|
 |
|
|||||||
|
|||||||||
© 2007 American Public Health Association
DOI: 10.2105/AJPH.2005.069443
RESEARCH AND PRACTICE |
Arleen F. Brown and Alfonso Ang are with the Division of General Internal Medicine and Health Services Research, University of California, Los Angeles. Anne R. Pebley is with the School of Public Health, University of California, Los Angeles, and the Rand Corporation, Santa Monica, Calif.
Correspondence: Requests for reprints should be sent to Arleen F. Brown, MD, PhD, UCLA Division of General Internal Medicine and Health Services Research, 911 Broxton Plaza, Los Angeles, CA 90024 (e-mail: abrown{at}mednet.ucla.edu).
 |
ABSTRACT |
|---|
 TOP ABSTRACT INTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
|---|
Objectives. We sought to determine whether the association between neighborhood characteristics and health differs for people with and without a chronic condition.
Methods. We analyzed data from 2536 adults from the Los Angeles Family and Neighborhood Survey and evaluated the relationship between the presence of a chronic condition at the individual level, neighborhood socioeconomic status (SES), and self-rated health. We constructed multilevel models to evaluate the relationship between the neighborhood SES index and self-rated health for people with and without chronic conditions, after adjustment for other individual characteristics.
Results. Having a chronic condition was associated with substantially poorer self-rated health among participants in a deprived area than among those in a more advantaged area.
Conclusions. Residence in a disadvantaged neighborhood may be associated with barriers to the management of a chronic condition. Further work is needed to identify the specific characteristics of disadvantaged areas associated with poorer self-rated health for adults with chronic conditions.
|
INTRODUCTION |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
|---|
Residence in a socioeconomically deprived neighborhood has been linked to all-cause mortality,1–3 functional decline,4 poorer health status,5,6 and higher incidence and prevalence of chronic conditions such as diabetes, cardiovascular disease, and cancer.2,7–16 People living in deprived neighborhoods are likely to experience multiple dimensions of poor environmental and social quality, including higher-priced yet lower-quality foods, high crime rates, poor-quality housing, limited transportation, toxic environments, and lower social cohesion and social support, all of which may contribute to poorer health.13,17–22 Adults with chronic conditions may be particularly vulnerable to these dimensions of neighborhood deprivation.
Models of chronic disease management, such as the Chronic Care Model23–25 and the Disablement Framework,26 highlight the importance of community resources to the management of chronic conditions. Yet research on chronic conditions tends to emphasize clinical care, the health care system, and individual factors, and only infrequently examines the role of the neighborhood environment in the management of chronic disease.
There are, however, several mechanisms through which the neighborhood context may differentially affect the health of people with chronic conditions. Among adults with conditions such as diabetes, cardiovascular disease, arthritis, and asthma, adequate disease management often requires continuous clinical follow-up, self-care, and complex medication, dietary, and exercise regimens,24 all of which may be influenced by neighborhood factors such as available health care, access to exercise facilities and nutritious foods, and environments otherwise conducive to self-management. Thus, the characteristics of local areas, such as limited availability or accessibility of health services, infrastructure deprivation, environmental stressors, and social interactions that promote an unhealthy lifestyle19,20,27 may be associated with greater reductions in health status among adults with chronic conditions than among those without a chronic condition.
We conducted a cross-sectional analysis of the 2000–2001 Los Angeles Family and Neighborhood Survey (LAFANS) to examine whether the neighborhood socioeconomic environment contributes to differences in self-rated health among persons with and without a chronic condition. We chose to evaluate self-rated health because it is closely associated with several health outcomes, including morbidity28,29 and mortality,30,31 and determinants of health ratings have been shown to differ between people with and without chronic conditions.32–34 We hypothesized that, independent of individual income or education, lower neighborhood socioeconomic status would be associated with lower self-rated health and that the association would be strongest for persons with a chronic condition.
|
METHODS |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
|---|
For the analyses, we used data from LAFANS Wave 1, a longitudinal study of families in a stratified probability sample of census tracts in Los Angeles County conducted in 2000–2001. The design of LAFANS is presented elsewhere.35,36 Briefly, 1652 census tracts in Los Angeles County were stratified on the basis of the percentage of people living in poverty as obtained from 1997 estimates. Census tracts were classified as very deprived (90%–100% of residents living in poverty), deprived (60%–89%), and not deprived (1%–59%). In a representative sample of 65 tracts (20 very deprived, 20 deprived, and 25 not deprived), 40 to 50 dwelling units were sampled at random, with an oversample of households with children. Within each household, LAFANS randomly selected 1 adult (aged 18 years or older), who was interviewed in person. These analyses include data only from the randomly sampled adults in the LAFANS Wave 1 cohort.
