METHODS: This study is a pragmatic, cluster-randomised, parallel-group, matched pair, controlled trial with blinded outcome assessment. Randomisation is performed using a computer-generated table with a 1:1 allocation comparing the SIMSP and the POHP involving 28 preschools in the Kampar district, Perak, Malaysia. The intervention consists of preschool visits by a group of dental therapists, in-class oral health lessons and daily toothbrushing conducted by class teacher, child home toothbrushing supervised by parents, and infographic oral health messages to parents. The control consists of the existing POHP that involves preschool visits by a group of dental therapists only. The trial lasts for 6 months. Primary outcome variable is the mean plaque score change after 6 months. To determine the feasibility of the SIMSP, a process evaluation will be conducted using the perspectives of dental therapists, teachers, and parents on the appropriateness, effectiveness, facilitators, and barriers to the SIMSP implementation as well as an audit trail to assess the trial intervention.
DISCUSSION: Cluster randomisation may lead to a random effect and cluster selection bias. These factors will be accounted for when analysing the data and interpreting the outcomes. The effectiveness of the SIMSP will be evaluated by comparing the results with those of the POHP.
TRIAL REGISTRATION: ClinicalTrials.gov NCT04339647 . Registered on 5 April 2020 - Retrospectively registered.
METHODS: Latent class analysis (LCA) was employed to model the co-occurrence of PTEs in two school samples of adolescents from India (n = 411) and Malaysia (n = 469). Demographic correlates (i.e., sex, age, household composition, parent education) of the latent classes and the association between latent class membership and probable diagnosis of posttraumatic stress disorder (PTSD) were examined.
RESULTS: The LCA identified three latent classes for the Indian sample: 'Low Risk - moderate sexual trauma', 'Moderate Risk', and 'High Risk'. Similarly, three classes were also identified for the Malaysian sample: 'Low Risk', 'Moderate Risk', and 'High Risk'. Membership of 'Moderate Risk' was associated with male sex in both samples, and with older age and lower levels of parental education attainment in the Malaysian sample. No correlates of 'High Risk' class were identified in either sample. Membership of the 'High Risk' class was significantly associated with probable PTSD diagnosis in both samples, while membership of the 'Moderate Risk' class was associated with probable PTSD diagnosis in the Malaysian sample.
CONCLUSION: Findings from this study correspond with Western studies indicating co-occurrence of PTEs to be common and to represent a salient risk factor for the development of PTSD.
Methods: A total of 101 currently enrolled pre-university students were recruited for this cross-sectional study. They answered a questionnaire about their demographic details and their frequency of backpack usage. Their backpacks were weighed for four consecutive school days. The Roland-Morris Disability Questionnaire and Body Discomfort Chart were used to rate discomfort levels.
Results: The use and weight of a backpack were not significantly associated with low back pain, as indicated by the Roland-Morris Disability Questionnaire and Body Discomfort Chart (p > 0.05).
Conclusion: This study did not find an association between the use of a backpack and low back pain in Malaysian pre-university students.
Methods: Cross-sectional data from 62 developing countries were used to run several multivariate linear regressions. R2 was used to compare the powers of MPI with income-poverties (income poverty gaps [IPG] at 1.9 and 3.1 USD) in explaining LE.
Results: Adjusting for controls, both MPI (β =-0.245, P<0.001) and IPG at 3.1 USD (β=-0.135, P=0.044) significantly correlates with LE, but not IPG at 1.9 USD (β=-0.147, P=0.135). MPI explains 12.1% of the variation in LE compared to only 3.2% explained by IPG at 3.1 USD. The effect of MPI on LE is higher on female (β=-0.210, P<0.001) than male (β=-0.177, P<0.001). The relative influence of the deprivation indictors on LE ranks as follows (most to least): Asset ownership, drinking water, cooking fuel, flooring, child school attendance, years of schooling, nutrition, mortality, improved sanitation, and electricity.
Conclusion: Interventions to reduce poverty and improve LE should be guided by MPI, not income poverty indices. Such policies should be female-oriented and prioritized based on the relative influence of the various poverty deprivation indicators on LE.