METHODS: Prospective case finding was performed from June to December 2009. Those who presented with signs and symptoms of CHIKV infection were investigated. We designed a case control study to assess the risk factors. Assessment consisted of answering questions, undergoing a medical examination, and being tested for the presence of IgM antibodies to CHIKV. Descriptive epidemiological studies were conducted by reviewing both the national surveillance and laboratory data. Multivariable logistic regression analysis was performed to determine risk factors contributing to the illness. Cases were determined by positive to RT-PCR or serological for antibodies by IgM. CHIKV specificity was confirmed by DNA sequencing.
RESULTS: There were 129 suspected cases and 176 controls. Among suspected cases, 54.4% were diagnosed to have CHIKV infection. Among the controls, 30.1% were found to be positive to serology for antibodies [IgM, 14.2% and IgG, 15.9%]. For analytic study and based on laboratory case definition, 95 were considered as cases and 123 as controls. Those who were positive to IgG were excluded. CHIKV infection affected all ages and mostly between 50-59 years old. Staying together in the same house with infected patients and working as rubber tappers were at a higher risk of infection. The usage of Mosquito coil insecticide had shown to be a significant protective factor. Most cases were treated as outpatient, only 7.5% needed hospitalization. The CHIKV infection was attributable to central/east African genotype CHIKV.
CONCLUSIONS: In this study, cross border activity was not a significant risk factor although Thailand and Malaysia shared the same CHIKV genotype during the episode of infections.
DESIGN: Population-based, cross-sectional study. Receiver operating characteristic curves were used to determine the cut-off values of BMI with optimum sensitivity and specificity for the detection of three cardiovascular risk factors: diabetes mellitus, hypertension and hypercholesterolaemia. Gender-specific logistic regression analyses were used to examine the association between BMI and these cardiovascular risk factors.
SETTING: All fourteen states in Malaysia.
SUBJECTS: Malaysian adults aged ≥18 years (n 32 703) who participated in the Third National Health and Morbidity Survey in 2006.
RESULTS: The optimal BMI cut-off value for predicting the presence of diabetes mellitus, hypertension, hypercholesterolaemia or at least one of these cardiovascular risk factors varied from 23.3 to 24.1 kg/m2 for men and from 24.0 to 25.4 kg/m2 for women. In men and women, the odds ratio for having diabetes mellitus, hypertension, hypercholesterolaemia or at least one cardiovascular risk factor increased significantly as BMI cut-off point increased.
CONCLUSIONS: Our findings indicate that BMI cut-offs of 23.0 kg/m2 in men and 24.0 kg/m2 in women are appropriate for classification of overweight. We suggest that these cut-offs can be used by health professionals to identify individuals for cardiovascular risk screening and weight management programmes.