The main predictors in the analysis were the socioeconomic status of the neighborhood of residence and the presence of a chronic condition. We assigned each tract a neighborhood socioeconomic status (SES) index.3 The SES index is the unweighted average of 5 census variables (percentage of individuals 25 years or older without a high school degree, median family income, median home value, percentage blue collar individuals, and percentage unemployed individuals), with the direction reversed for some variables; it was constructed from census tract–level data obtained from Summary File 3 of the 2000 US Census.37 We used an unweighted sum because a principal components analysis indicated that all 5 components contributed equally to the first factor, which accounted for 93% of the variance. Both the composite and the individual census variables were evaluated in separate models, but because we were interested in assessing multiple dimensions of the neighborhood socioeconomic environment and the findings did not differ appreciably, we used the SES index in these analyses.
The SES index was evaluated in separate analyses as either a continuous variable or in the 3 categories from the original sampling frame ("very deprived," "deprived," and "not deprived" census tracts). The results were similar for the 2 strategies; we present the results obtained from the second strategy. To test alternative definitions of "neighborhoods," we constructed the SES index scores at both smaller (census block group) and larger (census tract) levels and compared final versions of the models with these 2 different definitions. Because we found no substantial difference in the results and prior literature suggests a high correlation between census block and census tract indictors,38 we present the census tract–level analyses.
The primary individual predictor was a report of a physician’s diagnosis of 1 or more chronic conditions, as determined by the question, "Has a doctor ever told you that you have ... ?" followed by a list of conditions that included hypertension, arthritis, diabetes, and a chronic lung problem (asthma, chronic bronchitis, or chronic obstructive pulmonary disease). These conditions, which were included in analyses either individually or as an unweighted sum of the conditions, were the ones most commonly reported by study participants and are conditions that generally require substantial self-care.
Because depression is associated with health status and quality of life,39 we also evaluated it as a separate covariate in the regression model. Depression was defined as either ever having received a physician’s diagnosis of "major depression" or screening positive for depressive symptoms on the short form of the Composite International Diagnostic Interview.40 Because not all the randomly sampled adults in the cohort underwent screening for depression, we present separate results that include depression as a covariate in the portion of the sample that was screened for depression.
The dependent variable was self-rated health, which was measured by the question, "How would you rate your overall health?" Response categories were poor, fair, good, very good, and excellent. Poorer self-rated health has been associated with mortality and functional limitations in longitudinal studies.41 Unadjusted analyses were conducted using analyses of variance (repeated-measures analysis of variance [ANOVA]) and t tests. We evaluated self-rated health as a dichotomous variable (fair or poor vs good, very good, or excellent) and constructed weighted logistic regression models. Because self-rated health has an ordinal response scale, we conducted a sensitivity analysis that evaluated the outcome as an ordered categorical variable (poor or fair, good, very good, and excellent). The poor and fair categories were combined because only 4% of the overall sample rated their health as poor. The score test42,43 supported the proportional odds assumption (
238 = 47, P = .15). Multilevel ordered logistic models with random intercepts were constructed using Predictive Quasi Likelihood approximation procedures in HLM 5 software (Scientific Software International, Lincolnwood, Ill). Calculation of the intraclass correlation coefficient in the null (or empty) model suggested that 16.7% of the variance in self-rated health was between census tracts. After control for sociodemographic and clinical variables, the variance in self-rated health between census tracts was reduced to 10.8%.
