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Adult obesity prevention policies are largely untargeted, but their effects are limited on a global scale.
The high prevalence of obesity is expected to increase substantially in the next decade
.
If the information available in the national public health system can be used to determine the population at the highest risk of weight gain, new targeted methods can be used to supplement the population-wide prevention of obesity
.
Adult obesity prevention policies are largely untargeted, but their effects are limited on a global scale.
An emerging data science opportunity to identify people at high risk of weight gain comes from longitudinal population-based electronic health records (EHR)
Recently, studies have used population-based EHR to respond to long-term calls to monitor basic population weight data in the context of weight changes, and according to sociodemographic factors, it presents a 1-year, 5-year, and 10-year pattern of BMI changes within individuals
In this longitudinal , population- based cohort study , the study used relevant EHR data from 400 primary care clinics in the UK (linked through clinical practice research data), which were obtained through the Caliber program
.
In this longitudinal , population- based cohort study , the study used relevant EHR data from 400 primary care clinics in the UK (linked through clinical practice research data), which were obtained through the Caliber program
- The study included 2092260 eligible individuals whose BMI measurements exceeded 9 million
.
A radical change in policy is needed to focus on individuals who are most at risk of weight gain (i.
Literature source: https://pubmed.
80599.
net/34481555/ https://pubmed.
80599.
net/34481555/ Leave a message here
