Comparing Stroke Profiles and Outcomes between Urban and Rural India: A Secondary Analysis of the SPRINT INDIA Trial
Bibliographic record
Abstract
INTRODUCTION: Stroke causes significant death and disability, with urban-rural disparities in healthcare and limited studies in India, despite its rural majority of 70%. The post hoc study aimed to explore differences in stroke profiles, risk factors, and outcomes between urban and rural participants using data from the Secondary Prevention by Structured Semi-Interactive Stroke Prevention Package in India (SPRINT INDIA) trial. METHODS: The SPRINT INDIA trial was a multi-center randomized clinical trial across 31 Indian sites. Data were collected between April 28, 2018, and November 30, 2021. Index stroke patients, aged 18 and older, presenting within 2 days to 3 months of symptom onset, were randomized using a centralized web-based system into intervention or control groups. The intervention included SMS, videos, and an interactive educational workbook for secondary stroke prevention in 11 Indian languages. Baseline data captured in a case report form included participants' urban or rural locations. The primary outcome was a composite endpoint that included recurrent stroke, high-risk transient ischemic attack (TIA), acute coronary syndrome (ACS), and all-cause mortality within 1 year after randomization. The trial is registered by Clinicaltrials.gov (NCT03228979) and Clinical Trials Registry-India (CTRI/2017/09/009600). RESULTS: The trial enrolled 4,298 sub-acute stroke patients, out of which 3,038 (70.68%) were followed up, of which 1,620 (53.32%) were urban and 1,418 (46.68%) were rural. The primary composite outcome (recurrent stroke, high-risk TIA, ACS, and mortality) was higher in urban areas compared to rural areas (61 [3.8%] vs. 34 [2.4%]; p = 0.018) at 1-year follow-up. All cases of high-risk TIA occurred in urban participants (p < 0.001). Urban participants were more educated (795 [49.1%] vs. rural 394 [27.8%]; p < 0.001), with higher rates of dyslipidemia (335 [20.7%] vs. 247 [17.4%]; p = 0.023), and higher body mass index (25.17 ± 4.31 vs. 24.76 ± 4.23; p = 0.008). Behavioral risk factors of alcohol intake and smoking tobacco were higher in rural patients compared to urban patients (65 [4.6%] vs. 73 [4.5%]; p < 0.001 and 59 [4.2%] vs. 65 [4.0%]; p < 0.001, respectively). CONCLUSION: Urban patients show higher stroke recurrence and lifestyle-related conditions, while rural patients face more behavioral risks like smoking and alcohol use. To address these disparities, requires targeted interventions; urban patients would benefit from lifestyle-focused programs, such as dietary improvements and stress management. For rural patients, programs should focus on reducing behavioral risks like smoking and alcohol use through community-based education and accessible cessation support services.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".