MATERIALS AND METHODS: A systematic search of observational studies conducted in ASEAN countries between 1 January 2010 and 31 December 2020 was performed in the Medline, PubMed and Google Scholar databases. The quality of studies was evaluated based on The Joanna Briggs Institute Checklist. The analysis was performed with Review Manager software version 5.4. Metaanalysis of the estimates from primary studies was conducted by adjusting for possible publication bias and heterogeneity.
RESULTS: Twenty-five studies including 19924 postnatal mothers were included in this review. The pooled prevalence of PPD is 22.32% (95% CI: 18.48, 26.17). Thailand has the highest prevalence of PPD with a pooled prevalence of 74.1% (95% CI: 64.79, 83.41). The prevalence of PPD was highest when the assessment for PPD was conducted up to 6 weeks postpartum with a pooled prevalence of 25.24% (95% CI: 14.08, 36.41). The identified determinants of PPD were unplanned pregnancy, term pregnancy, lack of family support and physical violence. There were limited studies done and high heterogeneity in terms of quality, methodology, culture, screening method and time of PPD measurement.
CONCLUSIONS: Approximately one in five postpartum women in ASEAN countries had PPD. The risk factor that lowers the risk of PPD is unplanned and term pregnancies, while women with a lack of family support and experienced physical violence increase the risk of PPD. Robust prevalence studies are needed to assess the magnitude of this problem in ASEAN countries.
METHODS: This cross-sectional study was performed in two Malaysian health clinics by using the Malay version of a self-administered questionnaire. This instrument contains a diabetes care profile, a 21-item version of the Depression Anxiety Stress Scales (DASS21), and a Malaysian Medication Adherence Score (MalMAS). Simple and multiple logistic regression analyses were performed.
RESULTS: A total of 338 type II diabetes mellitus patients responded (response rate 93.1%). The proportion of patients with poor glycaemic control was 76.0%. Multiple logistic regression analysis showed that 1) social support scores [Adj. OR (95% CI): 1.06 (1.03,1.10); p = 0.001]; 2) unemployment [Adj. OR (95% CI): 0.46 (0.22,0.95); p = 0.035]; 3) pensioner status [Adj. OR (95% CI): 0.28 (0.13,0.61); p = 0.001]; and 4) perception of diabetes as interfering with daily living activities [Adj. OR (95% CI): 3.18 (1.17,8.70); p = 0.024] were significant factors for poor glycaemic control.
CONCLUSIONS: Unemployment, perception of diabetes' interference with daily living activities, and social support are significantly correlated with poor glycaemic control. Further studies assessing other important clinical and psychosocial factors that may influence glycaemic control are suggested. A younger age range of participants is recommended for better outcomes and interventional implementation of findings.