Evidence map›Paper›PMID 37252288›Full record

ArticleSSM - population health2023

Potential gaps in income support policies for those in poor health: The case of the earned income tax credit-A cross sectional analysis.

Seth A Berkowitz, Gaurav Dave, Atheendar S Venkataramani

Abstract read
In one paragraph

Article in SSM - population health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Trial
  2. Conducting Actionable Economic Policy Research to Address Community Violence Inequities.Prevention science : the official journal of the Society for Prevention Research · 2026
    Article
  3. Article
  4. Article
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.

Seth A BerkowitzDivision of General Medicine and Clinical Epidemiology, Department of Medicine, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA.
Gaurav DaveDivision of General Medicine and Clinical Epidemiology, Department of Medicine, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA.
Atheendar S VenkataramaniDivision of Health Policy, Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA.

Funding

Research BaseP30DK092926 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI MARY ELLEN MICHELE HEISLER, ADESUWA B OLOMU · 2011 to 2026
$10.0M
Sci-Tech Core-Population Dynamics Research CenterP2CHD044964 · NICHD · UNIVERSITY OF PENNSYLVANIA · PI EMILIO ALBERTO PARRADO · 2018 to 2026
$5.3M
NICHD NIH HHS P2C HD044964NIDDK NIH HHS P30 DK092926
6 · The paper itself

Abstract

Background: The federal Earned Income Tax Credit (EITC) is the primary income support program for low-income workers in the U.S., but its design may hinder its effectiveness when poor health limits, but does not preclude, work. Methods: Cross-sectional analysis of nationally-representative U.S. Census Current Population Survey (CPS) data covering 2019. Working-age adults eligible to receive federal EITC were included in this study. Poor health, as indicated by self-report of at least one problem with hearing, vision, cognitive function, mobility, dressing and bathing, or independence, was the exposure. The main outcome was federal EITC benefit category, categorized as no benefit, phase-in (income too low for the maximum benefit), plateau (maximum benefit), phase-out (income above threshold for maximum benefit), or earnings too high to receive any benefit. We estimated EITC benefit category probabilities by health status using multinomial logistic regression. We further examined whether other government benefits provided additional income support to those in poor health. Results: 41,659 participants (representing 87.1 million individuals) were included. 2,724 participants (representing 5.6 million individuals) reported poor health. In analyses standardized over age, gender, race, and ethnicity, those in poor health, compared with those not in poor health, were more likely to be in the no benefit (2.40% vs. 0.30%, risk difference 2.10 percentage points [95%CI 1.75 to 2.46 percentage points]), and phase-in (9.28% vs. 2.74%, risk difference 6.54 percentage points [95%CI 5.82 to 7.26 percentage points]) categories. Differences in resources by health status persisted even after accounting for other government benefits. Conclusions: EITC program design creates an important gap in income support for those for whom poor health limits work, which is not closed by other programs. Filling this gap is an important public health goal.

Indexed as

Earned income tax creditHealth equitySocial insuranceSocial policySocioeconomic factors

Identifiers

PMID37252288
PMCPMC10209707

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.