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The impact of universal health coverage and COVID-19 pandemic on out-of-pocket expenses in Thailand: an analysis of household survey from 1994 to 2021

2023· article· en· W6921001458 on OpenAlexaff

Bibliographic record

VenueFigshare · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPandemicDescriptive statisticsMedical expensesCoronavirus disease 2019 (COVID-19)Value (mathematics)Universal coverage

Abstract

fetched live from OpenAlex

After Thailand achieved Universal Health Coverage (UHC) in 2002, the extent of financial risk protection has not been assessed in the long term, especially after the COVID-19 pandemic. Therefore, this study aims to revisit the impact of UHC on out-of-pocket expenses (OOPE) for health and to descriptively explore the impact of COVID-19 on OOPE. This study was a secondary data analysis and used data from the Socio-Economic Survey from 1994 to 2021 in Thailand. The effect of UHC on the percentage of OOPE in total health expenditures (THE) from 1994 to 2019 was investigated with an interrupted time-series analysis. Descriptive analyses of OOPE in absolute value during the COVID-19 were conducted. The percentage of OOPE in THE significantly decreased both before (β −2.02%; 95% CI: −2.70% to − 1.33%) and during (β 1.41%; 95% CI: 0.70% to 2.11%) the UHC period. During the pandemic, total household OOPE for medical equipment was found to have rapidly increased from 643 million THB in 2019 to 9.4 billion THB in 2020. The trend of providing financial risk protection (measured by OOPE/THE) in Thailand continues until 2019. Providing medical equipment in sufficient and equally accessible manners should be prioritized during the future pandemic.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.341
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.218
GPT teacher head0.357
Teacher spread0.139 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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