Health disparities across the lifespan: Meaning, methods and mechanisms.

Over the past two decades, exponential growth of empirical research has fueled markedly increased concern about health disparities. In this paper, we show the progression of research on socioeconomic status (SES) and health through several eras. The first era reflected an implicit threshold model of the association of poverty and health. The second era produced evidence for a graded association between SES and health where each improvement in education, income, occupation, or wealth is associated with better health outcomes. Moving from description of the association to exploration of pathways, the third era focused on mechanisms linking SES and health, whereas the fourth era expanded on mechanisms to consider multilevel influences, and a fifth era added a focus on interactions among factors, not just their main effects or contributions as mediators. Questions from earlier eras remain active areas of research, while later eras add depth and complexity

Alternative causal inference methods in population health research: Evaluating tradeoffs and triangulating evidence.

Population health researchers from different fields often address similar substantive questions but rely on different study designs, reflecting their home disciplines. This is especially true in studies involving causal inference, for which semantic and substantive differences inhibit interdisciplinary dialogue and collaboration. In this paper, we group nonrandomized study designs into two categories: those that use confounder-control (such as regression adjustment or propensity score matching) and those that rely on an instrument (such as instrumental variables, regression discontinuity, or differences-in-differences approaches). Using the Shadish, Cook, and Campbell framework for evaluating threats to validity, we contrast the assumptions, strengths, and limitations of these two approaches and illustrate differences with examples from the literature on education and health. Across disciplines, all methods to test a hypothesized causal relationship involve unverifiable assumptions, and rarely is there clear justification for exclusive reliance on one method. Each method entails trade-offs between statistical power, internal validity, measurement quality, and generalizability. The choice between confounder-control and instrument-based methods should be guided by these tradeoffs and consideration of the most important limitations of previous work in the area. Our goals are to foster common understanding of the methods available for causal inference in population health research and the tradeoffs between them; to encourage researchers to objectively evaluate what can be learned from methods outside one's home discipline; and to facilitate the selection of methods that best answer the investigator's scientific questions

Indirect effect of financial strain on daily cortisol output through daily negative to positive affect index in the Coronary Artery Risk Development in Young Adults Study

Daily affect is important to health and has been linked to cortisol. The combination of high negative affect and low positive affect may have a bigger impact on increasing HPA axis activity than either positive or negative affect alone. Financial strain may both dampen positive affect as well as increase negative affect, and thus provides an excellent context for understanding the associations between daily affect and cortisol. Using random effects mixed modeling with maximum likelihood estimation, we examined the relationship between self-reported financial strain and estimated mean daily cortisol level (latent cortisol variable), based on six salivary cortisol assessments throughout the day, and whether this relationship was mediated by greater daily negative to positive affect index measured concurrently in a sample of 776 Coronary Artery Risk Development in Young Adults (CARDIA) Study participants. The analysis revealed that while no total direct effect existed for financial strain on cortisol, there was a significant indirect effect of high negative affect to low positive affect, linking financial strain to elevated cortisol. In this sample, the effects of financial strain on cortisol through either positive affect or negative affect alone were not significant. A combined affect index may be a more sensitive and powerful measure than either negative or positive affect alone, tapping the burden of chronic financial strain, and its effects on biology

Indirect effect of financial strain on daily cortisol output through daily negative to positive affect index in the Coronary Artery Risk Development in Young Adults Study

Daily affect is important to health and has been linked to cortisol. The combination of high negative affect and low positive affect may have a bigger impact on increasing HPA axis activity than either positive or negative affect alone. Financial strain may both dampen positive affect as well as increase negative affect, and thus provides an excellent context for understanding the associations between daily affect and cortisol. Using random effects mixed modeling with maximum likelihood estimation, we examined the relationship between self-reported financial strain and estimated mean daily cortisol level (latent cortisol variable), based on six salivary cortisol assessments throughout the day, and whether this relationship was mediated by greater daily negative to positive affect index measured concurrently in a sample of 776 Coronary Artery Risk Development in Young Adults (CARDIA) Study participants. The analysis revealed that while no total direct effect existed for financial strain on cortisol, there was a significant indirect effect of high negative affect to low positive affect, linking financial strain to elevated cortisol. In this sample, the effects of financial strain on cortisol through either positive affect or negative affect alone were not significant. A combined affect index may be a more sensitive and powerful measure than either negative or positive affect alone, tapping the burden of chronic financial strain, and its effects on biology

