Impact of The Covid-19 Pandemic on Tuberculosis Prevalence: A Systematic Review of Regional Trends In Nigeria
Bibliographic record
Abstract
Background: The COVID-19 pandemic caused an unprecedented disruption to health systems worldwide, diverting resources and attention from various endemic diseases, including tuberculosis. Among the countries with the highest burden of TB in the world, Nigeria grappled with enormous challenges in TB management during this period, with visible trends of marked regional variations in prevalence. Objective: The study assessed the effect of the COVID-19 pandemic on trends in TB prevalence in Nigeria, using national and regional trends between the Northern and Southern parts of the country, before and during the pandemic. Methods: In accordance with PRISMA criteria, this systematic review included research that was published between 2017 to 2023. Boolean operators designed to find pertinent research on the prevalence of tuberculosis in Nigeria were used to obtain peer-reviewed papers from African Journals Online (AJOL), PubMed, ScienceDirect, and Semantic Scholar. Cross-sectional studies documenting the prevalence of tuberculosis in Northern and Southern Nigeria prior to the pandemic (2017–2019) and during the pandemic (2020–2023) were included in the inclusion criteria. Covidence software was used to filter the studies, and the Newcastle-Ottawa Scale was used to evaluate the studies’ quality and bias risk. Descriptive statistical techniques were utilised to synthesise and compare the data, which was extracted with an emphasis on research design, demographic characteristics, geographic regions, and TB prevalence rates. Results: Nationally, TB prevalence increased from 15.8% pre-pandemic to 28.8% during the pandemic, with disruption of healthcare services. In the Northern region, it reduced trivially from 12% to 10%, probably due to decentralization in healthcare facilities and enhanced public health measures, while in the Southern region, there is a remarkable increase from 15% to 40%, driven by high urban density, overload in the healthcare system, and socio-economic adversities. Conclusion: The pandemic significantly marred TB prevalence in Nigeria, with glaring regional disparities. Whereas the North put up a good fight through community-based interventions, the urban vulnerabilities in the South accelerated TB burden. These findings underline the imperative of equitable health care investment, integrated public health strategies, and socio-economic interventions as ways to mitigate the dual burden of the pandemics and TB.
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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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".