WHAT ACCOUNTS FOR HEALTH DISPARITIES? education survey: HOUSTON SURVEYS in a( ) critical time

Transcription

1 WHAT ACCOUNTS FOR The 12 houston HEALTH DISPARITIES? education survey: FINDINGS FROM THE Public PercePtions HOUSTON SURVEYS in a(1-13) critical time #

2 April November 1413 Kinder Institute for Urban Research Rice University, MS 8 6 Main Street Houston, TX 775 Telephone: (713) Written by Stephen L. L. Klineberg, Principal Principal Investigator Investigator Jie Wu, Research Research Project Project Manager Manager Kiara Douds, Post-Baccalaureate Fellow Cristina Barrera, Research Intern Contact us for more information at kinder@rice.edu. Contact us for more information at kinder@rice.edu Copyright 13 by Kinder Institute for Urban Research. All rights reserved. Copyright 14 by Kinder Institute for Urban Research. All rights reserved.

3 TABLE OF CONTENTS INTRODUCTION... 3 SOCIAL STRUCTURES AND HEALTH DISPARITIES... 4 Measuring Self-Rated Health... 4 Figure 1 Self-Rated Health Status in Harris County, from Two Data Sources Aging and Health... 5 Figure 2 Self-Rated Health Status by Age Group (1-13 KIHAS, Combined) Figure 3 The Prevalence of Serious Health Problems by Age Group among the Respondents Who Reported Fair or Poor Health (HAHS) The Relationship between Inequality and Health Table 1 Figure 4 Self-Rated Health Status across Major Metropolitan Counties, Ranked by Their GINI Coefficients (11 ACS 5-Year Estimates) The Percent of Residents in Fair or Poor Health, by the GINI Coefficients of their Counties (13 BRFSS) The Individual Contributions of Education, Income, and Ethnicity... 7 The Role of Education... 8 Figure 5 Self-Rated Health Status by Educational Attainment (1-13 KIHAS, Combined) The Importance of Income... 9 Figure 6 Self-Rated Health Status by Annual Household Income (1-13 KIHAS, Combined) The Impact of Ethnicity... Figure 7 Figure 8 Figure 9 Levels of Household Income in Five Ethnic Communities (1-13 KIHAS, Combined) Differences in Self-Rated Health Status in Five Ethnic Communities (1-13 KIHAS, Combined) Differences in Self-Rated Health Status among U.S.-Born Asians and in Houston s Four Largest Asian Immigrant Communities (1-13 KIHAS, Combined) The Structural Correlates of Self-Rated Health Table 2 Figure Logistic Regression of the Structural Correlates on Self-Rated Health Status (1-13 KIHAS, Combined) The Perceived Determinants of a Person s Health, Average Ratings (HAHS) ACCESS TO HEALTH CARE...15 Health Insurance Coverage Figure 11 Health Insurance Coverage by Ethnicity among Respondents Aged 18 to 64 (1-13 KIHAS, Combined) Table 3 Logistic Regression of Health Insurance Coverage on Self-Rated Health Status Controlling for the Structural Variables (1-13 KIHAS, Combined) Immigration and Health Insurance among Hispanics Table 4 Logistic Regression of Immigration Status and of Insurance Coverage on Self-Rated Health Status among Latinos, Controlling for the Structural Variables Health Disparities 1

4 Access to Quality Health Care in Houston Figure 12 Figure 13 Respondents Ratings of the Quality of Health Care Available to Them in the Houston Area, by Household Income (HAHS) Respondents Ratings of the Quality of Health Care Available to Them in the Houston Area, by Insurance Coverage (HAHS) Medical Visits ENVIRONMENTS, NEIGHBORHOODS, AND HEALTH... The Importance of Air and Water Quality... Air Quality... Figure 14 Figure 15 Table 5 Ratings of Air Quality and Concerns about Air Pollution (HAHS) Self-Reported Health Status by Proximity to the APWL ZIP Codes (1-13 KIHAS, Combined) Logistic Regression of Proximity to the APWL ZIP Codes on Self-Reported Health, Controlling for the Structural Variables and for Insurance Coverage Water Quality Figure 16 Ratings of Water Quality and Concerns about Water Pollution (HAHS) Neighborhood Characteristics and Health-Related Behaviors Food Insecurity Figure 17 The USDA Map of Houston Area Food Deserts, Reflecting Both Income and Access Physical Activity Figure 18 Figure 19 Respondents Satisfaction with Their Level of Physical Activity by Their Self-Rated Health Status (HAHS) The Reported Frequency of Vigorous Physical Activity by Income Level (HAHS) Walkability Figure Neighborhood Amenities in the Houston Area by the Location of Home Residence (HAHS) The Use of Public Resources Figure 21 Figure 22 The Perceived Physical Activity in the Neighborhood by Proximity to Local Parks and Trails (HAHS) The Availability of Health-Promoting Resources and Their Frequency of Use during the Past Year among Harris County Residents (HAHS) Participation and Belonging... Table 6 Logistic Regression of the Composite Measures of Neighborhood Belonging and Participation on Self-Reported Health Status (12 KIHAS) SUMMARY AND CONCLUSIONS APPENDIX A. THE 12 HAHS METHODOLOGY APPENDIX B. LOCAL HEALTH ADVISORS REFERENCES Kinder Institute for Urban Research

5 INTRODUCTION This report seeks to identify the forces that account for health disparities in the Houston region. It makes use of questions asked in the past 13 years of the annual Kinder Institute Houston Area Survey (KIHAS), buttressed by findings from the third of the institute s three focused surveys, known as the SHEA studies ( Surveys of Health, Education, and the Arts ). Supported by a grant from Houston Endowment Inc., the studies were designed to assess the experiences, beliefs, and attitudes of Harris County residents with regard specifically to these important areas of urban life. The three separate surveys and the reports 1 presenting their central findings complement the Kinder Institute s continuing annual studies (the KIHAS), which have been tracking, through 33 years of systematic surveys ( ), the demographic patterns, experiences, attitudes, and beliefs of Houston area residents during a period of remarkable change. 2 In the process of developing the survey instrument to be used for the SHEA Houston Area Health Survey (HAHS), the research team at the Kinder Institute met periodically during 11 and 12 with local and national public health experts to fashion the questions we would ask of a scientificallyselected representative sample of 1, Harris County residents. The goal was to measure systematically area residents self-reported health status, their experience with Houston s health care delivery systems, and the health-related characteristics of their neighborhoods. The actual telephone interviews were conducted by the Survey Research Institute at the University of Houston between June 6 and July 17, 12, with 7 percent of the respondents reached by landline and percent by cell phone. After the interviews were completed, the data were weighted to correct for variations in the likelihood of selection and to align the sample more closely with known population characteristics, such as gender, age, education, race and ethnicity, home ownership, and county population and density. The weighting procedures provide a more accurate and reliable reflection of the actual attitudes and experiences to be found within the Harris County population as a whole. As in previous surveys, the final (weighted) sample distributions still slightly overrepresent respondents aged 6 and older and underrepresent those aged to 44, but they mirror almost perfectly the census figures for Harris County with regard to the distributions by gender, ethnicity, and educational attainment. Unless otherwise indicated, the findings presented in the following charts and discussed below are based on the weighted data. Appendix A provides additional information about these and other aspects of the survey methodology. Listed in Appendix B are the local advisors who helped the research team develop the survey instrument and offered comments on the preliminary findings and on earlier drafts of this report. The survey instrument that was used in the HAHS and the responses given by Harris County residents on all the questions can be found on the institute s website at kinder.rice.edu/shea. We begin this report with an analysis of the findings from both the HAHS and the past 13 years of the KIHAS in an effort to identify empirically the most important structural forces (such as age, education, income, and ethnicity) that demonstrably underlie and help to account for the wide disparities in the health and well-being of Harris County residents. We go on to explore the bases for individual differences in area residents access to quality health care and to assess the importance of the relationship between health insurance coverage and self-reported health status. Finally, we ask about the health effects of environmental pollution and of the social characteristics of neighborhoods, and we briefly mention some of the initiatives under way in the Houston area that may make a significant difference in shaping the overall health of this community. Health Disparities 3

