Evidence map›Paper›PMID 32778062›Full record

ArticleBMC nephrology2020

Exploring reasons for state-level variation in incidence of dialysis-requiring acute kidney injury (AKI-D) in the United States.

Zijin Chen, Charles E McCulloch, Neil R Powe, Michael Heung, Rajiv Saran, Meda E Pavkov, Nilka Rios Burrows, Raymond K Hsu, Chi-Yuan Hsu, Centers for Disease Control and Prevention Chronic Kidney Disease Surveillance Team Neil Powe

Open access · goldAbstract read
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In one paragraph

Article in BMC nephrology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.8field-weighted citation impact, top 14% 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

5 citing papers in PubMed, 15 citations in OpenAlex.

  1. Article
  2. Article
  3. Observational
  4. Review
  5. Article
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 4 institutions in 2 countries.

Zijin ChenDepartment of Nephrology, Ruijin Hospital affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China. chenzijin1030@126.com.
Charles E McCullochDepartment of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA.
Neil R PoweUCSF Center for Vulnerable Populations, Department of Medicine, University of California, San Francisco, California, USA.
Michael HeungDivision of Nephrology, Department of Medicine and Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA.
Rajiv SaranDivision of Nephrology, Department of Medicine and Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA.
Meda E PavkovDivision of Diabetes Translation, Centers for Disease Control and Prevention, Atlanta, GA, USA.
Nilka Rios BurrowsDivision of Diabetes Translation, Centers for Disease Control and Prevention, Atlanta, GA, USA.
Raymond K HsuDivision of Nephrology, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA.
Chi-Yuan HsuDivision of Nephrology, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA.
Centers for Disease Control and Prevention Chronic Kidney Disease Surveillance Team Neil Powe
University of California, San Francisco · USCenters for Disease Control and Prevention · USUniversity of Michigan · USSan Francisco General Hospital · US

Funding

Regional Pilot And Feasibility Study Grants ProgramP30DK020572 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Scott Soleimanpour · 2013 to 2026
$24.3M
Research BaseP30DK092926 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI MARY ELLEN MICHELE HEISLER, ADESUWA B OLOMU · 2011 to 2026
$10.0M
Acute kidney injury among patients with chronic kidney diseaseR01DK114014 · NIDDK · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI HSU, CHI-YUAN · 2018 to 2021
$3.0M
Patient Oriented Research in Kidney DiseaseK24DK092291 · NIDDK · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI HSU, CHI-YUAN · 2011 to 2020
$1.2M
Enhancing appreciation of the clinical significance of acute kidney injuryK23DK100468 · NIDDK · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI HSU, RAYMOND · 2013 to 2017
$892k
Clinical Outcomes after Dialysis-Requiring Acute Kidney InjuryR03DK111881 · NIDDK · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI HSU, RAYMOND · 2017 to 2018
$159k
ACL HHS U18DP006255CDC HHS NU58DP006255-01-00NCCDPHP CDC HHS U18 DP006255NIDDK NIH HHS K23 DK100468NIDDK NIH HHS K24 DK092291NIDDK NIH HHS P30 DK020572NIDDK NIH HHS P30 DK092926NIDDK NIH HHS R01 DK114014NIDDK NIH HHS R03 DK111881
6 · The paper itself

Abstract

backgroundThere is considerable state-level variation in the incidence of dialysis-requiring acute kidney injury (AKI-D). However, little is known about reasons for this geographic variation.

methodsNational cross-sectional state-level ecological study based on State Inpatient Databases (SID) and the Behavioral Risk Factor Surveillance System (BRFSS) in 2011. We analyzed 18 states and six chronic health conditions (diabetes mellitus [diabetes], hypertension, chronic kidney disease [CKD], arteriosclerotic heart disease [ASHD], cancer (excluding skin cancer), and chronic obstructive pulmonary disease [COPD]). Associations between each of the chronic health conditions and AKI-D incidence was assessed using Pearson correlation and multiple regression adjusting for mean age, the proportion of males, and the proportion of non-Hispanic whites in each state.

resultsThe state-level AKI-D incidence ranged from 190 to 1139 per million population. State-level differences in rates of hospitalization with chronic health conditions (mostly < 3-fold difference in range) were larger than the state-level differences in prevalence for each chronic health condition (mostly < 2.5-fold difference in range). A significant correlation was shown between AKI-D incidence and prevalence of diabetes, ASHD, and COPD, as well as between AKI-D incidence and rate of hospitalization with hypertension. In regression models, after adjusting for age, sex, and race, AKI-D incidence was associated with prevalence of and rates of hospitalization with five chronic health conditions--diabetes, hypertension, CKD, ASHD and COPD--and rates of hospitalization with cancer.

conclusionsResults from this ecological analysis suggest that state-level variation in AKI-D incidence may be influenced by state-level variations in prevalence of and rates of hospitalization with several chronic health conditions. For most of the explored chronic conditions, AKI-D correlated stronger with rates of hospitalizations with the health conditions rather than with their prevalences, suggesting that better disease management strategies that prevent hospitalizations may translate into lower incidence of AKI-D.

Indexed as

Renal DialysisAcute Kidney InjuryAgedAged, 80 and overBlack or African AmericanComorbidityFemaleGeographyHispanic or LatinoHospitalizationHumansIncidenceLinear ModelsMaleMiddle AgedPrevalenceAKI-DChronic health conditionEcological studyGeographic variation

Identifiers

PMID32778062
PMCPMC7418406
OpenAlexW3048120865

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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.