How Group-Based Cardiovascular Health Education Affects Treatment Adherence and Blood Pressure Control among Insured Hypertensive Nigerians: A Pre-Test, Post-Test Study
Bibliographic record
Abstract
In sub Saharan Africa (SSA), access to affordable hypertension care through health insurance is increasing. But due to poor adherence, hypertension treatment outcomes often remain poor. Patient-centered educational interventions may reverse this trend. Using a pre-test/post-test design, in this study we investigated the effects of a structured cardiovascular health education program (CHEP) on treatment adherence, blood pressure (BP) control and body mass index (BMI) among Nigerian hypertensive patients who received guideline-based care in a rural primary care facility, in the context of a community based health insurance program. Study participants included 149 insured patients with uncontrolled BP and/or poor self-reported medication adherence after 12 months of guideline-based care. All patients received three group-based educational sessions and usual primary care over 6 months. We evaluated changes in self-reported adherence to prescribed medications and behavioral advice (primary outcomes); systolic BP (SBP) and/or diastolic BP (DBP) and BMI (secondary outcomes); and beliefs about hypertension and medications (explora- tory outcomes). Outcomes were analyzed with descriptive statistics and regression analysis. 140 patients completed the study (94%). At 6 months, more participants reported high adherence to medications and behavioral advice than at baseline: respectively, 101 (72%) versus 70 (50%), (p < 0.001) and 126 (90%) versus 106 (76%), (p < 0.001). Participants with controlled BP doubled from 34 (24%) to 65 (46%), (p = 0.001). The median SBP and DBP decreased from 129.0 to 122.0 mmHg, (p = 0.002) and from 80.0 to 73.5 mmHg, (p < 0.001), respectively. BMI did not change (p = 0.444). Improved medication adherence was associated with a decrease in medication concerns (p = 0.045) and improved medication self-efficacy (p < 0.001). By positively influencing patient perceptions of medications, CHEP strengthened medication adherence and, consequently, BP reduction among insured hypertensive Nigerians. This educational approach can support cardiovascular disease prevention programs for Africa’s growing hypertensive population.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".