Insights for Conducting Large-Scale Surveys with Veterans Who Have Experienced Homelessness.
Aerin J deRussyAudrey L JonesErika L AustinAdam J GordonLillian GelbergSonya E GabrielianKevin R RiggsJohn R BlosnichAnn Elizabeth MontgomerySally K HolmesAllyson L VarleyApril E HogeStefan G KerteszPublished in: Journal of social distress and the homeless (2021)
Surveys of underserved patient populations are needed to guide quality improvement efforts but are challenging to implement. The goal of this study was to describe recruitment and response to a national survey of Veterans with homeless experience (VHE). We randomly selected 14,340 potential participants from 26 U.S. Department of Veterans Affairs (VA) facilities. A survey contract organization verified/updated addresses from VA administrative data with a commercial address database, then attempted to recruit VHE through 4 mailings, telephone follow-up, and a $10 incentive. We used mixed-effects logistic regressions to test for differences in survey response by patient characteristics. The response rate was 40.2% (n=5,766). Addresses from VA data elicited a higher response rate than addresses from commercial sources (46.9% vs 31.2%, p <.001). Residential addresses elicited a higher response rate than business addresses (43.8% vs 26.2%, p<. 001). Compared to non-respondents, respondents were older, less likely to have mental health, drug, or alcohol conditions, and had fewer VA housing and emergency service visits. Collectively, our results indicated a national mailed survey approach is feasible and successful for reaching VA patients who have recently experienced homelessness. These findings offer insight into how health systems can obtain perspectives of socially disadvantaged groups.
Keyphrases
- mental health
- quality improvement
- mental illness
- cross sectional
- end stage renal disease
- healthcare
- public health
- electronic health record
- case report
- ejection fraction
- chronic kidney disease
- newly diagnosed
- patient safety
- drinking water
- air pollution
- peritoneal dialysis
- machine learning
- colorectal cancer screening
- patient reported outcomes