BMJ 1998;317:917-921 ( 3 October )

Papers

Income distribution, socioeconomic status, and self rated health in the United States: multilevel analysis

Bruce P Kennedy, deputy director, a Ichiro Kawachi, associate professor, b Roberta Glass, research specialist, a Deborah Prothrow-Stith, professor. a

a Division of Public Health Practice, Harvard School of Public Health, 718 Huntington Avenue, Boston, MA 02115, USA, b Department of Health and Social Behavior, Harvard School of Public Health

Correspondence to: Dr Kennedy kennedy{at}hsph.harvard.edu

    Abstract
Top
Abstract
Introduction
Data and methods
Results
Discussion
Conclusion
References

Objective: To determine the effect of inequalities in income within a state on self rated health status while controlling for individual characteristics such as socioeconomic status.
Design: Cross sectional multilevel study. Data were collected on income distribution in each of the 50 states in the United States. The Gini coefficient was used to measure statewide inequalities in income. Random probability samples of individuals in each state were collected by the 1993 and 1994 behavioural risk factor surveillance system, a random digit telephone survey. The survey collects information on an individual's income, education, self rated health and other health risk factors.
Setting: All 50 states.
Subjects: Civilian, non-institutionalised (that is, non-incarcerated and non-hospitalised) US residents aged 18 years or older.
Main outcome measure: Self rated health status.
Results: When personal characteristics and household income were controlled for, individuals living in states with the greatest inequalities in income were 30% more likely to report their health as fair or poor than individuals living in states with the smallest inequalities in income.
Conclusions: Inequality in the distribution of income was associated with an adverse impact on health independent of the effect of household income.

Key messages

  • Inequalities in the statewide distribution of income are associated with self rated fair or poor health, even after individual income and other risk factors are accounted for

  • The effects of income distribution on self rated health were not limited only to those in the lowest income groups; those in the middle income groups in states with the greatest inequalities in income rated themselves as having poorer health than those in middle income groups in states with the smallest inequalities

  • The effects of income inequality on self rated health are as strong as other individual risk factors

  • Social and economic policies that affect income distribution may have important consequences for the health of the population


    Introduction
Top
Abstract
Introduction
Data and methods
Results
Discussion
Conclusion
References

Inequalities in health associated with socioeconomic status are large and they are growing. Most theories that explain these inequalities use indicators of socioeconomic status associated with the individual such as income, educational attainment, or occupation.1-4 A new hypothesis focuses on the distribution of income within a society as a predictor of health.5 Numerous ecological studies have provided support for this hypothesis showing that unequal distributions of income are associated with mortality in populations and life expectancy both between and within nations.5-9

These ecological studies indicate that inequalities in income may have extraindividual or contextual effects that structure the social environment in ways that affect the health of a population.10-12 It is not clear at what level of analysis the contextual effects of income inequality are best specified (for example, state, county, or neighbourhood) and whether it is more appropriate to test these using multilevel analyses (for example, of the state and individual) that can account for confounding variables at the level of the individual. Researchers have suggested that the relation between income distribution and mortality in populations may be due to a statistical artefact produced from the curvilinear relation of individual income to mortality.13 Others have argued that models using ecological data are more prone to model misspecification due to a greater likelihood of unmeasured confounding variables at the individual level.14 In a multilevel study that used both individual and ecological variables Fiscella and Franks found that the ecological effect of inequalities in income measured at the community level on mortality disappeared after controlling for family income.14 They concluded that previous studies using purely ecological variables may have overstated the relation between inequalities in income and mortality in populations as other studies had not controlled for the confounding of individual income.

This study was conducted to examine income inequalities measured by state to predict individual morbidity, as measured by self rated health status, while adjusting for potentially confounding individual variables. The potential confounding variables measured included household income, educational attainment, age, sex, race, whether an individual had health insurance, whether an individual smoked, and whether an individual was obese. Using a multilevel model that includes both ecological variables (in this case income distribution within a state) and individual variables militates against the possibility of model misspecification and the attribution of a contextual effect where none exists. 15 16

    Data and methods
Top
Abstract
Introduction
Data and methods
Results
Discussion
Conclusion
References

Sources of data
Data on individuals' reported health status, income, demographic variables, and access to health care were drawn from the results of the combined 1993-4 survey of the behavioural risk factor surveillance system. The telephone survey is conducted by state health departments under the direction of the Behavioral Surveillance Branch of the National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention. The states use probability samples in which all households with telephones have a non-zero chance of inclusion; the samples are designed to produce comparable estimates for the civilian, non-institutionalised (that is, non-incarcerated and non-hospitalised) population aged 18 or older.

