult to obtain and were not available by race and ethnicity. Data for obese and overweight and fruit and vegetable consumption from California Health Interview Survey (CHIS) was from years 2003‐2005 and the same data from Oregon Behavioral Risk Factor Surveillance System (BRFSS) was from years 2004‐2007. As is common, assuring that the community voice is thoroughly represented in primary data collection was challenging. For example, the qualitative data gathered in this assessment did not include representation from all of the tribes within the Sutter Coast HSA. Further, comprehensive information on health assets such as small community‐based organizations was difficult to locate and catalog in a systematic manner. Lastly, it is important to 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. 40
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, inter‐
related variables such as socioeconomic 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 between some of the variables that were collected and analyzed to describe the hospital service area. Hospital community benefit managers and personnel can use this expanded understanding of community health, along with the results of this assessment, to target specific interventions and improve health outcomes in some of the area’s more vulnerable communities. By knowing where to focus implementation plans, and the specific conditions and health outcomes experienced by their residents, nonprofit hospitals can develop programs and deliver services that address the underlying contributors to negative health outcomes. 41
Appendices Appendix A Key Informant Interview Protocol Project Objective To provide necessary information for Sutter Coast Hospital 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. Ask KI to provide a brief overview of his/her background and areas of expertise. Objective #1: To understand the predominant health issues in a HSA and the experiences of residents experiencing such health issues. Question #1: What are the biggest health issues, conditions, and/or diseases the community or those you serve struggle with? Probes:  Diabetes, high blood pressure, heart disease, cancer  Mental Health or substance abuse  Other issues, including those that are emerging that often go undetected Question #2: Who [which specific sub‐group(s)] within the community appear(s) to struggle with these issues the most? Probes:  How do you know, what leads you to make this conclusion?  Describe race/ethnic makeup of HSA to KI if needed o Subgroups within the larger categories  Where in the community do these groups live?  Describe family status of HSA to KI if needed  Describe the socioeconomic status of the HSA to KI if needed  Describe the overall vulnerability of the HSA to KI if needed Objective #2: In reference to the health outcomes mentioned above, determine influences of both the individual and community environment (physical structure and living conditions): Question #3: What do you think is causing these health issues and health conditions you’ve described? 1. Individual behavior 
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Activities or behaviors of specific groups Dietary behaviors Attitudes and beliefs Cultural or community norms or beliefs in the community around what it is to be “healthy” Stress and anxiety Physical activity, exercise 42
2. Physical Environment (Physical structure and Living conditions) 
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Aspect of the built environment Sidewalks building structures transportation routes Places to engage in activity Perception of safety Lack of places to exercise Access to health foods Access to preventative services, access to basic healthcare Question #4: What are some challenges that [your community, your HSA] faces in staying healthy? Probes: 
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Behaviors common to your community? Cultural norms and beliefs held by any subgroup, especially those identified above Smoking Diet, relationship with food Physical activity, relationship with one’s body Safety Access to preventative services, access to basic healthcare [For specific KIs] Policies, laws, regulations (provide example if needed) Objective 3: Determine opportunities and solutions for living healthy in the community. Question #5: What does the community have that helps improve or maintain health? Probes:  Shifting social and community norms and beliefs  Public health awareness  Opportunities to exercise  Access to fresh produce, healthier diet  Areas for families to gather  Sense of community safety  Access to preventative services, access to basic healthcare  Access to policy makers and local elected officials Questions #6: Of all those you noted above, what is the biggest thing needed to improve the overall health of the community? Probes: 
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Policies? Partnerships? Economic growth? Physical environment Question #7: What do the hospital systems need to know about the community? What ideas do you have for the hospital systems to improve the health of the community? 43
