Evidence map›Paper›PMID 41521580›Full record

ArticleAmerican journal of community psychology2026

The intersection of health inequalities and COVID-19: Evidence from National Health Insurance Big Data in South Korea.

Jaehyun Nam, Sarah Jiyoon Kwon, Wonik Lee, Eunji Kim

Abstract read
In one paragraph

Article in American journal of community psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Jaehyun NamDepartment of Social Welfare, Pusan National University, Busan, South Korea.
Sarah Jiyoon KwonDepartment of Social Welfare, University of Seoul, Seoul, South Korea.ORCID 0000-0002-6493-6049
Wonik LeeDepartment of Social Welfare, Pusan National University, Busan, South Korea.ORCID 0000-0001-9126-0067
Eunji KimDepartment of Social Welfare, Pusan National University, Busan, South Korea.

Funding

This study was supported by 2023 BK21 FOUR Program of Pusan National University.
6 · The paper itself

Abstract

Health inequalities persist along lines of income and wealth, shaped by unequal access to healthcare, differences in health behaviors, and pre-existing chronic conditions. The COVID-19 pandemic further put families in Korea under health strain and worsened their health outcomes. This study investigates how pre-existing socioeconomic disparities intersect with health outcomes. Using the national administrative health big data provided by the Korean National Health Insurance Service, we employ logistic regression models for 10,459,043 randomly sampled cases from a total of more than 34 million registered positive cases in the country. We specifically examine whether and to what extent individuals from lower income backgrounds with positive cases are more or less likely to experience negative health outcomes. Our findings reveal that individuals from higher income backgrounds are less likely to experience negative health outcomes compared to the lowest income group, including hospitalization (odds ratio [OR] = 0.45, confidence interval [CI] = 0.44, 0.46), severe illness (OR = 0.70, CI = 0.61, 0.81), and fatalities (OR = 0.40, CI = 0.38, 0.41). Furthermore, our subsample analyses based on various demographic characteristics consistently showed that individuals from higher income backgrounds face a lower risk of adverse health outcomes. These results indicate that disadvantaged individuals are disproportionately affected by the health crisis, deepening health inequities. This paper emphasizes the need for targeted public policies.

Indexed as

COVID-19Health Status DisparitiesNational Health ProgramsAdultAgedBig DataFemaleHospitalizationHumansIncomeLogistic ModelsLow Socioeconomic StatusMaleMiddle AgedRepublic of KoreaSARS-CoV-2COVID‐19health inequalitiessocial determinants of health

Identifiers

PMID41521580
PMCPMC13289520

What OpenQuestion holds

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LicenceCC BY
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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.