At the individual level, the model included the main predictor (the presence of a chronic condition) and individual covariates, including demographic characteristics (age, gender, race/ethnicity, household income, education, immigrant status), body mass index, and health behaviors (smoking, alcohol use, and a physician visit in the prior year). A separate model also included depression as a covariate. Many of these characteristics have been associated with health status in previous research.32,39,44,45 At the tract level, the main predictor was the neighborhood SES index. The models also included cross-level interactions between the neighborhood SES index and the presence of a chronic condition. Separate models were also constructed for each of the most common chronic conditions (hypertension, arthritis, diabetes, and chronic lung disease); these models included an interaction term between the neighborhood SES index and the chronic condition. Another model included an unweighted sum of chronic conditions. The logistic models included sampling weights that take into consideration both nonresponse and the over-sample of poor households and households with children.36
A potential confounder in research on neighborhood effects is "residential selection": people in better health may choose to live in more advantaged neighborhoods. Although we could not directly assess the effect of selection on health outcomes in these cross-sectional analyses, we were able to evaluate health ratings among people with the greatest residential stability (i.e., those who had lived at the same address for 5 or more years). We also evaluated interaction terms between the neighborhood SES index and either individual income or individual education. Because these individual-level interactions were not significant and did not appreciably alter our findings, they are not presented in our final results.
We derived the relative risks and the 95% confidence intervals by bootstrapping with replacement over 1000 repetitions.46,47 The expected values for each category of chronic condition and neighborhood poverty were then calculated. The analyses were performed using the statistical packages HLM 5, SAS version 9.1 (SAS Institute Inc, Cary, NC), and Stata version 9.0 (Stata Corp, College Station, Tex).
|
RESULTS |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
|---|
The sample comprised 2536 adults (response rate=70%), 848 of whom reported 1 or more of the chronic conditions of interest. Compared with the randomly sampled adults included in LAFANS, nonrespondents did not differ by race/ethnicity, gender, income, or education but were more likely to be the head of household.36 Older people, women, Whites, and African Americans were all more likely to report a chronic condition (Table 1
). People with chronic conditions also had lower educational attainment and annual household income. Compared with those who did not report a chronic condition, people with 1 or more chronic conditions were not more likely to live in the poorest census tracts, but in the aggregate, their tracts of residence had slightly lower median SES index scores and they were more likely to have lived in the same residence for 5 years or longer. Those with chronic conditions had higher rates of reporting poor or fair health than did those without a chronic condition (38% vs 14%, P<.001).
|
Characteristics of the neighborhoods of residence of the study participants are presented in Table 2
. Participants were divided into very deprived, deprived, and not deprived census tracts. Across these 3 groups, we present the median SES index score and the 5 census variables that comprise the SES index.
|
In multivariate analyses, having a chronic condition and the interaction terms between having a chronic condition and living in a deprived or very deprived census tract were all associated with lower self-rated health (Table 3
). Other characteristics associated with lower health ratings were male gender, older age, immigrant status, less education, lower income, body mass index more than 30 kg/m2, current tobacco use, binge drinking, and visiting a health care provider in the prior year.
|
Table 4
shows the adjusted percentages of participants expected to report good, very good, or excellent health, categorized by chronic condition and census tract status. The reference group for all comparisons was those without a chronic condition who resided in nondeprived tracts. Among people without a chronic condition, the adjusted predicted percentage of people who reported their health as good, very good, or excellent was 92.4% for those residing in nondeprived census tracts, 80.2% for those in deprived tracts, and 73.2% for those in very deprived tracts. People with 1 or more chronic conditions had lower predicted ratings: good, very good, or excellent health was reported by 88.4% of those in nondeprived tracts, 63.3% of those in deprived tracts, and 52.4% of those in very deprived tracts. The differences between the adjusted means for persons with a chronic condition (compared with those without one) were significantly higher for residents in deprived or very deprived areas than for those of nondeprived areas. With the exception of asthma, similar relationships were observed for the each of the chronic conditions.
|
Sensitivity analyses conducted with multilevel ordered logistic regression models found the same associations between self-rated health and area of residence, the presence of a chronic condition, and the interaction term between the 2. Similar results were also observed when we evaluated the number of chronic conditions as opposed to just the presence of a chronic condition and for analyses restricted to people who had lived in the census tract for 5 or more years.
