• Journal of applied mathematics & informatics
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• v.18 no.1_2
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• pp.311-320
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• 2005

Studies in the evolutionary biology of aging require good estimates of the age-dependent mortality rate coefficient (one of the Gompertz parameters). In this paper we introduce an alternative algorithm for estimating this parameter. And we discuss the sensitivity of the estimates to changes in the other model parameters.

• Journal of Korean Academy of Nursing
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• v.41 no.1
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• pp.92-100
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• 2011

Purpose: Mortality Provability Model (MPM) II is a model for predicting mortality probability of patients admitted to ICU. This study was done to test the validity of MPM II for critically ill neurological patients and to determine applicability of MPM II in predicting mortality of neurological ICU patients. Methods: Data were collected from medical records of 187 neurological patients over 18 yr of age who were admitted to the ICU of C University Hospital during the period from January 2008 to May 2009. Collected data were analyzed through $X^2$ test, t-test, Mann-Whiteny test, goodness of fit test, and ROC curve. Results: As to mortality according to patients' general and clinically related characteristics, mortality was statistically significantly different for ICU stay, hospital stay, APACHE III score, APACHE predicted death rate, GCS, endotracheal intubation, and central venous catheter. Results of Hosmer-Lemeshow goodness-of-fit test were MPM $II_0$ ($X^2$=0.02, p=.989), MPM $II_24$ ($X^2$=0.99 p=.805), MPM $II_48$ ($X^2$=0.91, p=.822), and MPM $II_72$ ($X^2$=1.57, p=.457), and results of the discrimination test using the ROC curve were MPM $II_0$, .726 (p<.001), MPM $II_24$, .764 (p<.001), MPM $II_48$, .762 (p<.001), and MPM $II_72$, .809 (p<.001). Conclusion: MPM II was found to be a valid mortality prediction model for neurological ICU patients.

• Communications for Statistical Applications and Methods
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• v.13 no.1
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• pp.177-190
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• 2006

The aim of this study is to propose a Bayesian model for fitting mortality rate of colon cancer. For the analysis of mortality rate of a disease, factors such as age classes of population and spatial characteristics of the location are very important. The model proposed in this study allows the age class to be a random effect in addition to its conventional role as the covariate of a linear regression, while the spatial factor being a random effect. The model is fitted using Metropolis-Hastings algorithm. Posterior expected predictive deviances, standardized residuals, and residual plots are used for comparison of models. It is found that the proposed model has smaller residuals and better predictive accuracy. Lastly, we described patterns in disease maps for colon cancer.

• Journal of Chest Surgery
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• v.54 no.2
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• pp.88-98
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• 2021

Background: This study aimed to develop a new risk prediction model for operative mortality in a Korean cohort undergoing heart valve surgery using the Korea Heart Valve Surgery Registry (KHVSR) database. Methods: We analyzed data from 4,742 patients registered in the KHVSR who underwent heart valve surgery at 9 institutions between 2017 and 2018. A risk prediction model was developed for operative mortality, defined as death within 30 days after surgery or during the same hospitalization. A statistical model was generated with a scoring system by multiple logistic regression analyses. The performance of the model was evaluated by its discrimination and calibration abilities. Results: Operative mortality occurred in 142 patients. The final regression models identified 13 risk variables. The risk prediction model showed good discrimination, with a c-statistic of 0.805 and calibration with Hosmer-Lemeshow goodness-of-fit p-value of 0.630. The risk scores ranged from -1 to 15, and were associated with an increase in predicted mortality. The predicted mortality across the risk scores ranged from 0.3% to 80.6%. Conclusion: This risk prediction model using a scoring system specific to heart valve surgery was developed from the KHVSR database. The risk prediction model showed that operative mortality could be predicted well in a Korean cohort.

• Journal of Preventive Medicine and Public Health
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• v.39 no.1
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• pp.13-20
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• 2006

Objectives: To propose a risk-adjustment model with using insurance claims data and to analyze whether or not the outcomes of non-emergent and isolated coronary artery bypass graft surgery (CABG) differed between the low- and high-volume hospitals for the patients who are at different levels of surgical risk. Methods: This is a cross-sectional study that used the 2002 data of the national health insurance claims. The study data set included the patient level data as well as all the ICD-10 diagnosis and procedure codes that were recorded in the claims. The patient's biological, admission and comorbidity information were used in the risk-adjustment model. The risk factors were adjusted with the logistic regression model. The subjects were classified into five groups based on the predicted surgical risk: minimal (<0.5%), low (0.5% to 2%), moderate (2% to 5%), high (5% to 20%), and severe (=20%). The differences between the low- and high-volume hospitals were assessed in each of the five risk groups. Results: The final risk-adjustment model consisted of ten risk factors and these factors were found to have statistically significant effects on patient mortality. The C-statistic (0.83) and Hosmer-Lemeshow test ($x^2=6.92$, p=0.55) showed that the model's performance was good. A total of 30 low-volume hospitals (971 patients) and 4 high-volume hospitals (1,087 patients) were identified. Significant differences for the in-hospital mortality were found between the low- and high-volume hospitals for the high (21.6% vs. 7.2%, p=0.00) and severe (44.4% vs. 11.8%, p=0.00) risk patient groups. Conclusions: Good model performance showed that insurance claims data can be used for comparing hospital mortality after adjusting for the patients' risk. Negative correlation was existed between surgery volume and in-hospital mortality. However, only patients in high and severe risk groups had such a relationship.

