Prevention of chronic diseases has
emerged as an urgent issue due to increasing prevalence of the chronic diseases
and their effects on medical care, public health and economic burden. A
multitude of large cohort studies have data on incidence rates and
predictors of various chronic diseases. However, approaches for utilization of
these costly collected data and translation of these valuable results to
inform and guide clinical disease prevention practice are not well
developed. In this paper, the authors proposed a novel conceptual group/community
disease prevention design strategy based on large cohort study data.
The data from
participants (n = 3516; 2056 women) aged 45 to 74 years old and the
diabetes risk prediction model from Strong Heart Study (SHS) were used. The SHS
is a population-based cohort study of cardiovascular disease (CVD) and its risk
factors for American Indians in southwestern Oklahoma, central Arizona, and
North and South Dakota. A conceptual group/community disease prevention design
strategy based on large cohort data was initiated. The application of the
proposed strategy for group diabetes prevention was illustrated.
The results
indicated that the strategy might provide reasonable solutions to the
prevention design issues. These issues included complex associations of a
disease with its combined and correlated risk factors, individual differences,
choosing intervention risk factors and setting their appropriate, attainable,
gradual and adaptive goal levels for different subgroups, and assessing
effectiveness of the prevention program.
In conclusion, the
proposed design strategy considers the complex associations of a disease with
its combined and correlated risk factors and individual differences; provides
ways to simultaneously set gradual, attainable and safe goals for all risk
factors in different subgroups; and forms an adaptive intervention frame. The
proposed design strategy represents a way to utilize or translate available
valuable results and costly collected data from large cohort studies for
clinical disease prevention practice, and can be applied for group/community
diseases preventions.
Article by Wenyu Wang, et al, from USA.
Full access: http://mrw.so/3zRWvb
Image by Dana Arena, from Flickr-cc.
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