|
DISCUSSION |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
|---|
In this analysis of a population-based study of adults in Los Angeles County, for both people with a chronic condition and those without, the difference in self-rated health was greater among those living in low-SES census tracts than among those in high-SES census tracts. Not only were the presence of a chronic condition and lower individual SES (e.g., income and education) associated with lower health status, but having a chronic condition was associated with substantially poorer self-rated health among people in a deprived area than among those in a more advantaged area. Participants with chronic conditions had substantially lower individual income and were less educated than were the other study participants. Although a lower neighborhood SES index was associated with poorer health status after adjustment for individual income and education, it is important to consider that their higher level of socioeconomic disadvantage may have made participants with chronic conditions particularly vulnerable to neighborhood deprivation.
Chronic stress (related to crime, poor housing quality, and infrastructure deprivation) and lower or poorer availability of resources (such as access to health care, food availability, and transportation) may contribute to the larger negative association between neighborhood socioeconomic environment and health for adults with chronic conditions. Among adults with diabetes, for example, chronic stress has been associated with poorer glycemic control through 2 mechanisms: health behaviors, such as lower rates of adherence to medication or less physical activity, and neurohormonal pathways.48
An important mediator of the relationship between neighborhood deprivation and lower self-rated health may be functional status. A recent study, for example, indicates that the impact of functional limitation on quality of life is more that 4 times that of the chronic condition by itself.49 Inadequate neighborhood resources, including access to health care, safe places to exercise, healthy foods, and transportation, may directly contribute to functional impairment. These factors may pose a greater barrier for adults with chronic conditions, particularly those with existing disability, and lead to reduced physical activity, poorer dietary patterns, and lower rates of visits to health providers. Allostatic load, the cumulative burden associated with the body’s adaptation to chronic stress, may be another important mechanism through which neighborhood characteristics influence health outcomes for adults with chronic conditions.50
We observed different relationships for some of the chronic conditions, most notably chronic lung conditions. We cannot be certain why, for conditions such as asthma, chronic obstructive pulmonary disease, and chronic bronchitis, there is no difference in self-rated health status according to the neighborhood SES index; other factors may be involved. For example, residence near a freeway or other determinants of local air quality may be more important predictors of self-rated health among people with chronic lung problems.51 Another explanation for these findings may be the smaller sample size in this group; still another could be that asthma, COPD, and chronic bronchitis are heterogenous conditions, yet differences in their individual health ratings are masked by grouping them into 1 category.
Among the limitations of these analyses are that we used cross-sectional data and have no information on change in health status or other health outcomes. Additionally, people in worse health may be more likely to move to or remain in more deprived neighborhoods. However, restricting the analyses to people who had lived in the same area for 5 or more years and controlling for duration of residence in these census tracts produced no significant change in the results. Another limitation is that although we tested for alternate definitions of neighborhoods using both tract and block group data, census-derived characterizations of neighborhoods may not reflect aspects of the social and physical environment that influence the health and behaviors of individuals. However, prior work from LAFANS suggests that census-tract definitions of neighborhood size are highly correlated with respondents’ reports of the size and boundaries of their neighborhoods.52 Still, further work is needed to better understand the characteristics of areas that influence the health-related experiences of residents.
Another limitation is that the chronic conditions were self-reported, with no independent verification of the results. Data on the validity of self-reported data on chronic conditions suggest that accuracy of self-report (with either the medical record or physical examination used as the gold standard) varies by condition. Several validation studies have compared methods of identifying people with the chronic conditions included in our analyses. Although self-reported diabetes shows the strongest agreement with the medical record, there is lower, but generally good, agreement between self-report and medical record diagnoses of hypertension.53 In contrast, there is substantial underreporting of asthma54 and osteoarthritis.55–57 Nonetheless, we found similar patterns for the association between neighborhood deprivation and health ratings for hypertension, arthritis, and diabetes.
