Examining associations between social vulnerability indices and COVID-19 incidence and mortality with spatial-temporal Bayesian modeling.

This study compares two social vulnerability indices, the U.S. CDC SVI and SoVI (the Social Vulnerability Index developed at the Hazards Vulnerability & Resilience Institute at the University of South Carolina), on their ability to predict the risk of COVID-19 cases and deaths.

Developing a granular scale environmental burden index (EBI) for diverse land cover types across the contiguous United States.

Critical to identifying the risk of environmentally driven disease is an understanding of the cumulative impact of environmental conditions on human health. Here we describe the methodology used to develop an environmental burden index (EBI). The EBI is calculated at U.

Uncertainty in geospatial health: challenges and opportunities ahead.

Purpose: Uncertainty is not always well captured, understood, or modeled properly, and can bias the robustness of complex relationships, such as the association between the environment and public health through exposure, estimates of geographic accessibility and cluster detection, to name a few.

Methods: We review current challenges and future opportunities as geospatial data and analyses are applied to the field of public health. We are particularly interested in the sources of uncertainty in geospatial data and how this uncertainty may propagate in spatial analysis.

Comparison of Estimated Severe Acute Respiratory Syndrome Coronavirus 2 Seroprevalence Through Commercial Laboratory Residual Sera Testing and a Community Survey.

We compared severe acute respiratory syndrome coronavirus 2 seroprevalence estimated from commercial laboratory residual sera and a community household survey in metropolitan Atlanta during April and May 2020 and found these 2 estimates to be similar (4.94% vs 3.18%).

Predicting coliform presence in private wells as a function of well characteristics, parcel size and leachfield soil rating.

Public water systems must be tested frequently for coliform bacteria to determine whether other pathogens may be present, yet no testing or disinfection is required for private wells. In this paper, we identify whether well age, type of well, well depth, parcel size, and soil ratings for a leachfield can predict the probability of detecting coliform bacteria in private wells using a multivariate logistic regression model. Samples from 1163 wells were analyzed for the presence of coliform bacteria between October 2017 and October 2019 across Gaston County, North Carolina, USA.

Estimating the prevalence and spatial clusters of coal workers' pneumoconiosis cases using medicare claims data, 2011-2014.

Background: Workers employed in the coal mining sector are at increased risk of respiratory diseases, including coal workers' pneumoconiosis (CWP). We investigated the prevalence of CWP and its association with sociodemographic factors among Medicare beneficiaries.

Methods: We used 5% Medicare Limited Data Set claims data from 2011 to 2014 to select Medicare beneficiaries with a diagnosis of ICD-9-CM 500 (CWP).

Residential mobility impacts relative risk estimates of space-time clusters of chlamydia in Kalamazoo County, Michigan.

We determine the impact of residential mobility in the prevalence and transmission dynamics of sexually transmitted infections. We illustrate our approach on reported chlamydia infections obtained from the Michigan Disease Surveillance System for Kalamazoo County, USA, from 2006 to 2014. We develop two scenarios, one with fixed residential addresses and one considering residential mobility.

Modelling individual vulnerability to sexually transmitted infections to optimise intervention strategies: analysis of surveillance data from Kalamazoo County, Michigan, USA.

Objective: We modelled individual vulnerability to STI using personal history of infection and neighbourhood characteristics.

Methods: Retrospective chlamydia and gonorrhoea data of reported confirmed cases from Kalamazoo County, Michigan for 2012 through 2014 were analysed. Unique IDs were generated from the surveillance data in collaboration with local health officials to track the individual STI histories.