Background: Workers’ health condition is an important issue. It affects not only the well-being of workers but also the firms and society as a whole through medical costs and productivity losses due to absenteeism an...Background: Workers’ health condition is an important issue. It affects not only the well-being of workers but also the firms and society as a whole through medical costs and productivity losses due to absenteeism and presenteeism. Data and Methods: Data were obtained from 1136 employees at an operational site of a large corporation. The dataset contained both medical checkups and working record information. Health factors affecting long-term absence (over three days in three months) were analyzed. Logistic regression models and the procedure for selecting proper covariates based on likelihood test statistics and the Akaike information criterion were used. Results: Among health factors, high-density lipoprotein cholesterol (HDL-C) and blood sugar levels were important in the selected model. For HDL-C, the odds ratio (OR) based on one standard deviation difference was 0.75 with a 95% confidence interval (CI) of 0.59 - 0.95. For blood sugar, the OR was 1.20 with a 95% CI of 1.01 - 1.42. Improving HDL-C and blood sugar levels would reduce long-term absence by 25% and 20%, respectively. Conclusion: Controlling HDL-C and blood sugar levels is important to reduce long-term absenteeism. These factors can be improved by modifying eating habits. Since the operational site has its own company cafeterias, which most employees use, nutritional intervention is relatively easy with little or no cost. It may be worthwhile to implement nutritional intervention, especially for patients with low HDL-C or high blood sugar levels. Limitations: The results of this study were based on one operational site of a corporation. The employees were mainly operators working inside the building. The results may be different from other types of jobs and working conditions, such as fieldwork. Analyses of different types of jobs and working conditions are necessary.展开更多
Background: Cerebrovascular disease is a worldwide health problem. Stroke, a type of cerebrovascular disease, caused by sudden loss of blood flow to parts of the brain, is the world’s second-leading cause of death an...Background: Cerebrovascular disease is a worldwide health problem. Stroke, a type of cerebrovascular disease, caused by sudden loss of blood flow to parts of the brain, is the world’s second-leading cause of death and third-leading cause of disability. It is critical to analyze risk factors to prevent cerebrovascular disease. Data and Methods: The risk factors for cerebrovascular disease were analyzed using data from 2,678,054 medical checkups obtained from the JMDC Claims Database. Logit models were used, and the odds ratio (OR) and confidence interval (CI) were calculated. The sample period was from January 2005 to September 2019. Results: Age and heart disease history were very important nonmodifiable factors. The OR comparing persons aged 70 to those aged 50 was 2.05 with a 95% CI of 1.92 - 2.05. A heart disease history was also an especially important factor (OR 2.29, 95% CI 2.18 - 2.41). Among the modifiable factors, triglyceride level and recent large weight change were very important factors, changing the risk of cerebrovascular disease by about 30%. Other significant modifiable factors were diastolic blood pressure, urine protein, having breakfast, walking ability and smoking;each of these changed the risk of cerebrovascular disease by about 10%. Taking medications to control hypertension, hyperglycemia and hypercholesterolemia respectively increased the risk of cerebrovascular disease. In particular, taking antihypertensive medications nearly doubled the risk (OR 1.93, 95% CI 1.86 - 2.00). Conclusion: It is very important for individuals with risk factors to improve their physical conditions to prevent cerebrovascular disease. Taking medications to control blood pressure, glucose level, and cholesterol might introduce risks for cerebrovascular disease. Since all medications have side effects, it is necessary to carefully manage the use of these medications to minimize the negative side effects. Limitations: The dataset was observatory. There were no experimental interventions. Further, the dataset contains only information of Japanese individuals, and the results might differ in other countries. As the data comprised claims from employment-based health insurance, the dataset includes no subjects aged 76 or over and relatively few aged 70 - 75. We need to analyze data of other countries and elderly people.展开更多
Background: Diabetes is a noncommunicable disease caused by high levels of blood glucose, and it is currently one of the most important public health problems in the world. It is important to know the prevalence of di...Background: Diabetes is a noncommunicable disease caused by high levels of blood glucose, and it is currently one of the most important public health problems in the world. It is important to know the prevalence of diabetes, the factors affecting blood glucose levels, and the percentage of people with diabetes taking medications. Data and Methods Data and Methods: We analyzed the distribution of blood glucose levels and prevalence of diabetes using 10,917,173 observations obtained from the JMDC Claims Database in Japan. The factors that may affect blood glucose levels were analyzed by a regression model using 5,472,205 observations. Treatment with diabetes medications was analyzed with 9,932,854 and 5,466,361 observations using a method to approximate the inverse of probability by a continuous piecewise linear function. Results: The prevalence of diabetes in 2019 was estimated to be 9.63% in males and 5.33% in females ages 20 - 79;10.78% and 7.04% for ages 20 - 89;and 10.93% and 7.65% for ages 20 - 99, respectively. In addition to age and gender, the important variables affecting blood glucose levels were <em>BMI</em>, <em>SBP</em>, <em>Triglyceride</em>, <em>ALT</em>, <em>AST</em> and <em>GGP</em>. The percentage taking medications increased up to a blood glucose level of around 175 mg/dL, but declined over that. Conclusion: The prevalence of diabetes in Japan was estimated using a very large dataset, and considering age, gender, and time trends. Some variables may be effective for controlling blood glucose levels. Nearly half of those at a serious stage of diabetes took no medications. Proper medical care for these individuals is necessary to prevent worsening diabetes and serious complications. Limitations: The dataset was observatory, and did not include those age 80 or over. Revising medical care systems to include those outside of hospitals is necessary;however, practical approaches have not yet been developed.展开更多
Machine learning methods, one type of methods used in artificial intelligence, are now widely used to analyze two-dimensional (2D) images in various fields. In these analyses, estimating the boundary between two regio...Machine learning methods, one type of methods used in artificial intelligence, are now widely used to analyze two-dimensional (2D) images in various fields. In these analyses, estimating the boundary between two regions is basic but important. If the model contains stochastic factors such as random observation errors, determining the boundary is not easy. When the probability distributions are mis-specified, ordinal methods such as probit and logit maximum likelihood estimators (MLE) have large biases. The grouping estimator is a semiparametric estimator based on the grouping of data that does not require specific probability distributions. For 2D images, the grouping is simple. Monte Carlo experiments show that the grouping estimator clearly improves the probit MLE in many cases. The grouping estimator essentially makes the resolution density lower, and the present findings imply that methods using low-resolution image analyses might not be the proper ones in high-density image analyses. It is necessary to combine and compare the results of high- and low-resolution image analyses. The grouping estimator may provide theoretical justifications for such analysis.展开更多
The original version of this article (Nawata, K. (2022) “An analysis of risk factors affecting cerebrovascular disease,” Health, 14, 866-882. DOI: https://doi.org/10.4236/health.2022.148061) was published as some re...The original version of this article (Nawata, K. (2022) “An analysis of risk factors affecting cerebrovascular disease,” Health, 14, 866-882. DOI: https://doi.org/10.4236/health.2022.148061) was published as some results data reported mistakenly. The author wishes to correct the errors.展开更多
文摘Background: Workers’ health condition is an important issue. It affects not only the well-being of workers but also the firms and society as a whole through medical costs and productivity losses due to absenteeism and presenteeism. Data and Methods: Data were obtained from 1136 employees at an operational site of a large corporation. The dataset contained both medical checkups and working record information. Health factors affecting long-term absence (over three days in three months) were analyzed. Logistic regression models and the procedure for selecting proper covariates based on likelihood test statistics and the Akaike information criterion were used. Results: Among health factors, high-density lipoprotein cholesterol (HDL-C) and blood sugar levels were important in the selected model. For HDL-C, the odds ratio (OR) based on one standard deviation difference was 0.75 with a 95% confidence interval (CI) of 0.59 - 0.95. For blood sugar, the OR was 1.20 with a 95% CI of 1.01 - 1.42. Improving HDL-C and blood sugar levels would reduce long-term absence by 25% and 20%, respectively. Conclusion: Controlling HDL-C and blood sugar levels is important to reduce long-term absenteeism. These factors can be improved by modifying eating habits. Since the operational site has its own company cafeterias, which most employees use, nutritional intervention is relatively easy with little or no cost. It may be worthwhile to implement nutritional intervention, especially for patients with low HDL-C or high blood sugar levels. Limitations: The results of this study were based on one operational site of a corporation. The employees were mainly operators working inside the building. The results may be different from other types of jobs and working conditions, such as fieldwork. Analyses of different types of jobs and working conditions are necessary.
