Comparing Stroke Profiles and Outcomes between Urban and Rural India: A Secondary Analysis of the SPRINT INDIA Trial
Notice bibliographique
Résumé
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 <ext-link ext-link-type="uri" xlink:href="http://Clinicaltrials.gov" xmlns:xlink="http://www.w3.org/1999/xlink">Clinicaltrials.gov</ext-link> (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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
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