METHODS: Secondary data analysis of adults aged 18 years and over from the National Health and Morbidity Survey 2019 was conducted in this study. Characteristics of respondents and those who utilised oral healthcare were described using complex sample descriptive statistics. Logistic regression analysis was performed to examine the association between the dependent and independent variables. Dependent variable was oral healthcare utilisation in the last 12 months. Independent variables were demographic and socioeconomic factors (predisposing, enabling and need characteristics) based on Andersen's Behavioural Model.
RESULTS: A total of 11,308 respondents, estimated to represent 21.7 million adults aged 18 years and over in Malaysia were included in the analysis. Prevalence of oral healthcare utilisation in the last 12 months was 13.2%. Demographic factors of sex, age, marital status, and socioeconomic factors of education level and occupation as well as health belief such as medical check-up were significantly related to oral healthcare utilisation. Enabling factor of household income quintile had significant association with oral healthcare utilisation. Inequalities were observed; females (OR = 1.57, 95% CI = 1.25, 1.96), younger adults (OR = 1.64, 95% CI = 1.15, 2.33), those who were married (OR = 1.65, 95% CI = 1.23, 2.22), those with higher education (OR = 2.21, 95% CI = 1.23, 3.99), those who had medical check-up in the last 12 months (OR = 1.86, 95% CI = 1.53, 2.25) and those with higher income (OR = 1.43, 95% CI = 1.04, 1.96) were more likely to utilise oral healthcare.
CONCLUSION: Understanding factors associated with utilisation of oral healthcare could help in formulating effective interventions to improve oral healthcare utilisation. Demographic and socioeconomic factors are strong determinants of oral healthcare utilisation in Malaysia. Appropriate interventions to strengthen the existing programmes aimed to promote regular and timely oral health check-ups are needed to improve oral healthcare utilisation.
METHODS: The data from the 2019 National Health and Morbidity Survey (NHMS), a nationwide cross-sectional survey with a two-stage stratified random sampling design, was used in this research. The study included respondents who were 18 years and older (n = 11,674). Data were obtained via face-to-face interviews using validated questionnaires. Descriptive and complex sample logistic regression analyses were employed as appropriate.
RESULTS: 5.7% of the adult population were informal caregivers. Provision of informal care were significantly associated with the female sex (OR = 1.52, 95% CI [1.21, 1.92]), those aged 36-59 years (OR = 1.61, 95% CI [1.15, 2.25]), and those who reported illness in the past 2 weeks (OR = 1.79, 95% CI [1.38, 2.33]). The risk of having their health affected were associated with female caregivers (OR = 3.63, 95% CI [1.73, 7.61]), those who received training (OR = 2.10, 95% CI [1.10, 4.00]) and those who provided care for 2 years or more (OR = 1.91, 95% CI [1.08, 3.37]). The factors associated with the effects on work were ethnicity, received training and had no assistance to provide the care. In terms of effect on social activities, female caregivers (OR = 1.96, 95% CI [1.04, 3.69]) and caregivers who received training were more likely (OR = 2.19, 95% CI [1.22, 3.93]) to have their social activities affected.
CONCLUSION: Our study revealed that sex, age, and self-reported illness were factors associated with being an informal caregiver in Malaysia. Informal caregivers faced effects on their health, work, and social activities which may be detrimental to their well-being. This understanding is crucial for planning support for caregivers.
METHODS: One hundred thirty-one FH patients, 68 RUC and 214 matched NC were recruited. Fasting lipid profile, biomarkers of inflammation (hsCRP), endothelial activation (sICAM-1 and E-selectin) and oxidative stress [oxidized LDL (oxLDL), malondialdehyde (MDA) and F2-isoprostanes (ISP)] were analyzed and independent predictor was determined using binary logistic regression analysis.
RESULTS: hsCRP was higher in FH and RUC compared to NC (mean ± SD = 1.53 ± 1.24 mg/L and mean ± SD = 2.54 ± 2.30 vs 1.10 ± 0.89 mg/L, p 0.05). FH was an independent predictor for sICAM-1 (p = 0.007), ox-LDL (p