Mariana C. Arcaya

Area variations in health: A spatial multilevel modeling approach

Both space and membership in geographically-embedded administrative units can produce variations in health, resulting in geographic clusters of good and poor health. Despite important differences between these two types of dependence, one is easily mistaken for the other, and the possibility that both are at work is commonly ignored. We fit a series of hierarchical and spatially-explicit multilevel models to a U.S. county-level life dataset of life expectancy in 1999 to demonstrate approaches for data analysis and interpretation when multiple sources of area-clustering are present. We demonstrate the methods to detect, interpret, and differentiate evidence of spatial and geographic membership effects and discuss key considerations for analyzing data with spatial or/and membership dimensions. We find evidence that life expectancy is driven by both within-state geographic process, and by spatial processes. We argue that considering spatial and membership processes simultaneously yields valuable insights into the patterning of area variations in health.

Inequalities in health: definitions, concepts, and theories.

Individuals from different backgrounds, social groups, and countries enjoy different levels of health. This article defines and distinguishes between unavoidable health inequalities and unjust and preventable health inequities. We describe the dimensions along which health inequalities are commonly examined, including across the global population, between countries or states, and within geographies, by socially relevant groupings such as race/ethnicity, gender, education, caste, income, occupation, and more. Different theories attempt to explain group-level differences in health, including psychosocial, material deprivation, health behavior, environmental, and selection explanations. Concepts of relative versus absolute; dose–response versus threshold; composition versus context; place versus space; the life course perspective on health; causal pathways to health; conditional health effects; and group-level versus individual differences are vital in understanding health inequalities. We close by reflecting on what conditions make health inequalities unjust, and to consider the merits of policies that prioritize the elimination of health disparities versus those that focus on raising the overall standard of health in a population.Individuals from different backgrounds, social groups, and countries enjoy different levels of health. This article defines and distinguishes between unavoidable health inequalities and unjust and preventable health inequities. We describe the dimensions along which health inequalities are commonly examined, including across the global population, between countries or states, and within geographies, by socially relevant groupings such as race/ethnicity, gender, education, caste, income, occupation, and more. Different theories attempt to explain group-level differences in health, including psychosocial, material deprivation, health behavior, environmental, and selection explanations. Concepts of relative versus absolute; dose–response versus threshold; composition versus context; place versus space; the life course perspective on health; causal pathways to health; conditional health effects; and group-level versus individual differences are vital in understanding health inequalities. We close by reflecting on what conditions make health inequalities unjust, and to consider the merits of policies that prioritize the elimination of health disparities versus those that focus on raising the overall standard of health in a population.

Research on neighborhood effects on health in the United States: A systematic review of study characteristics

Neighborhood effects on health research has grown over the past 20 years. While the substantive findings of this literature have been published in systematic reviews, meta-analyses, and commentaries, operational details of the research have been understudied. We identified 7140 multi-level neighborhoods and health papers published on US populations between 1995 and 2014, and present data on the study characteristics of the 256 papers that met our inclusion criteria. Our results reveal rapid growth in neighborhoods and health research in the mid-2000s, illustrate the dominance of observational cross-sectional study designs, and show a heavy reliance on single-level, census-based neighborhood definitions. Socioeconomic indicators were the most commonly analyzed neighborhood variables and body mass was the most commonly studied health outcome. Well-known challenges associated with neighborhood effects research were infrequently acknowledged. We discuss how these results move the agenda forward for neighborhoods and health research.

Effects of Proximate Foreclosed Properties on Individuals’ Systolic Blood Pressure in Massachusetts, 1987 to 2008

Background— No studies have examined the effects of local foreclosure activity on neighbors’ blood pressure, despite the fact that spillover effects of nearby foreclosures include many known risk factors for increased blood pressure. We assessed the extent to which living near foreclosed properties is associated with subsequent systolic blood pressure (SBP) measurements. Methods and Results— We used 6590 geocoded observations collected from 1740 participants in the Framingham Offspring Cohort across 5 waves (1987–2008) of the Framingham Heart Study to create a longitudinal record of exposure to nearby foreclosure activity. We distinguished between real estate–owned foreclosures, which typically sit vacant, and foreclosures purchased by third-party buyers, which are generally put into productive use. Counts of lender-owned foreclosed properties within 100 m of participants’ homes were used to predict measured SBP and odds of being hypertensive. We assessed whether self-reported alcoholic drinks per week and measured body mass index helped to explain the relationship between foreclosure activity and SBP. Each additional real estate–owned foreclosure located within 100 m of a participant’s home was associated with an increase in SBP of 1.71 mm Hg (P=0.03; 95% confidence interval, 0.18–3.24) after adjustment for individual- and area-level confounders but not with odds of hypertension. The presence of foreclosures purchased by third-party buyers was not associated with SBP or with hypertension. Body mass index and alcohol consumption attenuated the effect of living near real estate–owned foreclosures on SBP in fully adjusted models. Conclusions— Real estate–owned foreclosed properties may put nearby neighbors at risk for increased SBP, with higher alcohol consumption and body mass index partially mediating this relationship.

