Evidence map›Paper›PMID 35193593›Full record

ArticlePopulation health metrics2022

Trends and patterns of disparities in diabetes and chronic kidney disease mortality among US counties, 1980-2014.

Ali H Mokdad, Laura Dwyer-Lindgren, Amelia Bertozzi-Villa, Rebecca W Stubbs, Chloe Morozoff, Shreya Shirude, Sam B Finegold, Charlton Callender, Mohsen Naghavi, Christopher J L Murray

Open access · goldAbstract read
In one paragraph

Article in Population health metrics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.6field-weighted citation impact, top 18% 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

9 citing papers in PubMed, 12 citations in OpenAlex.

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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

10 authors at 3 institutions in 1 country.

Ali H MokdadInstitute for Health Metrics and Evaluation, University of Washington, 3980 15th Ave NE, Seattle, WA, 98195, USA. mokdaa@uw.edu.
Laura Dwyer-LindgrenInstitute for Health Metrics and Evaluation, University of Washington, 3980 15th Ave NE, Seattle, WA, 98195, USA.
Amelia Bertozzi-VillaInstitute for Health Metrics and Evaluation, University of Washington, 3980 15th Ave NE, Seattle, WA, 98195, USA.
Rebecca W StubbsInstitute for Health Metrics and Evaluation, University of Washington, 3980 15th Ave NE, Seattle, WA, 98195, USA.
Chloe MorozoffInstitute for Health Metrics and Evaluation, University of Washington, 3980 15th Ave NE, Seattle, WA, 98195, USA.
Shreya ShirudeInstitute for Health Metrics and Evaluation, University of Washington, 3980 15th Ave NE, Seattle, WA, 98195, USA.
Sam B FinegoldInstitute for Health Metrics and Evaluation, University of Washington, 3980 15th Ave NE, Seattle, WA, 98195, USA.
Charlton CallenderInstitute for Health Metrics and Evaluation, University of Washington, 3980 15th Ave NE, Seattle, WA, 98195, USA.
Mohsen NaghaviInstitute for Health Metrics and Evaluation, University of Washington, 3980 15th Ave NE, Seattle, WA, 98195, USA.
Christopher J L MurrayInstitute for Health Metrics and Evaluation, University of Washington, 3980 15th Ave NE, Seattle, WA, 98195, USA.
Institute for Health Metrics and Evaluation · USUniversity of Washington · USSeattle University · US

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 5P30AG047845NIA NIH HHS P30 AG047845
6 · The paper itself

Abstract

introductionDiabetes and chronic kidney diseases are associated with a large health burden in the USA and globally.

objectiveTo estimate age-standardized mortality rates by county from diabetes mellitus and chronic kidney disease. DESIGN AND

settingValidated small area estimation models were applied to de-identified death records from the National Center for Health Statistics (NCHS) and population counts from the census bureau, NCHS, and the Human Mortality Database to estimate county-level mortality rates from 1980 to 2014 from diabetes mellitus and chronic kidney disease (CKD). EXPOSURES: County of residence. MAIN OUTCOMES AND MEASURES: Age-standardized mortality rates by county, year, sex, and cause.

resultsBetween 1980 and 2014, 2,067,805 deaths due to diabetes were recorded in the USA. The mortality rate due to diabetes increased by 33.6% (95% UI: 26.5%-41.3%) between 1980 and 2000 and then declined by 26.4% (95% UI: 22.8%-30.0%) between 2000 and 2014. Counties with very high mortality rates were found along the southern half of the Mississippi river and in parts of South and North Dakota, while very low rates were observed in central Colorado, and select counties in the Midwest, California, and southern Florida. A total of 1,659,045 deaths due to CKD were recorded between 1980 and 2014 (477,332 due to diabetes mellitus, 1,056,150 due to hypertension, 122,795 due to glomerulonephritis, and 2,768 due to other causes). CKD mortality varied among counties with very low mortality rates observed in central Colorado as well as some counties in southern Florida, California, and Great Plains states. High mortality rates from CKD were observed in counties throughout much of the Deep South, and a cluster of counties with particularly high rates was observed around the Mississippi river. CONCLUSIONS AND RELEVANCE: This study found large inequalities in diabetes and CKD mortality among US counties. The findings provide insights into the root causes of this variation and call for improvements in risk factors, access to medical care, and quality of medical care.

Indexed as

Diabetes MellitusHypertensionRenal Insufficiency, ChronicCensusesFemaleHumansMaleMortalityRisk FactorsUnited StatesChronic kidney diseaseDiabetesDisparitiesMortality

Identifiers

PMID35193593
PMCPMC8862531
OpenAlexW4212887843

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.