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Record W2599755717 · doi:10.1007/s00268-017-4007-6

Cost‐Effectiveness of Two Government District Hospitals in Sub‐Saharan Africa

2017· article· en· W2599755717 on OpenAlexaff
Caris Grimes, Rebekah Law, Anna Dare, Nigel Day, Sophie Reshamwalla, Michael Murowa, Peter George, Thaim B Kamara, Nyengo Mkandawire, Andrew Leather, Christopher Lavy

Bibliographic record

VenueWorld Journal of Surgery · 2017
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGovernment (linguistics)Sierra leoneMedicineCommissionInvestment (military)Health economicsSocioeconomicsEnvironmental healthEconomic growthPublic healthBusinessNursingEconomicsFinancePolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: District hospitals in sub-Saharan Africa are in need of investment if countries are going to progress towards universal health coverage, and meet the sustainable development goals and the Lancet Commission on Global Surgery time-bound targets for 2030. Previous studies have suggested that government hospitals are likely to be highly cost-effective and therefore worthy of investment. METHODS: A retrospective analysis of the inpatient logbooks for two government district hospitals in two sub-Saharan African hospitals was performed. Data were extracted and DALYs were calculated based on the diagnosis and procedures undertaken. Estimated costs were obtained based on the patient receiving ideal treatment for their condition rather than actual treatment received. RESULTS: Total cost per DALY averted was 26 (range 17-66) for Thyolo District Hospital in Malawi and 363 (range 187-881) for Bo District Hospital in Sierra Leone. CONCLUSION: This is the first published paper to support the hypothesis that government district hospitals are very cost-effective. The results are within the same range of the US$32.78-223 per DALY averted published for non-governmental hospitals.

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)0.004
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 score0.028
Threshold uncertainty score0.491

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
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 head0.049
GPT teacher head0.327
Teacher spread0.278 · 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

Citations7
Published2017
Admission routes1
Has abstractyes

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