Impact Assessment of Corona Virus Disease 2019 (COVID-19) on Health Services in Katsina State, Nigeria
Bibliographic record
Abstract
Introduction: In the wake of the novel COVID-19 pandemic the health service disruption with the resultant widespread health consequences associated with the virus has become abundantly clear to all. Our primary objective was to determine the impacts of the COVID-19 epidemic on primary health care performance indicators in Katsina state.Material and Methods: Data was pulled and analyzed for trends and coverage of selected performance indicators from Quarter 1, 2019 to Quarter 2, 2020. Data sources were administrative data from District Health Information Software. An indicator each was analyzed from the following health thematic areas: Child health, Routine Immunization, Family planning, HIV/AIDS Care and Treatment, Labor and Delivery, Malaria and Antenatal Care (ANC). Descriptive and inferential statistical analyses were carried out using Statistical Package for Social Sciences version 20. Time series analysis with Auto-Regressive and Integrated Moving Average (ARIMA) modeling on indicators was used to study trends of performance over time. Simple Linear Regression (SLR) analysis was used to report coefficients of relationships at intercept and period points.Results: ANC 4th visit decreased abysmally from 65% to 46%, pentavalent vaccine 3 also declined consistently from 83% to 74% during the active lock down period. The study was also able to identify rising numbers of <5 mortality rate (from 2% to 19%) and a corresponding decrease in Pentavalent vaccine 3 coverage over time. These finding were significant (P = 0.01) across periodic quarters of 2019 and 2020.Conclusion: The current study was able to demonstrate, using ARIMA and SLR modeling, the decline in ANC 4th visit and pentavalent vaccine coverage in Katsina state, Nigeria during the active lock down phases.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".