Evidence map›Paper›PMID 33827600›Full record

ArticleInternational journal for equity in health2021

The convergence of racial and income disparities in health insurance coverage in the United States.

De-Chih Lee, Hailun Liang, Leiyu Shi

Abstract read
In one paragraph

Article in International journal for equity in health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 77 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
77citing 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

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

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17 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

3 authors.

De-Chih LeeDepartment of Information Management, Da-Yeh University, No.168, University Rd., Dacun, Changhua, 51591, Taiwan.
Hailun LiangSchool of Public Administration and Policy, Renmin University of China, No.59 Zhongguancun, Beijing, 100872, China. hliang16@jhu.edu.ORCID 0000-0002-9917-411X
Leiyu ShiJohns Hopkins Primary Care Policy Center, 624 N. Broadway, Baltimore, MD, 21205, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThis study applied the vulnerability framework and examined the combined effect of race and income on health insurance coverage in the US. DATA SOURCE: The household component of the US Medical Expenditure Panel Survey (MEPS-HC) of 2017 was used for the study. STUDY

designLogistic regression models were used to estimate the associations between insurance coverage status and vulnerability measure, comparing insured with uninsured or insured for part of the year, insured for part of the year only, and uninsured only, respectively. DATA COLLECTION/EXTRACTION

methodsWe constructed a vulnerability measure that reflects the convergence of predisposing (race/ethnicity), enabling (income), and need (self-perceived health status) attributes of risk. PRINCIPAL

findingsWhile income was a significant predictor of health insurance coverage (a difference of 6.1-7.2% between high- and low-income Americans), race/ethnicity was independently associated with lack of insurance. The combined effect of income and race on insurance coverage was devastating as low-income minorities with bad health had 68% less odds of being insured than high-income Whites with good health.

conclusionResults of the study could assist policymakers in targeting limited resources on subpopulations likely most in need of assistance for insurance coverage. Policymakers should target insurance coverage for the most vulnerable subpopulation, i.e., those who have low income and poor health as well as are racial/ethnic minorities.

Indexed as

IncomeAgedChildFemaleHealth ExpendituresHealth Services AccessibilityHumansInsurance CoverageInsurance, HealthMaleMedically UninsuredUnited StatesDisparityEthnicityHealth insuranceRace

Identifiers

PMID33827600
PMCPMC8025443

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.