Modelling the Impact of Obesity Reduction on the Prevalence of Hypertension in India: A Discrete-Event Microsimulation Approach
Mustafa, A.
Show abstract
Obesity is one of the most significant risk factors of non-communicable diseases, disability, and premature death. Due to its profound impact on health, researchers have started classifying it as a disease rather than a mere abnormality. India, following the global trend, is experiencing a surge in obesity prevalence, posing a critical research question about the potential impact of obesity reduction on NCD incidence and related disorders. This study employs discrete-event dynamic microsimulation modelling to investigate how changes in BMI distribution in early years of life can influence the prevalence of hypertension, one of the most prevalent diseases in India. The microsimulation modelling approach enables the simulation of individual-level real-life behaviors and interactions within a given population. The model simulated the lives of 100,000 individuals aged 20 over the next 50 years till age 70. Baseline characteristics, prevalence rates, and transition probabilities were derived from diverse data sources, including Census 2011, the National Family Health Survey - V (NFHS-5), and the Longitudinal Aging Study in India (LASI, 2017-18). The study explores the impact of two scenarios on hypertension prevalence: (i) a one-unit reduction in mean BMI level at baseline, and (ii) a one-unit reduction in the standard deviation of BMI distribution at baseline. Results indicate that a one-unit reduction in mean BMI level at baseline could lead to a 5% reduction in hypertension prevalence at age 70, while a one-unit reduction in the standard deviation of BMI distribution at baseline could result in a 7.5% reduction. These findings underscore the importance of targeting children and adolescents with elevated BMI values to mitigate the later-life prevalence of hypertension. Additionally, the study highlights the significance of promoting the use of microsimulation modelling in health research in the Indian context.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Economic Evaluation of Hypertension screening in Iran using Markov model 95%
- Undiagnosed hypertension and its associated factors in India: A rural-urban contrast from the National Family Health Survey (2019-21) 94%
- Increase trajectories of tendon micro vibration intensity during ankle plantar flexion: A longitudinal data analysis using latent curve models 94%
Similar papers in this journal
- Measuring the impact of nonpharmaceutical interventions on the SARS-CoV-2 pandemic at a city level: An agent-based computational modeling study of the City of Natal 94%
- Changes in the prevalence of the common risk factors for non-communicable diseases in Uganda between 2014 and 2023: Informed by nationally representative cross-sectional surveys 93%
- Overweight and Obesity among Women at Reproductive Age 15-49 Years Old in Cambodia: Data Analysis of Cambodia Demographic and Health Survey 2014 92%
Similar papers in this journal
Similar papers in this journal
- Predicting mortality, duration of treatment, pulmonary embolism and required ceiling of ventilatory support for COVID-19 inpatients: A Machine-Learning Approach 94%
- Quantifying the Effects of Social Distancing on the Spread of COVID-19 93%
- A two-phase stochastic dynamic model for COVID-19 mid-term policy recommendations in Greece: a pathway towards mass vaccination 92%
Similar papers in this journal
- The association of lifestyle with cardiovascular and all-cause mortality based on machine learning: A Prospective Study from the NHANES 93%
- Body Mass Index Asian Populations category and stroke and heart disease in the adult population: A longitudinal study of The Indonesia Family Life Survey (IFLS) 2007 and 2014 92%
- A Coupled Experimental and Statistical Approach for an Assessment of the Airborne Infection Risk in Event Locations 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.