Evidence map›Paper›PMID 32989362›Full record

ArticleEthnicity & disease2020

Trends in Obesity and Medical Expenditure among Women with Diabetes, 2008-2016: Differences by Race/Ethnicity.

Joni S Williams, Kaiwei Lu, Olaitan Akinboboye, Oluwatoyin Olukotun, Zhipeng Zhou, Sneha Nagavally, Leonard E Egede

Abstract read
In one paragraph

Article in Ethnicity & disease, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Trends in Healthcare Expenditures among Adults in the United States by Cancer Diagnosis Status, 2008-2016: A Cross-Sectional Study.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2022
    Article
  4. 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

7 authors.

Joni S WilliamsDivision of General Internal Medicine, Department of Medicine, Medical College of Wisconsin, Milwaukee, WI.
Kaiwei LuCenter for Advancing Population Science (CAPS), Medical College of Wisconsin, Milwaukee, WI.
Olaitan AkinboboyeInstitute of Health and Equity, Department of Public and Community Health, Medical College of Wisconsin, Milwaukee, WI.
Oluwatoyin OlukotunCenter for Advancing Population Science (CAPS), Medical College of Wisconsin, Milwaukee, WI.
Zhipeng ZhouClinical Trials Center, Cardiovascular Research Foundation, New York, NY.
Sneha NagavallyCenter for Advancing Population Science (CAPS), Medical College of Wisconsin, Milwaukee, WI.
Leonard E EgedeDivision of General Internal Medicine, Department of Medicine, Medical College of Wisconsin, Milwaukee, WI.

Funding

HOME DM-BAT: Home-based Diabetes-Modified Behavioral Activation Treatment for Low Income Seniors with T2DM Diversity SupplementR01DK118038 · NIDDK · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI EGEDE, LEONARD E. · 2019 to 2023
$3.5M
Financial Incentives And Nurse Coaching to Enhance Diabetes Outcomes (FINANCE-DM) - Diversity SupplementR01DK120861 · NIDDK · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI EGEDE, LEONARD E. · 2019 to 2023
$3.4M
Technology Intensified Diabetes Education Study in AA with T2DM (TIDES)K24DK093699 · NIDDK · MEDICAL UNIVERSITY OF SOUTH CAROLINA · PI EGEDE, LEONARD E. · 2011 to 2020
$1.5M
NIDDK NIH HHS K24 DK093699NIDDK NIH HHS R01 DK118038NIDDK NIH HHS R01 DK120861
6 · The paper itself

Abstract

Objectives: Diabetes results in $327 billion in medical expenditures annually, while obesity, a risk factor for type 2 diabetes, leads to more than $147 billion in expenditure annually. The aims of this study were: 1) to evaluate racial/ethnic trends in obesity and medical expenditures; and 2) to assess incremental medical expenditures among a nationally representative sample of women with diabetes. Methods: Nine years of data (2008-2016) from the Medical Expenditure Panel Survey Full Year Consolidated File (unweighted = 11,755; weighted = 10,685,090) were used. The outcome variable was medical expenditure. The primary independent variable was race/ethnicity defined as non-Hispanic Black (NHB), Hispanic, or non-Hispanic White (NHW). Covariates included age, education, marital status, income, insurance, employment, region, comorbidity, and year. Cochran-Armitage tests determined statistical significance of trends in obesity and mean expenditure. Two-part modeling using Probit and gamma distribution was used to assess incremental medical expenditure. Data were clustered to 2008-2010, 2011-2013, 2014-2016. Results: Trends in medical expenditures differed significantly between NHB and NHW women between 2008-2016 (P<.001). Hispanic women paid $1,291 less compared with NHW women, after adjusting for relevant covariates. There were no significant differences in obesity trends from 2008-2016 between NHB (P=.989) or Hispanic women with diabetes (P=.938) compared with NHW women with diabetes. Conclusions: These findings suggest the need to further understand the factors associated with differences in trends for medical expenditures between NHB and NHW women with diabetes and incremental medical expenditures in Hispanic women with diabetes compared with NHW women with diabetes.

Indexed as

AdultAgedBlack or African AmericanComorbidityDiabetes Mellitus, Type 2FemaleHealth ExpendituresHispanic or LatinoHumansMiddle AgedObesitySurveys and QuestionnairesUnited StatesWhite PeopleDiabetesDisparitiesMedical ExpenditureObesityRace/EthnicityWomen

Identifiers

PMID32989362
PMCPMC7518529

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.