In 1993 questions about health as related to quality of life were added. From the question about perceived health (Would you say that in general your health is: excellent, very good, good, fair, or poor?17) we created a dichotomous outcome measure equal to 1 if the respondent answered fair or poor. A review of 27 studies has shown that this simple measure of self rated health has strong predictive validity for mortality, independent of other physiological, behavioural, and psychosocial risk factors.18 Furthermore, it has been shown in longitudinal studies that self rated health predicts the onset of disability.19-23

Demographic data collected by the survey included information on race, age, sex, educational attainment, and household income. The proportion of people reporting fair or poor health remains relatively constant until age 40 and then increases linearly. To model this relation we created a variable that measures the number of years a respondent is older than age 40. Educational attainment was categorised as having less than a high school education, having a high school education, having attended some university or a trade school, or being a university graduate. Measures of access to health care included determining whether the person had health insurance and whether the person had had a health check up visit within the past two years. Household composition was categorised as one adult living alone, two or more adults with no children, one adult with any children, and two or more adults with children. Race was categorised as white, black, or other. Annual household income was categorised as <$10 000, $10 000 to $14 999, $15 000 to $19 999, $20 000 to $24 999, $25 000 to $34 999, $35 000 to $49 000, and >= $50 000.

For our variable on income inequality we used data on the Gini coefficient.24 The Gini coefficient is constructed using data on household income from the population survey for the years 1990-2. Income data are adjusted for differences among states in taxes and cash transfers as well as differences in household composition using an equivalence scale (with the equivalence elasticity 0.5). The sample was divided into four categories based on the distribution of the coefficient. Category 1 represents the states with small inequalities in income and includes states with coefficients less than one standard deviation below the mean coefficient for the United States (0.332). This included nine states with coefficients <0.320: Connecticut, Delaware, Hawaii, Iowa, North Dakota, South Dakota, Utah, Vermont, and Wisconsin. Category 2 included 13 states with coefficients between one standard deviation below the mean and the mean (0.320 to 0.331): Indiana, Kansas, Maine, Maryland, Minnesota, Montana, Nebraska, New Mexico, Ohio, Oregon, Pennsylvania, Rhode Island, and Wyoming. Category 3 included 18 states with coefficients between the mean and one standard deviation above the mean (0.332-0.355): Alaska, Arizona, Arkansas, Colorado, Idaho, Massachusetts, Michigan, Missouri, Nevada, New Hampshire, New Jersey, North Carolina, Oklahoma, South Carolina, Tennessee, Virginia, West Virginia, and Washington. Category 4 includes the states with the greatest inequalities in income (coefficients >0.355): Alabama, California, Florida, Georgia, Illinois, Kentucky, Louisiana, Mississippi, New York, and Texas.

Analysis of data
The behavioural risk factor surveillance system uses a three stage sampling design, which requires special statistical techniques to account for clustering when calculating standard errors. All logistic regression procedures were calculated using Software for Survey Data Analysis (SUDAAN, version 5.50, Research Triangle Institute, Research Triangle Park, NC) which takes the survey design into account when estimating standard errors. It can also be used for multilevel models using clustered data (such as for individuals within states). The estimation procedure takes into account the violation of independence among individuals in the same cluster (state) and estimates the appropriate standard errors.

Logistic regression was used to examine the relation between state income inequality and self reports of fair or poor health while controlling for an individual's household income. A second logistic regression model examined the same relation while adjusting for the demographic characteristics of age, sex, and race. A third model adjusted for factors that might be pathways through which inequalities in income influence health status. These additional factors included health insurance status, recent use of healthcare services, household composition, smoking status, obesity, and educational attainment. Finally, we stratified the sample by household income and by race (white, black, or other) and used adjusted models to determine whether the relation between health status and income inequality differed by income or race.

    Results
Top
Abstract
Introduction
Data and methods
Results
Discussion
Conclusion
References

The total sample size for the combined 1993-4 datasets was 205 245, ranging from a total of 1259 responses in Wyoming to 8800 in Maryland. The percentage of respondents within a state reporting fair to poor health was strongly correlated with mortality within the state (r=0.58).

Table 1 summarises characteristics of the respondents and bivariate relations with reported fair or poor health. Women were slightly overrepresented in the sample (58.3% women v 41.7% men). In simple bivariate analyses, more women reported fair or poor health than men (15.4% v 13.1%), as did blacks compared to whites (20.3% v 13.8%). In the bivariate associations income was strongly associated with fair or poor health; for the whole sample, 32% of those in the lowest income category (<$10 000) reported fair or poor health compared to only 6.3% in the highest income category (>$35 000). This represented a fivefold gradient effect for household income on health. A similar gradient was seen for education: 45% of those with no education or with less than a high school education reported fair or poor health compared with 6% of university graduates. Obesity, smoking, household composition, health insurance coverage, and other demographic variables were also associated with fair or poor health.

