Kansas City University, Department of Emergency Medicine, Kansas City, Missouri.
University of Tennessee, Knoxville, Tennessee.
West J Emerg Med. 2022 Feb 23;23(2):200-210. doi: 10.5811/westjem.2021.10.52539.
From the perspective of social determinants, homelessness perpetuates poor health and creates barriers to effective chronic disease management, necessitating frequent use of emergency department (ED) services. In this study we developed a screening algorithm (checklist) from common comorbidities observed in the homeless population in the United States. The result was a theoretical screening tool (checklist) to aid healthcare workers in the ED, including residents, medical students, and other trainees, to provide more efficacious treatment and referrals for discharge.
In this retrospective cohort study we used the Nationwide Emergency Department Sample (NEDS) to investigate comorbidities and ED utilization patterns relating to 23 injury-related, psychiatric, and frequent chronic medical conditions in the US adult (≥18 years of age) homeless population. Cases were identified from the NEDS database for 2014-2017 using International Classification of Diseases, 9th and 10 revisions, and Clinical Classification Software diagnosis codes. We performed a two-step cluster analysis including pathologies with ≥10% prevalence in the sample to identify shared comorbidities. We then compared the clusters by sociodemographic and ED-related characteristics, including age, gender, primary payer, and patient disposition from the ED. Chi-square analysis was used to evaluate categorical variables (ie, gender, primary payer, patient disposition from the ED), and analysis of variance for continuous variables (age).
The study included 1,715,777 weighted cases. The two-step cluster analysis identified nine groups denominated by most prevalent disease: 1) healthy; 2) mixed psychiatric; 3) major depressive disorder (MDD); 4) psychosis; 5) addiction; 6) essential hypertension; 7) chronic obstructive pulmonary disease (COPD); 8) infectious disease; and (9) injury. The MDD, COPD, infectious disease, and Injury clusters demonstrated the highest prevalence of co-occurring disease, with the MDD cluster displaying the highest proportion of comorbidities. Although the addiction cluster existed independently, substance use was pervasive in all except the healthy cluster (prevalence 36-100%). We used the extracted screening algorithm to establish a screening tool (checklist) for ED healthcare workers, with physicians as the first point of contact for the initial use of the screening tool.
Healthcare workers in the ED, including residents, medical students, and other trainees, provide services for homeless ED users. Screening tools (checklists) can help coordinate care to improve treatment, referrals, and follow-up care to reduce hospital readmissions. The screening tool may expedite targeted interventions for homeless patients with commonly occurring patterns of disease.
从社会决定因素的角度来看，无家可归现象使健康状况恶化，并为有效管理慢性疾病设置障碍，导致频繁使用急诊部（ED）服务。在这项研究中，我们从美国无家可归人群中常见的合并症角度出发，开发了一种筛选算法（清单）。其结果是一个理论上的筛选工具（清单），以帮助急诊部的医疗保健工作者，包括住院医师、医学生和其他受训人员，为提供更有效的治疗和出院转介提供帮助。
在这项回顾性队列研究中，我们使用全国急诊部样本（NEDS）调查了与美国成年（≥18 岁）无家可归人群中 23 种与损伤相关的、精神科的和常见的慢性医疗条件相关的合并症和 ED 利用模式。通过使用国际疾病分类，第 9 和第 10 修订版以及临床分类软件诊断代码，从 NEDS 数据库中确定了 2014 年至 2017 年的病例。我们对样本中患病率≥10%的病理进行了两步聚类分析，以确定共同的合并症。然后，我们根据社会学人口统计学和 ED 相关特征（包括年龄、性别、主要支付者和从 ED 出院的患者）对聚类进行了比较。使用卡方分析评估了分类变量（即性别、主要支付者、从 ED 出院的患者），并使用方差分析评估了连续变量（年龄）。
研究包括 1715777 例加权病例。两步聚类分析确定了九个由最常见疾病命名的组别：1）健康；2）混合精神病；3）重度抑郁症（MDD）；4）精神病；5）成瘾；6）原发性高血压；7）慢性阻塞性肺疾病（COPD）；8）传染病；和 9）损伤。MDD、COPD、传染病和损伤组显示出最高的共病发生率，而 MDD 组显示出最高的合并症比例。尽管成瘾组独立存在，但除健康组外，所有组均普遍存在药物滥用（患病率 36-100%）。我们使用提取的筛选算法为 ED 医疗保健工作者建立了一个筛选工具（清单），医生是初始使用筛选工具的第一联系人。
ED 的医疗保健工作者，包括住院医师、医学生和其他受训人员，为 ED 的无家可归用户提供服务。筛选工具（清单）可以帮助协调护理，以改善治疗、转介和随访护理，以减少医院再次入院。该筛选工具可以加快针对经常出现疾病模式的无家可归患者的靶向干预。
