Los Angeles County

Community Health Profile Project

UCLA Clinical Translational Science Institute (CTSI)

Data Sub-Committee One-Year Report

Version: June 27, 2013

David Zingmond, MD, PhD

Associate Professor of Medicine in Residence, Division of General Internal Medicine and Health

Services Research, UCLA Department of Medicine

Ami M. Shah, MPH

Health Policy Research Associate, UCLA Center for Health Policy Research

E. Richard Brown, PhD (deceased)

Professor, Department of Health Policy and Management, UCLA Fielding School of Public Health

Gerald Kominski, PhD

Professor, Department of Health Policy and Management, UCLA Fielding School of Public Health

Director, UCLA Center for Health Policy Research

EXECUTIVE SUMMARY

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Local health data have the potential to inspire communities and inform investigators, which

collectively can drive change toward improved health in Los Angeles County (LAC). This report

details results of initial efforts by the UCLA CTSI Community Engagement Research Program

(CERP)’s Health Services Data Subcommittee to engage with communities and identify and quantify

disease prevalence and health care utilization, along with associated social determinants of health.

Using data from the UCLA-based California Health Interview Survey (CHIS) and state collected

hospital discharge and emergency department encounter data from the Office of State Health

Planning and Development (OSHPD), investigators of the data subcommittee crafted geographic

descriptions of health outcomes at the county health district level (Figure 1, page 2) and shared

these outcomes with various community collaborators in the first year of the project. An initial set

of indicators served as a starting point for dialogue with the communities about a proposed analysis

plan.

Overall, there were four main categories of community collaborators with whom we engaged.

These were health clinics and systems, community-based organizations, academic-community

partnerships, and the health department. Eleven presentations were made to these groups during

year one of this project.

Community involvement followed a process of recruitment and engagement. Areas within the

health districts were first identified based on knowledge of and relationships with specific

neighborhoods and ethnic enclaves. Community collaborators were approached based on current

research relationships with UCLA investigators (e.g., the 70 Block Project, Magnolia Place

Community Initiative) and other groups associated with the CTSI UCLA CERP. Community

stakeholders and leaders from these areas were invited to participate via phone, email and in

person to provide feedback on presentation materials and proposed analysis plans. We also

reached out to colleagues at the LAC Department of Public Health (DPH) Division of Chronic Disease

and Injury Prevention and the LAC DPH Office of Assessment and those involved in preparing

community health needs assessments (CHNAS) because of their need for local health data.

More specifically, community feedback included the need for more granular data estimates that

represent unique neighborhoods in which these groups are working, along with newly proposed

measures of health (e.g., fast food environment and cardiovascular disease risk). Additional data

sources on the food environment and park space availability were also examined in response to

community needs.

Results of about 6 months of back and forth community engagement efforts helped to reshape and

refine the data analyses plans. A final list of indicators along with a new set of geographic areas and

neighborhoods were developed for future analysis, along with newly developed relationships to

continue the translation of health data. The net impact of engaging with communities is the

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inclusion of a community perspective on health needs when shaping the analysis plan, new

community collaborations, guidance to CTSI researchers and partners, and re-evaluation and

follow-up over the course of the CTSI engagement.

The analytic portions of this work resulted in the creation of profiles of health and health care

outcomes covering the six core areas, plus related risk behaviors of the UCLA CTSI consortium

mapped to the 26 health districts (HDs), which comprise of the 8 Service Planning Areas (SPAs) in

Los Angeles County (Figure 1, page 2).

For the overall hospital-centered care outcomes based on discharge data, the highest rates of

preventable hospitalizations are clustered in the central and southern portions of the county (SPAs

4 and 6), plus a separate region, the Antelope Valley (SPA1); while the lowest rates cluster in the

coastal health districts and the most eastern inland valley communities of the county.

Self-reported health behaviors and disease prevalence derived from the CHIS sometimes differ from

the hospital-centered care measures. Some areas of high hospital-centered care also reported high

prevalence of disease in CHIS. However, in other areas, high hospital-centered care was observed

with low prevalence of disease in CHIS. Disease prevalence measures have far less variation than

that seen in the hospital care measures and do not follow the same consistent patterns.

Examining the hospital-centered care outcomes in relation to population level disease prevalence

and risk factors provide important context to the descriptive health status of each LAC district. It is

possible that individuals living in certain health districts may have better access to health care and

chronic disease management, reflected by average or relatively high overall disease prevalence,

coupled with lower hospital utilization. For example, Glendale has the third highest asthma

prevalence (17%) but is ranked as having an average overall preventable asthma hospitalization rate

(195 per 100,000). Other districts have disproportionately greater disease severity (based on high

rates of hospital-utilization for a given condition) and comparably high disease prevalence in the

population. For example, the Southwest district has one of the top five diabetes prevalence and

preventable hospitalization rates in LAC. Finally, there are some districts where disease prevalence

is reported as very low, while hospital-centered care measures are high. The data in this case could

reflect a lack of awareness of the underlying illness among individuals living in these areas and

presents a noteworthy opportunity to examine whether improved access to quality ambulatory

care would improve health.

Taken together, results of the community engagement and analytic portions create a roadmap for

moving forward. Plans include:

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Further focus estimates for community partners to more granular regions (e. g. , cities,

neighborhoods) defined by zip code or census tracts as the most granular level of data

available (see Appendix C for map of proposed neighborhoods)

Use results to engage community partners in areas where awareness of a health condition

appears to be low, but hospital-centered care is high

Implications of health care reform and expanded coverage of insurance on possible increase

in diagnosis of health conditions and/or changes in rates of preventable hospitalizations

Explore dynamic (interactive) mapping of estimates that could be linked to local resource

availability

Supplement area use estimates with mortality estimates from the state death master file

Supplement area use estimates with cancer incidence rates from the state cancer registry

Report back to community collaborators to share, interpret, and translate findings into

meaningful solutions to address health concerns

As part of these early stages of the UCLA CTSI, this LAC Community Health Profile project report

responds to the critically important need to assess and identify the communities that could most

benefit from UCLA’s innovation and research, and where key synergies could be formed with the

CTSI. The results of this community collaborative process will ensure that the information about

inequities in health, along with possible roadmap of solutions on how to improve matters, will reach

those communities that are most affected and are the very people who can drive dramatic and

sustainable change.

TABLE OF CONTENTS

INTRODUCTION…………………………………………………………………………………………………………. pages 1-2

DATA FINDINGS…………………………………………………………………………………………………………. pages 3-12

DISCUSSION………………………………………………………………………………………………………………. pages 13-14

POLICY IMPLICATIONS, RECOMMENDATIONS, AND FUTURE DIRECTIONS…………………. page 15

APPENDICES ……………………………………………………………………………………………………………….pages 16-43

ACKNOWLEDGEMENTS

The UCLA Clinical Translational Science Institute’s Health Services Research Data Subcommittee of

the Community Engagement Research Program (CERP) acknowledges this project as the brainchild

of Dr. Rick Brown, a passionate advocate for evidence-based policies and programs. He envisioned

the synthesis of various data sources at the local level as an opportunity to inform the UCLA CTSI

and drive real sustainable change for those communities in Los Angeles County that need it most.

We also acknowledge Sitaram Vangala, M.S., Senior Statistician, Department of Medicine Statistics

Core, David Geffen School of Medicine at UCLA who analyzed the hospital discharge data and

calculated the Agency for Healthcare Research and Quality (AHRQ) Prevention Quality Indicators,

Inpatient Quality Indicators and additional quality measures under the direction and leadership of

Dr. David Zingmond. Hongjian Yu, Ph.D., Director of Statistical Support at the UCLA Center for

Health Policy Research, and his colleagues Yueyan Wang, Ph.D., Statistician, and Melanie Levy, M.S.,

Assistant Statistician, produced the small area estimates from the California Health Interview

Survey (CHIS) for the 26 Los Angeles County health districts. In addition, we recognize the time and

effort put forth by Héctor Alcalá, Ph.D. candidate, and Diane Tan, Ph.D. candidate and UCLA CTSI

fellow, who helped prepare data tables and maps for this report. None of this work would have

been possible without support from the UCLA CTSI.

SUGGESTED CITATION

D Zingmond, AM Shah, ER Brown, GM Kominski. Los Angeles County Community Health Profile Project

(Data Sub-Committee One-Year Report). Los Angeles, CA: UCLA Center for Health Policy Research,

Department of Medicine and Clinical Translational Sciences Institute, April 2013.

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INTRODUCTION

The UCLA Clinical Translational Science Institute (CTSI) Health Services Research Data

Subcommittee, as part of the Community Engagement Research Program (CERP), engaged with

community collaborators to assemble local data for the purpose of improving health in Los Angeles

County (LAC).

We engaged with communities to analyze and examine health survey and hospital discharge data

and identified variations in health and hospital-centered care outcomes for LAC health districts. The

Data Subcommittee consulted with various community stakeholders including representatives from

health clinics and systems, community-based organizations, academic-community partnerships and

the health department to identify measures of health that would serve as valuable evidence to

shape community plans, health initiatives, local health policies and the evaluation of programs.

Feedback received from this collaborative process informed the final list and definition of

indicators and refined the data analyses designed to quantify the health status of and illustrate

geographic variation among LAC health districts. The syntheses of these data, as summarized in

this Los Angeles County Community Health Profile, illustrate the burden of disease and

preventable hospitalizations for six clinical domains of the CTSI (diabetes/obesity, cardiovascular

and cerebrovascular disease, cancer, addiction, mental health, and HIV). Results of this initial

exploratory phase of the project (supported by one-year of CTSI funding) address the following

proposed project aims:

1. Engage with community stakeholders in an iterative process to inform and refine data analysis

plans to produce health data that are relevant and meaningful to community-based

improvement efforts

2. Develop an analysis plan based on community engagement efforts outlined in Aim 1 to

examine population health and healthcare

3. Develop an analysis plan based on community engagement efforts outlined in Aim 1 to examine

disease-specific preventable hospitalizations and emergency department (ED) encounters

4. Produce a report that incorporates the profile of LAC communities, with focused analyses of

key geographic areas engaged in per Aim 1

The evidence outlined in this Los Angeles County Community Health Profile may serve as an

empirical guide for investigators, community providers, policy makers and other stakeholders to:

(1) identify culturally tailored and community driven solutions for the specific population health

and healthcare needs identified; (2) recommend evidence-based solutions on how to meet these

needs; and (3) direct greater resources to those communities most in need.

Preliminary findings have already begun to inform the work of CTSI investigators and community

collaborators while enabling the Data Subcommittee to build relationships with additional

community partners. This level of engagement has enhanced our analyses and we anticipate will

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mobilize community stakeholders in all of the health districts, with support from the UCLA CTSI,

to begin discussions on how to address high rates of preventable hospitalizations and poor

population health, while shaping CTSI’s strategies on how to move toward improved health and

health care in LAC.

Figure 1. Map of Los Angeles County Service Planning Areas and Health Districts

DATA FINDINGS

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This section provides a brief description of the data tables and maps produced for this report. First,

we summarize the general demographic and socioeconomic characteristics of the 26 health

districts, and then we describe hospital-utilization rates and disease prevalence estimates profiling

the districts based on the CTSI core measures, when available.

Measures of disease prevalence and hospital utilization (based on definitions of preventable

hospitalizations) are included for the following conditions: diabetes; hypertension; coronary heart

failure and related heart attacks and chest pain; heart disease and cardiovascular disease risk;

procedures to treat ischemic heart disease; pulmonary diseases – including asthma and COPD

(chronic obstructive pulmonary disease); acute mental illness and serious psychological distress;

and a summary measure ranking all chronic disease related hospitalizations. In addition, measures

of cancer screening and mortality and obesity and sedentary behavior along with structural

measures of the built environment are included. Definitions of all measures and the methods

employed to produce these final outcomes are detailed in Appendix B, page 25-42.

Tables and maps are also presented within this section to highlight selected findings and illustrate

those districts with the greatest hospital-case for ambulatory care sensitive conditions (ACSCs) and

disease prevalence. Data are presented in relative terms to show differences across health districts

and ranked for certain outcomes. It is our intention that these data help inform CTSI investigators

and community collaborators and to encourage further exploration of where, why and how there

may be opportunities to intervene, and ultimately improve patient outcomes and community health.

DATA SOURCES AND METHODS

Disease prevalence estimates are modeled based on the 2009 California Health Interview Survey

(CHIS), which is a state-wide random digit-dial telephone survey. Most measures come from asking

whether a respondent reported ever having been diagnosed with a particular condition (e.g., “Have

you ever been told by a doctor that you have diabetes?”). For a list of all the CHIS constructs and

details small area estimation procedures employed see Appendix B.

Hospital utilization is defined by the Agency for Healthcare Research and Quality (AHRQ)’s

prevention quality indicators (PQIs) and inpatient quality indicators (IQIs). PQIs are a set of

measures that identify conditions for which good outpatient care or early intervention can prevent

complications or more severe disease and potential hospitalizations. IQIs are a set of measures that

reflect the quality of care inside hospitals and utilization of procedures that may be associated with

lower mortality. Counts of hospital admissions and ER encounters were produced and PQIs and IQIs

were calculated as rates per 100,000 based on data from Office of Statewide Health Planning and

Development (OSHPD) for the 26 LAC health districts. For more details on the measures produced

and methods employed, see Appendix B.