Another potential limitation is that certain groups, including poorer or less educated people, may be less likely to report a chronic condition owing to lower rates of diagnosis or a lack of awareness of the condition. A recent study suggests that less education and more co-morbid conditions are associated with under-reporting of conditions including hypertension and diabetes,58 but the absolute differences observed for different levels of education were relatively small. Misclassification of people in more deprived census tracts with undiagnosed disease, however, may have led to an overestimation of the magnitude of the difference in self-rated health between those with and those without a chronic condition in deprived and very deprived tracts.
Our findings of a differential in health status for those with and those without a chronic condition by level of neighborhood deprivation suggest a need to identify the specific characteristics of the built and social environment that are associated with substantially lower health ratings among persons with chronic conditions in the most deprived neighborhoods. An important step is for researchers and health care organizations to systematically collect information on the environmental and social factors that may serve as barriers to those with chronic conditions. An understanding of how neighborhood influences health can enhance efforts to improve health through urban planning, housing policies, and modifying the food resource environment.
In the clinical setting, enhanced awareness of the association between neighborhood factors and health for people with chronic conditions may help health care providers identify adults who are most likely to be affected by disadvantaged neighborhood environments; they can then tell them where and how to obtain important services, such as better food, improved transportation, and low-cost exercise facilities. Further work is also needed to clarify the specific socioeconomic forces and structural characteristics that influence health outcomes in general and those that contribute to the differential observed for people with chronic conditions. Previous studies have shown that for adults with diabetes, intensive clinical and behavioral interventions benefit those who are less educated59 or have low literacy levels60 more than those who are more educated or literate. We have yet to determine whether intensive clinical, behavioral, or policy interventions can modify neighborhood effects on people with chronic conditions.
|
Acknowledgments |
|---|
A. F. Brown received support from the Center for Health Improvement in Minority Elders/Resource Centers for Minority Aging Research at the University of California, Los Angeles (UCLA), from the National Institutes of Health (NIH), National Institute of Aging (grant AG–02–004), the UCLA/Drew Project EXPORT/NIH National Center on Minority Health and Health Disparities (grant P20 MD00148), and the Paul D. Beeson Career Development Award (grant AG 26748).
We thank Hope Watkins for her assistance with the preparation of the article.
Human Participant Protection
These analyses were approved by the UCLA institutional review board.
|
Footnotes |
|---|
Peer Reviewed
Contributors
A. F. Brown originated and supervised the study, conducted analyses, and led the writing of the article. A. Ang completed the analyses and assisted with the writing. A. R. Pebley led the original data collection and assisted with the study and the writing. All authors helped to conceptualize ideas, interpret findings, and review drafts of the article.
Accepted for publication June 28, 2006.
|
References |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
|---|
1. Haan M, Kaplan GA, Camacho T. Poverty and health. Prospective evidence from the Alameda County Study. Am J Epidemiol. 1987;125(6): 989–998.
2. Yen IH, Kaplan GA. Neighborhood social environment and risk of death: multilevel evidence from the Alameda County Study. Am J Epidemiol. 1999;149: 898–907.
3. Winkleby MA, Cubbin C. Influence of individual and neighbourhood socioeconomic status on mortality among black, Mexican-American, and white women and men in the United States. J Epidemiol Community Health. 2003;57:444–452.
4. Balfour JL, Kaplan GA. Neighborhood environment and loss of physical function in older adults: evidence from the Alameda County Study. Am J Epidemiol. 2002;155(6):506–515.
5. Patel KV, Eschbach K, Rudkin LL, Peek MK, Markides KS. Neighborhood context and self-rated health in older Mexican-Americans. Ann Epidemiol. 2003;13:620–628.[CrossRef][Web of Science][Medline]
6. Stafford M, Marmot M. Neighbourhood deprivation and health: does it affect us all equally? Int J Epidemiol. 2003;32:357–366.
7. Harburg E, Erfurt JC, Chape C, Hauenstein LS, Schull WJ, Schork MA. Socioecological stressor areas and black–white blood pressure: Detroit. J Chronic Dis. 1973;26(9):595–611.[CrossRef][Web of Science][Medline]
8. Devesa SS, Diamond EL. Socioeconomic and racial differences in lung cancer incidence. Am J Epidemiol. 1983;118(6):818–831.