6 SOCIAL STRUCTURES AND HEALTH DISPARITIES Measuring Self-Rated Health of neighborhoods, and we briefly mention some of the initiatives under way in the Houston area that Early may in the make Houston a significant Area Health difference Survey in (HAHS), shaping the Figure overall 1 presents health of the this responses community. on this question the respondents were asked: In general, would you from both sources: the HAHS and the combined say that your overall state of physical health these 13 years of the KIHAS (1-13). A plurality days is excellent, very good, good, fair, or poor? of all Harris County residents (32 to 38 percent) SOCIAL STRUCTURES AND said HEALTH This simple measure of self-reported health has that they were in good health; another 44 to 46 percent reported that their health was either DISPARITIES been found to be a remarkably reliable indicator of a person s actual state of health. Responses on this excellent or very good. Fully one-fifth (19 to question closely correlate with the results of physical examinations Self-Rated by health professionals, Health 3 and they current state of health as fair or poor. The 21 percent) of the survey participants rated their Measuring are strong predictors of mortality. 4 Health of Houston Survey, conducted by the Early in the Houston Area Health Survey (HAHS), the School respondents of Public Health were asked: at the In University general, of would Texas, you That say same that question your overall was included state of in physical each of the health these also days found is excellent, that percent very of good, Harris good, County fair, residents been reported found their to be health a remarkably status as fair reliable or poor. 5 or poor? past 13 years This simple (1-13) measure of the of annual self-reported Kinder health has indicator Institute Houston of a person s Area Survey actual (KIHAS), state of health. which Responses has The close on this correspondence question closely among correlate these various with the data results reached of more physical than, examinations Harris County by health residents professionals, sources 3 and strengthens they are strong confidence predictors in the reliability of of during those years. When asking about the role of the findings. We will be particularly interested to mortality. 4 demographic or structural factors in accounting for learn what the surveys can tell us about those individual differences in self-rated health, we will percent of area residents who indicate that their That same question was included in each of the past 13 years (1-13) of the annual Kinder make use of that more extensive set of data. We will current state of health is only fair or poor. Institute Houston Area Survey (KIHAS), which has reached more than, Harris County draw on the HAHS for its rich additional measures residents during those years. When asking about the role of demographic or structural factors in of various environmental and neighborhood characteristics and their impact on health outcomes. accounting for individual differences in self-rated health, we will make use of that more extensive set of data. We will draw on the HAHS for its rich additional measures of various environmental and neighborhood characteristics and their impact on health outcomes. Figure Figure 1 - Self-Rated Self-Rated Health Health Status Status in in Harris Harris County, County, from from Two Two Data Data Sources Sources Excellent Very good Good Fair Poor In general, would you say that your overall state of physical health these days is excellent, very good, good, fair, or poor? 6 5 The 12 HAHS 1-13 KIHAS, combined Figure 4 Kinder 1 presents Institute for the Urban responses Research on this question from both sources: the HAHS and the combined 13 years of the KIHAS (1-13). A plurality of all Harris County residents (32 to 38 percent) said that they were in good health; another 44 to 46 percent reported that their health was either excellent or very good. Fully one-fifth (19 to 21 percent) of the survey participants rated their

7 Aging and Health Not surprisingly, age is significantly associated with self-reported health status: The older survey participants were consistently more likely than the younger respondents to give negative ratings when asked about their current state of health. In the HAHS, 24 percent of those who were over the age of 6 reported that their overall physical health was fair or poor, compared to just 15 percent of respondents aged 18 to 29. Figure 2 shows that this relationship is confirmed by the more extensive KIHAS data: Over the 13 years of surveys, 32 percent of all the respondents aged 6 or older at the time of the interviews said they were in fair or poor health, but this was true of just 16 percent of the survey participants who were under the age of. Not surprisingly, age is significantly associated with self-reported health statu participants were consistently more likely than the younger respondents to giv when asked about their current state of health. In the HAHS, 24 percent of tho the age of 6 reported that their overall physical health was fair or poor, comp percent of respondents aged 18 to 29. Figure 2 shows that this relationship is c more extensive KIHAS data: Over the 13 years of surveys, 32 percent of all th 6 or older at the time of the interviews said they were in fair or poor health, b just 16 percent of the survey participants who were under the age of. Figure 2 Self-Rated Health Status by Age Group (1-13 KIHAS, Combined) Figure 2 - Self-Rated Health Status by Age Group (1-13 KIHAS, Combined) 6 5 In general, would you say that your overall state of health these days is excellent, very good, good, fair, or poor? (N=4,1) -44 (N=5,262) (N=5,639) 6+ (N=4,365) "Excellent" or "Very Good" "Good" "Fair" or "Poor" The HAHS asked the respondents if they had any physical health problems right now that prevent you from doing any of the things people your age normally can do? More than a third (34 percent) of the survey participants who were over the age of 6 said they did indeed have a physical problem of that sort, but this was true for just 5 percent of the respondents under age. Not surprisingly, those of any age who said their health was fair or poor were far more likely to report having a physical problem that affected their daily lives. from engaging in normal activities. Fewer than a tenth (8 percent) of the younger respondents who It is interesting to note that said people s they assessments were in generally of their poor health also indicated that they were currently dealing with a overall state of physical health debilitating seem to health be based problem, on but the from number engaging for whom in normal this is activities. true increased Fewer consistently than a tenth with (8 percent) age of the you to reach 69 percent among those aged said 6 they and were older. in generally poor health also indicated that they were currentl different considerations at different ages. Looking just debilitating health problem, but the number for whom this is true increased c at the one-fifth of all the respondents who rated their to reach Figure 69 percent 3 - The among Prevalence those aged of Serious 6 and older. Health Problems currently by Age dealing Group with a among the Respondents Who from engaging in normal activities. Figure Fewer 3 The than Prevalence a tenth of (8 Serious percent) Health of the Problems younger by respondents Age Group among who the Respondents Who current state of health as fair or poor, Figure 3 shows said they were in generally poor Reported health Fair also or indicated Poor Health that (HAHS) they were the debilitating percent health in each problem, of the four but the age from number groups engaging for who whom in also normal this said is activities. true increased Reported Fewer consistently than Fair a tenth or with Poor (8 percent) age Health of the (HAHS) younger respondents who they to reach had 69 a physical percent among health those problem aged said that they prevented were in generally them poor Reported health Fair also or indicated Poor Health that (HAHS) they were currently dealing with a 6 and older. debilitating 92 health problem, but the number for whom this is Do true you increased have any consistently physical health with age from engaging in normal activities. to 9 reach Fewer 69 percent than a tenth among those aged 6 and older. (8 percent) of the younger respondents who said they problems right now that prevent Figure 3 The Prevalence of Serious Health 92 8 Problems by Age Group among the Respondents Who you from doing any of the things were Reported in generally Fair or Poor poor Health health (HAHS) also indicated that they 9 69 people your age normally can do? Figure 7 3 The Prevalence 63 of Serious Health Problems by Age Group among the Respondents Who were currently dealing with a debilitating Reported Fair health or Poor problem, but the number 92 for whom this is true increased Do you 7 have any physical health Health (HAHS) 8 62 [N=5] consistently 9 with age to reach 69 percent among 92 those problems right now that prevent Do you have any physical health aged 68and older. you from doing any of 31 the things Figure 3 The Prevalence of Serious Health Problems by Age Group among the Re 5 people your age normally can do? 37 [N=5] problems right now that prevent you from 38 doing any of the things 31 Do you hav problems rig you from do people your [N=5] As other 6 research has shown, 6 younger people are 69 No people serious your health age problems normally can do? [N=5] more likely 5 to evaluate their current health status on 6 At least one serious health problem No seriou the basis of their participation in risky behaviors (e.g., 8 5 smoking, poor diet, lack of exercise). In contrast, older At least o adults overwhelmingly refer to functional limitations and specific physical problems As other when research assessing has their shown, 6 No serious health problems 8 younger people are more likely to evaluate their current health status on the basis of their participation At in least risky one behaviors serious health (e.g., problem smoking, poor No serious diet, health lack of problems state of health. exercise). In 8 As other research has shown, 6 younger people are more likely to evaluate the contrast, older adults overwhelmingly refer to functional limitations and specific status on the basis of their participation At in least risky one behaviors serious health (e.g., problem physical problems when assessing 6-95 their state of health. smoking, po exercise). In contrast, older adults overwhelmingly Health Disparities refer to functional 5 limitat physical problems 45-59when assessing 6-95 their state of health. As other research has shown, 6 The younger Relationship people are more between likely Inequality to evaluate their and current Health health status on the basis of their participation in risky behaviors (e.g., smoking, As other research has shown, The 6 poor diet, lack of exercise). In contrast, older adults younger people are more likely to evaluate their current health Table overwhelmingly 1 shows the data refer from to the functional 13 Behavioral Relationship limitations Risk and Factor between specific Surveillance Inequality System and (BRFSS) Health The HAHS asked the respondents if they had any physical health problems r you from doing any of the things people your age normally can do? More tha percent) of the survey participants who were over the age of 6 said they did i physical problem of that sort, but this was true for just 5 percent of the respon Not surprisingly, those of any age who said their health was fair or poor were report having a physical problem that affected their daily lives. It is interesting to note that people s assessments of their overall state of physi be based on different considerations at different ages. Looking just at the onerespondents who rated their current state of health as fair or poor, Figure 3 sho 6 Kinder Institute for Urban Research

