Impact of point-of-care screening for hypertension, diabetes and progression of chronic kidney disease in rural Manitoba Indigenous communities
Bibliographic record
Abstract
BACKGROUND: In 2013-2015, we conducted point-of-care screening for hypertension, diabetes and chronic kidney disease in rural and remote Indigenous communities in Manitoba, Canada. In this study, we aimed to determine whether optimal follow-up care was provided, defined as proportion of individuals with appropriate kidney disease laboratory testing, medication prescriptions and physician visits. METHODS: We linked screening data from participants to provincial administrative data sets to evaluate whether frequencies of laboratory testing, prescriptions of disease-modifying medications, and primary care and nephrology visits differed in the 18 months before and after screening. We also conducted a propensity score matching analysis to compare outcomes between screened and unscreened adults. RESULTS: Of 1353 adults who received the screening intervention and who had complete administrative data available, 44% were at risk of kidney failure at screening. Among these individuals, frequencies of comprehensive laboratory testing (estimated glomerular filtration rate and urine albumin to creatinine ratio) improved by 17.0% (95% confidence interval [CI] 11.5 to 22.5), anti-hyperglycemic medications improved by 4.4% (95% CI 1.0 to 7.8), and nephrology visits for participants meeting referral criteria improved by 5.9% (95% CI 3.4 to 8.5). We observed significant improvements in laboratory testing, antihyperglycemic medications and nephrology visits in the screened group compared with the 1:1 matched comparison group. INTERPRETATION: Point-of-care screening programs in rural and remote Indigenous communities are adaptable methods for increasing awareness, monitoring risk and treating chronic diseases. Interventions such as the development of a national screening program could improve chronic disease care in high-risk populations.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".