Cardiovascular Diseases ; epidemiology ; etiology ; China ; epidemiology ; Diabetes Complications ; complications ; epidemiology ; Diabetic Foot ; epidemiology ; etiology ; Diabetic Nephropathies ; epidemiology ; etiology ; Diabetic Neuropathies ; epidemiology ; etiology ; Diabetic Retinopathy ; epidemiology ; etiology ; Humans

Cardiovascular Diseases ; epidemiology ; etiology ; Environmental Exposure ; adverse effects ; Humans

Body Weight ; Cardiovascular Diseases/etiology* ; Humans ; Japan/epidemiology* ; Risk Factors

Surveys of noncommunicable diseases were performed in six communities in Papua New Guinea during 1985-1986. Results are reported here with respect to blood pressure and associated factors in adults. Mean systolic and diastolic blood pressures were lowest, and hypertension was rarest (less than 2%), in three rural/semirural villages on Karkar Island, Madang Province. Intermediate values for blood pressure and moderate prevalence of hypertension (3-6%) were observed in rural and urban Tolai communities in East New Britain Province. A periurban village in the Eastern Highlands Province displayed the highest mean blood pressures and prevalence of hypertension (12% in men and 5% in women). There was a modest rise in mean systolic blood pressure with age in most groups, but the age-related rise in diastolic pressure was much less pronounced. Other cardiovascular risk factors--body mass index (BMI), and plasma cholesterol, glucose and insulin concentrations--were lowest in the least developed rural villages on Karkar Island and highest in the urban Tolai and periurban highland communities. Both systolic and diastolic blood pressures were significantly (and positively) related to age, male sex, BMI and speaking a non-Austronesian language. It is concluded that there is now a considerable variation in the prevalence of hypertension, and the levels of blood pressure and other cardiovascular risk factors, in different communities in Papua New Guinea.
Adult ; Blood Pressure ; Cardiovascular Diseases - etiology ; Diabetes Mellitus - epidemiology ; Hypertension - complications ; Obesity - epidemiology ; Papua New Guinea

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*

In 2019, cardiovascular disease (CVD) accounted for 46.74% and 44.26% of all deaths in rural and urban areas, respectively. Two out of every five deaths were due to CVD. It is estimated that about 330 million patients suffer from CVD in China. The number of patients suffering from stroke, coronary heart disease, heart failure, pulmonary heart disease, atrial fibrillation, rheumatic heart disease, congenital heart disease, lower extremity artery disease and hypertension are 13.00 million, 11.39 million, 8.90 million, 5.00 million, 4.87 million, 2.50 million, 2.00 million, 45.30 million, and 245.00 million, respectively. Given that China is challenged by the dual pressures of population aging and steady rise in the prevalence of metabolic risk factors, the burden caused by CVD will continue to increase, which has set new requirements for CVD prevention and treatment and the allocation of medical resources in China. It is important to reduce the prevalence through primary prevention, increase the allocation of medical resources for CVD emergency and critical care, and provide rehabilitation services and secondary prevention to reduce the risk of recurrence, re-hospitalization and disability in CVD survivors. The number of people suffering from hypertension, dyslipidemia and diabetes in China has reached hundreds of millions. Since blood pressure, blood lipids, and blood glucose levels rise mostly insidiously, vascular disease or even serious events such as myocardial infarction and stroke often already occured at the time of detection in this population. Hence, more strategies and tasks should be taken to prevent risk factors such as hypertension, dyslipidemia, diabetes, obesity, and smoking, and more efforts should be made in the assessment of cardiovascular health status and the prevention, treatment, and research of early pathological changes.
Cardiovascular Diseases/etiology* ; China/epidemiology* ; Diabetes Mellitus ; Dyslipidemias ; Humans ; Hypertension/epidemiology* ; Myocardial Infarction ; Risk Factors ; Stroke/epidemiology*

OBJECTIVE: To explore the obesity distribution in old people and the relation between senile obesity and health. METHODS: First, a questionnaire was designed which included chronic disease history, body mass index (BMI), physiological value, biochemistry index, anti-oxidation index, diagnosis of diseases, etc. Second, the measure and detection methods were unified; and the last, the investigation was made along with daily clinical work by clinicians. RESULTS: We received 391 questionnaires. The overweight rate was 36.1% and the obesity rate was 7.9% . Total anti-oxidation activity in serum (TAS) and superoxide dismutase (SOD) decreased with body mass index (BMI), and the value in the obesity group was the lowest; Malonaldehyde (MDA) of overweight obesity was the largest. The mean blood pressure, blood fat, and blood glucose as well as the prevalence of cardiovascular disease, hyperlipemia, and glycuresis increased with BMI; and the value in the obesity group was the largest. CONCLUSION The prevalence of the senile obesity was below the average and the senile obesity complications were various and serious, and perhaps related to imbalance of free radical's production and cleanup, so the senile obesity seriously harmed old people's health.
Aged ; Body Mass Index ; Cardiovascular Diseases ; epidemiology ; etiology ; Cerebrovascular Disorders ; epidemiology ; etiology ; China ; epidemiology ; Female ; Humans ; Male ; Middle Aged ; Obesity ; complications ; epidemiology ; Overweight ; Prevalence ; Risk Factors

