The indirect cost due to pulmonary Tuberculosis in patients receiving treatment in Bauchi State—Nigeria
Bibliographic record
Abstract
OBJECTIVE: To determine the time spent and income lost by patients and their households for seeking tuberculosis diagnosis and treatment in Bauchi State-Nigeria. METHOD: A cross sectional study where 242 TB patients were sampled from 27 out of 67 facilities providing TB services in a north-eastern state of Nigeria. Sampling was stratified based on facility type, patients' HIV status and gender. RESULTS: The income lost among the hospitalized group was estimated at $156/patient and about $114 in the non-hospitalized patients group. Age, gender, facility of diagnosis, level of education and occupation were significant (p-values <0.05) associated with total (both patients and their households) income lost. However, AFB sputum-smear result and HIV status had no significant effects on the income lost. Hospitalised patients spent an average time of 924.98 hours for diagnosis and treatment whereas the non-hospitalised spent an average of 141.29 hours. The estimated US dollar valued of these hours was US517.98 and US$79.13 for hospitalised and non-hospitalised patient groups respectively. Hospitalisation and the facility of diagnosis were statistically significant (p-value <0.05) predictors of the time patients and household spent on TB. CONCLUSION: Tuberculosis poses causes tremendous burden in terms of time and productivity lost to both patients and their households in Bauchi State Nigeria.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".