8 The Relationship between Inequality and Health Table 1 shows the data from the 13 Behavioral Risk Factor Surveillance System (BRFSS) presenting the proportion of residents in the nation s ten most populous urban counties (not including the New York boroughs) who indicated that they were in only fair or poor health. 7 When compared to the other large urban regions, Harris County ranks in the middle, with 18 percent of Houston area residents in this study reporting fair or poor health, compared to 23 percent in Los Angeles and 14 percent in San José. The GINI coefficients listed in the right-hand column of Table 1 provide a standard measure of income inequality within each of these urban counties: If all households in the region had exactly the same level of income, the GINI coefficient would be zero; a value of one would mean that a single household had all the income, and all others had no income at all. The localities with a high GINI coefficient are those with a disproportionate number of rich and poor residents; lower GINI coefficients reflect populations with more even income distributions. The table shows clearly that the differences in self-rated health across these metropolitan areas tend to mirror their differences in income inequality. Table 1 Self-Rated Health Status across Major Metropolitan Counties (13 BRFSS, Excluding NYC Table - Self-Rated Health Status across Major Metropolitan Counties (13 PRFSS, Excluding NYC Boroughs), Ranked by Their GINI Coefficients (11 American Community Survey 5-Year Estimates) Boroughs, Table 1 Ranked Self-Rated by Health Their Status GINI across Coefficients Major Metropolitan (11 American Counties Community (13 BRFSS, Survey Excluding 5-Year NYC Estimates) Boroughs), Ranked by Their GINI Coefficients (11 American Community Survey 5-Year Estimates) Major U.S. City County County Percent in Fair or GINI Coefficient (7-11 ACS Major U.S. City County County Population Percent Poor in Fair Health or GINI (13 Coefficient (7-11 five-year ACS estimates) Population Poor Health (13 BRFSS) five-year estimates) Philadelphia, PA Philadelphia 1,536,471 BRFSS).497 Philadelphia, PA Philadelphia 1,536, Los Angeles, CA Los Angeles 9,889, Los Angeles, CA Los Angeles 9,889, Dallas, Dallas, TX TX Dallas 2,416,14 2,416, Houston, TX TX Harris 4,18, Chicago, IL IL Cook 5,217,8 5,217, San Antonio, TX Bexar 1,756, San Antonio, TX Bexar 1,756, San Diego, CA San Diego 3,1, San Diego, CA San Diego 3,1, Phoenix, AZ Maricopa 3,88, Phoenix, San José, AZ CA Maricopa Santa Clara 1,89,378 3,88, San José, CA Santa Clara 1,89, Figure 4 The Percent of Residents in Fair or Poor Health, by the GINI Coefficients of Their Counties Figure ( BRFSS) The Percent of Residents in Fair or Poor Health, by the GINI Coefficients of Their Counties (13 Figure BRFSS) 4 The Percent of Residents in Fair or Poor Health, by the GINI Coefficients of Their Counties (13 BRFSS) PA LA LEGEND 22 SJ San José (Santa Clara Co.) 18 HO PX Phoenix (Maricopa Co.) SA PA SD San Diego (San Diego Co.) 16 SD CH DA SA San Antonio LEGEND (Bexar Co.) PX CH Chicago (Cook SJ San Co.) José (Santa Clara Co.) 1814 SJ HO DA Dallas (Dallas PX Phoenix Co.) (Maricopa Co.) SA HO Houston (Harris Co.) 12 SD San Diego (San Diego Co.) 16 SD CH DA PA Philadelphia (Philadelphia Co.) LA Los Angeles SA San (Los Angeles Antonio Co.) (Bexar Co.) PX CH Chicago (Cook Co.) SJ DA Dallas (Dallas Co.) County GINI Coefficient HO Houston (Harris Co.) 12 PA Philadelphia (Philadelphia Co.) 6 Kinder Institute for Urban Research Figure 4 illustrates the positive linear relationship between the levels of income inequality LA Los in each Angeles (Los Angeles Co.) metro area and the proportion of residents reporting that they were in fair or poor health. The localities.44 where the.45 levels of inequality.46 are lower.47 than they.48 are in Houston.49(e.g., San.5 José, PERCENT OF RESIDENTS IN POOR OR FAIR HEALTH PERCENT OF RESIDENTS IN POOR OR FAIR HEALTH LA

9 Figure 4 illustrates the positive linear relationship between the levels of income inequality in each metro area and the proportion of residents reporting that they were in fair or poor health. The localities where the levels of inequality are lower than they are in Houston (e.g., San José, Phoenix) tend to have healthier populations, whereas those with higher levels of inequality (Philadelphia, Los Angeles) have more residents reporting that their health was only fair or poor. Lopez (4) examined metropolitan areas across all 5 states and found that, with each.1 point rise in the GINI index, the proportion of individuals in the area who reported fair or poor health increased by 4 percent. 8 Socioeconomic factors are strongly linked to health outcomes. The Individual Contributions of Education, Income, and Ethnicity The data in Table 1 clearly suggest that income differences contribute importantly to the health disparities that exist across the country. Higher levels of socioeconomic status (SES) generally mean greater household wealth, healthier neighborhood conditions, and more support systems that encourage health-promoting activities. Each of the separate components that define the structural inequalities in America (education, income, and ethnicity) may be significant individual contributors to the relationship between SES and self-reported health, or they may be associated with health outcomes simply because of their correlation with other more powerful structural variables. Thus, African Americans and Latinos may have worse health outcomes than Anglos or Asians because they also have lower levels of education and income and not because of any distinctive factors (such as wealth disparities, stress, or discrimination) that are specific to ethnic differences. Or does ethnicity independently contribute to the health disparities, even after the separate effects of education and income are statistically controlled? We will make use of logistic regression to disentangle the degree to which each of these structural components is individually associated with self-reported health. First, we examine the straightforward correlations of health status with education, income, and ethnicity, and consider the reasons why each factor might indeed be a separate and cumulative predictor of health status. Then, with the use of logistic regression, we will be able to test the independent correlation of each separate factor with self-rated health, after controlling for the impact of all the others. Health Disparities 7

10 The Role of Education. A person s educational attainment can influence health in a variety of ways. Several national studies suggest that more education in and of itself, even when controlling for its close association with income, enhances life expectancy and fosters better health. 9, Educational attainment, at all levels of income, increases access to basic health information and the likelihood of engaging in healthy behaviors, such as regular physical exercise and scheduling check-ups on a regular basis. 11 The 3 National Assessment of Adult Literacy found that education is highly correlated with measures of health literacy, the ability to obtain, process, and understand basic health information and services in order to make appropriate health decisions. 12 Education is also associated with important psychological factors, such as the sense of efficacy, or the belief in personal control over one s life circumstances: The extent to which individuals are confident that they can influence what happens in their lives has been shown to be linked with higher levels of self-rated health. 13 Educational attainment, even beyond its relationship with income, may improve health by reducing overall stress and promoting healthier lifestyle choices. Making use of the rich data from 13 years of the KIHAS (1-13), Figure 5 reveals a powerful linear relationship between educational attainment and self-reported health status. Over one-third (35 percent) of area residents who had less than a high school education said their current state of health was fair or poor, but this was the case for just 11 percent of the respondents with college degrees. Fully 63 percent of the survey participants who had post-graduate educations reported their health as excellent or very good, compared to just 31 percent of those who had dropped out of high school. Clearly, as education levels increase, health status improves. We will explore below whether this relationship continues to hold even after rigorously controlling for the separate effects of income, ethnicity, gender, and age. below whether this relationship continues to hold even after rigorously controlling for the separate effects of income, ethnicity, gender, and age. below whether this relationship continues to hold even after rigorously controlling for the separate effects Figure 5 Self-Rated Health Status by Educational Attainment (1-13 KIHAS, Combined) Figure of 5 income, - Self-Rated ethnicity, Health gender, Status and by Educational age. Attainment (1-13 KIHAS, Combined) Figure 75 Self-Rated Health Status by Educational Attainment ( KIHAS, Combined) "Excellent" or "Very Good" "Good" 18 "Excellent" "Fair" or "Poor" or "Very Good" 11 "Good" Less than HS HS diploma Some college College Postgraduate (N=2,411) (N=4,664) (N=5,954) degree education "Fair" or "Poor" (N=4,484) (N=2,729) Less than HS HS diploma Some college College Postgraduate "What (N=2,411) is the highest (N=4,664) grade of school (N=5,954) or year of college degree that you've education completed?" (N=4,484) (N=2,729) PERCENT OF RESPONDENTS The Importance "What is the of highest Income. grade The of school direct or impact year of college of income that you've on health completed?" seems likely to be at least as 8 Kinder Institute for Urban Research powerful as the relationship with education. People with higher household incomes typically live longer, healthier lives than less affluent individuals, since their higher earnings are associated with The Importance of Income. The direct impact of income access to resources that influence health and mortality 14,15 on health seems likely to be at least as and foster healthier lifestyle choices. 16 powerful as the relationship with education. People with higher household incomes typically live

11 The Importance of Income. The direct impact of income on health seems likely to be at least as powerful as the relationship with education. People with higher household incomes typically live longer, healthier lives than less affluent individuals, since their higher earnings are associated with access to resources that influence health and mortality, 14,15 and foster healthier lifestyle choices. 16 Higher-paying jobs are more likely to have health-related employee benefits and employer-sponsored health insurance. Beyond their greater access to quality health care, more affluent individuals are likely to experience less stress and to live in neighborhoods with higher levels of social cohesion, greater access to nutritious foods and ample opportunities for physical activity and recreation. In this era of growing inequalities, income differences in health and well-being are likely to be particularly stark. Figure 6 shows the relationship between selfreported health status and household incomes in the KIHAS surveys. Of all area residents who reported household incomes of $15, or less (below the poverty level for a family of two), considerably more than a third (38 percent) said they were in fair or poor health. As income levels rise above $25,, the percent of respondents reporting poor health declines, while the number saying that their current physical health is excellent or very good increases in a stepwise fashion. At the highest levels (the households reporting incomes of more than $75,), only percent of the survey participants said their current state of health was fair or poor, and 63 percent reported that they were in very good or excellent health. i Again, we need to ask whether this relationship is due specifically to differences in household income: Does the connection between income and health exist because respondents with higher incomes are also likely to have higher levels of educational attainment, or are education and income separate and independent contributors to a person s state of health? Meanwhile, of course, both education and income are closely tied to differences in ethnic backgrounds, so we also need to consider the role of ethnicity as we seek to identify the specific social structural forces that independently account for health disparities among Houston-area residents. Figure 6 Self-Rated Health Status by Annual Household Income (1-13 KIHAS, Combined) Figure 6 - Self-Rated Health Status by Annual Household Income (1-13 KIHAS, Combined) Less than $15, (N=1,359) $15,1 to $25, (N=1,975) $25,1 to $35, (N=2,66) 48 Again, i Almost we need percent to of ask the whether respondents this in relationship the 1-13 is KIHAS due specifically declined to report to differences their household in household incomes. There were income: no significant Does differences the connection between between these individuals income and and the health rest of exist the respondents because respondents in their distributions with higher on education, incomes ethnicity are or age. also likely to have higher levels of educational attainment, or are education and income separate and independent contributors to a person s state of health? Meanwhile, of course, both education and income are closely tied to differences in ethnic backgrounds, so Health we also Disparities need 9 to consider the role of ethnicity as we seek to identify the specific social structural forces that independently account for health disparities among Houston-area residents $35,1 to $5, (N=2,2) 15 $5,1 to $75, (N=2,633) More than $75, (N=4,282) "Excellent" or "Very Good" "Good" "Fair" or "Poor" "Please stop me when I reach the category that includes your total household income in the past year; that is, the income for all members of the household during the past year."

