Evidence map›Paper›PMID 39522526›Full record

ArticleLancet (London, England)2024

Disparities in wellbeing in the USA by race and ethnicity, age, sex, and location, 2008-21: an analysis using the Human Development Index.

Laura Dwyer-Lindgren, Parkes Kendrick, Mathew M Baumann, Zhuochen Li, Chris Schmidt, Dillon O Sylte, Farah Daoud, Wichada La Motte-Kerr, Robert W Aldridge, Catherine Bisignano and 3 more

Abstract read
In one paragraph

Article in Lancet (London, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

  1. Sports team participation in high school and adolescent and young adult depression and anxiety across sex, race/ethnicity, and socioeconomic status.Journal of research on adolescence : the official journal of the Society for Research on Adolescence · 2026
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors.

Laura Dwyer-LindgrenInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA; Department of Health Metrics Sciences, University of Washington, Seattle, WA, USA. Electronic address: ladwyer@uw.edu.
Parkes KendrickInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Mathew M BaumannInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Zhuochen LiInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Chris SchmidtInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Dillon O SylteInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Farah DaoudInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Wichada La Motte-KerrInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Robert W AldridgeInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA; Department of Health Metrics Sciences, University of Washington, Seattle, WA, USA.
Catherine BisignanoInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Simon I HayInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA; Department of Health Metrics Sciences, University of Washington, Seattle, WA, USA.
Ali H MokdadInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA; Department of Health Metrics Sciences, University of Washington, Seattle, WA, USA.
Christopher J L MurrayInstitute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA; Department of Health Metrics Sciences, University of Washington, Seattle, WA, USA.

Funding

NICHD NIH HHS 75N94023C00004
6 · The paper itself

Abstract

backgroundThe Human Development Index (HDI)-a composite metric encompassing a population's life expectancy, education, and income-is used widely for assessing and comparing human development and wellbeing at the country level, but does not account for within-country inequality. In this study of the USA, we aimed to adapt the HDI framework to measure the HDI at an individual level to examine disparities in the distribution of wellbeing by race and ethnicity, sex, age, and geographical location.

methodsWe used individual-level data on adults aged 25 years and older from the 2008-21 American Community Survey (ACS) Public Use Microdata Sample. We extracted information on race and ethnicity, age, sex, location (Public Use Microdata Areas), educational attainment, and household income and size. We merged these data with estimated life tables by race and ethnicity, sex, age, location (county), and year, generated using Bayesian small-area estimation models applied to death certificate data from the National Vital Statistics System. For each individual in the ACS, we used these combined data to estimate years of education, household consumption, and expected lifespan; converted each of these three features into an index using a percentile score; and calculated the HDI as the geometric mean of these three indices. Finally, we grouped individuals into yearly HDI deciles.

findingsYears of education, household consumption, and expected lifespan-and thus the HDI-varied considerably among adults in the USA during the 2008-21 period. For most race and ethnicity and sex groups, the mean HDI increased gradually from 2008 to 2019, then declined in 2020 due to declines in expected lifespan, although there were systematic differences in the distribution of the HDI by race and ethnicity and sex. In the lowest HDI decile, there was over-representation (ie, >10% of the total population of a given race and ethnicity and sex group) of American Indian and Alaska Native (AIAN) males (50% [SE 0·2] in decile, mean annual population in decile 0·37 million [SE 0·002]), Black males (40% [<0·1], 4·67 million [0·006]), AIAN females (23% [0·1], 0·19 million [0·001]), Latino males (21% [<0·1], 3·27 million [0·006]), Black females (14% [<0·1], 1·86 million [0·004]), and Latina females (13% [<0·1], 2·07 million [0·006]). Given differences in total population size, however, White males were the largest population group in the lowest decile (27% [<0·1] of the lowest decile, 5·87 million [0·012]), followed by Black males (22% [<0·1]) and Latino males (15% [<0·1]). There were notable differences in these patterns by age group: for example, for the 25-44 years age group, the lowest HDI decile had even greater over-representation of AIAN males (66% [0·2] in decile, 0·22 million [0·001]) and Black males (46% [<0·1], 2·52 million [0·005]) than the 85 years and older age group (22% [1·1], <0·01 million [<0·001]; and 20% [0·3], 0·03 million [<0·001]). By contrast, the lowest decile had an under-representation of Asian females (2% [<0·1], 0·06 million [<0·001]) in the 25-44 years age group, but an over-representation in the 85 years and older age group (25% [0·3], 0·03 million [<0·001]). The lowest HDI decile for the 25-44 years age group was primarily male (76% [<0·1], 6·44 million [0·009]) whereas for age 85 years and older it was predominantly female (71% [0·1], 0·42 million [0·002]). In the highest HDI decile, shifts in the composition of the population by age were particularly large for White males, who made up 5% (0·1; 0·39 million [0·001]) of this decile in the 25-44 years age group, but 49% (0·2; 0·29 million [0·001]) in the 85 years and older age group. From 2012 to 2021, the proportion of the population living in the lowest HDI decile varied substantially by location, and a disproportionately high share of the population living in locations in much of the southern half of the USA, Appalachia, and Rust Belt states were in the lowest HDI decile.

interpretationSubstantial disparities in wellbeing exist within the USA and are heavily influenced by race and ethnicity (due to racism), sex, age, and geographical location. These disparities are not immutable, but improvement is not a given, and gains can be fleeting in the face of a crisis such as the COVID-19 pandemic. Sustained action to ensure that everyone has meaningful access to a high-quality education, the means to earn a sufficient income, and the opportunity to live a long and healthy life is needed to reduce these disparities and should focus on the populations and locations that are worst off.

fundingState of Washington and National Institute on Minority Health and Health Disparities.

Indexed as

Health Status DisparitiesLife ExpectancyAdultAgedAged, 80 and overAge FactorsAmerican Indian or Alaska NativeBlack or African AmericanEducational StatusEthnicityFemaleHispanic or LatinoHuman DevelopmentHumansIncomeMale

Identifiers

PMID39522526
PMCPMC11654742

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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.