Disease risk among elderly smokers is considered to be doubled due to their smoking habits and age as compared with elderly non-smokers. The investigators conducted risk assessments of smoking for respiratory symptoms among elderly people.A questionnaire survey on smoking habits and respiratory symptoms was conducted among 3, 000 persons of 56 years of age and over who were randomly selected from suburban residents in a prefecture in Japan in October, 1997. A total 1, 954 or 65.1% of individuals responded, consisting of 42.8% for men and 57.2% for women, with an average age of 73.6 years.In addition to descriptive analysis, multiple logistic regression analysis was conducted. The results are summarized as follows:Smokers accounted for 28.1% of men and 3.6% of women. Among all age-groups, the highest rate of smokers was observed in men of 56-69 years old (34.7%) which was lower than the national average rate for the 60-69 year-old group (56.1% of men and 14.5% of women in ‘97). The odds ratios and 95 percent confidence interval (95%CI) for “having phlegm every day” and “having phlegm for more than 4 days a week” among smokers were 2.06 (95%CI=1.41-3.01) and 2.77(95%CI=1.80-4.27). Significantly higher odds ratios among smokers were also observed for “wheezing” and “shortness of breath when hurrying”.Odds ratios for some respiratory symptoms including “having phlegm for more than 4 days per week” among inhalers were significantly high compared with non-smokers, whereas those among non-inhalers were not significantly different from 1.0.Odds ratios for symptoms of phlegm and wheezing were significantly higher (Odds ratio ≥2.0) among heavy smokers (Brinkman Index [B. I.] >900) compared to non-smokers, while odds ratios of the same symptoms were not different from 1.0 among light smokers (B.I. ≤500).
symptoms <1> ; Respiratory ; Smoking ; Elderly ; Male population group

Objective：To investigate the characteristics of studies registered in the field of rehabilitation medicine.Methods：The university hospital medical information network clinical trials registry database was searched for domestic clinical trials associated with rehabilitation medicine that were registered after June 2005. We extracted information about studies and analyzed their registration trends and overall characteristics.Results：Among the 21,410 registered trials, we found 529 trials associated with rehabilitation. The purpose of this study was to investigate efficacy in 65％ of the studies. Among these studies, 54％ were parallel-group comparison studies, 50％ were registered retrospectively, and 85％ did not publish any results. In comparison studies, 86％ were randomized controlled studies, and 47％ were open-label trials.Conclusion：An increasing trend of registration was observed. However, we found several problems in registration. Prospective registration is important to decrease publication and outcome reporting biases. Education for the relevant study protocol and registration might improve the quality of clinical study in domestic rehabilitation medicine.

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*