Evidence map›Paper›PMID 39655003›Full record

ArticleJournal of clinical and translational science2024

Examining individual- versus population-level social determinants of health in a cluster randomized trial of health coaches for patients with multiple chronic conditions.

Mary E Charlson, Martin T Wells, James Hollenberg, Rosio Ramos, Guillerma Maritza Martinez, Martin J Gerard, Andrea Cassells, T J Lin, Ilana Mittleman, Alice Eggleston and 1 more

Abstract read
In one paragraph

Article in Journal of clinical and translational science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Trial
  2. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Mary E CharlsonWeill Cornell Medicine, Department of Medicine, New York, NY, USA.
Martin T WellsWeill Cornell Medicine, Department of Medicine, New York, NY, USA.
James HollenbergWeill Cornell Medicine, Department of Medicine, New York, NY, USA.
Rosio RamosWeill Cornell Medicine, Department of Medicine, New York, NY, USA.
Guillerma Maritza MartinezWeill Cornell Medicine, Department of Medicine, New York, NY, USA.
Martin J GerardWeill Cornell Medicine, Department of Medicine, New York, NY, USA.
Andrea CassellsClinical Directors Network (CDN), New York, NY, USA.
T J LinClinical Directors Network (CDN), New York, NY, USA.
Ilana MittlemanClinical Directors Network (CDN), New York, NY, USA.
Alice EgglestonAllianceChicago, Chicago, IL, USA.
Jonathan N TobinClinical Directors Network (CDN), New York, NY, USA.ORCID https://orcid.org/0000-0003-4722-539X

Funding

Research Design, Data, and Analytics CoreP30DK092949 · NIDDK · UNIVERSITY OF CHICAGO · PI Ronald T. Ackermann, MARSHALL H CHIN · 2011 to 2026
$10.0M
NIDDK NIH HHS P30 DK092949
6 · The paper itself

Abstract

Introduction: Social determinants of health (SDOH) are an important contributor to health status and health outcomes. In this analysis, we compare SDOH measured both at the individual and population levels in patients with high comorbidity who receive primary care at Federally Qualified Health Centers in New York and Chicago and enrolled in the Tipping Points trial. Methods: We analyzed individual- and population-level measures of SDOH in 1,488 patients with high comorbidity (Charlson Comorbidity Index ≥ 4) enrolled in Tipping Points. At the individual level, we used a standardized patient-reported questionnaire. At the population level, we employed patient addresses to calculate the Social Deprivation Index (SDI) and Area Deprivation Index. Multivariable regressions were conducted in addition to qualitative feedback from stakeholders. Results: Individual-level SDOH are distinct from population-level measures. Significant component predictors of population SDI are being unhoused, unable to pay for utilities, and difficulty accessing medical transportation. Qualitative findings mirrored these results. High comorbidity patients report significant SDOH challenges at the individual level. Fitting a binomial generalized linear model, the comorbidity score is significantly predicted by the composite individual SDOH index ( Conclusions: Individual- and population-level SDOH measures provide different risk assessments. The use of community-level SDI data is informative in the aggregate but should not be used to identify patients with individual unmet social needs. Health systems should implement a standardized individualized assessment of unmet SDOH needs and build strong, enduring partnerships with community-based organizations that can provide those services.

Indexed as

cluster randomized controlled trialFederally Qualified Health Centershealth coachesmultiple chronic conditionsSocial determinants of health

Identifiers

PMID39655003
PMCPMC11626581

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.