Evidence map›Paper›PMID 41379774›Full record

Observational studyPLoS medicine2025

From sex differences to sex inequalities in life expectancy: A cross-country observational benchmarking analysis.

Angela Y Chang, Emily K Johnson, Sarah Bolongaita, Kent Buse, Sarah J Hawkes, Omar Karlsson, Felicia M Knaul, Margaret E Kruk, Ole F Norheim, Osondu Ogbuoji and 2 more

Abstract readObservational Study
In one paragraph

Observational study in PLoS medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

12 authors.

Angela Y ChangDanish Institute for Advanced Study, University of Southern Denmark, Odense, Denmark.ORCID https://orcid.org/0000-0003-3947-3588
Emily K JohnsonDanish Institute for Advanced Study, University of Southern Denmark, Odense, Denmark.ORCID https://orcid.org/0000-0001-9213-9533
Sarah BolongaitaBergen Centre for Ethics and Priority Setting, Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway.ORCID https://orcid.org/0000-0003-0886-0454
Kent BuseGlobal 50/50, Cambridge, United Kingdom.
Sarah J HawkesGlobal 50/50, Cambridge, United Kingdom.
Omar KarlssonDuke University Population Research Institute, Durham, North Carolina, United States of America.
Felicia M KnaulDepartment of Medicine, David Geffen School of Medicine, University of California, Los Angeles, California, United States of America.
Margaret E KrukQuEST Center, Washington University School of Medicine in St. Louis, St. Louis, Missouri, United States of America.ORCID https://orcid.org/0000-0002-9549-8432
Ole F NorheimBergen Centre for Ethics and Priority Setting, Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway.ORCID https://orcid.org/0000-0002-5748-5956
Osondu OgbuojiCentre for Policy Impact in Global Health, Duke University, Durham, North Carolina, United States of America.
David WatkinsDepartment of Global Health, University of Washington, Seattle, Washington, United States of America.ORCID https://orcid.org/0000-0001-6341-9595
Dean T JamisonInstitute for Global Health Sciences, University of California, San Francisco, California, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe answer to whether females or males have better health, and which sex is the more disadvantaged, has depended in part on the metric and how the inequality is measured. This study introduces a new method for analyzing and interpreting sex inequalities in health outcomes-defined as the avoidable sex differences in health outcomes-that is systematic and potentially more objective. For this paper, we focus on life expectancy at different ages. METHODS AND

findingsWe introduce the adjusted sex ratio as a measure of sex inequalities and determining sex disadvantage. First, we calculated the sex ratio of life expectancy at ages 0, 5, 15, 35, 50, and 70. To understand what is achievable under favorable conditions, we identified countries in the 5th percentile of the highest life expectancy for each sex and used these values as benchmarks, and calculated the sex ratio of these best-performing countries ("frontier"). We calculated the country- and age-specific adjusted sex ratio by dividing country sex ratios by frontier sex ratios. This assumes that theoretically, under the current risk and healthcare environments, females all over the world have the potential to live up to the life expectancy of the females in the frontier countries, and separately, all males to their male-specific frontier. An adjusted ratio of greater than one indicates male disadvantage, while below one indicates female disadvantage. To avoid overinterpreting small differences, we defined a narrow range around equality (ratio of 1) within which we do not label either sex as disadvantaged. Before adjustment, males in all countries (except two) and at all ages had lower life expectancy than females. After adjustment, between 13% (at age 0) and 33% (at age 70) of the 237 countries shift from male to female disadvantage in life expectancy. More than half of the countries remain male-disadvantaged, indicating that males are generally disadvantaged in terms of life expectancy in most countries, even after our adjustments. India and approximately half of the countries in the Middle East and North Africa, North Atlantic, sub-Saharan Africa, and Western Pacific and Southeast Asia show female disadvantage. The number of countries with female disadvantage rises with age, especially in sub-Saharan Africa and Western Pacific and Southeastern Asia. Central and Eastern Europe show substantial male disadvantage across nearly all ages, even with adjustment. Our frontier selection and buffer range are empirical choices, and other definitions could be equally valid. Although our sex-specific benchmarks use the best-performing countries for each sex, they are not meant to represent purely biological differences, as observed sex gaps in life expectancy may also reflect unmeasured genetic variation, environmental exposures, and their interactions with sex.

conclusionThis study provides a novel, potentially more objective method for assessing sex inequalities in health outcomes, and presents the trends across countries, age, and time.

Indexed as

BenchmarkingHealth Status DisparitiesLife ExpectancyAdolescentAdultAgedChildChild, PreschoolFemaleHumansInfantInfant, NewbornMaleMiddle AgedSex FactorsSex Ratio

Identifiers

PMID41379774
PMCPMC12697978

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.