Background and Objective: A multitude of large cohort studies have collected data on incidence and covariates/risk factors of various chronic diseases. However, approaches for utilization of these large data and trans...Background and Objective: A multitude of large cohort studies have collected data on incidence and covariates/risk factors of various chronic diseases. However, approaches for utilization of these large data and translation of the valuable results to inform and guide clinical disease prevention practice are not well developed. In this paper, we proposed, based on large cohort study data, a novel conceptual cost-effective disease prevention design strategy for a target group when it is not affordable to include everyone in the target group for intervention. Methods and Results: Data from American Indian participants (n = 3516;2056 women) aged 45 - 74 years in the Strong Heart Study, the diabetes risk prediction model from the study, a utility function, and regression models were used. A conceptual cost-effective disease prevention design strategy based on large cohort data was initiated. The application of the proposed strategy for diabetes prevention was illustrated. Discussion: The strategy may provide reasonable solutions to address cost-effective prevention design issues. These issues include complex associations of a disease with its significant risk factors, cost-effectively selecting individuals at high risk of developing disease to undergo intervention, individual differences in health conditions, choosing intervention risk factors and setting their appropriate, attainable, gradual and adaptive goal levels for different subgroups, and assessing effectiveness of the prevention program. Conclusions: The strategy and methods shown in the illustrative example can also be analogously adopted and applied to other diseases preventions. The proposed strategy provides a way to translate and apply epidemiological study results to clinical disease prevention practice.展开更多
Background and Objective: 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...Background and Objective: 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 we proposed a novel conceptual group/community disease prevention design strategy based on large cohort study data. Methods and Results: The data from participants (n = 3516;2056 women) aged 45 to 74 years and the diabetes risk prediction model from Strong Heart Study were used. The Strong Heart Study is a population-based cohort study of cardiovascular disease and its risk factors in American Indians. 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. Discussion: The strategy may provide reasonable solutions to the prevention design issues. These issues include 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. Conclusions: The strategy and methods shown in the illustration example can be analogously adopted and applied for other diseases preventions. The proposed strategy for a target group/community in a population provides a way to translate and apply epidemiological study results to clinical disease prevention practice.展开更多
Background and Objective: American Indians have a high prevalence of diabetes and higher incidence of stroke than that of whites and blacks in the U.S. Stroke risk prediction models based on data from American Indians...Background and Objective: American Indians have a high prevalence of diabetes and higher incidence of stroke than that of whites and blacks in the U.S. Stroke risk prediction models based on data from American Indians would be of clinical and public health value. Methods and Results: A total of 3483 (2043 women) Strong Heart Study participants free of stroke at baseline were followed from 1989 to 2010 for incident stroke. Overall, 297 stroke cases (179 women) were identified. Cox models with stroke-free time and risk factors recorded at baseline were used to develop stroke risk prediction models. Assessment of the developed stroke risk prediction models regarding discrimination and calibration was performed by an analogous C-statistic (C) and a version of the Hosmer-Lemeshow statistic (HL), respectively, and validated internally through use of Bootstrapping methods. Results: Age, smoking status, alcohol consumption, waist circumference, hypertension status, antihypertensive therapy, fasting plasma glucose, diabetes medications, high/low density lipoproteins, urinary albumin/creatinine ratio, history of coronary heart disease/heart failure, atrial fibrillation, or Left ventricular hypertrophy, and parental history of stroke were identified as the significant optimal risk factors for incident stroke. Discussion: The models produced a C = 0.761 and HL = 4.668 (p = 0.792) for women, and a C = 0.765 and HL = 9.171 (p = 0.328) for men, showing good discrimination and calibration. Conclusions: Our stroke risk prediction models provide a mechanism for stroke risk assessment designed for American Indians. The models may be also useful to other populations with high prevalence of obesity and/or diabetes for screening individuals for risk of incident stroke and designing prevention programs.展开更多
文摘Background and Objective: A multitude of large cohort studies have collected data on incidence and covariates/risk factors of various chronic diseases. However, approaches for utilization of these large data and translation of the valuable results to inform and guide clinical disease prevention practice are not well developed. In this paper, we proposed, based on large cohort study data, a novel conceptual cost-effective disease prevention design strategy for a target group when it is not affordable to include everyone in the target group for intervention. Methods and Results: Data from American Indian participants (n = 3516;2056 women) aged 45 - 74 years in the Strong Heart Study, the diabetes risk prediction model from the study, a utility function, and regression models were used. A conceptual cost-effective disease prevention design strategy based on large cohort data was initiated. The application of the proposed strategy for diabetes prevention was illustrated. Discussion: The strategy may provide reasonable solutions to address cost-effective prevention design issues. These issues include complex associations of a disease with its significant risk factors, cost-effectively selecting individuals at high risk of developing disease to undergo intervention, individual differences in health conditions, choosing intervention risk factors and setting their appropriate, attainable, gradual and adaptive goal levels for different subgroups, and assessing effectiveness of the prevention program. Conclusions: The strategy and methods shown in the illustrative example can also be analogously adopted and applied to other diseases preventions. The proposed strategy provides a way to translate and apply epidemiological study results to clinical disease prevention practice.
文摘Background and Objective: 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 we proposed a novel conceptual group/community disease prevention design strategy based on large cohort study data. Methods and Results: The data from participants (n = 3516;2056 women) aged 45 to 74 years and the diabetes risk prediction model from Strong Heart Study were used. The Strong Heart Study is a population-based cohort study of cardiovascular disease and its risk factors in American Indians. 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. Discussion: The strategy may provide reasonable solutions to the prevention design issues. These issues include 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. Conclusions: The strategy and methods shown in the illustration example can be analogously adopted and applied for other diseases preventions. The proposed strategy for a target group/community in a population provides a way to translate and apply epidemiological study results to clinical disease prevention practice.
文摘Background and Objective: American Indians have a high prevalence of diabetes and higher incidence of stroke than that of whites and blacks in the U.S. Stroke risk prediction models based on data from American Indians would be of clinical and public health value. Methods and Results: A total of 3483 (2043 women) Strong Heart Study participants free of stroke at baseline were followed from 1989 to 2010 for incident stroke. Overall, 297 stroke cases (179 women) were identified. Cox models with stroke-free time and risk factors recorded at baseline were used to develop stroke risk prediction models. Assessment of the developed stroke risk prediction models regarding discrimination and calibration was performed by an analogous C-statistic (C) and a version of the Hosmer-Lemeshow statistic (HL), respectively, and validated internally through use of Bootstrapping methods. Results: Age, smoking status, alcohol consumption, waist circumference, hypertension status, antihypertensive therapy, fasting plasma glucose, diabetes medications, high/low density lipoproteins, urinary albumin/creatinine ratio, history of coronary heart disease/heart failure, atrial fibrillation, or Left ventricular hypertrophy, and parental history of stroke were identified as the significant optimal risk factors for incident stroke. Discussion: The models produced a C = 0.761 and HL = 4.668 (p = 0.792) for women, and a C = 0.765 and HL = 9.171 (p = 0.328) for men, showing good discrimination and calibration. Conclusions: Our stroke risk prediction models provide a mechanism for stroke risk assessment designed for American Indians. The models may be also useful to other populations with high prevalence of obesity and/or diabetes for screening individuals for risk of incident stroke and designing prevention programs.