9. Jenkins CD. Social environment and cancer mortality in men. N Engl J Med. 1983;308(7):395–398.[Web of Science][Medline]
10. Crombie IK, Kenicer MB, Smith WC, Tunstall-Pedoe HD. Unemployment, socioenvironmental factors, and coronary heart disease in Scotland. Br Heart J. 1989;61( 2):172–177.
11. Adler NE, Boyce WT, Chesney MA, Folkman S, Syme L. Socioeconomic inequalities in health: no easy solution. JAMA. 1993;269(4):3140–3145.
12. Diez-Roux AV, Nieto FJ, Muntaner C, et al. Neighborhood environments and coronary heart disease: a multilevel analysis. Am J Epidemiol. 1997;146:48–63.
13. Roberts EM. Socioeconomic position and health: the independent contribution of community socioeconomic context. Ann Rev Sociol. 1999;25:489–516.[CrossRef][Web of Science]
14. Yen IH, Syme SL. The social environment and health: a discussion of the epidemiological literature. Annu Rev Public Health. 1999;20:287–308.[CrossRef][Web of Science][Medline]
15. Diez Roux AV, Merkin SS, Arnett D, et al. Neighborhood or residence and incidence of coronary heart disease. N Engl J Med. 2001;345:99–106.
16. Morenoff J, Lynch JW. What makes a place healthy? Neighborhood influences on racial/ethnic disparities in health over the life course. In: Anderson NB, Bulatao RA, Cohen B, eds. Critical Perspectives on Racial and Ethnic Differences in Health in Late Life. Washington, DC: National Academy Press; 2004:406–449.
17. Macintyre S, Maciver S, Sooman A. Area, class and health: should we be focusing on places or people? Int Soc Policy. 1993;22:213–234.
18. Sampson R, Raudenbush S, Earls F. Neighborhoods and violent crime: a multilevel study of collective efficacy. Science. 1997;277:918–924.
19. Diez Roux AV. Bringing context back into epidemiology: variables and fallacies in multilevel analysis. Am J Public Health. 1998;88:216–222.
20. Pickett KE, Pearl M. Multilevel analyses of neighbourhood socioeconomic context and health outcomes: a critical review. J Epidemiol Community Health. 2001; 55(2):111–122.
21. Evans GW, Kantrowitz E. Socioeconomic status and health: the potential role of environmental risk exposure. Annu Rev Public Health. 2002;23:303–331.[CrossRef][Web of Science][Medline]
22. Steptoe A, Feldman P. Neighborhood problems as sources of chronic stress: development of a measure of neighborhood problems, and associations with socioeconomic status and health. Ann Behav Med. 2001;23: 177–185.[CrossRef][Web of Science][Medline]
23. Bodenheimer T, Wagner EH, Grumbach K. Improving primary care for patients with chronic illness. N Engl J Med. 2002;288:1775–1779.
24. Wagner EH, Austin BT, Davis C, Hindmarsh M, Bonomi A. Improving chronic illness care: translating evidence into action. Health Aff. 2001;20:64–78.
25. Glasgow RE, Wagner EH, Kaplan RM, Vinicor F, Smith L, Norman J. If diabetes is a public health problem, why not treat it as one? A population-based approach to chronic illness. Ann Behav Med. 1999; 21(2): 159–170.[Web of Science][Medline]
26. Verbrugge LM, Jette AM. The disablement process. Soc Sci Med. 1994;38(1):1–14.[CrossRef][Web of Science][Medline]
27. Ellen IG, Mijanovich T, Dillman K. Neighborhood effects on health: exploring the links and assessing the evidence. J Urban Aff. 2001;23:391–408.[CrossRef]
28. Branch LG, Lu L. Transition probabilities to dependency, institutionalization and death among the elderly over a decade. J Aging Health. 1989;1:370–408.
29. Mor V, Wilcox V, Rakowski W, Hiris J. Functional transitions among the elderly: patterns, predictors, and related hospital use. Am J Public Health. 1994;84(8): 1274–1280.
30. Mossey JM, Shapiro E. Self-rated health: a predictor of mortality among the elderly. Am J Public Health. 1982;72(8):800–808.