12 The Impact of Ethnicity. The recent unprecedented immigration streams into this country since the 1965 reform of the previously restrictive laws have transformed the ethnic composition of the Houston and American populations. Throughout almost all of its history, Houston was essentially a bi-racial, Anglo-dominated Southern city. In the course of the past years, after the oil-boom collapse in 1982, it has been transformed into the single most ethnically diverse large metropolitan region in the country. In the U.S. Census counted 4.1 million people living in Harris County, of whom just 33 percent were Anglos (or non-hispanic whites). Harris County s population was now 41 percent Hispanic, 18 percent African-American, and 8 percent Asian or other. closely associated with self-reported health. Well over half (55 percent) of the U.S.-born Anglos said their current state of health these days was excellent or very good; this was the case for 48 percent of the Asians, 46 percent of the U.S.-born Hispanics, 41 percent of the U.S.-born blacks, and just 32 percent of the Hispanic immigrants. Fully percent of the Hispanic immigrants and 28 percent of the U.S.-born blacks said they were currently in fair or poor health. The U.S.-born Hispanics generally reported better health than blacks or Latino immigrants, but worse health than Anglos or Asians. Figure 7 utilizes the combined data from the KIHAS Figure 7 - Levels of Household Income in Five Ethnic (1-13) to show the distributions of household 7 Communities Levels of Household (1-13 Income in Five KIHAS, Ethnic Communities Combined) (1-13 KIHAS, Combined) Figure income across Harris Figure County s 7 Levels five major of Household ethnic Income in Five 6 Ethnic Communities ( KIHAS, Combined) groups, with Hispanics divided into U.S.-born and immigrant Figure 7 Levels communities. of Household 6 Among Income the in U.S.-born Five Ethnic Anglo respondents, 6 Communities ( KIHAS, Combined) Levels of Household Income in 43 Five percent Ethnic 5reported Communities total household ( KIHAS, Combined) incomes of more than $75,; this was true for percent of the Asians, but 52 for just percent of the Less than $25, U.S.-born Hispanics, 16 percent of U.S.-born blacks, 7 $25,1 to $75, and 7 percent 38 of the Hispanic immigrants. Fully 35 More than $75, percent of the African-American Less than $25, respondents and US-Born Anglos All Asians US-Born US-Born Blacks Hispanic 45 percent of the Hispanic (N=4,392) (N=893) Hispanics (N=4,1) Immigrants immigrants reported Less than (N=2,516) $25, 7 $25,1 (N=1,833) to $75, total household 19 incomes of less than $25, (which The social and economic disadvantages associated with being black or Latino will inevitably 14 would 16 Less than $25, 7 $25,1 to $75, More than $75, put them just above the poverty level for a generate differences in health status. Moreover, the ethnic disparities in life circumstances have family of four); only 14 percent US-Born of Anglos Anglos and All 19 Asians 7 been $25,1 shown US-Born to to accumulate $75, US-Born over Blacks More the life than course, Hispanic $75, and they are typically transferred across (N=4,392) (N=893) generations, Hispanics impacting (N=4,1) the education and Immigrants health outcomes of children and grandchildren as well. percent of Asians had incomes below $25,. US-Born Anglos All Asians US-Born US-Born Such Figure Blacks More (N=2,516) ethnic 8 than differences Hispanic - Differences $75, may well in be Self-Rated strong (N=1,833) predictors Health of health Status disparities in and of themselves, (N=4,392) (N=893) Hispanics (N=4,1) even beyond Immigrants their close relationship to education and income. US-Born Anglos The social All Asians and economic US-Born disadvantages US-Born Blacks associated Hispanic in Five Ethnic Communities (1-13 KIHAS, The social and economic (N=2,516) disadvantages Figure associated 8 Differences (N=1,833) with in Self-Rated being black Health or Status Latino Five will Ethnic inevitably Communities (1-13 KIHAS, (N=4,392) (N=893) Hispanics (N=4,1) Immigrants Figure Combined) 8 presents the racial and ethnic differences in self-rated health status among the KIHAS with being black or Latino generate will differences inevitably in generate health status. Combined) respondents. Moreover, The the data ethnic make it disparities clear that ethnicity in life is indeed circumstances closely associated have The social and economic (N=2,516) (N=1,833) with self-reported differences in health status. been disadvantages shown Moreover, to accumulate associated the ethnic over with the being health. life course, 6 black or Well over 55 and Latino half they (55 percent) are will typically inevitably of the U.S.-born transferred Anglos across said their current state of health these generate differences in days was excellent or very good; this was the case for 48 percent of the Asians, 46 percent of the l and economic disparities disadvantages in life circumstances associated generations, health status. with have impacting Moreover, Figure being been 8 black shown Differences the the education ethnic disparities or Latino Self-Rated will and 5inevitably health in Health outcomes life circumstances Status in 48 Five of children have Ethnic 46 Communities and grandchildren (1-13 KIHAS, as well. 17 been shown to accumulate U.S.-born Hispanics, 41 percent of the U.S.-born blacks, and just 32 percent of the Hispanic differences to in accumulate health status. over Moreover, the Such life ethnic over the course, the ethnic differences life course, and disparities they are in life circumstances immigrants. Fully have 41 Figure 8 Differences Combined) may and well they be are strong typically predictors transferred of health across disparities in generations, impacting the education and health in Self-Rated outcomes Health of Status children percent of the Hispanic immigrants and 28 percent of the U.S.-born blacks in Five and Ethnic grandchildren Communities 37 (1-13 as well. 17 and of themselves, KIHAS, 37 wn to accumulate typically over transferred the life course, across even and generations, they are typically impacting transferred said they across were currently fair or poor health. 33 The U.S.-born Hispanics 32 generally reported better Figure 8 Differences in Self-Rated Combined) beyond their close 6 relationship to education and income. Such ethnic differences may well Health be Status strong in Five predictors 55 Ethnic Communities of health disparities (1-13 ns, impacting health than blacks 29 KIHAS, in or Latino immigrants, but worse health than Anglos or Asians. the the education education and and health health outcomes outcomes of of children children and grandchildren as well. 17 and of themselves, even Combined) beyond their close ic differences grandchildren may well be as strong Figure relationship well. 17 predictors 8 presents to Such ethnic of health the education racial differences disparities and and ethnic income. may in Figure and differences of 9 shows themselves, in self-rated health status 21 among the KIHAS importance of 15 country of origin in accounting for the variability in self-rated ond their close relationship to education respondents. 5 and income. The data 46 make it clear health that 37 ethnicity status among is Houston s indeed closely varied Asian-American associated 37 communities. with self-reported A clear majority of the well Figure be 8 strong presents predictors the "Excellent" or "Ver 48 of health disparities in and health. racial and Well ethnic 46 over differences half (55 percent) in self-rated immigrants from India, Pakistan, and Bangladesh 32 (61 percent) and from the Philippines ( of the U.S.-born health status Anglos among said the their KIHAS current 28 state of health these "Good" of respondents. themselves, The even data beyond make their it clear close that relationship ethnicity 41 is indeed percent), closely 33 along associated with the U.S.-born with 32 self-reported Asians (53 percent), said that their current state of health was presents the racial and ethnic differences days was 37 in excellent self-rated or health very good; 29 status this among to education and income. either 37 was the excellent case KIHAS 31 for 48 percent or very 28 of the Asians, 46 percent of the health. Well over half (55 percent) good. This was true for just 39 percent of the immigrants from "Fair" China, or "Poor" of the U.S.-born Anglos said their current 21 state of health these nts. The data make it clear that ethnicity U.S.-born is Hispanics, indeed 33 closely 41 percent 31 associated 17 of 32 the Taiwan, with U.S.-born self-reported and Hong US-Born Kong, and All Asians 34 US-Born of the Vietnamese US-Born immigrants. Hispanic 29 These contrasts in selfreported Hispanic blacks, and just 32 percent of the Hispanic days was excellent or ell over half (55percent) of the immigrants. very good; this U.S.-born Anglos Fully was the said 17 percent case for their current of the 48 percent of state of health immigrants the Asians, Anglos closely these parallel (N=1,315) and 46 the 28 percent differences percent of the Hispanics among of the Blacks U.S.-born Asian "Excellent" communities Immigrants or blacks "Very in Good" their overall U.S.-born Hispanics, Figure 8 presents the racial and ethnic differences in levels of income (N=5,916) and education, and hence (N=3,572) in the life circumstances (N=5,818) (N=2,659) that they experience. excellent or very good; this was said 41 percent the case they were of the for 48 currently U.S.-born percent of in the fair blacks, Asians, or poor and 46 health. just 32 percent The percent of U.S.-born of the the Hispanics Hispanic ii 17 "Excellent" generally or "Very "Good" reported Good" better immigrants. Fully percent 15 Hispanics, self-rated 41 percent health of the status U.S.-born health among than of the Hispanic immigrants and 28 percent of the U.S.-born blacks blacks, the blacks KIHAS and or just Latino respondents. percent The of data the make Hispanic it clear immigrants that ethnicity and 28 is percent indeed US-Bornof the Figure All U.S.-born Asians 9 Differences US-Born blacks in Self-Rated US-BornHealth Hispanic Status among U.S.-born Asians and in Houston s Four L 32 percent immigrants, of the but Hispanic worse "Excellent" health or "Very than Good" Anglos "Good" or Asians. "Fair" or "Poor" said they were currently in fair or poor health. The U.S.-born Hispanics generally reported better ts. Fully health than blacks or Latino immigrants, but worse "Good" "Fair" or "Poor" Anglos health were currently in fair or poor health. Figure The 9 shows U.S.-born US-Born the importance Hispanics All Asians generally of country Asian (N=1,315) than US-Born Immigrant Anglos Hispanics reported of origin US-Born Communities or Asians. Blacks better in accounting Hispanic (1-13 for Immigrants the KIHAS, variability Combined) in self-rated (N=5,916) "Fair" (N=3,572) or "Poor" (N=5,818) (N=2,659) n blacks or Latino Kinder immigrants, Institute for but health Urban worse status Research health among Anglos than Houston s Anglos (N=1,315) or varied Asians. ii Hispanics A comprehensive Asian-American Blacks report on Harris communities. Immigrants County s Asian communities A clear was majority released in of 12. the It presents the findings from Figure 9 shows US-Born the importance All Asians of US-Born country US-Born Hispanic (N=5,916) of origin in accounting (N=3,572) three focused 7 for surveys (N=5,818) the conducted variability (N=2,659) by the Kinder in self-rated Institute in 1995, 2, and 11, reaching more than 5 Asians in Anglos immigrants (N=1,315) Hispanics from India, Blacks Pakistan, Immigrants and Bangladesh (61 percent) and from the Philippines (52 health status each of these three 61 years, with 24 percent of the interviews conducted in Vietnamese, Cantonese, Mandarin, and (N=5,916) among Houston s varied (N=3,572) Asian-American (N=5,818) (N=2,659) communities. A clear majority of the shows the importance of country percent), of origin along in accounting Figure with 9 the Differences U.S.-born for the variability in Asians Self-Rated Korean. 6The (53 in report Health self-rated percent), is available Status at said among that U.S.-born their current Asians and state in Houston s of health Four was Largest immigrants from India, Pakistan, and 53 tus among Houston s varied Asian-American either Figure excellent 9 Differences communities. Asian or Bangladesh very Immigrant (61 in Self-Rated good. A Communities This percent) clear Health was majority Status true (1-13 and for from among of just the KIHAS, the Philippines U.S.-born 39 percent Combined) (52 Asians of and the in immigrants Houston s Four from 12 5 Kinder Institute for Urban Research 47 Largest China, percent), along with the U.S.-born Asians (53 percent), said that their current state of health was

