Factors influencing treatment outcomes of tuberculosis patients attending health facilities in Galkayo Puntland, Somalia
Bibliographic record
Abstract
AIM: This study evaluated the underlying factors associated with poor tuberculosis (TB) treatment outcomes among patients attending health care facilities in Galkayo, Puntland, Somalia. METHODS: An institution-based cross-sectional study was conducted between 2016 and 2017 in three selected TB clinics. Data were collected from 400 TB patients, through medical record review and structured questionnaire. Multivariate logistic regression analyses were performed. RESULTS: Of the 400 TB respondents, 57.3% were new cases, 12.3% had smear-negative TB and 12.5% had extrapulmonary TB. The median age was (35.66 ± 13.16) with majority being male (65.5%). Overall, 85% of patients were successfully treated, 9.7% failed and 5.3% defaulted. Multivariate analysis revealed that patient's body weight (odds ratio [OR]: 1.078); diabetes (OR: 8.022); family size (OR: 3.851); patients' delay in diagnosis (OR: 11.946); frequency of receiving anti-TB medication (OR: 9.068); smoker (OR: 5.723); category of patients (retreatment versus new, OR: 5.504; retreatment versus transfer in, OR: 4.957); health facilities (OR: 6.716) and treatment duration (OR: 132.091) were independent factors associated with poor TB outcomes. CONCLUSIONS: Our findings highlight the need to improve TB services for vulnerable groups. They also emphasize the need for health system strengthening, public awareness and risk of treatment interruption. This may reduce both patients' delay in seeking care and TB treatment failure in Galkayo district.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".