Hospital Differences in Cesarean Deliveries in Massachusetts (US) 2004–2006: The Case against Case-Mix Artifact

Objective We examined the extent to which differences in hospital-level cesarean delivery rates in Massachusetts were attributable to hospital-level, rather than maternal, characteristics. Methods Birth certificate and maternal in-patient hospital discharge records for 2004–06 in Massachusetts were linked. The study population was nulliparous, term, singleton, and vertex births (NTSV) (n = 80,371) in 49 hospitals. Covariates included mothers age, race/ethnicity, education, infant birth weight, gestational age, labor induction (yes/no), hospital shift at time of birth, and preexisting health conditions. We estimated multilevel logistic regression models to assess the likelihood of a cesarean delivery Results Overall, among women with NTSV births, 26.5% births were cesarean, with a range of 14% to 38.3% across hospitals. In unadjusted models, the between-hospital variance was 0.103 (SE 0.022); adjusting for demographic, socioeconomic and preexisting medical conditions did not reduce any hospital-level variation 0.108 (SE 0.023). Conclusion Even after adjusting for both socio-demographic and clinical factors, the chance of a cesarean delivery for NTSV pregnancies varied according to hospital, suggesting the importance of hospital practices and culture in determining a hospitals cesarean rate.

Role of health in predicting moves to poor neighborhoods among Hurricane Katrina survivors

Significance Although neighborhood outcomes and health may influence each other reciprocally, existing studies overwhelmingly focus on neighborhood effects on health. Health’s influence on neighborhood is largely viewed as a nuisance that may bias neighborhood effects estimates. However, if health shapes whether individuals attain better neighborhoods, understanding selection processes may advance both health and urban policy objectives. We follow a socially vulnerable cohort of Hurricane Katrina survivors from 2003–2010 and find that although health was not associated with neighborhood poverty before the disaster, those with pre-Katrina health problems ended up living in poorer neighborhoods years after the storm. Understanding whether and how poor health impedes poverty deconcentration efforts may help inform programs and policies designed to help low-income families move into—and stay in—higher opportunity neighborhoods. In contrast to a large literature investigating neighborhood effects on health, few studies have examined health as a determinant of neighborhood attainment. However, the sorting of individuals into neighborhoods by health status is a substantively important process for multiple policy sectors. We use prospectively collected data on 569 poor, predominantly African American Hurricane Katrina survivors to examine the extent to which health problems predicted subsequent neighborhood poverty. Our outcome of interest was participants’ 2009–2010 census tract poverty rate. Participants were coded as having a health problem at baseline (2003–2004) if they self-reported a diagnosis of asthma, high blood pressure, diabetes, high cholesterol, heart problems, or any other physical health problems not listed, or complained of back pain, migraines, or digestive problems at baseline. Although health problems were not associated with neighborhood poverty at baseline, those with baseline health problems ended up living in higher poverty areas by 2009–2010. Differences persisted after adjustment for personal characteristics, baseline neighborhood poverty, hurricane exposure, and residence in the New Orleans metropolitan area, with baseline health problems predicting a 3.4 percentage point higher neighborhood poverty rate (95% confidence interval: 1.41, 5.47). Results suggest that better health was protective against later neighborhood deprivation in a highly mobile, socially vulnerable population. Researchers should consider reciprocal associations between health and neighborhoods when estimating and interpreting neighborhood effects on health. Understanding whether and how poor health impedes poverty deconcentration efforts may help inform programs and policies designed to help low-income families move to—and stay in—higher opportunity neighborhoods.

