S. V. Subramanian

Race/Ethnicity, Gender, and Monitoring Socioeconomic Gradients in Health: A Comparison of Area-Based Socioeconomic Measures—The Public Health Disparities Geocoding Project

Use of multilevel frameworks and area-based socioeconomic measures (ABSMs) for public health monitoring can potentially overcome the absence of socioeconomic data in most US public health surveillance systems. To assess whether ABSMs can meaningfully be used for diverse race/ethnicity-gender groups, we geocoded and linked public health surveillance data from Massachusetts and Rhode Island to 1990 block group, tract, and zip code ABSMs. Outcomes comprised death, birth, cancer incidence, tuberculosis, sexually transmitted infections, childhood lead poisoning, and nonfatal weapons-related injuries. Among White, Black, and Hispanic women and men, measures of economic deprivation (e.g., percentage below poverty) were most sensitive to expected socioeconomic gradients in health, with the most consistent results and maximal geocoding linkage evident for tract-level analyses.

Demographic and health surveys: a profile

Demographic and Health Surveys (DHS) are comparable nationally representative household surveys that have been conducted in more than 85 countries worldwide since 1984. The DHS were initially designed to expand on demographic, fertility and family planning data collected in the World Fertility Surveys and Contraceptive Prevalence Surveys, and continue to provide an important resource for the monitoring of vital statistics and population health indicators in low- and middle-income countries. The DHS collect a wide range of objective and self-reported data with a strong focus on indicators of fertility, reproductive health, maternal and child health, mortality, nutrition and self-reported health behaviours among adults. Key advantages of the DHS include high response rates, national coverage, high quality interviewer training, standardized data collection procedures across countries and consistent content over time, allowing comparability across populations cross-sectionally and over time. Data from DHS facilitate epidemiological research focused on monitoring of prevalence, trends and inequalities. A variety of robust observational data analysis methods have been used, including cross-sectional designs, repeated cross-sectional designs, spatial and multilevel analyses, intra-household designs and cross-comparative analyses. In this profile, we present an overview of the DHS along with an introduction to the potential scope for these data in contributing to the field of micro- and macro-epidemiology. DHS datasets are available for researchers through MEASURE DHS at www.measuredhs.com.

Choosing area based socioeconomic measures to monitor social inequalities in low birth weight and childhood lead poisoning: The Public Health Disparities Geocoding Project (US)

Study objectives: To determine which area based socioeconomic measures can meaningfully be used, at which level of geography, to monitor socioeconomic inequalities in childhood health in the US. Design: Cross sectional analysis of birth certificate and childhood lead poisoning registry data, geocoded and linked to diverse area based socioeconomic measures that were generated at three geographical levels: census tract, block group, and ZIP code. Setting: Two US states: Massachusetts (1990 population=6 016 425) and Rhode Island (1990 population=1 003 464). Participants: All births born to mothers ages 15 to 55 years old who were residents of either Massachusetts (1989–1991; n=267 311) or Rhode Island (1987–1993; n=96 138), and all children ages 1 to 5 years residing in Rhode Island who were screened for lead levels between 1994 and 1996 (n=62 514 children, restricted to first test during the study period). Main results: Analyses of both the birth weight and lead data indicated that: (a) block group and tract socioeconomic measures performed similarly within and across both states, while ZIP code level measures tended to detect smaller effects; (b) measures pertaining to economic poverty detected stronger gradients than measures of education, occupation, and wealth; (c) results were similar for categories generated by quintiles and by a priori categorical cut off points; and (d) the area based socioeconomic measures yielded estimates of effect equal to or augmenting those detected, respectively, by individual level educational data for birth outcomes and by the area based housing measure recommended by the US government for monitoring childhood lead poisoning. Conclusions: Census tract or block group area based socioeconomic measures of economic deprivation could be meaningfully used in conjunction with US public health surveillance systems to enable or enhance monitoring of social inequalities in health in the United States.

Painting a Truer Picture of US Socioeconomic and Racial/Ethnic Health Inequalities: The Public Health Disparities Geocoding Project

OBJECTIVES We describe a method to facilitate routine monitoring of socioeconomic health disparities in the United States. METHODS We analyzed geocoded public health surveillance data including events from birth to death (c. 1990) linked to 1990 census tract (CT) poverty data for Massachusetts and Rhode Island. RESULTS For virtually all outcomes, risk increased with CT poverty, and when we adjusted for CT poverty racial/ethnic disparities were substantially reduced. For half the outcomes, more than 50% of cases would not have occurred if population rates equaled those of persons in the least impoverished CTs. In the early 1990s, persons in the least impoverished CT were the only group meeting Healthy People 2000 objectives a decade ahead. CONCLUSIONS Geocoding and use of the CT poverty measure permit routine monitoring of US socioeconomic inequalities in health, using a common and accessible metric.

