Evidence map›Paper›PMID 34017188›Full record

ArticleClinicoEconomics and outcomes research : CEOR2021

Influence of Comorbidities on Healthcare Expenditures and Perceived Physical and Mental Health Status Among Adults with Multiple Sclerosis: A Propensity Score-Matched US National-Level Study.

Sandipan Bhattacharjee, Zufan Yegezu, Kristin Kollecas, Kevin Duhrkopf, Lobat Hashemi, Nupur Greene

Open access · goldAbstract read
In one paragraph

Article in ClinicoEconomics and outcomes research : CEOR, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
1.5field-weighted citation impact, top 11% 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

3 citing papers in PubMed, 1 synthesis or guideline pooled it, 5 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Review
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

6 authors at 4 institutions in 1 country.

Sandipan BhattacharjeeHealth Outcomes Division, College of Pharmacy, The University of Texas at Austin, Austin, TX, USA.ORCID 0000-0003-4917-5138
Zufan YegezuDepartment of Pharmacy Practice and Science, College of Pharmacy, The University of Arizona, Tucson, AZ, USA.
Kristin KollecasNeurology, Immunology, & Inflammation, Sanofi Genzyme, Cambridge, MA, USA.
Kevin DuhrkopfNeurology, Immunology, & Inflammation, Sanofi Genzyme, Cambridge, MA, USA.
Lobat HashemiNeurology, Immunology, & Inflammation, Sanofi Genzyme, Cambridge, MA, USA.
Nupur GreeneNeurology, Immunology, & Inflammation, Sanofi Genzyme, Cambridge, MA, USA.
Sanofi (United States) · USGenesys (United States) · USThe University of Texas at Austin · USUniversity of Arizona · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo evaluate the effect of comorbidities on healthcare expenditures and perceived physical and mental health status among adults with multiple sclerosis (MS) compared to propensity score-matched non-MS controls.

methodsA retrospective, cross-sectional, matched cohort study was conducted using Medical Expenditure Panel Survey (2005-2015) data. The base study sample consisted of adults (age ≥18 years) who were alive and had positive total healthcare expenditures during the survey calendar year. Adults with MS were propensity-matched (1:1) to non-MS controls based on age, gender, and race/ethnicity using greedy matching algorithm. Healthcare expenditures consisted of total and subtypes of expenditures. Health status consisted of perceived physical and mental health status. Comorbidities were identified using ICD-9-CM and Clinical Classification System codes. Ordinary least squares regression and multinomial logistic regression were used to analyze the healthcare expenditures and health status variables, respectively.

resultsFinal study sample consisted of 541 adults in each MS and non-MS control groups after propensity score matching. After adjusting for potential confounders, individuals with MS had greater total and subtypes of expenditures compared to non-MS controls, and several comorbidities (eg, depression, hypertension) were significantly associated with increased healthcare expenditures. Yearly average total expenditures (expressed in 2018 US$) were significantly (p<0.001) higher for adults with MS ($29,396) than propensity score-matched non-MS adults ($7875). Moreover, after adjusting for all individual-level factors, adults with MS experienced 363% (p<0.001) higher total expenditures compared to propensity score-matched non-MS controls. Individuals with MS were more likely to report poorer physical and good mental health status compared to propensity score-matched non-MS controls, and several comorbidities (eg, anxiety, depression) were significant independent predictors of poorer health status. For example, adults with MS were four times more likely (OR: 4.10, 95% CI: 2.42-6.96) to report fair/poor physical health status compared to excellent/very good physical health status compared with non-MS controls. Adults with MS were 42% (OR: 1.42, 95% CI: 1.01-1.99) more likely than propensity score-matched non-MS controls to report good rather than very good or excellent mental health status. However, there was no difference between adults with MS and propensity score-matched non-MS controls in terms of reporting fair or poor than very good or excellent mental health status.

conclusionFindings from this study indicate substantial economic and health status burdens among adults with MS at the US national-level that are significantly influenced by comorbidities.

Indexed as

comorbiditiesexpendituresmental health statusmultiple sclerosisphysical health statuspropensity score matching

Identifiers

PMID34017188
PMCPMC8129918
OpenAlexW3163939923

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.