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Spatial and Temporal Patterns of Chronic Disease Burden in the U.S., 2018-2021
Jocelyn V Hunyadi1, Kehe Zhang1, Qian Xiao2
1Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas; Center for Spatial-Temporal Modeling for Applications in Population Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas.
Insights
A new Chronic Disease Burden Index (CDBI) reveals high disease burdens in southern US counties, particularly in rural areas. This index helps identify vulnerable populations for targeted public health interventions and equitable healthcare access.
Area of Science:
- Public Health
- Epidemiology
- Geographic Information Systems (GIS)
Background:
- Chronic diseases are leading causes of death and disability in the U.S.
- Existing individual-level indices do not capture population-level chronic disease burden.
- A quantitative tool is needed to understand geographic patterns and impacts of chronic diseases for public health strategies.
Purpose of the Study:
- To construct a county-level Chronic Disease Burden Index (CDBI).
- To identify geographic and temporal patterns of chronic disease burden.
- To investigate the association between CDBI and social vulnerability.
Main Methods:
- Utilized 20 health measures from CDC's PLACES database (2018-2021).
- Constructed annual county-level CDBIs using principal component analysis.
- Identified geographic hotspots with Getis-Ord Gi* and assessed associations with social vulnerability using logistic regression.
Main Results:
- High chronic disease burden clusters in the southern U.S., with persistent burden in KY and WV.
- Increased burden observed in OH and TX.
- Significant association between CDBI and social vulnerability index (OR=7.6); nonmetro-urban counties showed elevated CDBI (OR=14.6).
Conclusions:
- The CDBI is an effective population-level tool for assessing chronic disease burden.
- Identifying high-burden, vulnerable communities is essential for resource allocation.
- This approach aids in enhancing equitable healthcare access and understanding health disparities.
Introduction:
Chronic diseases are primary causes of mortality and disability in the U.S. Although individual-level indices to assess the burden of multiple chronic diseases exist, there is a lack of quantitative tools at the population level. This gap hinders the understanding of the geographical distribution and impact of chronic diseases, crucial for effective public health strategies. This study aims to construct a Chronic Disease Burden Index (CDBI) for evaluating county-level disease burden, to identify geographic and temporal patterns, and investigate the association between CDBI and social vulnerability.
Methods:
A total of 20 health measures from CDC's PLACES database (2018-2021) were used to construct annual county-level CDBIs through principal component analysis. Geographic hotspots of chronic disease burden were identified using Getis-Ord Gi*. Multinomial logistic regression models and bivariate maps were used to assess the association between CDBI and CDC's social vulnerability index. Analyses were conducted in 2023-2024.
Results:
Counties with high chronic disease burden were predominantly clustered in the southern U.S. High persistent chronic disease burden was prevalent in Kentucky and West Virginia, while increased burden was observed in Ohio and Texas. Chronic disease burden was highly associated with social vulnerability index (ORQ5 vs Q1=7.6, 95% CI: [6.6, 8.8]), with nonmetro-urban counties experiencing elevated CDBI (OR=14.6, 95% CI: [9.7, 21.9]).
Conclusions:
The CDBI offers an effective tool for assessing chronic disease burden at the population level. Identifying high-burden and vulnerable communities is a crucial first step toward facilitating resource allocation to enhance equitable healthcare access and advancing understanding of health disparities.
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