The impact of COVID-19 on the tuberculosis control activities in Addis Ababa
Bibliographic record
Abstract
March 2020 and series of announcements of set of measures, proclamation and directives have been enacted to fight the coronavirus pandemic. These have implications for the regular health services including the TB control program. This brief communication assesses the impact of the COVID-19 response on the TB control activities of Addis Ababa health centers based on research project data. We compared the patient flows in pre-COVID-19 period (quarter 1, Q1) and during COVID-19 (quarter 2, Q2 and quarter 3, Q3) of 2020 at 56 health centers in Addis Ababa from all 10 sub-cities per sub-city. The patient flow declined from 3,473 in Q1 to 1,062 in Q2 and 1,074 in Q3, which is a decrease by 62-76% and 52-80% in Q2 and Q3 respectively as compared to that of Q1. In Q2, Kolfe keranio and Kirkos sub-cities recorded the biggest decline (76 and 75% respectively) whereas Yeka sub-city had the least decline (62%). In Q3, Kirkos sub-city had the biggest decline (80%) and Addis ketema sub-city had the lowest (52%). We conclude that the series of measures, state of emergency proclamation and government directives issued to counter the spread of COVID-19 and the public response to these significantly affected the TB control activities in Addis Ababa city as attested by the decrease in the patient flow at the clinics. Health authorities may inform the public that essential health services are still available and open to everyone in need of these services.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".