© 2006 American Public Health Association DOI: 10.2105/AJPH.2004.052076
The authors are with the Sinai Urban Health Institute, Chicago, Ill. Steven Whitman is also with the Rosalind Franklin University of Medicine and Science, North Chicago. Correspondence: Requests for reprints should be sent to Ami M. Shah, MPH, Sinai Urban Health Institute, Mount Sinai Hospital, 1500 S California Ave, Room K-439, Chicago, IL 60608 (e-mail: suhi{at}sinai.org).
Objectives. Although local-level chronic disease and risk factor data are not typically available, they are valuable for guiding public health interventions and policies. To present a case for disaggregated community-level health data, we conducted a study exploring the relevance of such data to research on health disparities. Methods. We designed a population-based health survey to gather information on many health measures, 13 of which are presented here. Interviews were conducted with 1699 adults (1875 years) in 6 Chicago community areas between September 2002 and April 2003. Results. Statistically significant variations in health measures were found between the 6 communities themselves (108 of 195 pairwise comparisons were significant) and between the communities and Chicago as a whole (35 of 54 comparisons were significant). Conclusions. The local-level variations in health revealed in this study emphasize that geographic and racial/ethnic health disparities are still prominent in Chicago and shed light on the limitations of existing city- and regional-level data.
Until recently, local-level public health data have not been routinely collected and thus are not readily avalable. Existing data that can be geocoded to the county, city, or community level are derived from traditional surveillance systems (e.g., vital records and communicable disease registries), and provide information on small-area trends and variances in mortality,2,3 measures related to birth outcomes,4 and infectious diseases.5 However, they offer little local information on the determinants of morbidity and mortality.6,7 Such information is derived from health surveys, often conducted at the national (e.g., National Health Interview Survey [NHIS]) and state (e.g., Behavioral Risk Factor Surveillance System [BRFSS]) levels. Although these data are essential in terms of national public health policies and health monitoring, they are typically not available at the local level. Social epidemiologists and public health practitioners have responded to this growing need for local health data.810 For instance, Northridge et al., gathering data at the local level, found that the smoking prevalence rate in Harlem (42%) was notably different from the rate in New York State as a whole (25%) and the rate among non-Hispanic Blacks residing in the state (25%).11 Others have conducted health surveys designed to gather these important data at the county (e.g., Los Angeles County Health Survey12 and SeattleKing County Survey13), city (e.g., New York City Health and Nutrition Examination Survey14), and community or neighborhood (e.g., New York City health disparities report15 and New York City Community Health Survey16) levels. Even the Centers for Disease Control and Prevention, which conducts state-based health surveys (i.e., BRFSS surveys), has recognized the importance of local-level data, designing the Selected Metropolitan/Micropolitan Area Risk Trends Project to mathematically estimate health-related prevalence proportions in smaller geographic areas.17,18 As urban settings become increasingly diverse and certain populations are disproportionately affected by disease,19,20 variations in the health status of these smaller geographic areas may be substantial,2 and such variations must be considered if true advances in disease prevention and control are to be achieved.810,2123 To explore such differences, we conducted a household survey in 6 diverse communities of Chicago to examine health profile differences.
Community Areas Chicago is divided into 77 officially designated community areas that are often used as a basis for describing the citys health conditions, delivering health care services, and implementing community-based interventions.24 Figure 1
We selected these community areas for various social and political reasons, but our primary interest was their role in shaping local policies and developing community interventions. We selected North Lawndale and South Lawndale because we are affiliated with the Sinai Health System, which serves these communities (Figure 1 The contiguous communities of West Town and Humboldt Park, located west of downtown Chicago, are interesting in an epidemiological sense in that they are both facing transitions related to urban development. In addition, they are home to energetic and dedicated community-based organizations that were eager to use the data gathered here to implement changes. The population of West Town is one-half White, one-quarter Mexican, and one-quarter Puerto Rican, whereas that of Humboldt Park is one-half Black, one-quarter Mexican, and one-quarter Puerto Rican. Finally, we selected Roseland, a predominantly Black community on the south side, and Norwood Park, a predominantly White community on the north side, because they represented 2 geographically and racially disparate communities. According to 2000 US census data, median household incomes in the 6 study communities ranged from $18000 to $53 000, compared with $39000 for Chicago and $42 000 for the United States as a whole. Overall, the communities were reflective of different geographic areas but were not selected to be representative of the city of Chicago.
