Racial disparities in health are well-documented and represent a significant public health concern in the US. Racism-related factors contribute to poorer health and higher mortality rates among Blacks compared to other racial groups. However, methods to measure racism and monitor its associations with health at the population-level have remained elusive. In this study, we investigated the utility of a previously developed Internet search-based proxy of area racism as a predictor of Black mortality rates. Area racism was the proportion of Google searches containing the “N-word” in 196 designated market areas (DMAs). Negative binomial regression models were specified taking into account individual age, sex, year of death, and Census region and adjusted to the 2000 US standard population to examine the association between area racism and Black mortality rates, which were derived from death certificates and mid-year population counts collated by the National Center for Health Statistics (2004–2009). DMAs characterized by a one standard deviation greater level of area racism were associated with an 8.2% increase in the all-cause Black mortality rate, equivalent to over 30,000 deaths annually. The magnitude of this effect was attenuated to 5.7% after adjustment for DMA-level demographic and Black socioeconomic covariates. A model controlling for the White mortality rate was used to further adjust for unmeasured confounders that influence mortality overall in a geographic area, and to examine Black-White disparities in the mortality rate. Area racism remained significantly associated with the all-cause Black mortality rate (mortality rate ratio = 1.036; 95% confidence interval = 1.015, 1.057; p = 0.001). Models further examining cause-specific Black mortality rates revealed significant associations with heart disease, cancer, and stroke. These findings are congruent with studies documenting the deleterious impact of racism on health among Blacks. Our study contributes to evidence that racism shapes patterns in mortality and generates racial disparities in health.
The lack of progress in reducing health disparities suggests that new approaches are needed if we are to achieve meaningful, equitable, and lasting reductions. Current scientific paradigms do not adequately capture the complexity of the relationships between environment, personal health and population level disparities. The public health exposome is presented as a universal exposure tracking framework for integrating complex relationships between exogenous and endogenous exposures across the lifespan from conception to death. It uses a social-ecological framework that builds on the exposome paradigm for conceptualizing how exogenous exposures “get under the skin”. The public health exposome approach has led our team to develop a taxonomy and bioinformatics infrastructure to integrate health outcomes data with thousands of sources of exogenous exposure, organized in four broad domains: natural, built, social, and policy environments. With the input of a transdisciplinary team, we have borrowed and applied the methods, tools and terms from various disciplines to measure the effects of environmental exposures on personal and population health outcomes and disparities, many of which may not manifest until many years later. As is customary with a paradigm shift, this approach has far reaching implications for research methods and design, analytics, community engagement strategies, and research training.
Does replacing the term “citizen science” do more harm than good?
Context A disaster is indiscriminate in whom it affects. Limited research has shown that the poor and medically underserved, especially in rural areas, bear an inequitable amount of the burden. Objective To review the literature on the combined effects of a disaster and living in an area with existing health or health care disparities on a community’s health, access to health resources, and quality of life. Methods We performed a systematic literature review using the following search terms: disaster, health disparities, health care disparities, medically underserved, and rural. Our inclusion criteria were peer-reviewed, US studies that discussed the delayed or persistent health effects of disasters in medically underserved areas. Results There has been extensive research published on disasters, health disparities, health care disparities, and medically underserved populations individually, but not collectively. Conclusions The current literature does not capture the strain of health and health care disparities before and after a disaster in medically underserved communities. Future disaster studies and policies should account for differences in health profiles and access to care before and after a disaster.
Social, ecological, and climatic factors interact creating a heterogeneous matrix that determines the spatiotemporal distribution of mosquitoes and human risks of exposure to the diseases they transmit. We explore linkages between the social and institutional processes behind residential abandonment, urban ecology, and the interactions of socio-ecological processes with abiotic drivers of mosquito production. Specifically, we test the relative roles of infrastructure degradation and vegetation for explaining the presence of Aedes albopictus Skuse 1894 to better predict spatial heterogeneity in mosquito exposure risk within urban environments. We further examine how precipitation interacts with these socially underpinned biophysical variables. We use a hierarchical statistical modeling approach to assess how environmental and climatic conditions over 3 years influence mosquito ecology across a socioeconomic gradient in Baltimore, MD. We show that decaying infrastructure and vegetation are important determinants of Ae. albopictus infestation. We demonstrate that both precipitation and vegetation influence mosquito production in ways that are mediated by the level of infrastructural decay on a given block. Mosquitoes were more common on blocks with greater abandonment, but when precipitation was low, mosquitoes were more likely to be found in higher-income neighborhoods with managed container habitat. Likewise, although increased vegetation was a negative predictor of mosquito infestation, more vegetation on blocks with high abandonment was associated with the largest mosquito populations. These findings indicate that fine spatial scale modeling of mosquito habitat within urban areas is needed to more accurately target vector control.
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