Utilizing social determinants of health to identify most vulnerable neighborhoods-Latent class analysis and GIS map

Yongwen Jiang; Karyn Backus; Marc Camardo; Jennifer Morin; Orlando Velazco; Trace Kershaw

doi:10.1016/j.ypmed.2024.107997

Utilizing social determinants of health to identify most vulnerable neighborhoods-Latent class analysis and GIS map

Prev Med. 2024 May 9:184:107997. doi: 10.1016/j.ypmed.2024.107997. Online ahead of print.

Authors

Yongwen Jiang¹, Karyn Backus², Marc Camardo³, Jennifer Morin³, Orlando Velazco⁴, Trace Kershaw⁵

Affiliations

¹ Surveillance, Analysis and Reporting Unit, Health Statistics and Surveillance Section, Connecticut Department of Public Health, Hartford, CT 06134, USA. Electronic address: Yongwen.Jiang@ct.gov.
² Surveillance, Analysis and Reporting Unit, Health Statistics and Surveillance Section, Connecticut Department of Public Health, Hartford, CT 06134, USA.
³ Maternal and Child Health Epidemiology Unit, Maternal Child Health and Access to Care Section, Connecticut Department of Public Health, Hartford, CT 06134, USA.
⁴ Office of Health Equity, Public Health Systems Improvement, Connecticut Department of Public Health, Hartford, CT 06134, USA.
⁵ Department of Social and Behavioral Sciences, School of Public Health, Yale University, New Haven, CT 06510, USA.

PMID: 38729527
DOI: 10.1016/j.ypmed.2024.107997

Abstract

Objectives: Public Health officials are often challenged to effectively allocate limited resources. Social determinants of health (SDOH) may cluster in areas to cause unique profiles related to various adverse life events. The authors use the framework of unintended teen pregnancies to illustrate how to identify the most vulnerable neighborhoods.

Methods: This study used data from the U.S. American Community Survey, Princeton Eviction Lab, and Connecticut Office of Vital Records. Census tracts are small statistical subdivisions of a county. Latent class analysis (LCA) was employed to separate the 832 Connecticut census tracts into four distinct latent classes based on SDOH, and GIS mapping was utilized to visualize the distribution of the most vulnerable neighborhoods. GEE Poisson regression model was used to assess whether latent classes were related to the outcome. Data were analyzed in May 2021.

Results: LCA's results showed that class 1 (non-minority non-disadvantaged tracts) had the least diversity and lowest poverty of the four classes. Compared to class 1, class 2 (minority non-disadvantaged tracts) had more households with no health insurance and with single parents; and class 3 (non-minority disadvantaged tracts) had more households with no vehicle available, that had moved from another place in the past year, were low income, and living in renter-occupied housing. Class 4 (minority disadvantaged tracts) had the lowest socioeconomic characteristics.

Conclusions: LCA can identify unique profiles for neighborhoods vulnerable to adverse events, setting up the potential for differential intervention strategies for communities with varying risk profiles. Our approach may be generalizable to other areas or other programs.

Key messages: What is already known on this topic Public health practitioners struggle to develop interventions that are universally effective. The teen birth rates vary tremendously by race and ethnicity. Unplanned teen pregnancy rates are related to multiple social determinants and behaviors. Latent class analysis has been applied successfully to address public health problems. What this study adds While it is the pregnancy that is not planned rather than the birth, access to pregnancy intention data is not available resulting in a dependency on teen birth data for developing public health strategies. Using teen birth rates to identify at-risk neighborhoods will not directly represent the teens at risk for pregnancy but rather those who delivered a live birth. Since teen birth rates often fluctuate due to small numbers, especially for small neighborhoods, LCA may avoid some of the limitations associated with direct rate comparisons. The authors illustrate how practitioners can use publicly available SDOH from the Census Bureau to identify distinct SDOH profiles for teen births at the census tract level. How this study might affect research, practice or policy These profiles of classes that are at heightened risk potentially can be used to tailor intervention plans for reducing unintended teen pregnancy. The approach may be adapted to other programs and other states to prioritize the allocation of limited resources.

Keywords: Census tract; Geographic information systems (GIS); Latent class analysis (LCA); Social determinants of health (SDOH); Teen birth rate; Unintended teen pregnancy.