Sample
Survey Instrument A certified translator translated the instrument and all supporting materials into Spanish. When available, translated questions from existing health surveys were used. Appropriate modifications of the Spanish instrument were made as a result of cognitive interviews in Spanish and pretest interviews conducted with native Spanish speakers to ensure accurate comprehension of the translated questions. After several iterations over a 5-month period, the final adult instrument was reviewed and approved by the survey design committee and a questionnaire review committee from the Survey Research Laboratory at the University of Illinois at Chicago, the organization subcontracted to implement the survey.
Data Collection Community leaders from the survey design committee sent an advance letter to households selected for the survey. Interviewers made at least 12 attempts to screen and interview the randomly selected adult from each household at different times of the day and different days of the week. Most (85%) of the interviews, which were approximately 1 hour in duration, were conducted during evening and weekend hours. Interviewees received a health information packet (in Spanish or English) along with $40 for their time. The survey was administered via computer-assisted personal interviewing techniques to reduce the potential for errors related to data entry or skip patterns. Ten percent of each interviewers work was validated at random for quality assurance purposes. The goal of conducting at least 300 face-to-face interviews in each community area was met in 5 of the 6 communities. Only 190 interviews were completed in Norwood Park, the predominantly White community area with the highest median household income.
Response Rate The overall response rate, based on a conservative calculation procedure outlined by the American Association for Public Opinion Research,28 was 43.2%. In this procedure, all originally sampled buildings and households were included in the denominator.29 That is, unoccupied housing, households that no longer existed, and households where interviewers were not able to locate residents were included, in addition to individuals who refused to participate.
Measures Health conditions. We asked respondents whether they had ever been diagnosed with high blood pressure, arthritis, asthma, depression, or diabetes by a doctor, nurse, or other health professional. In addition, as a means of determining the prevalence of obesity within each community, we used individuals self-reported height and weight to calculate their body mass index. Health behaviors. We also asked about physical activity and smoking. Respondents were asked how many times a week they engaged in moderate activities for at least 30 minutes at a time. We calculated the percentages who engaged in such activities at least 5 times a week (in accord with the guidelines of the Centers for Disease Control and Prevention). Consistent with the Chicago BRFSS survey, we defined current smokers as those who responded yes to the questions "Have you smoked at least 100 cigarettes in your entire life?" and "Do you currently smoke cigarettes?" Health care access. We asked a series of questions designed to determine health care coverage and access to health care services. Respondents were asked whether they currently had any type of health insurance or medical coverage. Also, they were asked whether, at any point during the preceding 12 months, they needed but did not obtain dental care because they could not afford it. They were asked the same question about prescription medications. Both of these latter questions were identical to questions asked in the NHIS. In addition, we asked about cancer screening. Women were asked "Have you ever had a mammogram or breast x-ray?" and "How long has it been since you had your last mammogram?" We analyzed the proportion of women age 40 years or older who had a mammogram in the past year. Also, all respondents were asked "Have you ever had a sigmoidoscopy or colonoscopy?" and "How long ago did you have one of these tests?" We assessed the percentages of adults 50 years or older who had undergone one of these tests.