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General characteristics

Tables B2a and B2b (in Appendix B) present some general characteristics of Los Angeles County

(page 30-31). More than 15% of adults in 12 out of 26 of the health districts (HDs) are age 65 and

over. Alhambra and Harbor have the highest proportion of older adults and Southeast and Compton

have the lowest. The proportion of low-income adults (under 100% FPL) ranges from 7% in Torrance

to 39% in Southeast. In 9 of the 26 HDs, less than ¾ of adults (age 25 and over) have a high school

education. The lowest estimated percentage of adults with a high school education is 37%

(Southeast) and 48% (South). HDs with the highest proportion of adults with limited English

proficiency (LEP) are Southeast (48%), Central (44%), San Antonio (43%), and East LA (42%),

followed by Northeast, El Monte, and Alhambra (38%).

LAC is a diverse region with a majority-minority demographic breakdown. Non-Hispanic Whites

form the majority of residents (West and Glendale) in only two health districts (HDs) and constitute

less than 10% of residents in seven HDs (Table B2). In five of these HDs, Compton, East LA, South,

Southeast, and Southwest, less than 5% of the residents are non-Hispanic Whites. In 16 of the 26

HDs, Hispanic individuals are at least 40% of the population in the respective districts. In five HDs,

Hispanic individuals are at least 70% of the population in the HDs. The lowest estimated percentage

of Hispanic individuals is 14% (West) and the highest is 89% (East LA).

Uninsured estimates were defined by adults age 18-64 that were uninsured at any point in the past

year. The uninsured estimates ranged from 16% (West) to 64% (Southeast), with a median of 29%

uninsured. Five HDs have rates greater than 40% (San Antonio, East LA, Central, South, and

Southeast) and another five HDs have rates lower than 20% (West, Torrance, Harbor, San Fernando,

and Foothill). Despite the wide range, there is a high overall rate of uninsurance, where one in every

three people is uninsured in LAC (Appendix Table B2b, page 31).

Diabetes related preventable hospitalizations and prevalence

We examined the four AHRQ-defined diabetes measures – short-term complications, long-term

complications, uncontrolled diabetes, and diabetic lower extremity amputation. Overall, the health

districts with the highest total rates for these measures geographically cluster together in SPA 6 –

South, Compton, Southwest, Southeast, and part of SPA 8 - Inglewood (Figure 2, page 5). Total rates

track each of the four individual diabetes measures. The West health district has rates 1/5 of those

in South (88 v. 432 per 100,000), which is the district with the highest rates of preventable inpatient

hospitalizations due to diabetes and related conditions (Appendix Table B3, page 32). These were

compared to CHIS estimates of diabetes prevalence by health district (Appendix Table B3). The

highest reported prevalence was estimated in the South HD (33%) versus the lowest in the Glendale

HD (3%).

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Health districts with the highest rates of hospitalizations for diabetes for the most part also have

relatively high diabetes prevalence and those with low rates of hospitalizations have relatively low

disease prevalence. For instance, South and Southwest HDs also have some of the highest disease

prevalence estimates (33% and 15%, respectively). However, for Southeast, where we see one of

the highest rates of hospitalizations (358 per 100,000), there is relatively low-to-moderate disease

prevalence (7%) – see Appendix Table B3, page 32.

Inpatient hospitalizations

(rate per 100,000)

Figure 2. Inpatient Hospitalizations Due To Uncontrolled, Short and Long-Term Diabetes

Complications And Lower Extremity Amputations In Los Angeles County Health Districts

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Hypertension – prevalence and related hospitalizations

Hypertension-derived hospital encounters were broken out between ED encounters not leading to

hospitalization, admissions, and total encounters (ED encounters plus admissions). Admissions

reflect hypertensive-related complications (hypertension, hypertensive emergency, hypertension

plus heart failure, hypertension plus renal failure), while ED encounters are qualitatively different

(AHRQ PQI#7). By far the highest rates of admissions and overall encounters are clustered in

districts that are located primarily in SPA 6, South LA (Southwest, South, Compton, Inglewood, and

Southeast), occurring roughly four times more often than the lowest overall encounters in West,

Hollywood and the eastern region of the county (SPA 3), see Appendix Table B4 (page 33). This

pattern of hospital-utilization is similar to that of the preventable hospitalizations due to diabetes

described earlier.

In contrast, prevalence rates vary from a reported low of 14% in Southeast to 52% in South

(Appendix B, Table B4). Because of the high reported rate of hypertension prevalence, South HD

actually appears closer to the median in terms of the proportion of hospitalizations relative to

disease prevalence when compared to the districts with high overall rates of hypertension-related

hospital care (e. g., Southeast district). Interestingly, Glendale, which has low disease prevalence,

has a high burden of hospitalizations (3rd most, see Appendix Table B4, page 33). Table 1 below

summarizes these findings.

Table 1. Los Angeles County Health Districts with the best and worst hypertension related

hospital outcomes and their corresponding hypertension disease prevalence

HEALTH DISTRICT HOSPITAL ADMISSIONS AND ER

ENCOUNTERS HYPERTENSION

PREVALENCE

Top 5 districts (rates/100,000) %

Southwest 418 34

South 411 52

Compton 363 31

Inglewood 340 28

Southeast 311 14

Bottom 5 districts (rate/100,000) %

Foothill 188 36

Hollywood-Wilshire 185 27

Alhambra 182 28

El Monte 180 30

West 110 25 Source: OSHPD 2007-2009 average rates, see appendix table B4 for data on additional districts and California Health Interview Survey 2009 small area estimates

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Cardiac Disease – estimated prevalence, occurrence and treatment of ischemic heart disease, and heart failure related hospitalizations

Occurrence of ischemic heart disease hospitalizations contrasts between rates of acute illness

(acute myocardial infarction (AMI) and angina (chest pain)) and procedures to treat ischemic heart

disease (bypass surgery and coronary angioplasty). On the one hand, rates of hospitalization per

100,000 population for acute heart attacks are highest in Whittier, Antelope Valley, and Glendale

and lowest in Southeast and West, see Appendix table B5 (page 34) for details.

Common surgical procedures to treat ischemic heart disease include CABG (coronary artery bypass

graft) and PCI (percutaneous coronary intervention). Rates of these cardiac procedures tend to

occur in areas where acute rates of ischemic heart disease are highest. Table 2 below presents the

top five health districts with the highest rates of AMI and cardiac procedures, compared to the

average for LAC. Areas with highest procedure rates also include areas with the highest rates of

AMI, e. g., Whittier, Antelope Valley, and Glendale. However, there are some areas where acute

rates of ischemic heart disease do not have the highest rates of cardiac procedures. For example,

the Southwest district has the lowest receipt of cardiac procedures, but is actually fourth in terms of

rate of AMI. See Appendix Table B6 (page 35) for details.

Table 2. Top five Los Angeles County Health Districts with the highest rates of acute myocardial

infarction (AMI) and associated rates of cardiac procedures, including CABG (coronary artery

bypass graft) and PCI (percutaneous coronary intervention)

AMI (heart attack) Rates per 100,000

Cardiac procedure Rates per 100,000

Los Angeles County 155 466

Whittier 235 565

Antelope Valley 228 626

Glendale 198 735

Southwest 168 349

East LA 165 458 Source: OSHPD 2007-2009 average rates, see Appendix Table B6 (page 35) or data on additional districts.

Heart-related disease prevalence and hospitalizations cannot be directly combined due to the

inexact definition of heart disease in the CHIS, but are most closely tracked by answering questions

regarding “ever had heart disease” (not otherwise defined) or via a composite measure of two or

more ischemic heart disease risk factors (hypertension, diabetes, smoking, obesity or low physical

activity). The highest rates of heart disease reported by CHIS occur in Whittier, Long Beach, and

Harbor HDs while the lowest rates occur in the East LA, South, and Inglewood HDs. This is to be

contrasted with the composite risk factor measure, which is highest in the South HD (50%) and

lowest in Pasadena (14%) and Glendale (11%) HD (Appendix Table B7, page 36).

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Cardiac heart failure is qualitatively different than these other heart disease related measures. The

highest rates of hospitalizations for heart failure are clustered in the southern HDs of the county,

comprised of SPAs 6 and 8 (Southwest, South, Compton, Inglewood, Bellflower, Southeast, Long

Beach, and Harbor), while the West and Hollywood HDs have the lowest (Appendix Table B5).

Pulmonary disease –asthma and COPD- related preventable hospitalizations and prevalence

Asthma occurrence differs compared to other health measures since air quality may differ

throughout the county. The highest rates of asthma-related hospital encounters (admissions plus

ED visits) occur in the South HD (578 per 100,000) and in the Antelope Valley HD (557 per 100,000),

while the lowest occurs in the West (135 per 100,000) (Appendix Table B8, page 37).

Figure 3. Current asthma prevalence in Los Angeles County Health Districts

among young adults age 18-44

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Current asthma prevalence is defined as individuals who report that they were ever diagnosed with

asthma and has asthma now; OR ever diagnosed with asthma and had an asthma attack/episode in

the last year. Interestingly, per CHIS estimates, rates of current asthma prevalence are highest in

the South HD (41%) and lowest in the Southeast HD (1%), which are in the area, SPA6 (Figure 3,

page 8). Other high rates of asthma prevalence were observed in Antelope Valley (19%) and

Glendale (17%) (Appendix Table B8, page 37).

When one considers the proportion of asthma visits divided by asthma prevalence, the number of

hospital visits (inpatient and ED encounters) for asthma is not disproportionately greater in the

South and Antelope HDs. However, the Southeast, Southwest, and East LA districts, which have the

lowest asthma prevalence rates (<2%), have the highest burden of preventable hospital-care for

asthma. Hospital encounters for COPD in older adult populations (45+ years old) are roughly evenly

divided between ED visits and actual hospital admissions. Of note, the southern cluster of HDs

(mostly in SPA 6), with the exception of Antelope Valley, accounts for most of the areas with the

greatest rates of preventable hospital visits, which consistently demonstrates high rates of disease

(Appendix Table B9, page 38).

Acute mental health- related hospitalizations and severe psychological distress prevalence

Admissions with a primary psychiatric diagnosis generally follow a pattern consistent with that for

medical diagnoses. Mental health admissions (inpatient and ED encounters) ranged from 460 per

100,000 in West HD to 1,388 in the South HD. Of note, three out of the five health districts with the

highest admission rates are in county health districts South, Southeast, and Southwest (which are in

SPA 6) and the two other districts, Central and Pasadena, which are in SPA 4 and 6, respectively

(Appendix Table B10, page 39). HDs with the lowest admission rates are either on the coast

(Torrance and West) or clustered in the eastern half of Los Angeles County (East LA, Alhambra, El

Monte, San Antonio, Whittier, and Pomona).

A review of the actual admissions reveals that most hospitalizations occur with a type of care

reported as “medical” rather than psychiatric, suggesting that the county’s general acute care

facilities are used as staging areas to evaluate and stabilize patients medically before placement in

dedicated psychiatric inpatient wards or free-standing facilities.

Serious Psychological Distress (SPD) is often used as a proxy measure for severe mental illness in a

population. Adult respondents were asked six questions, known as the “Kessler 6”, to assess

symptoms of distress during a 30‐day period in the last year. Findings from CHIS regarding SPD are

only moderately concordant with the findings from actual hospitalizations for acute psychiatric

conditions. Southeast (27%) and Central (14%) have the highest rates of SPD and West (3%) has the

lowest, which are similar to their rank in relation to mental health hospital-care (Appendix Table

B10). However, in some districts, we do not see the same pattern of use. For instance, the South HD

has low SPD (4%) but the highest rates of hospital-care and the San Antonio HD (in SPA7) has high

SPD (10%) and the sixth lowest rate of hospital-care (595 per 100,000).

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Table 3. Average Ranked Health Districts for

8 Preventable Hospitalizations (1)

Health District Rank

West 1

Alhambra 2

Torrance 3

Hollywood-Wilshire 4

San Fernando 5

Pomona 6

Harbor 7

West Valley 8

El Monte 9

East Valley 10

Glendale 11

Pasadena 12

Foothill 13

San Antonio 14

Whittier 15

Long Beach 16

East LA 17

Bellflower 18

Central 19

Northeast 20

Inglewood 21

Southeast 22

Antelope Valley 23

Compton 24

Southwest 25

South 26 (1) Average rankings were calculated based on

preventable hospitalizations from diabetes, hypertension,

heart failure, heart attack, CP, asthma, COPD and a

mental illness. Source: OSHPD 2007-2009 average rates

per 100,000

Summary ranking of major chronic diseases

To synthesize the many tables of data we have

produced, we calculated the average rank of

total preventable hospitalizations for eight

major conditions: diabetes, hypertension, heart

failure, heart attack, CP, asthma, COPD and a

mental illness (Table 3).

The highest rates of preventable

hospitalizations are clustered in the center and

southern portions of the county (e. g., districts

South, Southwest and Compton) in a clear,

contiguous grouping of health districts plus a

separate region, the Antelope Valley. The best

overall measures cluster in the coastal health

districts and the most eastern inland valley

communities of the county (West and

Alhambra).

Disease prevalence rates do not follow the same

patterns. High rates of disease are only

sometimes indicative of areas where there is

the greatest burden of chronic diseases overall.

For instance, the South district has the highest

rates of diabetes (33%), hypertension (52%),

and asthma (40%) but has some of the lowest

prevalence of heart disease (3%) and serious

psychological distress (4%), a proxy for mental

illness in the population.

