Health differences between rural and non-rural Texas counties based on 2023 County Health Rankings

Background: Place matters for health. In Texas, growing rural populations face a variety of structural, social, and economic disparities that position them for potentially worse health outcomes. The current study contributes to understanding rural health disparities in a state-specific context.

Methods: Using 2023 County Health Rankings data from the University of Wisconsin Population Health Institute, the study analyzes rural/non-rural county differences in Texas across six composite indexed domains of health outcomes (length of life, quality of life) and health factors (health behavior, clinical care, socioeconomic factors, physical environment) with a chi-square test of significance and logistic regression.

Results: Quartile ranking distributions of the six domains differed between rural and non-rural counties. Rural Texas counties were significantly more likely to fall into the bottom quartile(s) in the domains of length of life and clinical care and less likely to fall into the bottom quartile(s) in the domains of quality of life and physical environment. No differences were found in the domains of health behavior and socioeconomic factors. Findings regarding disparities in length of life and clinical care align with other studies examining disease prevalence and the unavailability of many health services in rural Texas. The lack of significant differences in other domains may relate to indicators that are not present in the dataset, given studies that find disparities relating to other underlying factors.

Conclusions: Texas County Health Rankings data show differences in health outcomes and factors between rural and non-rural counties. Limitations of findings relate to the study's cross-sectional design and parameters of the secondary data source. Ultimately, results can help state health stakeholders, especially those in community or operational contexts with limited resources or access to more detailed health statistics, to use the CHR dataset to consider more relevant local interventions to address rural health disparities.