31. Idler EL, Kasl SV, Lemke JH. Self-evaluated health and mortality among the elderly in New Haven, Connecticut, and Iowa and Washington counties, Iowa, 1982–1986. Am J Epidemiol. 1990;131(1):91–103.
32. Cott CA, Gignac MA, Badley EM. Determinants of self rated health for Canadians with chronic disease and disability. J Epidemiol Community Health. 1999; 53(11):731–736.[Abstract]
33. Molarius A, Janson S. Self-rated health, chronic diseases, and symptoms among middle-aged and elderly men and women. J Clin Epidemiol. 2002;55(4):364–370.[CrossRef][Web of Science][Medline]
34. Froom P, Melamed S, Triber I, Ratson NZ, Hermoni D. Predicting self-reported health: the CORDIS study. Prev Med. 2004;39(2):419–423.[CrossRef][Web of Science][Medline]
35. Sastry N, Ghosh-Dastidar B, Adams J, Pebley AR. The design of a multilevel longitudinal survey of children, families, and communities: the Los Angeles Family and Neighborhood Survey. Soc Sci Res. 2006;35(4): 1000–1024.[CrossRef][Web of Science]
36. Sastry N, Pebley AR. Non-response in the Los Angeles Family and Neighborhood Survey. Rand Corp, Santa Monica, Calif, 2003. Available at: http://www.rand.org/pubs/drafts/DRU2400.7. Accessed March 5, 2007.
37. 2000 US Census, Summary File 3. Available at: http://www.census.gov/Press-Release/www/2002/sumfile3.html. Accessed January 25, 2007.
38. Diez Roux AV, Kiefe CI, Jacobs DR Jr, et al. Area characteristics and individual-level socioeconomic position indicators in three population-based epidemiologic studies. Ann Epidemiol. 2001;11(6):395–405.[CrossRef][Web of Science][Medline]
39. Ruo B, Rumsfeld JS, Hlatky MA, Liu H, Browner WS, Whooley MA. Depressive symptoms and health-related quality of life: the Heart and Soul Study. JAMA. 2003;290(2):215–221.
40. Knäuper B, Cannell CF, Schwarz N, Bruce ML, Kessler RC. Improving accuracy of major depression age-of-onset reports in the US National Comorbidity Survey. Int J Methods Psychiatr Res. 1999;8(1):39–48.[CrossRef]
41. Idler EL, Russell LB, Davis D. Survival, functional limitations, and self-rated health in the NHANES I epidemiologic follow-up study, 1992. Am J Epidemiol. 2000;152:874–883.
42. Holtbrugge W, Schumacher M. A comparison of regression models for the analysis of ordered categorical data. Appl Stat. 1991;40:249–259.[CrossRef]
43. Ananth CV, Kleinbaum DG. Regression models for ordinal responses: a review of methods and applications. Int J Epidemiol. 1997;26:1323–1333.
44. Okosun IS, Choi S, Matamoros T, Dever GE. Obesity is associated with reduced self-rated general health status: evidence from a representative sample of white, black, and Hispanic Americans. Prev Med. 2001;32(5): 429–436.[CrossRef][Web of Science][Medline]
45. Ford ES, Moriarty DG, Zack MM, Mokdad AH, Chapman DP. Self-reported body mass index and health-related quality of life: findings from the Behavioral Risk Factor Surveillance System. Obes Res. 2001;9(1):21–31.[Web of Science][Medline]
46. Zhang J, Yu KF. What’s the relative risk? JAMA. 1998;290:1690–1691.
47. Carpenter J, Bithell J. Bootstrap confidence intervals: when, which, what? A practical guide for medical statisticians. Stat Med. 2000;19:1141–1164.[CrossRef][Web of Science][Medline]
48. Barglow P, Hatcher R, Edidin DV, et al. Stress and metabolic control in diabetes: psychosomatic evidence and evaluation of methods. Psychosom Med. 1984;46:127–144.
49. Netuveli G, Wiggins RD, Hildon Z, Montgomery SM, Blane D. Functional limitation in long standing illness and quality of life: evidence from a national survey. BMJ. 2005;331(7529):1382–1383.