Statistical Analysis
In addition, we compared prevalence rates for each community area measure with rates for Chicago as a whole and evaluated statistical significance at the .05
Table 2
In a similar manner, and consistent with data on insurance coverage, Norwood Park residents were more likely than residents of other communities to obtain needed dental care and prescription medicines. Only 9% of these individuals, compared with 34% in West Town and 33% in Humboldt Park, had not obtained needed dental care in the previous 12 months because they could not afford it. Of the 15 pairwise comparisons made for this measure, 5 were statistically significant. Similarly, 4% of Norwood Park residents, compared with 24% of North Lawndale and 23% of Humboldt Park residents, had not obtained needed prescription medicines in the previous 12 months because they could not afford them. In this case, 9 of 15 pairwise comparisons were significant. Chicago data were not available for either of these variables. Of the total of 195 tests (15 pairwise comparisons for each of the 13 measures) examining differences between measures, 108 were statistically significant (10 would have expected by chance alone from the unadjusted significance levels used). Finally, of the 54 tests (9 measures with available Chicago data for each of the 6 community areas) involving comparisons with Chicago data, 35 were significant (3 would have been expected by chance).
The overarching question addressed in this analysis was whether there were substantial variations in the health measures assessed between the community areas themselves and between the community areas and Chicago as a whole. Our data indicate that considerable variations existed in both instances. However, it should be noted that our analyses were exploratory and that the sample size was not selected to detect a particular effect size. As such, it is possible that any differences we have described here as statistically significant may not be meaningful or important, but it is also possible that meaningful or important differences did not reach the level of statistical significance. The variations identified demonstrate that existing national and even state surveys may not reflect the health conditions of local (often diverse) communities and suggest that available Chicago data may be inadequate in terms of representing the health of the citys 77 community areas. Previous analyses of vital statistics and communicable disease registry data have revealed considerable variations in health among these Chicago communities as well.3 Our survey data supplement such information and further identify substantial differences in the current status and determinants of health among these populations. For instance, North Lawndale and South Lawndale are adjacent to one another, yet they have very different health profiles. To consider just 1 measure, 39% of the former communitys residents are smokers, compared with only 20% of those of the latter community. If data are examined in an aggregated fashion, contextual differences in the demographic and health profiles of specific communities will not be identified, leading to difficulties in identifying and mounting effective community-based public health and public policy programs. Another example of the importance of community-level data can be found in comparisons of North Lawndale and Roseland. Although both are composed virtually entirely of African American residents, Roseland has a much higher median household income level ($38000), one that is similar to Chicago and national levels. North Lawndales median household income ($18000), in contrast, makes it one of the poorest of Chicagos 77 community areas. Despite this substantial economic difference, Roselands residents exhibited statistically significant advantages on only 4 of the 13 health measures assessed in this study (asthma, depression, insurance, and access to prescription medicines). These similarities, as opposed to the much larger differences that one might expect on the basis of the 2 communities median household incomes, raise important questions about the relation between race and class.3135 Again, such a provocative finding can be obtained only through disaggregation of data at the community level. Although it is one of the largest cities in the United States and its population is diverse, Chicagolabeled in a seminal study as "hypersegregated"36 (i.e., segregated on many dimensions simultaneously)has proven to be an ideal setting for small area studies. A strength of this study is that some of the community areas assessed were homogeneous, lending valuable information to assessments of racial and ethnic health disparities. In fact, recent reports37,38 based on health status