Table 2 shows the multivariate odds ratios for the effects of income inequality on health. The unadjusted odds ratio of the Gini coefficient for income inequality shows that there is an increased risk of about 30% for fair or poor health for individuals living in states with greater inequalities in income. This effect is attenuated when household income is added to the model but the odds ratios remain significant (table 2). The effect remains fairly stable even after adjusting for demographic variables and risk factors. The effect of income inequality on health in the fully adjusted model stratified by race was similar for blacks (adjusted odds ratio for the states with the greatest income inequality 1.37, 95% confidence interval 1.08 to 1.74) and for whites (1.27, 1.19 to 1.36) (table 2). Although women reported being in fair or poor health slightly more often than men (15.4% v. 13.1%) this difference was not significant in the fully adjusted model (odds ratio for men 1.05, 1.00 to 1.09) (table 2).

                              
View this table:
[in this window]
[in a new window]
 

Table 1. Characteristics of 205 245 respondents and number reporting fair or poor health

                              
View this table:
[in this window]
[in a new window]
 

Table 2. Odds ratios (95% confidence intervals) for respondents reporting fair or poor health. Level of inequalities in income is adjusted for characteristics of the respondents

We also examined the effects of income inequality stratified by household income (table 3). The effects of income inequality on health were most pronounced among those whose incomes were <$20 000; those in this category had about a 30% increase in the risk of fair or poor health in the states with the greatest inequalities in income. These effects remained stable after adjusting for a variety of individual characteristics. Inequalities in income were also associated with an increased risk of reporting fair or poor health in about 20% of those in the middle income group ($20 000 to $35 000) although this was not as pronounced as among those in the lower income group. These effects remained stable after adjusting for other risk factors.

                              
View this table:
[in this window]
[in a new window]
 

Table 3. Odds ratio (95% confidence interval) of Gini coefficient for each model for respondents reporting fair or poor health. Level of income inequality is adjusted for individual characteristics and stratified by income

    Discussion
Top
Abstract
Introduction
Data and methods
Results
Discussion
Conclusion
References

Fiscella and Franks found that the ecological effect of income inequality at the level of the community disappears after including family income in a model.14 Contrary to Fiscella and Franks, we found an independent effect of income inequality on reported health after adjusting for potentially confounding individual variables, including household income. Furthermore, the effect of income distribution, although attenuated somewhat, did not change significantly when household income was included in the model. When stratified by income the effects of inequalities in income were strongest among those with lower incomes. The income inequality variable did not have a significant effect on the reported health of those in the highest income group yet the effect was present for those in the middle income group.

Fiscella and Franks suggested that earlier findings from ecological studies may have been confounded by individual income.14 They suggested that income inequality was capturing the compositional effect of individual income on mortality---that is, areas with more poor individuals have greater inequalities in income and, since being poor is associated with higher mortality, there is a spurious association between income inequality and mortality. Our findings do not support their conclusions but suggest that there is an independent contextual effect of income distribution, measured at state level, on individual ratings of health.

There are a number of potential explanations for the differences between our findings and those of Fiscella and Franks. Firstly, they used a different level of aggregation which may have been too small to allow income distribution to exert an effect independent of individual income. It is not clear at what ecological level income inequality can be expected to be meaningful. For example, income inequality measured within very poor and very affluent communities would not have the same meaning as inequalities was measured across them. As Wilkinson points out, it is not the inequality within Harlem that matters but how Harlem compares with the larger society.25

Secondly, this study used self rated health as the outcome; this variable may be more sensitive to income inequality. However, studies have shown that self rated health is linked to mortality. This measure of self rated health has high predictive validity for mortality.18 In this study self rated health aggregated to the level of the state was strongly correlated with the total age adjusted mortality rates for the state (r=0.58) and the correlation with the Gini coefficient, although larger, was similar to that of mortality (0.62 v 0.51).

Another reason for the divergence in findings may be due to measurement error in the calculation of income inequality. As Fiscella and Franks noted their samples were not meant to be representative of the communities to which the ecological variables were assigned.14 Furthermore, the measures of inequality were derived from the distribution of income for each area's sample and were truncated at $25 000. This may have resulted in an underestimation of the true degree of inequality and thus attenuated its effects in the multilevel model. A more appropriate index of income inequality could be derived from income data from an independent source such as census data from the county (as was done in this study).

    Conclusion
Top
Abstract
Introduction
Data and methods
Results
Discussion
Conclusion
References

Inequalities in income at the state level exert an independent effect on an individual's risk of reporting fair or poor health. This suggests that the ecological relation between income inequality and mortality is not simply an artefact caused by the compositional effect of aggregated individual incomes. 13 14 There seems to be a clear contextual effect of income inequality on health status.