Question #8: What else does our team need to know about the community that hasn’t already been addressed? 44
Appendix B List of Key Informants Name & Title Hilda Yepes Contreras, Site Administrator Jessica VanArsdale, MD, MPH Director of Health Research Clarke Moore, Board Member Geneva Wiki, Executive Director Nanette Yandell, MPH Public Health Policy Coordinator Agency Del Norte Community Health Center California Center for Rural Policy Del Norte Health Care District Wild Rivers Community Foundation Del Norte County Department of Health and Human Services Area of Expertise Date Community Health 4/8/13 Rural Health and Health Research 4/9/13 Community Health 4/10/13 Community and Tribal Health 4/11/13 Community Health and Health Policy 4/24/13 Annette Klinefelter, Manager of Planning and Public Development Jessica Delaney, AmeriCorps VISTA Curry Community Health Public Health, Community Assessment 5/15/13 Gary Blatnick, Director Del Norte County Department of Health and Human Services Community Health, Mental Health 6/11/13 Father Bernie Lindley St. Timothy’s Episcopal Church Community Health 6/25/13 Ronda Richie, Program Manager Howonquet Head Start Community and Tribal Health 6/26/13 Francisca Van Lith, Volunteer St. Timothy’s Clinic Community Health 6/27/13 Appendix C Focus Group Interview Protocol Demographic Make‐up of Group: Date of Focus Group: Location: Conducted by: Total # of participants: # male: # female: Total number of participants by race/ethnicity: _____ Caucasian _____ Caucasian – Slavic _____ African American _____ Hispanic/Latino _____ Native American _____ Asian _____ More than one race Total number of participants by insurance status: _____ no coverage at all _____ gov’t program _____ commercial ins Estimate average age of all participants: Introductory language for the 2013 CHNA and the role of focus groups As you may know, the State of California requires nonprofit hospitals to conduct community health needs assessments every three years, and to use the results of these to develop community benefit plans, or how each hospital will invest resources into the community to improve overall health. Now the Federal government through the Affordable Care Act has imposed the same requirement on nonprofit hospitals throughout the United States. I have several important questions I’d like to ask over the next hour or so. Please feel free to respond openly and candidly to every question. I want to record our interview so that I can be sure I capture everything you say. We will transcribe the recording and analyze the transcriptions of this and similar interviews in order to paint a complete picture of health of [name of specific community, group, condition, etc]. This interview is confidential, however, we may use quotes from the transcription in the writing of our final report, but the quotes will not be attributed directly to you. Before we get going I also want to ask you to sign an informed consent stating your agreement to participate in this interview, and giving me permission to record and use the recording in the larger needs assessment [introduce informed consent form and get signed before beginning interview]. If needed, begin by stating the project’s objective….. Project Objective To provide necessary information for Sutter Coast Hospital 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. Objective #1: To understand the predominant health issues in a HSA. Question #1: What are the biggest health issues, conditions, and/or diseases you or your family struggles with? Probes: 46
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Diabetes, high blood pressure, heart disease, cancer Mental Health or substance abuse Other issues, including those that are emerging that often go undetected Objective #2: In reference to the health outcomes mentioned above, determine influences of both the individual and community environment (physical structure and living conditions). Question #2: What do you think is causing these health issues and health conditions you’ve described? 3. Individual behavior 
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Activities or behaviors of specific groups Dietary behaviors Attitudes and beliefs Cultural or community norms or beliefs in the community around what it is to be “healthy” Stress and anxiety Physical activity, exercise 4. Physical Environment (Physical structure and Living conditions) 
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Aspects of the built environment Sidewalks Building structures Transportation routes Places to engage in activity Perception of safety Lack of places to exercise Access to health foods 