Self-reported disease prevalence measures the proportion of people living with a particular

condition, but it does not necessarily measure how well individuals manage their condition. On

the one hand, there are some districts that have high rates of disease prevalence and consistently

high rates of preventable hospitalizations: the South district not only has the highest rate of

diabetes prevalence but it also has the highest rate of hospitalizations due to uncontrolled and

complications due to diabetes (433 per 100,000) – see Appendix Table B3. On the other hand,

there are some districts that have low rates of self-reported disease and high rates of

hospitalizations. For example, the Southeast district has the 3rd highest rate of hypertension

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hospitalizations and 4th highest rates of diabetes hospitalizations but some of the lowest

estimates of disease prevalence for both conditions. While data findings are inconsistent, they

present an interesting opportunity where better outpatient care could possibly improve health

since there are high rates of preventable hospitalizations and low self-reporting or awareness of

diagnosed chronic health conditions in the population.

Breast and colorectal cancer screening and mortality

Compliance of cancer screening recommendations can ensure early detection and increase cancer

survival rates. Screening compliance can also be a proxy measure of access to routine primary care

and access to routine preventive health services. We examined data for breast and colorectal

cancer screening compliance for the 26 health districts in relation to known mortality rates at the

SPA level.

Though there has been some controversy in recent years regarding breast cancer screening for

women age 40 and over, we measure compliance as having had a mammogram at least once in the

past two years. Mammogram screening history based on this guideline for the 26 LAC health

districts ranging range from 67% in Hollywood-Wilshire to 86% of in the Northeast district

(Appendix Table B11, page 40). Screening rates overall appear relatively similar across the different

districts despite substantial differences in poverty and insurance status by health district and higher

rates of mortality rates among African American women (34 deaths per 100,000 compared to 23

deaths per 100,000 among White women in LAC) and in Antelope Valley and South LA SPAs (25

compared to 20 per 100,000 in LAC overall).1

Colorectal cancer screening compliance was defined for adults age 50 and over who had at least

one or more of the following: a FOBT in the last year, flexible sigmoidoscopy in the last five years,

double-contrast barium enema in the last five years, or a colonoscopy in the last 10 years.

Colorectal cancer screening compliance varies more broadly and ranges from 43% the Southeast

district to 78% in Torrance (Appendix Table B11, page 40). Low colorectal cancer screening rates

may be associated with the disproportionately higher colorectal cancer mortality rates among

African Americans (22 deaths per 100,000) and in the South SPA (18 death per 100,000) compared

to Los Angeles County (14 deaths per 100,000).1

Obesity, sedentary activity, and the built environment

The effect of the built environment on health has been a major public health concern - it affects

what we eat and how active we are and plays a major role in shaping our daily behaviors and habits.

Specifically, a population’s eating habits may be influenced by their access to healthy food outlets

and their level of physical activity may be influenced by access to safe parks or green space.

1 Los Angeles County Department of Public Health, Mortality in Los Angeles 2008, Dec 2011.

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Estimates of obesity (body mass index greater or equal to 30 kg/m2) and sedentary activity were

produced as potential modifiable risk factors toward improved health. We also calculated the

number of fast food outlets per 100,000 residents and mean park acreage per 1,000 residents for

each of the 26 health districts of LAC as two measures of the built environment.

The central health districts tended to have the highest rates of obesity as well as the most fast food

outlets per 100,000 residents. San Antonio, El Monte, Bellflower, Whittier, and Harbor had the top

five highest rates of obesity in LAC in that order (Appendix Table B12, page 41). Central, Torrance,

East LA, Bellflower, and Hollywood-Wilshire were among the health districts with the most fast food

outlets per 100,000 residents. In addition, HDs with some of the lowest values of average park

space also had some of the lowest rates of sedentary behavior (e.g., Torrance – prevalence of

sedentary behavior: 7.1% and average park space: 4.1 acres per 1,000 residents), while certain

health districts with some of the most park space per 1,000 residents also had some of the highest

rates of sedentary behavior (e.g. , Antelope Valley – prevalence of sedentary behavior: 21.8% and

average park space: 47.8 acres per 1,000 residents).

13

DISCUSSION

Evaluation of health and healthcare across Los Angeles County health districts using estimates from

state hospital record abstracts, Census population estimates, and CHIS demonstrates consistent

patterns across the illnesses studied. As is well known, clusters of illnesses appear worse in health

districts located primarily in SPA 6 (South) and SPA 1 (Antelope Valley) as well as portions of SPA 4

(Metro). Not surprisingly, health and wealth are highly correlated such that affluent areas of the

county (West, Torrance, and Pasadena) tend to have lower hospitalization rates.

Although patterns of use are consistent with historical data and distribution of wealth, the variation

between health districts is striking. Depending on the type of illness, the range of acute

hospitalizations can vary up to a factor of five (e.g., for diabetes complications). Such variation

reflects a number of likely underlying factors. Hypothetically, limited or delayed access to quality

ambulatory care leads to lack of disease management, loss of treatment, greater severity of

disease, and greater complications. This also leads to increased hospital use, even if overall severity

of illness is no different.

In some situations, these descriptive data do not follow expected patterns. For instance, rates of

acute mental health admissions were particularly high in Pasadena. Though we have not analyzed

exactly why this is the case, anecdotal feedback from community groups suggest that this is likely

because many mental health social service agencies are available in Pasadena. Though such

contextual information is not conclusive, continued community input and interpretation of findings

help support and understand the findings.

Rates of hospitalizations in most districts are consistently high in relation to the disease prevalence

in the population. Some districts however show lower disease prevalence (e.g., diabetes,

hypertension and asthma) than the corresponding hospital data. There are a number of possible

explanations for this. First, CHIS does not measure severity of disease rather prevalence in the

overall population. Thus, it is possible that of the cases in a given population, that those that are

severe (though preventable) are few. Second, prevalence is based on self-reported data from CHIS,

a health survey. For example, to assess diabetes prevalence respondents are asked: “Has a doctor

ever told you that you have diabetes?” In areas with low rates of disease, survey respondents

(especially those who lack access to ambulatory care, insurance or a doctor) may not be aware of

their condition because they may not have seen a doctor to have had the opportunity to be

diagnosed with a given condition or disease. It is possible that these populations are more likely to

be uninsured and heavily immigrant with large numbers of non-English speaking or undocumented

individuals. However, no definitive conclusions or causal inferences can be drawn from these initial

analyses.

14

By involving community organizations and representatives in the process of designing the analysis

plan, the findings described here have been enriched with a unique perspective and include

feedback on what indicators and geographic level of analyses would be most useful. Analyzing and

assembling the data in response to the initial feedback from community stakeholders is only the

first phase of this project. Findings will continue to be shared with and reported back to community

stakeholders, hospital administrators, health officials and other change agents who are primed to

create culturally tailored and locally acceptable solutions with academic expertise and support of

the UCLA CTSI for real and sustained change.

We present these data findings as a call for action. A measure of our success will be whether and

how the findings translate into meaningful programs and policies to address the health disparities

documented in this LAC Community Health Profile. We have begun to identify which districts have

disproportionately higher rates of preventable disease relative to the proportion affected by the

condition in the overall population, suggesting the need for improved access to quality primary care

and a supportive environment for better self-management of chronic disease. We have also shown

which health districts could benefit from further data examination to determine why rates of

preventable hospitalizations are high or low and where there is significant variation in health and

health care. With the overall goal of improved health in LAC, sharing knowledge and advances in

medical practices, reallocating resources, and building local capacity that is supported by the CTSI

will ensure optimal disease management in the health care delivery system and ultimately

improved health in LAC.

15

POLICY IMPLICATIONS, RECOMMENDATIONS, AND FUTURE DIRECTIONS

Findings from these analyses suggest potential areas for further work and collaboration between

the CTSI and local communities for creating strategic ways to use these data for innovation,

intervention, and disease tracking. Significant concentrations of illness suggest that resource

allocation and targeting could best be directed regionally with programs that can affect community

health beyond just a narrow target population. In other words, because multiple illnesses cluster

geographically, the choice of community interventions should be made in such a way that benefits

spread beyond just single targeted illnesses. For example, a stroke prevention program in SPA 6

might have the added benefits of raising awareness of risk factors of stroke for the entire adult

population – hypertension, diabetes, hyperlipidemia, and obesity – which would in turn affect not

only stroke, but also adverse events due to these illnesses and their downstream complications.

In year 2 of this effort, we plan to:

Further focus estimates for community partners to more granular regions (e. g. , cities,

neighborhoods) defined by zip code or census tracts as the most granular level of data

available (see Appendix D for map of proposed neighborhoods).

Explore dynamic (interactive) mapping of estimates that could be linked to local resource

availability.

Supplement area use estimates with mortality estimates from the state death master file

Supplement area use estimates with cancer incidence rates from the state cancer registry

Report back to community collaborators to share, interpret, and translate findings into

meaningful solutions to address health concerns

16

APPENDICES

Appendix A. Community Engagement Approach and Impact..…………………………………pages 17-22

Table A1. Community Engagement Log

Table A2. List of Presentations, CTSI Los Angeles County Health Profile

Appendix B. Data Methods and Tables ………………………………………………………………….pages 25-42

Figure B1. Los Angeles County 8 Service Planning Areas and 26 Health Districts

Table B1. Population Health Indicators selected to be produced from CHIS Small Area Estimates

Table B2a. Demographic characteristics

Table B2b. Socio-economic characteristics and uninsured rates

Table B3. Diabetes preventable hospitalization rates and disease prevalence

Table B4. Hypertension preventable hospitalizations and disease prevalence

Table B5. Heart failure, acute illness and angina without procedure

Table B6. Procedures to treat ischemic heart disease

Table B7. Heart disease prevalence and ischemic heart disease/cardiovascular disease risk factors

Table B8. Asthma preventable hospitalizations and current asthma (18-44yrs)

Table B9. COPD preventable hospitalizations (45yrs+)

Table B10. Preventable hospitalizations due to mental illness and serious psychological distress

Table B11. Breast and colorectal cancer screening

Table B12. Prevalence of obesity, sedentary behavior, fast food outlets and park acreage

Appendix C. Map of neighborhood proposed for future analyses …………………………..page 43

Appendix D. Age- and Sex- Standardized Hospitalization Rates…..…………………………..pages 44-46

17

Appendix A. Community Engagement Approach and Impact

To ensure that the final data assembled for this project are relevant and valuable to communities,

we engaged with several community groups to shape the exploratory phase of data analyses, select

a limited set of key health indicators for analyses and revise the analysis plans as needed when

feasible. Community engagement has enriched the research process and has implications for the

interpretation and translation data findings. Reaching out to community groups started in May

2012 and continues to evolve with input from 16 unique organizations representing four types of

community groups. This appendix details how we engaged with community groups, solicited their

input and refined our analyses as a result.

We first identified groups with whom we had established relationships (through the efforts of Dr.

Rick Brown, the initial PI on the project, and referrals from Dr. Arleen Brown). For instance, we

established a partnership with the Community Health Councils as part of the CTSI Healthy

Community Neighborhood Initiative, and shared preliminary findings with the health committees of

the 70 Block Project and Magnolia Place Community Initiative. In general, initial estimates of

disease prevalence along with counts or rates of hospitalization were presented either at the

Service Planning Area (SPA) or health district level (when available) to illustrate the type of

information that could be available in producing this Los Angeles County profile. In response, we

gained essential insight on how to further guide our analyses and dissemination plans.

Next, we reached out to, and in some cases responded to, the data needs of those involved in

preparing community health needs assessments (CHNAs) required for clinics, hospitals, or a health

department and for the data needs of investigators working in the community through the UCLA

Community Engagement Research Program (CERP). In addition, we met with colleagues at the LAC

Department of Public Health and Division of Chronic Disease and Injury Prevention and the LAC

Department of Public Health Division of Assessment and received important feedback on how data

are translated into local policies and action. They provided insight on new, more granular

geographic areas that would be pertinent and meaningful for analyses, such as city and

unincorporated Census Designated Places. In this case, we gained important insight regarding the

geographies of interest to community planning and exchanged knowledge and ideas regarding the

small area estimation methodologies.

The Community Health Councils has likely been an ideal scenario in which we have gone back and

forth to discuss, analyze and reanalyze data. Our efforts have evolved and cross-fertilized with other

UCLA community engagement work and has led to several iterations of analyses, particularly related

to high rates of coronary heart failure in South LA, and several presentations since March 2012

(Appendix B). Upon presenting the data at several internal meetings, we found that investigators

were interested in sharing data with their community partners, pursuing new funding to address

18

problems identified, and establishing a working relationship with us in pursuit of improving health in

South LA.

Another interesting example comes from a community-based researcher, Miki Carpenter, MPH,

PhD, Director of Assessment and Program Development, Western University of Health Sciences in

San Gabriel Valley. Dr. Carpenter reached out to the UCLA Center for Health Policy Research in need

of local data and was enthused by our plans to produced small area estimates. She reinforced

feedback from the health department and other local organizations the importance of producing

city level estimates and recommended measures of the built environment. To show her

appreciation and values of this work, she writes:

“I must thank you again for considering our community needs and looking at the Health

Districts within the SPA so that we actually could look at differences between these areas to

further define the work of our collaborative. Los Angeles County is so large and even within

SPA 3, it is difficult to know where to begin work in a geographical area so large. Your data for

both health districts and for cities will be used along with the demographic information we

have available to us to create a starting point for the conversation with our collaborative

members and stakeholders about the priorities and action plan to promote health equity in the

San Gabriel Valley”. - Email from Miki Carpenter, November 30, 2012

In addition, we shared initial estimates of diabetes at the health district level with Dr. Carpenter to

support efforts of the City of Hope and Assembly member Roger Hernández to organize an

inaugural Diabetes Summit in October 2012. Presenters discussed, diet, exercise, access to healthy

foods, bike paths, transportation planning, safe routes to school, and how to advocate for health in

policies. Attendees of the summit included community members, elected officials, and local

stakeholders. This half-day program involved panel presenters who addressed the physiological

origins of diabetes and issues related to the environment, systems, and policies that can impact

health. Their initiative has great potential for improved health and the data we are producing for

this project is helping to shape their plans. Furthermore, Dr. Carpenter is working with the local

YMCA and with State Senator Dr. Ed Hernandez to further dissemninate findings, raise awareness

about pertinent health issues and begin a concerted dialogue to address health disparities in San

Gabriel Valley.

