Evidence map›Paper›PMID 36461071›Full record

ArticlePopulation health metrics2022

State-level metabolic comorbidity prevalence and control among adults age 50-plus with diabetes: estimates from electronic health records and survey data in five states.

Russell Mardon, Joanne Campione, Jennifer Nooney, Lori Merrill, Maurice Johnson, David Marker, Frank Jenkins, Sharon Saydah, Deborah Rolka, Xuanping Zhang and 2 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.1field-weighted citation impact, top 46% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. US public health surveillance, reimagined.Learning health systems · 2024
    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

12 authors at 2 institutions in 1 country.

Russell MardonWestat, 1600 Research Blvd, Rockville, MD, 20850, USA. russmard@gmail.com.ORCID 0000-0001-6028-9131
Joanne CampioneWestat, 1600 Research Blvd, Rockville, MD, 20850, USA.
Jennifer NooneyWestat, 1600 Research Blvd, Rockville, MD, 20850, USA.
Lori MerrillWestat, 1600 Research Blvd, Rockville, MD, 20850, USA.
Maurice JohnsonWestat, 1600 Research Blvd, Rockville, MD, 20850, USA.
David MarkerWestat, 1600 Research Blvd, Rockville, MD, 20850, USA.
Frank JenkinsWestat, 1600 Research Blvd, Rockville, MD, 20850, USA.
Sharon SaydahUS Centers for Disease Control and Prevention, 1600 Clifton Rd., Atlanta, GA, 30329, USA.
Deborah RolkaUS Centers for Disease Control and Prevention, 1600 Clifton Rd., Atlanta, GA, 30329, USA.
Xuanping ZhangUS Centers for Disease Control and Prevention, 1600 Clifton Rd., Atlanta, GA, 30329, USA.
Sundar ShresthaUS Centers for Disease Control and Prevention, 1600 Clifton Rd., Atlanta, GA, 30329, USA.
Edward GreggUS Centers for Disease Control and Prevention, 1600 Clifton Rd., Atlanta, GA, 30329, USA.
Westat (United States) · USCenters for Disease Control and Prevention · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAlthough treatment and control of diabetes can prevent complications and reduce morbidity, few data sources exist at the state level for surveillance of diabetes comorbidities and control. Surveys and electronic health records (EHRs) offer different strengths and weaknesses for surveillance of diabetes and major metabolic comorbidities. Data from self-report surveys suffer from cognitive and recall biases, and generally cannot be used for surveillance of undiagnosed cases. EHR data are becoming more readily available, but pose particular challenges for population estimation since patients are not randomly selected, not everyone has the relevant biomarker measurements, and those included tend to cluster geographically.

methodsWe analyzed data from the National Health and Nutritional Examination Survey, the Health and Retirement Study, and EHR data from the DARTNet Institute to create state-level adjusted estimates of the prevalence and control of diabetes, and the prevalence and control of hypertension and high cholesterol in the diabetes population, age 50 and over for five states: Alabama, California, Florida, Louisiana, and Massachusetts.

resultsThe estimates from the two surveys generally aligned well. The EHR data were consistent with the surveys for many measures, but yielded consistently lower estimates of undiagnosed diabetes prevalence, and identified somewhat fewer comorbidities in most states.

conclusionsDespite these limitations, EHRs may be a promising source for diabetes surveillance and assessment of control as the datasets are large and created during the routine delivery of health care.

trial registrationNot applicable.

Indexed as

Diabetes MellitusElectronic Health RecordsAdultComorbidityHumansMiddle AgedPrevalenceSelf ReportDiabetes mellitusElectronic health recordsEpidemiologic methodsHealth and Retirement StudyHigh cholesterolHypertensionNational Health and Nutritional Examination SurveyPopulation surveillance

Identifiers

PMID36461071
PMCPMC9719142
OpenAlexW4310785201

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.