Income inequality, mortality, and self rated health: meta-analysis of multilevel studies

Objective To provide quantitative evaluations on the association between income inequality and health. Design Random effects meta-analyses, calculating the overall relative risk for subsequent mortality among prospective cohort studies and the overall odds ratio for poor self rated health among cross sectional studies. Data sources PubMed, the ISI Web of Science, and the National Bureau for Economic Research database. Review methods Peer reviewed papers with multilevel data. Results The meta-analysis included 59 509 857 subjects in nine cohort studies and 1 280 211 subjects in 19 cross sectional studies. The overall cohort relative risk and cross sectional odds ratio (95% confidence intervals) per 0.05 unit increase in Gini coefficient, a measure of income inequality, was 1.08 (1.06 to 1.10) and 1.04 (1.02 to 1.06), respectively. Meta-regressions showed stronger associations between income inequality and the health outcomes among studies with higher Gini (≥0.3), conducted with data after 1990, with longer duration of follow-up (>7 years), and incorporating time lags between income inequality and outcomes. By contrast, analyses accounting for unmeasured regional characteristics showed a weaker association between income inequality and health. Conclusions The results suggest a modest adverse effect of income inequality on health, although the population impact might be larger if the association is truly causal. The results also support the threshold effect hypothesis, which posits the existence of a threshold of income inequality beyond which adverse impacts on health begin to emerge. The findings need to be interpreted with caution given the heterogeneity between studies, as well as the attenuation of the risk estimates in analyses that attempted to control for the unmeasured characteristics of areas with high levels of income inequality.

Future Directions in Residential Segregation and Health Research: A Multilevel Approach

The authors examine the research evidence on the effect of residential segregation on health, identify research gaps, and propose new research directions. Four recommendations are made on the basis of a review of the sociological and social epidemiology literature on residential segregation: (1) develop multilevel research designs to examine the effects of individual, neighborhood, and metropolitan-area factors on health outcomes; (2) continue examining the health effects of residential segregation among African Americans but also initiate studies examining segregation among Hispanics and Asians; (3) consider racial/ethnic segregation along with income segregation and other metropolitan area factors such as poverty concentration and metropolitan governance fragmentation; and (4) develop better conceptual frameworks of the pathways that may link various segregation dimensions to specific health outcomes.

Bonding versus bridging social capital and their associations with self rated health: a multilevel analysis of 40 US communities

Study objective: Few studies have distinguished between the effects of different forms of social capital on health. This study distinguished between the health effects of summary measures tapping into the constructs of community bonding and community bridging social capital. Design: A multilevel logistic regression analysis of community bonding and community bridging social capital in relation to individual self rated fair/poor health. Setting: 40 US communities. Participants: Within community samples of adults (n = 24 835), surveyed by telephone in 2000–2001. Main results: Adjusting for community sociodemographic and socioeconomic composition and community level income and age, the odds ratio of reporting fair or poor health was lower for each 1-standard deviation (SD) higher community bonding social capital (OR = 0.86; 95% = 0.80 to 0.92) and each 1-SD higher community bridging social capital (OR = 0.95; 95% CI = 0.88 to 1.02). The addition of indicators for individual level bonding and bridging social capital and social trust slightly attenuated the associations for community bonding social capital (OR = 0.90, 95% CI = 0.84 to 0.97) and community bridging social capital (OR = 0.96, 95% CI = 0.89 to 1.03). Individual level high formal bonding social capital, trust in members of one’s race/ethnicity, and generalised social trust were each significantly and inversely related to fair/poor health. Furthermore, significant cross level interactions of community social capital with individual race/ethnicity were seen, including weaker inverse associations between community bonding social capital and fair/poor health among black persons compared with white persons. Conclusions: These results suggest modest protective effects of community bonding and community bridging social capital on health. Interventions and policies that leverage community bonding and bridging social capital might serve as means of population health improvement.