In the end, there were four major types of community groups with whom we engaged. These

included representatives from health systems, community-based organizations, academic-

community partnerships and the LAC health department (Table A1). Feedback from all of these

groups have informed future plans to produce even more granular data estimates that represent

unique neighborhoods in which projects are working, along with newly proposed measures of

health (e. g. , fast food environment and cardiovascular disease risk). A comprehensive email was

also sent out on Sept 19, 2012 with a proposed plan for next steps including a map of proposed

19

geographic areas and list of final indicators for analysis. Final feedback and email responses

approving this effort were received before analyses were completed for this project. Because of

limited resources, we completed analyses for health districts and will ask for additional funding to

do analyses at a more granular level later.

In the end, the initial set of indicators served as a starting point for dialogue with the partners and

community collaborators about our planned data analysis and resulted in a final list of indicators

(appendix table B1, page 24) and a new set of geographic neighborhoods were then developed for

future translation of findings (appendix C). The net impact of engaging with communities included:

obtaining community perceptions of health needs, guiding CTSI researchers, and allowing for re-

evaluation and follow-up over the course of the CTSI engagement with communities.

20

Table A1. Community Engagement Log

Name Title Affiliation Typology SPA Contact Type Date Feedback

Lark Galloway

Gilliam

Executive

Director

Community Health

Councils

CBO 6 In person 3/28/2012 Presentation to the Southside Health Coalition of the

CHCs - on health care reform by Dylan Roby

Rad Cunningham Data Analyst Southside Health

Coalition -

Community Health

Councils

CBO 6 Phone/Email Jun 2012

onward;

spoke at length

on 7/27/12

Interest in community planning areas (CPAs) and zip

code level estimates; 15 council districts; focus on quality

of care and coverage; # of eligible under health care

expansion; interest in risk factor of disease outcomes, e.

g. , physical activity and other environmental factors;

concerned with food deserts and vacant spaces and HH

distance to the highway

Fatima Morales Policy Analyst LA Action for

Coverage Coalition

6 Phone/Email 7/27/2012 Interested in local level data on eligibility for ACA and

ultimately shared data on legislative districts from CHIS

with her. She reached out again in Dec 2012

Loretta Jones Executive

Director

Healthy African

American Families

CBO 6 In person 7/11/2012 Presentation at the Cancer Disparities. Had several

questions and requested for additional data re mortality,

stage of diagnosis, and information on how much money

is invested in research versus interventions.

Audrey Simons Community

Services/Grant

Administrator

Mission Community

Hospital

Health

System

2 Phone 6/14/2012 Chat about data needs for the hospital and connected

me to Joni Novosel

Joni Novosel Executive

Director

Valley Care

Community

Consortium

Health

System

2 Phone 7/3/2012 Discussed Kaiser data portal and need for data relevant

to community benefit reports; working as part of a

collaborative of hospitals in the San Fernando and Santa

Clarita Valleys (SPA 2)

21

Name Title Affiliation Typology SPA Contact Type Date Feedback

Joni Novosel

(Continued from

previous page)

Executive

Director

Valley Care

Community

Consortium

Health

System

2 Phone 7/11/2012 Follow up on health data needs. Shared information on

Kaiser's Community Health Needs Assessment (CHNA)

data collection efforts and exchanged ideas on how our

data could contribute to this.

Cheryl Barrit Preventive

Health Bureau

Manager

Long Beach

Department of

Health and Human

Services

Health

Dept

8 Phone/Email Several calls Interest in health and health utilization data for 10 zip

codes comprising the city of Long Beach. Specifically

asked for Health Profiles for each zip code to support

their efforts for health department accreditation and to

produce CHNAs (Community Health Needs

Assessments).

Miki Carpenter Director of

Assessment and

Program

Development

College of

Optometry -

Western University

of Health Sciences

University-

Community

Partnership

3 Phone 6/20/2012-

12/1/2012

Initial chat about her interest in health district level data

(referral from Kathleen Abanilla, CHIS data access

manager); Explored her data needs further -- e. g. ,

interested in social determinants of health (green space;

food environment); and would love city level estimates

eventually. Planning a Health Equity Summit in the

fall/winter and would like to have district level data to

share as a starting point for their discussion around

disparities; interested in estimates of childhood obesity,

emotional well-being, neighborhood safety perception;

children's screen time; health literacy, civic engagement;

food deserts; City level estimates would also be most

valuable - especially certain cities with high poverty rates

- Azuza in Foothills, Pomona in Pomona; City of El Monte

or Baldwin Park in El Monte; Also very interested in data

by race/ethnicity.

22

Name Title Affiliation Typology SPA Contact Type Date Feedback

Paul Simon Director,

Division of

Chronic Disease

and Injury

Prevention

LA County Dept of

Public Health

Health

Dept

All In person 6/29/2012 Gained insight on meaningful geographic boundaries -e.

g. , health districts are not as important to planning as

before; Put me in touch with the office of health

assessment and epidemiology for possible collaboration

and sharing of shape files for city and community/census

defined areas that are meaningful

Amy Lighthouse/

Susie Baldwin/

Margaret Shi

Office of Health

Assessment and

Epidemiology

LA County Dept of

Public Health

Health

Dept

All Phone/ In

person

Between

Aug-Oct 2012

Knowledge exchange re RFE Index on food environment;

their small area report of smoking in cities and CDPs and

their CPA (Community Plan Areas) - we may want to

consider these finer geographies. Helped inform new

health district boundaries

Ami Pascual-

Spear

Director of

Foundation

Relations |

Communications

East LA Community

Corporation

CBO 7 Phone 7/16/2012;

9/19/2012

Ongoing emails exchanged to regarding their interest in

local data for a grant proposal; Also expressed and

shared information with us re grocery stores, food retail

outlets and food desert related info in East LA

Karen Leung

and Sergio

Morales

Administrative

Analyst

Chinatown Service

Center

CBO/health

clinic

4 In person 10/4/2012 Shared data on patients from Hector Rodriguez' study

and discussed opportunities to use and share the CTSI

findings

Vanessa Vazquez Assistant

Director

Magnolia

Community

Initiative

CBO 4 and 6 Calls and In

Person

Nov 2012-Jan

2013

Shared information and planning for the Health

Committee presentation, connected by Moira Inkelas

23

Appendix A2. List of Presentations, CTSI Los Angeles County Health Profile, October 2011-February 2012

Date Presentation Audience # Attendees Speaker (invited by)

29-Nov-11 Los Angeles County Community

Health Profile Project

UCLA CTSI / HSR Broxton 30 Steve Wallace (invited by CTSI to present

on behalf of Rick Brown)

28-Mar-12 ACA Impact in South LA South LA Roundtable, Community Health Councils 15 Dylan Roby (invited by Roberto Vargas)

11-Jul-12 Cancer-Related Health Disparities

in LAC

Healthy African American Families: Community

Academic Council Meeting of the Charles Drew

University and UCLA Cancer Community Outreach,

Prevention and Control Program

15 Ami Shah (invited by Roberto Vargas)

6-Sep-12 LAC Preliminary Findings/Report

Back to CTSI

Health Services Research CTSI Report Back, HSR

Broxton

20 Ami Shah and David Zingmond (invited

by CTSI HSR group/ Martin Shapiro)

28-Sep-12 LAC "Hot Spot" Analysis Health Services Research Seminar 25 Ami Shah (invited by Lee Jennings,

Katherine Kahn/David Zingmond)

9-Oct-12 Understanding cardiovascular

disease in South Los Angeles

South LA Roundtable, Community Health Councils 15 Ami Shah (on behalf of Dr. Vargas)

15-Nov-12 LAC Community Health

Assessment

CERP Monthly Update, HSR / Broxton 15 Ami Shah (and Dr. Zingmond)

4-Dec-12 Understanding cardiovascular

disease in South LA

CERP Learning Network, Ackerman 15-20 Ami Shah (with Annie Park/Jessica Jew

on behalf of Dr. Vargas)

4-Dec-12 LAC Community Health

Assessment

CERP Learning Network, Ackerman 20 David Zingmond (and Ami Shah)

24

Date Presentation Audience # Attendees Speaker (invited by)

18-Dec-12 LAC Community Health

Assessment

Magnolia Place Initiative, Research Team 10 Ami Shah (invited by Moira Inkeles)

10-Jan-12 Cardiovascular Disease in South LA South LA Roundtable, Community Health Councils 8 Ami Shah/Jessica Jew with Dr. Vargas

14-Jan-12 LAC Community Health

Assessment

Los Angeles County Health Department and

Department of Health Services

9 David Zingmond (with Ami Shah, Arleen

Brown, and Martin Shapiro)

25

Appendix B. METHODS OF DATA ANALYSIS

For these year 1 analyses, we primarily employed two readily available sources of health and

healthcare data focusing on detailed information on Los Angeles County (LAC) for these analyses:

(1) the 2009 California Health Interview Survey (CHIS), a representative population-based phone

survey of California households administered by the UCLA Center for Health Policy Research; and (2)

hospital discharge and emergency department encounter record abstracts between 2007 and 2009

collected annually at all general acute care California hospitals by the Office of Statewide Health

Planning and Development (OSHPD). Data from OSHPD are identified down to the zip code level.

Examining these data sources, investigators profiled the health and healthcare needs for the 26

health districts that comprise LAC (Figure B1, which shows the eight larger Service Planning Areas

(SPAs) and the 26 smaller Health Districts).

Figure B1. Los Angeles County 8 Service Planning Areas and 26 Health Districts

26

Small area modeling of population health and disease prevalence

Using the most recent available CHIS data (2009), we produced estimates of disease prevalence and

other related factors from CHIS 2009. Because CHIS data are geocoded, small area estimates can be

modeled. Each wave of CHIS includes over 8,000 responses for adults from LAC. A statistical

method called small area estimation (SAE) was used to produce population health statistics for the

26 LAC health districts in Los Angeles County. SAEs borrow “strength” of data from larger domain to

produce estimates for smaller geographic areas or population groups that are not otherwise

available from the survey sample and when sample sizes are too small to produce stable estimates

using traditional, direct estimate methods. Over the past 10 years, the UCLA Center for Health

Policy Research has used SAE to produce estimates of various health indicators for different

geographic areas.2,3

Table B1. Population Health Indicators selected to be produced from CHIS Small Area Estimates

INDICATOR UNIVERSE DESCRIPTION

Uninsured anytime 18-64 Proportion of non-elderly adults uninsured at any point in the last year Current asthma 18-44/

45+ Defined as ever diagnosed with asthma and have asthma now; OR ever diagnosed with asthma and had an asthma attack/episode in the last year

Diabetes 18+/ 45+ Proportion of adults ever diagnosed with diabetes Hypertension 18+ Proportion of adults ever diagnosed with high blood pressure, excluding

borderline high blood pressure Heart disease 45+ Proportion of adults ever diagnosed with heart disease Obesity 18+ Proportion of adults with BMI greater than or equal to 30kg/m

2

Sedentary activity 18+ Physical inactivity or sedentary behavior. Specifically, the person reports less than 10 minutes of any type of physical activity (moderate physical activity, vigorous physical activity and any type of walking)

Risk of cardio-vascular disease

18+ Proportion of adults that have at least TWO risk factors associated with cardiovascular disease (hypertension, diabetes, cigarette smoking, obesity, physical inactivity)

Psychological distress

18+ Serious Psychological Distress (SPD) is often used as a proxy measure for severe mental illness in a population. Adult respondents were asked 6 questions, known as the “Kessler 6”, to assess symptoms of distress during a 30‐day period in the last year.

Routine mammography

40+ Proportion of women age 40 and over who had a mammogram in the last 2 years

Colorectal cancer screening compliance

50-75 Proportion of adults age 50 -75 and over who is compliant with current recommendations for sigmoidoscopy, colonoscopy, and FOBT screenings. Recommendations include fecal occult blood test (FOBT) every year or flexible sigmoidoscopy every 5 years, or colonscopy every ten years.

2 Mendez-Luck CA, Yu H, Meng YY, Jhawar M, Wallace S, Estimating Health Conditions for Small Areas: Asthma Symptom Prevalence for State Legislative Districts, Los Angeles: Health Services Research, 2007, 42(6):2389-2409. 3 Yu H, Meng YY, Mendez-Luck CA, Jhawar M, Wallace S, “Meeting Local Data Needs: Small Area Estimation of Health Insurance Coverage for California Legislative Districts,” American Journal of Public Health, 2007, 97(4): 731-737.

27

Eleven health indicators were selected based on investigator and community stakeholder input

(Appendix Table B1, page 24). Estimates, along with their respective 95% confidence interval, are

presented in the data tables. Measures were constructed based on standard definitions and often

matched the CHIS online query system (www.AskCHIS.org) and CHIS Health Profiles at the SPA level.

District-level profiles were calculated employing CHIS SAEs methodology based on estimates of

2010 US Census counts for specific demographic distributions. Estimates were restricted to certain

age criteria in order to match definitions of ambulatory care sensitive conditions.