Gravelle14 argued that the effect of income inequality on the risk of death was an artefact resulting from the non-linear shape of the relation between average income and mortality rates and the use of aggregate rather than individual data (the ecological fallacy). Years ago we suggested that the non-linear relation between average income and mortality is a sufficient but not necessarily the only explanation for the association between inequalities in income and health.26 This multilevel analysis suggests that the association is not a product of the ecological fallacy and that a contextual effect of income inequality is apparent even among individuals whose personal incomes are above the poverty line. Social and economic policies that affect income distribution may have important consequences for population health.

    Acknowledgments

We thank Professor Timothy Smeeding for providing data on the Gini coefficient.

Contributors: This study was based on the core ideas of BPK and IK. They assume primary responsibility for the design of the study, interpretation of the findings, and the writing of the paper. RG participated in designing the study, performed all of the data analysis, and participated in writing the paper. DP-S discussed the paper's core ideas, assisted with the interpretation of the findings, and participated in the writing of the paper.

Funding: BPK and IK are recipients of Robert Wood Johnson Foundation investigator awards in health policy research.

Conflict of interest: None.

    References
Top
Abstract
Introduction
Data and methods
Results
Discussion
Conclusion
References

  1. Black D, Morris JN, Smith C, Townsend P, Whitehead M. Inequalities in health: the Black report. London: Penguin , 1988.
  2. Smith GD, Shipley MJ, Rose G. Magnitude and causes of socioeconomic differentials in mortality: further evidence from the Whitehall study. J Epidemiol Community Health 1990; 44: 265-270[Abstract/Free Full Text].
  3. Pappas G, Queen S, Hadden W, Fisher G. The increasing disparity in mortality between socioeconomic groups in the United States, 1960 and 1986. N Engl J Med 1993; 329: 103-109[Abstract/Free Full Text].
  4. Adler NE, Boyce T, Chesney MA, Cohen S, Folkman S, Kahn RL, et al. Socioeconomic status and health: the challenge of the gradient. Am Psychol 1994; 49: 15-24[Medline].
  5. Wilkinson RG. Income distribution and life expectancy. BMJ 1992; 304: 165-168.
  6. Wilkinson RG. Unhealthy societies: the afflictions of inequality. London: Routledge , 1996.
  7. Kennedy BP, Kawachi I, Prothrow-Stith D. Income distribution and mortality: cross sectional ecological study of the Robin Hood index in the United States [correction appears in BMJ 1996;312:1194]. BMJ 1996; 312: 1004-1007[Abstract/Free Full Text].
  8. Kaplan GA, Pamuk ER, Lynch JW, Cohen RD, Balfour JL. Inequality in income and mortality in the United States: analysis of mortality and potential pathways. BMJ 1996; 312: 999-1003[Abstract/Free Full Text].
  9. Ben Shlomo Y, White IR, Marmot M. Does the variation in the socioeconomic characteristics of an area affect mortality? BMJ 1996; 312: 1013-1014[Free Full Text].
  10. Wilkinson RG. Health inequalities: relative or absolute material standards. BMJ 1997; 314: 591-595[Free Full Text].
  11. Smith GD. Income inequality and mortality: why are they related? BMJ 1996; 312: 987-988[Free Full Text].
  12. Kawachi I, Kennedy BP, Lochner K, Prothrow-Stith D. Social capital, income inequality, and mortality. Am J Public Health 1997; 87: 1491-1498[Abstract/Free Full Text].
  13. Gravelle H. How much of the relation between population mortality and unequal distribution of income is a statistical artefact? BMJ 1998; 316: 382-385[Free Full Text].
  14. Fiscella K, Franks P. Poverty or income inequality as predictor of mortality: longitudinal cohort study. BMJ 1997; 314: 1724-1727[Abstract/Free Full Text].
  15. Blalock H. Contextual effects models: theoretical and methodological issues. Am Rev Sociol 1984; 10: 353-372.
  16. Hauser R. Contextual effects models: a cautionary tale. Am J Sociol 1970; 75: 645-664.
  17. Hagan HC, Moriarty DG, Zack MM, Scherr PA, Brackbill R. Measuring health-related quality of life for public health surveillance. Public Health Rep 1994; 109: 665-672[Medline].
  18. Idler El, Benyamini Y. Self-rated health and mortality: a review of twenty-seven community studies. J Health Soc Behav 1997; 38: 21-37[Medline].
  19. Ferraro KF, Farmer MM, Wybraniec JA. Health trajectories: long-term dynamics among black and white adults. J Health Soc Behav 1997; 38: 38-54[Medline].
  20. Idler EL, Kasl S. Self-ratings of health: do they also predict change in functional ability? J Gerontol B Psychol Sci Soc Sci 1995; 50B: S344-S353[Abstract].
  21. Mor V, Murphy J, Materson-Allen S, Willey C, Razmpour A, Jackson ME, et al. Risk of functional decline among well elders. J Clin Epidemiol 1989; 42: 895-904[Medline].
  22. Wilcox VL, Kasl SV, Idler EL. Self-rated health and physical disability in elderly survivors of a major medical event. J Gerontol B Psychol Sci Soc Sci 1996; 51B: S96-104[Abstract].
  23. Farmer MM, Ferraro KF. Distress and perceived health: mechanisms of health decline. J Health Soc Behav 1997; 39: 298-311.
  24. Atkinson AB, Rainwater L, Smeeding TM. Income distribution in OECD countries: evidence from the Luxembourg income study. Paris: Organisation for Economic Cooperation and Development , 1995.
  25. Wilkinson RG. Income inequality summarises the health burden of individual deprivation. BMJ 1997; 314: 1728-1729.
  26. Kawachi I, Levine S, Miller SM, Lasch K, Amick B. Income inequality and life expectancy: theory, research and policy. Boston: New England Medical Center , 1994(Joint Program on Society and Health, working paper No 94-2.)

