Skip to main content

Rural Hospital Closures and Mortality From Time-Sensitive Conditions in Texas.

JAMA network open2026-09-28PubMed 42804204
Design
Design 9 of 10
Novelty
Novelty 8 of 10
Journal
Journal 8 of 10
Clinical
Clinical 9 of 10

Summary

Using county-year data from Texas between 2006 and 2019, this study applied two complementary difference-in-differences designs to evaluate rural hospital closures. Closure exposure was associated with 11.52 to 11.60 additional annual deaths per 100,000 residents from time-sensitive conditions, representing a 5.4% increase from baseline mortality. Associations were concentrated in acute emergencies and were robust to several sensitivity analyses.

Key Findings

  • Fourteen rural hospitals closed among the 175 Texas counties studied during the observation period.
  • Hospital closures were associated with approximately 11.6 additional annual deaths per 100,000 residents from time-sensitive conditions in the adjacent-county analysis.
  • The estimated increase corresponded to a 5.4% rise over baseline mortality and was concentrated in acute emergencies.

Clinical Implications

Sepsis systems should incorporate regional access, transport times, referral capacity, and post-closure emergency coverage into preparedness planning. Maintaining or replacing rural emergency capacity may be important for reducing delays in time-sensitive treatment.

Why It Matters

The study moves beyond individual clinical management to demonstrate a population-level relationship between loss of rural emergency infrastructure and mortality from time-sensitive diseases, including sepsis. Its quasi-experimental design strengthens the policy relevance of the findings.

Limitations

  • The ecological county-level design cannot determine which individual patients experienced delayed or unavailable sepsis care.
  • The study was conducted in Texas, so effects may differ in other states or health systems with different transport and hospital networks.

Future Directions

Future studies should link individual emergency medical service, transfer, treatment-timing, and mortality data to identify causal pathways, and should evaluate telemedicine, mobile critical-care transport, and regionalized sepsis networks as mitigation strategies.

Study Information

Study Type
Cohort
Research Domain
Prevention/Prognosis
Evidence Level
II - Population-level quasi-experimental cohort using difference-in-differences methods, stronger for causal inference than a conventional retrospective association study but not randomized.
Study Design