Neighborhood Self-Selection: The Role of Pre-Move Health Factors on the Built and Socioeconomic Environment

Residential self-selection bias is a concern in studies of neighborhoods and health. This bias results from health behaviors predicting neighborhood choice. To quantify this bias, we examined associations between pre-move health factors (body mass index, walking, and total physical activity) and post-move neighborhood factors (County Sprawl Index, Census tract socioeconomic status (SES)) in the Nurses’ Health Study (n = 14,159 moves from 1986–2008). Individuals in the highest quartile of pre-move BMI (BMI > 28.4) compared to the lowest quartile (BMI < 22.5) moved to counties that averaged 2.57 points lower on the sprawl index (95% confidence interval −3.55, −1.59) indicating that individuals moved to less dense counties; however, no associations were observed for pre-move walking nor total physical activity. Individuals with higher pre-move BMI tended to move to Census tracts with lower median income and home values and higher levels of poverty. Analyses examining the change in neighborhood environments after a move demonstrated that healthy pre-move behaviors were associated with moves to worse socioeconomic environments. This type of self-selection would bias results downward, underestimating the true relationship between SES and physical activity. Generally, the magnitudes of associations between pre-move health factors and neighborhood measures were small and indicated that residential self-selection was not a major source of bias in analyses in this population.

Urban sprawl and body mass index among displaced Hurricane Katrina survivors

OBJECTIVE Existing research suggests that walkable environments are protective against weight gain, while sprawling neighborhoods may pose health risks. Using prospective data on displaced Hurricane Katrina survivors, we provide the first natural experimental data on sprawl and body mass index (BMI). METHODS The analysis uses prospectively collected pre- (2003-2005) and post-hurricane (2006-2007) data from the Resilience in Survivors of Katrina (RISK) project on 280 displaced Hurricane Katrina survivors who had little control over their neighborhood placement immediately after the disaster. The county sprawl index, a standardized measure of built environment, was used to predict BMI at follow-up, adjusted for baseline BMI and sprawl; hurricane-related trauma; and demographic and economic characteristics. RESULTS Respondents from 8 New Orleans-area counties were dispersed to 76 counties post-Katrina. Sprawl increased by an average of 1.5 standard deviations (30 points) on the county sprawl index. Each one point increase in sprawl was associated with approximately .05kg/m(2) higher BMI in unadjusted models (95%CI: .01-.08), and the relationship was not attenuated after covariate adjustment. CONCLUSIONS We find a robust association between residence in a sprawling county and higher BMI unlikely to be caused by self-selection into neighborhoods, suggesting that the built environment may foster changes in weight.

Do minority and poor neighborhoods have higher access to fast-food restaurants in the United States?

BACKGROUND Disproportionate access to unhealthy foods in poor or minority neighborhoods may be a primary determinant of obesity disparities. We investigated whether fast-food access varies by Census block group (CBG) percent black and poverty. METHODS We measured the average driving distance from each CBG population-weighted centroid to the five closest top ten fast-food chains and CBG percent black and percent below poverty. RESULTS Among 209,091 CBGs analyzed (95.1% of all US CBGs), CBG percent black was positively associated with fast-food access controlling for population density and percent poverty (average distance to fast-food was 3.56 miles closer (95% CI: -3.64, -3.48) in CBGs with the highest versus lowest quartile of percentage of black residents). Poverty was not independently associated with fast-food access. The relationship between fast-food access and race was stronger in CBGs with higher levels of poverty (p for interaction <0.0001). CONCLUSIONS Predominantly black neighborhoods had higher access to fast-food while poverty was not an independent predictor of fast-food access.

A Health Impact Assessment of Proposed Public Transportation Service Cuts and Fare Increases in Boston, Massachusetts (U.S.A.)

Transportation decisions have health consequences that are often not incorporated into policy-making processes. Health Impact Assessment (HIA) is a process that can be used to evaluate health effects of transportation policy. We present a rapid HIA, conducted over eight weeks, evaluating health and economic effects of proposed fare increases and service cuts to Boston, Massachusetts’ public transportation system. We used transportation modeling in concert with tools allowing for quantification and monetization of multiple pathways. We estimated health and economic costs of proposed public transportation system changes to be hundreds of millions of dollars per year, exceeding the budget gap the public transportation authority was required to close. Significant health pathways included crashes, air pollution, and physical activity. The HIA enabled stakeholders to advocate for more modest fare increases and service cuts, which were eventually adopted by decision makers. This HIA was among the first to quantify and monetize multiple pathways linking transportation decisions with health and economic outcomes, using approaches that could be applied in different settings. Including health costs in transportation decisions can lead to policy choices with both economic and public health benefits.