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Large Cohort Data Based Group or Community Disease Prevention Design Strategy: Strong Heart Study

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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