Effect of free distribution of medicines on the process of care for adult patients with type 1 and type 2 diabetes and hypertension: post hoc analysis of randomised controlled trial findings
Bibliographic record
Abstract
OBJECTIVES: The Carefully Selected and Easily Accessible at No charge Medicines randomised controlled trial showed that patients receiving free access to medicines had improved diabetes and hypertension outcomes compared with patients who had usual access to medicines. In this study, we aimed to test the impact of providing free access to medicine to people with diabetes and hypertension on process of care indicators. DESIGN: In this post hoc analysis of randomised controlled trial findings, we identified process of care indicators for the management of diabetes and hypertension using relevant guidelines. The following process of care indicators were identified for diabetes management: encounters with healthcare professionals, blood pressure measurements, self-monitoring of blood glucose, annual eye and foot examination, annual administration of the influenza vaccine, and laboratory testing for glycated haemoglobin (HbA1c), low-density lipoprotein-cholesterol, serum creatinine and urine albumin to creatinine ratio. We identified the following process of care indicators for hypertension: encounters with healthcare professionals, blood pressure measurements, self-measuring of blood pressure, and serum tests for electrolytes, HbA1c, lipids and creatinine. Chart extractions were performed for all patients and the indicators for diabetes and hypertension were recorded. We compared the indicators for patients in each arm of the trial. RESULTS: The study included 268 primary care patients. Free distribution of medicines may improve self-monitoring behaviours (adjusted rate ratio (aRR) 1.30; 95% CI 0.66 to 2.57) and reduce missed primary care appointments for patients with diabetes (aRR 0.80; 95% CI 0.48 to 1.33) or hypertension (aRR 0.41; 95% CI 0.18 to 0.90). Free distribution may also reduce primary care and consultant appointments and laboratory testing in patients with hypertension. CONCLUSIONS: Improving medicine accessibility for patients with diabetes and hypertension not only improves surrogate health outcomes but also improves the patient experience and may also reduce healthcare costs by encouraging self-monitoring. TRIAL REGISTRATION NUMBER: The randomised controlled trial mentioned is clinicaltrials.gov identifier: NCT02744963.
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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 | 0.036 | 0.061 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.021 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".