Browsing by Author "Jon Zelner"

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    Has the relationship between wealth and HIV risk in Sub-Saharan Africa changed over time? A temporal, gendered and hierarchical analysis.
    (2021-05-29) Emily Andrus; Sanyu A Mojola; Elizabeth Moran; Marisa Eisenberg; Jon Zelner
    This study examines the relationship between wealth and HIV infection in Sub-Saharan Africa to determine whether and how this relationship has varied over time, within and across countries, by gender, and urban environment. The analysis draws on DHS and AIS data from 27 Sub-Saharan African countries, which spanned the 14 years between 2003 and 2016. We first use logistic regression analyses to assess the relationship between individual wealth, HIV infection and gender by country and year stratified on urban environment. We then use meta-regression analyses to assess the relationship between country level measures of wealth and the odds of HIV infection by gender and individual level wealth, stratified on urban environment. We find that there is a persistent and positive relationship between wealth and the odds of HIV infection across countries, but that the strength of this association has weakened over time. The rate of attenuation does not appear to differ between urban/rural strata. Likewise, we also find that these associations were most pronounced for women and that this relationship was persistent over the study period and across urban and rural strata. Overall, our findings suggest that the relationship between wealth and HIV infection is beginning to reverse and that in the coming years, the relationship between wealth and HIV infection in Sub-Saharan Africa may more clearly mirror the predominant global picture.
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    There are no equal opportunity infectors: Epidemiological modelers must rethink our approach to inequality in infection risk
    (2022-02-09) Jon Zelner; Nina B Masters; Ramya Naraharisetti; Sanyu A Mojola; Merlin Chowkwanyun; Ryan Malosh
    Mathematical models have come to play a key role in global pandemic preparedness and outbreak response: helping to plan for disease burden, hospital capacity, and inform nonpharmaceutical interventions. Such models have played a pivotal role in the COVID-19 pandemic, with transmission models-and, by consequence, modelers-guiding global, national, and local responses to SARS-CoV-2. However, these models have largely not accounted for the social and structural factors, which lead to socioeconomic, racial, and geographic health disparities. In this piece, we raise and attempt to clarify several questions relating to this important gap in the research and practice of infectious disease modeling: Why do epidemiologic models of emerging infections typically ignore known structural drivers of disparate health outcomes? What have been the consequences of a framework focused primarily on aggregate outcomes on infection equity? What should be done to develop a more holistic approach to modeling-based decision-making during pandemics? In this review, we evaluate potential historical and political explanations for the exclusion of drivers of disparity in infectious disease models for emerging infections, which have often been characterized as "equal opportunity infectors" despite ample evidence to the contrary. We look to examples from other disease systems (HIV, STIs) and successes in including social inequity in models of acute infection transmission as a blueprint for how social connections, environmental, and structural factors can be integrated into a coherent, rigorous, and interpretable modeling framework. We conclude by outlining principles to guide modeling of emerging infections in ways that represent the causes of inequity in infection as central rather than peripheral mechanisms.