Evidence map›Paper›PMID 41168778›Full record

ArticleHealth research policy and systems2025

Health insurance coverage in Mexico: progress, inequalities and remaining challenges towards UHC2030.

Edson Serván-Mori, Diego Cerecero-García, Sergio Meneses-Navarro, Thomas Hone, Alejandro Mohar-Betancourt, Octavio Gómez-Dantés

Abstract read
In one paragraph

Article in Health research policy and systems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. 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

6 authors.

Edson Serván-Mori *Center for Health Systems Research, The National Institute of Public Health, Avenida Universidad 655, Colonia Santa María Ahuacatitlán, 62100, Cuernavaca, Morelos, Mexico.ORCID http://orcid.org/0000-0001-9820-8325
Diego Cerecero-García *Center for Health Systems Research, The National Institute of Public Health, Avenida Universidad 655, Colonia Santa María Ahuacatitlán, 62100, Cuernavaca, Morelos, Mexico.ORCID http://orcid.org/0000-0001-5368-9241
Sergio Meneses-NavarroCenter for Health Systems Research, The National Institute of Public Health, Avenida Universidad 655, Colonia Santa María Ahuacatitlán, 62100, Cuernavaca, Morelos, Mexico.ORCID http://orcid.org/0000-0002-6542-6454
Thomas HoneDepartment of Primary Care and Public Health, Public Health Policy Evaluation Unit, School of Public Health, Imperial College London, London, UK.ORCID http://orcid.org/0000-0003-0703-6973
Alejandro Mohar-BetancourtEpidemiology and Biomedical Cancer Research Unit, National Cancer Institute and Biomedical Research Institute, National Autonomous University of Mexico, Mexico City, Mexico.ORCID http://orcid.org/0000-0002-4730-4787
Octavio Gómez-DantésCenter for Health Systems Research, The National Institute of Public Health, Avenida Universidad 655, Colonia Santa María Ahuacatitlán, 62100, Cuernavaca, Morelos, Mexico. ocogomez@yahoo.com.

Funding

NIHR GHPSR NIHR150067
6 · The paper itself

Abstract

backgroundUniversal health coverage (UHC) requires strong institutional capacity, equity-oriented policies, sustained political and financial commitment and public trust. However, public confidence in many health systems, including Mexico's, has been chronically undermined. This study aims to document Mexico's health coverage trajectory by offering a comprehensive, disaggregated and longitudinal assessment of insurance coverage from 2000 to 2023 - highlighting both achievements and setbacks in the context of UHC2030 goals.

methodsThis study used nationally representative data from Mexico's National Household Income and Expenditure Survey (ENIGH) from 2000 to 2022, with projections for 2023. Households were classified into mutually exclusive health insurance categories on the basis of institutional affiliation. National and subnational trends in coverage were analysed, with attention to major reforms and disruptions. A distance-to-frontier metric quantified the gap between 2023 coverage and each state's historical maximum, enabling assessment of progress toward UHC goals.

resultsBetween 2000 and 2015, Mexico reduced the uninsured population from 55% to 6.2%, largely driven by Seguro Popular (SP) expansion benefiting Indigenous peoples, rural and low-income households in high-deprivation states. Following SP's dismantling in 2019, the launch of Health Institute for Welfare (INSABI), and the COVID-19 pandemic, uninsured rates rose sharply to 29.1% by 2023. The greatest losses in coverage occurred in southern states and among marginalized groups, deepening territorial and social inequalities. The decline in mixed public coverage further reflects system fragmentation and eroding public trust. The distance-to-frontier analysis revealed that several states need to more than double their coverage to regain previous levels.

conclusionsMexico's experience highlights that health coverage gains are reversible without strong institutional foundations, political consensus and social legitimacy. Rebuilding and sustaining UHC requires deliberate efforts to address structural inequalities, strengthen institutions and restore public trust. For other low- and middle-income countries, this case emphasizes the urgent need for institutions restructured to foster adaptive capacity alongside equity-focused strategies to achieve and sustain UHC.

Indexed as

Healthcare DisparitiesInsurance CoverageInsurance, HealthUniversal Health InsuranceCOVID-19Health Care ReformHumansMedically UninsuredMexicoSocioeconomic FactorsHealth insurance coverageHealth system fragmentationInequalities in health coverageMexicoSocial protectionUniversal health coverage

Identifiers

PMID41168778
PMCPMC12577427

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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.