Because of the computational intensity of producing small area estimates, not every possible

measure from CHIS as proposed could be created within the constraints of the project budget and

timeframe. Measures under consideration but not yet implemented because of these limitations

include: health behaviors such as tobacco use, alcohol use, consumption of fresh produce and

“obesogenic” foods, physical activity levels, food insecurity, and clinical preventive services such as

receipt of flu shots. If there is demand for the estimates and resources permitting, it is possible to

produce these in the future.

Disease-specific hospitalizations and emergency department visits

Using the 2007 to 2009 hospital discharge and emergency department encounter data, the team

profiled disease-specific rates by zip code in LAC. Zip code counts of cases were aggregated up to

the health district (HD) and Service Planning Area (SPA) levels using weighted mapping based on

the estimated proportion of the zip code’s population in the aggregate geographic unit (e. g. , HD

and SPA). Disease rates were calculated based upon Census 2000 population estimates with a

planned update to 2010 Census estimates, which only became available recently. In addition to raw

rates, we also calculated rates standardized to the age and gender distribution of the entire state.

Standardization does not appear to dramatically change the general results. Standardized results are

available upon request.

Finally, we calculated a modified rate for certain hospitalized conditions (diabetic complications,

hypertension, and asthma), equal to the rate divided by predicted disease prevalence (derived from

CHIS small area estimates). This modified rate, or “burden” measure is an estimate of the

hospitalization rate among individuals with that disease (the actual at-risk population) in each

geographic region. Quantifying the at-risk population is difficult and thus using the general

population as the denominator is a compromise choice for many conditions (e. g. heart failure,

ischemic heart disease, and specific cancers).

Cases were identified using for the following illnesses and treatments based upon definitions

derived from the Agency for Health Research and Quality (AHRQ) (1) prevention quality indicators

(PQI) for ambulatory care sensitive (ACS) conditions, (2) AHRQ inpatient quality indicators (IQI),

and supplemental conditions for CTSI emphasis areas not captured by (1) and (2). One advantage

28

of the AHRQ case definitions is that the coding is updated annually to reflect changes in the ICD9-

CM coding convention.

PQI Conditions: http://www. qualityindicators. ahrq. gov/Modules/pqi_resources. asp

PQI #01 Diabetes Short-Term Complications PQI #11 Bacterial Pneumonia

PQI #03 Diabetes Long-Term Complications PQI #12 Urinary Tract Infection

PQI #05 Chronic Obstructive Pulmonary Disease (COPD)

or Asthma in Older Adults

PQI #13 Angina without Procedure

PQI #07 Hypertension PQI #14 Uncontrolled Diabetes

PQI #08 Congestive Heart Failure (CHF) PQI #15 Asthma in Younger Adults

PQI #10 Dehydration PQI #16 Rate of Lower-Extremity

Amputation

IQI Definitions: http://www. qualityindicators. ahrq. gov/modules/iqi_resources. aspx

IQI #08 Esophageal Resection IQI #15 Acute Myocardial Infarction (AMI)

IQI #09 Pancreatic Resection IQI #16 Congestive Heart Failure (CHF)

IQI #11 Abdominal Aortic Aneurism (AAA) Repair IQI #17 Acute Stroke

IQI #12 Coronary Artery Bypass Graft (CABG) IQI #18 Gastrointestinal Hemorrhage

IQI #13 Craniotomy IQI #19 Hip Fracture

IQI #14 Hip Replacement IQI #20 Pneumonia

IQI #30 Percutaneous Transluminal Coronary Angioplasty

(PTCA)

IQI #31 Carotid Endarterectomy

Conditions within the CTSI Domains not captured by the PQI and IQI Condition Sets- labeled as

AQI (additional quality indicators) for this report:

Colon and rectal cancer resection Lung cancer resection

HIV/AIDS (primary diagnosis) Breast cancer resection

Hysterectomy for cervical cancer and uterine cancer Ovarian cancer resection

Hospitalization for acute mental health condition Hospitalization for substance abuse

Using the emergency department and inpatient data, we profiled rates of admission for serious

mental health disorders and overall rates of emergency encounters for mental health conditions

and for substance abuse. Unlike medical conditions, emergent psychiatric admission were

estimated through identification of inpatient admissions from the OSHPD inpatient file and

through transfers from emergency departments for admission to outside facilities (not captured

by the inpatient file) using the OSHPD emergency department file.

In addition to CHIS and OSHPD analyses, we examined some contextual factors. Structural and

environmental measures important to understanding the availability and access to healthy food

and park acreage were included and derived from the following sources:

29

Park acreage – California Protected Area Database (CPAD), Version 1. 6, Census 2000, LA County

GIS Data Portal. Park acreage per 1,000 population was plotted and mapped with sedentary

behavior.

Food environment – Business addresses of food outlet data file4 from the Los Angeles County

Department of Public Health (LACDPH) were geocoded and used to calculate the number of fast

food outlets per 100,000 population based on the US Census 2000 for each health district in

LAC.

Mapping of results

Using the HD results, the research team used ARCGIS 10 to map results for Los Angeles County by

health district for select outcomes. These maps provide geographic context for the tabular findings.

Currently, maps are static and unsearchable.

We used health district boundaries from 2012 (based on US Census 2010 boundaries) to map the

CHIS and hospitalization rates. CHIS estimates were produced using population distribution from

Census 2010. However, the hospitalization rates were calculated prior the availability of 2012

health district boundaries and were thus calculated based on 2002 boundaries (based on US

Census 2000). The 2012 health district boundaries differ slightly from the 2002 boundaries in that a

few thousand individuals may have been redistributed and counted in a different health district

than they were in before. We cannot be certain of the actual number of individuals residing within

these affected census block groups and whether boundary changes reflect an actual change in

population for each health district. The portion or area of an affected block group that was part of

a different health district may have contained no or only a few residents. Because the total

population for each health district are relatively large (approximately a couple hundred thousand

residents per health district), changes in the population distribution that may have resulted from

this boundary change are negligible and not likely to affect the distribution of outcomes presented,

making our maps as reliable and valid as they can be.

4 The food outlet data file contains information on all retailers selling food products in Los Angeles County based

on permits submitted by retailers.

Table B2a: Demographics characteristics by Los Angeles County health district, 18yrs and older, Census 2010 (1)

30

Total Population

(#) 18-64yrs

(%) 65yrs+

(%) Female

(%) Latino

(%) NH White

(%) NH Black

(%) NH Asian

(%) Other (2)

(%)

3 Alhambra 270,500 81. 1 18. 9 52. 9 25. 2 15. 2 1. 0 57. 3 1. 3

5 Antelope Valley 267,000 88. 2 11. 8 50. 9 39. 6 39. 5 14. 0 4. 2 2. 9

6 Bellflower 261,500 85. 1 14. 9 51. 9 42. 1 25. 2 7. 4 22. 7 2. 6 9 Central 271,000 87. 5 12. 5 45. 8 49. 9 19. 1 7. 0 22. 0 2. 0

12 Compton 187,000 89. 8 10. 2 52. 4 69. 4 2. 7 25. 4 1. 0 1. 5

16 East LA 143,500 85. 2 14. 8 51. 4 88. 8 4. 6 0. 5 5. 5 0. 6

19 East Valley 335,000 87. 3 12. 7 50. 2 48. 9 37. 7 4. 0 7. 2 2. 2

23 El Monte 314,000 85. 9 14. 1 50. 9 64. 9 8. 3 1. 2 24. 6 1. 0

25 Foothill 230,500 83. 0 17. 0 52. 6 33. 7 38. 9 6. 2 19. 0 2. 2 27 Glendale 269,000 81. 6 18. 4 52. 8 17. 0 62. 7 1. 5 15. 8 3. 0

31 Harbor 144,000 81. 3 18. 7 51. 4 43. 8 37. 9 4. 4 11. 4 2. 5

34 Hollywood-Wilshire 400,500 87. 1 12. 9 49. 0 30. 4 41. 1 7. 3 18. 6 2. 6

37 Inglewood 297,500 87. 3 12. 7 52. 4 46. 8 11. 9 29. 3 9. 4 2. 7

40 Long Beach 349,000 87. 6 12. 4 51. 6 35. 5 34. 4 12. 8 13. 6 3. 7

47 Northeast 222,500 86. 4 13. 6 51. 0 71. 0 12. 2 1. 6 14. 0 1. 3

50 Pasadena 112,000 83. 1 16. 9 51. 7 29. 6 42. 3 10. 1 15. 3 2. 7

54 Pomona 401,000 84. 7 15. 3 52. 0 42. 0 28. 1 4. 6 23. 2 2. 1

58 San Antonio 290,000 89. 2 10. 8 51. 2 87. 0 8. 2 1. 4 2. 7 0. 8

62 San Fernando 365,000 86. 8 13. 2 50. 4 37. 3 44. 9 3. 8 11. 7 2. 3

69 South 119,500 91. 2 8. 8 52. 3 70. 1 0. 8 27. 9 0. 2 1. 0

72 Southeast 108,000 92. 8 7. 2 49. 5 85. 0 0. 8 12. 9 0. 6 0. 7

75 Southwest 275,500 86. 4 13. 6 53. 3 46. 6 5. 1 41. 5 4. 4 2. 5

79 Torrance 351,500 82. 8 17. 2 51. 6 21. 2 44. 1 7. 5 23. 6 3. 6

84 West 535,000 83. 3 16. 7 52. 1 14. 3 63. 1 5. 6 13. 4 3. 7

86 West Valley 656,500 84. 7 15. 3 51. 3 34. 3 47. 7 4. 0 11. 3 2. 7

91 Whittier 236,000 83. 8 16. 2 52. 0 66. 1 24. 9 1. 2 6. 4 1. 4

Notes: (1) All estimates are based on 2012 health district boundaries; (2) "Other" includes American Indian, Alaska Native, Native Hawaiian and other Pacific Islander, other race and two or more races

Table B2b. Socioecomomic status by Los Angeles County health district

31

Under 100% FPL (1), 18yrs+

(%)

HS or more (1), 25yrs+

(%)

Limited English Proficiency (1), 5yrs+

(%)

Uninsured anytime in past yr (2), 18-64yrs

% 95%LCI 95UCI

3 Alhambra 11. 4 78. 3 38. 0 29. 4 24. 9 33. 9 5 Antelope Valley 18. 0 77. 7 14. 9 22. 6 18. 8 26. 4 6 Bellflower 9. 5 80. 4 23. 5 34. 3 29. 4 39. 2 9 Central 29. 3 63. 8 44. 0 47. 2 41. 8 52. 6

12 Compton 21. 2 56. 3 33. 1 27. 8 23. 5 32. 2 16 East LA 20. 7 53. 9 41. 9 45. 6 40. 2 51. 0 19 East Valley 15. 4 72. 2 30. 7 28. 3 23. 8 32. 7 23 El Monte 14. 2 63. 4 38. 1 25. 7 21. 5 29. 8 25 Foothill 9. 8 84. 8 18. 4 19. 8 16. 3 23. 2 27 Glendale 10. 5 87. 2 27. 0 29. 5 25. 0 34. 0 31 Harbor 15. 3 78. 6 22. 6 18. 1 14. 9 21. 4 34 Hollywood-Wilshire 18. 7 81. 2 34. 5 30. 4 25. 8 35. 0 37 Inglewood 18. 4 73. 8 26. 0 35. 0 30. 0 39. 9 40 Long Beach 19. 0 78. 6 21. 7 30. 2 25. 6 34. 8 47 Northeast 20. 6 61. 5 38. 2 25. 1 21. 0 29. 2 50 Pasadena 13. 3 83. 5 18. 6 28. 3 23. 9 32. 7 54 Pomona 10. 3 81. 3 21. 4 20. 9 17. 3 24. 5 58 San Antonio 18. 8 53. 4 43. 1 42. 4 37. 1 47. 7 62 San Fernando 8. 2 83. 2 17. 0 18. 3 15. 0 21. 5 69 South 32. 3 47. 6 36. 0 52. 5 47. 1 57. 9 72 Southeast 38. 7 36. 6 47. 8 63. 6 58. 5 68. 7 75 Southwest 27. 1 64. 9 27. 6 33. 0 28. 2 37. 8 79 Torrance 7. 0 89. 1 16. 9 17. 3 14. 2 20. 4 84 West 11. 1 93. 2 11. 5 15. 9 13. 0 18. 9 86 West Valley 13. 7 81. 5 24. 9 25. 0 20. 9 29. 1 91 Whittier 9. 2 75. 9 20. 2 28. 5 24. 1 32. 9

Source: (1) 5-year American Community Survey Estimates 2010; and (2) California Health Interview Survey 2009 Small Areas Estimates

Table B3. Preventable hospitalizations due to diabetes and related complications (1) and diabetes prevalence (2) by Los Angeles County health district

32

Sources: (1) OSHPD 2007-2009 raw averages of AHRQ Quality Indicator definitions, counts per 100,000 population based on Census 2000 population. (2) CHIS 2009 small

area estimations include regression modeling of CHIS 2009 responses according to Census 2010 population distribution

HD Health District PQI#01

short-term complications

rate per 100,000

PQI #03 long-term

complications rate per 100,000

PQI #14 uncontrolled

diabetes rate per 100,000

PQI #16 lower extremity

amputations rate per 100,000

TOTAL PQI rate per 100,000

Diabetes Prevalence

% 95% LCI 95% UCI

3 Alhambra 26. 3 117. 6 13. 6 4. 8 162. 3 7. 8 6. 0 9. 6 5 Antelope Valley 88. 6 154. 5 30. 0 6. 9 280. 0 11. 1 8. 6 13. 6 6 Bellflower 48. 0 172. 8 27. 8 11. 7 260. 2 14. 7 11. 5 17. 9 9 Central 44. 5 152. 2 32. 3 6. 8 235. 7 6. 4 4. 9 7. 9