Access to preventative services, access to basic healthcare Objective #3: Determine opportunities and solutions for living healthy in the community. Question #3: What does your community have that helps you improve or maintain your health? Probes:  Shifting social and community norms and beliefs  Public health awareness  Opportunities to exercise  Access to fresh produce, healthier diet  Areas for families to gather  Sense of community safety  Access to preventative services, access to basic healthcare  Access to policy makers and local elected officials Questions #4: Of all those you noted above, what is the biggest thing needed to improve the overall health of your community? Probes: 
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Policies? Partnerships? 47
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Economic growth? Physical environment Question #5: What do the hospital systems need to know about your community? What ideas do you have for the hospital systems to improve the health of your community? Question #6: What else does our team need to know about your community that hasn’t already been addressed? 48
Appendix D List of Focus Groups Location Date Age Chetco Activity Center 12 participants Brookings Food Bank 12 participants 6/25/
13 6/27/
13 50s‐
70s 40s‐
60s Mountain School, Gasquet 9 participants Family Resource Center, Crescent City 5 participants Yurok Tribal Office, Klamath 9 participants College of the Redwoods, Crescent City 7 participants 6/26/
13 20s‐
50s 6/28/
13 Demographic Information Seniors, low and fixed income Caucasian, seniors, low income Caucasian, low income Government insurance, uninsured Government insurance, private insurance, uninsured 20s‐
30s Caucasian, young families, low income Government insurance, uninsured 7/23/
13 30s‐
60s Government insurance, uninsured 7/24/
13 20s‐
50s Native American, Caucasian, seniors, low income Caucasian, Hispanic, low income Insurance Government insurance, private insurance, uninsured Government and private insurance Appendix E Detailed Health Needs Table Health Need Lack of access to primary and preventive care Contributing Factors 
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Lack of access to follow‐up treatment and specialty care 
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Lack of providers who accept publically insured or uninsured patients Clinics located mainly in cities and are difficult for rural populations to reach Patients must wait for a long time or are unable to secure appointments People only seek treatment for acute conditions or serious injuries The demand for services exceeds capacity Patients experience extreme difficulty getting referrals for specialty care Many must travel long distances to receive specialty care Ongoing conditions requiring specialty services not are properly managed (i.e. cancer treatment, dialysis, pain management) 
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Associated Health Outcomes Diabetes Heart disease Hypertension Asthma COPD Cancer Mental health Diabetes Heart disease Hypertension Renal disease Asthma COPD Cancer Mental health Supportive Data 
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Qualitative Health assets Designated HP shortage area ED visits and hospitalization rates for ambulatory care sensitive conditions (asthma, COPD, diabetes, hypertension) Health assets % uninsured Qualitative 50
Health Need Lack of access to prescription medications and medical equipment Contributing Factors 
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Lack of affordable health insurance and medical coverage 
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Limited access to mental health services 
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Cost of prescriptions medication and equipment to manage chronic conditions is prohibitive Individuals are forced to choose between food, rent or medication Some rural communities do not have pharmacies or drug stores 
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Cost of co‐pays is prohibitive Private heath insurance is too expensive for many individuals People “in the middle” struggle to afford medical care Recently unemployed struggle to access services without insurance People may be eligible for public programs but are not aware of how to enroll Many adults go without care except for emergencies Limited mental health services available, especially for the uninsured and youth Stigmas around seeking care, especially in small communities Existing programs and services 
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Associated Health Outcomes Diabetes Asthma Hypertension Mental health Supportive Data 
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% uninsured Qualitative Diabetes Heart disease Hypertension Asthma COPD Cancer Mental health 
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% uninsured Qualitative Mental health Substance abuse 
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Qualitative Health assets ED and hospitalization rates for mental health and substance abuse % uninsured 
51
Health Need 
Lack of substance abuse treatment and rehabilitation, both inpatient and outpatient 
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Lack of economic opportunity 
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Limited access to healthy foods Associated Health Outcomes Contributing Factors 
