Evidence map›Paper›PMID 31995211›Full record

ArticleJAMA network open2020

Quantification of Neighborhood-Level Social Determinants of Health in the Continental United States.

Marynia Kolak, Jay Bhatt, Yoon Hong Park, Norma A Padrón, Ayrin Molefe

Abstract read
In one paragraph

Article in JAMA network open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 117 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
117citing papers in PubMed, 1 pooled it
–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

117 citing papers in PubMed, 1 synthesis or guideline pooled it.

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57 more citing papers are in PubMed but not listed here.

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

5 authors.

Marynia KolakCenter for Spatial Data Science, Searle Chemistry Laboratory, University of Chicago, Chicago, Illinois.
Jay BhattCenter for Health Innovation, American Hospital Association, Chicago, Illinois.
Yoon Hong ParkCenter for Spatial Data Science, Searle Chemistry Laboratory, University of Chicago, Chicago, Illinois.
Norma A PadrónCenter for Health Innovation, American Hospital Association, Chicago, Illinois.
Ayrin MolefeCenter for Health Innovation, American Hospital Association, Chicago, Illinois.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

importanceAn association between social and neighborhood characteristics and health outcomes has been reported but remains poorly understood owing to complex multidimensional factors that vary across geographic space.

objectivesTo quantify social determinants of health (SDOH) as multiple dimensions across the continental United States (the 48 contiguous states and the District of Columbia) at a small-area resolution and to examine the association of SDOH with premature mortality within Chicago, Illinois. DESIGN, SETTING, AND

participantsIn this cross-sectional study, census tracts from the US Census Bureau from 2014 were used to develop multidimensional SDOH indices and a regional typology of the continental United States at a small-area level (n = 71 901 census tracts with approximately 312 million persons) using dimension reduction and clustering machine learning techniques (unsupervised algorithms used to reduce dimensions of multivariate data). The SDOH indices were used to estimate age-adjusted mortality rates in Chicago (n = 789 census tracts with approximately 7.5 million persons) with a spatial regression for the same period, while controlling for violent crime. MAIN OUTCOMES AND MEASURES: Fifteen variables, measured as a 5-year mean, were selected to characterize SDOH as small-area variations for demographic characteristics of vulnerable groups, economic status, social and neighborhood characteristics, and housing and transportation availability at the census-tract level. This SDOH data matrix was reduced to 4 indices reflecting advantage, isolation, opportunity, and mixed immigrant cohesion and accessibility, which were then clustered into 7 distinct multidimensional neighborhood typologies. The association between SDOH indices and premature mortality (defined as death before age 75 years) in Chicago was measured by years of potential life lost and aggregated to a 5-year mean. Data analyses were conducted between July 1, 2018, and August 30, 2019.

resultsAmong the 71 901 census tracts examined across the continental United States, a median (interquartile range) of 27.2% (47.1%) of residents had minority status, 12.1% (7.5%) had disabilities, 22.9% (7.6%) were 18 years and younger, and 13.6% (8.1%) were 65 years and older. Among the 789 census tracts examined in Chicago, a median (interquartile range) of 80.4% (56.3%) of residents had minority status, 10.2% (8.2%) had disabilities, 23.2% (10.9%) were 18 years and younger, and 9.5% (7.1%) were 65 years and older. Four SDOH indices accounted for 71% of the variance across all census tracts in the continental United States in 2014. The SDOH neighborhood typology of extreme poverty, which is of greatest concern to health care practitioners and policy advocates, comprised only 9.6% of all census tracts across the continental United States but characterized small areas of known public health crises. An association was observed between all SDOH indices and age-adjusted premature mortality rates in Chicago (R2 = 0.63; P < .001), even after accounting for violent crime and spatial structures. CONCLUSIONS AND RELEVANCE: The modeling of SDOH as multivariate indices rather than as a singular deprivation index may better capture the complexity and spatial heterogeneity underlying SDOH. During a time of increased attention to SDOH, this analysis may provide actionable information for key stakeholders with respect to the focus of interventions.

Indexed as

Health StatusAdultAgedChicagoCross-Sectional StudiesFemaleHumansMaleMiddle AgedMinority GroupsModels, StatisticalQuality of LifeResidence CharacteristicsRisk FactorsSocial Determinants of HealthSocioeconomic Factors

Identifiers

PMID31995211
PMCPMC6991288

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.