12 Compton 88. 5 250. 3 29. 3 17. 2 385. 3 10. 9 8. 4 13. 4 16 East LA 39. 9 212. 9 28. 3 12. 8 293. 9 10. 5 8. 1 12. 8 19 East Valley 37. 2 111. 6 17. 6 5. 8 172. 2 6. 5 5. 0 8. 1 23 El Monte 36. 0 172. 5 21. 3 9. 6 239. 5 26. 1 21. 2 31. 0 25 Foothill 36. 1 115. 5 12. 0 4. 1 167. 6 13. 3 10. 4 16. 3 27 Glendale 23. 4 85. 3 14. 0 2. 6 125. 3 3. 3 2. 5 4. 1 31 Harbor 54. 0 144. 5 13. 9 10. 2 222. 5 15. 3 12. 0 18. 6 34 Hollywood-Wilshire 29. 5 97. 2 15. 2 4. 7 146. 6 7. 7 5. 9 9. 5 37 Inglewood 69. 9 193. 7 22. 8 13. 6 299. 9 10. 5 8. 1 12. 9 40 Long Beach 58. 7 162. 5 25. 5 7. 9 254. 6 9. 9 7. 6 12. 2 47 Northeast 51. 8 180. 5 30. 7 8. 0 271. 0 13. 7 10. 7 16. 7 50 Pasadena 40. 0 132. 4 12. 8 7. 8 192. 9 5. 5 4. 1 6. 8 54 Pomona 41. 3 118. 6 15. 8 8. 3 184. 0 6. 1 4. 6 7. 6 58 San Antonio 48. 0 178. 7 30. 2 10. 7 267. 5 22. 4 18. 0 26. 8 62 San Fernando 32. 9 98. 1 10. 2 4. 3 145. 6 5. 9 4. 5 7. 3 69 South 111. 3 256. 5 46. 0 18. 9 432. 6 33. 3 27. 7 38. 9 72 Southeast 81. 2 227. 0 38. 5 11. 0 357. 8 6. 7 5. 1 8. 3 75 Southwest 97. 7 221. 3 38. 9 12. 7 370. 6 14. 8 11. 6 18. 0 79 Torrance 40. 7 106. 4 9. 0 7. 8 164. 0 10. 2 7. 9 12. 5 84 West 20. 9 57. 4 7. 3 2. 8 88. 3 7. 9 6. 1 9. 8 86 West Valley 32. 0 92. 2 10. 4 4. 1 138. 7 7. 0 5. 4 8. 7 91 Whittier 41. 1 158. 3 23. 6 14. 8 237. 7 10. 2 7. 8 12. 5

Table B4. Preventable hospitalizations due to hypertension (1) and hypertension prevalence (2) by Los Angeles County health district

33

HD# Health districts

PQI#7, Inpatient admissions,

rate per 100,000

PQI#7, ER encounters,

rate per 100,000 TOTAL hospitalizations

rate per 100,000

Hypertension Prevalence

% 95% LCI 95%UCI

3 Alhambra 42. 2 139. 8 182. 0 28. 2 25. 0 31. 3

5 Antelope Valley 59. 0 188. 9 247. 9 24. 8 21. 9 27. 7

6 Bellflower 62. 2 237. 4 299. 6 31. 4 28. 1 34. 8

9 Central 80. 3 188. 5 268. 8 25. 9 22. 9 28. 9

12 Compton 76. 0 286. 8 362. 7 31. 1 27. 8 34. 4

16 East LA 51. 2 166. 5 217. 6 15. 9 13. 8 18. 0

19 East Valley 45. 0 167. 1 212. 1 21. 2 18. 7 23. 8

23 El Monte 39. 4 140. 1 179. 5 30. 2 27. 0 33. 5

25 Foothill 45. 1 143. 3 188. 4 36. 2 32. 6 39. 8

27 Glendale 55. 3 192. 0 247. 3 18. 3 16. 0 20. 6

31 Harbor 31. 8 203. 7 235. 5 33. 1 29. 7 36. 6

34 Hollywood-Wilshire 52. 5 132. 6 185. 0 26. 9 23. 9 30. 0

37 Inglewood 74. 6 265. 0 339. 6 27. 8 24. 7 30. 9

40 Long Beach 58. 9 215. 2 274. 2 25. 2 22. 3 28. 1

47 Northeast 66. 6 182. 5 249. 1 26. 7 23. 7 29. 7

50 Pasadena 74. 9 170. 1 245. 0 23. 6 20. 8 26. 4

54 Pomona 37. 1 164. 5 201. 6 27. 5 24. 4 30. 6

58 San Antonio 59. 5 195. 6 255. 0 37. 9 34. 2 41. 5

62 San Fernando 39. 7 168. 5 208. 2 23. 9 21. 1 26. 8

69 South 105. 8 305. 3 411. 1 51. 5 47. 6 55. 3

72 Southeast 88. 0 223. 0 311. 0 14. 1 12. 2 16. 0

75 Southwest 106. 7 311. 1 417. 8 33. 9 30. 4 37. 4

79 Torrance 33. 3 178. 2 211. 5 25. 1 22. 1 28. 0

84 West 21. 4 88. 6 110. 0 24. 9 22. 0 27. 8

86 West Valley 38. 2 175. 9 214. 0 27. 9 24. 8 31. 1

91 Whittier 35. 3 180. 4 215. 7 26. 0 23. 0 29. 0 Sources: (1) OSHPD 2007-2009 raw averages of AHRQ Quality Indicator definitions, counts per 100,000 population based on Census 2000 population. (2) CHIS 2009 small area estimations include regression modeling of CHIS 2009 responses according to Census 2010 population distribution.

Table B5. Congestive heart failure (CHF-PQI#8), Acute Illness (Acute Myocardial Infarction-IQI#15) and Angina without Procedure (PQI #13) hospital admissions and ER encounters by Los Angeles County health district

34

HD# Health District

CHF, Inpatient

admissions PQI#8

CHF, ED encounters

PQI #8

CHF, Inpatient

and ED Total PQI #8

Acute Myocardial Infarction (AMI),

IQI#15 Overall

Angina without procedure, Inpatient

admissions PQI#13

Angina without procedure,

ED encounters PQI #13

Angina without procedure,

Inpatient and ED Total PQI #13

3 Alhambra 283. 1 20. 6 303. 7 140. 6 32. 0 9. 7 41. 7 5 Antelope Valley 368. 6 49. 3 417. 9 228. 3 42. 0 31. 4 73. 5 6 Bellflower 298. 5 45. 0 343. 5 162. 0 39. 0 18. 2 57. 2 9 Central 357. 3 46. 4 403. 7 134. 5 44. 3 6. 8 51. 1

12 Compton 416. 1 75. 9 492. 0 142. 6 46. 8 22. 9 69. 7 16 East LA 330. 0 33. 6 363. 6 164. 7 51. 0 9. 6 60. 6 19 East Valley 269. 5 31. 2 300. 7 139. 9 27. 2 8. 5 35. 7 23 El Monte 287. 9 26. 0 314. 0 153. 7 30. 9 6. 4 37. 3 25 Foothill 319. 1 33. 7 352. 8 163. 7 25. 2 11. 1 36. 3 27 Glendale 315. 2 29. 5 344. 6 197. 9 27. 4 12. 9 40. 3 31 Harbor 301. 4 57. 8 359. 2 138. 9 28. 4 15. 0 43. 3 34 Hollywood-Wilshire 253. 1 29. 8 282. 9 135. 0 23. 9 6. 9 30. 8 37 Inglewood 406. 5 65. 3 471. 8 145. 8 40. 4 18. 3 58. 7 40 Long Beach 278. 3 48. 6 326. 8 151. 1 28. 0 16. 2 44. 1 47 Northeast 306. 7 28. 8 335. 5 156. 3 47. 0 12. 1 59. 0 50 Pasadena 297. 5 26. 9 324. 3 138. 1 20. 4 14. 7 35. 1 54 Pomona 283. 2 49. 2 332. 4 151. 3 26. 4 9. 5 36. 0 58 San Antonio 271. 3 33. 3 304. 7 131. 7 36. 8 12. 3 49. 1 62 San Fernando 250. 1 29. 8 279. 9 153. 8 33. 5 16. 9 50. 4 69 South 579. 8 86. 3 666. 2 160. 9 60. 6 17. 6 78. 2 72 Southeast 431. 3 53. 1 484. 4 120. 8 45. 1 12. 4 57. 4 75 Southwest 549. 5 102. 0 651. 5 167. 6 57. 9 17. 9 75. 8 79 Torrance 279. 0 45. 2 324. 2 142. 5 24. 1 12. 0 36. 1 84 West 209. 7 22. 4 232. 1 131. 3 12. 6 6. 1 18. 6 86 West Valley 266. 9 36. 6 303. 5 154. 9 18. 8 10. 6 29. 4 91 Whittier 309. 8 32. 0 341. 8 235. 0 29. 0 6. 3 35. 3

Source: OSHPD 2007-2009 raw averages of AHRQ Prevention Quality Indicators definitions per 100,000 based on Census 2000 population.

Table B6. Procedures to treat ischemic heart disease hospital procedures, including coronary artery bypass graft (CABG) – IQI #12 and percutaneous coronary intervention (PCI) – IQI#30 by Los Angeles County health district, among adults age 45 and over

35

# Health District IQI#12 CABG

2007-2009 average rate per 100,000

IQI#30 PCI 2007-2009 average

rate per 100,000

Total Interventions 2007-2009 average

rate per 100,000

3 Alhambra 99. 0 290. 1 389. 1 5 Antelope Valley 94. 2 531. 7 625. 9 6 Bellflower 133. 2 352. 6 485. 8 9 Central 90. 3 299. 1 389. 4

12 Compton 107. 5 361. 6 469. 1 16 East LA 103. 2 354. 9 458. 1 19 East Valley 100. 3 372. 0 472. 2 23 El Monte 109. 5 293. 3 402. 8 25 Foothill 95. 2 329. 3 424. 5 27 Glendale 150. 5 584. 8 735. 3 31 Harbor 78. 4 382. 3 460. 8 34 Hollywood-Wilshire 74. 7 351. 1 425. 8 37 Inglewood 76. 7 356. 9 433. 6 40 Long Beach 129. 1 407. 7 536. 8 47 Northeast 99. 8 370. 3 470. 1 50 Pasadena 103. 5 350. 3 453. 8 54 Pomona 125. 4 263. 1 388. 5 58 San Antonio 99. 2 354. 7 453. 8 62 San Fernando 118. 7 397. 3 516. 0 69 South 73. 1 340. 5 413. 6 72 Southeast 70. 0 310. 4 380. 4 75 Southwest 66. 0 283. 1 349. 1 79 Torrance 73. 4 450. 5 523. 9 84 West 85. 4 336. 3 421. 7 86 West Valley 116. 2 360. 0 476. 3 91 Whittier 149. 4 415. 5 564. 9

Source: OSHPD 2007-2009 raw averages of AHRQ Inpatient Quality Indicators definitions per 100,000 based on Census 2000 population.

Table B7. Prevalence of Heart Disease and Cardio-Vascular Disease (CVD) Risk (defined as the proportion of adults with at least 2 risk factors associated with CVD, including high blood pressure, diabetes, smoking, obesity or sedentary behavior) by Los Angeles County health district

36

Health District Heart Disease Prevalence 95% CI 95% CI CVD Risk 95% CI 95% CI

3 Alhambra 4. 9% 4. 0% 5. 8%

18. 0% 15. 4% 20. 6%

5 Antelope Valley 6. 7% 5. 5% 8. 0%

28. 3% 24. 7% 31. 8%

6 Bellflower 4. 7% 3. 8% 5. 5%

26. 7% 23. 2% 30. 1%

9 Central 7. 5% 6. 1% 8. 8%

24. 0% 20. 8% 27. 2%

12 Compton 7. 6% 6. 2% 9. 0%

26. 7% 23. 3% 30. 1%

16 East LA 3. 2% 2. 6% 3. 9%

22. 6% 19. 5% 25. 6%

19 East Valley 6. 5% 5. 3% 7. 7%

23. 3% 20. 2% 26. 5%

23 El Monte 4. 6% 3. 8% 5. 5%

30. 7% 27. 0% 34. 4%

25 Foothill 8. 5% 7. 0% 10. 0%

20. 2% 17. 4% 23. 0%

27 Glendale 4. 9% 4. 0% 5. 8%

10. 7% 9. 0% 12. 4%

31 Harbor 8. 6% 7. 0% 10. 1%

26. 7% 23. 3% 30. 1%

34 Hollywood-Wilshire 4. 5% 3. 6% 5. 3%

18. 3% 15. 7% 20. 9%

37 Inglewood 3. 4% 2. 8% 4. 1%

25. 3% 22. 0% 28. 6%

40 Long Beach 10. 2% 8. 4% 12. 0%

21. 5% 18. 6% 24. 5%

47 Northeast 3. 7% 3. 0% 4. 4%

23. 1% 20. 0% 26. 2%

50 Pasadena 5. 6% 4. 5% 6. 6%

13. 5% 11. 5% 15. 6%

54 Pomona 4. 5% 3. 7% 5. 4%

19. 0% 16. 3% 21. 7%

58 San Antonio 4. 0% 3. 3% 4. 8%

35. 2% 31. 2% 39. 2%

62 San Fernando 4. 9% 3. 9% 5. 8%

17. 4% 14. 9% 19. 9%

69 South 3. 2% 2. 6% 3. 8%

49. 7% 45. 4% 54. 1%

72 Southeast 4. 2% 3. 4% 5. 0%

16. 9% 14. 4% 19. 3%

75 Southwest 5. 9% 4. 8% 7. 0%

28. 8% 25. 2% 32. 4%

79 Torrance 5. 5% 4. 5% 6. 6%

19. 0% 16. 3% 21. 7%

84 West 8. 2% 6. 8% 9. 7%

17. 8% 15. 2% 20. 3%

86 West Valley 3. 4% 2. 8% 4. 1%

18. 2% 15. 6% 20. 8%

91 Whittier 12. 4% 10. 3% 14. 6% 19. 5% 16. 8% 22. 3%

Source: CHIS 2009 small area estimates include regression modeling of CHIS 2009 responses according to CHIS 2010 population distribution.