Supportive Data have been cut due to lack of funding People have difficulty building trusting relationships with providers due to high turn over Limited services available, especially for uninsured Behavioral health issues exacerbated by lack of treatment options People self medicate to relieve stress and anxiety related to living in poverty Lack of supportive services creates barriers to achieving and maintaining sobriety Multigenerational poverty is prevalent in many communities There are limited employment opportunities in the area and some people must work two or three jobs to “get by” Poverty impacts all aspects of life, including health Most jobs that do not require a college degree are physically demanding 
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Fresh produce and healthy foods are often more expensive 
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Mental health Substance abuse 
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Health assets Qualitative ED and hospitalization rates for substance abuse % uninsured Stress, anxiety 
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% uninsured % unemployed Qualitative Diabetes Obesity 
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mRFEI Fruit and vegetable 52
Health Need Contributing Factors 
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Lack of access to safe and affordable housing 
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Limited access to transportation 
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than processed There is an abundance of fast food in urban areas Many small communities do not have grocery stores, residents rely on gas stations for food Produce is often expensive or of poor quality in rural areas People with special dietary needs (diabetic, cultural) have difficulty getting necessary food Associated Health Outcomes  Heart Disease  Hypertension Supportive Data 
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consumption % overweight or obese Qualitative Low income families cannot afford to move from housing that is poorly maintained Some housing has mold, poor interior air quality and inefficient weatherization Very limited emergency and transitional housing is available There is a fixed homeless population in the area that resides in campgrounds and on beaches and lack access to sanitation 
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Respiratory illness Stress and anxiety 
Qualitative Public transit runs infrequently or does not stop near delivery points for health care services Rural areas may not have any public transit options, and people in rural areas become 
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Diabetes Heart disease Hypertension Asthma COPD Cancer 
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Qualitative ED visits and hospitalization rates for ambulatory care sensitive conditions (asthma, COPD, diabetes, hypertension) 53
Health Need Contributing Factors 
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Limited access to safe and affordable places to exercise 
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Lack of access to dental care and preventive services 
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isolated if they cannot drive Cost of gas prohibitive to accessing services, especially specialty care the requires long distance travel Poor weather and limitations of available equipment create challenges with patient transport Classes, gyms and youth sports are too expensive for many low income families People in rural areas must drive elsewhere access to sports and recreation activities, cost may be prohibitive Many areas lack sidewalks and adequate lighting or lanes for bicycles, pedestrians do not feel safe walking in high traffic areas Inclement weather deters people from exercising outdoors People do not feel safe walking alone or allowing children to play outside in some areas For uninsured adults, extraction is often the only option and dental conditional go untreated People experience long wait times for dental appointments Children are presenting severe Associated Health Outcomes  Mental health  Stress, isolation Supportive Data 
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Diabetes Hypertension Heart Disease Obesity Stress 
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Qualitative Health assets % overweight or obese 
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Chronic pain Infections Pain medication abuse and dependency 
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Health assets % uninsured Qualitative 54
Health Need 
Lack of health literacy 
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Limited access to health and nutrition education Associated Health Outcomes Contributing Factors 
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dental problems at very young ages People must travel out of the area for specialty services People do not make the connection between behavior, lifestyle choices and their health
