CoChat
Sign Up
Cited by 346Insurance, Mortality, Demography, Risk ManagementHealth disparities and outcomesHealthcare Systems and Public Health

The reversal of fortunes: trends in county mortality and cross-county mortality disparities in the United States

Open access
Ezzati, Majid, Friedman, Ari B, Kulkarni, Sandeep C, Murray, Christopher J. L·2008·PLoS Medicine
DOI 10.1371/journal.pmed.0050066PMC PMC2323303
Open this paper in CoChat — free accountOpen PDF

Abstract

BACKGROUND: Counties are the smallest unit for which mortality data are routinely available, allowing consistent and comparable long-term analysis of trends in health disparities. Average life expectancy has steadily increased in the United States but there is limited information on long-term mortality trends in the US counties This study aimed to investigate trends in county mortality and cross-county mortality disparities, including the contributions of specific diseases to county level mortality trends. METHODS AND FINDINGS: We used mortality statistics (from the National Center for Health Statistics [NCHS]) and population (from the US Census) to estimate sex-specific life expectancy for US counties for every year between 1961 and 1999. Data for analyses in subsequent years were not provided to us by the NCHS. We calculated different metrics of cross-county mortality disparity, and also grouped counties on the basis of whether their mortality changed favorably or unfavorably relative to the national average. We estimated the probability of death from specific diseases for counties with above- or below-average mortality performance. We simulated the effect of cross-county migration on each county's life expectancy using a time-based simulation model. Between 1961 and 1999, the standard deviation (SD) of life expectancy across US counties was at its lowest in 1983, at 1.9 and 1.4 y for men and women, respectively. Cross-county life expectancy SD increased to 2.3 and 1.7 y in 1999. Between 1961 and 1983 no counties had a statistically significant increase in mortality; the major cause of mortality decline for both sexes was reduction in cardiovascular mortality. From 1983 to 1999, life expectancy declined significantly in 11 counties for men (by 1.3 y) and in 180 counties for women (by 1.3 y); another 48 (men) and 783 (women) counties had nonsignificant life expectancy decline. Life expectancy decline in both sexes was caused by increased mortality from lung cancer, chronic obstructive pulmonary disease (COPD), diabetes, and a range of other noncommunicable diseases, which were no longer compensated for by the decline in cardiovascular mortality. Higher HIV/AIDS and homicide deaths also contributed substantially to life expectancy decline for men, but not for women. Alternative specifications of the effects of migration showed that the rise in cross-county life expectancy SD was unlikely to be caused by migration. CONCLUSIONS: There was a steady increase in mortality inequality across the US counties between 1983 and 1999, resulting from stagnation or increase in mortality among the worst-off segment of the population. Female mortality increased in a large number of counties, primarily because of chronic diseases related to smoking, overweight and obesity, and high blood pressure.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1371/journal.pmed.0050066

Cites

50 of 50 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Most studies conducted to date have compared mortality in psychosis with that in the national rather than local population, thereby ignoring geographical variation in mortality rates.

    “Further, most studies conducted to date have compared mortality in psychosis with that in the national rather than local population,9 thereby ignoring geographical variation in mortality rates.17,18”

    From Mortality in Schizophrenia and Other Psychoses: A 10-Year Follow-up of the ӔSOP First-Episode Cohort (2015) · cites this paper as [17]

  • The study was able to compare mortality in this cohort with that in the local general population of the defined catchment areas, addressing variation in general population mortality by place.

    “We were further able to compare mortality in this cohort with that in the local general population of the defined catchment areas, thereby addressing the issue that mortality rates in the general population vary (markedly) by place”

    From Mortality in Schizophrenia and Other Psychoses: A 10-Year Follow-up of the ӔSOP First-Episode Cohort (2015) · cites this paper as [17]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Related

Authors
Ezzati, M., Friedman, A. B., Kulkarni, S. C., & Murray, C. J. L.
Affiliations
Harvard University (US)
Corresponding author
Ezzati, Majid
ORCID
Venue
PLoS Medicine
Publisher
Public Library of Science (PLoS)
Published
2008-04-22
Volume
5
Issue
4
Pages
e66
ISSN
  • 1549-1277 (ISSN-L)
  • 1549-1676
DOI
10.1371/journal.pmed.0050066
PMC
PMC2323303
OpenAlex
W2147204063
Cited by
346
References
50
Citations by year
2026: 1 · 2025: 7 · 2024: 8 · 2023: 14 · 2022: 13 · 2021: 18 · 2020: 17 · 2019: 18 · 2018: 19 · 2017: 20 · 2016: 20 · 2015: 21 · 2014: 23 · 2013: 22 · 2012: 30
FWCI
84.25
Type
journal article
Language
en
Update notices
correction (2008-05-13)
Open access
Licence
Locations
  • PLoS Medicine (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, other-oa)
  • PubMed Central (publishedVersion, other-oa)
  • PubMed (publishedVersion)
  • Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
  • ScholarlyCommons (University of Pennsylvania) (submittedVersion, other-oa)
  • ScholarlyCommons (University of Pennsylvania) (submittedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • University Libraries (University of Maryland) (submittedVersion)
  • Figshare (submittedVersion, cc-by)
  • Digital Repository at the University of Maryland (University of Maryland College Park)
Funders
  • Centers for Disease Control and Prevention
  • U.S. Public Health Service (U36 CCU300430-23)
Topics
  • Social Sciences › Demography › Insurance, Mortality, Demography, Risk Management
  • Social Sciences › Health › Health disparities and outcomes
  • Health Sciences › Medicine › Epidemiology › Healthcare Systems and Public Health
Keywords
life expectancy, health disparities, mortality inequality, cardiovascular mortality decline, non-communicable diseases, lung cancer mortality, chronic obstructive pulmonary disease, diabetes mortality, HIV mortality, homicide, sex differences in mortality, county-level mortality, United States counties
MeSH terms
Adolescent, Adult, Aged, Aged, 80 and over, Child, Child, Preschool, Demography, Female, Geography, Humans, Infant, Infant, Newborn, Life Expectancy, Male, Middle Aged, Mortality, Risk Factors, Socioeconomic Factors, United States, Censuses

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review