Rural Stroke Surveillance and Establishment of Acute Stroke Care Pathway Using Frontline Health Workers in Rural Northwest India: The Ludhiana Experience
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The role of community health workers (CHWs) in stroke surveillance and in establishing the stroke care pathway has not been studied. The aim of the study was to evaluate the feasibility of using CHWs in the public health system to identify stroke patients for population-based stroke registration and to study the establishment of acute stroke care pathway in rural areas of Ludhiana, Punjab, Northwest India. METHODS: Two rural blocks in Ludhiana district, comprising 164 villages and a population of 259,778, were selected. Phase-1 (feasibility study) was from August to November 2016 and phase-2 from December 2016 to November 2018. All first-ever stroke cases in adults (aged ≥18 years) were included. The accredited social health activists (ASHAs) were trained to identify stroke patients in the community, who were later evaluated by a neurologist. Stroke characteristics were recorded, and the outcome was assessed at 6 months using modified Rankin scale (0-2, good outcome). FINDINGS: During phase-2, 359 first-ever stroke patients and 102 stroke mimics were identified. The age-standardized incidence rate was 218.5/100,000 and 197∙6/100,000 for each year. Half (52.4%) of the patients reached health-care facilities within 4.5 h, yet none of them received thrombolysis. Very few patients (1.9%) utilized free government 108 ambulance service to reach a health-care facility. Out of 359 stroke cases, the majority (306, 85.23%) were reported by ASHAs and 14.77% were reported by other sources. Brain imaging was available in 127 (35.4%) patients, and 100 (78.7%) had ischemic stroke. The most common risk factor was hypertension (320, 89%) and drug abuse (154, 42.9%). At 6 months, 168 (64%) patients had a good outcome. CONCLUSION: ASHAs were able to identify stroke patients in the villages. Despite high numbers of patients reaching health-care facilities within a window period, the hospitals were unable to provide acute stroke treatment like thrombolysis. The health-care system needs to be strengthened to improve stroke care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".