Many people do not understand how to care for themselves or manage chronic conditions Cultural beliefs and diets may not support positive health outcomes People do not know how to read food labels or use fresh foods to prepare healthy meals People have difficulty understanding and following written instructions Fees associated with available classes are cost prohibitive Classes are offered sporadically or at times that are not convenient, low attendance is an issue People are often not aware of existing educational resources 
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Diabetes Hypertension Heart disease COPD Obesity Mental health Supportive Data 
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Diabetes Hypertension Heart disease COPD Obesity Mental health 
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Qualitative Health assets ED visits and hospitalization rates of ambulatory care sensitive conditions (asthma, COPD, diabetes, hypertension) % overweight or obese Fruit and vegetable consumption Qualitative Health assets ED visits and hospitalization rates of ambulatory care sensitive conditions (asthma, COPD, diabetes, hypertension) % overweight or obese Fruit and vegetable consumption Appendix F Health Assets Identified for SCH CHNA Name Zip Code Asthma/ Lung Disease Diabetes Hypertension Mental Health Nutrition Substance Abuse Tobacco Medical Services Specialty Other Dental S=screening services; M=disease management services; E=education services; I=information available; CM=case management; C=counseling services; R=referral services; A=advocacy services; P=programs offered Del Norte Senior Center 95531 P Yurok Food Distribution Program 95531 P Del Norte Childcare Council 95531 E, P E, I, R County of Del Norte Veterans Services 95531 I, CM, R I, CM, R Birth and Beyond 95531 E E Childbirth, breast feeding, newborn care Del Norte Family Resource Center 95531 Del Norte Community Health Center 95531 P E P P E, P Yes Del Norte Mobile Dental Van 95531 Yes Name Zip Code Asthma/ Lung Disease Diabetes Hypertension Mental Health Nutrition Substance Abuse Tobacco Medical Services Specialty Other Dental 56
Del Norte Community Wellness Center and Garden 95531 P Community Assistance Network 95531 P Coastal Connections 95531 I, R, P I, R, P I, R, P County of Del Norte Child Support Services 95531 I, P I Del Norte and Adjacent Trib
al Lands Building Healthy Co
mmunities Initiative 95531 E E E Decreasing violence in the community First 5 Del Norte 95531 E E Wild Rivers Community Foundation 95531 P Grants Del Norte County Public Health 95531 E, P E E, R, P Del Norte County Alcohol and other Drug Services 95531 P Del Norte County Mental Health 95531 P Name Zip Code Asthma/ Lung Disease Diabetes Hypertension Mental Health Nutrition Substance Abuse Tobacco Medical Services Specialty Other Dental 57
United Indian Health Services: Elk Valley 95531 P United Indian Health Services: Crescent City 95531 P In‐Home Support Services 95531 P Sutter Coast Walk‐in clinic 95531 P Non‐life threatening Sutter Coast Hospital 95531 P P S, M, P Advanced Illness Management 95531 P Hospice CalFresh 95531 P Children’s Health Collaborative 95531 E, P Veteran's Hall 95531 P Crescent City Nursing and Rehab Center 95531 P Nursing home Sutter Coast Home Health Care 95531 P P Transition from hospital to home Hypertension Mental Health Nutrition Substance Abuse Tobacco Medical Services C P Dental Diabetes 95531 Other Asthma/ Lung Disease Harrington House Specialty Name Zip Code 58
Domestic Violence Programs Shelter, legal, 24‐hour hotline Medication Adults 18 and management, over with assistance to development
doctor's al disabilities appointments, etc. Rural Human Services, Supportive Living Services 95531 E, A Lighthouse Community Church 95531 P Veteran’s Hall 95531 P Rural Human Services 95531 P Del Norte County Fairgrounds 95531 P Our Daily Bread Ministries 95531 P P Grace Lutheran Church 95531 P Yurok Tribal programs: TANF 95548 E Cash assistance Child care Diabetes support group 95548 P Name Zip Code Asthma/ Lung Disease Diabetes Hypertension Mental Health Nutrition Substance Abuse Tobacco Medical Services Specialty Other Dental 59
United Indian Health Services: Klamath Health Clinic 95548 P Klamath Senior Center 95548 Klamath Community Center 95548 P Howonquet Head Start 95567 E, P Childcare Xaa‐wan'‐k'wvt Nutrition Center 95567 P Howonquet Health Center 95567 P Yes Smith River Methodist Church 95567 P Brookings Harbor Medical Center 97415 R, P Lab, x‐ray Chetco Medical Center 97415 P Internal medicine Meals on Wheels 97415 P Senior services Chetco Activity Center 97415 P Senior services Classes, recreation Brookings/Harbor Food Bank 97415 P Name Zip Code Asthma/ Lung Disease Diabetes Hypertension Mental Health Nutrition Substance Abuse Tobacco Medical Services Specialty Other Dental 60
