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Background Research on the influence of heart failure on mortality after Alzheimer's disease diagnosis is limited. Objective To evaluate the association between comorbid heart failure and mortality following Alzheimer's disease diagnosis, particularly considering sex differences. Methods We analyzed administrative claims data from Japan, involving 32,363 individuals (11,064 men and 21,299 women) aged 75 or older newly diagnosed with Alzheimer's disease, with 7% having comorbid heart failure. Cox proportional hazard models and population attributable fractions (PAFs) were used to evaluate the association between comorbid heart failure and mortality within one year following Alzheimer's disease diagnosis. Results Individuals with Alzheimer's disease and heart failure had a multivariate-adjusted hazard ratio of 1.51 (95% confidence interval [CI], 1.32–1.73) for mortality during the one-year follow-up period compared to those with Alzheimer's disease and without heart failure. Subgroup analysis by sex revealed a higher mortality hazard ratio in women of 1.63 (95% CI, 1.36–1.95) than that in men of 1.39 (95% CI, 1.13–1.71). Further age and sex subgroup analysis indicated that women across all age brackets—75–79, 80–84, and ≥ 85 years—had higher mortality hazard ratios. The PAF for heart failure increased with age in both sexes, with women having higher PAFs than men, and the sex difference in PAF being most pronounced in the 75–79 age category (men: 1.4%, women: 4.0%). Conclusions Hazard ratios and PAFs for mortality associated with comorbid heart failure in newly diagnosed Alzheimer's disease are higher in women than in men, which persists across all age subgroups.

Objectives We evaluated the number of hospitalizations among public assistance recipients for each major classification according to the International Classification of Diseases 10th Revision (ICD-10), adjusting for sex and age differences in the general Japanese population. This study aimed to provide a comprehensive assessment of hospitalization patterns among public assistance recipients by disease category.Methods We used indirect methods to adjust for sex and age, with public assistance recipients and the entire Japanese population as the observation and reference groups, respectively. We calculated the standardized hospitalization ratios (SHRs) for each major classification based on the ICD-10. We only used publicly available government statistics, including data from the 2020 Patient Survey, for hospitalization rates according to sex, age, and major classification. Additionally, we used data from the 2020 National Survey on Public Assistance Recipients conducted for the number of public assistance recipients by sex and age groups and data from the 2020 Survey on the Actual Status of Medical Assistance conducted for the number of hospitalizations by major classification.Results After adjusting for age, the overall SHR was 1.49. The major classifications with the high SHRs for men and women were "V. Mental and behavioural disorders" (SHR for men; 4.06, women; 3.45) and "IV. Endocrine, nutritional, and metabolic diseases" (SHR for men; 2.40, women; 1.47). Conversely, the major classifications with low SHRs were "XVI. Certain conditions originating in the perinatal period" (SHR; 0.43) and "VII. Diseases of the eye and adnexa" (SHR; 0.44) for men. For women, these were "XV. Pregnancy, childbirth, and the puerperium" (SHR; 0.17) and "VII. Diseases of the eye and adnexa" (SHR; 0.27).Conclusion After adjusting for age, hospitalization status among public assistance recipients was higher overall than in the general Japanese population. However, if divided based on major classifications, higher and lower rates were observed compared with the general population. In assessing the status of medical assistance for public assistance recipients, research should be conducted by disease classification, considering the significant differences in age composition between public assistance recipients and the general Japanese population.

BACKGROUND: Estimating the prevalence of schizophrenia in the general population remains a challenge worldwide, as well as in Japan. Few studies have estimated schizophrenia prevalence in the Japanese population and have often relied on reports from hospitals and self-reported physician diagnoses or typical schizophrenia symptoms. These approaches are likely to underestimate the true prevalence owing to stigma, poor insight, or lack of access to health care among respondents. To address these issues, we previously developed an artificial neural network (ANN)-based schizophrenia classification model (SZ classifier) using data from a large-scale Japanese web-based survey to enhance the comprehensiveness of schizophrenia case identification in the general population. In addition, we also plan to introduce a population-based survey to collect general information and sample participants matching the population's demographic structure, thereby achieving a precise estimate of the prevalence of schizophrenia in Japan. OBJECTIVE: This study aimed to estimate the prevalence of schizophrenia by applying the SZ classifier to random samples from the Japanese population. METHODS: We randomly selected a sample of 750 participants where the age, sex, and regional distributions were similar to Japan's demographic structure from a large-scale Japanese web-based survey. Demographic data, health-related backgrounds, physical comorbidities, psychiatric comorbidities, and social comorbidities were collected and applied to the SZ classifier, as this information was also used for developing the SZ classifier. The crude prevalence of schizophrenia was calculated through the proportion of positive cases detected by the SZ classifier. The crude estimate was further refined by excluding false-positive cases and including false-negative cases to determine the actual prevalence of schizophrenia. RESULTS: Out of 750 participants, 62 were classified as schizophrenia cases by the SZ classifier, resulting in a crude prevalence of schizophrenia in the general population of Japan of 8.3% (95% CI 6.6%-10.1%). Among these 62 cases, 53 were presumed to be false positives, and 3 were presumed to be false negatives. After adjustment, the actual prevalence of schizophrenia in the general population was estimated to be 1.6% (95% CI 0.7%-2.5%). CONCLUSIONS: This estimated prevalence was slightly higher than that reported in previous studies, possibly due to a more comprehensive disease classification methodology or, conversely, model limitations. This study demonstrates the capability of an ANN-based model to improve the estimation of schizophrenia prevalence in the general population, offering a novel approach to public health analysis.

Aim Japan faces a public health challenge of dementia, further complicated by the increasing complications from diabetes within its rapidly aging population. This study assesses the impact of diabetes on mortality and hospitalization among individuals aged ≥75 years with new dementia diagnoses. Methods We analyzed administrative claims data in Japan from 73 324 individuals aged ≥75 years with dementia, of whom 17% had comorbid diabetes. Dementia and diabetes were identified from the International Classification of Diseases, Tenth Revision codes. We used Kaplan–Meier survival analysis, Cox proportional hazards analysis, and population attributable fractions (PAFs) to evaluate the impact on mortality and hospitalization after dementia diagnosis. Results One‐year mortality and 1‐year hospitalization probabilities in individuals with dementia and diabetes (10.3% and 31.7%, respectively) were higher than those without diabetes (8.3% and 25.4%, respectively). The adjusted hazard ratios for individuals with diabetes, as compared to those without, were 1.126 (95% confidence interval [CI], 1.040–1.220) for mortality and 1.191 (95% CI, 1.140–1.245) for hospitalization. The PAFs from the comorbidity of dementia and diabetes were 2.2% for mortality and 3.1% for hospitalization. Subgroup analysis showed that the PAFs were highest in men aged 75–79 years and women aged 80–84 years for mortality and in individuals aged 75–79 for hospitalization. Conclusion During the early postdiagnosis period, comorbid diabetes increases mortality and hospitalization risks in older adults with dementia. The variation in disease burden across age groups underscores the need for age‐specific health care strategies to manage comorbid diabetes in individuals with dementia. Geriatr Gerontol Int 2024; 24: 773–781.

Aim Several studies have shown that dairy consumption in old age is effective in preventing frailty. However, there is a lack of evidence regarding the association between milk consumption during middle age and the development of frailty in old age. Therefore, we carried out an investigation to explore the association between milk consumption during middle age and development of frailty examined after over 15 years of follow up in a long‐term cohort study in Japan. Methods We studied 265 participants aged 60–79 years (212 men and 53 women) in 2018, who participated in both the baseline survey in 2002 and the frailty assessment in 2018. The amount of milk consumption (g/day) at baseline was age‐ and energy‐adjusted, and classified into three categories (no, low and high consumption: 0 g/day, ≤135.86 g/day, >135.86 g/day in men and 0 g/day, ≤126.44 g/day, >126.44 g/day in women). Odds ratios (OR) and 95% confidence intervals (CI) for prefrailty/frailty after adjusting for lifestyles at baseline, stratified by sex, were estimated using logistic regression analysis. Results The prevalence of prefrailty/frailty in 2018 was 37.7% and 28.3% in men and women, respectively. Milk consumption categories were inversely associated with the prevalence of prefrailty/frailty in men (OR 0.34, 95% CI 0.14–0.84 in low consumption; OR 0.31, 95% CI 0.10–0.95 in high consumption; P < 0.05), but not in women (OR 0.53, 95% CI 0.11–2.65; P = 0.44). Conclusions In this study, milk intake in middle‐aged men was inversely associated with the prevalence of prefrailty/frailty later in life. Geriatr Gerontol Int 2024; 24: 700–705.

