Evaluation of Neighborhood Socioeconomic Characteristics and Advance Care Planning Among Older Adults.
Evaluation of Neighborhood Socioeconomic Characteristics and Advance Care Planning Among Older Adults.
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DOI：
10.1001/jamanetworkopen.2020.29063
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发表时间：
2020-12-01
影响因子：
13.8
通讯作者：
Sudore RL
中科院分区：
文献类型：
--
作者：
Nouri S;Lyles CR;Rubinsky AD;Patel K;Desai R;Fields J;DeRouen MC;Volow A;Bibbins-Domingo K;Sudore RL
提前护理计划的比率是否因社区社会经济地位（nSES）而异？在这项针对旧金山弗朗西斯科湾区13104名老年人的横断面研究中，与nSES高的社区相比，那些生活在nSES低的社区的老年人进行高级护理计划的几率较低，最低五分之一的老年人的几率低29%。映射地理编码数据允许识别5个低nSES和提前护理规划的旧金山弗朗西斯科社区。这些发现可能有助于为多层次干预措施的发展提供信息，包括有针对性的，以社区为基础的方案交付给需求最高和最不利的地区。这项横断面研究调查了来自加州大学旧金山分校弗朗西斯科65岁及以上健康患者的数据，以及社区社会经济地位和提前护理计划率之间是否存在关联。提前护理计划（ACP）在社会经济地位不利的老年人中很低。有必要采取针对性的社区办法来增加非加太，但人们对非加太的社区模式知之甚少。研究邻里社会经济地位（nSES）和ACP之间的关联，并确定低nSES和低ACP率的社区。这项横断面研究检查了加州旧金山弗朗西斯科大学的电子健康记录（EHR）数据和来自旧金山弗朗西斯科湾区9个县的基于地点的数据。参与者是2017年7月居住在旧金山弗朗西斯科湾区的65岁以上的初级保健患者。于二零二零年五月至六月进行统计分析。患者的家庭地址被地理编码并分配到美国人口普查区。主要因素nSES是一个综合了收入、教育、贫困、就业、职业和住房或租金价值等地区水平指标的指数，根据湾区所有美国人口普查区域的分布情况分为五分之一（Q1 =最低nSES）。协变量来自EHR，包括医疗保健使用（前一年的初级保健、门诊专科、急诊科和住院患者）。ACP定义为EHR中的扫描文件（例如，预先指令）、ACP当前程序术语代码或ACP注释类型。共纳入13104例患者，平均（SD）年龄为75（8）岁，其中7622例为女性患者（58.2%），897例患者（6.8%）被确定为黑人，913人3788人（28.9%）为亚洲/太平洋岛民，748人（5.7%）为其他少数种族/民族，2393人（18.3%）自我报告他们更喜欢说非英语语言。其中，3827例患者（29.2%）记录了ACP。该队列分布于所有5个nSES五分位数（Q1：1426例患者[10.9%]; Q2：1792例患者[13.7%]; Q3：2408例患者[18.4%]; Q4：3330例患者[25.4%]; Q5：4148例患者[31.7%]）。与Q5相比，在调整医疗保健使用后，所有nSES较低的五分位数均显示ACP的分级优势较低（Q1：调整优势比[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]。一个双变量地图的ACP的nSES允许识别5个社区与低nSES和ACP。在这项研究中，在调整医疗保健使用后，较低的nSES与较低的ACP记录相关。使用EHR和基于地点的数据，确定了低nSES和低ACP的老年人社区。这是与社区合作制定有针对性的、以社区为基础的干预措施以切实增加非加太国家方案的第一步。
Do rates of advance care planning vary by neighborhood socioeconomic status (nSES)? In this cross-sectional study of 13 104 older adults in the San Francisco Bay Area, those living in neighborhoods with lower nSES had lower odds of advanced care planning compared with those in neighborhoods with high nSES, with those in the lowest quintile having 29% lower odds. Mapping geocoded data allowed identification of 5 San Francisco neighborhoods with both low nSES and advance care planning. These findings may help to inform the development of multilevel interventions, including targeted, community-based program delivery to areas with the highest need and greatest disadvantage. This cross-sectional study examines data from University of California San Francisco Health patients aged 65 years and older and whether there is an association between neighborhood socioeconomic status and rates of advance care planning. 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.
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