St. Timothy’s Episcopal Church 97415 P St. Timothy’s Clinic 97415 I, R I, R S, P Curry Public Transit Dial a Ride 97415 Transportation Brookings Harbor Lions Club 97415 P P Hearing aids, mobile health screening unit, eye glasses Seaview Senior Living 97415 P Senior assisted living Primary, urgent, specialty care Curry Medical Center 97415 P Outpatient dept. of Curry Gen in Gold Beach Brookings Psychiatry 97415 P Sutter Coast Health Center at Brookings‐Harbor 97415 P Outpatient primary care Lab Coast Physical Therapy 97415 P Physical therapy Mental Health Nutrition Substance Abuse Tobacco Dental Hypertension Other Diabetes 97415 Specialty Asthma/ Lung Disease Pacific Vision Medical Center Medical Services Name Zip Code 61
P Vision Outreach Gospel Clinic 97415 P P Shelter, clothing, household goods, car seats Brookings Seventh Day Adventist 97415 P Star of the Sea Catholic Church 97415 P Brookings Presbyterian Church 97415 P Trinity Lutheran Church 97415 P Azalea Middle School 97415 P Mobile Dental throughout Curry County Yes Curry Health Foundation: Ready to Smile 97444 Appendix G Data Dictionary and Quantitative Data 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. This is followed by a discussion of the approaches used to address challenges in data collection across the boundary between the states of California and Oregon. 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 and Curry county (Datasheer, L.L.C., 2012) was compared to the 2010 ZCTA boundaries. All ZIP codes (whether PO Box or unique ZIP code) 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 63
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. State Boundary Issues State agencies, such as public health offices, are often the primary source of health outcome data used to assess community health needs. This presented special challenges for this assessment because the Health Service Area (HSA) for Sutter Coast Hospital includes ZCTAs in both California and Oregon. While every effort was made to collect data from a single, consistent source, no single source could be identified for mortality data. As a result, mortality variables for the portions of the HSA in Oregon and California were obtained from different sources. Mortality variables were available at the ZIP code level in Del Norte County, but at the county level in Curry County. Additionally, while the definitions of the variables were compared to make sure they were consistent, it is possible that coding and data collection standards between the sources could lead influence the resulting reported values. Because of these two issues, comparisons between rates across the state boundary should be done with caution. 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 (U.S. Census Bureau, 2013a; U.S. Census Bureau, 2013b) Variable Name Definition Geographic Data Source Level Asian Hispanic or Latino and Tract 2010 American Population Race, Not Hispanic or Community Latino, Asian alone Survey 5 Year Estimates Table DP05 Black Hispanic or Latino and Tract 2010 American Population Race, Not Hispanic or Community Latino, Black or Survey 5 Year African American Estimates Table alone DP05 Hispanic Hispanic or Latino and Tract 2010 American Population Race, Hispanic or Community Latino (of any race) Survey 5 Year Estimates Table 64
Variable Name Definition Geographic Level Native American Population Hispanic or Latino and Tract Race, Not Hispanic or Latino, American Indian and Alaska Native alone Pacific Islander Hispanic or Latino and Tract Population Race, Not Hispanic or Latino, Native Hawaiian and Other Pacific Islander alone White Hispanic or Latino and Tract Population Race, Not Hispanic or Latino, White alone Total Households Total Households Tract Married Households Married‐couple family Tract household Single Female Headed Households Female householder, no husband present, family household Single Male Headed Male householder, no Tract wife present, family household Non‐Family Households Nonfamily household Tract Tract Data Source DP05 2010 American Community Survey 5 Year Estimates Table DP05 2010 American Community Survey 5 Year Estimates Table DP05 2010 American Community Survey 5 Year Estimates Table DP05 2010 American Community Survey 5 Year Estimates Table S1101 2010 American Community Survey 5 Year Estimates Table S1101 2010 American Community Survey 5 Year Estimates Table S1101 2010 American Community Survey 5 Year Estimates Table S1101 2010 American Community Survey 5 Year Estimates Table S1101 65
Variable Name Definition Population in Total poverty under Poverty (Under .50; .50 to .99 100% Federal Poverty Level) Geographic Level Tract Population in Total poverty under Tract Poverty (Under .50; .50 to .99; 1.00 to 125% Federal 1.24 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 poverty under Tract .50; .50 to .99; 1.00 to 1.24; 1.25 to 1.49; 1.50 to 1.84; 1.85 to 1.99 Total Population by Tract Age Group Total Population Tract Total Population Total Population Block Asian/Pacific Islander Population Total Population, One ZCTA, State Race, Asian, Not Hispanic or Latino; Total Population, One Race, Native Hawaiian and Other Pacific Islander, Not Hispanic or Latino Total Population, One ZCTA, State Race, Black or African American, Not Black Population Data Source 2010 American Community Survey 5 Year Estimates Table C17002 2010 American Community Survey 5 Year Estimates Table C17002 2010 American Community Survey 5 Year Estimates Table C17002 2010 American Community Survey 5 Year Estimates Table DP05 2010 American Community Survey 5 Year Estimates Table DP05 2010 Census Summary File 1 Table P1 2010 Census Summary File 1 Table QTP14 2010 Census Summary File 1 Table QTP14 66
