Evidence map›Paper›PMID 38459412›Full record

ArticleJournal of general internal medicine2024

Changes in Healthcare Spending Attributable to High Deductible Health Plan Offer Among Enrollees with Comorbid Substance Use Disorder and Cardiovascular Disease.

Julia C P Eddelbuettel, Alene Kennedy-Hendricks, Mark K Meiselbach, Elizabeth A Stuart, Haiden A Huskamp, Alisa B Busch, Mara A G Hollander, Cameron Schilling, Colleen L Barry, Matthew D Eisenberg

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of general internal medicine, 2024. 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

10 authors.

Julia C P EddelbuettelPhD Program in Health Policy, Harvard University, Cambridge, MA, USA. julia_eddelbuettel@g.harvard.edu.ORCID 0000-0002-9134-6570
Alene Kennedy-HendricksDepartment of Health Policy & Management, Johns Hopkins University, Baltimore, MD, USA.
Mark K MeiselbachDepartment of Health Policy & Management, Johns Hopkins University, Baltimore, MD, USA.
Elizabeth A StuartDepartment of Biostatistics, Johns Hopkins University, Baltimore, MD, USA.
Haiden A HuskampDepartment of Health Care Policy, Harvard Medical School, Boston, MA, USA.
Alisa B BuschMcLean Hospital, Belmont, MA, USA.
Mara A G HollanderDepartment of Public Health Sciences, University of North Carolina Charlotte, Charlotte, NC, USA.
Cameron SchillingDepartment of Health Policy & Management, Johns Hopkins University, Baltimore, MD, USA.
Colleen L BarryBrooks School of Public Policy, Cornell University, Ithaca, NY, USA.
Matthew D EisenbergDepartment of Health Policy & Management, Johns Hopkins University, Baltimore, MD, USA.

Funding

TRAINING IN HEALTH SERVICES RESEARCHT32HS000029 · AHRQ · JOHNS HOPKINS UNIVERSITY · PI DY, SYDNEY MORSS · 1987 to 2024
$8.3M
Consumer-Directed Health Plans and Substance Use Disorder TreatmentR01DA044201 · NIDA · JOHNS HOPKINS UNIVERSITY · PI BARRY, COLLEEN L, EISENBERG, MATTHEW · 2018 to 2021
$2.9M
Mental Health Services and Systems Training ProgramT32MH109436 · NIMH · JOHNS HOPKINS UNIVERSITY · PI Matthew Eisenberg, Elizabeth A. Stuart · 2017 to 2026
$1.7M
AHRQ HHS T32 HS000029AHRQ HHS T32HS000029NIDA NIH HHS R01 DA044201NIDA NIH HHS R01DA044201NIMH NIH HHS T32 MH109436NIMH NIH HHS T32MH109436
6 · The paper itself

Abstract

backgroundThe rise in prevalence of high deductible health plans (HDHPs) in the United States may raise concerns for high-need, high-utilization populations such as those with comorbid chronic conditions. In this study, we examine changes in total and out-of-pocket (OOP) spending attributable to HDHPs for enrollees with comorbid substance use disorder (SUD) and cardiovascular disease (CVD).

methodsWe used de-identified administrative claims data from 2007 to 2017. SUD and CVD were defined using algorithms of ICD 9 and 10 codes and HEDIS guidelines. The main outcome measures of interest were spending measure for all non-SUD/CVD-related services, SUD-specific services, and CVD-specific services, for all services and medications specifically. We assessed both total and OOP spending. We used an intent-to-treat two-part model approach to model spending and computed the marginal effect of HDHP offer as both the dollar change and percent change in spending attributable to HDHP offer.

resultsOur sample included 33,684 enrollee-years and was predominantly white and male with a mean age of 53 years. The sample had high demonstrated substantial healthcare utilization with 94% using any non-SUD/CVD services, and 84% and 78% using SUD and CVD services, respectively. HDHP offer was associated with a 17.0% (95% CI = [0.07, 0.27] increase in OOP spending for all non-SUD/CVD services, a 21.1% (95% CI = [0.11, 0.31]) increase in OOP spending for all SUD-specific services, and a 13.1% (95% CI = [0.04, 0.23]) increase in OOP spending for all CVD-specific services. HDHP offer was also associated with a significant increase in OOP spending on non-SUD/CVD-specific medications and SUD-specific medications, but not CVD-specific medications.

conclusionsThis study suggests that while HDHPs do not change overall levels of annual spending among enrollees with comorbid CVD and SUD, they may increase the financial burden of healthcare services by raising OOP costs, which could negatively impact this high-need and high-utilization population.

Indexed as

Cardiovascular DiseasesDeductibles and CoinsuranceHealth ExpendituresSubstance-Related DisordersAdultAgedComorbidityFemaleHumansInsurance, HealthMaleMiddle AgedUnited Statescardiovascular diseasehealthcare spendinghigh deductible health planssubstance use disorder

Identifiers

PMID38459412
PMCPMC11306437

What OpenQuestion holds

Textmetadata
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.