(Accepted 18 June 1998)

© BMJ 1998
Add to CiteULike CiteULike   Add to Complore Complore   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us   Add to Digg Digg   Add to Reddit Reddit   Add to StumbleUpon StumbleUpon   Add to Technorati Technorati    What's this?

Relevant Articles

Child wellbeing and income inequality in rich societies: ecological cross sectional study
Kate E Pickett and Richard G Wilkinson
BMJ 2007 335: 1080. [Abstract] [Full Text] [PDF]

Income distribution, socioeconomic status, and self rated health in US
Jeffrey Milyo, Bruce P Kennedy, Ichiro Kawachi, Roberta Glass, and Deborah Prothrow-Stith
BMJ 1999 318: 1417. [Extract] [Full Text]

This article has been cited by other articles:

  • Cagney, K. A., Glass, T. A., Skarupski, K. A., Barnes, L. L., Schwartz, B. S., Mendes de Leon, C. F. (2009). Neighborhood-Level Cohesion and Disorder: Measurement and Validation in Two Older Adult Urban Populations. J Gerontol B Psychol Sci Soc Sci 64B: 415-424 [Abstract] [Full text]  
  • Willson, A. E. (2009). `Fundamental Causes' of Health Disparities: A Comparative Analysis of Canada and the United States. International Sociology 24: 93-113 [Abstract]  
  • Gadalla, T. M., Fuller-Thomson, E. (2008). Examining the Lag Time Between State-Level Income Inequality and Individual Disabilities: A Multilevel Analysis. AJPH 98: 2187-2190 [Abstract] [Full text]  
  • Kondo, N, Subramanian, S V, Kawachi, I, Takeda, Y, Yamagata, Z (2008). Economic recession and health inequalities in Japan: analysis with a national sample, 1986-2001. J. Epidemiol. Community Health 62: 869-875 [Abstract] [Full text]  
  • Wilkinson, R. G., Pickett, K. E. (2008). Income Inequality and Socioeconomic Gradients in Mortality. AJPH 98: 699-704 [Abstract] [Full text]  
  • Pickett, K. E, Wilkinson, R. G (2007). Child wellbeing and income inequality in rich societies: ecological cross sectional study. BMJ 335: 1080-1080 [Abstract] [Full text]  
  • Bobak, M., Murphy, M., Rose, R., Marmot, M. (2007). Societal characteristics and health in the former communist countries of Central and Eastern Europe and the former Soviet Union: a multilevel analysis. J. Epidemiol. Community Health 61: 990-996 [Abstract] [Full text]  
  • Rostila, M. (2007). Social capital and health in European welfare regimes: a multilevel approach. Journal of European Social Policy 17: 223-239 [Abstract]  
  • Collison, D., Dey, C., Hannah, G., Stevenson, L. (2007). Income inequality and child mortality in wealthy nations. J Public Health (Oxf) 29: 114-117 [Abstract] [Full text]  
  • Yorkston, E., Turner, C., Schluter, P. J, McClure, R. (2007). Quantifying the effect of a community-based injury prevention program in Queensland using a generalized estimating equation approach. Inj. Prev. 13: 191-196 [Abstract] [Full text]  
  • Backlund, E., Rowe, G., Lynch, J., Wolfson, M. C, Kaplan, G. A, Sorlie, P. D (2007). Income inequality and mortality: a multilevel prospective study of 521 248 individuals in 50 US states. Int J Epidemiol 36: 590-596 [Abstract] [Full text]  
  • Woolf, S. H., Johnson, R. E., Phillips, R. L. Jr, Philipsen, M. (2007). Giving Everyone the Health of the Educated: An Examination of Whether Social Change Would Save More Lives Than Medical Advances. AJPH 97: 679-683 [Abstract] [Full text]  
  • Henriksson, G., Allebeck, P., Weitoft, G. R., Thelle, D. (2007). Are manual workers at higher risk of death than non-manual employees when living in Swedish municipalities with higher income inequality?. Eur J Public Health 17: 139-144 [Abstract] [Full text]  
  • Pei, X, Rodriguez, E (2006). Provincial income inequality and self-reported health status in China during 1991-7.. J. Epidemiol. Community Health 60: 1065-1069 [Abstract] [Full text]  
  • Sullivan, M. D., Edlund, M. J., Zhang, L., Unutzer, J., Wells, K. B. (2006). Association between mental health disorders, problem drug use, and regular prescription opioid use.. Arch Intern Med 166: 2087-2093 [Abstract] [Full text]  
