Methods: A cross-sectional survey was conducted in a low-income housing area in Kuala Lumpur, Malaysia. Data were collected using a questionnaire via face-to-face interviews by trained enumerators in order to obtain details on sociodemographic characteristics and dietary practices.
Results: Descriptive statistics showed that 86.7% of the respondents in the low-income community consumed fruit and vegetables less than five times per day, 11.7% consumed carbonated and sweetened drinks more than twice per day and about 25% consumed fast food more than four times per month. In total, 65.2% (n=945) did not have healthy dietary practices. Binary logistic regression showed that age, education and ethnicity were significant predictors of unhealthy dietary practices among the low-income community. Those in the 30-59 years age group had higher odds (odds ratio 1.65, p=0.04) of practising an unhealthy diet as compared with those older than 60 years of age.
Conclusion: Unhealthy dietary practices were found to be common among the low-income group living in an urban area. Healthy lifestyle intervention should be highlighted so that it can be adopted in the low-income group.
METHODS: A cross-sectional study was conducted at the National Heart Institute of Malaysia involving 503 patients who were hospitalized during the year prior to the survey.
RESULTS: The mean annual out-of-pocket health spending for IHD was MYR3045 (at the time US$761). Almost 16% (79/503) suffered from catastrophic health spending (out-of-pocket health spending ≥40% of household non-food expenditures), 29.2% (147/503) were unable to pay for medical bills, 25.0% (126/503) withdrew savings to help meet living expenses, 16.5% (83/503) reduced their monthly food consumption, 12.5% (63/503) were unable to pay utility bills and 9.0% (45/503) borrowed money to help meet living expenses.
CONCLUSIONS: Overall, the economic impact of IHD on patients in Malaysia was considerable and the prospect of economic hardship likely to persist over the years due to the long-standing nature of IHD. The findings highlight the need to evaluate the present health financing system in Malaysia and to expand its safety net coverage for vulnerable patients.
METHODS: PLHIV enrolled in the Therapeutics, Research, Education and AIDS Training in Asia (TREAT Asia) HIV Observational Database (TAHOD) who initiated ART with a CD4 count 1 year were censored at 12 months. Competing risk regression was used to analyse risk factors with loss to follow-up as a competing risk.
RESULTS: A total of 1813 PLHIV were included in the study, of whom 74% were male. With 73 (4%) deaths, the overall first-year mortality rate was 4.27 per 100 person-years (PY). Thirty-eight deaths (52%) were AIDS-related, 10 (14%) were immune reconstituted inflammatory syndrome (IRIS)-related, 13 (18%) were non-AIDS-related and 12 (16%) had an unknown cause. Risk factors included having a body mass index (BMI) 100 cells/μL: SHR 0.12; 95% CI 0.05-0.26) was associated with reduced hazard for mortality compared to CD4 count ≤ 25 cells/μL.
CONCLUSIONS: Fifty-two per cent of early deaths were AIDS-related. Efforts to initiate ART at CD4 counts > 50 cell/μL are associated with improved short-term survival rates, even in those with late stages of HIV disease.
METHODS: We analysed Demographic and Health Survey data on tobacco use collected from large nationally representative samples of men and women in 54 LMICs. We estimated the weighted prevalence of any current tobacco use (including smokeless tobacco) in each country for 4 educational groups and 4 wealth groups. We calculated absolute and relative measures of inequality, that is, the slope index of inequality (SII) and relative index of inequality (RII), which take into account the distribution of prevalence across all education and wealth groups and account for population size. We also calculated the aggregate SII and RII for low-income (LIC), lower-middle-income (lMIC) and upper-middle-income (uMIC) countries as per World Bank classification.