Variable Name Definition Hispanic Population Native American Population White Population Male Population Hispanic or Latino Total Population, Hispanic or Latino (of any race) Total Population, One Race, American Indian and Alaska Native, Non Hispanic or Latino
Total Population, Once Race, White, Not Hispanic or Latino Total Male Population Geographic Level Data Source ZCTA, State 2010 Census Summary File 1 Table QTP3 2010 Census Summary File 1 Table QTP14 ZCTA, State ZCTA, State ZCTA, State Female Population Total Female Population ZCTA, State Population by Age Group: Under 1, 1‐4, 5‐
14, 15‐24, 25‐
34, 35‐44, 45‐
54, 55‐64, 65‐
74, 75‐84, and 85 and over Total Population Total Male and Female Population by Age Group ZCTA, State Total Population 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 2010 Census Summary File 1 Table PCT12 Table B2. ZIP Demographic Information (US Census Bureau, 2011) Variable Geographic Level Percent Households 65 years or ZCTA Older In Poverty Percent Families with Children in ZCTA Poverty Source 2011 American Community Survey Table B17017 2011 American Community Survey Table S1702 Percent Single Female Headed Households in Poverty Percent Population 25 or Older Without a High School Diploma Percent Non‐White or Hispanic 2011 American Community Survey Table S1702 2011 American Community Survey Table S1501 2011 American Community ZCTA ZCTA ZCTA 67
Survey Table B03002
Population Population 5 Years or Older who speak Limited English Percent Unemployed ZCTA Percent Renter Occupied Households ZCTA Percent Uninsured Population County ZCTA 2011 American Community Survey Table B16004 2011 American Community Survey Table S2301 2011 American Community Survey Table DP04 2011 American Community Survey Table DP03 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 B3 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, non‐
Hispanic Native American). Table B3. 2011 OSHPD Hospitalization and Emergency Department Discharge Data by ZIP code Category Variable Name ICD9/E‐Codes Chronic Diabetes 250 Disease Heart Disease 410‐417, 428, 440, 443, 444, 445, 452 Hypertension 401‐405 Stroke 430‐436, 438 Respiratory Asthma 493‐494 Chronic Obstructive Pulmonary 490‐496 Disease (COPD) Mental Mental Health 290, 293‐298, 301‐
Health 302, 310‐311 Mental Health, Substance Abuse 291‐292, 303‐305 20
Injuries Unintentional Injury E800‐E869, E880‐
E929 20
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) Cancer Other Indicators 68
Assault Self Inflicted Injury Accidents Breast Cancer Colorectal Cancer Lung Cancer Prostate Cancer Hip Fractures Tuberculosis HIV STDs Oral cavity/dental West Nile Virus Acute Respiratory Infections Urinary Tract Infections (UTI) Complications related to pregnancy E960‐E969, E999.1 E950‐E959 E814, E826 174, 175 153, 154 162, 163 185 820 010‐018, 137 042‐044 042‐044, 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 2010 were collected at the ZIP code level from the California Department of Public Health (CDPH) and at the county level from the Oregon Health Authority. 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 for ZIP codes in Del Norte County. 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 B4 consequently also lists the years for which each variable was collected. Table B4. California Department of Public Health Birth and Mortality Data by ZIP Code; Oregon Health Authority Birth and Mortality Data by County Variable Name CA Years CA ICD10 OR Years OR ICD10 Codes Collected Codes Collected Total Deaths 2010 2010 Male Deaths 2010 2010 Female Deaths 2010 2010 Population by Age 2006‐2010 2010 Group: Under 1, 1‐4, 5‐14, 15‐24, 25‐34,45‐54, 55‐64, 65‐74, 75‐84, and 85 and over Diseases of the Heart 2010 I00‐I09, I11, 2010 I00‐I09, I11, 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 Alcohol Induced Intentional Self Harm (Suicide) Essential Hypertension & Hypertensive Renal Disease Nephritis, Nephrotic Syndrome and Nephrosis Parkinson’s Viral Hepatitis Arteriosclerosis Homicide All Other Causes Total Births Births with Infant Birthweight Under 1500 Grams, 1500‐
69
2010 2010 I13, I20‐I51 C00‐C97 2010 I13, I20‐I51 C00‐C97 I60‐I69 2010 I60‐I69 2010 J40‐J47 2010 J40‐J47 2010 2010 2010 2010 2010 2010 G30 V01‐X59, Y85‐Y86 E10‐E14 J09‐J18 G30 V01‐X59, Y85‐
Y86 E10‐E14 J09‐J18 2010 K70, K73‐K74 ‐‐ ‐‐ 2010 U03, X60‐X84, 2010 Y87.0 I10, I12, I15 2010 2010 2010 ‐‐ ‐‐ ‐‐ ‐‐ 2010 2006‐2010 2006‐2010 N00‐N07, N17‐N19, N25‐N27 ‐‐ ‐‐ ‐‐ ‐‐ Residual Codes 2010 2010 ‐‐ 2010 ‐‐ E24.4, F10, G31.2, G62.1, G72.1, I42.6, K29.2, K70, K85.2, K86.0, R78.0, X45, X65, and Y15 X60‐X84, Y87.0 I10, I12, I15 2010 N00‐N07, N17‐
