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通过系统动力学解决高血压差异问题:社区健康联系的见解

Addressing Hypertension Disparities via Systems Dynamics: Insights From Community Health Connections.

作者信息

Krishna Kavin, Franciosa Mark

机构信息

Health Policy, Stanford University, Redwood City, USA.

Internal Medicine, Community Health Connections, Fitchburg, USA.

出版信息

Cureus. 2024 Sep 6;16(9):e68763. doi: 10.7759/cureus.68763. eCollection 2024 Sep.

DOI:10.7759/cureus.68763
PMID:39371701
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11456158/
Abstract

Background and objective Hypertension remains a major public health challenge in the United States, disproportionately affecting various demographic groups. Significant disparities persist in hypertension prevalence and control due to interactions between socioeconomic factors, healthcare access, and systemic inequities. In this study, we aimed to determine the impact of socioeconomic and healthcare factors on hypertension control among patients at Community Health Connections (CHC), a Federally Qualified Health Center serving 36 communities in North Central Massachusetts, and identify effective interventions using systems dynamics modeling to promote health equity. Materials and methods This was a retrospective observational study using data from 2023 CHC patients, and the National Health and Nutrition Examination Survey (NHANES) 2011-2014. Systems dynamics modeling was employed to visualize interactions among factors influencing hypertension outcomes. The study included 4,870 CHC patients. Participants were selected based on clinical records and comprised 3,690 White participants (76%), 464 Black/African American participants (10%), 108 Asian participants (2%), and 608 classified as Other/Non-reported (12%). The cohort included 2,490 males (51%) and 2,380 females (49%). Socioeconomic factors (e.g., race, age, insurance status) and healthcare access were the study variables. The primary outcome was hypertension control, defined as a blood pressure reading of <140/90 mmHg. The study measured control rates across different demographic groups and assessed the impact of socioeconomic and healthcare factors on these rates. Results Among the 4,870 CHC patients, 3,007 (62%) achieved hypertension control. The overall hypertension control rate varied significantly by race: White: 68.6%, Black/African American: 61.6%, Asian: 63.2%, and Other/Non-reported: 65.6%. Insured patients had a control rate of 67.7%, compared to 37.5% for uninsured patients (p<0.001). Systems dynamics models illustrated how socioeconomic disparities and healthcare access issues amplify health inequities. Key interventions identified include, but are not limited to, multidisciplinary care teams, community health worker programs, and telehealth services. Conclusions Addressing hypertension disparities among CHC patients requires a systemic approach integrating socioeconomic, healthcare, and policy-related interventions. Systems dynamics modeling provides a framework for designing and implementing targeted interventions, enhancing health equity, and improving hypertension control outcomes in underserved populations. Further research is needed to test the effectiveness of these interventions before their broad implementation.

摘要

背景与目的

在美国,高血压仍然是一项重大的公共卫生挑战,对不同人口群体的影响存在不均衡性。由于社会经济因素、医疗保健可及性和系统性不平等之间的相互作用,高血压患病率和控制情况仍存在显著差异。在本研究中,我们旨在确定社会经济和医疗保健因素对社区健康连接中心(CHC)患者高血压控制的影响。CHC是一家联邦合格健康中心,服务于马萨诸塞州中北部的36个社区,并使用系统动力学模型确定有效的干预措施以促进健康公平。

材料与方法

这是一项回顾性观察研究,使用了2023年CHC患者的数据以及2011 - 2014年的国家健康和营养检查调查(NHANES)数据。采用系统动力学模型来可视化影响高血压结果的因素之间的相互作用。该研究纳入了4870名CHC患者。参与者根据临床记录选取,包括3690名白人参与者(76%)、464名黑人/非裔美国参与者(10%)、108名亚洲参与者(2%)以及608名被归类为其他/未报告的参与者(12%)。该队列包括2490名男性(51%)和2380名女性(49%)。社会经济因素(如种族、年龄、保险状况)和医疗保健可及性为研究变量。主要结局为高血压控制,定义为血压读数<140/90 mmHg。该研究测量了不同人口群体的控制率,并评估了社会经济和医疗保健因素对这些率的影响。

结果

在4870名CHC患者中,3007名(62%)实现了高血压控制。总体高血压控制率因种族而异:白人:68.6%,黑人/非裔美国:61.6%,亚洲:63.2%,其他/未报告:65.6%。参保患者的控制率为67.7%,未参保患者为37.5%(p<0.001)。系统动力学模型说明了社会经济差异和医疗保健可及性问题如何加剧健康不平等。确定的关键干预措施包括但不限于多学科护理团队、社区卫生工作者项目和远程医疗服务。

结论

解决CHC患者之间的高血压差异需要一种综合社会经济、医疗保健和政策相关干预措施的系统方法。系统动力学模型为设计和实施有针对性的干预措施、增强健康公平以及改善服务不足人群的高血压控制结果提供了一个框架。在广泛实施这些干预措施之前,需要进一步研究来测试其有效性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/274c/11456158/aecb85d3632e/cureus-0016-00000068763-i04.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/274c/11456158/2b754f13e285/cureus-0016-00000068763-i01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/274c/11456158/47f3571041f8/cureus-0016-00000068763-i02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/274c/11456158/3cb9ad366e94/cureus-0016-00000068763-i03.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/274c/11456158/aecb85d3632e/cureus-0016-00000068763-i04.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/274c/11456158/2b754f13e285/cureus-0016-00000068763-i01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/274c/11456158/47f3571041f8/cureus-0016-00000068763-i02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/274c/11456158/3cb9ad366e94/cureus-0016-00000068763-i03.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/274c/11456158/aecb85d3632e/cureus-0016-00000068763-i04.jpg

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