Prevalence of risk factors of non-communicable diseases in Kerala, India: results of a cross-sectional study

Sarma, P. S.; Sadanandan, Rajeev; Thulaseedharan, Jissa Vinoda; Soman, Biju; Kannan, Srinivasan; Varma, Ravi Prasad; Nair, Manju R; Pradeepkumar, A S; Jeemon, Panniyammakal; Thankappan, KR; Kutty, Raman

doi:10.1136/bmjopen-2018-027880

Cited by 101 publications

(58 citation statements)

References 34 publications

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“…The major comorbidities in our cohort were hypertension, diabetes, obstructive sleep apnea, dyslipidemia, coronary artery disease hypothyroidism, and epilepsy. Kerala is a state with a higher burden of these disorders compared to the rest of the country [32,33] making it important to determine the relevance of these factors on conversion risk. None of the comorbidities had a significant bearing on dementia conversion in our MCI cohort as opposed to the findings of other communitybased cohorts [27,[29][30][31].…”

Section: Discussionmentioning

confidence: 99%

Baseline Predictors of Longitudinal Cognitive Outcomes in Persons with Mild Cognitive Impairment

Poulose¹,

Varma²,

Surendran³

et al. 2023

Dement Geriatr Cogn Disord

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Introduction: The study aimed to explore longitudinal cognitive outcomes and to ascertain predictors of conversion to dementia in a hospital-based mild cognitive impairment (MCI) cohort classified according to the neuropsychological phenotype at baseline. Materials and Methods: Subjects aged >55 years who had a clinical diagnosis of MCI at initial visit between 2010 and 2018, with at least one formal neuropsychological assessment at baseline and follow-up of a minimum of 2 years were included. The prospective study was completed based on evaluation at last follow-up to gauge conversion to dementia, quantification of performance on activities of daily living and when available, longitudinal neuropsychological test scores. Results: Ninety-five patients with MCI met the inclusion criteria with a mean age of 68.4 ± 6.4 years at baseline and a mean duration of follow-up for 6.4 ± 3.2 years. The cumulative conversion rate to dementia was 22.2% (21/95) and the annualized conversion rate was 3.3% per year of follow-up. The majority of subjects who had converted had multidomain MCI (66%). Only white matter changes on MRI brain revealed correlation with baseline neuropsychology tests. The multivariate logistic regression analysis revealed the utility of lower baseline list recognition (adjusted odds ratio: 0.735 [95% confidence interval: 0.589–0.916]; p 0.006), lower immediate logical memory (0.885 [0.790–0.990]; p 0.03), and high perseverative error scores on set shifting (3.116 [1.425–6.817]; p 0.004) as predictors of conversion. A model score of +2.615 could predict conversion with sensitivity of 72% and specificity of 98% over 6.4 years follow-up. Conclusion: There was a higher risk of conversion associated with multidomain MCI. Logistic regression-based estimations of dementia risk utilizing domain-based neuropsychology test scores in MCI have high specificity for diagnosis at baseline.

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Section: Discussionmentioning

confidence: 99%

Baseline Predictors of Longitudinal Cognitive Outcomes in Persons with Mild Cognitive Impairment

Poulose¹,

Varma²,

Surendran³

et al. 2023

Dement Geriatr Cogn Disord

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“…The difference could be due to the passage of time and study area variations among the surveys. The risk factors prevalence is also higher than reports from South Africa [33], India [34], China [35], and the WHO global report (23% physical inactivity, 38% alcohol use, and 22% tobacco use) [36]. Differences might be due to variations in participants' sociodemographic and economic characteristics.…”

Section: Discussionmentioning

confidence: 76%

Noncommunicable disease risk factors and cardiovascular risk among adults in Ethiopia: A cross‐sectional study