文摘Background: Cerebrovascular disease is a worldwide health problem. Stroke, a type of cerebrovascular disease, caused by sudden loss of blood flow to parts of the brain, is the world’s second-leading cause of death and third-leading cause of disability. It is critical to analyze risk factors to prevent cerebrovascular disease. Data and Methods: The risk factors for cerebrovascular disease were analyzed using data from 2,678,054 medical checkups obtained from the JMDC Claims Database. Logit models were used, and the odds ratio (OR) and confidence interval (CI) were calculated. The sample period was from January 2005 to September 2019. Results: Age and heart disease history were very important nonmodifiable factors. The OR comparing persons aged 70 to those aged 50 was 2.05 with a 95% CI of 1.92 - 2.05. A heart disease history was also an especially important factor (OR 2.29, 95% CI 2.18 - 2.41). Among the modifiable factors, triglyceride level and recent large weight change were very important factors, changing the risk of cerebrovascular disease by about 30%. Other significant modifiable factors were diastolic blood pressure, urine protein, having breakfast, walking ability and smoking;each of these changed the risk of cerebrovascular disease by about 10%. Taking medications to control hypertension, hyperglycemia and hypercholesterolemia respectively increased the risk of cerebrovascular disease. In particular, taking antihypertensive medications nearly doubled the risk (OR 1.93, 95% CI 1.86 - 2.00). Conclusion: It is very important for individuals with risk factors to improve their physical conditions to prevent cerebrovascular disease. Taking medications to control blood pressure, glucose level, and cholesterol might introduce risks for cerebrovascular disease. Since all medications have side effects, it is necessary to carefully manage the use of these medications to minimize the negative side effects. Limitations: The dataset was observatory. There were no experimental interventions. Further, the dataset contains only information of Japanese individuals, and the results might differ in other countries. As the data comprised claims from employment-based health insurance, the dataset includes no subjects aged 76 or over and relatively few aged 70 - 75. We need to analyze data of other countries and elderly people.
文摘Background: Diabetes is a noncommunicable disease caused by high levels of blood glucose, and it is currently one of the most important public health problems in the world. It is important to know the prevalence of diabetes, the factors affecting blood glucose levels, and the percentage of people with diabetes taking medications. Data and Methods Data and Methods: We analyzed the distribution of blood glucose levels and prevalence of diabetes using 10,917,173 observations obtained from the JMDC Claims Database in Japan. The factors that may affect blood glucose levels were analyzed by a regression model using 5,472,205 observations. Treatment with diabetes medications was analyzed with 9,932,854 and 5,466,361 observations using a method to approximate the inverse of probability by a continuous piecewise linear function. Results: The prevalence of diabetes in 2019 was estimated to be 9.63% in males and 5.33% in females ages 20 - 79;10.78% and 7.04% for ages 20 - 89;and 10.93% and 7.65% for ages 20 - 99, respectively. In addition to age and gender, the important variables affecting blood glucose levels were <em>BMI</em>, <em>SBP</em>, <em>Triglyceride</em>, <em>ALT</em>, <em>AST</em> and <em>GGP</em>. The percentage taking medications increased up to a blood glucose level of around 175 mg/dL, but declined over that. Conclusion: The prevalence of diabetes in Japan was estimated using a very large dataset, and considering age, gender, and time trends. Some variables may be effective for controlling blood glucose levels. Nearly half of those at a serious stage of diabetes took no medications. Proper medical care for these individuals is necessary to prevent worsening diabetes and serious complications. Limitations: The dataset was observatory, and did not include those age 80 or over. Revising medical care systems to include those outside of hospitals is necessary;however, practical approaches have not yet been developed.
文摘Machine learning methods, one type of methods used in artificial intelligence, are now widely used to analyze two-dimensional (2D) images in various fields. In these analyses, estimating the boundary between two regions is basic but important. If the model contains stochastic factors such as random observation errors, determining the boundary is not easy. When the probability distributions are mis-specified, ordinal methods such as probit and logit maximum likelihood estimators (MLE) have large biases. The grouping estimator is a semiparametric estimator based on the grouping of data that does not require specific probability distributions. For 2D images, the grouping is simple. Monte Carlo experiments show that the grouping estimator clearly improves the probit MLE in many cases. The grouping estimator essentially makes the resolution density lower, and the present findings imply that methods using low-resolution image analyses might not be the proper ones in high-density image analyses. It is necessary to combine and compare the results of high- and low-resolution image analyses. The grouping estimator may provide theoretical justifications for such analysis.
文摘The original version of this article (Nawata, K. (2022) “An analysis of risk factors affecting cerebrovascular disease,” Health, 14, 866-882. DOI: https://doi.org/10.4236/health.2022.148061) was published as some results data reported mistakenly. The author wishes to correct the errors.
