The life-course impact of smoking on hypertension, myocardial infarction and respiratory diseases

The Author(s) 2017. The objective of this study was to examine the impact of smoking on respiratory diseases, hypertension and myocardial infarction, with a particular focus from a life-course perspective. In this study, 28,577 males from a Chinese longitudinal survey were analysed. The effects of smoking on the risk of respiratory diseases, hypertension and myocardial infarction were assessed from a life-course perspective and a current view separately. No significant associations were found between smoking and the risk of incident respiratory diseases, hypertension and myocardial infarction in the group younger than 35. Among study participants aged between 36-55 and 56-80, smoking was positively associated with the risk of incident respiratory diseases, hypertension and myocardial infarction from the life-course perspective, and the risk increased with age. In contrast, the results from a current view showed inverse associations between smoking and the risk of the diseases mentioned above. Our findings highlight that it is essential to quantify the effects of smoking from a life-course perspective in future research and to suggest that smokers quit smoking as soon as possible, regardless of the temporary side effects of quitting

Missed Opportunities in Preventing Hospital Readmissions: Redesigning Post‐Discharge Checkup Policies

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/147049/1/poms12858.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147049/2/poms12858_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147049/3/poms12858-sup-0001-AppendixS1.pd

A risk analysis based on a two-stage delayed diagnosis regression model with application to chronic disease progression

This paper presents a two-stage regression model for quantifying different stages of a disease progression with delayed diagnosis time and for identifying the risk factors associated with each stage. Conventional chronic disease progression studies reported replied on the assumption that the time of the confirmation of a disease state by diagnosis is the start time of this disease state. Clearly this will lead to biased estimates of progression since the disease state should have already occurred before the diagnosis, but the true occurrence time is unknown. This later confirmation is called the delayed diagnosis in this paper and a delay-time modelling procedure is developed for the identification of the unknown stages of progression. A hazard-based regression model is also proposed for a further risk analysis. We apply the developed methods to hepatitis C data and the analysis shows that considering the delayed diagnosis significantly improved the model fit in comparison with the conventional model. We also find that the risk factors associated with each stage are more significant, particularly in the second stage of progression, than those based on the conventional model. We conclude that such delayed phenomena in diagnosis should be taken into account when modelling the chronic disease progression process and conducting related risk analysis. © 2011 Elsevier B.V. All rights reserved