46: Refining Community Characteristic Composites Representing American Community Survey Item Data

Wali Johnson Co-Author
Vanderbilt University Medica Center
 
Irene Feurer Co-Author
Vanderbilt University Medical Center
 
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.

Keywords

Latent variable models and principal components analysis

American Community Survey data

United States county community characteristics

Social determinants of health 

Abstracts


Main Sponsor

Survey Research Methods Section