Association of Maternal Stature With Offspring Mortality, Underweight, and Stunting in Low- to Middle-Income Countries

CONTEXT Although maternal stature has been associated with offspring mortality and health, the extent to which this association is universal across developing countries is unclear. OBJECTIVE To examine the association between maternal stature and offspring mortality, underweight, stunting, and wasting in infancy and early childhood in 54 low- to middle-income countries. DESIGN, SETTING, AND PARTICIPANTS Analysis of 109 Demographic and Health Surveys in 54 countries conducted between 1991 and 2008. Study population consisted of a nationally representative cross-sectional sample of children aged 0 to 59 months born to mothers aged 15 to 49 years. Sample sizes were 2,661,519 (mortality), 587,096 (underweight), 558,347 (stunting), and 568,609 (wasting) children. MAIN OUTCOME MEASURES Likelihood of mortality, underweight, stunting, or wasting in children younger than 5 years. RESULTS The mean response rate across surveys in the mortality data set was 92.8%. In adjusted models, a 1-cm increase in maternal height was associated with a decreased risk of child mortality (absolute risk difference [ARD], 0.0014; relative risk [RR], 0.988; 95% confidence interval [CI], 0.987-0.988), underweight (ARD, 0.0068; RR, 0.968; 95% CI, 0.968-0.969), stunting (ARD, 0.0126; RR, 0.968; 95% CI, 0.967-0.968), and wasting (ARD, 0.0005; RR, 0.994; 95% CI, 0.993-0.995). Absolute risk of dying among children born to the tallest mothers (> or = 160 cm) was 0.073 (95% CI, 0.072-0.074) and to those born to the shortest mothers (< 145 cm) was 0.128 (95% CI, 0.126-0.130). Country-specific decrease in the risk for child mortality associated with a 1-cm increase in maternal height varied between 0.978 and 1.011, with the decreased risk being statistically significant in 46 of 54 countries (85%) (alpha = .05). CONCLUSION Among 54 low- to middle-income countries, maternal stature was inversely associated with offspring mortality, underweight, and stunting in infancy and childhood.

Ethnicity and nativity status as determinants of perceived social support: testing the concept of familism.

Research has demonstrated a protective effect of social support on health. Social support is most often treated as an independent variable. However, as with disease risk factors, which are not randomly distributed, health-promoting resources such as social support are also systematically patterned. For example, in the USA, family support is thought to be high among Latinos, Mexican Americans in particular. Using data from the Project on Human Development in Chicago Neighborhoods, we explored the relationships between ethnicity/nativity status, socioeconomic status (SES) and perceived social support from family and friends. We also assessed the role of retention of culture-measured as primary language spoken at home-on social support. Finally, we tested whether SES moderated the relationship between ethnicity/nativity status and social support. Foreign and US-born Latinos, most notably, foreign-born Mexicans, reported higher family support compared to non-Latino whites. Primary language spoken at home seems to account for the relationship between ethnicity/nativity and familial social support. Mexican-born and US-born Latino immigrants reported lower social support from family at higher levels of SES. Each ethnic minority group reported lower perception of friend support compared to non-Latino whites. There was a strong SES gradient in subjective support from friends with higher support reported among those with higher SES. This study provides evidence for the notion that Latinos in the USA, specifically foreign-born Mexicans, may rely on family ties for support more than do non-Latino whites. Findings also help identify ethnicity/nativity status, primary language spoken and SES as determinants of social support. Specifically, the higher familial social support found among Latino immigrants may be due to retention of culture. Effect modification by SES suggests that Latinos of lower and higher SES may differ with regard to the traditionally-held value of familism.

Neighbourhood influences on health

Outstanding issues in the neighbourhood research agenda Although multilevel studies help to tease apart contextual from compositional influences on health, they do not in themselves consider other threats to causal inference, particularly selection and endogeneity.1 Endogeneity occurs when people choose to move to a particular neighbourhood—for example, one with cleaner air or medical amenities—because of an existing health problem (reverse causation). Endogeneity can also occur because of the presence of unobserved common prior causes of neighbourhood-level exposures and health outcomes (confounding)—for example, it is commonly supposed that the presence of fast-food outlets in a neighbourhood increases the risk of obesity for local residents. However, it is equally plausible that the decision of fast food franchises to open their businesses in particular locations occurs in response to the tastes of local residents. In this instance, taste for fatty food is an unobserved variable that is related to both the location of outlets as well as the risk of obesity. Generally speaking, epidemiological studies to date have seldom attempted to deal with these threats to causal inference. Arguably, the problems we have described could be overcome by collecting data on a comprehensive range of unobserved variables and controlling for them. Alternatively, analysts could overcome some of the limitations of observational data by importing methods developed in other social sciences, such as instrumental variable estimation.2 Instrumental variable estimation has long been used in economics. The goal is to manipulate the exposure of interest (eg, neighbourhood poverty) by identifying variables (instruments) that cause exogenous …