Trends and determining factors associated with adherence to antiretroviral therapy (ART) in Cameroon: a systematic review and analysis of the CAMPS trial
Bibliographic record
Abstract
BACKGROUND: The benefits of antiretroviral therapy (ART) cannot be experienced if they are not taken as prescribed. Yet, not all causes of non-adherence are dependent on the patient. Having to pay for medication reduces adherence rates. Non- adherence has severe public health implications which must be addressed locally and globally. This paper seeks to describe the trends in adherence rates reported in Cameroon and to investigate the determinants of adherence to ART in the Cameroon Mobile Phone SMS (CAMPS) trial. METHODS: We conducted a systematic review of electronic databases (PubMed, Google Scholar, Web of Science, CINAHL, EMBASE and PSYCINFO) for publications on adherence to ART in Cameroon (from January 1999 to May 2012) and described the trend in reported adherence rates and the factors associated with adherence. Data were extracted in duplicate. We used multivariable analyses on the baseline data for 200 participants in the CAMPS trial to determine the factors associated with adherence in four models using different measures of adherence (more than 90% or 95% on the visual analogue scale, no missed doses and a composite measure: 100% on the visual analogue scale, no missed doses and all pills taken on time). RESULTS: We identified nine studies meeting our inclusion criteria. Adherence to ART in Cameroon has risen steadily between 2000 and 2010, corresponding to reductions in the cost of medication. The factors associated with adherence to ART in Cameroon are grouped into patient, medication and disease related factors. We also identified factors related to the health system and the patient-provider relationship. In the CAMPS trial, education, side effects experienced and number of reminder methods were found to improve adherence, but only using multiple reminder methods was associated with better adherence in all the regression models (Adjusted Odds Ratio [AOR] 4.11, 95% Confidence Interval [CI] 1.89, 8.93; p<0.001; model IV). CONCLUSIONS: Reducing the cost of ART is an important aspect of ensuring adequate adherence rates. Using multiple reminder methods may have a cumulative effect on adherence to ART, but should be investigated further.
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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 | 0.023 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.013 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".