Impact of the Ebola virus disease outbreak (2014–2016) on tuberculosis surveillance activities by Guinea’s National Tuberculosis Control Program: a time series analysis
Bibliographic record
Abstract
BACKGROUND: Most countries in Subsaharan Africa have well-established National Tuberculosis Control Programs with relatively stable routine performances. However, major epidemiological events may result in significant disruptions. In March 2014, the World Health Organization announced the outbreak of Ebola virus disease in Guinea, a country with a high incidence of TB and HIV. Our study aimed to assess the impact of the Ebola virus disease outbreak on TB notification, treatment, and surveillance, using main indicators. METHODS: This is a retrospective cohort study that compared TB trends using surveillance data from the periods before (2011-2013), during (2014-2016), and after (2017-2018) Ebola virus disease outbreak. A time-series analysis was conducted to investigate the linkages between the decline in TB notification and the Ebola virus disease outbreak through cross-correlation. The lag in the cross-correlation test was evaluated using ANCOVA type II delayed variable dependent model. The surveillance system was assessed using TB surveillance standards and benchmarks and vital registration systems recommended by WHO, compared with those of 2015 during the Ebola virus disease. RESULTS: The rate of reporting of TB declined from 120 cases per 100,000 in 2011 to 100 cases per 100,000 in 2014, at the peak of the Ebola virus disease outbreak. The time-series cross-correlation test of all notified cases of TB and Ebola showed a significant lag of - 0.4 (40%), reflecting a drop in the rate of notification (F-value = 5.7 [95% CI: 0.2-21.3]). The Ebola virus disease had no negative impact on patient treatment outcomes (F-value = 1.3 [95% CI: 0.0-8.8]). Regarding the surveillance system, five out of 13 WHO standards and benchmarks were met following their evaluation in 2019, after the Ebola virus disease outbreak, compared to three in 2015. CONCLUSION: Major epidemics such as the Ebola virus disease outbreak may have a significant impact on well-established TB control programs as shown in the example of Guinea. Sudden disruptions of routine performance may lead programs to improve their surveillance system. The experience acquired in the fight against EVD and the investments made should make it possible to prepare the health system in a coherent manner for the other probable episodes.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".