A Sustainable Community-Based Model of Noncommunicable Disease Risk Factor Surveillance (Shraddha-Jagrithi Project): Protocol for a Cohort Study
Notice bibliographique
Résumé
BACKGROUND: India has a massive noncommunicable disease (NCD) burden, at an enormous cost to the individual, family, society, and health system at large, despite which prevention and surveillance are relatively neglected. If diagnosed early and treated adequately, risk factors for atherosclerotic cardiovascular disease would help decrease the mortality and morbidity burden. Surveillance for NCDs, creating awareness, positive lifestyle changes, and treatment are the proven measures known to prevent the progression of the disease. India is in a stage of rapid epidemiological transition, with the state of Kerala being at the forefront, pointing us towards likely disease burden and outcomes for the rest of the country in the future. A previous study done by the same investigators in a population of >100,000 revealed poor awareness, treatment of NCDs, and poor adherence to medicines in individuals with CVD. OBJECTIVE: This study aimed at assessing a sustainable, community-based surveillance model for NCDs with corporate support fully embedded in the public health system. METHODS: Frontline health workers will check all individuals in the target group (≥age 30 years) with further follow-up and treatment planned in a "spoke and hub" model using the public health system of primary health centers as spokes to the hubs of taluk or district hospitals. All data entry done by frontline health workers will be on a tablet PC, ensuring rapid acquisition and transfer of participant health details to primary health centers for further follow-up and treatment. RESULTS: The model will be evaluated based on the utilization rate of various services offered at all tier levels. The proportions of the target population screened, eligible individuals who reached the spoke or hub centers for risk stratification and care, and community-level control for hypertension and diabetes in annual surveys will be used as indicator variables. The model ensures diagnosis and follow-up treatment at no cost to the individual entirely through the tiered public health system of the state and country. CONCLUSIONS: Surveillance for NCDs is an essential facet of health care presently lacking in India. Atherosclerotic cardiovascular disease has a long gestation period in progression to the symptomatic phase of the disease, during which timely preventive and lifestyle measures would help prevent disease progression if implemented. Unfortunately, several asymptomatic individuals have never tested their plasma glucose, serum lipid levels, or blood pressure and are unaware of their disease status. Our model, implemented through the public health system using frontline health workers, would ensure individuals aged≥30 years at risk of disease are identified, and necessary lifestyle modifications and treatments are given. In addition, the surveillance at the community level would help create a general awareness of NCDs and lead to healthier lifestyle habits. TRIAL REGISTRATION: Clinical Trial Registry India CTRI/2018/07/014856; https://tinyurl.com/4saydnxf. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/27299.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,036 | 0,026 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,003 |
| Méta-épidémiologie (sens large) | 0,003 | 0,003 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,004 | 0,003 |
| Intégrité de la recherche | 0,003 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,078 | 0,017 |
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 source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».