Evidence map›Paper›PMID 31961379›Full record

ArticleJAMA internal medicine2020

Association of Social Mobility With the Income-Related Longevity Gap in the United States: A Cross-Sectional, County-Level Study.

Atheendar Venkataramani, Sebastian Daza, Ezekiel Emanuel

Open access · hybridAbstract read
In one paragraph

Article in JAMA internal medicine, 2020. 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
2.3field-weighted citation impact, top 9% of its field
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, 28 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Review
  9. Review
  10. Article
  11. Article
  12. Article
  13. Early Exposure to County Income Mobility and Adult Individual Health in the United States.The journals of gerontology. Series B, Psychological sciences and social sciences · 2022
    Article
  14. Article
  15. Article
  16. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors at 2 institutions in 1 country.

Atheendar VenkataramaniDepartment of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia.
Sebastian DazaCenter for Demography and Ecology, University of Wisconsin-Madison.
Ezekiel EmanuelDepartment of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia.
University of Pennsylvania · USUniversity of Wisconsin–Madison · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: Despite substantial research, the drivers of the widening gap in life expectancy between rich and poor individuals in the United States-known as the longevity gap-remain unknown. The hypothesis of this study is that social mobility may play an important role in explaining the longevity gap. Objective: To assess whether social mobility is associated with income-related differences in life expectancy in the United States. Design, Setting, and Participants: This cross-sectional, ecological study used data from 1559 counties in the United States to assess the association of social mobility with average life expectancy at age 40 years by sex and income quartile among adult men and women over the period of January 2000 through December 2014. Bayesian generalized linear multilevel regression models were used to estimate the association, with adjustment for a range of socioeconomic, demographic, and health care system characteristics. Exposures: County-level social mobility, here operationalized as the association of the income rank of individuals born during the period of January 1980 through December 1982 (based on tax record data, averaged over the period January 2010 through December 2012) with the income ranks of their parents (averaged over the period January 1996 through December 2000) using the location where the parent first claimed the child as a dependent at age 15 years to identify counties. Main Outcomes and Measures: The main outcome was life expectancy at age 40 years by sex and income quartile. Results: The sample consisted of 1559 counties, which represented 93% of the US population in 2000. Each 1-SD increase in social mobility-equivalent to the difference between a low-mobility state, such as Alabama (ranked 49th on this measure), and a higher-mobility state, such as Massachusetts (ranked 23rd on this measure)-was associated with a 0.38-year (95% credible interval [CrI], 0.29-0.47) and a 0.29-year (95% CrI, 0.21-0.38) increase in county-level life expectancy among men and women, respectively, in the lowest income quartile. Estimates for life expectancies among county residents in the highest income quartile were smaller in magnitude and not robust to covariate adjustment (men: 0.10-year [95% CrI, -0.02 to 0.22] increase; women: 0.08-year [95% CrI, -0.05 to 0.20] increase). Increasing social mobility in all counties to the value of the highest social mobility county was associated with decreases in the life expectancy gap between the highest and lowest income quartiles by 1.4 (95% CrI, 0.7-2.1) years for men and 1.1 (95% CrI, 0.5-1.6) years for women nationally, representing a 20% decrease. Conclusions and Relevance: In this cross-sectional study, higher county-level social mobility was associated with smaller county-level gaps in life expectancy by income. These findings motivate further investigation of causal relationships between policies that shift social mobility and health outcomes.

Indexed as

IncomeLongevitySocial MobilityAdultAgedAged, 80 and overCross-Sectional StudiesFemaleHumansLife ExpectancyMaleMiddle AgedPovertyRisk FactorsSocioeconomic FactorsUnited States

Identifiers

PMID31961379
PMCPMC6990844
OpenAlexW3001018177

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.