  • Borrell, L. N., Crawford, N. D. (2006). Race, Ethnicity, and Self-Rated Health Status in the Behavioral Risk Factor Surveillance System Survey. Hispanic Journal of Behavioral Sciences 28: 387-403 [Abstract]  
  • Zimmerman, F. J, Bell, J. F (2006). Income inequality and physical and mental health: testing associations consistent with proposed causal pathways.. J. Epidemiol. Community Health 60: 513-521 [Abstract] [Full text]  
  • Materia, E., Cacciani, L., Bugarini, G., Cesaroni, G., Davoli, M., Mirale, M. P., Vergine, L., Baglio, G., Simeone, G., Perucci, C. A. (2005). Income inequality and mortality in Italy. Eur J Public Health 15: 411-417 [Abstract] [Full text]  
  • Baum, F. (2005). Wealth and health: the need for more strategic public health research. J. Epidemiol. Community Health 59: 542-545 [Abstract] [Full text]  
  • Elgar, F. J., Roberts, C., Parry-Langdon, N., Boyce, W. (2005). Income inequality and alcohol use: a multilevel analysis of drinking and drunkenness in adolescents in 34 countries. Eur J Public Health 15: 245-250 [Abstract] [Full text]  
  • Carlson, P. (2005). Relatively poor, absolutely ill? A study of regional income inequality in Russia and its possible health consequences. J. Epidemiol. Community Health 59: 389-394 [Abstract] [Full text]  
  • Shi, L., Macinko, J., Starfield, B., Politzer, R., Wulu, J., Xu, J. (2005). Primary Care, Social Inequalities, and All-Cause, Heart Disease, and Cancer Mortality in US Counties, 1990. AJPH 95: 674-680 [Abstract] [Full text]  
  • Blazer, D. G., Sachs-Ericsson, N., Hybels, C. F. (2005). Perception of Unmet Basic Needs as a Predictor of Mortality Among Community-Dwelling Older Adults. AJPH 95: 299-304 [Abstract] [Full text]  
  • De Vogli, R., Mistry, R., Gnesotto, R., Cornia, G. A. (2005). Has the relation between income inequality and life expectancy disappeared? Evidence from Italy and top industrialised countries. J. Epidemiol. Community Health 59: 158-162 [Abstract] [Full text]  
  • Castilla, E. J. (2004). Organizing Health Care: A Comparative Analysis of National Institutions and Inequality Over Time. International Sociology 19: 403-435 [Abstract]  
  • Tay, J. B, Kelleher, C. C, Hope, A., Barry, M., Gabhainn, S. N., Sixsmith, J. (2004). Influence of sociodemographic and neighbourhood factors on self rated health and quality of life in rural communities: findings from the Agriproject in the Republic of Ireland. J. Epidemiol. Community Health 58: 904-911 [Abstract] [Full text]  
  • Brown, A. F., Ettner, S. L., Piette, J., Weinberger, M., Gregg, E., Shapiro, M. F., Karter, A. J., Safford, M., Waitzfelder, B., Prata, P. A., Beckles, G. L. (2004). Socioeconomic Position and Health among Persons with Diabetes Mellitus: A Conceptual Framework and Review of the Literature. Epidemiol Rev 26: 63-77 [Full text]  
  • Subramanian, S. V., Kawachi, I. (2004). Income Inequality and Health: What Have We Learned So Far?. Epidemiol Rev 26: 78-91 [Full text]  
  • Fritzell, J., Nermo, M., Lundberg, O. (2004). The impact of income: assessing the relationship between income and health in Sweden. Scand J Public Health 32: 6-16 [Abstract]  
  • Macinko, J. A., Shi, L., Starfield, B., Wulu, J. T. Jr. (2003). Income Inequality and Health: A Critical Review of the Literature. Med Care Res Rev 60: 407-452 [Abstract]  
  • Subramanian, S., Kawachi, I. (2003). The association between state income inequality and worse health is not confounded by race. Int J Epidemiol 32: 1022-1028 [Abstract] [Full text]  
  • Subramanian, S., Kawachi, I. (2003). Response: In defence of the income inequality hypothesis. Int J Epidemiol 32: 1037-1040 [Full text]  
  • Kim Bao Giang, , Allebeck, P. (2003). Self-reported illness and use of health services in a rural district of Vietnam: findings from an epidemiological field laboratory. Scand J Public Health 31: 52-58 [Abstract]  
  • Subramanian, S V, Delgado, I, Jadue, L, Vega, J, Kawachi, I (2003). Income inequality and health: multilevel analysis of Chilean communities. J. Epidemiol. Community Health 57: 844-848 [Abstract] [Full text]  