Electronic Health Records and Population Health Research

Adoption of electronic health records (EHRs) by clinical practices and hospitals in the US has increased substantially since 2009, and offers opportunities for population health researchers to access rich structured and unstructured clinical data on large, diverse, and geographically distributed populations. However, because EHRs are intended for clinical and administrative use, the data must be curated for effective use in research. We describe EHRs, examine their use in population health research, and compare the strengths and limitations of these applications to traditional epidemiologic methods. To date, EHR data have primarily been used to validate prior findings, to study specific diseases and population subgroups, to examine environmental and social factors and stigmatized conditions, to develop and implement predictive models, and to evaluate natural experiments. Although primary data collection may provide more reliable data and better population retention, EHR-based studies are less expensive and require less time to complete. In addition, large patient samples that can be readily identified from EHR data enable researchers to evaluate simultaneously multiple risk factors and/or outcomes while maintaining study power. In addition to current advantages, improved capture of social, behavioral, environmental, and genetic data, and use of natural language processing, clinical biobanks, and personal sensing via smartphone should further enable EHR researchers to understand complex diseases with multifactorial etiologies. Integrating emerging technologies with clinical care could lead to innovative approaches to precision public health, reduce health care spending on individuals, and directly improve population health

The Differential Effects of Sleep Quality and Quantity on the Relationship between SES and Health

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73341/1/j.1749-6632.1999.tb08162.x.pd

Obesity and the food environment: income and ethnicity differences among people with diabetes: the Diabetes Study of Northern California (DISTANCE).

ObjectiveIt is unknown whether any association between neighborhood food environment and obesity varies according to individual income and/or race/ethnicity. The objectives of this study were to test whether there was an association between food environments and obesity among adults with diabetes and whether this relationship differed according to individual income or race/ethnicity.Research design and methodsSubjects (n = 16,057) were participants in the Diabetes Study of Northern California survey. Kernel density estimation was used to create a food environment score for each individual's residence address that reflected the mix of healthful and unhealthful food vendors nearby. Logistic regression models estimated the association between the modeled food environment and obesity, controlling for confounders, and testing for interactions between food environment and race/ethnicity and income.ResultsThe authors found that more healthful food environments were associated with lower obesity in the highest income groups (incomes 301-600% and >600% of U.S. poverty line) among whites, Latinos, and Asians. The association was negative, but smaller and not statistically significant, among high-income blacks. On the contrary, a more healthful food environment was associated with higher obesity among participants in the lowest-income group (<100% poverty threshold), which was statistically significant for black participants in this income category.ConclusionsThese findings suggest that the availability of healthful food environments may have different health implications when financial resources are severely constrained

Social isolation and all-cause mortality: a population-based cohort study in Denmark.

Social isolation is associated with increased mortality. Meta-analytic results, however, indicate heterogeneity in effect sizes. We aimed to provide new evidence to the association between social isolation and mortality by conducting a population-based cohort study. We reconstructed the Berkman and Syme's social network index (SNI), which combines four components of social networks (partnership, interaction with family/friends, religious activities, and membership in organizations/clubs) into an index, ranging from 0/1 (most socially isolated) to 4 (least socially isolated). We estimated cumulative mortality and adjusted mortality rate ratios (MRR) associated with SNI. We adjusted for potential important confounders, including psychiatric and somatic status, lifestyle, and socioeconomic status. Cumulative 7-year mortality in men was 11% for SNI 0/1 and 5.4% for SNI 4 and in women 9.6% for SNI 0/1 and 3.9% for SNI 4. Adjusted MRRs comparing SNI 0/1 with SNI 4 were 1.7 (95% CI: 1.1-2.6) among men and 1.6 (95% CI: 0.83-2.9) among women. Having no partner was associated with an adjusted MRR of 1.5 (95% CI: 1.2-2.1) for men and 1.7 (95% CI: 1.2-2.4) for women. In conclusion, social isolation was associated with 60-70% increased mortality. Having no partner was associated with highest MRR