Trends of HIV testing among sexually active young females in Ethiopia: a multivariable decomposition analysis of 2005-2016 EDHS
Bibliographic record
Abstract
Introduction: Even though HIV testing and care is highly targeted in different strategies in Ethiopia, most sexually active young females do not know their HIV status.Moreover, prior trend analysis has not been conducted among sexually active young females in Ethiopia.Therefore, this study aimed to assess the trend and determinants of HIV testing change among sexually active young females in Ethiopia. Material and methods:The current study utilized three consecutive Ethiopia Demographic and Health Survey (EDHS) datasets.A total of 1,201.9,3,094, and 2,864.7 sexually active female youths were included in 2005, 2011, and 2016 EDHS surveys, respectively.Data were analyzed using Stata version 14.0.Logit-based decomposition analysis was performed to identify contributing factors for HIV testing change, and statistical significance was determined with p-value.Results: The trend of HIV testing increased from 7% in 2005 to 52.5% in 2016 EDHS.The analysis revealed that 19% of the overall change in HIV testing was due to the change in female youths composition.Changes in the composition of young females according to region, age, economic status, age of sexual initiation, and comprehensive HIV/ AIDS knowledge were the major sources of the change.Greater than 80% of the increase in HIV testing was due to differences in coefficient.The change in behaviors of married youths and early starting of sex life were the contributing factors to change of HIV testing over the last decade.Conclusions: Change of HIV testing among sexually active female youths increased in the last 10 years in Ethiopia.However, in order to achieve the HIV-related targets in the country, programmatic interventions targeting uneducated women is needed.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".