Article

Socioeconomic Status in Health Research: One Size Does Not Fit All

Center on Social Disparities in Health and Department of Family and Community Medicine, University of California, San Francisco 94143-0900, USA.
JAMA The Journal of the American Medical Association (Impact Factor: 35.29). 01/2006; 294(22):2879-88. DOI: 10.1001/jama.294.22.2879
Source: PubMed

ABSTRACT

Problems with measuring socioeconomic status (SES)-frequently included in clinical and public health studies as a control variable and less frequently as the variable(s) of main interest-could affect research findings and conclusions, with implications for practice and policy. We critically examine standard SES measurement approaches, illustrating problems with examples from new analyses and the literature. For example, marked racial/ethnic differences in income at a given educational level and in wealth at a given income level raise questions about the socioeconomic comparability of individuals who are similar on education or income alone. Evidence also shows that conclusions about nonsocioeconomic causes of racial/ethnic differences in health may depend on the measure-eg, income, wealth, education, occupation, neighborhood socioeconomic characteristics, or past socioeconomic experiences-used to "control for SES," suggesting that findings from studies that have measured limited aspects of SES should be reassessed. We recommend an outcome- and social group-specific approach to SES measurement that involves (1) considering plausible explanatory pathways and mechanisms, (2) measuring as much relevant socioeconomic information as possible, (3) specifying the particular socioeconomic factors measured (rather than SES overall), and (4) systematically considering how potentially important unmeasured socioeconomic factors may affect conclusions. Better SES measures are needed in data sources, but improvements could be made by using existing information more thoughtfully and acknowledging its limitations.

Download full-text

Full-text

Available from: Catherine Cubbin
  • Source
    • "The authors have declared that no competing interests exist. will aid health officials in resource allocation and provision of health services so as to reduce stroke disparities[11]. Thus, the objective of this study was to investigate geographic and temporal disparities in stroke, and identify areas with statistically significant higher risk of stroke hospitalizations and deaths in Florida. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Background: Identifying geographic areas with significantly high risks of stroke is important for informing public health prevention and control efforts. The objective of this study was to investigate geographic and temporal patterns of stroke hospitalization and mortality risks so as to identify areas and seasons with significantly high burden of the disease in Florida. The information obtained will be useful for resource allocation for disease prevention and control. Methods: Stroke hospitalization and mortality data from 1992 to 2012 were obtained from the Florida Agency for Health Care Administration. Age-adjusted stroke hospitalization and mortality risks for time periods 1992-94, 1995-97, 1998-2000, 2001-03, 2004-06, 2007-09 and 2010-12 were computed at the county spatial scale. Global Moran's I statistics were computed for each of the time periods to test for evidence of global spatial clustering. Local Moran indicators of spatial association (LISA) were also computed to identify local areas with significantly high risks. Results: There were approximately 1.5 million stroke hospitalizations and over 196,000 stroke deaths during the study period. Based on global Moran's I tests, there was evidence of significant (p<0.05) global spatial clustering of stroke mortality risks but no evidence (p>0.05) of significant global clustering of stroke hospitalization risks. However, LISA showed evidence of local spatial clusters of both hospitalization and mortality risks with significantly high risks being observed in the north while the south had significantly low risks of stroke deaths. There were decreasing temporal trends and seasonal patterns of both hospitalization and mortality risks with peaks in the winter. Conclusions: Although stroke hospitalization and mortality risks have declined in the past two decades, disparities continue to exist across Florida and it is evident from the results of this study that north Florida may, in fact, be part of the stroke belt despite not being in any of the traditional stroke belt states. These findings are useful for guiding public health efforts to reduce/eliminate inequities in stroke outcomes and inform policy decisions. There is need to continually identify populations with significantly high risks of stroke to better guide the targeting of limited resources to the highest risk populations.
    Full-text · Article · Jan 2016 · PLoS ONE
  • Source
    • "A longstanding and well-established literature has described a positive relationship between income and other measures of socioeconomic status such as wealth or education and health and well-being (Adler & Rehkopf, 2008; Marmot & Wilkinson, 2005; Braveman et al., 2005; Lantz et al., 1998). The Earned Income Tax Credit (EITC), a broad-based income support program that raises millions of Americans out of poverty, has been well covered by others in this issue. "
    [Show abstract] [Hide abstract]
    ABSTRACT: A longstanding and well-established literature has described a positive relationship between income and other measures of socioeconomic status such as wealth or education and health and well-being (Adler & Rehkopf, 2008; Marmot & Wilkinson, 2005; Braveman et al., 2005; Lantz et al., 1998). The Earned Income Tax Credit (EITC), a broad-based income support program that raises millions of Americans out of poverty, has been well covered by others in this issue. Examining the effect of changes in EITC benefits and their relation to health outcomes is especially useful in deepening our understanding of how income impacts health, because these policy changes can provide a source of income variation that is relatively exogenous to individual or household characteristics. Perhaps more importantly, it provides an opportunity to broaden our views of both health and economic policy by exploring the relationship between them. [http://bit.ly/1ljhSEZ]
    Full-text · Article · Nov 2015
  • Source
    • "The literature has consistently shown that intergenerational income mobility has remained constant in the US over several decades (Lee & Solon, 2009; McIntosh & Munk, 2009). Moreover, healthy parents from high-SES families are likely to be on a trajectory for excellent health later in life (Van de Mheen et al., 1998; Braveman et al., 2005). Healthy parents with socioeconomic resources would then be able to provide material conditions for their children to be healthy (Teasdale et al., 1990). "
    [Show abstract] [Hide abstract]
    ABSTRACT: This study used US National Longitudinal Study of Youth data to explore how exposure to different socioeconomic conditions (proxied by maternal education) before birth can shape child weight. Using endogenous selection regression models, the findings suggest that educational selectivity affects weight gain. Mothers whose mothers graduated from high school were more likely to complete high school, and mothers reared in an intact family had higher levels of education. However, mothers who had given birth as a teenager had the same educational outcomes as mothers who gave birth in their post-teenage years. Based on this intergenerational educational selectivity, caretaking (e.g. breast-feeding) was found to be associated with a lower child body mass index (BMI), while negative maternal characteristics (e.g. mothers with high BMIs) were associated with higher child BMIs. Thus, educational selectivity influences child health through values passed on to the child and the lifestyle in which the child is reared. Maternal education may be tied to parenting, which relates to child obesity risk.
    Full-text · Article · Aug 2015 · Journal of Biosocial Science
Show more