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Table_1_Social Determinants Predicting the Knowledge, Attitudes, and Practices of Women Toward Zika Virus Infection.DOCX (22.45 kB)

Table_1_Social Determinants Predicting the Knowledge, Attitudes, and Practices of Women Toward Zika Virus Infection.DOCX

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posted on 2020-06-03, 13:28 authored by Mari Kannan Maharajan, Kingston Rajiah, Jo-Ann Singco Belotindos, Marilou S. Basa

Objective: To investigate the factors predicting knowledge, attitude, and practices (KAP) toward Zika virus infection among women population in Cebu City, Philippines.

Study Design: A cross-sectional survey was conducted from March 2018 to May 2018. Ethical practices were followed. A total of 702 women was approached and finally 516 completed the survey.

Methods: Descriptive analysis was undertaken for the participants' characteristics. Kolmogorov–Smirnov test was applied to declare the nature of data distribution. To determine the role of socio-demographic characteristics on KAP, differences in socio-demographic status were compared with the KAP scores using the one-way analysis of variance or Kruskal–Wallis test with p < 0.05 as significant. Logistic regression analysis was used to determine the predictors of each KAP domain (good and poor).

Results: There was a significant positive correlation between level of education and KAP scores. Also, there was a significant positive correlation between employment and KAP scores. Knowledge score was a significant predictor of practice score (b = 1.261, p = 0.024), and attitude score was also a significant predictor of practice score (b = 0.183, p = 0.039). However, knowledge score was not a significant predictor of attitude score (b = 0.316, p = 0.247).

Conclusions: The present findings provided an overall view of KAP on Zika virus infection among females in Philippines and the socio-demographic factors that affected their KAP. Women with postgraduate education and being in higher profession were the predictors influencing the KAP scores of this female population. Women with postgraduate education was the strongest predictor.

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