Evidence map›Paper›PMID 41645166›Full record

ArticleBMC public health2026

Treatment costs and determinants in PM

Pheerasak Assavanopakun, Kannika Jarernwong, Sate Sampattagul, Jinjuta Panumasvivat

Abstract read
In one paragraph

Article in BMC public health, 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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0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Pheerasak Assavanopakun *Department of Community Medicine, Faculty of Medicine, Chiang Mai University, 110 Intawaroros Road, Sri Phum Subdistrict, 50200, Chiang Mai, Thailand.ORCID http://orcid.org/0000-0002-3929-5585
Kannika Jarernwong *Department of Industrial Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, 50200, Thailand.ORCID http://orcid.org/0000-0002-4327-7654
Sate SampattagulDepartment of Industrial Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, 50200, Thailand.ORCID http://orcid.org/0000-0003-4167-416X
Jinjuta PanumasvivatDepartment of Community Medicine, Faculty of Medicine, Chiang Mai University, 110 Intawaroros Road, Sri Phum Subdistrict, 50200, Chiang Mai, Thailand. jinjuta.p@cmu.ac.th.ORCID http://orcid.org/0000-0002-0479-4923

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEnvironmental issues related to air pollution in Southeast Asia have persisted for more than a decade, especially in Thailand. This study aims to estimate the treatment costs of respiratory diseases caused by exposure to ambient PM₂.₅ and to identify the factors that influence these costs.

methodsThis retrospective study analyzed secondary data on OPD and IPD respiratory disease treatment costs from government hospitals, along with ambient PM₂.₅ data from low-cost monitoring stations, to estimate the cost of illness across 25 districts in Chiang Mai during Thailand's fiscal year 2023. Economic cost was estimated using the Cost-of-Illness method formula: Economic Cost Loss = Health Impact × Treatment Cost. K-means cluster analysis was used to classify estimated costs into minimum, medium, and maximum cost scenarios. Multiple linear regression was applied to identify significantly associated factors with treatment cost.

resultsUnder the maximum cost scenario identified through K-means cluster analysis stratification, the total treatment cost associated with an average PM₂.₅ concentration of 42.59 µg/m³ was 460,122.58 USD, averaging 41.62 USD per case. Each 1 µg/m³ increase in PM2.5 was associated with a cost rise ranging from 403.84 to 13,159.87 USD. Non-infectious respiratory diseases incurred costs approximately two times higher than infectious ones. The estimate of maximum treatment burden for respiratory disease cases was highest in urban areas, totaling 102,878.88 USD. The urban area showed a significantly higher cost of treatment both in OPD and IPD cases (p < 0.001). Moreover, higher healthcare levels and older age were associated with higher costs in OPD cases. In IPD cases, length of hospital stay was a significant predictor.

conclusionsAmbient PM₂.₅ exposure contributes significantly to the economic burden of respiratory diseases in polluted areas. These highlight the importance of pollution control policies and healthcare resource planning in high-risk areas.

trial registrationnot applicable.

Indexed as

Air PollutantsAir PollutionCost of IllnessHealth Care CostsParticulate MatterRespiratory Tract DiseasesAdolescentAdultAgedChildChild, PreschoolFemaleHumansInfantMaleMiddle AgedAir PollutantsParticulate MatterEconomic burdenPM2.5Respiratory diseasesTreatment costs

Identifiers

PMID41645166
PMCPMC12977856

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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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 Socratic graph.