  • Sanmartin, C, Ross, N A, Tremblay, S, Wolfson, M, Dunn, J R, Lynch, J (2003). Labour market income inequality and mortality in North American metropolitan areas. J. Epidemiol. Community Health 57: 792-797 [Abstract] [Full text]  
  • Shi, L., Macinko, J., Starfield, B., Wulu, J., Regan, J., Politzer, R. (2003). The Relationship Between Primary Care, Income Inequality, and Mortality in US States, 1980-1995. J Am Board Fam Med 16: 412-422 [Abstract] [Full text]  
  • McLeod, C. B., Lavis, J. N., Mustard, C. A., Stoddart, G. L. (2003). Income Inequality, Household Income, and Health Status in Canada: A Prospective Cohort Study. AJPH 93: 1287-1293 [Abstract] [Full text]  
  • Osler, M., Christensen, U., Due, P., Lund, R., Andersen, I., Diderichsen, F., Prescott, E. (2003). Income inequality and ischaemic heart disease in Danish men and women. Int J Epidemiol 32: 375-380 [Abstract] [Full text]  
  • Hillemeier, M. M., Lynch, J., Harper, S., Raghunathan, T., Kaplan, G. A. (2003). Relative or Absolute Standards for Child Poverty: A State-Level Analysis of Infant and Child Mortality. AJPH 93: 652-657 [Abstract] [Full text]  
  • Blakely, T, Atkinson, J, O'Dea, D (2003). No association of income inequality with adult mortality within New Zealand: a multi-level study of 1.4 million 25-64 year olds. J. Epidemiol. Community Health 57: 279-284 [Abstract] [Full text]  
  • Shi, L., Forrest, C. B., von Schrader, S., Ng, J. (2003). Vulnerability and the Patient-Practitioner Relationship: The Roles of Gatekeeping and Primary Care Performance. AJPH 93: 138-144 [Abstract] [Full text]  
  • Kim Bao Giang, , Allebeck, P. (2003). Self-reported illness and use of health services in a rural district of Vietnam: findings from an epidemiological field laboratory. Scand J Public Health 31: 52-58 [Abstract]  
  • Kawachi, I, Subramanian, S V, Almeida-Filho, N (2002). A glossary for health inequalities. J. Epidemiol. Community Health 56: 647-652 [Abstract] [Full text]  
  • Weich, S, Lewis, G, Jenkins, S P (2002). Income inequality and self rated health in Britain. J. Epidemiol. Community Health 56: 436-441 [Abstract] [Full text]  
  • Porta, M., Borrell, C., Copete, J. L (2002). Commentary: Theory in the fabric of evidence on the health effects of inequalities in income distribution. Int J Epidemiol 31: 543-546 [Full text]  
  • Krokstad, S, Kunst, A E, Westin, S (2002). Trends in health inequalities by educational level in a Norwegian total population study. J. Epidemiol. Community Health 56: 375-380 [Abstract] [Full text]  
  • Krokstad, S., Westin, S. (2002). Health inequalities by socioeconomic status among men in the Nord-Trondelag Health Study, Norway. Scand J Public Health 30: 113-124 [Abstract]  
  • Elliott, L J, Blanchard, J F, Beaudoin, C M, Green, C G, Nowicki, D L, Matusko, P, Moses, S (2002). Geographical variations in the epidemiology of bacterial sexually transmitted infections in Manitoba, Canada. Sex. Transm. Infect. 78: i139-144 [Abstract] [Full text]  
  • Kawachi, I (2002). Income inequality and economic residential segregation. J. Epidemiol. Community Health 56: 165-166 [Full text]  
  • Sturm, R. (2002). The Effects Of Obesity, Smoking, And Drinking On Medical Problems And Costs. Health Aff (Millwood) 21: 245-253 [Abstract] [Full text]  
  • Mackenbach, J. P (2002). Income inequality and population health. BMJ 324: 1-2 [Full text]  
  • Osler, M., Prescott, E., Gronbak, M., Christensen, U., Due, P., Engholm, G. (2002). Income inequality, individual income, and mortality in Danish adults: analysis of pooled data from two cohort studies. BMJ 324: 13-13 [Abstract] [Full text]  
  • Shibuya, K., Hashimoto, H., Yano, E. (2002). Individual income, income distribution, and self rated health in Japan: cross sectional analysis of nationally representative sample. BMJ 324: 16-16 [Abstract] [Full text]  
  • Sturm, R., Gresenz, C. R. (2002). Relations of income inequality and family income to chronic medical conditions and mental health disorders: national survey. BMJ 324: 20-20 [Abstract] [Full text]  
