A model of underlying socioeconomic vulnerability in human populations: evidence from variability in population health and implications for public health

Drawing from insights into the variability of complex biologic systems we propose that the health of human populations reflects the interrelationship between underlying vulnerabilities (determined by population-level social and economic factors; e.g., income distribution) and capacities (determined by population-level salutary resources, e.g., social capital) and how populations, shaped by these vulnerabilities and capacities, respond to intermittent stressors (e.g., economic downturns) and protective events (e.g., introduction of a school). Monitoring this dynamic at the population-level can be accomplished by examining not only rates of illness and mortality, but variability in rates, either between populations or within populations over time. We used mortality data from New York City neighborhoods between 1990 and 2001 to test two related hypotheses consistent with this model of population health: (a) There is greater variability in mortality rates at a point in time between neighborhoods that are characterized by socioeconomic vulnerability; and (b) there is greater variability in mortality rates over time within neighborhoods that are characterized by socioeconomic vulnerability. We found that neighborhoods characterized by social and economic vulnerability displayed substantial variability in particular mortality rates. Mortality rates displaying the greatest variability were from causes that may be sensitive to social conditions (e.g., homicide or HIV/AIDS rates). Variability in population health existed both between neighborhoods with underlying vulnerability at one point in time and within vulnerable neighborhoods over time. The results of this analysis are consistent with a theory of underlying socioeconomic vulnerabilities of human populations and suggest that variability in population health may be an important consideration in population health assessment.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/40366/2/Galea_A Model of Underlying Socioeconomic Vulnerability_2005.pd

Social capital and violence in the United States, 1974-1993

Social capital is a characteristic of communities. Cross-sectional studies have shown that social capital is inversely associated with homicide and violent crime. We hypothesized that variations in social capital in US states over time can predict variations in regional homicide mortality both across and within time periods. We analyzed serial crosssectional data for measures of social capital and age-adjusted homicide rates between 1974 and 1993. We used perception of social trust and per capita membership in voluntary associations, obtained from responses to the General Social Surveys, as the principal measures of social capital. We controlled for potential confounding by mean levels of income, urbanization, and region. Measures of perceived trust were strongly inversely correlated with homicide rates in an aggregate cross-sectional analysis (r = -0.51, p < 0.001) and also within each time period. Social capital was an independent predictor of rates of violence when controlling for income, region, and urbanization ( p < 0.001 ). Homicide rates also predicted levels of social capital in adjusted models ( p < 0.001 ). To investigate directionality of this relationship we developed Markov transition matrices that described the change in the states’ levels of social capital and homicide across time intervals. Analysis of the transitional probabilities confirmed that a simple unidirectional association between social capital and violence was not sufficient to describe this association. There is likely an impact of violence on levels of perceived trust in communities that complements the hypothesized effect of social capital on homicide. We conclude that the relationship between social capital and violence over time is non-linear and dynamic. More complex analytic models describing the relationship between violence and ecological social determinants need to be considered.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/40331/2/Galea_Social Capital and Violence in the United States_2002.pd

Pesticide spraying for West Nile virus control and emergency department asthma visits in New York City, 2000

Pyrethroid pesticides were applied via ground spraying to residential neighborhoods in New York City during July–September 2000 to control mosquito vectors of West Nile virus (WNV). Case reports link pyrethroid exposure to asthma exacerbations, but population-level effects on asthma from large-scale mosquito control programs have not been assessed. We conducted this analysis to determine whether widespread urban pyrethroid pesticide use was associated with increased rates of emergency department (ED) visits for asthma. We recorded the dates and locations of pyrethroid spraying during the 2000 WNV season in New York City and tabulated all ED visits for asthma to public hospitals from October 1999 through November 2000 by date and ZIP code of patients’ residences. The association between pesticide application and asthma-related emergency visits was evaluated across date and ZIP code, adjusting for season, day of week, and daily temperature, precipitation, particulate, and ozone levels. There were 62,827 ED visits for asthma during the 14-month study period, across 162 ZIP codes. The number of asthma visits was similar in the 3-day periods before and after spraying (510 vs. 501, p = 0.78). In multivariate analyses, daily rates of asthma visits were not associated with pesticide spraying (rate ratio = 0.92; 95% confidence interval, 0.80–1.07). Secondary analyses among children and for chronic obstructive pulmonary disease yielded similar null results. This analysis shows that spraying pyrethroids for WNV control in New York City was not followed by population-level increases in public hospital ED visit rates for asthma

Variability and Vulnerability at the Ecological Level: Implications for Understanding the Social Determinants of Health

Objectives. We examined variability in disease rates to gain understanding of the complex interactions between contextual socioeconomic factors and health. Methods. We compared mortality rates between New York and California counties in the lowest and highest quartiles of socioeconomic status (SES), assessed rate variability between counties for various outcomes, and examined correlations between outcomes’ sensitivity to SES and their variability. Results. Outcomes with mortality rates that differed most by county SES were among those whose variability across counties was high (e.g., AIDS, homicide, cirrhosis). Lower- SES counties manifested greater variability among outcome measures. Conclusions. Differences in health outcome variability reflect differences in SES impact on health. Health variability at the ecological level might reflect the impact of stressors on vulnerable populations.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/40316/2/Karpati_Variability and Vulnerability at the Ecological_2002.pd