Prevalence of Human Immunodeficiency Virus and Malaria Co-Infection in Nigeria: A Review of Published Literature
Bibliographic record
Abstract
Background: Human immunodeficiency virus and malaria are significant global health challenges. Both diseases contribute to the global burden of disease and poverty notable amongst low-income countries, including sub-Sahara Africa and Nigeria. There are little or no available review articles on the prevailing epidemiological data on human immunodeficiency virus and malaria interaction in Nigeria. Aim: This literature review aims to update knowledge on human immunodeficiency virus and malaria co-infection and determine the prevalence of human immunodeficiency virus and malaria from published literature. Method: This work reviewed published articles on human immunodeficiency virus/malaria co-infection published in English in PubMed, Google Scholar, and ScienceDirect. An internet search on Google Scholar was also conducted for studies that were conducted between 2007 and 2017 in Nigeria. Result: The literature review indicated that the highest prevalence of human immunodeficiency virus and malaria co-infection in Nigeria is 86.2%. The mean ± SD among the HIV-malaria co-infected group and the negative malaria group in all of Nigeria’s geo-political zones are 36.6 ± 25.5 and 19.5 ± 15.3, respectively. The highest mean prevalence of 64.5% was reported in human immunodeficiency virus patients co-infected with malaria in the northwest zone. The use of highly active antiretroviral therapy is also associated with a reduced mean positive prevalence of 31.26%. Conclusion: Human immunodeficiency virus-positive individuals across all the geopolitical zones in Nigeria are at high risk of malaria. Findings from this review of literature will provide additional information on HIV-malaria co-infection prevalence and guide public health prevention, control and management practice.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".