Interaksi 13 Faktor Risiko Leptospirosis

Abstract

Interaction 13 Risk Factors In Leptospirosis

Background: Leptospirosis is a zoonotic disease, caused by infection with spiral-shaped bacteria of the genus Leptospira. Leptospirosis attacks the animal and human pathogens. Efforts to scale baseline mapping problem of leptospirosis in the city of Semarang is necessary because since 2005, continues to increase. Identify risk factors that influenced the incidence of leptospirosis is the purpose of this study.

Method: The method used in this study is case control. Categories leptospirosis cases were patients who recorded at the hospital in Semarang, 2009, while the control is a person who does not suffer from leptospirosis, proven by the results of teck dry lepto negative dot and for 2 times the incubation period (60 days) showed no clinical symptoms of leptospirosis since the respondents states participated in the study

Result:The results showed the respondents included in the exclusion criteria for both case and control sample of 255 respondents found out. Interaction of risk factors in multivariate showed that only 4 variables that showed statistically significance. a). The probability is not always wear PPE will boost the 266.3 times the incidence of leptospirosis. b). Communities with low education would increase the probability of leptospirosis incidence of 255.2 times, c). Contact with the flesh or body parts of dead animals increase the probability of
leptospirosis incidence of 77.8 tim es. d). The probability of incidence of leptospirosis increased 44.3 times if the respondent contact with standing water. The probability of occurrence of leptospirosis can be described by the equation:

Conclusion: From the results of this study concluded priority cope with leptospirosis in the city of Semarang is the problem; a). Use personal protective equipment (PPE); b). Problems caused by low public education; c). Contact with meat or body parts of dead animals and d). Contact with stagnant water.

Keywords: leptospirosis, case control, binary logistic regression, multivariate