Background and Objective: Excellent generalizability is the precondition for the widespread practical implementation of machine learning models. In our previous study, we developed the schizophrenia classification model (SZ classifier) to identify potential schizophrenia patients in the Japanese population. The SZ classifier has exhibited impressive performance during internal validation. However, ensuring the robustness and generalizability of the SZ classifier requires external validation across independent sample sets. In this study, we aimed to present an external validation of the SZ classifier using outpatient data. Methods: The SZ classifier was trained by using online survey data, which incorporate demographic, health-related, and social comorbidity features. External validation was conducted using an outpatient sample set which is independent from the sample set during the model development phase. The model performance was assessed based on the sensitivity and misclassification rates for schizophrenia, bipolar disorder, and major depression patients. Results: The SZ classifier demonstrated a sensitivity of 0.75 when applied to schizophrenia patients. The misclassification rates were 59% and 55% for bipolar disorder and major depression patients, respectively. Conclusions: The SZ classifier currently encounters challenges in accurately determining the presence or absence of schizophrenia at the individual level. Prior to widespread practical implementation, enhancements are necessary to bolster the accuracy and diminish the misclassification rates. Despite the current limitations of the model, such as poor specificity for certain psychiatric disorders, there is potential for improvement if including multiple types of psychiatric disorders during model development.

Abstract Objectives This study aimed to develop an approach to enhance the model precision by artificial images. Materials and Methods Given an epidemiological study designed to predict 1 response using f features with M samples, each feature was converted into a pixel with certain value. Permutated these pixels into F orders, resulting in F distinct artificial image sample sets. Based on the experience of image recognition techniques, appropriate training images results in higher precision model. In the preliminary experiment, a binary response was predicted by 76 features, the sample set included 223 patients and 1776 healthy controls. Results We randomly selected 10 000 artificial sample sets to train the model. Models’ performance (area under the receiver operating characteristic curve values) depicted a bell-shaped distribution. Conclusion The model construction strategy developed in the research has potential to capture feature order related information and enhance model predictability.

Background In Japan, challenges were reported in accurately estimating the prevalence of schizophrenia among the general population. Retrieving previous studies, we investigated that patients with schizophrenia were more likely to experience poor subjective well-being and various physical, psychiatric, and social comorbidities. These factors might have great potential for precisely classifying schizophrenia cases in order to estimate the prevalence. Machine learning has shown a positive impact on many fields, including epidemiology, due to its high-precision modeling capability. It has been applied in research on mental disorders. However, few studies have applied machine learning technology to the precise classification of schizophrenia cases by variables of demographic and health-related backgrounds, especially using large-scale web-based surveys. Objective The aim of the study is to construct an artificial neural network (ANN) model that can accurately classify schizophrenia cases from large-scale Japanese web-based survey data and to verify the generalizability of the model. Methods Data were obtained from a large Japanese internet research pooled panel (Rakuten Insight, Inc) in 2021. A total of 223 individuals, aged 20-75 years, having schizophrenia, and 1776 healthy controls were included. Answers to the questions in a web-based survey were formatted as 1 response variable (self-report diagnosed with schizophrenia) and multiple feature variables (demographic, health-related backgrounds, physical comorbidities, psychiatric comorbidities, and social comorbidities). An ANN was applied to construct a model for classifying schizophrenia cases. Logistic regression (LR) was used as a reference. The performances of the models and algorithms were then compared. Results The model trained by the ANN performed better than LR in terms of area under the receiver operating characteristic curve (0.86 vs 0.78), accuracy (0.93 vs 0.91), and specificity (0.96 vs 0.94), while the model trained by LR showed better sensitivity (0.63 vs 0.56). Comparing the performances of the ANN and LR, the ANN was better in terms of area under the receiver operating characteristic curve (bootstrapping: 0.847 vs 0.773 and cross-validation: 0.81 vs 0.72), while LR performed better in terms of accuracy (0.894 vs 0.856). Sleep medication use, age, household income, and employment type were the top 4 variables in terms of importance. Conclusions This study constructed an ANN model to classify schizophrenia cases using web-based survey data. Our model showed a high internal validity. The findings are expected to provide evidence for estimating the prevalence of schizophrenia in the Japanese population and informing future epidemiological studies.

The physical, psychiatric, and social comorbidities interfere with the everyday activities of community-dwelling individuals with schizophrenia and increase the risk of their readmission. However, these comorbidities have not been investigated comprehensively in Japan. We conducted a self-reported internet survey in February 2022 to identify individuals aged 20–75 years with and without schizophrenia using a prevalence case-control study. The survey compared physical comorbidities such as being overweight, hypertension, and diabetes; psychiatric comorbidities such as depressive symptoms and sleep disturbances; social comorbidities such as employment status, household income, and social support between participants with and without schizophrenia. A total of 223 participants with schizophrenia and 1776 participants without schizophrenia were identified. Participants with schizophrenia were more likely to be overweight and had a higher prevalence of hypertension, diabetes, and dyslipidemia than participants without schizophrenia. Additionally, depressive symptoms, unemployment, and non-regular employment were more prevalent in participants with schizophrenia than those without schizophrenia. These results highlight the necessity of comprehensive support and interventions addressing physical, psychiatric, and social comorbidities in individuals with schizophrenia in the community. In conclusion, effective interventions for managing comorbidities in individuals with schizophrenia are necessary to enable them to continue to live in the community.

