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Record W2041500594 · doi:10.1111/tmi.12032

Financial burden of health care for <scp>HIV</scp>/<scp>AIDS</scp> patients in Vietnam

2012· article· en· W2041500594 on OpenAlexaff
Bach Xuan Tran, Anh Thuy Duong, Long Thành Nguyễn, Jongnam Hwang, Binh Nguyen, Quynh T. Nguyen, Vuong Minh Nong, Phu Xuan Vu, Arto Öhinmaa

Bibliographic record

VenueTropical Medicine & International Health · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsInstitute of Health EconomicsUniversity of Alberta
Fundersnot available
KeywordsMedicineHealth careHo chi minhFamily medicineHuman immunodeficiency virus (HIV)Developing countryEnvironmental healthLow incomeSocioeconomicsEconomic growth

Abstract

fetched live from OpenAlex

OBJECTIVE: To assess the out-of-pocket (OOP) payments for health-care services of HIV/AIDS patients, and identify associated factors in Vietnam. METHODS: Cross-sectional multisite survey of 1016 HIV/AIDS patients attending 7 hospitals and health centres in Ha Noi, Hai Phong and Ho Chi Minh City in 2012. RESULTS: HIV/AIDS patients used inpatient and outpatient care on average 5.1 times (95% CI = 4.7-5.4) besides ART services. Inpatient care cost US$ 461 on average and outpatient care US$ 50. Mean annual health-care expenditure for HIV/AIDS patients was US$ 188 (95% CI = 148-229). 35.1% of households (95% CI = 32.2-38.1) experienced catastrophic health expenditure; 73.3% (95% CI = 70.6-76.1) of households would be affected if ART were not subsidised. Being a patient at a provincial clinic, male sex, unstable employment, being in the poorest income quintile, a CD4 count of <200 cells/mL and not yet receiving ART increased the likelihood of catastrophic medical expense. CONCLUSIONS: HIV/AIDS patients in Vietnam frequently use medical services and incur OOP payments for health care. Scaling up free-of-charge ART services, earlier access to and initiation of ART, and decentralisation and integration of HIV/AIDS-related services could reduce their financial burden.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.272
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.033
GPT teacher head0.305
Teacher spread0.272 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations40
Published2012
Admission routes1
Has abstractyes

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