Recent HIV infection and associated factors among newly diagnosed HIV cases in the Southwest Ethiopia Regional State: HIV case-based surveillance analysis (2019–2022)
Bibliographic record
Abstract
BACKGROUND: Globally, there were an estimated 39 million people living with HIV with 1.3 million new HIV infections by the end of 2023. The Sub-Saharan Africa accounted 51% of new HIV infections. HIV case-based Surveillance collects data on newly diagnosed HIV cases, recent HIV infections, and other sentinel events, aiding evidence-based decision making. There is limited evidence on these in Ethiopia. The objective of this study is to determine the incidence proportion of recent infections and associated factors among newly diagnosed HIV cases and their distribution by person, place, and time in the Southwest Ethiopia Regional State. METHODS: A retrospective analysis was conducted on HIV case-based surveillance dataset (July 2019 to June 2022) from the Southwest Ethiopia Regional State. Recent HIV infection is an infection that acquired within the last 12 months as diagnosed by Asante recency test kits. Data were analyzed using SPSS version 26. ArcGIS version 10.8 was used for mapping recent infections. Logistic regression was employed to identify factors associated with recent infections. In multivariable logistic regression analysis, variables with p-value < 0.05 and an adjusted odds ratio with 95% confidence interval were considered to declare significant association. RESULTS: A total of 1,167 newly diagnosed HIV cases (eligible cases) were identified. Among these, 786 (67.3%) recency tests were performed. The mean age of individuals with recent infection was 28.4 years. The proportion of recent infection is 89 (11.3%, 95% CI: 11.2, 11.5%). The highest proportion of recent infection is reported from the West Omo zone (42.9%), whereas 13.2% in Bench Sheko zone. Recent infection is significantly associated with age 15-24 years [AOR = 7.14, 95%CI: 2.89,17.57], age 25-34 years [AOR = 5.34, 95%CI: 2.20,12.94], females [AOR = 2.03, 95%CI: 1.26,3.25], and contact history with the index case [AOR = 0.48, 95%CI: 0.28, 0.83]. The incidence of recent infection increased from 86 (in 2019/20) to 132 (in 2022) recent infections per 1,000 newly diagnosed cases. CONCLUSIONS: Recent HIV infection is a public health concern in the Southwest Ethiopia Regional State with an increasing incidence. Targeted prevention efforts are necessary, especially for females and younger people.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".