indicators drawn solely from vital records and communicable disease registries have demonstrated substantial and even increasing BlackWhite disparities at the city level in Chicago. Our study adds to a more general picture of the citys health conditions in that we examined disparities at the community area level and analyzed health measures not available in existing databases. The kinds of disaggregated data used in our investigation are necessary to fully appreciate and ultimately remedy disparities in communities such as those assessed here. Although it is not surprising that individuals of lower socioeconomic status fare worse than those in better financial situations in terms of health measures, the extent of such inequities has rarely been documented. Researchers can use the present local-level data to continue to investigate how peoples place of residence may affect their health,39,40 how socioeconomic factors correlate with health risk factors,41 and how self-reported survey data combined with analyses of existing birth and death certificates can provide in-depth profiles of community health conditions.2,3,5,42,43
Methodological Considerations Also, there was notable variation in participation rates according to community area. In North Lawndale, the poorest of the areas assessed, the occupancy rate (percentage of occupied households) was 85%, whereas the refusal rate was 10%. In Norwood Park, the richest area assessed, the occupancy rate was 98% and the refusal rate was 35%. Thus, although many houses (about 15%) in North Lawndale were vacant or burned down, 90% of the potential participants located completed the survey. In Norwood Park, conversely, although most of the housing units were occupied, only 65% of those contacted agreed to participate in the survey. This was also one of the main reasons why we completed only 190 surveys in Norwood Park instead of the goal of 300. Our difficulties in this community were consistent with reports from the 2000 census, according to which Chicagos more affluent communities had the highest refusal rates in the city. Overall, 87% of the eligible respondents we were able to contact agreed to participate in and complete the survey. Factors that may have contributed to this relatively high participation rate included the $40 incentive offered, the partnerships established with community-based organizations, and the persistence of interviewers who, before defining a household as "nonresponding," visited that household at least 12 times. Overall, the demographic characteristics of the respondents (in terms of gender, age, and race/ethnicity) closely reflected the characteristics of their respective census blocks. Studies have shown that the health status of survey nonrespondents is usually worse than that of respondents.44,45 If this is the case, then our results for health conditions may represent underestimates, whereas our health access results may represent overestimates. It is unclear precisely how our response rate affected our findings, but this is one of the challenges presented by all population-based surveys.
Financial Considerations
Implications for Future Research With community area prevalence and risk factor data available, these Chicago communities are at a considerable advantage in developing effective community-based initiatives designed to improve the health of their residents. For instance, setting priorities and planning for improved health in South Lawndale may involve ensuring access to health care services, whereas in Humboldt Park the focus may be on diabetes outreach and management. In fact, survey findings have already been successful in guiding local foundations to invest in programs related to arthritis, weight loss, and asthma management in these Chicago communities.47 Finally, our findings can inform policymakers, community leaders, and researchers in their efforts to advocate for equitable distribution of resources, particularly in the context of widening health disparities in Chicago37,38 and other similar urban centers and in achieving Healthy People 2010 goals.48 Our survey data document, emphasize, and reinforce the fact that geographic and racial/ethnic health disparities are still prominent in Chicago, and thus, they offer opportunities for action and development of effective interventions and policies at the local level.
Generous funding for this project was provided by the Robert Wood Johnson Foundation (grant 043026) and the Chicago Community Trust (grant C2003-00844). This project would not have been possible without the support, time, and dedication of the epidemiologists and researchers at the Sinai Urban Health Institute.
Human Participant Protection
Peer Reviewed
Contributors Accepted for publication October 5, 2005.
