ABSTRACT
Risk prediction models play an important role in the primary prevention of cardiovascular diseases (CVD) in the elderly population. There are fifteen papers about CVD risk prediction models developed for the elderly domestically and internationally, of which the definitions of disease outcome vary widely. Ten models were reported with insufficient information about study methods or results. Ten models were at high risk of bias. Thirteen models presented moderate discrimination in internal validation, and only four models have undertaken external validation. The CVD risk prediction models for the elderly differed from those for the general population in terms of model algorithm and the effect size of association between predictor and outcome, and the prediction performance of the models for the elderly attenuated. In the future, high-quality external validation researches are necessary to provide more solid evidence. Different ways, including adding new predictors, using competing risk model algorithms, machine learning methods, or joint models, and altering the prediction time horizon, should be explored to optimize the current models.
Subject(s)
Humans , Aged , Cardiovascular Diseases/epidemiology , Algorithms , Machine LearningABSTRACT
Chronic kidney disease (CKD) is an important global public health problem that greatly threatens population health. Application of risk prediction model is a crucial way for the primary prevention of CKD, which can stratify the risk for developing CKD and identify high-risk individuals for more intensive interventions. By now, more than twenty risk prediction models for CKD have been developed worldwide. There are also four domestic risk prediction models developed for Chinese population. However, none of these models have been recommended in clinical guidelines yet. The existing risk prediction models have some limitations in terms of outcome definition, predictors, strategies for handling missing data, and model derivation. In the future, the applications of emerging biomarkers and polygenic risk scores as well as advances in machine learning methods will provide more possibilities for the further improvement of the model.
Subject(s)
Humans , Renal Insufficiency, Chronic , Risk Factors , BiomarkersABSTRACT
Epidemiology is a discipline integrating methodology and applied science, whose mission is to prevent and control diseases and promote health. This review introduces the new progress of epidemiology from five aspects: communicable diseases, chronic diseases, systems epidemiology, implementation research and big data of health care. New projects and constantly emerging technologies in the field of infectious diseases are inspiring, while more attention should be paid to the environmental factors of pathogen variation. In the field of chronic diseases, there is an urgent need to study the multimorbidity of the elderly. The role of infectious inducers and human microbiota in the occurrence and development of chronic diseases has been gradually revealed. Systems epidemiology, which is of great significance to achieve precision prevention is a new branch and an important supplement of modern epidemiology. Implementation research, is a bridge connecting basic scientific research and public health practice and will provide evidence to support the effective implementation of the Health China Action Plan. The development of health care big data is based on digital public health, which provides a broad research platform and abundant data resources for epidemiology, and will promote the fundamental transformation of the service and management mode of public health.
ABSTRACT
@#To evaluate the association between the frequency of bowel movement (BMF) and the risk of Parkinson's disease (PD). In this study, 510 134 participants from the China Kadoorie Biobank (CKB) were included after excluding those who reported to had been diagnosed with cancer at baseline survey. The baseline survey was conducted from 2004 to 2008. The study used the data from the baseline survey and follow-up until December 31, 2016. Cox proportional hazards regression models were used to estimate the s and the 95s of risk of PD diagnosis with BMF. During an average follow-up period of (9.9±1.9) years, 808 participants were diagnosed with PD. Compared with participants who had bowel movements every day, the multivariable-adjusted (95) for those who had bowel movements<3 times/week, once every 2-3 days, and>1 time/day were 3.62 (2.88-4.54), 2.13 (1.74-2.60), and 0.81 (0.63-1.05), respectively. The linear trend test results of the association between BMF and risk of PD diagnosis was significant (<0.001). Compared with the participants who had bowel movements ≥1 time/day, the multivariable-adjusted (95) for those who had bowel movements<1 time/day was 3.13 (2.32-4.23) within the 5 years of follow- up and was 2.48 (2.05-3.01) beyond the 5 years of follow-up. The gender specific results were similar. The association of BMF<1 time/day with risk of PD diagnosis was stronger in older participants. The participants with low BMF at baseline survey would have higher risk for PD diagnosis in the subsequent 10 years on average. Since abnormal decrease of BMF is easy to be found, programs could be set up for the early screening of PD in older people, along with other early symptoms of PD.