Using a dynamic adherence Markov model to assess the efficiency of Respiratory Medication Therapy Adherence Clinic (RMTAC) on asthma patients in Malaysia

Background Respiratory Medication Therapy Adherence Clinic (RMTAC) is an initiative by the Ministry of Health (MOH) Malaysia to improve patients’ medication adherence, as an adjunct to the usual physician care (UC). This study aimed to evaluate the cost-effectiveness of combined strategy of RMTAC and UC (RMTAC + UC) vs. UC alone in asthma patients, from the MOH Malaysia perspective. Methods A lifetime horizon dynamic adherence Markov model with monthly cycle was developed, for quality-adjusted life year (QALY) gained and hospitalization averted outcomes. Transition probabilities of composite asthma control and medication adherence, utilities, costs, and mortality rates due to all causes were measured from local data sources. Effectiveness, exacerbation rates, and asthma mortality rates were taken from non-local data sources. One-way sensitivity analysis (SA) was conducted for assessing parameter uncertainties, whereas probabilistic SA (PSA) was conducted on a different set of utilities and effectiveness data. Costs were adjusted to 2014 US dollars ($). Both costs and benefits were discounted at a 3% rate annually. Results RMTAC + UC was found to be a dominant alternative compared to UC alone; $− 13,639.40 ($− 109,556.90 to $104,445.54) per QALY gained and $− 428.93 ($− 521.27 to ($− 328.69)) per hospitalization averted. These results were found to be robust against changes in all parameters except utilities in the one-way SA, and for both scenarios in PSA. Conclusions RMTAC + UC is more effective and yet cheaper than UC alone, from the MOH perspective. For the benefit of both MOH and patients, RMTAC is thus recommended to be remained, and expanded to more healthcare settings where possible. Electronic supplementary material The online version of this article (10.1186/s12962-018-0156-1) contains supplementary material, which is available to authorized users.

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PID https://www.doi.org/10.1186/s12962-018-0156-1
PID pmid:30377414
PID pmc:PMC6195711
URL http://europepmc.org/articles/PMC6195711
URL http://dx.doi.org/10.1186/s12962-018-0156-1
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URL https://academic.microsoft.com/#/detail/2896888218
URL https://doaj.org/toc/1478-7547
URL http://link.springer.com/content/pdf/10.1186/s12962-018-0156-1.pdf
URL https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6195711
URL https://dx.doi.org/10.1186/s12962-018-0156-1
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URL http://link.springer.com/article/10.1186/s12962-018-0156-1/fulltext.html
URL https://link.springer.com/article/10.1186/s12962-018-0156-1
URL http://link.springer.com/article/10.1186/s12962-018-0156-1
URL https://resource-allocation.biomedcentral.com/articles/10.1186/s12962-018-0156-1
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Author Asrul Shafie, 0000-0002-5629-9270
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Collected From Europe PubMed Central; PubMed Central; ORCID; UnpayWall; Datacite; DOAJ-Articles; Crossref; Microsoft Academic Graph
Hosted By Europe PubMed Central; Cost Effectiveness and Resource Allocation
Journal Cost Effectiveness and Resource Allocation, ,
Publication Date 2018-10-01
Publisher BMC
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Language English
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Source https://science-innovation-policy.openaire.eu/search/publication?articleId=dedup_wf_001::f3d9b5803cbbc31ea8eeeddb55a2e6ff
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Last Updated 24 December 2020, 20:52 (CET)
Created 24 December 2020, 20:52 (CET)