13 Figure 8 Differences in Self-Rated Health Status in Five Ethnic Communities (1-13 KIHAS, Combined) 6 55 Figure shows the importance of country 46 of origin Many independent studies confirm that Anglos in accounting for the variability in self-rated health 41 generally experience the best health and the lowest age-adjusted status among Houston s varied Asian-American mortality over the life course, communities. A 29 clear majority of the immigrants whereas 28 blacks and Hispanics have much higher from India, Pakistan, and Bangladesh (61 percent) levels of infant mortality and obesity Are there and from the Philippines 17 (52 percent), along with forces beyond differences in socioeconomic 15 the U.S.-born Asians (53 percent), said that their conditions that account for "Excellent" the racial or and "Very ethnic Good" current state of health was either excellent or very disparities in self-reported health status? The good. This was true for just 39 percent of the immigrants from China, Taiwan, and Hong Kong, and well differ from those of Anglos "Fair" for "Poor" reasons hav- daily lived experiences of blacks "Good" and Latinos may 34 percent of US-Born the Vietnamese All Asians immigrants. US-Born These US-Born ing to do not Hispanic only with the well-known contrasts contrasts in self-reported Anglos health (N=1,315) closely parallel Hispanics the Blacks in education Immigrants and income, but also with the vast differences among (N=5,916) the Asian communities (N=3,572) in their (N=5,818) discrepancies (N=2,659) in family assets, the experience of overall levels of income and education, and hence in persistent discrimination, and the continual high the life circumstances that they experience. ii levels of stress that result from these realities. Figure 9 Differences in Self-Rated Health Status among U.S.-born Asians and in Houston s Four Largest Asian Figure Immigrant 9 - Differences Communities in Self-Rated (1-13 Health Status KIHAS, among Combined) U.S.-born Asians and in Houston s Four Largest Asian Immigrant Communities (1-13 KIHAS, Combined) Indians / Pakistanis (N=292) US-born Asians (N=3) Filipinos (N=62) Chinese / Taiwanese (N=254) Vietnamese (N=226) "Excellent" or "Very Good" "Good" "Fair" or "Poor" Many independent studies confirm that Anglos generally experience the best health and the lowest age-adjusted mortality over the life course, 18 whereas blacks and Hispanics have much higher levels of infant mortality and obesity. 19 Are there forces beyond differences in socioeconomic conditions that account for the racial and ethnic disparities in self-reported health status? The daily lived experiences of blacks and Latinos may well differ from those of Anglos for reasons having to do not only with the well-known contrasts in education and income, but also with the vast discrepancies in family assets, the experience of persistent discrimination, and the continual high levels of stress that result from these realities. ii A comprehensive report on Harris County s Asian communities was released in 12. It presents the findings from three focused surveys conducted by the Kinder Institute in 1995, 2, and 11, reaching more than 5 Asians in each of these three years, with 24 percent of the interviews conducted in Vietnamese, Cantonese, The SHEA Mandarin, Health and Survey Korean. The 13 report is available at Health Disparities 11

14 The Structural Correlates of Self-Rated Table 2 presents the results of the logistic regression, Health measuring the separate associations of these distinct The Structural Correlates of Self-Rated structural Health variables with the odds that Harris County residents will indicate that their current state of The rich data from the KIHAS make possible a health is fair or poor. The rich data from the KIHAS make possible a rigorous statistical examination of the individual rigorous statistical examination of the individual impact on self-reported health of each of these major structural factors, after taking account of the impact on self-reported health of each of these major The regression coefficients in the first column of the effects of all the others. To test these relationships, structural factors, after taking account of the effects table indicate we use the estimated the unweighted increase data (or decrease) from the 13 years of all the others. of pooled To test random these relationships, samples of Harris we use County in residents. the log odds Table of reporting 2 presents fair the or poor results health. of the For logistic the unweighted regression, data from measuring the 13 years the separate of pooled associations age, the of odds these represent distinct the structural increase variables in the proportion their reporting current poor state health of health with each is fair additional or poor. with the odds random samples that Harris of Harris County County residents residents. will indicate that year; Table 2 Logistic Regression of the Structural Correlates on Self-Rated Health Status (1-13 KIHAS, Table 2 - Logistic Combined) Regression iii of the Structural Correlates on Self-Rated Health Status (1-13 KIHAS, Combined) iii Independent Variable (A) Socioeconomic Status (1) Education a Coefficient Self-Reported Fair or Poor Health (KIHAS 1-13) Standard Error Odds Ratio (OR) High School Diploma Some College -.366** College Degree -.863*** (2) Income b $35,51 to $5, -.462*** $5,1 to $75, -.631*** More than $75, -.958*** (B) Demographic Characteristics (3) Ethnicity c Blacks +.423*** Latinos +.8*** Asians (4) Age +.21*** (5) Female Constant -.685*** N 6,792 a Reference group: Less than high school. b Reference group: Less than $35,. c Reference group: Anglos. *p <.5, **p <.1. ***p <.1 (two-tailed tests). 1-OR iii To determine if there were ethnic differences in the relationships with health of the structural variables, we tested the interaction terms and found that none were statistically significant. We also ran the regressions separately for the years 1-7 and 8-13; we found no differences between the two models in the nature and strength of the coefficients for age, gender, iii To income, determine and if ethnicity, there were and ethnic only differences very slight differences in the relationships for education. with We health have of therefore structural combined variables, all 13 we tested years in the the regression interaction models. terms and found that none were statistically significant. We also ran the regressions separately for the years 1-7 and 8-13; we found no differences between the two models in the nature and strength of 12 Kinder the Institute coefficients for Urban for age, Research gender, income, and ethnicity, and only very slight differences for education. We have therefore combined all 13 years in the regression models.

