Neurocognitive decline as a major predictor of nonadherence to antiretroviral therapy among adults living with HIV in Dodoma region, central Tanzania
Bibliographic record
Abstract
Abstract Background The survival of people living with human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome largely depends on good adherence to antiretroviral medications. Neuropsychiatric conditions such as major depressive disorders (MDDs) and neurocognitive disorders, in particular, are common in the HIV population and attributed to suboptimal adherence to antiretroviral treatment and overall poor clinical outcomes. This study aimed to determine the association between neurocognitive disorders and nonadherence to antiretroviral therapy (ART) in the Dodoma region's adult population living with HIV. Methods The study was conducted in Dodoma Regional Referral Hospital using a cross‐sectional design to assess 397 participants through a systematic sampling approach. Montreal Cognitive Assessment was used to determine neurocognitive function, while the Simplified Medical Adherence Questionnaire was used to assess nonadherence to ART. Logistic regression analysis was computed to determine the association between cognitive decline and nonadherence to ART while controlling for sociodemographic and clinical confounders. Results Out of the 397 recruited participants, 266 (67.00%) and 41 (10.33%) met the criteria for neurocognitive decline and nonadherence to ART. Participants with cognitive impairment had a significantly poorer adherence rate than those without, even after controlling for confounders adjusted odds ratio (aOR): 2.183 (95% confidence interval [CI]: 1.031, 4.630, p = 0.0413). MDD was the only additional factor that remained significantly associated with ART nonadherence (aOR: 4.332, 95% CI: 1.634, 11.485, p = 0.0032). Conclusion Neurocognitive disorders are strong predictor of suboptimal adherence to ART; a comorbid neuropsychiatric condition such as MDD may further compromise the ART adherence rate leading to poor HIV care and poor clinical outcome. Further research with systematic and more robust studies in the field will provide a baseline to design and integrate appropriate care models to maximize ART adherence in HIV care. Integration of psychiatric services in HIV care can benefit the overall patient outcome.
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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 | 0.000 | 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".