Evidence map›Paper›PMID 27959996›Full record

ArticleJAMA2016

US County-Level Trends in Mortality Rates for Major Causes of Death, 1980-2014.

Laura Dwyer-Lindgren, Amelia Bertozzi-Villa, Rebecca W Stubbs, Chloe Morozoff, Michael J Kutz, Chantal Huynh, Ryan M Barber, Katya A Shackelford, Johan P Mackenbach, Frank J van Lenthe and 4 more

Abstract read
In one paragraph

Article in JAMA, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 153 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
153citing papers in PubMed, 2 pooled it
–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

153 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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  13. Pregnancy-related death rates by county, race, and ethnicity in the US, 2000-2019.Archives of public health = Archives belges de sante publique · 2025
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93 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

14 authors.

Laura Dwyer-LindgrenInstitute for Health Metrics and Evaluation, University of Washington, Seattle.
Amelia Bertozzi-VillaInstitute for Health Metrics and Evaluation, University of Washington, Seattle.
Rebecca W StubbsInstitute for Health Metrics and Evaluation, University of Washington, Seattle.
Chloe MorozoffInstitute for Health Metrics and Evaluation, University of Washington, Seattle.
Michael J KutzInstitute for Health Metrics and Evaluation, University of Washington, Seattle.
Chantal HuynhInstitute for Health Metrics and Evaluation, University of Washington, Seattle.
Ryan M BarberInstitute for Health Metrics and Evaluation, University of Washington, Seattle.
Katya A ShackelfordInstitute for Health Metrics and Evaluation, University of Washington, Seattle.
Johan P MackenbachDepartment of Public Health, Erasmus MC, Rotterdam, the Netherlands.
Frank J van LentheDepartment of Public Health, Erasmus MC, Rotterdam, the Netherlands.
Abraham D FlaxmanInstitute for Health Metrics and Evaluation, University of Washington, Seattle.
Mohsen NaghaviInstitute for Health Metrics and Evaluation, University of Washington, Seattle.
Ali H MokdadInstitute for Health Metrics and Evaluation, University of Washington, Seattle.
Christopher J L MurrayInstitute for Health Metrics and Evaluation, University of Washington, Seattle.

Funding

University of Washington Center for Demography and Economics of Aging-OverallP30AG047845 · NIA · UNIVERSITY OF WASHINGTON · PI MURRAY, CHRISTOPHER J.L. · 2014 to 2019
$3.8M
NIA NIH HHS P30 AG047845
6 · The paper itself

Abstract

Importance: County-level patterns in mortality rates by cause have not been systematically described but are potentially useful for public health officials, clinicians, and researchers seeking to improve health and reduce geographic disparities. Objectives: To demonstrate the use of a novel method for county-level estimation and to estimate annual mortality rates by US county for 21 mutually exclusive causes of death from 1980 through 2014. Design, Setting, and Participants: Redistribution methods for garbage codes (implausible or insufficiently specific cause of death codes) and small area estimation methods (statistical methods for estimating rates in small subpopulations) were applied to death registration data from the National Vital Statistics System to estimate annual county-level mortality rates for 21 causes of death. These estimates were raked (scaled along multiple dimensions) to ensure consistency between causes and with existing national-level estimates. Geographic patterns in the age-standardized mortality rates in 2014 and in the change in the age-standardized mortality rates between 1980 and 2014 for the 10 highest-burden causes were determined. Exposure: County of residence. Main Outcomes and Measures: Cause-specific age-standardized mortality rates. Results: A total of 80 412 524 deaths were recorded from January 1, 1980, through December 31, 2014, in the United States. Of these, 19.4 million deaths were assigned garbage codes. Mortality rates were analyzed for 3110 counties or groups of counties. Large between-county disparities were evident for every cause, with the gap in age-standardized mortality rates between counties in the 90th and 10th percentiles varying from 14.0 deaths per 100 000 population (cirrhosis and chronic liver diseases) to 147.0 deaths per 100 000 population (cardiovascular diseases). Geographic regions with elevated mortality rates differed among causes: for example, cardiovascular disease mortality tended to be highest along the southern half of the Mississippi River, while mortality rates from self-harm and interpersonal violence were elevated in southwestern counties, and mortality rates from chronic respiratory disease were highest in counties in eastern Kentucky and western West Virginia. Counties also varied widely in terms of the change in cause-specific mortality rates between 1980 and 2014. For most causes (eg, neoplasms, neurological disorders, and self-harm and interpersonal violence), both increases and decreases in county-level mortality rates were observed. Conclusions and Relevance: In this analysis of US cause-specific county-level mortality rates from 1980 through 2014, there were large between-county differences for every cause of death, although geographic patterns varied substantially by cause of death. The approach to county-level analyses with small area models used in this study has the potential to provide novel insights into US disease-specific mortality time trends and their differences across geographic regions.

Indexed as

Cause of DeathHumansMortalityUnited States

Identifiers

PMID27959996
PMCPMC5576343

What OpenQuestion holds

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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.