• Advances in environmental research
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• v.10 no.1
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• pp.17-41
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• 2021

Kazakhstan's cities experience high concentrations levels of atmospheric particulate matter (PM), which is well-known for its highly detrimental effect on the human health. A further increase in PM concentrations in the future could lead to a higher air pollution-caused morbidity and mortality, causing an increase in healthcare expenditures by the government. However, to prevent elevated PM concentrations in the future, more stringent standards could be implemented by lowering current maximum allowable PM concentration limit to Organization for Economic Co-operation and Development (OECD)'s limits. Therefore, this study aims to find out what impact this change in environmental policy towards PM has on state economy in the long run. Future PM10 and PM2.5 concentrations were estimated using multiple linear regression based on gross regional product (GRP) and population growth parameters. Dose-response model was based on World Health Organization's approach for the identification of mortality, morbidity and healthcare costs due to air pollution. Analysis of concentrations revealed that only 6 out of 21 cities of Kazakhstan did not exceed the EU limit on PM10 concentration. Changing environmental standards resulted in the 71.7% decrease in mortality and 77% decrease in morbidity cases in all cities compared to the case without changes in environmental policy. Moreover, the cost of morbidity and mortality associated with air pollution decreased by $669 million in 2030 and $2183 million in 2050 in case of implementation of OECD standards. Thus, changing environmental regulations will be beneficial in terms of both of mortality reduction and state budget saving.

• Journal of Trauma and Injury
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• v.35 no.1
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• pp.25-33
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• 2022

Purpose: The scoring system for traumatic liver injury (SSTLI) was developed in 2015 to predict mortality in patients with polytraumatic liver injury. This study aimed to validate the SSTLI as a prognostic factor in patients with polytrauma and liver injury through a generalized estimating equation analysis. Methods: The medical records of 521 patients with traumatic liver injury from January 2015 to December 2019 were reviewed. The primary outcome variable was in-hospital mortality. All the risk factors were analyzed using multivariate logistic regression analysis. The SSTLI has five clinical measures (age, Injury Severity Score, serum total bilirubin level, prothrombin time, and creatinine level) chosen based on their predictive power. Each measure is scored as 0-1 (age and Injury Severity Score) or 0-3 (serum total bilirubin level, prothrombin time, and creatinine level). The SSTLI score corresponds to the total points for each item (0-11 points). Results: The areas under the curve of the SSTLI to predict mortality on post-traumatic days 0, 1, 3, and 5 were 0.736, 0.783, 0.830, and 0.824, respectively. A very good to excellent positive correlation was observed between the probability of mortality and the SSTLI score (γ=0.997, P<0.001). A value of 5 points was used as the threshold to distinguish low-risk (<5) from high-risk (≥5) patients. Multivariate analysis using the generalized estimating equation in the logistic regression model indicated that the SSTLI score was an independent predictor of mortality (odds ratio, 1.027; 95% confidence interval, 1.018-1.036; P<0.001). Conclusions: The SSTLI was verified to predict mortality in patients with polytrauma and liver injury. A score of ≥5 on the SSTLI indicated a high-risk of post-traumatic mortality.

• Journal of Preventive Medicine and Public Health
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• v.31 no.4 s.63
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• pp.604-615
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• 1998

Although previous studies revealed the association of physical activity with mortality rate, it is unclear whether there is a linear trend between physical activity and mortality rate. In this study, the association of physical activity with the risk of all-cause mortality was analysed using Cox's proportional hazard model for a cohort of 14,204 healthy Korean men aged 40-59 years followed up for 4 years(Jan. 1993-Dec. 1996). Physical activity and other life style were surveyed by a postal questionnaire in December 1992. Total of 14,204 subjects were grouped into quartiles by physical activity. Using death certificate data, 123 deaths were identified. The second most active quartile had a lowest mortality .ate with relative risk of 0.44(95% C.I. : 0.23-0.84) compared with most sedentary quartile, showing a J-shape pattern of physical activity-mortality curve. By examining the difference in proportion of cause of the death between most active quartile and the other quartiles, there was no significant difference of proportional mortality from cardiovascular deaths, cerebrovascular deaths or deaths from trauma. The covariates were stratified into two group between which the trend of RR was compared to test the effect modification. There was no remarkable effect modification by alcohol intake, smoking, body mass index, calorie consumption, percent fat consumption. In conclusion, moderate activity was found to have more protective effect on all-cause mortality than vigorous activity and that the J-shape pattern of physical activity-mortality curve was not due to the difference of mortality pattern or effect modification by alcohol intake, smoking, body mass index, calorie consumption and percent fat consumption.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.14
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• pp.5685-5689
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• 2014

Background: It is well known that smoking is a preventable factor for all-cause mortality; however, it is still questionable how many years after smoking cessation that people will have reduced risk for mortality, in particular in those with a high interest in their own health. We aimed to examine the association between time since quitting smoking and total mortality among past-smokers relative to current smokers. Materials and Methods: We enrolled 36,446 health examinees that voluntarily taken with diverse health check-up packages of high cost burden in 1995-2003 and followed them till death by 2004. The history of cigarette smoking consumption was collected using a self-administrative questionnaire at the first visit time. Mortality risk by smoking cessation years was analyzed using Cox's proportional hazard model. Results: Compared to non-smokers, male smokers over 15 pack-years had higher risk for total mortality (HR=1.60, 95%CI 1.23-2.14). The mortality risk in female smokers with same pack-years was more pronounced than that in male smokers (HR=2.83, 95%CI 1.17-7.04) despite a small number of cases. Compared to current smokers, a decrease of total mortality was observed among those who ceased smoking, and inverse dose-response was found with years after cessation: RR 0.98 (95%CI, 0.64-1.41) (<2 yrs), 0.60 (95%CI, 0.43-0.83) (3-9 yrs), and 0.58 (95%CI, 0.43-0.79) (${\geq}10$ yrs). Conclusions: A reduced risk of total mortality was observed after 3 years of smoking cessation. Our findings suggest that at least 3 years of smoking cessation may contribute to reduce premature mortality among Asian men.

• Korea journal of population studies
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• v.26 no.2
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• pp.33-62
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• 2003

The purpose of this study is to analyze the relative importance of three factor -socioeconomic development, public health development, egalitarian nature of socioeconomic development- affecting mortality declines. Infant mortality rate and life expectancy at birth are used as the mortality index, that is the dependent variables, while GNP is used as the indicator of socioeconomic development, primary school enrollment ratio of female as the indicator of egalitarian nature of socioeconomic development, population per hospital bed as the indicator of public health. The data of these variables are collected two time-periods -before 1970 and during 1970-1980- over 50 countries. The explanatory data analysis is used as the statistical technique. We can find whether the relationship between dependent variable and independent variables are linear or nonlinear, and which case is the influential case in our model. The main results of this study are followings. First, the association between infant mortality rates and four indices are not linear. The most important factor explaining the variation of infant mortality is GNP, while primary enrollment of female is the second and GINI is the third important factor. However, population per hospital bed does not have a significant effect on the infant mortality rates in this study. Second, life expectancy at birth is log-linearly related to GNP. Unlike infant mortality rates, the most important factor explaining the variation of life expectance at birth is women's education and the next important factor GNP, and then the third one GINI. But, still population per hospital bed is not significantly related to the variation of life expectance in this study.

• Journal of the Korean Statistical Society
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• v.12 no.1
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• pp.24-35
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• 1983

In fitting models to data, selection of the most desirable estimation method and determination of the adequacy of fitted model are the central issues. This paper compares the maximum likelihood estimators and the minimum logit chi-square estimators, both being best asymptotically normal, when logit models are fitted to infant mortality data. Chi-square goodness-of-fit test and likelihood ratio one are also compared. The analysis infant mortality data shows that the outlying observations do not necessarily result in the same impact on goodness-of-fit measures.

