Pitfalls of Single Measurement Screening for Diabetes and Hypertension in Community-Based Settings
Bibliographic record
Abstract
Background: Cross-sectional screening programs are used to detect and refer individuals with non-communicable diseases to healthcare services. We evaluated the positive predictive value of cross-sectional measurements for Diabetes Mellitus (DM) and hypertension (HTN) as part of a community-based disease screening study, 'Vukuzazi' in rural South Africa. Methods: We conducted community-based screening for HTN and DM using the World Health Organization STEPS protocol and glycated haemoglobin A1c (HbA1c) testing, respectively. Nurses conducted follow-up home visits for confirmatory diagnostic testing among individuals with a screening BP above 140/90 mmHg and/or HbA1c above 6.5% at the initial screen, and without a prior diagnosis. We assessed the positive predictive value of the initial screening, compared to the follow up measure. We also sought to identify a screening threshold for HTN and DM with greater than 90% positive predictive value. Results: Of 18,027 participants enrolled, 10.2% (1,831) had a screening BP over 140/90 mmHg. Of those without a prior diagnosis, 871 (47.6%) received follow-up measurements. Only 51.2% (451) of those with completed follow-up measurements had a repeat BP>140/90 mmHg at the home visit and were referred to care. To achieve a 90% correct referral rate, a systolic BP threshold of 192 was needed at first screening. For DM screening, 1,615 (9.0%) individuals had an HbA1c > 6.5%, and of those without a prior diagnosis, 1,151 (71.2%) received a follow-up blood glucose. Of these, only 34.1% (395) met criteria for referral for DM. To ensure a 90% positive predictive value i.e. a screening HbA1c of >16.6% was needed. Conclusions: A second home-based screening visit to confirm a diagnosis of DM and HTN reduced health system referrals by 48% and 66%, respectively. Two-day screening programmes for DM and HTN screening might save individual and healthcare resources and should be evaluated carefully in future cost effectiveness evaluations.
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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 | 0.170 | 0.386 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".