ArticleBMJ open2019
Association between access to social service resources and cardiometabolic risk factors: a machine learning and multilevel modeling analysis.
Article in BMJ open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- Socioeconomic Factors and Their Role in Metabolic Dysfunction-Associated Steatotic Liver Disease: A Comprehensive Review.Liver international : official journal of the International Association for the Study of the Liver · 2026Review
- Predicting incident cardio-metabolic disease among persons with and without depressive and anxiety disorders: a machine learning approach.Social psychiatry and psychiatric epidemiology · 2025Article
- Exploring predictors of substance use disorder treatment engagement with machine learning: The impact of social determinants of health in the therapeutic landscape.Journal of substance use and addiction treatment · 2024Article
- Article
- Relationships between enabling services use and access to care among adults with cardiometabolic risk factors: findings from the 2014 National Health Center Patient Survey.BMC health services research · 2022Article
- Geospatial Analysis of Food Deserts and Their Impact on Health Outcomes in Children with Cystic Fibrosis.Nutrients · 2021Article
- A scoping review on the use of machine learning in research on social determinants of health: Trends and research prospects.SSM - population health · 2021Article
- Social and Behavioral Determinants of Health in the Era of Artificial Intelligence with Electronic Health Records: A Scoping Review.Health data science · 2021Article
- The Relationship between Food Security Status and Sleep Disturbance among Adults: A Cross-Sectional Study in an Indonesian Population.Nutrients · 2020Article
- Including Social and Behavioral Determinants in Predictive Models: Trends, Challenges, and Opportunities.JMIR medical informatics · 2020Article
- Part II: A Qualitative Study of Social Risk Screening Acceptability in Patients and Caregivers.American journal of preventive medicine · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
objectivesInterest 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.
designCross-sectional.
settingData 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.
resultsResources associated with lower BMI included more food resources (-0.08 kg/m
conclusionsAccess 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.
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Registered trials
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