FINDINGS: Male tobacco use was highest in Bangladesh (70.3%) and lowest in Sao Tome (7.4%), whereas female tobacco use was highest in Madagascar (21%) and lowest in Tajikistan (0.22%). Among men, educational inequalities varied widely between countries, but aggregate RII and SII showed an inverse trend by country wealth groups. RII was 3.61 (95% CI 2.83 to 4.61) in LICs, 1.99 (95% CI 1.66 to 2.38) in lMIC and 1.82 (95% CI 1.24 to 2.67) in uMIC. Wealth inequalities among men varied less between countries, but RII and SII showed an inverse pattern where RII was 2.43 (95% CI 2.05 to 2.88) in LICs, 1.84 (95% CI 1.54 to 2.21) in lMICs and 1.67 (95% CI 1.15 to 2.42) in uMICs. For educational inequalities among women, the RII varied much more than SII varied between the countries, and the aggregate RII was 14.49 (95% CI 8.87 to 23.68) in LICs, 3.05 (95% CI 1.44 to 6.47) in lMIC and 1.58 (95% CI 0.33 to 7.56) in uMIC. Wealth inequalities among women showed a pattern similar to that of men: the RII was 5.88 (95% CI 3.91 to 8.85) in LICs, 1.76 (95% CI 0.80 to 3.85) in lMIC and 0.39 (95% CI 0.09 to 1.64) in uMIC. In contrast to men, among women, the SII was pro-rich (higher smoking among the more advantaged) in 13 of the 52 countries (7 of 23 lMIC and 5 of 7 uMIC).
INTERPRETATION: Our results confirm that socioeconomic inequalities tobacco use exist in LMIC, varied widely between the countries and were much wider in the lowest income countries. These findings are important for better understanding and tackling of socioeconomic inequalities in health in LMIC.
METHODS: Data of 328 eligible housewives who participated in the MyBFF@Home study was used. Intervention group of 169 subjects were provided with an intervention package which includes physical activity (brisk walking, dumbbell exercise, physical activity diary, group exercise) and 159 subjects in control group received various health seminars. Physical activity level was assessed using short-International Physical Activity Questionnaire. The physical activity level was then re-categorized into 4 categories (active intervention, inactive intervention, active control and inactive control). Physical activity, blood glucose and lipid profile were measured at baseline, 3rd month and 6th month of the study. General Linear Model was used to determine the effect of physical activity on glucose and lipid profile.
RESULTS: At the 6th month, there were 99 subjects in the intervention and 79 control group who had complete data for physical activity. There was no difference on the effect of physical activity on the glucose level and lipid profile except for the Triglycerides level. Both intervention and control groups showed reduction of physical activity level over time.
CONCLUSION: The effect of physical activity on blood glucose and lipid profile could not be demonstrated possibly due to physical activity in both intervention and control groups showed decreasing trend over time.
METHODS: A systematic review and meta-analysis of interventional studies assessing quality improvement processes, interventions, and structure in developing country trauma systems was conducted from November 1989 to August 2020 according to the Preferred Reporting Items of Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Studies were included if they were conducted in an LMIC population according to World Bank Income Classification, occurred in a trauma setting, and measured the effect of implementation and its impact. The primary outcome was trauma mortality.
RESULTS: Of 37,575 search results, 30 studies were included from 15 LMICs covering five WHO regions in a qualitative synthesis. Twenty-seven articles were included in a meta-analysis. Implementing a pre-hospital trauma system reduced overall trauma mortality by 45% (risk ratio (RR) 0.55, 95% CI 0.4 to 0.75). Training first responders resulted in an overall decrease in mortality (RR 0.47, 95% CI 0.28 to 0.78). In-hospital trauma training with certified courses resulted in a reduction of mortality (RR 0.71, 95% CI 0.62 to 0.78). Trauma audits and trauma protocols resulted in varying improvements in trauma mortality.
CONCLUSION: There is evidence that quality improvement processes, interventions, and structure can improve mortality in the trauma systems in LMICs.
METHODS: Grey literature was searched at the library of the University of Kebangsaan, Malaysia, on database engines Google Scholar and Science Direct with specific key words to screen papers published from January 2001 to June 2016. They were reviewed to identify the key factors affecting scaling up of health-related pilot projects. Full-text articles were selected, and their reference lists were checked to look for relevant papers. They were short-listed and analysed using thematic approach.