1. Shaping a Health Statistics Vision for the 21st Century. Washington, DC: US Dept of Health and Human Services; 2002. 2. Fang J, Bosworth W, Madhavan S, Cohen H, Alderman MH. Differential mortality in New York City (19881992), part two: excess mortality in the South Bronx. Bull N Y Acad Med. 1995;72:483499.[Web of Science][Medline] 3. Whitman S, Silva A, Shah A, Ansell D. Diversity and disparity: GIS and small area analysis in six Chicago neighborhoods. J Med Syst. 2004;28:397411.[CrossRef][Medline] 4. Krieger N, Chen JT, Waterman PD, Soobader MJ, Subramanian SV, Carson R. Choosing area based socioeconomic measures to monitor social inequalities in low birthweight and childhood lead poisoning: the Public Health Disparities Geocoding Project (US). J Epidemiol Community Health. 2003;57:186199. 5. Krieger N, Waterman PD, Chen JT, Soobader MJ, Subramanian S. Monitoring socioeconomic inequalities in sexually transmitted infections, tuberculosis, and violence: geocoding and choice of area-based socioeconomic measuresthe Public Health Disparities Geocoding Project (US). Public Health Rep. 2003;118: 240260.[CrossRef][Web of Science][Medline] 6. McGinnis JM, Foege WH. Actual causes of death in the United States. JAMA. 1993;270:22072212. 7. Mokdad AH, Marks JS, Stroup DF, Gerberding JL. Actual causes of death in the United States, 2000. JAMA. 2004;29:12381245. 8. Friedan TR. Asleep at the switch: local public health and chronic disease. Am J Public Health. 2004; 94:20592061. 9. Simon PA, Wold CM, Cousineau MR, Fielding JE. Meeting the data needs of a local health department: the Los Angeles County Health Survey. Am J Public Health. 2001;91:19501952. 10. Fielding JE, Frieden TR. Local knowledge to enable local action. Am J Prev Med. 2004;27:183184.[CrossRef][Web of Science][Medline] 11. Northridge ME, Morabia A, Ganz ML, et al. Contribution of smoking to excess mortality in Harlem. Am J Epidemiol. 1998;147:250258. 12. The Health of Angelenos: A Comprehensive Report of the Health of Residents of Los Angeles County. Los Angeles, Calif: Los Angeles County Dept of Health Services; 2000. 13. Seattle/King County Epidemiology, Planning, and Evaluation Unit. Communities Count 2000: social and health indicators across King County. Available at: http://www.metrokc.gov/health/reports/cc2k/cc2kintro.pdf. Accessed May 3, 2006. 14. New York City Dept of Health and Mental Hygiene. New York City Health and Nutrition Examination Survey (NYC HANES). Available at: http://www.nyc.gov/html/doh/html/hanes/hanes.html. Accessed May 3, 2006. 15. Karpati A, Kerker B, Mostashari F, et al. Health disparities in New York City. Available at: http://www.nyc.gov/html/doh/downloads/pdf/epi/disparities-2004.pdf. Accessed May 3, 2006. 16. New York City Dept of Health and Human Hygiene. New York City Community Health Survey 2002. Available at: http://www.nyc.gov/html/doh/html/survey/survey.shtml. Accessed May 3, 2006. 17. Centers for Disease Control and Prevention. SMART: selected metropolitan and micropolitan area risk trends. Available at: http://apps.nccd.cdc.gov/brfss-smart/index.asp. Accessed May 3, 2006. 18. Centers for Disease Control and Prevention. 2002 IL BRFSS SMART (selected metropolitan/micropolitan area risk trends for Illinois). Available at: http://apps.nccd.cdc.gov/brfss-smart/MMSAProjAreas.asp?state=IL. Accessed May 3, 2006. 19. Fullilove RE, Fullilove MT, Northridge ME, et al. Risk factors for excess mortality in Harlem: findings from the Harlem Household Survey. Am J Prev Med. 1999;16:S22S28. 20. Northridge ME, Meyer IH, Dunn L. Overlooked and underserved in Harlem: a population-based survey of adults with asthma. Environ Health Perspect. 2002; 110:S217S220. 21. Healthy Communities 2000: Model Standards. Guidelines for Community Attainment of the Year 2000 National Health Objectives. 3rd ed. Washington, DC: American Public Health Association; 1991. 22. Brownson RC, Bright FS. Chronic disease control in public health practice: looking back and moving forward. Public Health Rep. 2004;119:230238.[CrossRef][Web of Science][Medline] 23. Howell EM, Pettit KL, Ormond BA, Kingsley GT. Using the National Neighborhood Indicators Partnership to improve public health. J Public Health Manage Pract. 