Cost of three models of care for drug-resistant tuberculosis patients in Nigeria
Bibliographic record
Abstract
BACKGROUND: Nigeria accounts for a significant proportion of the global drug-resistant tuberculosis (DR-TB) burden, a large proportion of which goes untreated. Different models for managing DR-TB treatment with varying levels of hospitalization are in use across Nigeria, however costing evidence is required to guide the scale up of DR-TB care. We aimed to estimate and compare the costs of different DR-TB treatment and care models in Nigeria. METHODS: We estimated the costs associated with three models of DR-TB treatment and care: Model (A) patients are hospitalized throughout the 8-month intensive phase, Model (B) patients are partially hospitalized during the intensive phase and Model (C) is entirely ambulatory. Costs of treatment, in-patient and outpatient care and diagnostic and monitoring tests were collected using a standardized data collection sheet from six sites through an ingredient's approach and cost models were based on the Nigerian National Tuberculosis, Leprosy and Buruli Ulcer Guideline - Sixth Edition (2014) and Guideline for programmatic and clinical management of drug-resistant tuberculosis in Nigeria (2015). RESULTS: Assuming adherence to the Nigerian DR-TB guidelines, the per patient cost of Model A was $18,528 USD, Model B $15,159 USD and Model C $9425 USD. Major drivers of cost included hospitalization (Models A and B) and costs of out-patient consultations and supervision (Model C). CONCLUSION: Utilizing a decentralized ambulatory model, is a more economically viable approach for the expansion of DR-TB care in Nigeria, given that patient beds for DR-TB treatment and care are limited and costs of hospitalized treatment are considerably more expensive than ambulatory models. Scale-up of less expensive ambulatory care models should be carefully considered in particular, when treatment efficacy is demonstrated to be similar across the different models to allow for patients not requiring hospitalization to be cared for in the least expensive way.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".