Alamnia,

Sargent,

Kelly

2023

Public Health Challenges

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BackgroundNoncommunicable diseases (NCDs) are rising in developing countries, posing substantial health, social, and economic consequences. Targeted action to address NCDs is difficult without data on the prevalence and social distribution of the risk factors.MethodsA cross‐sectional survey was conducted among randomly sampled adults in Bahir Dar, Northwest Ethiopia. Sociodemographic, lifestyle risk factors, knowledge, and anthropometric measurements were collected. Chi‐square and logistic regression analyses were conducted to identify predictors for NCD risk factors and cardiovascular risk.ResultsThis study involved 417 participants, with a mean age of 35.6 ± 12.6 years, in which 54.7% were females. The prevalence of inadequate fruit/vegetable intake was 95.7%, alcohol consumption 53.0%, physical inactivity 37.9%, tobacco use 1.9%, khat use 5.1%, hypertension 20.7%, overweight/obesity 29.6%, and abdominal obesity 67.4%. Overall, 55% of adults have clusters of three or more risk factors, and 14.6% have a moderate (10%–20%) 10‐year cardiovascular risk. Factors associated with NCD risk factors are sex, age, religion, marital status, occupation, and income. Ten‐year cardiovascular risk is higher in men (adjusted odd ratio (OR) 4.2, 95% CI 1.5–11.4), in adults with abdominal obesity (adjusted OR 5.8, 95% CI 1.4–24.2), and in those with clustered risk factors (adjusted OR 4.0, 95% CI 1.6–10.3).ConclusionThis study shows that adults in Northwest Ethiopia are at high risk of developing NCDs. Coordinated, multisectoral interventions at all levels of the socio‐ecological model are needed to reduce the risk factors.

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“…The AMCHSS, the public health division of SCTIMST, was a partner in the customization of the DHIS-2 software for the Indian context, which was piloted in its field practice area in Athiyannur block in Thiruvananthapuram. This led to many field-based research initiatives on digital health, spanning from its use in infectious diseases [119][120][121][122] to non-communicable diseases [123][124][125]. In recent times AMCHSS is moving ahead with infectious disease modeling and the use of data science approaches to large-scale data [126][127][128][129][130].…”

Section: Digital Health-initiatives From the State Of Kerala (India)mentioning

confidence: 99%

Digital health: trends, opportunities and challenges in medical devices, pharma and bio-technology

Kasoju

Sasi

et al. 2023

CSIT

Self Cite

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Digital health interventions refer to the use of digital technology and connected devices to improve health outcomes and healthcare delivery. This includes telemedicine, electronic health records, wearable devices, mobile health applications, and other forms of digital health technology. To this end, several research and developmental activities in various fields are gaining momentum. For instance, in the medical devices sector, several smart biomedical materials and medical devices that are digitally enabled are rapidly being developed and introduced into clinical settings. In the pharma and allied sectors, digital health-focused technologies are widely being used through various stages of drug development, viz. computer-aided drug design, computational modeling for predictive toxicology, and big data analytics for clinical trial management. In the biotechnology and bioengineering fields, investigations are rapidly growing focus on digital health, such as omics biology, synthetic biology, systems biology, big data and personalized medicine. Though digital health-focused innovations are expanding the horizons of health in diverse ways, here the development in the fields of medical devices, pharmaceutical technologies and biotech sectors, with emphasis on trends, opportunities and challenges are reviewed. A perspective on the use of digital health in the Indian context is also included.

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Prevalence of risk factors of non-communicable diseases in Kerala, India: results of a cross-sectional study

Cited by 101 publications

References 34 publications

Baseline Predictors of Longitudinal Cognitive Outcomes in Persons with Mild Cognitive Impairment

Baseline Predictors of Longitudinal Cognitive Outcomes in Persons with Mild Cognitive Impairment

Noncommunicable disease risk factors and cardiovascular risk among adults in Ethiopia: A cross‐sectional study

Digital health: trends, opportunities and challenges in medical devices, pharma and bio-technology

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