N19, N25‐N27 2010 2010 2010 2010 G20‐G21 B15‐B19 I72‐I78 X85‐Y09, Y87.1 70
2499 Grams Infant Mortality Rate 2006‐2010 2010 Low Birth Weight 2010 Rate Behavioral and environmental data were collected from a variety of sources, and at various geographic levels. Table B5 lists the sources of these variables, and lists the geographic level at which they were reported. 71
Table B5. Behavioral and environmental variable sources Category Variable Year Definition Healthy Overweight and 2003‐2005 Percent of population with Eating/ Active Obese (California) self‐reported height and Living weight corresponding to overweight or obese BMIs (BMI greater than 25) Overweight and 2006 ‐ 2009 Percent of population with Obese (Oregon) self‐reported height and weight corresponding to overweight or obese BMIs (BMI greater than 25) 5 a day Fruit and 2003‐2005 Percent of population age 5 Vegetable and over consuming five Consumption servings of fruit and (California) vegetables a day 5 a day Fruit and 2006 ‐ 2009 Percent of population age 5 Vegetable and over consuming five Consumption servings of fruit and (Oregon) vegetables a day Modified Retail 2011 (Data Percent of total retail food Food Environment from 2008 outlets that are healthy Index (mRFEI) and 2009) outlets Food Deserts 2013 USDA Defined food desert tracts Reporting Unit ZIP Code Data Source CHIS/Healthycity.org
County Oregon Behavioral Risk Factor Surveillance System/Oregon Health Authority ZIP Code CHIS/Healthycity.org
County Tract Tract Oregon Behavioral Risk Factor Surveillance System/Oregon Health Authority Centers for Disease Control and Prevention, Division of Nutrition, Physical Activity, and Obesity ,http://www.cdc.gov/ob
esity/downloads/2_16_
mrfei_data_table.xls Food Access Research Atlas, USDA Economic 72
Certified Farmers Markets (California) Registered Farmers Markets (Oregon) 2012 Physical location of certified farmers markets Location 2013 Physical location of registered farmers markets Location Parks 2010 Location Parks 2012 U.S. Parks, includes local, county, regional, state, and national parks and forests CA State Park Boundaries 2011‐12 Parks 2010 OR State Parks Location Parks 2013 Brookings City Parks Location Location Research Service, http://www.ers.usda.go
v/data‐products/food‐
access‐research‐
atlas/download‐the‐
data.aspx http://www.cafarmersm
arkets.com/ http://www.oregonfarm
ersmarkets.org/ Esri Cal‐Atlas Geospatial Clearinghouse, http://projects.atlas.ca.
gov/projects/calstprksb
ndys Oregon Parks and Recreation Department, via Oregon Geospatial Enterprise Office, http://www.oregon.gov
/DAS/CIO/GEO/pages/al
phalist.aspx Brookings, OR Public Works & Development Other Indicators 73
Crime 2011 Major Crimes (Homicide, Municipality/ Forcible Rape, Robbery, Jurisdiction Aggravated Assault, Burglary, Motor Vehicle theft, Larceny, Arson) Locations of traffic accidents Location resulting in fatalities Traffic Accidents Resulting in Fatalities Health Professional Shortage Areas (Primary Care) 2010 2013 Federally designated primary care health professional shortage areas, which may be defined based on geographic areas or distributions of people in specific demographic groups Services FBI Uniform Crime Reports National Highway Transportation Safety Administration US Dept. of Health and Human Services Health Resources and Services Administration Data Warehouse Feature Service (HDW_Mapping); http://ims.hrsa.gov 74
General Processing Steps Rate Smoothing All OSHPD and all single‐year CDPH variables were collected for all ZIP codes in California. OSHPD variables were also collected for all ZIP codes in Curry county. 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 for California ZCTAs 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 75
with an older population to 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. Because of data availability constraints, not state rates were calculated for ZCTAs in Curry County. 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 for ZCTAs in Del Norte County, 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. 76
Processing for Specific Variables 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 non‐
White or Hispanic population, percent single female headed 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 for ZCTAs in Del Norte County 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 77
Life expectancy at birth values are reported for ZCTAs in Del Norte county in years, and were derived from period life tables created in the statistical software program R using the Human Ecology, Evolution, and Health 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 Zip‐
Codes.com: http://www.Zip‐Codes.com Datasheer, L.L.C. (2013). Zip‐Codes.com. Retrieved February 16, 2013, from http://www.zip‐
codes.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/cgi‐
bin/web/node/75 78
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 3‐900051‐
07‐0, 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
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