A systematic review and meta-analysis of non-adherence to anti-diabetic medication: Evidence from low‐ and middle‐income countries
Bibliographic record
Abstract
Objective: There is lack of evidence on the burden of and factors associated with non-adherence to anti-diabetic medication among individuals living with diabetes in low-and middle-income countries (LMICs). Therefore, we carried out a systematic literature review and meta-analytic synthesis to estimate non-adherence to anti-diabetic medication reported among adults in LMICs and to explore factors affecting non-adherence. Methods: We systematically searched MEDLINE and Embase to identify studies investigating non-adherence to anti-diabetic medications published between January 2000 and May 2020. Cross-sectional studies that had been conducted among individuals with diabetes in LMICs were eligible for the selection process. Critical appraisal of the included studies was carried out using the Newcastle Ottawa Scale. Meta-analysis was carried out using Stata 14.2. Random effects model was used to compute the pooled proportion at 95% confidence interval. Results: Forty-three studies met the inclusion criteria, of which 13 studies were used in meta-analysis. The pooled proportion of non-adherence to anti-diabetic medications using the eight-item Morisky Medication Adherence Scale (MMAS) was 43.4% (95% CI: 17.5–69.4; p=0.000) and 29.1% (95% CI: 19.8–38.4; p=0.000) when using the cut-off at 80 or 90%. The pooled proportion of non-adherence was 29.5% (95% CI: 25.5–33.5; p=0.098) when using the four-item Morisky Medication Adherence Scale. The factors for non-adherence based on World Health Organization demonstrated considerable variation of non-adherence to ant-diabetic medication in LMICs depending on the methods used to estimate non-adherence. Conclusions: These findings demonstrate a significantly higher proportion of medication non-adherence among individuals with diabetes in LMIC settings when MMAS-8 item scale was used and low when 80-90% cut-off scales were used. Various factors, such as disease factors, therapy-related factors, healthcare system factor, patient-centered factors, and social and economic factors contributed to non-adherence. Therefore, comprehensive multifaceted strategies are urgently needed to address factors associated with anti-diabetic medication non-adherence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.013 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.006 | 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 teacher head, 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".