OBJECTIVES: The aim of this study was to evaluate the association between Agent Orange exposure and self-reported diseases in Korean Vietnam veterans. METHODS: A postal survey of 114 562 Vietnam veterans was conducted. The perceived exposure to Agent Orange was assessed by a 6-item questionnaire. Two proximity-based Agent Orange exposure indices were constructed using division/brigade-level and battalion/company-level unit information. Adjusted odds ratios (ORs) for age and other confounders were calculated using a logistic regression model. RESULTS: The prevalence of all self-reported diseases showed monotonically increasing trends as the levels of perceived self-reported exposure increased. The ORs for colon cancer (OR, 1.13), leukemia (OR, 1.56), hypertension (OR, 1.03), peripheral vasculopathy (OR, 1.07), enterocolitis (OR, 1.07), peripheral neuropathy (OR, 1.07), multiple nerve palsy (OR, 1.14), multiple sclerosis (OR, 1.24), skin diseases (OR, 1.05), psychotic diseases (OR, 1.07) and lipidemia (OR, 1.05) were significantly elevated for the high exposure group in the division/brigade-level proximity-based exposure analysis, compared to the low exposure group. The ORs for cerebral infarction (OR, 1.08), chronic bronchitis (OR, 1.05), multiple nerve palsy (OR, 1.07), multiple sclerosis (OR, 1.16), skin diseases (OR, 1.05), and lipidemia (OR, 1.05) were significantly elevated for the high exposure group in the battalion/company-level analysis. CONCLUSIONS: Korean Vietnam veterans with high exposure to Agent Orange experienced a higher prevalence of several self-reported chronic diseases compared to those with low exposure by proximity-based exposure assessment. The strong positive associations between perceived self-reported exposure and all self-reported diseases should be evaluated with discretion because the likelihood of reporting diseases was directly related to the perceived intensity of Agent Orange exposure.
2,4,5-Trichlorophenoxyacetic Acid/*poisoning ; 2,4-Dichlorophenoxyacetic Acid/*poisoning ; Cardiovascular Diseases/epidemiology/etiology ; Defoliants, Chemical/*poisoning ; Endocrine System Diseases/epidemiology/etiology ; Gastrointestinal Diseases/epidemiology/etiology ; Humans ; Logistic Models ; Male ; Middle Aged ; Neoplasms/epidemiology/etiology ; Neuromuscular Diseases/epidemiology/etiology ; Odds Ratio ; Prevalence ; Republic of Korea/epidemiology ; Respiratory Tract Diseases/epidemiology/etiology ; *Self Report ; Tetrachlorodibenzodioxin/*poisoning ; *Veterans ; Vietnam Conflict

OBJECTIVE: To develop and validate a three-year risk prediction model for new-onset cardiovascular diseases (CVD) among female patients with breast cancer. METHODS: Based on the data from Inner Mongolia Regional Healthcare Information Platform, female breast cancer patients over 18 years old who had received anti-tumor treatments were included. The candidate predictors were selected by Lasso regression after being included according to the results of the multivariate Fine & Gray model. Cox proportional hazard model, Logistic regression model, Fine & Gray model, random forest model, and XGBoost model were trained on the training set, and the model performance was evaluated on the testing set. The discrimination was evaluated by the area under the curve (AUC) of the receiver operator characteristic curve (ROC), and the calibration was evaluated by the calibration curve. RESULTS: A total of 19 325 breast cancer patients were identified, with an average age of (52.76±10.44) years. The median follow-up was 1.18 [interquartile range (IQR): 2.71] years. In the study, 7 856 patients (40.65%) developed CVD within 3 years after the diagnosis of breast cancer. The final selected variables included age at diagnosis of breast cancer, gross domestic product (GDP) of residence, tumor stage, history of hypertension, ischemic heart disease, and cerebrovascular disease, type of surgery, type of chemotherapy and radiotherapy. In terms of model discrimination, when not considering survival time, the AUC of the XGBoost model was significantly higher than that of the random forest model [0.660 (95%CI: 0.644-0.675) vs. 0.608 (95%CI: 0.591-0.624), P < 0.001] and Logistic regression model [0.609 (95%CI: 0.593-0.625), P < 0.001]. The Logistic regression model and the XGBoost model showed better calibration. When considering survival time, Cox proportional hazard model and Fine & Gray model showed no significant difference for AUC [0.600 (95%CI: 0.584-0.616) vs. 0.615 (95%CI: 0.599-0.631), P=0.188], but Fine & Gray model showed better calibration. CONCLUSION It is feasible to develop a risk prediction model for new-onset CVD of breast cancer based on regional medical data in China. When not considering survival time, the XGBoost model and the Logistic regression model both showed better performance; Fine & Gray model showed better performance in consideration of survival time.
Humans ; Female ; Adult ; Middle Aged ; Adolescent ; Breast Neoplasms/epidemiology* ; Cardiovascular Diseases/etiology* ; Proportional Hazards Models ; Logistic Models ; China/epidemiology*

Air Pollutants ; chemistry ; toxicity ; Cardiovascular Diseases ; epidemiology ; etiology ; Environmental Exposure ; Humans ; Particle Size ; Particulate Matter ; chemistry ; toxicity ; Respiratory Tract Diseases ; epidemiology ; etiology