Related Experiment Video
Updated: Jan 27, 2026
Constructing and Visualizing Models using Mime-based Machine-learning Framework
Published on: July 22, 2025
Association between access to social service resources and cardiometabolic risk factors: a machine learning and
Seth A Berkowitz1, Sanjay Basu2, Atheendar Venkataramani3
1Department of General Medicine and Clinical Epidemiology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, North Carolina, USA.
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/m2 per additional resource, 95% CI -0.13 to -0.03 kg/m2), employment resources (-0.05 kg/m2, 95% CI -0.11 to -0.002 kg/m2) and nutrition resources (-0.07 kg/m2, 95% CI -0.13 to -0.01 kg/m2). 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.
More Related Videos
12:18A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
08:51Author Spotlight: Unveiling Neural Mechanisms Through Automated Evaluation of Motor Learning and Myelin Plasticity Studies Using the Erasmus Ladder
Published on: December 15, 2023
Related Concept Videos
01:27Associative Learning
Classical conditioning, also known...
02:10Social Scripts
01:26Factors Affecting the Risk of Infection
The integrity and count of the white blood cells help the body resist pathogens and fight infection. When impaired, it reduces the body's resistance to pathogens. The acidic pH levels of the gastrointestinal, genitourinary tracts, and skin...
01:41Social Traps
01:29Factors Influencing Attraction V: Social Skills
00:52Social Proof