[Community-based epidemiological study on hypertension and diabetes--community-based preventive medicine by Yang-Ming...

A community-based preventive medicine project was carried out by the Yang-Ming Crusade, organized by more than 180 students of National Yang-Ming Medical College, in July 1989. All of the registered residents over 40 years old of 13 villages (from 8 towns) were chosen for the study population. Their blood pressure, urine sugar, urine protein, high and weight were taken by the crusaders through door by door interviews. The screening positive cases were referred to the local Group Practice Center for confirm diagnosis and follow-up treatment. Among 7598 study population. 5088 (67%) were interviewed including male 48.9% and female 51.0%. About one third of the interviewee were in 50-59 age group. 762 hypertensive cases out of 3462 interviewee with blood pressure taken were found. The prevalence of hypertension was 21.0%. 334 diabetes cases out of 4713 interviewee with urine sugar taken were found. The prevalence of diabetes was 7.1%. Among 762 hypertensive cases, 560 (77.1%) were self-recognized and treated with the following methods: modern medicine 79.2%, traditional medicine 8.7%, drug stores 6.4%, and herbs 2.8%. Among 334 diabetes cases, 203 (60.8%) were self-recognized and only 171 (51.2%) were treated with the following methods: modern medicine 79.5%, traditional medicine 8.2%, drug stores 3.5%, and herbs 4.1%. Univariate analysis on hypertension showed that age, ethnicity, obesity, diabetes, hypertension history of mother, and hypertension history of siblings were significant variables. All of the above 6 variables were significant in multiple logistic regression analysis. Univariate analysis on diabetes showed that age, ethnicity, obesity, hypertension, diabetes history of father, diabetes history of mother, and diabetes history of siblings were significant variables. However only 4 variables, age, hypertension, diabetes history of mother, and diabetes history of siblings, were significant in multiple logistic regression analysis.