15 for all the other independent variables in the model the coefficient represents the increase or decrease in the odds of reporting poor health compared to the respondents in the designated reference category. Thus, after controlling for the effects of income, age, ethnicity, and gender, the regression model shows that the odds of reporting poor health decrease at each higher level of education (high school diploma, some college, college degree), when respondents with that amount of education are compared to those who have less than a high school education (the referent category). The results indicate that it is only when the levels of schooling reach beyond high school that educational attainment is significantly associated with better health. Just having a high school diploma offers a statistically indistinguishable health advantage over having less than a high school education. In this increasingly global, high-tech, knowledge-based economy, education beyond high school is now a critical determinant of a person s ability to obtain a job that pays more than poverty wages. This new reality is increasingly clear to the general public as well. In the 13 KIHAS, 73 percent of all Harris County residents agreed with the suggestion that, to succeed in today s world, it is necessary to get an education beyond high school. Fewer than one in four believed instead that there are many ways to succeed with no more than a high school diploma. The table indicates that respondents with a college degree have an Odds Ratio (OR) of.422 of reporting their health as only fair or poor. This means that they are 42 percent as likely as the respondents without a high school education, or 58 percent less likely (1- OR) than those in the reference category, to report being in fair or poor health. And, again, this relationship holds even after rigorously controlling for the effects of differences among the respondents in their household incomes, ethnic backgrounds, age, and gender. In sum, educational attainment, at least when it entails education beyond high school, does indeed appear to be an independent factor that in and of itself is significantly associated with a person s self-reported health status. Similarly, each increase in the respondents levels of household income is significantly associated with decreasing likelihoods that they will report being in fair or poor health. Respondents with incomes between $35, and $5, had 37 percent lower odds (1-OR) of reporting poor health compared to those in households making less than $35,. Respondents with incomes above $75, (after controlling for differences in education, ethnicity, and age) were almost 62 percent less likely (1-OR) to report being in poor or fair health. Income, like education, is indeed a separate and significant independent correlate of self-reported health status. Table 2 indicates further this is also the case for ethnicity. Controlling for differences on all the other variables in the model, the African-American respondents were 53 percent more likely than Anglos (the Latinos were 36 percent more likely) to report being in fair or poor health. With all the other variables controlled, Asians are only slightly, and not significantly, more inclined than Anglos to report being in fair or poor health. For reasons other than, or in addition to, the ethnic differences in socioeconomic status, the black and Latino residents of Harris County are significantly more likely than Anglos to give low evaluations of their current state of health. Age, too, remains a significant predictor, with the other structural variables controlled: each one-year increase in the respondents ages, from 18 to 95, raises the odds of their reporting that they are in fair or poor health by an average of 2.2 percent. After considering all these other important factors, men and women have statistically identical odds of reporting poor health. In sum, the statistical analysis confirms that the separate structural forces captured by measures of educational attainment, household income, ethnicity, and age are indeed significantly associated with the health disparities, and each of these critical factors contributes independently to self-reported health status. We will continue to make use of the regression models to control for the effects of age, ethnicity, education, and income, as we consider the individual importance for self-reported health Health Disparities 13

16 of other more subtle factors, such as having health insurance, or engaging in health-related physical activities, or living in areas that are particularly prone to air pollution, or feeling comfortable and engaged in one s neighborhood. how much physical activity that person gets (with an average score of 8.6) or the amount of stress a person experiences (at 8.3). They viewed structural inequalities, such as a person s level of income (7.2) or a person s experience with racial or ethnic discrimination (6.2) as having less impact on that person s health. Not surprisingly, African Americans (at 63 percent) were more likely than Latinos (53 percent), who were more likely than Anglos (45 percent), to assert that living with racial discrimination has strong effects on a person s health, giving it a rating of 7 or more on the ten-point scale. Although (as we shall see) individually-based risk factors such as diet and physical activity may well contribute to health outcomes, the structural forces Age, measured too, remains Table a significant 2 are generally predictor, considered with the by other structural variables controlled: each oneyear health increase scholars in the to be respondents by far the more ages, from pervasive 18 to and 95, raises the odds of their reporting that they are critical in fair forces poor in accounting health by an for average individual of 2.2 differences in self-reported men and women health have status. statistically 21 As the identical findings odds of reporting poor health. percent. After considering all these other important factors, from the Houston surveys and numerous other Do the individual and behavior factors that the In sum, the statistical analysis confirms that the separate structural forces captured by measures of studies confirm, the sharp disparities in health that public identifies really contribute on their own to educational attainment, household income, ethnicity, and age are indeed significantly associated exist in this country primarily derive from the vast health outcomes after all the more powerful structural forces with the health disparities, and each of these critical factors contributes independently to selfreported social and health economic status. inequalities We will continue that are to make so deeply use of the regression models are taken to control into account? for the Aside from age, effects ingrained of age, in American ethnicity, education, society. 22 The and general income, public, as we consider socioeconomic the individual status, importance and institutionalized for selfreported however, health is more of other likely more to view subtle a person s factors, health such as as having inequalities, health insurance, is self-rated or engaging health in status also affected racial health-related primarily a matter physical of individual activities, or volition living in and areas specific that are particularly by more immediate prone to air and pollution, more individualized or experiences, such as having health insurance, partici- feeling health-related comfortable behaviors. and engaged in one s neighborhood. pating in healthy behaviors, or benefiting from the Although The respondents (as we shall in the see) HAHS individually-based were asked how risk much factors such health-related as diet and physical aspects of activity the surrounding may social and well they contribute thought a to person s health outcomes, health is determined the structural by forces measured natural in environment? Table 2 are generally We turn next to an assessment and of critical the importance forces in accounting of factors for such as these in considered each of several by health different scholars factors, be using by far a scale the more from pervasive individual 1 ( very little differences effect ) in to self-reported ( very strong health effect ). status. 21 As As the determining findings from health the Houston outcomes, surveys as we seek to assess and indicated numerous in Figure other studies, the confirm, survey participants the sharp disparities attributed the most powerful effects to individual and quality health care and health-promoting resources in how health well that this exist city in is this meeting country its obligation to provide primarily derive from the vast social and economic inequalities that are so deeply ingrained in American society. 22 The general public, however, is more likely to view a person s health as behavioral factors, such as what a person eats and to all its residents. primarily a matter of individual volition and specific health-related behaviors. Figure The - The Perceived Perceived Determinants Determinants of a Person s of a Person s Health, Health, Average Ratings Average (HAHS) Ratings (HAHS) EFFECT ON HEALTH (SCALE FROM 1 TO ) Diet and Physical Activity Amount of Stress How much do you think a person's health is determined by each of these factors? Using a scale from 1 to, where 1 means it has 'very little effect' and means it has a 'very strong effect,' how important is each of the following? Amount of Air and Water Social Support Pollution Level of Income Genetic Makeup 6.16 Racial Discrimination The 14 respondents Kinder Institute in the for HAHS Urban were Research asked how much they thought a person s health is determined by each of several different factors, using a scale from 1 ( very little effect ) to ( very strong effect ). As indicated in Figure, the survey participants attributed the most powerful effects to

17 ACCESS TO HEALTH CARE Health Insurance Coverage The respondents in the HAHS were asked if they were currently covered by any type of health insurance or health care plan. One-fifth (19 percent) said that they had no health insurance coverage at all. This figure somewhat underreports the actual number of the uninsured. The 9-11 ACS 3-year estimates from the U.S. Census indicated that 27 percent of all Harris County residents and 34 percent of those aged 18 to 64 were uninsured. Nationally, 15.4 percent of the total U.S. population and 21 percent of the population aged 18 to 64 were uninsured, meaning that there were some 48 million uninsured Americans in 12, according to the Census report. 23 between $37,51 and $62,5 were uninsured. In addition, close to 34 percent of the respondents who did not have a high school diploma said they had no health insurance, compared to 23 percent of high school graduates, 14 percent of those who had some college education, and just 9 percent of college graduates. When asked about their source of health insurance or health care coverage, 51 percent of the respondents who were insured said they were covered through an employer, and 21 percent through a government assistance program such as Medicare or Medicaid. Another 6 percent purchased their plan directly from a private insurance company, and 1 percent through a military or veteran program. Fewer than one percent The likelihood of having some kind of health insurance plan differs significantly by ethnic background County Hospital District s Gold Card (now known as of the respondents reported that they used the Harris and increases with age, income, and education. In the HarrisHealth Program ) as their health care plan; the HAHS, well over one-quarter (29 percent) of the the plan enables these individuals to receive limited respondents under age said they were uninsured, subsidized medical care at specified locations, but through a government assistance program such as Medicare or Medicaid. Another 6 percent as did 21 percent through of those aged to 44. The proportion dropped purchased to or 18 veteran percent their program. at plan ages directly Fewer 45 to than from 59, and one a private to percent insurance of the respondents company, and reported 1 percent that they through used a the military Harris technically they are not insured. purchased a government their plan directly assistance from program a private such insurance as Medicare company, or Medicaid. and 1 percent Another through 6 percent a military percent among or County veteran the respondents Hospital program. District s Fewer who were than Gold 6 one Card years percent (now old of known the Figure respondents as the 11 makes HarrisHealth reported use of that the Program ) they KIHAS used as data their Harris to health show the and older. County care plan; Hospital the plan District s enables Gold these Card individuals (now known to distribution receive as the limited HarrisHealth of subsidized health insurance Program ) medical coverage as care their at specified health in Houston s care locations, plan; the but plan technically enables they these are individuals not insured. to receive five major limited ethnic subsidized communities, medical among care at specified all respondents aged 18 to 64. Remarkably, almost half (48 locations, but technically they are not insured. Health insurance coverage also declines at the lower Figure 11 makes use of the KIHAS data to show ends of the socioeconomic scale. Nearly 28 percent percent) the distribution of the Hispanic of health immigrants insurance coverage in Harris in County Figure Houston s 11 makes five major use of ethnic the KIHAS communities, data to show among the all distribution respondents of aged health 18 insurance to 64. Remarkably, coverage in of the survey Houston s respondents almost half five (48 whose major percent) household ethnic of the communities, Hispanic incomes immigrants among said all they respondents in Harris were County uninsured. aged 18 said to they This 64. Remarkably, were was uninsured. the case for 28 were less than almost This $37,5 was half said the (48 case they percent) for did 28 of not percent the have Hispanic of any the immigrants U.S.-born percent Hispanics, in Harris of the County 21 U.S.-born percent said of they Hispanics, were U.S.-born uninsured. 21 percent blacks, 17 of the type of health This insurance percent was of the Asians, or case health for and 28 care 13 percent percent plan. of Almost of the native-born U.S.-born U.S.-born Hispanics, Anglos. blacks, 21 percent 17 percent of the U.S.-born of Asians, blacks, and 1317 percent percent of percent those who of Asians, reported and household 13 percent of incomes native-born of Anglos. native-born Anglos. Figure 11 Health Insurance Coverage by Ethnicity among Respondents Aged 18 to 64 (1 13 Figure KIHAS, 11 Combined) Health Insurance Coverage by Ethnicity among Respondents Aged 18 to 64 (1 13 Figure 11 - Health KIHAS, Insurance Combined) Coverage by Ethnicity among Respondents Aged 18 to 64 (1-13 KIHAS, Combined) 87 Do you and your family currently Do have you any and health your insurance? family currently have any health insurance? Insured 13 Insured Uninsured Uninsured US Born Anglos All Asians US Born Blacks US Born Hispanic US Born (N=3,761) Anglos All (N=636) Asians US Born (N=3,959) Blacks US Born Hispanics Hispanic Immigrants (N=3,761) (N=636) (N=3,959) Hispanics (N=2,791) Immigrants (N=1,931) (N=2,791) (N=1,931) Table 3 uses the KIHAS data and the logistic regression model to test the separate impact of Table having 3 uses health the insurance KIHAS data on self-reported and the logistic health regression status, when model the to effects test the of separate all the key impact structural of having health insurance self-reported health status, when the effects of all the key Health structural Disparities factors (age, ethnicity, education, and income) have been entered into the model. When all four of 15 factors the structural (age, ethnicity, factors are education, controlled, and is income) the sheer have fact been that entered the respondent into the is model. or is not When insured all four of the significantly structural factors associated are controlled, with self-reported is the sheer health fact status? that the The respondent answer is is yes. or is The not insured analyses make it significantly clear that having associated health with insurance self-reported is indeed health independently status? The correlated answer is with yes. reporting The analyses better make health. it