50. Seeman TE, Crimmins E. Social environment effects on health and aging: integrating epidemiologic and demographic approaches and perspectives. Ann N Y Acad Sci. 2001;954:88–117.[CrossRef][Web of Science][Medline]
51. Oyana TJ, Rogerson P, Lwebuga-Mukasa JS. Geographic clustering of adult asthma hospitalization and residential exposure to pollution at a United States–Canada border crossing. Am J Public Health. 2004; 94(7):1250–1257.
52. Sastry N, Pebley AR, Zonta M. Neighborhood definitions and the spatial dimensions of daily life in Los Angeles. Rand Working Paper DRU-2400/8-LAFANS. Available at: http://www.rand.org/pubs/drafts/DRU2400.8/index.html. Accessed January 25, 2007.
53. Bush TL, Miller SR, Golden AL, Hale WE. Self-report and medical record report agreement of selected medical conditions in the elderly. Am J Public Health. 1989;79:1554–1556.
54. Mohangoo AD, van der Linden MW, Schellevis FG, Raat H. Prevalence estimates of asthma or COPD from a health interview survey and from general practitioner registration: what’s the difference? Eur J Public Health. 2006;16:101–105.
55. Simpson CF, Boyd CM, Carlson MC, Griswold ME, Guralnik JM, Fried LP. Agreement between self-report of disease diagnoses and medical record validation in disabled older women: factors that modify agreement. J Am Geriatr Soc. 2004;52(1):123–127.[CrossRef][Web of Science][Medline]
56. Harlow SD, Linet MS. Agreement between questionnaire data and medical records: the evidence for accuracy of recall. Am J Epidemiol. 1989;129:233–248.
57. Bowlin SJ, Morrill BD, Nafziger AN, Jenkins PL, Lewis C, Pearson TA. Validity of cardiovascular disease risk factors assessed by telephone survey: the Behavioral Risk Factor Survey. J Clin Epidemiol. 1993;46(6): 561–571.[CrossRef][Web of Science][Medline]
58. Okura Y, Urban LH, Mahoney DW, Jacobsen SJ, Rodeheffer RJ. Agreement between self-report questionnaires and medical record data was substantial for diabetes, hypertension, myocardial infarction and stroke but not for heart failure. J Clin Epidemiol. 2004;57(10): 1096–1103.[CrossRef][Web of Science][Medline]
59. Goldman DP, Smith JP. Can patient self-management help explain the SES health gradient? Proc Natl Acad Sci U S A. 2002;99(16):10929–10934.
60. Rothman RL, DeWalt DA, Malone R, et al. Influence of patient literacy on the effectiveness of a primary care-based diabetes disease management program. JAMA. 2004;292(14):1711–1716.
This article has been cited by other articles:
|
|
 |
|
  K. Ladin, N. Daniels, and I. Kawachi Exploring the Relationship Between Absolute and Relative Position and Late-Life Depression: Evidence From 10 European Countries Gerontologist, February 1, 2010; 50(1): 48 - 59. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 E. D. Peterson, J. A. Spertus, D. J. Cohen, M. A. Hlatky, A. S. Go, B. G. Vickrey, J. L. Saver, and P. C. Hinton Vision and Creation of the American Heart Association Pharmaceutical Roundtable Outcomes Research Centers Circ Cardiovasc Qual Outcomes, November 1, 2009; 2(6): 663 - 670. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 R. Wexler, D. Feldman, D. Larson, L. T. Sinnott, L. A. Jones, and J. Miner Adoption of Exercise and Readiness to Change Differ Between Whites and African-Americans with Hypertension: A Report from The Ohio State University Primary Care Practice-Based Research Network (OSU-PCPBRN) J Am Board Fam Med, July 1, 2008; 21(4): 358 - 360. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 T. L. Gary, M. M. Safford, R. B. Gerzoff, S. L. Ettner, A. J. Karter, G. L. Beckles, and A. F. Brown Perception of Neighborhood Problems, Health Behaviors, and Diabetes Outcomes Among Adults With Diabetes in Managed Care: The Translating Research Into Action for Diabetes (TRIAD) Study Diabetes Care, February 1, 2008; 31(2): 273 - 278. [Abstract] [Full Text] [PDF]
|
|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |