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Zhong Min Li 4 Articles
Cardiovascular Health Metrics and All-cause and Cardiovascular Disease Mortality Among Middle-aged Men in Korea: The Seoul Male Cohort Study
Ji Young Kim, Young-Jin Ko, Chul Woo Rhee, Byung-Joo Park, Dong-Hyun Kim, Jong-Myon Bae, Myung-Hee Shin, Moo-Song Lee, Zhong Min Li, Yoon-Ok Ahn
J Prev Med Public Health. 2013;46(6):319-328.   Published online November 28, 2013
DOI: https://doi.org/10.3961/jpmph.2013.46.6.319
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  • 53 Crossref
AbstractAbstract PDF
Objectives

This study estimated the association of cardiovascular health behaviors with the risk of all-cause and cardiovascular disease (CVD) mortality in middle-aged men in Korea.

Methods

In total, 12 538 men aged 40 to 59 years were enrolled in 1993 and followed up through 2011. Cardiovascular health metrics defined the following lifestyle behaviors proposed by the American Heart Association: smoking, physical activity, body mass index, diet habit score, total cholesterol, blood pressure, and fasting blood glucose. The cardiovascular health metrics score was calculated as a single categorical variable, by assigning 1 point to each ideal healthy behavior. A Cox proportional hazards regression model was used to estimate the hazard ratio of cardiovascular health behavior. Population attributable risks (PARs) were calculated from the significant cardiovascular health metrics.

Results

There were 1054 total and 171 CVD deaths over 230 690 person-years of follow-up. The prevalence of meeting all 7 cardiovascular health metrics was 0.67%. Current smoking, elevated blood pressure, and high fasting blood glucose were significantly associated with all-cause and CVD mortality. The adjusted PARs for the 3 significant metrics combined were 35.2% (95% confidence interval [CI], 21.7 to 47.4) and 52.8% (95% CI, 22.0 to 74.0) for all-cause and CVD mortality, respectively. The adjusted hazard ratios of the groups with a 6-7 vs. 0-2 cardiovascular health metrics score were 0.42 (95% CI, 0.31 to 0.59) for all-cause mortality and 0.10 (95% CI, 0.03 to 0.29) for CVD mortality.

Conclusions

Among cardiovascular health behaviors, not smoking, normal blood pressure, and recommended fasting blood glucose levels were associated with reduced risks of all-cause and CVD mortality. Meeting a greater number of cardiovascular health metrics was associated with a lower risk of all-cause and CVD mortality.

Summary

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Impact of Individual and Combined Health Behaviors on All Causes of Premature Mortality Among Middle Aged Men in Korea: The Seoul Male Cohort Study
Chul Woo Rhee, Ji Young Kim, Byung Joo Park, Zhong Min Li, Yoon-Ok Ahn
J Prev Med Public Health. 2012;45(1):14-20.   Published online January 31, 2012
DOI: https://doi.org/10.3961/jpmph.2012.45.1.14
  • 9,753 View
  • 109 Download
  • 20 Crossref
AbstractAbstract PDF
Objectives

The aim of this study was to evaluate and quantify the risk of both individual and combined health behaviors on premature mortality in middle aged men in Korea.

Methods

In total, 14 533 male subjects 40 to 59 years of age were recruited. At enrollment, subjects completed a baseline questionnaire, which included information about socio-demographic factors, past medical history, and life style. During the follow-up period from 1993 to 2008, we identified 990 all-cause premature deaths using national death certificates. A Cox proportional hazard regression model was used to estimate the hazard ratio (HR) of each health risk behavior, which included smoking, drinking, physical inactivity, and lack of sleep hours. Using the Cox model, each health behavior was assigned a risk score proportional to its regression coefficient value. Health risk scores were calculated for each patient and the HR of all-cause premature mortality was calculated according to risk score.

Results

Current smoking and drinking, high body mass index, less sleep hours, and less education were significantly associated with all-cause premature mortality, while regular exercise was associated with a reduced risk. When combined by health risk score, there was a strong trend for increased mortality risk with increased score (p-trend < 0.01). When compared with the 1-9 score group, HRs of the 10-19 and 20-28 score groups were 2.58 (95% confidence intervals [CIs], 2.19 to 3.03) and 7.09 (95% CIs, 5.21 to 9.66), respectively.

Conclusions

Modifiable risk factors, such as smoking, drinking, and regular exercise, have considerable impact on premature mortality and should be assessed in combination.

