Effect of the COVID-19 pandemic on drug-resistant tuberculosis treatment outcomes at a national referral hospital in Sierra Leone, 2017 to 2022: a retrospective study
Bibliographic record
Abstract
Abstract Introduction Sierra Leone is one of the 30 high TB burden countries, with an incidence rate in 2023 of 286 per 100,000 population. Despite progress in case notification and treatment coverage, around 5,000 cases of TB in Sierra Leone are missing each year. Challenges with notification of drug-susceptible TB can result in drug-resistant TB. The COVID-19 pandemic has further compounded these challenges, resulting in increased drug-resistant TB cases and poor treatment outcomes. This study highlights the effect of COVID-19 on drug-resistant TB treatment outcomes. Methods We conducted a cross-sectional retrospective analysis of newly identified drug-resistant TB cases in Sierra Leone using data from the national drug-resistant TB database from January 2017 to December 2022, pre-COVID-19, during COVID-19, and post-COVID-19. Data was analysed using STATA. Descriptive analysis was used to summarise the demographic and clinical characteristics and treatment outcomes of drug-resitant TB cases. We used logistic regression to examine the association between time-period and treatment outcomes, after adjusting for age, gender, nutritional status, HIV status and treatment regimens. A p-value of <0.05 was considered statistically significant at 95% confidence level. Results Of the 701 drug-resistant TB patients, 383 (54.6%) were registered in the pre-COVID-19 period, 228 (32.5%) during COVID-19, and 92 (12.8%) in the post-COVID-19 period. Pre-treatment TB cases reduced from 359 (92.5%) in the pre-COVID-19 period to 80 (30.9%) in the COVID-19 period. New treatment cases increased from 29 (7.5%) to 159 (61.4%) during COVID-19. The proportion of drug-resistant TB that completed treatment decreased from 74.7% in the pre-COVID-19 period to 63.3% during COVID-19 and 68.5% post-COVID-19. There were more cases of successful treatment outcomes in the pre-COVID-19 period (74.7%) than in the COVID-19 period (63.3%). Compared with the pre-COVID-19 period, the odds of a successful outcome were 42% less than in the COVID-19 period (OR 0.58, 95% CI 0.42 to 0.82). Conclusion We reported a decline in the proportion of drug-resistant TB cases with successful treatment outcomes during COVID-19 and a rapid recovery in the post-COVID-19 period, emphasising the need for robust mitigation strategies for drug-resistant TB management during public health emergencies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".