Prevalence and Predictors of Neurocognitive Impairment in Ethiopian Population Living with HIV
Bibliographic record
Abstract
Mohammed Salahuddin,1,2 Md Dilshad Manzar,3 Hamid Yimam Hassen,4,5 Aleem Unissa,6 Unaise Abdul Hameed,7 David Warren Spence,8 Seithikurippu R Pandi-Perumal9 1Department of Pharmacy, College of Medicine and Health Sciences, Mizan-Tepi University (Mizan Campus), Mizan, Ethiopia; 2Pharmacology Division, Department of BioMolecular Sciences, University of Mississippi, Oxford, Mississippi, USA; 3Department of Nursing, College of Applied Medical Sciences, Majmaah University, Al Majmaah 11952, Saudi Arabia; 4Department of Public Health, College of Health Sciences, Mizan Tepi University, (Mizan Campus), Mizan, Ethiopia; 5Department of Primary and Interdisciplinary Care, College of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium; 6Malla Reddy College of Pharmacy, Hyderabad, Telangana, India; 7Department of Physiotherapy, Faculty of Medicine, Nursing and Health Sciences, Monash University, Australia; 8Independent Researcher, Toronto, ON M6K 2B4, Canada; 9Somnogen Canada Inc, Toronto, ON, CanadaCorrespondence: Mohammed Salahuddin Department of BioMolecular SciencesUniversity of Mississippi, School of Pharmacy, 331 Faser Hall, P.O. Box 1848, University, MS 38677-1848 Tel +1 662-609-3011Email smohamme@go.olemiss.eduBackground: Modern antiretroviral therapy has extended the life expectancies of people living with HIV; however, the prevention and treatment of their associated neurocognitive decline have remained a challenge. Consequently, it is desirable to investigate the prevalence and predictors of neurocognitive impairment to help in targeted screening and disease prevention.Materials and Methods: Two hundred and forty-four people living with HIV were interviewed in a study using a cross-sectional design and the International HIV Dementia Scale (IHDS). Additionally, the sociodemographic, clinical, and psychosocial characteristics of the patients were recorded. Chi-square and binary logistic regression analysis were used to determine the level of significance among the independent risk factors and probable neurocognitive impairment.Results: The point prevalence of neurocognitive impairment was found to be 39.3%. Participants’ characteristics of being older than 40 years (AOR= 2.81 (95% CI; 1.11– 7.15)), having a history of recreational drug use (AOR= 13.67 (95% CI; 6.42– 29.13)), and being non-compliant with prescribed medications (AOR= 2.99 (95% CI; 1.01– 8.87)) were independent risk factors for neurocognitive impairment.Conclusion: The identification of predictors, in the Ethiopian people living with HIV, may help in the targeted screening of vulnerable groups during cART follow-up visits. This may greatly help in strategizing and implementation of the prevention program, more so, because (i) HIV-associated neurocognitive impairment is an asymptomatic condition for considerable durations, and (ii) clinical trials on neurocognitive impairment therapies have been unsuccessful.Keywords: cART, HIV, IHDS, Africa, dementia, Ethiopia, recreational drugs
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".