COVID-19-related healthcare impacts: an uncontrolled, segmented time-series analysis of tuberculosis diagnosis services in Mozambique, 2017–2020
Bibliographic record
Abstract
INTRODUCTION: Currently, COVID-19 dominates the public health agenda and poses a permanent threat, leading to health systems' exhaustion and unprecedented service disruption. Primary healthcare services, including tuberculosis services, are at increased risk of facing severe disruptions, particularly in low-income and middle-income countries. Indeed, corroborating model-based forecasts, there is increasing evidence of the COVID-19 pandemic's negative impact on tuberculosis case detection. METHODS: Applying a segmented time-series analysis, we assessed the effects of COVID-19-related measures on tuberculosis diagnosis service across districts in Mozambique. Ministry health information system data were used from the first quarter of 2017 to the end of 2020. The model, performed under the Bayesian premises, was estimated as a negative binomial with random effects for districts and provinces. RESULTS: A total of 154 districts were followed for 16 consecutive quarters. Together, these districts reported 96 182 cases of all forms of tuberculosis in 2020. At baseline (first quarter of 2017), Mozambique had an estimated incidence rate of 283 (95% CI 200 to 406) tuberculosis cases per 100 000 people and this increased at a 5% annual rate through the end of 2019. We estimated that 17 147 new tuberculosis cases were potentially missed 9 months after COVID-19 onset, resulting in a 15.1% (95% CI 5.9 to 24.0) relative loss in 2020. The greatest impact was observed in the southern region at 40.0% (95% CI 30.1 to 49.0) and among men at 15% (95% CI 4.0 to 25.0). The incidence of pulmonary tuberculosis increased at an average rate of 6.6% annually; however, an abrupt drop (15%) was also observed immediately after COVID-19 onset in March 2020. CONCLUSION: The most significant impact of the state of emergency was observed between April and June 2020, the quarter after COVID-19 onset. Encouragingly, by the end of 2020, clear signs of health system recovery were visible despite the initial shock.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".