We assessed the effectiveness of practical instruction in parasitology for undergraduates at Jichi Medical School by examining grades on a practical examination. Two hundred six second-year medical students in 1997 and 1998 (103 students in each year) were enrolled in this study. The students took written and practical examinations at the end of the program. We found that grades on the practical examination were correlated with grades on the written examination (r=0.5664; p<0.001). The discrimination index ranged from 0.23 to 0.78. The percentage of correctly identified species was significantly higher when students studied live specimens than when they used other methods (p<0.0001 in both 1997 and 1998). The highest rates of correct identification (more than 90%) were for Anisakis species larvae and Enterobius vermicularis eggs in 1997 and for Anisakis species larvae, E. vermicularis eggs, and Anopheles mosquitoes in 1998. Results of neither written nor practical examinations differed significantly between students who chose biology at the entrance examination and those who did not. Our results suggest that undergraduates would gain a better understanding of parasitology by studying live specimens.

OBJECTIVES: This study aimed to identify factors associated with initiation and adherence of osteoporosis medication from a patient perspective. METHODS: A web-based survey was developed based on health behavior theories. Descriptive analyses were conducted for all survey items. Analyses in a structural equation modeling framework were conducted to identify factors associated with treatment initiation and adherence. RESULTS: Five hundred forty-five women completed the questionnaire. A majority were currently receiving medications for osteoporosis (n = 376, 69.0%) and 25.0% of these patients (n = 94) were considered adherent to their treatment. Knowledge was strongly associated with osteoporosis treatment initiation (standard error [SE], 0.58). Greater knowledge of disease was associated with increased likelihood of initiating medication. Medication complexity (SE, 0.49) and perceived susceptibility to fracture and loss of independence (SE, −0.37) were also associated with initiation. Perceived barriers (SE, −0.85) such as inconvenience, lack of efficacy and financial burden were observed to be the greatest obstacle to adherence. The greater the perceived barriers, the less likely patients were to adhere to medication. Patients' perception of self-efficacy (SE, 0.37) also affected adherence. The greater the patient perception of ability to independently manage their medication, the more likely they were to adhere to the medication. CONCLUSIONS: Different factors were found to be associated with initiation and adherence of osteoporosis medication. Patient knowledge of their disease and the perception of barriers were found to be the most influential. Empowering patients with the knowledge to better understand their disease and decreasing the perception of barriers through education initiatives may be effective in improving patient outcomes.
Education ; Female ; Health Behavior ; Humans ; Japan* ; Osteoporosis* ; Patient-Centered Care

BACKGROUND: Previous cardiovascular risk prediction models in Japan have utilized prospective cohort studies with concise data. As the health information including health check-up records and administrative claims becomes digitalized and publicly available, application of large datasets based on such real-world data can achieve prediction accuracy and support social implementation of cardiovascular disease risk prediction models in preventive and clinical practice. In this study, classical regression and machine learning methods were explored to develop ischemic heart disease (IHD) and stroke prognostic models using real-world data. METHODS: IQVIA Japan Claims Database was searched to include 691,160 individuals (predominantly corporate employees and their families working in secondary and tertiary industries) with at least one annual health check-up record during the identification period (April 2013-December 2018). The primary outcome of the study was the first recorded IHD or stroke event. Predictors were annual health check-up records at the index year-month, comprising demographic characteristics, laboratory tests, and questionnaire features. Four prediction models (Cox, Elnet-Cox, XGBoost, and Ensemble) were assessed in the present study to develop a cardiovascular disease risk prediction model for Japan. RESULTS: The analysis cohort consisted of 572,971 invididuals. All prediction models showed similarly good performance. The Harrell's C-index was close to 0.9 for all IHD models, and above 0.7 for stroke models. In IHD models, age, sex, high-density lipoprotein, low-density lipoprotein, cholesterol, and systolic blood pressure had higher importance, while in stroke models systolic blood pressure and age had higher importance. CONCLUSION Our study analyzed classical regression and machine learning algorithms to develop cardiovascular disease risk prediction models for IHD and stroke in Japan that can be applied to practical use in a large population with predictive accuracy.
Humans ; Cardiovascular Diseases/epidemiology* ; Prognosis ; Prospective Studies ; Japan/epidemiology* ; Stroke/etiology* ; Myocardial Ischemia/epidemiology* ; Risk Assessment/methods*