Association between access to social service resources and cardiometabolic risk factors: a machine learning and multilevel modeling analysis. Issue 3 (12th March 2019)

• Record Type: Journal Article
• Authors: Berkowitz, Seth A; Basu, Sanjay; Venkataramani, Atheendar; Reznor, Gally; Fleegler, Eric W; Atlas, Steven J
• Is Part Of: BMJ open. Volume 9:Issue 3(2019)
• Publication Date: 2019-03-12
• Subjects: cardiovascular disease -- food insecurity -- health disparities -- socioeconomic status; Medicine -- Research -- Periodicals; 610.72
• DOI: 10.1136/bmjopen-2018-025281
• Languages: English
• ISSNs: 2044-6055

Abstract

Abstract : 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.