Table B8. Asthma hospitalizations (1) and current asthma prevalence (2) by Los Angeles County health district, among young adults age 18-44

37

Health District

Inpatient admissions for Asthma PQI#15

rate per 100,000

ED encounters for Asthma PQI#15

rate per 100,000

TOTAL Asthma Visits

rate per 100,000

Asthma Prevalence % 95% LCI 95% UCI

3 Alhambra 17. 8 156. 2 174. 0 3. 3 2. 1 4. 5 5 Antelope Valley 73. 8 482. 7 556. 5 18. 5 12. 8 24. 2 6 Bellflower 38. 2 295. 5 333. 6 7. 0 4. 5 9. 5 9 Central 26. 3 256. 5 282. 8 4. 7 3. 0 6. 5

12 Compton 59. 1 399. 0 458. 1 9. 8 6. 4 13. 2 16 East LA 35. 6 263. 6 299. 1 2. 2 1. 4 3. 1 19 East Valley 33. 2 278. 5 311. 7 3. 3 2. 0 4. 5 23 El Monte 34. 9 241. 9 276. 8 4. 1 2. 6 5. 7 25 Foothill 44. 1 257. 6 301. 7 5. 8 3. 7 7. 9 27 Glendale 23. 5 171. 6 195. 2 16. 9 11. 5 22. 2 31 Harbor 26. 3 240. 6 266. 9 3. 8 2. 4 5. 2 34 Hollywood-Wilshire 28. 0 177. 5 205. 6 9. 8 6. 4 13. 2 37 Inglewood 44. 5 368. 2 412. 6 5. 8 3. 7 7. 9 40 Long Beach 45. 3 384. 7 430. 0 9. 2 6. 0 12. 4 47 Northeast 41. 8 259. 9 301. 7 4. 8 3. 1 6. 6 50 Pasadena 30. 6 241. 1 271. 7 3. 8 2. 4 5. 3 54 Pomona 32. 6 258. 7 291. 4 2. 4 1. 5 3. 3 58 San Antonio 37. 8 244. 1 281. 9 3. 4 2. 1 4. 6 62 San Fernando 31. 9 237. 4 269. 3 7. 8 5. 0 10. 5 69 South 102. 0 475. 8 577. 8 40. 2 31. 2 49. 2 72 Southeast 55. 9 308. 4 364. 3 1. 4 0. 8 2. 0 75 Southwest 60. 3 440. 8 501. 1 2. 1 1. 3 2. 9 79 Torrance 21. 1 172. 3 193. 4 2. 7 1. 7 3. 7 84 West 13. 5 135. 2 148. 7 10. 8 7. 1 14. 5 86 West Valley 38. 4 293. 6 332. 0 3. 2 2. 0 4. 4 91 Whittier 40. 2 310. 7 350. 8 5. 5 3. 5 7. 5

Source: (1) OSHPD 2007-2009 raw averages of AHRQ Prevention Quality Indicators definitions per 100,000 based on Census 2000 population. (2) CHIS 2009 small

area estimates include regression modeling of CHIS 2009 responses according to CHIS 2010 population distribution.

38

Table B9. Inpatient admissions and emergency department encounters for COPD (chronic obstructive pulmonary disease) by Los Angeles County health district, among adults age 45 and older

HD Health District PQI#5, Inpatient admissions for

COPD, rate per 100,000 PQI#5, ED encounters for COPD,

rate per 100,000 Total COPD visits, PQI#5

rate per 100,000

3 Alhambra 340. 4 242. 5 582. 9 5 Antelope Valley 983. 0 766. 5 1749. 5 6 Bellflower 503. 8 436. 4 940. 3 9 Central 837. 2 651. 3 1488. 5

12 Compton 869. 6 773. 3 1642. 9 16 East LA 486. 3 425. 8 912. 0 19 East Valley 463. 9 424. 6 888. 5 23 El Monte 436. 0 305. 8 741. 8 25 Foothill 392. 6 311. 4 704. 0 27 Glendale 432. 7 343. 0 775. 7 31 Harbor 378. 7 364. 9 743. 7 34 Hollywood-Wilshire 485. 8 339. 2 825. 0 37 Inglewood 662. 3 665. 5 1327. 8 40 Long Beach 614. 6 568. 6 1183. 2 47 Northeast 481. 3 480. 2 961. 5 50 Pasadena 511. 3 377. 0 888. 3 54 Pomona 356. 5 368. 5 724. 9 58 San Antonio 674. 5 424. 4 1098. 9 62 San Fernando 382. 7 360. 5 743. 1 69 South 1323. 0 1059. 9 2382. 9 72 Southeast 1131. 6 854. 1 1985. 6 75 Southwest 900. 9 794. 5 1695. 4 79 Torrance 384. 4 318. 7 703. 0 84 West 264. 6 268. 5 533. 1 86 West Valley 388. 9 384. 3 773. 2 91 Whittier 434. 3 422. 3 856. 6

Source: OSHPD 2007-2009 raw averages of AHRQ Prevention Quality Indicators definitions per 100,000 based on Census 2000 population.

39

Table B10. Mental health admissions (1) and prevalence of severe psychological distress (2) by Los Angeles County health district

Inpatient admissions,

rate per 100,000 ED encounters,

rate per 100,000

Total Mental Health

hospitalizations

Serious Psychological Distress

HD HD Name % 95% LCI 95% UCI

3 Alhambra 509. 3 24. 4 533. 7 4. 3 3. 4 5. 3 5 Antelope Valley 720. 7 25. 8 746. 4 8. 1 6. 4 9. 8 6 Bellflower 675. 0 23. 8 698. 8 7. 6 6. 0 9. 3 9 Central 1213. 4 73. 6 1287. 0 14. 2 11. 3 17. 0

12 Compton 812. 2 42. 4 854. 6 6. 2 4. 9 7. 6 16 East LA 472. 8 40. 3 513. 1 5. 9 4. 6 7. 2 19 East Valley 762. 1 43. 4 805. 5 6. 2 4. 8 7. 6 23 El Monte 534. 4 25. 9 560. 3 6. 1 4. 8 7. 5 25 Foothill 744. 1 19. 1 763. 3 3. 6 2. 8 4. 4 27 Glendale 729. 0 25. 9 754. 9 7. 5 5. 9 9. 1 31 Harbor 632. 1 33. 5 665. 6 4. 2 3. 2 5. 1 34 Hollywood-Wilshire 762. 3 45. 8 808. 1 9. 7 7. 6 11. 7 37 Inglewood 705. 4 44. 5 749. 9 9. 7 7. 6 11. 7 40 Long Beach 1133. 0 27. 6 1160. 6 7. 0 5. 5 8. 5 47 Northeast 852. 1 62. 4 914. 5 5. 4 4. 2 6. 6 50 Pasadena 1242. 3 23. 6 1265. 9 5. 3 4. 1 6. 4 54 Pomona 629. 8 22. 0 651. 9 9. 4 7. 4 11. 3 58 San Antonio 563. 0 32. 2 595. 2 10. 2 8. 1 12. 3 62 San Fernando 713. 1 40. 2 753. 3 4. 6 3. 5 5. 6 69 South 1315. 7 72. 3 1388. 0 4. 4 3. 4 5. 4 72 Southeast 1167. 7 61. 2 1228. 9 27. 0 22. 4 31. 6 75 Southwest 1154. 6 58. 3 1212. 8 9. 5 7. 5 11. 5 79 Torrance 427. 3 32. 6 459. 9 5. 8 4. 5 7. 0 84 West 515. 5 25. 5 541. 0 2. 9 2. 3 3. 6 86 West Valley 764. 6 41. 4 806. 0 7. 0 5. 5 8. 5 91 Whittier 587. 0 34. 6 621. 7 5. 1 4. 0 6. 2

Source: (1) OSHPD 2007-2009 raw averages of AHRQ Prevention Quality Indicators definitions per 100,000 based on Census 2000 population.

(2) CHIS 2009 small area estimates include regression modeling of CHIS 2009 responses according to CHIS 2010 population distribution.

40

Table B11. Breast and colorectal cancer screening by Los Angeles County health district

HD# Health district Mammogram in

last 2yrs, % 95% LCI 95% UCI

Colorectal screening

compliance, % 95% LCI 95% UCI

3 Alhambra 78. 2 75. 5 80. 9 70. 0 66. 2 73. 8 5 Antelope Valley 76. 1 73. 2 78. 9 66. 6 62. 6 70. 6 6 Bellflower 81. 8 79. 5 84. 2 60. 9 56. 7 65. 2 9 Central 70. 1 66. 7 73. 4 52. 4 47. 9 56. 9

12 Compton 83. 6 81. 4 85. 8 68. 5 64. 6 72. 4 16 East LA 75. 6 72. 7 78. 6 62. 0 57. 8 66. 3 19 East Valley 83. 1 80. 9 85. 3 58. 3 53. 9 62. 7 23 El Monte 80. 0 77. 5 82. 6 64. 3 60. 1 68. 4 25 Foothill 81. 0 78. 6 83. 5 68. 6 64. 7 72. 5 27 Glendale 77. 5 74. 8 80. 3 70. 5 66. 8 74. 3 31 Harbor 77. 2 74. 4 80. 0 64. 2 60. 1 68. 4 34 Hollywood-Wilshire 66. 5 63. 0 70. 0 66. 3 62. 2 70. 3 37 Inglewood 82. 4 80. 1 84. 7 74. 7 71. 3 78. 1 40 Long Beach 84. 0 81. 8 86. 1 62. 5 58. 2 66. 7 47 Northeast 86. 2 84. 3 88. 1 74. 6 71. 2 78. 0 50 Pasadena 75. 1 72. 2 78. 1 71. 2 67. 5 74. 9 54 Pomona 72. 0 68. 8 75. 2 57. 8 53. 4 62. 2 58 San Antonio 83. 3 81. 0 85. 5 52. 0 47. 5 56. 6 62 San Fernando 83. 7 81. 6 85. 9 63. 4 59. 2 67. 6 69 South 77. 9 75. 2 80. 7 46. 1 41. 6 50. 6 72 Southeast 77. 2 74. 4 80. 0 42. 5 38. 0 46. 9 75 Southwest 79. 5 76. 9 82. 1 68. 3 64. 4 72. 2 79 Torrance 77. 9 75. 2 80. 6 78. 0 74. 9 81. 1 84 West 82. 4 80. 1 84. 7 68. 4 64. 5 72. 3 86 West Valley 82. 6 80. 3 84. 9 72. 7 69. 2 76. 3 91 Whittier 82. 1 79. 7 84. 4 54. 6 50. 1 59. 0

Source: CHIS 2009 small area estimates include regression modeling of CHIS 2009 responses according to CHIS 2010 population distribution.

