A Data-Integrity Audit of Spatial Associations Among Surface Temperature, Health Vulnerability, and Social Vulnerability in Houston, Texas

Integrating spatial temperature and modeled health data requires compatible geographic units and valid treatment of prevalence estimates. This Houston-area study audits an intersected dataset and examines the sensitivity of its tract-level associations. Thirty health-prevalence fields exhibited an apparent multiplicative scaling pattern with record-specific factors. Reconstruction recovered 740 source tracts, including 659 under strict quality criteria. Identifier agreement was 434/435 (99.8%) in the directly checkable subset, not validation of all records; 642 assignments remained inferred, and the findings are conditional on the reconstructed crosswalk. Excluded tracts were cooler and less socially vulnerable. The reported land surface temperature (LST) field averaged 80.83 °F (27.13 °C); it was derived from Landsat 8 imagery acquired on 27 September 2022 using single-channel processing in ArcGIS Pro. This early-autumn observation is distinct from peak-summer or long-term heat exposure. LST had weak positive correlations with lack of health insurance (r = 0.199) and overall social vulnerability (r = 0.171). After adjustment for social vulnerability and tract share aged 65 years or older, standardized LST coefficients were 0.014 for lack of health insurance, −0.088 for obesity, −0.140 for current asthma, and −0.115 for stroke. In an eight-neighbor spatial-error specification, corresponding coefficients were 0.058, 0.021, −0.002, and −0.021; confidence intervals included zero for all three chronic-disease outcomes. Sensitivity analyses demonstrate that geographic selection, age composition, and spatial dependence materially affect interpretation. The principal contribution is the reproducible audit of the archived table; these ecological results do not establish heat-related disease risk or a validated basis for intervention targeting.