Objectives This study examined the social and behavioral factors influencing community leaders’ participation in community-based surveillance (CBS) of infectious diseases. We hypothesized that knowledge, attitudes, and perceptions significantly predict the behavioral likelihood of engaging in CBS activities. Methods: A cross-sectional study was conducted among 470 schoolteachers in Kelantan, Malaysia, from March to June 2024. Participants were selected using multistage sampling, with random selection of schools followed by convenience sampling of teachers. Data were collected using the validated KAP-CBS-ID questionnaire, which was based on the Theory of Reasoned Action and Health Belief Model. Structural equation modeling was used to analyze relationships among the study variables with the maximum likelihood robust estimator. Results: The final model demonstrated good fit (comparative fit index, 0.921; standardized root mean square residual, 0.071; root mean square error of approximation, 0.040 [90% confidence interval, 0.036–0.044]). Ten significant path relationships were identified, explaining 46.1% of the variance in behavioral likelihood. Knowledge of infectious diseases (β=0.419, p<0.001), subjective norms (β=0.235, p=0.038), and negative attitudes (β=–0.432, p<0.001) significantly influenced intention. Behavioral likelihood was directly predicted by intention (β =0.347, p<0.001), perceived susceptibility (β=0.310, p<0.001), perceived benefits (β=0.198, p=0.001), and perceived barriers (β=–0.132, p=0.008). Self-efficacy showed significant indirect effects through perceived benefits (β=0.785, p<0.001) and perceived susceptibility (β=0.765, p<0.001). Conclusion: Knowledge and psychosocial factors significantly inf luence community engagement in CBS of infectious diseases. Public health interventions should address these interconnected determinants to strengthen early disease detection and reporting systems.
Objectives This study aimed to describe and compare health-related quality of life (QoL) as measured by the World Health Organization Quality of Life–BREF (WHOQoL-BREF) and the EuroQol-5 Dimensions (EQ-5D) among the Malaysian population, examining differences by sociodemographic characteristics including age, income, sex, ethnicity, educational level, and occupation. Methods: This cross-sectional study used data from 19,402 individuals collected as part of a health and demographic surveillance system survey conducted in the Segamat district of Malaysia in 2018–2019. Descriptive statistics and measures of central tendency were produced. Differences in QoL among demographic sub-groups were examined using the t-test and analysis of variance, while the correlations between the WHOQoL-BREF and EQ-5D were evaluated using Pearson correlation coefficients. Results: Based on complete case analysis (n=19,129), the average scores for the 4 WHOQoLBREF domains were 28.2 (physical), 24.1 (psychological), 12.0 (social relationships), and 30.4 (environment). The percentages of participants not in full health for each EQ-5D dimension were 12.8% (mobility), 3.1% (self-care), 6.9% (usual activities), 20.9% (pain/discomfort), and 6.8% (anxiety/depression). Correlations between the 4 WHOQoL-BREF domains and the 5 EQ-5D dimensions were relatively weak, ranging from –0.06 (social relationships with self-care and pain/discomfort; p<0.001) to –0.42 (physical with mobility; p<0.001). Conclusion: Although health-related QoL as measured by the WHOQoL-BREF and the EQ-5D are correlated, these 2 measures should not be considered interchangeable. The choice between them should be guided by the specific research questions and the intended use of the data.
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Objectives
In light of the importance of physical activity, the aim of the present study is to examine the factors affecting participation in physical activity among adults in Malaysia. Methods
A logistic regression model and the Third National Health and Morbidity Survey consisting of 30,992 respondents were used. Results
Age, income, gender, education, marital status, region, house locality, job characteristics, and medical conditions are significantly associated with participation in physical activity. In particular, old individuals, high income earners, females, the well-educated, widowed or divorced individuals, East Malaysians, urban dwellers, the unemployed, and individuals who are not diagnosed with hypercholesterolemia are less likely to be physically active than others. Conclusion
Because sociodemographic and health factors play an important role in determining physical activity, the government should take them into account when formulating policy.
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