• Journal of Korean Society of Forest Science
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• v.100 no.1
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• pp.1-7
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• 2011

The objective of this study was to improve the performance of the existing individual-tree/distantindependent stand growth model in predicting the growth of Pinus koraiensis forest stands. The parameters of diameter growth and mortality prediction models were estimated using periodic annual increment (PAI) of permanent plots and the performance of the models were compared with that of the existing ones using mean anuual increment (MAI). The diameter growth model includes crown ratio, potential diameter growth and modifier to compute for competitions of trees of a stand. In deriving the mortality prediction model, the parameters were estimated based on PAI which was also estimated as the function of MAI due to the lacking of permanent plot data. The results of this study showed that the newly-estimated functions based on PAI provide more realistic patterns in diameter growth of individual trees. The new approach using PAI in mortality model seems to overcome the over-estimate problem by the MAI-based model in estimating mortality of stand trees.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.9
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• pp.4115-4121
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• 2015

Background: Colorectal cancer is the second most common cause of cancer death with half a million deaths per year. Incidence and mortality rates have demonstrated notable changes in Asian and African countries during the last few decades. In this study, we first aimed to determine the trend of colorectal cancer mortality rate in each Institute for Health Metrics and Evaluation (IHME) region, and then re-classify them to find more homogenous classes. Materials and Methods: Our study population consisted of 52 countries of Asia and North Africa in six IHME pre-defined regions for both genders and age-standardized groups from 1990 to 2010.We first applied simple growth models for pre-defined IHME regions to estimate the intercepts and slopes of mortality rate trends. Then, we clustered the 52 described countries using the latent growth mixture modeling approach for classifying them based on their colorectal mortality rates over time. Results: Statistical analysis revealed that males and people in high income Asia pacific and East Asia countries were at greater risk of death from colon and rectum cancer. In addition, South Asia region had the lowest rates of mortality due to this cancer. Simple growth modeling showed that majority of IHME regions had decreasing trend in mortality rate of colorectal cancer. However, re-classification these countries based on their mortality trend using the latent growth mixture model resulted in more homogeneous classes according to colorectal mortality trend. Conclusions: In general, our statistical analyses showed that most Asian and North African countries had upward trend in their colorectal cancer mortality. We therefore urge the health policy makers in these countries to evaluate the causes of growing mortality and study the interventional programs of successful countries in managing the consequences of this cancer.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.22
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• pp.10027-10031
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• 2014

Background: We investigated the risk of cancer mortality according to obesity status and metabolic health status using sampled cohort data from the National Health Insurance system. Materials and Methods: Data on body mass index and fasting blood glucose in the sampled cohort database (n=363,881) were used to estimate risk of cancer mortality. Data were analyzed using a Cox proportional hazard model (Model 1 was adjusted for age, sex, systolic blood pressure, diastolic blood pressure, total cholesterol level and urinary protein; Model 2 was adjusted for Model 1 plus smoking status, alcohol intake and physical activity). Results: According to the obesity status, the mean hazard ratios were 0.82 [95% confidence interval (CI), 0.75-0.89] and 0.79 (95% CI, 0.72-0.85) for the overweight and obese groups, respectively, compared with the normal weight group. According to the metabolic health status, the mean hazard ratio was 1.26 (95% CI, 1.14-1.40) for the metabolically unhealthy group compared with the metabolically healthy group. The interaction between obesity status and metabolic health status on the risk of cancer mortality was not statistically significant (p=0.31). Conclusions: We found that the risk of cancer mortality decreased according to the obesity status and increased according to the metabolic health status. Given the rise in the rate of metabolic dysfunction, the mortality from cancer is also likely to rise. Treatment strategies targeting metabolic dysfunction may lead to reductions in the risk of death from cancer.

• Communications for Statistical Applications and Methods
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• v.29 no.6
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• pp.679-694
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• 2022

Mortality risk is a significant threat to individual life, and quantifying the risk is necessary for making a national population plan and is a traditionally fundamental task in the insurance and annuity businesses. Like other advanced countries, the sustainability of life pensions and the management of longevity risks are becoming important in Asian countries entering the era of aging society. In this study, mortality and pension value sustainability trends are compared and analyzed based on national population and mortality data, focusing on four Asian countries from 1990 to 2017. The result of analyzing the robustness and accuracy of generalized linear/nonlinear models reveals that the Cairns-Blake-Dowd model, the nonparametric Renshaw-Haberman model, and the Plat model show low stability. The Currie, CBD M5, M7, and M8 models have high stability against data periods. The M7 and M8 models demonstrate high accuracy. The longevity risk is found to be high in the order of Taiwan, Hong Kong, Korea, and Japan, which is in general inversely related to the population size.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.8
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• pp.3893-3898
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• 2012

In previous studies we predicted future trends in cancer incidence for each prefecture in order to plan cancer control. Those predictions, however, did not take into account the characteristics of each prefecture. We therefore used the results of age-period-cohort analysis of incidence and mortality data of Osaka, and estimated the incidence and mortality of cancers at all sites and selected sites. The results reflect the characteristics of Osaka, which has and is expected to have large number of patients with liver cancer. We believe our results to be useful for planning and evaluating cancer control activities in Osaka. It would be worthwhile to base the estimation of cancer incidence and mortality in each prefecture on each population-based cancer registry.