  • Kelleher, C, Timoney, A, Friel, S, McKeown, D (2002). Indicators of deprivation, voting patterns, and health status at area level in the Republic of Ireland. J. Epidemiol. Community Health 56: 36-44 [Abstract] [Full text]  
  • Grundy, E, Holt, G (2001). The socioeconomic status of older adults: How should we measure it in studies of health inequalities?. J. Epidemiol. Community Health 55: 895-904 [Abstract] [Full text]  
  • Rauh, V. A., Andrews, H. F., Garfinkel, R. S. (2001). The Contribution of Maternal Age to Racial Disparities in Birthweight: A Multilevel Perspective. AJPH 91: 1815-1824 [Abstract] [Full text]  
  • Veugelers, P. J., Yip, A. M., Kephart, G. (2001). Proximate and Contextual Socioeconomic Determinants of Mortality: Multilevel Approaches in a Setting with Universal Health Care Coverage. Am J Epidemiol 154: 725-732 [Abstract] [Full text]  
  • Bhatia, R., Katz, M. (2001). Estimation of Health Benefits From a Local Living Wage Ordinance. AJPH 91: 1398-1402 [Abstract] [Full text]  
  • Shi, L., Starfield, B. (2001). The Effect of Primary Care Physician Supply and Income Inequality on Mortality Among Blacks and Whites in US Metropolitan Areas. AJPH 91: 1246-1250 [Abstract] [Full text]  
  • Mellor, J. M., Milyo, J. (2001). Reexamining the Evidence of an Ecological Association between Income Inequality and Health. Journal of Health Politics, Policy and Law 26: 487-522 [Abstract]  
  • Kawachi, I., Blakely, T. A. (2001). When Economists and Epidemiologists Disagree.... Journal of Health Politics, Policy and Law 26: 533-542  
  • Marmot, M., Wilkinson, R. G (2001). Psychosocial and material pathways in the relation between income and health: a response to Lynch et al. BMJ 322: 1233-1236 [Full text]  
  • WEICH, S., LEWIS, G., JENKINS, S. P. (2001). Income inequality and the prevalence of common mental disorders in Britain. Br. J. Psychiatry 178: 222-227 [Abstract] [Full text]  
  • Wasylenki, D. A. (2001). Inner city health. CMAJ 164: 214-215 [Full text]  
  • Blakely, T., Woodward, A., Razum, O., Ross, N. A, Wolfson, M., Berthelot, J.-M., Dunn, J., Kaplan, G., Lynch, J. (2000). Income inequality and mortality in Canada and the United States. BMJ 321: 1532-1532 [Full text]  
  • Kahn, R. S, Wise, P. H, Kennedy, B. P, Kawachi, I. (2000). State income inequality, household income, and maternal mental and physical health: cross sectional national survey. BMJ 321: 1311-1315 [Abstract] [Full text]  
  • Hillen, T., Schaub, R., Hiestermann, A., Kirschner, W., Robra, B.-P. (2000). Self rating of health is associated with stressful life events, social support and residency in East and West Berlin shortly after the fall of the wall. J. Epidemiol. Community Health 54: 575-580 [Abstract] [Full text]  
  • Lynch, J. W, Smith, G. D., Kaplan, G. A, House, J. S (2000). Income inequality and mortality: importance to health of individual income, psychosocial environment, or material conditions. BMJ 320: 1200-1204 [Full text]  
  • Ross, N. A, Wolfson, M. C, Dunn, J. R, Berthelot, J.-M., Kaplan, G. A, Lynch, J. W (2000). Relation between income inequality and mortality in Canada and in the United States: cross sectional assessment using census data and vital statistics. BMJ 320: 898-902 [Abstract] [Full text]  
  • Wolfson, M., Kaplan, G., Lynch, J., Ross, N., Backlund, E., Gravelle, H., Wilkinson, R. G (1999). Relation between income inequality and mortality: empirical demonstration • Diminishing returns to aggregate level studies • Two pathways, but how much do they diverge?. BMJ 319: 953-957 [Abstract] [Full text]  
  • Milyo, J., Kennedy, B. P, Kawachi, I., Glass, R., Prothrow-Stith, D. (1999). Income distribution, socioeconomic status, and self rated health in US. BMJ 318: 1417a-1417 [Full text]  

Rapid Responses:

Read all Rapid Responses

Authors ignore data in study of income inequality and health
Jeffrey Milyo
bmj.com, 20 Jan 1999 [Full text]
Access jobs at BMJ Careers
Whats new online at Student BMJ