Division of Palliative Medicine, Department of Medicine, University of California, San Francisco.
UCSF Center for Vulnerable Populations, Zuckerberg San Francisco General Hospital, San Francisco, California.
JAMA Netw Open. 2020 Dec 1;3(12):e2029063. doi: 10.1001/jamanetworkopen.2020.29063.
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.
DESIGN, SETTING, AND PARTICIPANTS: 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.
在社会经济处于不利地位的老年人群体中，预先医疗规划（ACP）的比例较低。需要制定以社区为基础的方法来增加 ACP，但社区 ACP 的模式尚未得到充分理解。
研究邻里社会经济地位（nSES）与 ACP 之间的关系，并确定 nSES 较低且 ACP 率较低的社区。
设计、地点和参与者：这项横断面研究检查了加利福尼亚大学旧金山分校电子健康记录（EHR）数据和来自旧金山湾区 9 个县的基于地点的数据。参与者是 2017 年 7 月在旧金山湾区居住且年龄在 65 岁或以上的初级保健患者。统计分析于 2020 年 5 月至 6 月进行。
患者的家庭住址进行了地理编码并分配到美国人口普查地段。主要因素 nSES 是一个综合区域收入、教育、贫困、就业、职业以及住房或租金价值指标的指数，分为五分位数，与整个湾区的所有美国人口普查地段的分布相匹配（Q1=最低 nSES）。协变量来自 EHR，包括医疗保健使用（初级保健、门诊专科、急诊和前一年的住院就诊）。
ACP 定义为 EHR 中的扫描文档（例如，预先指令）、ACP 当前程序术语代码或 ACP 笔记类型。
该队列包括 13104 名患者，平均（标准差）年龄为 75（8）岁，7622 名女性患者（58.2%），897 名患者（6.8%）被认定为黑人，913 名（7.0%）为拉丁裔，3788 名（28.9%）为亚裔/太平洋岛民，748 名（5.7%）为其他少数民族种族/族裔，2393 名（18.3%）自述更愿意说非英语。其中，3827 名患者（29.2%）有记录的 ACP。该队列分布在 nSES 的所有 5 个五分位数（Q1：1426 名患者[10.9%]；Q2：1792 名患者[13.7%]；Q3：2408 名患者[18.4%]；Q4：3330 名患者[25.4%]；Q5：4148 名患者[31.7%]）。与 Q5 相比，在调整了医疗保健使用情况后，所有较低的 nSES 五分位数的 ACP 几率均呈梯度降低（Q1：调整后的优势比[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]）。nSES 按 ACP 的双变量图允许确定 5 个邻里社区，这些社区既具有较低的 nSES，也具有较低的 ACP。
在这项研究中，在调整了医疗保健使用情况后，较低的 nSES 与较低的 ACP 记录相关。使用 EHR 和基于地点的数据，可以确定既有较低 nSES 又有较低 ACP 的老年人群体社区。这是与社区合作制定有针对性的社区为基础的干预措施以切实增加 ACP 的第一步。
