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Record W2157585847

Cost of tuberculosis diagnosis and treatment from the patient perspective in Lusaka, Zambia.

2008· article· en· W2157585847 on OpenAlexaff
Anne Aspler, Dick Menzies, Olivia Oxlade, Jane Banda, Lawrence Mwenge, Peter Godfrey‐Faussett, Helen Ayles

Bibliographic record

VenuePubMed · 2008
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsRoctest (Canada)
Fundersnot available
KeywordsMedicineInterquartile rangeTuberculosisIndirect costsTotal costCross-sectional studyHealth carePediatricsEmergency medicineFamily medicineSurgery
DOInot available

Abstract

fetched live from OpenAlex

SETTING: Urban primary health centres in Lusaka, Zambia. OBJECTIVES: 1) To estimate patient costs for tuberculosis (TB) diagnosis and treatment and 2) to identify determinants of patient costs. METHODS: A cross-sectional survey of 103 adult TB patients who had been on treatment for 1-3 months was conducted using a standardised questionnaire. Direct and indirect costs were estimated, converted into US$ and categorised into two time periods: 'pre-diagnosis/care-seeking' and 'post-diagnosis/treatment'. Determinants of patient costs were analysed using multiple linear regression. RESULTS: The median total patient costs for diagnosis and 2 months of treatment was $24.78 (interquartile range 13.56-40.30) per patient--equivalent to 47.8% of patients' median monthly income. Sex, patient delays in seeking care and method of treatment supervision were significant predictors of total patient costs. The total direct costs as a proportion of income were higher for women than men (P < 0.001). Treatment costs incurred by patients on the clinic-based directly observed treatment strategy were more than three times greater than those incurred by patients on the self-administered treatment strategy (P < 0.001). CONCLUSION: Clinic-based treatment supervision posed a significant economic burden on patients. The creation or strengthening of community-based treatment supervision programmes would have the greatest potential impact on reducing patients' TB-related costs.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.107
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.077
GPT teacher head0.307
Teacher spread0.230 · 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

Citations79
Published2008
Admission routes1
Has abstractyes

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