Evidence map›Paper›PMID 38290479›Full record

ArticleNeuroepidemiology2024

Cost-Effectiveness of a Government Policy to Incentivise Chronic Disease Management following Stroke: A Modelling Study.

Zhomart Orman, Dominique A Cadilhac, Nadine E Andrew, Monique F Kilkenny, Muideen T Olaiya, Amanda G Thrift, David Ung, Lachlan L Dalli, Leonid Churilov, Vijaya Sundararajan and 4 more

Abstract read
In one paragraph

Article in Neuroepidemiology, 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. Observational
  2. Costs of Neurological Disorders.Neuroepidemiology · 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

14 authors.

Zhomart OrmanHealth Economics and Policy Evaluation Research Group, Centre for Medicine Use and Safety, Faculty of Pharmacy and Pharmaceutical Sciences, Parkville, Victoria, Australia, zhomart.orman@monash.edu.
Dominique A CadilhacDepartment of Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia.
Nadine E AndrewPeninsula Clinical School, Central Clinical School, Monash University, Frankston, Victoria, Australia.
Monique F KilkennyDepartment of Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia.
Muideen T OlaiyaDepartment of Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia.
Amanda G ThriftDepartment of Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia.
David UngPeninsula Clinical School, Central Clinical School, Monash University, Frankston, Victoria, Australia.
Lachlan L DalliDepartment of Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia.
Leonid ChurilovMelbourne Medical School, University of Melbourne, Melbourne, Victoria, Australia.
Vijaya SundararajanDepartment of Medicine, St Vincent's Hospital, University of Melbourne, Fitzroy, Victoria, Australia.
Natasha A LanninDepartment of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia.
Mark R NelsonMenzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia.
Velandai SrikanthPeninsula Clinical School, Central Clinical School, Monash University, Frankston, Victoria, Australia.
Joosup KimDepartment of Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionLittle is known about the cost-effectiveness of government policies that support primary care physicians to provide comprehensive chronic disease management (CDM). This paper aimed to estimate the potential cost-effectiveness of CDM policies over a lifetime for long-time survivors of stroke.

methodsA Markov model, using three health states (stable, hospitalised, dead), was developed to simulate the costs and benefits of CDM policies over 30 years (with 1-year cycles). Transition probabilities and costs from a health system perspective were obtained from the linkage of data between the Australian Stroke Clinical Registry (cohort n = 12,368, 42% female, median age 70 years, 45% had CDM claims) and government-held hospital, Medicare, and pharmaceutical claims datasets. Quality-adjusted life years (QALYs) were obtained from a comparable cohort (n = 512, 34% female, median age 69.6 years, 52% had CDM claims) linked with Medicare claims and death data. A 3% discount rate was applied to costs in Australian dollars (AUD, 2016) and QALYs beyond 12 months. Probabilistic sensitivity analyses were used to understand uncertainty.

resultsPer-person average total lifetime costs were AUD 142,939 and 8.97 QALYs for those with a claim, and AUD 103,889 and 8.98 QALYs for those without a claim. This indicates that these CDM policies were costlier without improving QALYs. The probability of cost-effectiveness of CDM policies was 26.1%, at a willingness-to-pay threshold of AUD 50,000/QALY.

conclusionCDM policies, designed to encourage comprehensive care, are unlikely to be cost-effective for stroke compared to care without CDM. Further research to understand how to deliver such care cost-effectively is needed.

Indexed as

Cost-Benefit AnalysisQuality-Adjusted Life YearsStrokeAgedAged, 80 and overAustraliaChronic DiseaseDisease ManagementFemaleHealth PolicyHumansMaleMarkov ChainsMiddle AgedChronic disease managementCost-effectivenessEconomic evaluationMarkov modellingStroke

Identifiers

PMID38290479
PMCPMC11151971

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.