• Korean Journal of Mathematics
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• v.13 no.2
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• pp.161-176
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• 2005

We consider a model of population dynamics whose mortality function is unbounded. We note that the regularity of the solution depends on the growth rate of the mortality near the maximum age. We propose Gauss-Legendre methods along the characteristics to approximate the solution when the solution is smooth enough. It is proven that the scheme is convergent at fourth-order rate in the maximum norm. We also propose discontinuous Galerkin finite element methods to approximate the solution which is not smooth enough. The stability of the method is discussed. Several numerical examples are presented.

• Journal of Preventive Medicine and Public Health
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• v.52 no.1
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• pp.21-29
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• 2019

Objectives: The Regional Cardiocerebrovascular Center (RCCVC) Project designated local teaching hospitals as RCCVCs, in order to improve patient outcomes of acute cardiocerebrovascular emergencies by founding a regional system that can adequately transfer and manage patients within 3 hours. We investigated the effects of RCCVC establishment on treatment volume and 30-day mortality. Methods: We constructed a panel dataset by extracting all acute myocardial infarction cases that occurred from 2007 to 2016 from the Health Insurance Review and Assessment Service claims data, a national and representative source. We then used a panel fixed-effect model to estimate the impacts of RCCVC establishment on patient outcomes. Results: We found that the number of cases of acute myocardial infarction that were treated increased chronologically, but when the time effect and other related covariates were controlled for, RCCVCs only significantly increased the number of treatment cases of female in large catchment areas. There was no statistically significant impact on 30-day mortality. Conclusions: The establishment of RCCVCs increased the number of treatment cases of female, without increasing the mortality rate. Therefore, the RCCVCs might have prevented potential untreated deaths by increasing the preparedness and capacity of hospitals to treat acute myocardial infarction patients.

• Journal of agricultural medicine and community health
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• v.32 no.1
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• pp.13-26
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• 2007

Objectives: This study was conducted to investigate the association between health risk factors and mortality in Juam cohort. Methods: The subjects were 1,447 males and 1,889 females who had been followed up for 68.5 months to 1 January 2001. Whether they were alive or not was confirmed by the mortality data of the National Statistical Office. A total of 289 persons among them died during the follow-up period. The Cox's proportional hazard regression model was used for survival analysis. Results: Age, type of medical insurance, self cognitive health level, habit of alcohol drinking, smoking, exercise and BMI level were included in Cox's proportional hazard model by gender. The hazard ratio of age was 1.07(95% CI: 1.05-1.10) in men, 1.09(95% CI: 1.06-1.12) in women. The hazard ratio of medical aid(lower socioeconomic state) was 1.43(95% CI 1.02-2.19) in women. The hazard ratios of current alcohol drinking and current smoking were respectively 1.69(95% CI: 1.01-2.98), 1.52(95% CI: 1.02-2.28) in women. The hazard ratio of underweight was 1.56(95% CI 1.08-2.47) in men. The hazard ratios of underweight, normoweight, overweight, and obesity were respectively 1.63(95% CI: 1.02-2.67), 1.0(referent), 0.62(95% CI: 0.32-1.63), 1.27(95% CI: 0.65-3.06), which supported the U-shaped relationship between body mass index and mortality among the men over 65. Conclusions: The health risk factors increasing mortality were age, underweight in male, age, lower socioeconomic state, current alcohol drinking, current smoking in female. To evaluate long-term association between health risk factors and mortality, further studies need to be carried out.

• The Korean Journal of Applied Statistics
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• v.31 no.4
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• pp.529-538
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• 2018

Tuberculosis causes high morbidity and mortality. However, Korea still has the highest tuberculosis (TB) incidence and mortality among OECD countries despite decreasing incidence and mortality due to the development of modern medicine. Korea has now implemented various policy projects to prevent and control tuberculosis. This study analyzes the effects of public-private mix (PPM) tuberculosis control program on treatment outcomes and identifies the factors that affecting the success of TB treatment. We analyzed 130,000 new tuberculosis patient cohort from 2012 to 2015 using data of tuberculosis patient reports managed by the Disease Control Headquarters. A cumulative incidence function (CIF) compared the cumulative treatment success rates for each factor. We compared the results of the analysis using two popular types of competition risk models (cause-specific Cox's proportional hazards model and subdistribution hazard model) that account for the main event of interest (treatment success) and competing events (death).