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. 2024 Sep 4;19(9):e0293431.
doi: 10.1371/journal.pone.0293431. eCollection 2024.

Concurrent disease burden from multiple infectious diseases and the influence of social determinants in the contiguous United States

Emma Blake  1 , Este Stringham  1 , Chantel Sloan-Aagard  1
Affiliations

Concurrent disease burden from multiple infectious diseases and the influence of social determinants in the contiguous United States

Emma Blake et al. PLoS One. .

Abstract

Social determinants of health are known to underly excessive burden from infectious diseases. However, it is unclear if social determinants are strong enough drivers to cause repeated infectious disease clusters in the same location. When infectious diseases are known to co-occur, such as in the co-occurrence of HIV and TB, it is also unknown how much social determinants of health can shift or intensify the co-occurrence. We collected available data on COVID-19, HIV, influenza, and TB by county in the United States from 2019-2022. We applied the Kulldorff scan statistic to examine the relative risk of each disease by year depending on the data available. Additional analyses using the percent of the county that is below the US poverty level as a covariate were conducted to examine how much clustering is associated with poverty levels. There were three counties identified at the centers of clusters in both the adjusted and unadjusted analysis. In the poverty-adjusted analysis, we found a general shift of infectious disease burden from urban to rural clusters.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. High-risk cluster locations in the unadjusted analysis and poverty-adjusted analysis.
This figure shows high-risk cluster locations and radii for disease and year for the unadjusted analysis (left), and high-risk cluster locations and radii for disease and year for the poverty-adjusted analysis (right). The shapefile used was from the US Census Bureau publicly available TIGER/Line Shapefiles. Counties at the center of high-risk clusters in consecutive years (see S4 File) were found for COVID-19, HIV, and Influenza. Central counties for high-risk clusters for COVID-19 were in Miami-Dade County, FL, New York County, NY, Westmoreland County, PA and Yuma County, AZ. Counties at the center of regions with high disease burden of HIV in consecutive years were Anne Arundel County, MD, Lake County, FL, Los Angeles County, CA and San Francisco County, CA. The county with a high disease burden of influenza in consecutive years was Boone County, MO. Based on this analysis, none of the counties were at the center of high-risk clusters for TB in consecutive years.
Fig 2
Fig 2. Analysis type for counties found in high-risk clusters.
This figure shows the counties that were found to be at the center of a high relative risk cluster by analysis type: unadjusted or poverty-adjusted (adjusted for 125% below the poverty level). The shapefile used was from the US Census Bureau publicly available TIGER/Line Shapefiles.
Fig 3
Fig 3. Counties found in high-risk clusters with one or multiple diseases.
This figure shows the counties that were at the center of clusters by disease, one or multiple diseases. The shapefile used was from the US Census Bureau publicly available TIGER/Line Shapefiles.
Fig 4
Fig 4. High-risk clusters attributed to type of infectious disease in the unadjusted analysis.
These figures show the number of high-risk clusters attributed to the type of infectious disease from the unadjusted analysis.
Fig 5
Fig 5. High-risk clusters attributed to type of infectious disease in the poverty-adjusted analysis.
These figures show the number of high-risk clusters attributed to the type of infectious disease from the poverty-adjusted analysis.
See this image and copyright information in PMC

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