2003;9:235242.[Medline] 24. Chicago Fact Book Consortium. Local Community Fact Book: Chicago Metropolitan Area, 1990. Chicago, Ill: Academy Chicago Publishers; 1995. 25. Kish L. Survey Sampling. New York, NY: John Wiley & Sons Inc; 1965. 26. Sudman S. Applied Sampling. New York, NY: Academic Press Inc; 1976. 27. Troldahl V, Carter RE. Random selection of respondents within households in phone surveys. J Marketing Res. 1964;1:7176. 28. Standard Definitions: Final Dispositions of Case Codes and Outcome Rates for Surveys. Ann Arbor, Mich: American Association for Public Opinion Research; 2000. 29. Johnson TP, Owens L. Survey response rate reporting in the professional literature. In: Proceedings of the 2003 American Statistical Association Conference, Section on Survey Research Methods. Alexandria, Va: American Statistical Association; 2004:127133. 30. Stata Statistical Software, Version 8.0. College Station, Tex: Stata Corp; 2003. 31. Krieger N, Rowley DL, Hermann AA, Avery B, Phillips MT. Racism, sexism and social class: implications for studies of health, disease, and well being. Am J Prev Med. 1993;9(suppl 6):82122.[Web of Science][Medline] 32. Navarro V. Race or class versus race and class: mortality differentials in the United States. Lancet. 1990;336:12381240.[CrossRef][Web of Science][Medline] 33. Kaufman JS, Cooper RS. Seeking causal explanations in social epidemiology. Am J Epidemiol. 1999; 150:113120. 34. Muntaner C. Invited commentary: social mechanisms, race and social epidemiology. Am J Epidemiol. 1999;150:121126. 35. Ren XS, Amick BC, Williams DR. Racial/ethnic disparities in health: the interplay between discrimination and socioeconomic status. Ethn Dis. 1999;9: 151165.[Medline] 36. Massey DS, Denton NA. American Apartheid: Segregation and the Making of the Underclass. Cambridge, Mass: Harvard University Press; 1993. 37. Silva A, Whitman S, Margellos H, Ansell D. Evaluating Chicagos success in reaching the Healthy People 2000 goal of reducing health disparities. Public Health Rep. 2001;116:484494.[CrossRef][Web of Science][Medline] 38. Margellos H, Silva A, Whitman S. Comparison of health status indicators in Chicago: are black-white disparities worsening? Am J Public Health. 2004;94: 116121. 39. Acevedo-Garcia D, Lochner KA. Residential segregation and health. In: Kawachi I, Berkman LF, eds. Neighborhoods and Health. New York, NY: Oxford University Press Inc; 2003:265287. 40. Diez Roux AV. The examination of neighborhood effects on health: conceptual and methodological issues related to the presence of multiple levels of organization. In: Kawachi I, Berkman LF, eds. Neighborhoods and Health. New York, NY: Oxford University Press Inc; 2003:4564. 41. Barbeau EM, Krieger N, Soobader MJ. Working class matters: socioeconomic disadvantage, race/ethnicity, gender, and smoking in NHIS 2000. Am J Public Health. 2004;94:269278. 42. OCampo P. Invited commentary: advancing theory and methods for multilevel models of residential neighborhoods and health. Am J Epidemiol. 2003;157: 913. 43. Kawachi I, Berkman LF, eds. Neighborhoods and Health. New York, NY: Oxford University Press Inc; 2003. 44. Shahar E, Folsom AR, Jackson R. The effect of nonresponse on prevalence estimates for a referent population: insights from a population-based cohort study. Ann Epidemiol. 1996;6:498506.[CrossRef][Web of Science][Medline] 45. Cohen G, Duffy JC. Are nonrespondents to health surveys less healthy than respondents? J Off Stat. 2002;18:1323. 46. Mostashari F, Kerker BD, Hajat A, Miller N, Frieden TR. Smoking practices in New York City: the use of a population-based survey to guide policymaking and programming. J Urban Health. 2005;82: 5870.[Web of Science][Medline] 47. Whitman S, Shah AM. Progress report to the Chicago Community Trust, May 2005. Available at: http://www.sinai.org/urban/publications.asp. Accessed May 3, 2006. 48. Healthy People 2010: Understanding and Improving Health. Washington, DC: US Dept of Health and Human Services; 2000.
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