Summary

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    Journal of Preventive Medicine and Public Health.2013; 46(6): 319.     CrossRef
  • The combined effects of healthy lifestyle behaviors on all cause mortality: A systematic review and meta-analysis
    Martin Loef, Harald Walach
    Preventive Medicine.2012; 55(3): 163.     CrossRef
Power Estimation and Follow-Up Period Evaluation in Korea Radiation Effect and Epidemiology Cohort Study.
In Seong Cho, Minkyo Song, Yunhee Choi, Zhong Min Li, Yoon Ok Ahn
J Prev Med Public Health. 2010;43(6):543-548.
DOI: https://doi.org/10.3961/jpmph.2010.43.6.543
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AbstractAbstract PDF
OBJECTIVES
The objective of this study was to calculate sample size and power in an ongoing cohort, Korea radiation effect and epidemiology cohort (KREEC). METHOD: Sample size calculation was performed using PASS 2002 based on Cox regression and Poisson regression models. Person-year was calculated by using data from '1993-1997 Total cancer incidence by sex and age, Seoul' and Korean statistical informative service. RESULTS: With the assumption of relative risk=1.3, exposure:non-exposure=1:2 and power=0.8, sample size calculation was 405 events based on a Cox regression model. When the relative risk was assumed to be 1.5 then number of events was 170. Based on a Poisson regression model, relative risk=1.3, exposure:non-exposure=1:2 and power=0.8 rendered 385 events. Relative risk of 1.5 resulted in a total of 157 events. We calculated person-years (PY) with event numbers and cancer incidence rate in the non-exposure group. Based on a Cox regression model, with relative risk=1.3, exposure:non-exposure=1:2 and power=0.8, 136 245PY was needed to secure the power. In a Poisson regression model, with relative risk=1.3, exposure:non-exposure=1:2 and power=0.8, person-year needed was 129517PY. A total of 1939 cases were identified in KREEC until December 2007. CONCLUSIONS: A retrospective power calculation in an ongoing study might be biased by the data. Prospective power calculation should be carried out based on various assumptions prior to the study.
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  • Comparative Analysis of Driver Mutations and Transcriptomes in Papillary Thyroid Cancer by Region of Residence in South Korea
    Jandee Lee, Seonhyang Jeong, Hwa Young Lee, Sunmi Park, Meesson Jeong, Young Suk Jo
    Endocrinology and Metabolism.2023; 38(6): 720.     CrossRef
  • Cancer Risk in Adult Residents near Nuclear Power Plants in Korea - A Cohort Study of 1992-2010
    Yoon-Ok Ahn, Zhong Min Li
    Journal of Korean Medical Science.2012; 27(9): 999.     CrossRef
Reliability of Covariates in Baseline Survey of a Cohort Study: Epidemiological Investigation on Cancer Risk Among Residents Who Reside Near the Nuclear Power Plants in Korea.
Sanghyuk Bae, Bo Young Park, Zhong Min Li, Yoon Ok Ahn
J Prev Med Public Health. 2010;43(2):159-165.
DOI: https://doi.org/10.3961/jpmph.2010.43.2.159
  • 4,884 View
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  • 1 Crossref
AbstractAbstract PDF
OBJECTIVES
We evaluated the reliability of the possible covariates of the baseline survey data collected for the Epidemiological Investigation on Cancer Risk Among Residents Who Reside Near the Nuclear Power Plants in Korea. METHODS: Follow-up surveys were conducted for 477 participants of the cohort at less than 1 year after the initial survey. The mean interval between the initial and follow-up surveys was 282.5 days. Possible covariates were identified by analyzing the correlations with the exposure variable and associations with the outcome variables for all the variables. Logistic regression analysis with stepwise selection was further conducted among the possible covariates to select variables that have covariance with other variables. We considered that these variables can be representing other variables. Seven variables for the males and 3 variables for the females, which had covariance with other possible covariates, were selected as representative variables. The Kappa index of each variable was calculated. RESULTS: For the males, the Kappa indexes were as follow; family history of cancer was 0.64, family history of liver diseases in parents and siblings was 0.56, family history of hypertension in parents and siblings was 0.51, family history of liver diseases was 0.50, family history of hypertension was 0.44, a history of chronic liver diseases was 0.53 and history of pulmonary tuberculosis was 0.36. For females, the Kappa indexes were as follow; family history of cancer was 0.58, family history of hypertension in parents and siblings was 0.56 and family history of hypertension was 0.47. CONCLUSIONS: Most of the possible covariates showed good to moderate agreement.
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  • Cancer Risk in Adult Residents near Nuclear Power Plants in Korea - A Cohort Study of 1992-2010
    Yoon-Ok Ahn, Zhong Min Li
    Journal of Korean Medical Science.2012; 27(9): 999.     CrossRef

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