Evidence map›Paper›PMID 35654300›Full record

ArticleCancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology2022

Trends in Healthcare Expenditures among Adults in the United States by Cancer Diagnosis Status, 2008-2016: A Cross-Sectional Study.

Shannon L Walker, Joni S Williams, Kaiwei Lu, Aprill Z Dawson, Leonard E Egede

Abstract read
In one paragraph

Article in Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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

2 citing papers in PubMed.

  1. Article
  2. 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

5 authors.

Shannon L WalkerInstitute for Health and Equity (IHE), Medical College of Wisconsin, Milwaukee, Wisconsin.
Joni S WilliamsCenter for Advancing Population Science (CAPS), Medical College of Wisconsin, Milwaukee, Wisconsin.ORCID 0000-0001-8988-3556
Kaiwei LuCenter for Advancing Population Science (CAPS), Medical College of Wisconsin, Milwaukee, Wisconsin.
Aprill Z DawsonCenter for Advancing Population Science (CAPS), Medical College of Wisconsin, Milwaukee, Wisconsin.
Leonard E EgedeCenter for Advancing Population Science (CAPS), Medical College of Wisconsin, Milwaukee, Wisconsin.ORCID 0000-0003-1546-1515

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
Lowering the Impact of Food insecurity in African American Adults with Type 2 Diabetes Mellitus (LIFT-DM) - Diversity SupplementR01MD013826 · NIMHD · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI EGEDE, LEONARD E., WALKER, REBEKAH J · 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 DK120861NIMHD NIH HHS R01 MD013826
6 · The paper itself

Abstract

backgroundThis study aims to assess trends in direct medical expenditures and indirect costs between adults with and without a prior cancer diagnosis from 2008 to 2016.

methodsNine years of data (2008-2016) from the Medical Expenditure Panel Survey (weighted N = 236,811,875) were used. The outcomes included medical expenditures (total expenditure, inpatient, office-based, medications, outpatient, dental, emergency room visits, home health, other) and health-related missed workdays. The predictor was prior cancer diagnosis. Covariates included demographic characteristics, comorbidities, and calendar year at time of survey completion. Two-part statistical modeling with a combination of binomial and positive distributions was used to estimate medical expenditures and missed workdays. Data were clustered into five timepoints: 2008 to 2009, 2010 to 2011, 2012 to 2013, 2014 to 2015, and 2016.

resultsEleven percent of the sample (n = 25,005,230) had a prior cancer diagnosis. Compared with those without a prior cancer diagnosis, those with a prior cancer diagnosis had higher mean incremental total expenditures across all years. Between 2008 and 2016, the adjusted annual incremental total expenditures were $3,522 [95% confidence interval (CI), $3,072-$3,972]; office-based visits ($1,085; 95% CI, $990-$1180); inpatient hospitalizations ($810; 95% CI, $627-$992); outpatient appointments ($517; 95% CI, $434-$600); and medications ($409; 95% CI, $295-$523); and health-related missed workdays (0.75; 95% CI, 0.45-1.04) compared with adults without a prior cancer diagnosis.

conclusionsAdults with a prior cancer diagnosis had significantly increased healthcare expenditures and health-related missed workdays compared with those with no cancer diagnosis. IMPACT: Our findings highlight the need for increasing strategies to remedy the impact of increasing direct and indirect costs associated with cancer survivorship as the population grows and ages.

Indexed as

Health ExpendituresNeoplasmsAdultCross-Sectional StudiesDelivery of Health CareHumansSurveys and QuestionnairesUnited States

Identifiers

PMID35654300
PMCPMC9348759

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.