ABSTRACT
INTRODUCTION: Cardiovascular disease (CVD) is the primary cause of death in HIV patients. The number of HIV patients suffering from cardiovascular disease is almost twice as high as that of patients who are not HIV-positive. OBJECTIVE: The purpose of this study was to evaluate risk of cardiovascular disease among HIV-positive persons. METHODS: We conducted a cross-sectional study with HIV positive individuals at public health center and non-AIDS govermental organization. We enrolled people diagnosed with HIV, age over 30 years old, and on CVD medications. We collected data of demographic, anthropometric and clinical information, smoking history, and non-fasting cholesterol and blood glucose. Estimation of 10-years CVD risk was calculated using the BMI-based Framingham Risk Score. RESULTS: Of 150 participants enrolled, 66.7% were male and mean age was 38.09 (SD = 7.99) years. The mean current CD4 counts was 493.3 (SD = 139.8) cells/mm3. Female were younger, had a shorter duration living with HIV and a shorted duration of receiving ART than males. About 8.7% of respondents had a high risk of developing a CVD event in the next 10 years, and higher among females than males. The most common CVD risk factors were smoking, high blood pressure, and hypercholestrolemia. CONCLUSION: Our study demonstrates that HIV positive persons who are at risk for developing CVD in the next 10-years. There is an increasing need for educational programs on CVD prevention for the HIV-positive person and to further facilitate the identification of persons at elevated risk in routine practice.
ABSTRACT
BACKGROUND: Climatic and weather factors become important determinants of vector-borne diseases transmission like malaria. This study aimed to prove relationships between weather factors with considering human migration and previous case findings and malaria cases in endemic areas in Purworejo during 2005-2014. METHODS: This study employed ecological time series analysis by using monthly data. The independent variables were the maximum temperature, minimum temperature, maximum humidity, minimum humidity, precipitation, human migration, and previous malaria cases, while the dependent variable was positive malaria cases. Three models of count data regression analysis i.e. Poisson model, quasi-Poisson model, and negative binomial model were applied to measure the relationship. The least Akaike Information Criteria (AIC) value was also performed to find the best model. Negative binomial regression analysis was considered as the best model. RESULTS: The model showed that humidity (lag 2), precipitation (lag 3), precipitation (lag 12), migration (lag1) and previous malaria cases (lag 12) had a significant relationship with malaria cases. CONCLUSION: Weather, migration and previous malaria cases factors need to be considered as prominent indicators for the increase of malaria case projection.
