Evidence map›Paper›PMID 41966221›Full record

ArticleInternational journal of health policy and management2026

Economic Burden of Non-medicinal Poisoning From Healthcare Provider Perspective in 2020: A Prevalence-Based Cost-of-Illness Study in Thailand.

Mu Htay Kywel, Orathai Khiaocharoen, Chatchon Prasertworakul, Tanwa Khattiyod, Sitaporn Youngkong, Arthorn Riewpaiboon

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Article in International journal of health policy and management, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Mu Htay KywelMaster of Science Program in Social, Economic and Administrative Pharmacy, Mahidol University, Bangkok, Thailand.
Orathai KhiaocharoenThai CaseMix Centre, Division of Healthcare Information Standards Service, Health Systems Research Institute, Bangkok, Thailand.ORCID 0000-0002-9273-679X
Chatchon PrasertworakulThai CaseMix Centre, Division of Healthcare Information Standards Service, Health Systems Research Institute, Bangkok, Thailand.
Tanwa KhattiyodThai CaseMix Centre, Division of Healthcare Information Standards Service, Health Systems Research Institute, Bangkok, Thailand.
Sitaporn YoungkongDivision of Social and Administrative Pharmacy, Department of Pharmacy, Faculty of Pharmacy, Mahidol University, Bangkok, Thailand.ORCID 0000-0002-2448-3954
Arthorn RiewpaiboonDivision of Social and Administrative Pharmacy, Department of Pharmacy, Faculty of Pharmacy, Mahidol University, Bangkok, Thailand.ORCID 0000-0003-2959-3244

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBetween 2010 and 2019 in Thailand, hospital admissions due to toxic effects of non-medicinal substances (International Classification of Diseases 10th Revision [ICD-10] codes: T51-T65) ranged from 59.78 to 87.47 per 100 000 population. The objective of this study was to estimate the costs of non-medicinal poisoning from healthcare provider perspective, and identify factors associated with the costs in Thailand for the year 2020.

methodsThis was a prevalence-based cost-of-illness study conducted from healthcare provider perspective, analysing data from five hospitals (four regional and one provincial) across the Central, North, and Northeast regions of Thailand. We included all patients diagnosed with non-medicinal poisoning (ICD-10 codes: T51-T65) during the fiscal year 2020. Direct medical costs were calculated from hospital databases, estimating the cost per outpatient/emergency visit and the cost per hospital admission. Multiple regression analysis was used to determine the factors affecting these costs. All total costs were converted to international dollar (Int$) for 2020.

resultsA total of 3260 patients were included (2472 outpatient visits and 788 admissions). The mean age was 39 years, with 51% being male. The mean cost per outpatient visit was Int$ 47, and the mean cost per admission was Int$ 896. Key factors significantly associated with higher costs included patient type (outpatient vs admission), length of stay (LOS), age, insurance scheme, diagnosis group, and the presence of comorbidities.

conclusionThis study provided critical, updated data that can inform health policy by emphasizing the economic burden of non-medicinal poisoning. These findings underscore the need for strengthening poisoning prevention and early intervention services and offer essential data for conducting future economic evaluation studies of relevant interventions in Thailand.

Indexed as

Cost of IllnessHealth Care CostsPoisoningAdolescentAdultAgedFemaleHospitalizationHumansMaleMiddle AgedPrevalenceThailandYoung AdultCost-of-IllnessEconomic BurdenNon-medicinal PoisoningThailand

Identifiers

PMID41966221
PMCPMC12980026

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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.