Socio-Economic Determinants of HIV-Malaria Co-Infection among Adults in the North Central Zone, Nigeria
Bibliographic record
Abstract
Background: Globally, Human Immunodeficiency Virus, and malaria co-infection are responsible for high rates of disease and death predominantly in sub-Saharan Africa. However, the relationship between the socio-economic determinants of the human immunodeficiency virus and malaria co-infection has not been established. Therefore, this study aims to determine the socio-economic variables associated with human immunodeficiency virus and malaria co-infection among adults in peri-urban secondary hospitals in the North Central Zone, Nigeria. Method: A retrospective descriptive cross-sectional study was carried out among human immunodeficiency virus-positive patients at six selected peri-urban secondary hospital facilities in the North Central Zone, Nigeria. Continuos variable was compared using the student t-test, or Wilcoxon test, while the categorical variable was compared using Chi-square and Fisher’s exact test. The significance level was kept at p ≤ 0.05. Results: This study showed that patients of 61 years and above, those between 18 and 30 years of age are at risk of HIV/malaria co-infection RR 1.09 (0.92 - 1.31) and (95% CI), 1.02 (0.96 - 1.08). A significant relationship was reported between the likelihood of co-infection and education (p = 0.023), residence (p = 0.001), employment, (p < 0.001) and income (p < 0.001). Similarly, the highest proportion of malaria diagnosis 547 (80.9%) was among the un-employed patient’s contrary to the least proportion reported among employed patients 84 (68.3%). Using a logistic regression model, it was noted that the proportion of co-infection among HIV seropositive patients is negatively associated with their income. Conclusion: Findings from this study revealed a strong association between socio-economic variables and HIV/malaria co-infection among the study population. These socio-economic variables could serve as an essential indicator in any proposed intervention programme and could help to predict future co-infection rates in regions where both infectious diseases are dominant.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".