46: Refining Community Characteristic Composites Representing American Community Survey Item Data
Scott Rega
First Author
Vanderbilt University Medical Center
Irene Feurer
Presenting Author
Vanderbilt University Medical Center
Monday, Aug 4: 2:00 PM - 3:50 PM
2068
Contributed Posters
Music City Center
Our earlier work reported two US county-level composites, health/economics (HEC) and community capital/urbanicity (CCU), using American Community Survey (ACS) data and two other national health-related databases. We aim to develop new composites using 2023 5-year ACS estimates. One hundred two ACS variables among 3,144 counties were analyzed using principal components analysis. Standardized composite scores were computed for each county and their associations with HEC, CCU, Neighborhood Deprivation Index (NDI) and Urban-Rural Classification Scheme (URC) scores were evaluated. A 74-item, two-component solution that approximated "simple structure" and accounted for 41.4% of the total item covariance was interpreted to represent: 1) financial resources & educational attainment (FREA) and 2) age, demographic characteristics & urbanicity (ADU). Non-chance associations (r or rho, p<0.001) between FREA and HEC, CCU, NDI and URC were 0.77, 0.57, -0.88 and -0.44, respectively. For ADU they were 0.45, -0.69, -0.23 and 0.37. Patterns of associations indicated general concordance of the new composites with other measures. Future work will focus on smaller geographic entities using ACS data.
Latent variable models and principal components analysis
American Community Survey data
United States county community characteristics
Social determinants of health
Main Sponsor
Survey Research Methods Section
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