Comparison of Community-Level and Patient-Level Social Risk Data in a Network of Community Health Centers.
Comparison of Community-Level and Patient-Level Social Risk Data in a Network of Community Health Centers.
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DOI：
10.1001/jamanetworkopen.2020.16852
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发表时间：
2020-10-01
影响因子：
13.8
通讯作者：
Gottlieb LM
中科院分区：
文献类型：
--
作者：
Cottrell EK;Hendricks M;Dambrun K;Cowburn S;Pantell M;Gold R;Gottlieb LM
这项横断面研究通过将患者居住的人口普查区的社会剥夺指数得分与患者级社会风险筛查数据进行比较，探讨了社区级数据在准确识别具有社会风险的患者方面的效用。社区层面的数据（例如患者居住地人口普查区的社会剥夺指数评分）能否准确识别患者层面的社会风险？在这项包括 36-578 名患者的横断面研究中，10-858 名患者 (29.7%) 筛查出 1 种或多种社会风险呈阳性；具有至少 1 种社会风险的患者中 42% 居住在未被定义为弱势群体的社区。使用社区层面的数据来指导患者层面的活动可能会导致错过一些可以从社会风险针对性或社会风险知情护理中受益的患者。针对社会风险因素与健康之间关系的大量研究，人们对医疗机构中社会风险筛查的热情日益高涨，许多美国卫生系统正在尝试社会风险筛查举措。在缺乏标准社会风险筛查建议的情况下，一些卫生系统正在探索使用公开的社区级数据来识别生活在最脆弱社区的患者，以此来描述患者的社会和经济背景、识别具有潜在社会风险的患者和/或有针对性地开展社会风险筛查工作。通过将患者居住的人口普查区的社会剥夺指数得分与患者级社会风险筛查数据进行比较，探讨社区级数据在准确识别具有社会风险的患者方面的效用。使用 2016 年 6 月 24 日至 2018 年 11 月 15 日期间全国社区卫生中心网络电子健康记录中的患者级社会风险筛查数据进行的横断面研究，并与公开来源的地理编码社区级数据相关联。符合资格的患者是那些对有关食物、住房和/或财务资源紧张的社会风险筛查问题有记录的回答，并且具有足够质量进行地理编码的有效地址的患者。社会风险筛查记录在电子健康记录中。使用按四分位分层的人口普查区级社会剥夺指数评分来评估社区级社会风险。通过粮食不安全、住房不安全和财务资源紧张筛查应对措施来识别患者层面的社会风险。最终研究样本包括来自美国 13 个州的 36 578 名患者； 22 113 人 (60.5%) 接受公共保险，21 181 人 (57.9%) 为女性，17 578 人 (48.1%) 为白人，10 918 人 (29.8%) 为黑人。尽管 6516 名至少具有 1 个社会风险因素的患者（60.0%）位于人口普查区最贫困的四分位，但具有社会风险因素的患者居住在所有人口普查区。总体而言，社区级数据识别有无社会风险患者的准确率为 48.0%。尽管存在重叠，但患者层面和社区层面评估患者社会风险的方法并不相同。使用社区层面的数据来指导患者层面的活动可能意味着无法识别一些可以从有针对性的干预措施或护理调整中受益的患者。
This cross-sectional study explores the utility of community-level data for accurately identifying patients with social risks by comparing the social deprivation index score for the census tract where a patient lives with patient-level social risk screening data. Can community-level data (eg, social deprivation index score of the census tract where patients live) accurately identify patient-level social risks? In this cross-sectional study including 36 578 patients, 10 858 (29.7%) screened positive for 1 or more social risks; 42% of patients with at least 1 social risk lived in neighborhoods not defined as disadvantaged. Using community-level data to guide patient-level activities may result in missing some patients who can benefit from social risk–targeted or social risk–informed care. Responding to the substantial research on the relationship between social risk factors and health, enthusiasm has grown around social risk screening in health care settings, and numerous US health systems are experimenting with social risk screening initiatives. In the absence of standard social risk screening recommendations, some health systems are exploring using publicly available community-level data to identify patients who live in the most vulnerable communities as a way to characterize patient social and economic contexts, identify patients with potential social risks, and/or to target social risk screening efforts. To explore the utility of community-level data for accurately identifying patients with social risks by comparing the social deprivation index score for the census tract where a patient lives with patient-level social risk screening data. Cross-sectional study using patient-level social risk screening data from the electronic health records of a national network of community health centers between June 24, 2016, and November 15, 2018, linked to geocoded community-level data from publicly available sources. Eligible patients were those with a recorded response to social risk screening questions about food, housing, and/or financial resource strain, and a valid address of sufficient quality for geocoding. Social risk screening documented in the electronic health record. Community-level social risk was assessed using census tract–level social deprivation index score stratified by quartile. Patient-level social risks were identified using food insecurity, housing insecurity, and financial resource strain screening responses. The final study sample included 36 578 patients from 13 US states; 22 113 (60.5%) received public insurance, 21 181 (57.9%) were female, 17 578 (48.1%) were White, and 10 918 (29.8%) were Black. Although 6516 (60.0%) of those with at least 1 social risk factor were in the most deprived quartile of census tracts, patients with social risk factors lived in all census tracts. Overall, the accuracy of the community-level data for identifying patients with and without social risks was 48.0%. Although there is overlap, patient-level and community-level approaches for assessing patient social risks are not equivalent. Using community-level data to guide patient-level activities may mean that some patients who could benefit from targeted interventions or care adjustments would not be identified.
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影响因子：
3.3
作者：
Puterman, Eli;Adler, Nancy;Epel, Elissa
通讯作者：
Epel, Elissa
影响因子：
3.4
作者：
Butler, Danielle C.;Petterson, Stephen;Bazemore, Andrew W.
通讯作者：
Bazemore, Andrew W.
影响因子：
39
作者：
Gottlieb, Laura;Sandel, Megan;Adler, Nancy E.
通讯作者：
Adler, Nancy E.
影响因子：
12.7
作者：
Krieger, N;Chen, JT;Subramanian, SV
通讯作者：
Subramanian, SV
DOI：
10.3122/jabfm.2017.04.170079
发表时间：
2017-07-01
影响因子：
2.9
作者：
Byhoff, Elena;Cohen, Alicia J.;Tipirneni, Renuka
通讯作者：
Tipirneni, Renuka