Objectives: To examin whether public trust was associated with the utilization of COVID-19 Contact Confirming Application (COCOA) in those who self-reported a history of COVID-19. Study design: Cross-sectional study. Methods: Data were obtained from the Japan Society and New Tobacco Internet Survey, a nationwide online survey conducted from February to March 2021, which also assessed items related to COVID-19 and public trust. We included 453 participants with a history of COVID-19. Participants' reports of their general trust in the national government and the related policies, attitudes toward COVID-19 vaccination, and the adherence to the preventive measures against SARS-CoV-2 spread were compared between COCOA users and non-users controlling for age, sex, and socioeconomic statuses by analysis of covariance. Mediation analysis was conducted to examine whether public trust mediates the associations of certain participants' characteristics with COCOA utilization. Results: Seventy-six percent (344/453) reported the COCOA utilization. Compared to non-users, the users were younger, more likely to be men and had a tendency to have higher education. They were more willing to get COVID-19 vaccination, adherent to public health measures against the spread of the SARS-Cov-2, and more likely to express trust in government in general and policies related to COVID-19 independent of age, sex, and the socioeconomic status. Trust in government did not mediate the associations of age and education with COCOA utilization. Conclusions: The utilization of digital contact tracing technology for the health of public during pandemic was related to the degree of trust in the government in Japan.

Studies have reported that short-term blood pressure (BP) variability (BPV) is associated with type 2 diabetes mellitus (T2DM) incidence, but the association with long-term BPV remains unclear. The present study investigated the associations of long-term BPV as well as the time trend of BP changes over time with the incidence of T2DM. This study followed a cohort of 3017 Japanese individuals (2446 male, 571 female) aged 36-65 years from 2007 through March 31, 2019. The root-mean-square error (RMSE) and the slope of systolic BP (SBP) change regressed on year were calculated individually using SBP values obtained from 2003 to baseline (2007). A multivariable Cox proportional hazard model was applied to estimate hazard ratios (HRs) and corresponding 95% confidence intervals (CIs) for tertiles of SBP RMSE and continuous SBP slopes adjusted for age, sex, smoking status, regular exercise, sodium intake, family history of diabetes, sleep disorder, body mass index (BMI), SBP, and fasting blood glucose (FBG) at baseline, and BMI slope from 2003 to 2007. The highest RMSE tertile compared to the lowest was associated with a significantly higher incidence of T2DM after adjusting for covariates (HR: 1.79, 95% CI: 1.15, 2.78). The slope was also significantly associated with T2DM incidence until baseline SBP and FBG were adjusted (HR: 1.03, 95% CI: 0.99, 1.07). In conclusion, long-term SBP variability was significantly associated with an increased incidence of T2DM independent of baseline age, sex, BMI, SBP, FBG, lifestyle factors and BMI slope from 2003 until baseline.

The aim of the present study was to derive dietary patterns to explain variation in a set of nutrient intakes or in the measurements of waist circumference (WC) and fasting blood glucose (FBG) using reduced rank regression (RRR) and to prospectively investigate these patterns in relation to the risk of developing metabolic syndrome (MetS) and its components during the follow-up. The study participants were comprised of 2944 government employees aged 30–59 years without MetS. RRR was applied with 38 food groups as predictors and with two sets of response variables. The first set included intake of putatively beneficial nutrients, and the first factor retained was named the Healthy Dietary Pattern (HDP). The second one included baseline WC and FBG, and the first factor was named the Unhealthy Dietary Pattern (UHDP). Multivariable Cox proportional hazard model was used to estimate hazard ratio and 95% confidence intervals with adjustments for age, sex, total energy consumption and other potential confounders. During the 5-year median follow-up, we ascertained 374 cases of MetS. The HDP score was inversely associated with the incidence of MetS (p-trend = 0.009) and hypertension (p-trend = 0.002) and marginally significantly associated with elevated triglyceride and decreased high-density lipoprotein cholesterol (p-trend = 0.08). The UHDP score was linearly positively associated with the incidence of MetS and all its components (all p-trend < 0.05). Both the HDP and UHDP predicted the development of MetS and its components.