A joint spatial factor analysis model to accommodate data from misaligned areal units with application to Louisiana social vulnerability

Delivering spatially comparable inference on the risks of multiple severities of respiratory disease from spatially misaligned disease count data

Population-level disease risk varies between communities, and public health professionals are interested in mapping this spatial variation to monitor the locations of high-risk areas and the magnitudes of health inequalities. Almost all of these risk maps relate to a single severity of disease outcome, such as hospitalization, which thus ignores any cases of disease of a different severity, such as a mild case treated in a primary care setting. These spatially-varying risk maps are estimated from spatially aggregated disease count data, but the set of areal units to which these disease counts relate often varies by severity. Thus, the statistical challenge is to provide spatially comparable inference from multiple sets of spatially misaligned disease count data, and an additional complexity is that the spatial extents of the areal units for some severities are partially unknown. This paper thus proposes a novel spatial realignment approach for multivariate misaligned count data, and applies it to the first study delivering spatially comparable inference for multiple severities of the same disease. Inference is via a novel spatially smoothed data augmented MCMC algorithm, and the methods are motivated by a new study of respiratory disease risk in Scotland in 2017.

Potential Impact of 2020 US Decennial Census Data Collection on Disaster Preparedness and Population Mental Health

Increasing in frequency and impact in the United States and worldwide, disasters can lead to serious mental health consequences. Although US census data are essential for disaster preparedness and the identification of community-level risk factors for adverse postdisaster mental health outcomes, the US Census Bureau faces many challenges as we approach 2020 Decennial Census data collection. Despite the utility of the information provided by the Census and American Community Survey (ACS), the 2020 US Census and subsequent ACS data face threats to validity. As a result, public health funding could be misallocated, and disaster preparedness and response efforts misinformed; this can also contribute to the worsening of mental health inequities, particularly in the context of disaster. Undercutting the Census and the ACS, rich data sources that allow representation of all people in the United States, is a step backward in our effort to mitigate the population mental health consequences of disasters.