Neighborhood Socioeconomic Status Is Associated with Advance Care Planning Among Older Adults (W205D)

Objectives 1. Describe how community-level factors may affect advance care planning and why that matters. 2. Recognize how electronic health-record data, place-based data, and geographic information systems can be used to assess community patterns and community-level factors associated with advance care planning. Original Research Background Advance care planning (ACP) is low among vulnerable, older adults. There is a need for community-based approaches to increase ACP, but community patterns of ACP are poorly understood. Research Objectives To examine the association between neighborhood socioeconomic status (nSES) and ACP and identify communities with both low nSES and low rates of ACP. Methods Addresses of patients who receive primary care at UCSF, live in the Bay Area, and are ≥65 years old were geocoded then assigned to census tracts. ACP was defined as a scanned document, ACP CPT code, or ACP note type in the EHR. nSES, an index combining area-level measures of income, education, poverty, employment, occupation, and housing/rent values, was divided into quintiles scaled to Bay Area census tracts (Q1=lowest nSES). Covariates from the EHR included healthcare utilization (primary care, outpatient specialty, emergency department, and inpatient encounters in the prior year). We estimated the odds ratio of ACP across nSES quintiles using mixed-effects logistic regression with random effect for census tract. Results Over 13,000 patients were included in the cohort-mean age 75 (SD 8), 58% female, 48% people of color, and 18% non-English speaking. Nearly a third (29%) had documented ACP. The cohort was distributed across all 5 quintiles of nSES. Compared to Q5 and after adjusting for healthcare utilization, all lower quintiles showed a lower odds of ACP in a graded fashion (Q1: aOR=0.71 [0.61-0.84], Q2: 0.74 [0.64, 0.86], Q3: 0.81 [0.71, 0.93], Q4: 0.82 [0.72, 0.93]. A bivariable map of ACP by nSES allowed identification of five neighborhoods with both low nSES and ACP. Conclusion Low nSES is associated with low ACP documentation after adjusting for healthcare utilization. Using EHR and place-based data, we identified communities with both low nSES and low ACP. Implications for Research, Policy, or Practice This is a first step in partnering with communities to develop targeted interventions to meaningfully increase ACP.