Low–socioeconomic status (SES) households have little income or wealth to buffer against the negative impacts of adverse health events among adult household members. This research project links data from a nonprofit food distribution center, electronic medical records from a safety-net healthcare system, and publicly available residential appraisals for more than 3,000 households to provide insight into how low-SES households cope with health shocks experienced by resident adults. Three broad types of strategies are examined: changes in household structure, residential mobility, and use of social services. Of the households studied, 20.2 percent had at least one adult member who experienced a health shock. These households were more likely to gain additional adult household members and employed household members, were more likely to move residence and to move distances greater than one mile, and were less likely to visit the food distribution center after the shock. This research highlights how novel data linkages can help us to understand how health and social policies impact vulnerable populations.

Background: Social disadvantage predicts colorectal cancer outcomes across the cancer care continuum for many populations and places. For medically underserved populations, social disadvantage is likely intersectional—affecting individuals at multiple levels and through membership in multiple disadvantaged groups. However, most measures of social disadvantage are cross-sectional and limited to race, ethnicity, and income. Linkages between electronic health records (EHR) and external datasets offer rich, multilevel measures that may be more informative.Methods: We identified urban safety-net patients eligible and due for colorectal cancer screening from the Parkland-UT Southwestern PROSPR cohort. We assessed one-time screening receipt (via colonoscopy or fecal immunochemical test) in the 18 months following cohort entry via the EHR. We linked EHR data to housing and Census data to generate measures of social disadvantage at the parcel- and block-group level. We evaluated the association of these measures with screening using multilevel logistic regression models controlling for sociodemographics, comorbidity, and healthcare utilization.Results: Among 32,965 patients, 45.1% received screening. In adjusted models, residential mobility, residence type, and neighborhood majority race were associated with colorectal cancer screening. Nearly all measures of patient-level social disadvantage and healthcare utilization were significant.Conclusions: Address-based linkage of EHRs to external datasets may have the potential to expand meaningful measurement of multilevel social disadvantage. Researchers should strive to use granular, specific data in investigations of social disadvantage.Impact: Generating multilevel measures of social disadvantage through address-based linkages efficiently uses existing EHR data for applied, population-level research.

Background: Incidence rates of gastric cancer are increasing in young adults (age <50 years), particularly among Hispanic persons. We estimated incidence rates of early-onset gastric cancer (EOGC) among Hispanic and non-Hispanic White persons by census tract poverty level and county-level metro/nonmetro residence. Methods: We used population-based data from the California and Texas Cancer Registries from 1995 to 2016 to estimate age-adjusted incidence rates of EOGC among Hispanic and non-Hispanic White persons by year, sex, tumor stage, census tract poverty level, metro versus nonmetro county, and state. We used logistic regression models to identify factors associated with distant stage diagnosis. Results: Of 3,047 persons diagnosed with EOGC, 73.2% were Hispanic White. Incidence rates were 1.29 [95% confidence interval (CI), 1.24–1.35] and 0.31 (95% CI, 0.29–0.33) per 100,000 Hispanic White and non-Hispanic White persons, respectively, with consistently higher incidence rates among Hispanic persons at all levels of poverty. There were no statistically significant associations between ethnicity and distant stage diagnosis in adjusted analysis. Conclusions: There are ethnic disparities in EOGC incidence rates that persist across poverty levels. Impact: EOGC incidence rates vary by ethnicity and poverty; these factors should be considered when assessing disease risk and targeting prevention efforts.