Postal reminder with reduced burden of clinic information seeking for equitable participation in general health check-ups: A randomized controlled trial.

High participation rate and low inequality in participation are key to the program success of general health check-ups in Japan. This study examined the effectiveness of a postal reminder including nearest clinic information, compared to the standard postal reminder including details of all local clinics, on participation rate and income-based participation rate in general health checks. This was a single-blind, two-arm, prospective, randomized controlled study conducted at the Fukuoka Branch of Japan Health Insurance Association. Dependents (family members) of insured persons aged 40-69 years were randomly assigned (1:1) to the intervention group that received a tailored postal reminder intervention (showing information on the nearest clinic from each participant's address) or to the control group that received an original template postal reminder (containing just the URL of the website listing all available clinics). Allocation was concealed from participants and service providers of general health check-up. The primary outcome was participation in general health check-ups within 1 month of intervention. Between February 1 and February 10, 2017, 21,017 were randomly assigned to the intervention (n = 10,474) or control (n = 10,543) group. The participation rate in the intervention group was higher than control group (3.2% vs. 2.1%; OR: 1.55, 95% CI: 1.31-1.85, P < 0.001). The intervention effect was estimated to decrease as the income category increased (P for interaction = 0.037). Tailored postal reminders with information on the nearest clinic were able to improve the overall participation rate and reduce income-based inequality in participation for general health check-ups in Japan. Trial registration: UMIN-CTR, UMIN000042509, Registered 26 November 2020 - Retrospectively registered.

Dementia risks identified by vocal features via telephone conversations: A novel machine learning prediction model.

Due to difficulty in early diagnosis of Alzheimer's disease (AD) related to cost and differentiated capability, it is necessary to identify low-cost, accessible, and reliable tools for identifying AD risk in the preclinical stage. We hypothesized that cognitive ability, as expressed in the vocal features in daily conversation, is associated with AD progression. Thus, we have developed a novel machine learning prediction model to identify AD risk by using the rich voice data collected from daily conversations, and evaluated its predictive performance in comparison with a classification method based on the Japanese version of the Telephone Interview for Cognitive Status (TICS-J). We used 1,465 audio data files from 99 Healthy controls (HC) and 151 audio data files recorded from 24 AD patients derived from a dementia prevention program conducted by Hachioji City, Tokyo, between March and May 2020. After extracting vocal features from each audio file, we developed machine-learning models based on extreme gradient boosting (XGBoost), random forest (RF), and logistic regression (LR), using each audio file as one observation. We evaluated the predictive performance of the developed models by describing the receiver operating characteristic (ROC) curve, calculating the areas under the curve (AUCs), sensitivity, and specificity. Further, we conducted classifications by considering each participant as one observation, computing the average of their audio files' predictive value, and making comparisons with the predictive performance of the TICS-J based questionnaire. Of 1,616 audio files in total, 1,308 (81.0%) were randomly allocated to the training data and 308 (19.1%) to the validation data. For audio file-based prediction, the AUCs for XGboost, RF, and LR were 0.863 (95% confidence interval [CI]: 0.794-0.931), 0.882 (95% CI: 0.840-0.924), and 0.893 (95%CI: 0.832-0.954), respectively. For participant-based prediction, the AUC for XGboost, RF, LR, and TICS-J were 1.000 (95%CI: 1.000-1.000), 1.000 (95%CI: 1.000-1.000), 0.972 (95%CI: 0.918-1.000) and 0.917 (95%CI: 0.918-1.000), respectively. There was difference in predictive accuracy of XGBoost and TICS-J with almost approached significance (p = 0.065). Our novel prediction model using the vocal features of daily conversations demonstrated the potential to be useful for the AD risk assessment.

Our health, our planet: a cross-sectional analysis on the association between health consciousness and pro-environmental behavior among health professionals.

One possible predictive factor that affects both pro-environmental behavior and health behavior is health consciousness (a psychological state where an individual is aware of and involved in his/her health condition). We examined the relationship between health consciousness and two pro-environmental behaviors (recycling and green purchasing) within health professionals in a Japanese large hospital. Multivariate linear regression analysis revealed a significant association between health consciousness and recycling behavior, while there was no association between health consciousness and green purchasing behavior. We assume that health consciousness can certainly be a factor promoting pro-environmental behavior, but that it may have been insufficient to cause green purchasing, because of the organizational norm of recycling in the Japanese context. Given that there is previous evidence about the relationship between health consciousness and health behavior, health consciousness might be a predictive factor that encourages both health behavior and pro-environmental behavior simultaneously.

The role of ultrasound imaging in adult patients with testicular torsion: a systematic review and meta-analysis.

PURPOSE: Our aim was to determine the accuracy of ultrasound (US) examination-based testicular torsion diagnosis in adult patients with acute scrotal pain. METHODS: A comprehensive electronic search was performed using internet retrieval systems up to 5 August 2018 in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. The quality of eligible studies was assessed using Quality Assessment for Studies of Diagnostic Accuracy (QUADAS-2). The diagnostic value of ultrasound in patients with testicular torsion was evaluated using pooled estimates of sensitivity, specificity, likelihood ratio, and diagnostic odds ratio, as well as the summary receiver operating characteristics curve. RESULTS: Twenty-six studies with 2116 patients were included in the study. Overall diagnostic sensitivity was 0.86 [95% confidence interval (CI) 0.79-0.91] and specificity was 0.95 (95% CI: 0.92-0.97). Subgroup analysis of prospective studies showed pooled sensitivity of ultrasound for testicular torsion was 0.94 (95% CI 0.83-0.98), and pooled specificity was 0.98 (95% CI 0.94-1.00). Recent studies after 2010 showed diagnostic sensitivity of 0.95 (95% CI 0.84-0.99) and specificity of 0.98 (95% CI 0.93-0.99). CONCLUSIONS: This meta-analysis demonstrated that ultrasound represents an effective imaging modality for diagnosing testicular torsion in adult patients with acute scrotal pain.