18 Table 3 uses the KIHAS data and the logistic regression model to test the separate impact of having health insurance on self-reported health status, when the effects of all the key structural factors (age, ethnicity, education, and income) have been entered into the model. When all four of the structural factors are controlled, is the sheer fact that the respondent is or is not insured significantly associated with self-reported health status? The answer is yes. The analyses make it clear that having health insurance is indeed independently correlated with reporting better health. Across all categories of education, income, ethnicity, and age, the respondents who said they were currently covered by some type of health insurance or health care plan had on average 38 percent (1-OR=-.381) lower odds of reporting that their current state of health was fair or poor, compared to those who were uninsured. This suggests that simply expanding area residents access to health insurance may well have a significant positive impact on their overall health status, although the effects of differences in education, income, and ethnicity, not surprisingly, are generally even more important than having insurance in accounting for the health disparities. The Congressional Budget Office projects that the Affordable Care Act should result in its first year in 14 million previously uninsured Americans being able to obtain health insurance. The data reported here suggest that this expansion in access to health insurance coverage may well have positive effects on the overall health of residents in Harris County and across America. Unfortunately, the benefits of the new act will be less evident in Texas than elsewhere in the nation, since the state government so far has decided not to accept the federal funds that would expand Medicaid to the working poor, leaving a large gap in insurance coverage. Table 3 - Logistic Regression of Health Insurance Coverage on Self-Rated Health Status, Controlling for Table 3 Logistic Regression of Health Insurance Coverage on Self-Rated Health Status, Controlling for the Structural Variables the Structural (1-13 Variables KIHAS, (1-13 Combined) KIHAS, Combined) Self-Reported Fair or Poor Health (KIHAS 1-13) Independent Variable B SE OR 1-OR (A) Socioeconomic Status (1) Education a High School Diploma Some College -.256* College Degree -.781*** (2) Income b $35,51 to $5, -.383*** $5,1 to $75, -.524*** More than $75, -.777*** (B) Demographic Characteristics (3) Ethnicity c Black +.411*** Latino +.216** Asian (4) Age +.23*** (5) Female (C) Having Health Insurance d -.48*** Constant -.425** N 5,435 a Reference group: Less than high school. b Reference group: Less than $35,. c Reference group: Anglos. d The question about health insurance coverage was included in the 1, 3-12 surveys. *p <.5, **p <.1. ***p <.1 (two-tailed tests). 16 Kinder Institute for Urban Research The Congressional Budget Office projects that the Affordable Care Act should result in its first year in 14 million previously uninsured Americans being able to obtain health insurance. The data reported here suggest that this expansion in access to health insurance coverage may well have

19 Immigration and health insurance among Hispanics. We have seen (in Figure 8) that, among the participants in the 13 years of the KIHAS, the Hispanic immigrants (at percent) were more likely than the U.S.-born Latinos (21 percent) to report their health as fair or poor. The immigrants were also much more likely than the U.S.-born Latinos (by 47 to 27 percent) to say they had no health insurance. Table 4 tests the relationship between immigrant status and self-reported health among the Latino respondents, first after controlling for age, gender, education, and income, and then with health insurance coverage also entered into the equations. The first regression model indicates that Latino immigrants, despite their differences from U.S.-born Latinos in age, education, and income, do indeed (by 25 percent) have higher odds than their U.S.-born counterparts of reporting their current health status as fair or poor. The second regression reveals that this relationship is no longer statistically significant when health insurance coverage is entered into the model. The association between being an immigrant and reporting low levels of personal health is largely explained, the data indicate, by the fact that the Latino immigrants are so much less likely to be covered by health insurance. Once again, these analyses reinforce the importance of increasing access to affordable health insurance across the entire Houston community, if the goal is to improve the overall health of area residents. reported health among the Latino respondents, first after controlling for age, gender, education, and income, and then with health insurance coverage also entered into the equations. The first regression model indicates that Latino immigrants, despite their differences from U.S.- born Latinos in age, education, and income, do indeed (by 25 percent) have higher odds than their U.S.-born counterparts of reporting their current health status as fair or poor. The second regression reveals that this relationship is no longer statistically significant when health insurance coverage is entered into the model. The association between being an immigrant and reporting low levels of personal health is largely explained, the data indicate, by the fact that the Latino immigrants are so much less likely to be covered by health insurance. Once again, these analyses reinforce the importance of increasing access to affordable health insurance across the entire Houston community, if the goal is to improve the overall health of area residents. Table 4 - Logistic Regression of Immigration Status and Insurance Coverage on Self-Reported Health Table 4 Logistic Regression of Immigration Status and Insurance Coverage on Self-Reported Health Status Status among among Latinos, Latinos, Controlling for the Structural Variables Variables (1-13 (1-13 KIHAS, Combined) KIHAS, Combined) iv iv Self-Reported Fair or Poor Health (Latinos only) (KIHAS 1-13) (KIHAS 1-13) Independent Variable B SE OR 1-OR B SE OR 1-OR (A) Socioeconomic Status (1) Education a High School Diploma -.7* ** Some College -.419*** ** College Degree -.815*** *** (2) Income b $35,51 to $5, -.35*** * $5,1 to $75, -.788*** *** More than $75, -.929*** *** (B) Demographic Characteristics (3) Age +.24*** *** (4) Female (5) Latino Immigrants c +.224** (C) Having Health Insurance d -.284** Constant -1.76*** *** N 4,185 3,472 a Reference group: Less than high school. b Reference group: Less than $35,. c Reference group: U.S.-born Latinos. d The question about health insurance coverage was included in the 1, 3-12 surveys. *p <.5, **p <.1. ***p <.1 (two-tailed tests). iv To determine iv To determine if the if the relationships of the of the structural variables with self-reported health health differ differ among among the Latinos the Latinos by by their their immigrant status, we tested the interaction terms and found that none were statistically significant, so they are immigrant status, we tested the interaction terms and found that none were so they are not included in the final models. not included in the final models. Kinder Institute for Urban Research Health Disparities 17