41

Table B12. Adult obesity, sedentary behavior, fast food outlets and mean park acreage by Los Angeles County health district

HD Health District

Obesity Prevalence(1) (BMI>30kg/m2)

Sedentary behavior(1) Fast Food Outlets(2) # of outlets per

100,000

Mean Park Acreage (3)

acreage per 100,000 #

% 95% LCI 95% UCI %

95% LCI

95% UCI

3 Alhambra 16. 3 14. 2 18. 5 11. 4 9. 6 13. 2 54. 1 2. 3 5 Antelope Valley 28. 5 25. 3 31. 7 21. 8 18. 8 24. 9 63. 9 47. 8 6 Bellflower 30. 6 27. 2 33. 9 8. 7 7. 3 10. 1 90. 8 2. 3 9 Central 28. 2 25. 0 31. 4 8. 8 7. 3 10. 2 131. 1 8. 8

12 Compton 28. 2 25. 0 31. 4 11. 3 9. 5 13. 1 79. 5 1. 1 16 East LA 25. 7 22. 7 28. 7 7. 2 6. 0 8. 4 91. 0 1. 7 19 East Valley 17. 9 15. 5 20. 2 14. 4 12. 2 16. 7 70. 0 23. 7 23 El Monte 31. 7 28. 3 35. 1 16. 1 13. 7 18. 5 78. 1 9. 3 25 Foothill 17. 0 14. 8 19. 2 7. 3 6. 1 8. 5 81. 4 38. 2 27 Glendale 13. 7 11. 8 15. 5 15. 1 12. 8 17. 4 78. 2 22. 7 31 Harbor 28. 7 25. 5 31. 9 11. 3 9. 5 13. 1 69. 9 14

34 Hollywood-Wilshire 17. 8 15. 5 20. 1

10. 3 8. 7 12. 0 89. 7

3

37 Inglewood 23. 7 20. 8 26. 5 17. 8 15. 1 20. 4 84. 0 1. 3 40 Long Beach 22. 4 19. 7 25. 2 9. 5 7. 9 11. 0 not available^ 3. 5 47 Northeast 27. 5 24. 4 30. 6 11. 1 9. 3 12. 9 69. 1 2. 5 50 Pasadena 22. 6 19. 8 25. 3 7. 9 6. 5 9. 2 not available^ 14. 2 54 Pomona 16. 9 14. 7 19. 1 10. 9 9. 1 12. 6 76. 9 16. 8 58 San Antonio 36. 2 32. 5 39. 8 7. 5 6. 2 8. 8 86. 0^ 1. 1 62 San Fernando 18. 5 16. 1 20. 8 9. 7 8. 1 11. 3 71. 4 56. 6 69 South 26. 6 23. 5 29. 7 31. 1 27. 2 34. 9 53. 9 0. 7 72 Southeast 26. 3 23. 2 29. 4 10 8. 4 11. 7 84. 0 0. 5 75 Southwest 25. 4 22. 4 28. 4 13 11. 0 15. 1 78. 3 1. 6 79 Torrance 24. 9 21. 9 27. 8 7. 1 5. 9 8. 2 93. 0 4. 1 84 West 10. 9 9. 4 12. 4 12. 4 10. 4 14. 3 81. 0 78. 4 86 West Valley 18. 8 16. 4 21. 2 10. 3 8. 7 12. 0 80. 1 21. 6 91 Whittier 29. 0 25. 8 32. 2 81. 1 10. 0 13. 8 81. 1 17

42

^The cities of Long Beach (Long Beach HD), Pasadena (Pasadena HD), and Vernon (part of the San Antonio HD) inspect their on retail food

facilities irrespective of the Los Angeles County Department of Public Health's efforts. This is why no data is available for the Long Beach

and Pasadena health districts and why the number of fast food outlets per 100,000 residents in the San Antonio health district is

underestimated.

Notes:

(1) Sedentary behavior is defined as participating in less than 10 minutes of physical activity in the past week, which includes walking for

leisure or transportation. Census tracts with fewer than 1,000 residents and those that were entirely or almost entirely covered by the

Angeles National Forest, Hungry Valley State Vehicular Park, or Castaic Lake State Recreation Area were excluded from the calculations of

mean park average per 1,000 population.

(2) Fast food outlets are defined as presented by the California Center for Public Health Advocacy, PolicyLink, and UCLA Center for Health

Policy Research in Designed for Disease: The Link Between Local Food Environments and Obesity and Diabetes (April 2008).

Sources: (1) 2009 California Health Interview Survey (CHIS) Small Area Estimates; LA County Department of Public Health (LACDPH); and (3)

California Protected Areas Database (CPAD), Version 1.6; and U.S. Census 2000.

43

Appendix C. Map of proposed neighborhoods for future analysis

Average Standardized Hospitalization Rates by Health Condition and Los Angeles County Health District

HD Name HDPQI#01: short‐term 

complicationsPQI#03: long‐term complications

PQI#14: uncontrolled diabetes

PQI#16: lower extremity 

amputationsTotal Inpatient Admissions ED encounters

Inpatient admissions Total Hospitalizations

Alhambra 3 26.7 125.8 14.5 5.3 172.4 148.4 44.4 192.8Antelope Valley 5 87.8 187.8 35.1 8.9 319.6 221.0 71.2 292.2Bellflower 6 46.7 205.3 31.0 14.1 297.1 270.2 72.2 342.4Central 9 48.6 207.4 41.7 9.2 306.9 256.9 109.3 366.2Compton 12 96.0 356.6 39.7 26.1 518.4 389.0 109.8 498.8East LA 16 42.2 266.1 34.7 16.7 359.7 199.2 62.0 261.3East Valley 19 37.2 140.1 20.8 7.7 205.8 204.7 56.1 260.9El Monte 23 36.0 216.3 25.4 12.5 290.2 171.6 48.9 220.5Foothill 25 36.7 124.0 12.7 4.5 177.9 151.3 48.4 199.7Glendale 27 23.9 89.8 14.3 3.0 130.9 200.1 57.2 257.2Harbor 31 54.3 151.9 14.4 11.0 231.6 211.8 33.1 244.9Hollywood‐Wilshire 34 31.2 127.9 19.1 6.6 184.7 168.0 66.9 235.0Inglewood 37 73.8 242.9 27.9 18.3 362.9 323.1 94.8 417.9Long Beach 40 61.1 203.9 30.8 10.3 306.0 257.5 72.7 330.2Northeast 47 55.4 230.4 37.8 10.7 334.4 225.3 83.8 309.1Pasadena 50 43.4 161.6 14.8 10.2 230.0 200.1 90.2 290.3Pomona 54 41.4 142.9 17.6 10.2 212.2 188.1 44.5 232.6San Antonio 58 49.1 259.3 41.2 16.1 365.7 270.3 85.0 355.3San Fernando 62 32.8 120.0 12.0 5.4 170.2 205.8 48.7 254.5South 69 125.7 391.3 66.6 31.1 614.7 426.0 156.5 582.5Southeast 72 90.4 394.9 63.7 20.9 569.9 353.6 150.7 504.3Southwest 75 100.5 268.0 46.9 16.7 432.1 366.1 125.9 492.0Torrance 79 41.8 119.3 9.9 9.1 180.2 199.5 37.5 237.0West 84 21.4 64.7 8.1 3.2 97.5 99.1 24.2 123.3West Valley 86 31.8 107.2 11.5 5.0 155.5 203.7 44.4 248.1Whittier 91 41.5 181.3 26.9 17.4 267.1 201.6 39.8 241.3

Source: OSPHD 2007‐2009

Diabetes (Age 18+): PQIs=01, 03, 14, and 16 Hypertension (Age 18+): PQI#07

Average Standardized Hospitalization Rates by Health Condition and Los Angeles County Health District

Acute Myocardial Infarction (AMI) (Age 18+): IQI#15 Overall

HD Name HD ED encountersInpatient admissions Total Hospitalizations Total Hospitalizations CABG PCI Total Interventions ED encounters

Inpatient Admissions 

Total Hospitalizations 

Alhambra 3 21.9 295.9 317.8 151.3 21.9 295.9 317.8 155.2 17.7 172.9Antelope Valley 5 62.9 486.7 549.6 298.3 62.9 486.7 549.6 484.0 74.0 557.9Bellflower 6 54.1 363.1 417.2 197.4 54.1 363.1 417.2 294.3 38.0 332.2Central 9 63.9 495.2 559.0 187.9 63.9 495.2 559.0 269.4 28.1 297.6Compton 12 113.9 638.3 752.2 222.4 113.9 638.3 752.2 397.9 59.0 456.9East LA 16 41.2 404.7 445.9 207.2 41.2 404.7 445.9 265.7 35.9 301.6East Valley 19 40.3 351.5 391.7 181.9 40.3 351.5 391.7 281.3 33.3 314.7El Monte 23 33.5 377.0 410.4 200.8 33.5 377.0 410.4 242.6 35.1 277.6Foothill 25 36.0 339.5 375.5 176.7 36.0 339.5 375.5 256.3 44.0 300.3Glendale 27 30.1 320.7 350.8 210.9 30.1 320.7 350.8 170.9 23.4 194.4Harbor 31 59.6 310.9 370.5 145.1 59.6 310.9 370.5 240.7 26.3 267.0Hollywood‐Wilshire 34 39.3 334.7 374.0 179.5 39.3 334.7 374.0 180.2 28.8 209.0Inglewood 37 84.6 541.5 626.1 196.0 84.6 541.5 626.1 364.1 43.9 408.0Long Beach 40 60.8 356.9 417.7 196.6 60.8 356.9 417.7 382.9 44.9 427.9Northeast 47 37.3 396.8 434.1 204.8 37.3 396.8 434.1 264.1 42.6 306.8Pasadena 50 32.2 357.5 389.7 172.5 32.2 357.5 389.7 242.9 30.7 273.5Pomona 54 60.1 352.3 412.4 187.5 60.1 352.3 412.4 258.2 32.5 290.8San Antonio 58 49.6 406.9 456.5 200.2 49.6 406.9 456.5 244.6 37.9 282.4San Fernando 62 40.0 338.9 378.9 200.2 40.0 338.9 378.9 239.0 32.2 271.2South 69 133.9 916.6 1050.5 262.5 133.9 916.6 1050.5 471.5 101.0 572.5Southeast 72 95.6 794.3 889.9 228.4 95.6 794.3 889.9 312.8 56.9 369.6Southwest 75 125.6 678.0 803.6 209.5 125.6 678.0 803.6 436.4 59.5 495.9Torrance 79 52.3 326.1 378.4 165.3 52.3 326.1 378.4 172.2 21.1 193.3West 84 24.9 233.5 258.4 147.5 24.9 233.5 258.4 134.8 13.5 148.2West Valley 86 43.8 318.4 362.2 185.6 43.8 318.4 362.2 294.7 38.5 333.3Whittier 91 35.6 344.8 380.5 267.8 35.6 344.8 380.5 309.7 40.0 349.7

Source: OSPHD 2007‐2009

Congestive Heart Failure (CHF) (Age 18+): PQI#08

Coronary Artery Bypass Graft (CABG) (IQI#12) and Percutaneous Coronary 

Intervention (PCI) (IQI#30) Asthma (Age 18‐44): PQI#15

Average Standardized Hospitalization Rates by Health Condition and Los Angeles County Health District

HD Name HD ED encounters Inpatient Admissions

Total Hospitalizations ED Encounters

Inpatient Admissions Total Hospitalizations ED Encounters 

Inpatient Admissions 

Total Hospitalizations  ED encounters

Inpatient Admissions Total Hospitalizations

Alhambra 3 242.5 315.7 558.2 24.4 509.3 533.7 2.7 19.9 22.6 3.0 20.8 23.7Antelope Valley 5 774.3 1021.0 1795.3 25.8 720.7 746.4 1.0 27.7 28.8 13.8 57.4 71.1Bellflower 6 439.3 517.1 956.3 23.8 675.0 698.8 3.1 34.9 38.0 5.8 23.7 29.5Central 9 661.5 820.9 1482.4 73.6 1213.4 1287.0 4.8 46.2 51.0 55.7 162.5 218.2Compton 12 769.6 883.2 1652.7 42.4 812.2 854.6 4.4 35.9 40.3 12.2 60.6 72.9East LA 16 429.8 454.1 883.9 40.3 472.8 513.1 2.4 21.1 23.5 4.5 28.5 33.0East Valley 19 424.9 462.8 887.7 43.4 762.1 805.5 2.2 37.4 39.6 9.8 56.1 65.9El Monte 23 305.9 445.9 751.7 25.9 534.4 560.3 2.1 23.8 25.9 3.6 20.5 24.1Foothill 25 307.2 380.0 687.2 19.1 744.1 763.3 2.2 43.3 45.5 4.0 29.8 33.9Glendale 27 337.7 405.2 742.9 25.9 729.0 754.9 1.8 28.4 30.2 7.5 42.6 50.1Harbor 31 360.8 366.1 726.9 33.5 632.1 665.6 4.2 87.6 91.8 4.0 23.9 27.9Hollywood‐Wilshire 34 339.2 469.9 809.1 45.8 762.3 808.1 3.6 60.2 63.8 36.4 163.2 199.5Inglewood 37 661.7 670.3 1332.1 44.5 705.4 749.9 4.3 35.2 39.5 12.3 57.5 69.8Long Beach 40 571.9 608.1 1180.0 27.6 1133.0 1160.6 3.0 60.7 63.7 17.2 94.1 111.3Northeast 47 481.7 474.3 956.0 62.4 852.1 914.5 3.7 37.0 40.7 10.7 77.5 88.2Pasadena 50 375.1 490.3 865.5 23.6 1242.3 1265.9 3.6 45.0 48.6 7.0 59.6 66.6Pomona 54 370.2 366.5 736.8 22.0 629.8 651.9 4.6 35.2 39.8 5.7 24.3 30.0San Antonio 58 423.5 678.8 1102.4 32.2 563.0 595.2 3.3 24.3 27.6 5.6 43.6 49.2San Fernando 62 364.8 410.5 775.3 40.2 713.1 753.3 1.6 38.9 40.4 3.3 20.3 23.6South 69 1055.6 1324.2 2379.8 72.3 1315.7 1388.0 5.4 45.8 51.2 21.6 124.9 146.5Southeast 72 853.7 1163.3 2017.0 61.2 1167.7 1228.9 3.0 34.9 37.9 23.4 108.6 132.0Southwest 75 804.0 887.5 1691.4 58.3 1154.6 1212.8 4.9 34.9 39.8 28.9 155.4 184.3Torrance 79 319.2 385.4 704.6 32.6 427.3 459.9 4.7 53.7 58.4 2.7 16.3 19.0West 84 269.1 253.6 522.7 25.5 515.5 541.0 3.5 33.1 36.6 6.9 31.7 38.5West Valley 86 382.9 381.4 764.3 41.4 764.6 806.0 2.5 43.5 46.1 10.3 42.3 52.6Whittier 91 419.7 405.1 824.8 34.6 587.0 621.7 5.5 33.1 38.6 5.8 26.8 32.6

Source: OSPHD 2007‐2009

HIV/AIDS: AQI#01Mental Health: AQI#02COPD (Age 45+): PQI#05Intoxication/Substance Abuse: 

AQI#03