Using machine-learning approaches to predict non-participation in a nationwide general health check-up scheme.

BACKGROUND: In the time since the launch of a nationwide general health check-up and instruction program in Japan in 2008, interest in the formulation of an effective and efficient strategy to improve the participation rate has been growing. The aim of this study was to develop and evaluate models identifying those who are unlikely to undergo general health check-ups. We used machine-learning methods to select interventional targets more efficiently. METHODS: We used information from a local government database of Japan. The study population included 7290 individuals aged 40-74 years who underwent at least one general health check-up between 2012 and 2015. We developed four predictive models based on the extreme gradient boosting (XGBoost), random forest (RF), support vector machines (SVMs), and logistic regression (LR) algorithms, using machine-learning techniques, and compared the areas under the curves (AUCs) of the models with those of the heuristic method (which presumes that the individuals who underwent a general health check-up in the previous year will do so again in the following year). RESULTS: The AUCs for the XGBoost, RF, SVMs, LR, and heuristic models/method were 0.829 (95% confidence interval [CI]: 0.806-0.853), 0.821 (95% CI: 0.797-0.845), 0.812 (95% CI: 0.787-0.837), 0.816 (95% CI: 0.791-0.841), and 0.683 (95% CI: 0.657-0.708), respectively. XGBoost model exhibited the best AUC, and the performance was significantly better than that of SVMs (p = 0.034), LR (p = 0.017), and heuristic method (p < 0.001). However, the performance of XGBoost did not differ significantly from that of RF (p = 0.229). CONCLUSION: Predictive models using machine-learning techniques outperformed the existing heuristic method when used to predict participation in a general health check-up system by eligible participants.

Prediction models to identify individuals at risk of metabolic syndrome who are unlikely to participate in a health intervention program.

OBJECTIVES: Since the launch of a nationwide general health check-up and instruction program in Japan in 2008, interest in strategies to improve implementation of the program based on predictive analytics has grown. We investigated the performance of prediction models developed to identify individuals classified as "requiring instruction" (high-risk) who were unlikely to participate in a health intervention program. METHODS: Data were obtained from one large health insurance union in Japan. The study population included individuals who underwent at least one general health check-up between 2008 and 2013 and were identified as "requiring instruction" in 2013. We developed three prediction models based on the gradient boosted trees (GBT), random forest (RF), and logistic regression (LR) algorithms using machine-learning techniques and compared the areas under the curve (AUC) of the developed models with those of two conventional methods The aim of the models was to identify at-risk individuals who were unlikely to participate in the instruction program in 2013 after being classified as requiring instruction at their general health check-up that year. RESULTS: At first we performed the analysis using data without multiple imputation. The AUC values for the GBT, RF, and LR prediction models and conventional methods: 1, and 2 were 0.893 (95%CI: 0.882-0.905), 0.889 (95%CI: 0.877-0.901), 0.885 (95%CI: 0.872-0.897), 0.784 (95%CI: 0.767-0.800), and 0.757 (95%CI: 0.741-0.773), respectively. Subsequently, we performed the analysis using data after multiple imputation. The AUC values for the GBT, RF, and LR prediction models and conventional methods: 1, and 2 were 0.894 (95%CI: 0.882-0.906), 0.889 (95%CI: 0.887-0.901), 0.885 (95%CI: 0.872-0.898), 0.784 (95%CI: 0.767-0.800), and 0.757 (95%CI: 0.741-0.773), respectively. In both analyses, the GBT model showed the highest AUC among that of other models, and statistically significant difference were found in comparison with the LR model, conventional method 1, and conventional method 2. CONCLUSION: The prediction models using machine-learning techniques outperformed existing conventional methods: for predicting participation in the instruction program among participants identified as "requiring instruction" (high-risk).

Effect of providing risk information on undergoing cervical cancer screening: a randomized controlled trial.

BACKGROUND: In Japan, the cervical cancer screening rate is extremely low. Towards improving the cervical cancer screening rate, encouraging eligible people to make an informed choice, which is a decision-making process that relies on beliefs informed by adequate information about the possible benefits and risks of screening, has attracted increased attention in the public health domain. However, there is concern that providing information on possible risks of screening might prevent deter from participating. METHODS: In total, 1,912 women aged 20-39 years who had not participated in screening in the fiscal year were selected from a Japanese urban community setting. Participants were randomly divided into 3 groups. Group A received a printed reminder with information about the possible benefits of screening, group B received a printed reminder with information about possible benefits and risks, and group C received a printed reminder with simple information only (control group). RESULTS: Out of 1,912 participants, 169 (8.8%) participated in cervical cancer screening. In the intervention groups, 137 (10.9%) participated in cervical cancer screening, compared to only 32 (4.9%) of the control group (p < 0.001). In addition, logistic regression analysis revealed that there was no significant difference in screening rate between group A and group B (p = 0.372). CONCLUSIONS: Providing information on the possible risks of screening may not prevent people from taking part in cervical cancer screening among a Japanese non-adherent population.