20 standards quality of for health patient care care available and medical to you in research. the Houston Despite area such as resources, excellent, too go have one-third difficulty (34 percent) accessing rated quality the health care care. they Respondents could access in the as fair HAHS or poo we quality of health care available to you in the Houston area as excellent, go one-third The same (34 question percent) was rated asked the in health the KIHAS care they on two could different access occasions. fair or poo In negative ratings to the quality of health care available to them in the Houst The percent same in question 9. The was data asked are consistent in the KIHAS in suggesting on two different that more occasions. than one-t In negative rate the health ratings care to they quality can obtain of health as only care fair available or poor, to and them that in there Houst has b Access to Quality Health Care in Houston percent over the in course 9. of The the data past are two consistent decades in in their suggesting assessments that more of the than quality one-t rate the health care they can obtain as only fair or poor, and that there has b Houston is home to the largest medical complex in over dents said that the quality of care available to them Figure the 12 course shows of the the relationship past two decades between in household their assessments incomes of and the responde quality the world and boasts some of the highest standards in the Houston area was excellent or good, compared quality of health care available to them in the Houston area. Over half (51 for patient care and medical research. Despite such Figure respondents to just 1238 shows percent in the the 12 of relationship the HAHS uninsured. who between Fully reported 41 household percent annual incomes and of less responde than $ resources, too many area residents have difficulty ac- quality of the of uninsured health care rated available the quality to them of care in was they fair Houston could or poor. area. This Over was half true (51 for cessing quality health care. Respondents in the HAHS respondents with access household in Houston in the incomes 12 as fair HAHS of or more poor. who than reported $,. annual incomes of less than $ were asked to rate the quality of health care available quality of health care available to them was fair or poor. This was true for to you in the Houston area as excellent, good, fair, with household incomes of more than $,. or poor. Over one-third (34 percent) rated the health Figure Figure Respondents - Ratings Ratings of the of Quality the Quality of Health Care Available to The by care they could access as fair or poor. of Household Health Care Income Available (HAHS) to Them in the Houston Figure 12 Respondents Ratings of the Quality of Health Care Available to The Area, by Household Income (HAHS) by Household Income (HAHS) The same question was asked in the KIHAS on two What about the quality of health care available to different occasions. In 1992, 37 percent gave negative you in the Houston area? Would you say: excellent, What good, fair about or poor? the quality of health care available to ratings to the quality of health care available to them 7 you in the Houston area? Would you say: excellent, in the Houston area, as did 38 percent in 9. The 6 good, fair 51 or poor? data are consistent in suggesting that more than onethird of all area residents rate the health care they can obtain as only fair or poor, and that there has been no improvement over the course of the past two decades in their assessments of the quality of care they can Having health insurance, not surprisingly, is also strongly correlated with the respondents r access of the quality of health care available to them in the Houston area. As shown in Figure 13, th insured and uninsured respondents differ dramatically on this question. More than 7 percen Figure 12 shows the relationship between household the insured respondents Less than said that $37,51 the quality - of $62,51 care available - More to them than in the Houston area wa incomes and respondents ratings of the quality of excellent or good, $37,5 compared to just 62,5 38 percent of, the uninsured. $, Having health insurance, not surprisingly, is also strongly correlated with the respondents ratings Fully 41 percent of the unin rated the quality Less of care thanthey could $37,51 access - in Houston $62,51 as - fair More poor. than health care available of Having to the them quality health in of the health insurance, Houston care available not area. to them in the Houston "Excellent" area. As shown "Good" in Figure 13, the "Fair" or "Poor" surprisingly, is also strongly $37,5 correlated with 62,5 the respondents,ratings $, Having Over health half insurance, (51 percent) not insured of surprisingly, the of quality the and respondents uninsured of is also health strongly respondents care in available the correlated 12 differ to with dramatically This them the difference in respondents this the Houston ratings question. More than 7 percent of in access area. to quality As shown care in is surely Figure one 13, of the the reasons why having health insur Having of the quality HAHS health of who insurance, health care reported not available the insured insured surprisingly, annual and to respondents them incomes uninsured is also the strongly of respondents Houston said that less than correlated area. the quality differ As with is shown of care dramatically so strongly the in respondents Figure available "Excellent" related 13, this to the to them in the ratings question. higher ratings More "Good" Houston area was of than personal 7 percent health. of "Fair" The logistic or "Poor" regression model (not insured and uninsured respondents excellent of the $37,5 quality of said health that care the available the quality insured differ or good, to of respondents dramatically compared them health in the care Houston said on this to just available that question. 38 percent area. the quality As shown) More People of of confirms care than the in in Figure available 7 higher uninsured. percent that 13, having the income to of Fully them health in brackets 41 percent the insurance, Houston can of the even afford uninsured area after was better controlling insurance for the coverage, effects of incom tran the insured respondents said rated that the quality of of care available they could to them access insured and uninsured respondents differ dramatically on this question. education, in in the Houston More than ethnicity, as area fair 7 percent and was or age, poor. of is indeed significantly associated with the respondents ratings to them was fair or excellent poor. This or was good, true compared for just to onefifth quality just 38 percent clinics of and the uninsured. hospitals, Fully and 41 access percent to of a wider the uninsured array of medical specialists. Th excellent or good, compared to just 38 percent of the uninsured. Fully the insured respondents said rated that the quality of of care available they could to them access quality 41 percent in the Houston of health of the as area care uninsured People fair was available or poor. to them in the Houston area. rated the excellent or of good, those of care compared with they household This could access to just difference 38 percent incomes Houston of access the of more as fair uninsured. to quality than or poor. Fully care is income in 41 surely Figure percent one is higher also of 13 of the - the reflected income Respondents uninsured reasons why in brackets ethnic having differences: can afford better Ratings health of the insurance 81 percent insurance of the coverage, Anglo resp tran Quality rated the $,. quality of care they is This could so strongly difference access in related in Houston to higher access as to fair ratings quality or poor. of personal clinics said they and health. had hospitals, access The logistic to and excellent regression access to model a or wider good (not array health of medical care, but specialists. this was the Thc care Figure is surely of 13 Health one Respondents of Care the reasons Available Ratings why having to Quality Them health of in Health the insurance This difference in access to quality shown) care confirms is surely that one having of the health reasons insurance, why income of having blacks even is health after and also insurance 52 controlling reflected percent for in of ethnic the Hispanics. effects differences: of income, Houston Care 81 Available percent to of Them the in Anglo the Houston resp is so strongly related to higher is so ratings strongly of personal related to health. higher The ratings logistic of by regression personal Insurance Area, health. by model Coverage Insurance The (not (HAHS) logistic Coverage regression (HAHS) model (not This difference People in in higher access to income quality education, shown) brackets care ethnicity, confirms is surely can that one and afford having of age, the is better health reasons indeed insurance ethnicity, coverage, and age, transportation education, insurance, why significantly said having they even health had associated access after insurance with to excellent the respondents or good ratings of health the care, but this was the c shown) confirms that having health insurance, even after controlling for effects of income, controlling for the effects of income, is so strongly related to higher quality ratings of of health personal care health. available The to logistic them in regression the Houston model area. (not education, is indeed ethnicity, significantly to better and associated age, clinics is indeed and with the significantly of Having blacks health and respondents 45 associated 52 insurance, percent ratings of with the the of not respondents Hispanics. surprisingly, ratings is also of the strongly correlated with t shown) confirms that having health insurance, even after controlling for the effects of income, 41 quality quality of health care available to them in the Houston area. education, hospitals, of health care ethnicity, and available and access age, is to indeed a them wider in the significantly array Houston of medical area. of the quality of health care available to them in the Houston area. As show associated specialists. of health The care relationship available by Insurance to them with income Coverage the Houston is (HAHS) also area. reflected of the 35 quality 31 of health care available to them in the Houston area. As show with the respondents Figure 13 Respondents Ratings of the Quality Having insured of Health health and Care uninsured ratings insurance, Available of the respondents to not Them surprisingly, in the differ Houston dramatically is Area, also strongly this correlated question. with Mot quality Figure in 13 ethnic Respondents differences: Ratings Figure 81 of percent the 13 Quality Respondents of of the Health Anglo Ratings Care respondents Available of the Quality to Them of Health the Houston Care Available Area, to Them in the Houston Area, 28 by Insurance by Insurance Coverage (HAHS) insured and uninsured respondents differ dramatically on this question. Mo Figure Coverage 13 Respondents in the (HAHS) Ratings said of they 45 Quality had access of Health to Care excellent Available to Them in the Houston Area, 25 by Insurance 21 or good Coverage health (HAHS) care, but this was the case 22 Kinder Institute for Urban 41 Research for just percent of blacks and 52 percent of Hispanics Kinder Institute for Urban Research "Excellen Having health insurance, not surprisingly, 31 is also "Fair" 35 strongly correlated with the respondents ratings of "Poor" 25 the quality of health care 25 available to them in 28 the Yes, Insured Houston area. As shown in Figure 13, the insured and "Excellent" No, Not insured "Are you currently covered by any type of health insurance or health care plan?" uninsured respondents differ dramatically on this "Excellent" "Excellent" "Good" question. More than 7 percent of the insured respon- "Excellent" "Good" "Good" "Fair" A second regression (also not shown) indicates that the association between the respondents "Good" "Fair" "Fair" "Poor" 5 5 assessments of the quality of health care available to them and their self-reported health statu fully explained by "Fair" the relationships of each of these "Poor" two variables with socioeconomic status 5 Yes, Insured "Poor" No, Not insured residents with higher levels of age, education and income, and Anglos and Asians more than 18 Kinder Institute Yes, Insured for Urban "Are Research you currently "Poor" Yes, Insured No, covered Not insured by any type of health insurance or health care plan?" No, Not insured blacks or Latinos, are better able to access the kind of high-quality health care that results in "Are you currently Yes, Insured covered by any "Are type you of health currently insurance No, covered Not or insured by health any health, type care of plan?" and health they insurance are able or to health do so, care the plan?" data suggest, because they are also more likely to be c "Are you currently covered A second by any regression type of health (also insurance not shown) or health indicates by care health plan?" that insurance. the association between the respondents A second regression (also not assessments shown) indicates of the that quality the of association health care between available the respondents to them and their self-reported health status is PERCENT PERCENT OF OF RESPONDENTS PERCENT PERCENT OF OF RESPONDENTS PERCENT OF RESPONDENTS "Good"