Abstract
Advance care planning (ACP) is low among older adults with socioeconomic disadvantage. There is a need for tailored community-based approaches to increase ACP, but community patterns of ACP are poorly understood. To examine the association between neighborhood socioeconomic status (nSES) and ACP and to identify communities with both low nSES and low rates of ACP. This cross-sectional study examined University of California San Francisco electronic health record (EHR) data and place-based data from 9 San Francisco Bay Area counties. Participants were primary care patients aged 65 years or older and living in the San Francisco Bay Area in July 2017. Statistical analysis was performed from May to June 2020. Patients' home addresses were geocoded and assigned to US Census tracts. The primary factor, nSES, an index combining area-level measures of income, education, poverty, employment, occupation, and housing or rent values, was divided into quintiles scaled to the distribution of all US Census tracts in the Bay Area (Q1 = lowest nSES). Covariates were from the EHR and included health care use (primary care, outpatient specialty, emergency department, and inpatient encounters in the prior year). ACP was defined as a scanned document (eg, advance directive), ACP Current Procedural Terminology code, or ACP note type in the EHR. There were 13 104 patients included in the cohort-mean (SD) age was 75 (8) years, with 7622 female patients (58.2%), 897 patients (6.8%) identified as Black, 913 (7.0%) as Latinx, 3788 (28.9%) as Asian/Pacific Islander, and 748 (5.7%) as other minority race/ethnicity, and 2393 (18.3%) self-reported that they preferred to speak a non-English language. Of these, 3827 patients (29.2%) had documented ACP. The cohort was distributed across all 5 quintiles of nSES (Q1: 1426 patients [10.9%]; Q2: 1792 patients [13.7%]; Q3: 2408 patients [18.4%]; Q4: 3330 patients [25.4%]; Q5: 4148 patients [31.7%]). Compared with Q5 and after adjusting for health care use, all lower nSES quintiles showed a lower odds of ACP in a graded fashion (Q1: adjusted odds ratio [aOR] = 0.71 [95% CI, 0.61-0.84], Q2: aOR = 0.74 [95% CI, 0.64-0.86], Q3: aOR = 0.81 [95% CI, 0.71-0.93], Q4: aOR = 0.82 [95% CI, 0.72-0.93]. A bivariable map of ACP by nSES allowed identification of 5 neighborhoods with both low nSES and ACP. In this study, lower nSES was associated with lower ACP documentation after adjusting for health care use. Using EHR and place-based data, communities of older adults with both low nSES and low ACP were identified. This is a first step in partnering with communities to develop targeted, community-based interventions to meaningfully increase ACP.
Highlights
Advance care planning (ACP), a process by which people communicate their preferences for future medical care,[1,2] is associated with a higher likelihood of patients receiving care consistent with their goals and higher patient and family satisfaction with end of life care.[3,4,5,6] ACP rates are as low as 20% to 30% among older, socioeconomically disadvantaged populations—including people of color and those with lower income—compared with rates higher than 50% among older adults overall
Compared with Q5 and after adjusting for health care use, all lower neighborhood socioeconomic status (nSES) quintiles showed a lower odds of ACP in a graded fashion
In this study, lower nSES was associated with lower ACP documentation after adjusting for health care use
Summary
Advance care planning (ACP), a process by which people communicate their preferences for future medical care,[1,2] is associated with a higher likelihood of patients receiving care consistent with their goals and higher patient and family satisfaction with end of life care.[3,4,5,6] ACP rates are as low as 20% to 30% among older, socioeconomically disadvantaged populations—including people of color and those with lower income—compared with rates higher than 50% among older adults overall. Understanding community patterns of health behaviors, such as ACP, allows development of multilevel interventions, including targeted program delivery to areas with highest need and greatest disadvantage.[29] Given persistently low rates of ACP among people of color and those with lower income,[10,11] it is imperative to look beyond individual and health system level factors and engage communities in strategies to increase ACP.[30]
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