Effect and Implementation Experience of Intensive Adherence Counseling in A Public Hiv Care Center in Uganda: A Mixed-methods Study
Bibliographic record
Abstract
Abstract Background: Non-adherence to anti-retroviral therapy (ART) is responsible for up to 75% of the unsuppressed viral load among people living with HIV (PLWH) on ART. Intensive adherence counseling (IAC) is an intervention recommended by the World Health Organization to improve adherence. In 2016, the Ugandan Ministry of Health introduced IAC to improve viral load suppression. This study evaluated the effect and experiences of providing IAC in an urban HIV care center in Kampala, Uganda.Methods: This was a sequential explanatory mixed-method study that compared viral load suppression in the period with IAC intervention to a period without IAC at Kisenyi Health centre IV. Data were abstracted from patient files. The effect on viral load suppression of IAC and associated factors was analyzed using modified Poisson regression with robust standard errors. Using in-depth interviews and an inductive analysis approach in Atlas. ti, we also explored experiences of providing IAC among healthcare workers. Results: A total of 500 records were sampled: 249(49.8%) in the intervention period and 251(51.2%) in the pre-intervention period. The mean age of clients was 34.8 years (SD±12.8), and 326/500 (65.2%) were females. Majority were on a Lopinavir/ritonavir based regimen [314 (62.8%)], and the median duration on ART was 30.8 months (IQR: 12.5–51.7). Over the intervention period, all eligible clients received IAC [249/249 (100.0%)]. Of those, 143 (44.1%) achieved viral load suppression compared to 46 (26.3%) in the pre-intervention period. Receiving IAC significantly increased viral load suppression by 22% (aPR = 1.22, 95% CI: 1.01–1.47). Participants on Lopinavir/ritonavir-based regimen were less likely to suppress (aPR= 0.11, 95%CI: 0.08–0.15) than those on Efavirenz or Nevirapine based regimens. All the interviewed healthcare workers lauded IAC for improving ART adherence. However, non-disclosure, social-economic constraints, lack of a multidisciplinary team and work overload hindered adherence during IAC. Conclusions: The full potential of IAC in achieving viral suppression in this setting has not been reached, probably due to a combination of the health care system and patient-related factors. Provision of adequate IAC necessities and use of patient centered approach during IAC should be emphasized to obtain the maximum benefit of IAC.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".