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Enregistrement W4406036721 · doi:10.4103/ijmr.2010_132_05_529

Cardiovascular disease in India: Lessons learnt & challenges ahead

2010· article· en· W4406036721 sur OpenAlexaff
Dorairaj Prabhakaran, Salim Yusuf

Notice bibliographique

RevueThe Indian Journal of Medical Research · 2010
Typearticle
Langueen
DomaineBusiness, Management and Accounting
ThématiqueGlobal Public Health Policies and Epidemiology
Établissements canadiensHamilton Health SciencesMcMaster UniversityPopulation Health Research Institute
Organismes subventionnairesnon disponible
Mots-clésDiseaseIntensive care medicineMedicineInternal medicine

Résumé

récupéré en direct d'OpenAlex

Lifestyles of populations across the world have changed dramatically in the 20th century. These changes (collectively termed as epidemiological transition) have been brought about by a number of developments in science and technology that now affect every facet of human existence. Most human societies have moved from agrarian diets and active lives to fast foods and sedentary habits. Combined with increasing tobacco use, these changes have fuelled the epidemic of obesity, diabetes, hypertension, dyslipidaemia and cardiovascular diseases (CVD). In developed nations the rise in the burden of CVD occurred over several decades due to a long period of epidemiological transition. In India, perhaps because of the rapid pace of economic development, epidemiological changes have spanned a much shorter time. As a consequence, cardiovascular disease (CVD) has emerged as the leading cause of death all over India, with coronary heart disease (CHD) affecting Indians at least 5-6 years earlier than their western counterparts[1,2]. Current estimates from disparate cross-sectional studies indicate the prevalence of CHD to be between 7-13 per cent in urban and 2-7 per cent in rural India[3]. The spiralling rates of modifiable risk factors for CHD across the spectrum of rural to urban segments of our population have been demonstrated by several studies across India[3,4]. In addition, migration and urbanization have resulted in an increase in the prevalence of risk factors such as diabetes, overweight[5]. The economic impact of these transformations was estimated at 9 billion dollars in national income from premature deaths due to heart disease, stroke and diabetes in 2005 alone, with the projected estimates of 237 billion dollars by 2015. The out-of-pocket health expenses incurred by households increased from 31.6 per cent in 1995 to 47.3 per cent in 2004[6]. Modelling studies have estimated that if non-communicable diseases (NCDs) were completely eliminated, the estimated GDP in a year would have been 4-10 per cent higher[7]. The determinants and the control strategies of the CHD epidemic are multi-factorial, complex and interrelated (Fig.). We now know that the surge in CHD and their risk factors in varied settings are fuelled by modifiable risk factors which can be reduced by simple strategies. Most of these preventive strategies are in the domain of policy, health system intervention, health promotion and simple quality improvement programmes aimed at improving secondary prevention.Fig: The determinants and the control strategies of the coronary heart disease epidemic. *Established modifiable risk factors include smoking, alcohol, physical inactivity, low consumption of fruits and vegetables, overweight and obesity, increased blood pressure, impaired fasting glucose and dyslipidaemia.While the need for research directed towards implementing simple but effective CVD prevention strategies and health system strengthening to combat CVD is glaringly obvious, there is very little actual research output in these areas from India[8,9]. Key research areas to combat CVD should include: (i) cost-effective, innovative ways of reducing CVD risk through health policy and health system interventions; (ii) methods for ensuring integration of CVD care within health systems; (iii) health system financing strategies for individuals with CVD; (iv) best methods of applying existing knowledge for development, implementation and evaluation of CVD prevention programmes; (v) mechanistic research to identify the reasons for the younger age of onset of CVD and diabetes and their occurrence at a lower threshold of risk factors; and (vi) methods to implement health promotion measures to the population at large along with formulation and implementation of ‘HEART-friendly’ policy measures. The above described measures require multidisciplinary, multi-sectoral and multi-level co-ordination and action. Thus translational research, (both T1: from the laboratory to studies in humans and T2: from clinical research to clinical practice and beyond) is needed to develop multi-pronged approaches that address the patient, provider, healthcare systems, public health, and public policy for the prevention and control of CHD in India. What does this issue contribute? This special section is an attempt to highlight current research trends in the field of CHD and to identify gaps in knowledge. The contributors to this issue are key researchers in India with years of experience in CVD prevention and clinical care. They bring their personal insights, deliberate on the current scenario and identify future areas of research in CVD. The topics cover important areas that include epidemiology, genetics and clinical management with a special focus on the Indian context. These are specially written to provide important information for researchers and clinicians. We hope these articles will help stimulate future researchers and assist in planning their studies. We are extremely grateful to the authors who have taken time from their busy schedules and contributed to this issue.

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 distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,041
score de la tête « metaresearch » (Gemma)0,030
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Intégrité de la recherche
Catégories consensuellesMétarecherche
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,589
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0410,030
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,004
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,155
Tête enseignante GPT0,428
Écart entre enseignants0,273 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations8
Publié2010
Routes d'admission1
Résumé présentoui

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