Association between access to social service resources and cardiometabolic risk factors: a machine learning and multilevel modeling analysis
Association between access to social service resources and cardiometabolic risk factors: a machine learning and multilevel modeling analysis
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DOI：
10.1136/bmjopen-2018-025281
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发表时间：
2019-06-01
期刊：
影响因子：
2.9
通讯作者：
Atlas, Steven J.
中科院分区：
文献类型：
--
作者：
Berkowitz, Seth A.;Basu, Sanjay;Atlas, Steven J.
将社会需求未得到满足的患者与地区一级的资源联系起来，如邮政编码内的食品储藏室和就业中心，这种兴趣正在增长。然而，这些资源的存在是否与更好的健康结果有关尚不清楚。我们试图确定是否区域一级的资源，定义为组织，帮助个人与满足健康相关的社会需求，与较低水平的心脏代谢危险factors.Design横断面设置数据收集在初级保健网络在东部马萨诸塞州在2015年。参与者和主要和次要的结果措施123355参与者。主要结局是体重指数（BMI）。次要结局为收缩压（SBP）、低密度脂蛋白（LDL）胆固醇和血红蛋白A1 c（HbA 1c）。所有参与者都被纳入BMI分析。高血压患者被纳入SBP分析。具有降低胆固醇适应症的受试者被纳入LDL分析，糖尿病受试者被纳入HbA 1c分析。我们使用基于随机森林的机器学习算法来识别与研究结果相关的资源类型。然后，我们测试了ZIP级选定资源类型的关联（3个用于BMI分析，2个用于SBP和HbA 1c分析，1个用于LDL分析）与这些结果，使用多水平模型来解释个人水平、临床水平和其他地区水平的因素。（每种额外资源-0.08 kg/m（2），95% CI -0.13至-0.03 kg/m（2）），就业资源（-0.05 kg/m2，95% CI -0.11 ~-0.002 kg/m2）和营养资源（-0.07 kg/m2，95% CI -0.13 ~-0.01 kg/m2）。没有地区资源与SBP、LDL或HbA 1c的差异相关。结论获得特定的当地资源与更好的BMI相关。努力将患者与区域资源联系起来，并改善社区内的资源状况，可能有助于降低BMI并改善人口健康。
Objectives Interest in linking patients with unmet social needs to area-level resources, such as food pantries and employment centres in one's ZIP code, is growing. However, whether the presence of these resources is associated with better health outcomes is unclear. We sought to determine if area-level resources, defined as organisations that assist individuals with meeting health-related social needs, are associated with lower levels of cardiometabolic risk factors.Design Cross-sectional.Setting Data were collected in a primary care network in eastern Massachusetts in 2015.Participants and primary and secondary outcome measures 123 355 participants were included. The primary outcome was body mass index (BMI). The secondary outcomes were systolic blood pressure (SBP), low-density lipoprotein (LDL) cholesterol and haemoglobin A1c (HbA1c). All participants were included in BMI analyses. Participants with hypertension were included in SBP analyses. Participants with an indication for cholesterol lowering were included in LDL analyses and participants with diabetes mellitus were included in HbA1c analyses. We used a random forest-based machine-learning algorithm to identify types of resources associated with study outcomes. We then tested the association of ZIP-level selected resource types (three for BMI, two each for SBP and HbA1c analyses and one for LDL analyses) with these outcomes, using multilevel models to account for individual-level, clinic-level and other area-level factors.Results Resources associated with lower BMI included more food resources (-0.08 kg/m(2) per additional resource, 95% CI -0.13 to -0.03 kg/m(2)), employment resources (-0.05 kg/m(2), 95% CI -0.11 to -0.002 kg/m(2)) and nutrition resources (-0.07 kg/m(2), 95% CI -0.13 to -0.01 kg/m(2)). No area resources were associated with differences in SBP, LDL or HbA1c.Conclusions Access to specific local resources is associated with better BMI. Efforts to link patients to area resources, and to improve the resources landscape within communities, may help reduce BMI and improve population health.