Hypertension and diabetes control: faith-based centres offer a promise for expanding screening services and linkage to care in Ghana
Bibliographic record
Abstract
BACKGROUND: Hypertension and type 2 diabetes mellitus (T2DM) are important contributors to noncommunicable disease related morbidity and mortality. Health systems could benefit from exploring the use of Faith-Based Centres (FBC) to screen and link suspected cases for further care in order to help achieve Sustainable Development Goal (SDG) 3. The study investigated the role of faith-based screening for T2DM and hypertension and the linkage of cases to the healthcare system and examined the care cascade in the Kassena Nankana Districts of Northern Ghana. METHODS: We screened individuals from 6 FBCs for elevated blood pressure and hyperglycaemia. Suspected hypertension and T2DM cases were referred to health facilities for confirmation and subsequently followed them up for 3 months. We assessed the prevalence of behavioural and metabolic risk factors, including hypertension and T2DM, and the retention of referred cases in the healthcare system over follow up period. We further assessed levels of awareness, treatment and adequate control of hypertension and T2DM. RESULTS: ) and had a higher mean waist circumference (89.0 cm IQR 75-116 cm vs. 84.2 cm IQR 72-107 cm), hip circumference (101.5 ± 10.6 cm vs. 96.4 ± 8.6 cm) and waist-to-hip ratio (0.86 ± 0.1 cm vs. 0.87 ± 0.1 cm) than males. The prevalence of confirmed hypertension and T2DM was 27.9% and 3.5% respectively with no observed sex differences. We observed deficits in the hypertension and T2DM care cascade with reported low awareness, treatment and uncontrolled levels. A 3-month follow up showed a retention in care of 100% in month one and 94.9% in the third month. There was an increase in treatment (39.4% in month-1 and 82.8% in month-3) and control (26.3% in month-1 and 76.3% in month-3) of hypertension and T2DM combined. CONCLUSION: Faith-based centres have the potential to enhance the screening, linkage to the healthcare system, and management of hypertension and T2DM. This improvement over the routine system could lead to earlier diagnoses, a reduction in complications, and decreased premature mortality from cardiovascular diseases. Consequently, these efforts would contribute significantly to achieving SDG 3.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".