Predicting factors contributing to knowledge, attitudes and practices relating to Zika virus infection among the general public in Malaysia

Kingston Rajiah, Mari Kannan Maharajan, May Yee Woo, Yew Wing Yee, Shi Mun Cheah, Mai Ya Zhe

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
26 Downloads (Pure)

Abstract

Objective:
To identify the predicting factors that contribute to knowledge, attitude and practices relating to Zika virus infection among the general public in Malaysia.

Methods:
A cross-sectional study was conducted using a validated self-administered questionnaire. Descriptive analysis was done for participants’ socio-demographic profile. Contingency table analysis was done to analyse the associations between knowledge, attitudes, and practices (KAP) scores and socio-demographic profile. A Bonferroni-corrected P-value was used to find the significance of the associations and multiple comparisons were performed in a single data set. To determine the linear relationship between each independent variable and the dependent variable, Spearman rank correlation was performed. Cohen's correlation coefficient was evaluated to determine the strength of the effect size. Multiple correlations and regression analyses were performed to identify independent variables that predicts the dependent variable.

Results:
Multiple correlation analyses were conducted between respondents’ KAP score and independent variables (Age >60 years; Female gender; Selangor state; At least 1 pregnant woman per household). The independent variables such as ‘Female gender’, ‘Selangor state’ and ‘At least 1 pregnant woman per household’ were positively and significantly correlated with KAP score whereas, age >60 years was negatively and significantly correlated with the KAP scores.

Conclusions:
There were associations between four independent factors and the KAP scores, while only three factors contributed to changes in KAP scores among the public. Among these contributing factors, respondents’ age group was the strongest predictor.
Original languageEnglish
Pages (from-to)314-321
Number of pages8
JournalAsian Pacific Journal of Tropical Medicine
Volume13
Issue number7
Early online date17 Jun 2020
DOIs
Publication statusPublished (in print/issue) - 1 Jul 2020

Keywords

  • Zika virus
  • Infection
  • Public
  • Malaysia
  • Predictors

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