RESULTS: Of the 47 articles initially screened, 14(29.78%) were shortlisted. Thematic analysis of the selected articles suggested several key factors contributed to the successful scale-up of pilot projects. These factors included evidence-based and effective intervention, community readiness, government support, stakeholders' engagement, and monitoring and supervision.
CONCLUSIONS: To maximise health coverage in developing and low middle-income countries, scaling up of health interventions on a large scale is essential to improve the health and wellbeing of people. The identified key factors should be considered while planning the scale-up of any health project.
METHODS: Data from TUA cohort study involving 1366 older adults (aged 60 years and above) categorized as low-income were analysed, for risk of MCR syndrome based on defined criteria. Chi-square analysis and independent t test were employed to examine differences in socioeconomic, demographic, chronic diseases and lifestyle factors between MCR and non-MCR groups. Risk factors of MCR syndrome were determined using hierarchical logistic regression.
RESULTS: A total of 3.4% of participants fulfilled the criteria of MCR syndrome. Majority of them were female (74.5%, p = 0.001), single/widow/widower/divorced (55.3%, p = 0.002), living in rural area (72.3%, p = 0.011), older age (72.74 ± 7.08 year old, p
METHODS: Data for this study was extracted from the 2011 Bangladesh Demographic and Health Survey (BDHS-2011). In this survey, data was collected using a two-stage stratified cluster sampling approach. The chi-square test and a two-level logistic regression model were used for further analysis.
RESULTS: Data from 2231 children aged 6-59 months were included for analysis. The prevalence of child anemia was noted to be 52.10%. Among these anemic children, 48.40% where from urban environment and 53.90% were from rural areas. The prevalence of mild, moderate and severe anemia among children was 57.10, 41.40 and 1.50% respectively. The two-level logistic regression model revealed that the following factors were associated with childhood anemia: children of anemic mothers (p
METHODS: Through the Association of Southeast Asian Nations Costs in Oncology study, 1,294 newly diagnosed patients with cancer (Ministry of Health [MOH] hospitals [n = 577], a public university hospital [n = 642], private hospitals [n = 75]) were observed in Malaysia. Cost diaries and questionnaires were used to measure incidence of financial toxicity, encompassing financial catastrophe (FC; out-of-pocket costs ≥ 30% of annual household income), medical impoverishment (decrease in household income from above the national poverty line to below that line after subtraction of cancer-related costs), and economic hardship (inability to make necessary household payments). Predictors of financial toxicity were determined using multivariable analyses.
RESULTS: One fifth of patients had private health insurance. Incidence of FC at 1 year was 51% (MOH hospitals, 33%; public university hospital, 65%; private hospitals, 72%). Thirty-three percent of households were impoverished at 1 year. Economic hardship was reported by 47% of families. Risk of FC attributed to conventional medical care alone was 18% (MOH hospitals, 5%; public university hospital, 24%; private hospitals, 67%). Inclusion of expenditures on nonmedical goods and services inflated the risk of financial toxicity in public hospitals. Low-income status, type of hospital, and lack of health insurance were strong predictors of FC.
CONCLUSION: Patients with cancer may not be fully protected against financial hardships, even in settings with universal health coverage. Nonmedical costs also contribute as important drivers of financial toxicity in these settings.
METHODS: Data from four Western Pacific nations (N = 3,277) are used to test additive and multiplicative models of the relationships between financial strain, social relations, and psychological distress.
RESULTS: Financial strain is associated with higher levels of psychological distress in three of the four nations. Interactive models of the effects of financial strain and social relations on distress were uncovered in three of the four nations, but the type of social relation influencing the strain-distress relationship varied. Subjective-health and IADLs were significant predictors of psychological distress in all four nations.
DISCUSSION: Findings suggest that although financial strain is quite likely to lead to psychological distress among elders, this can be mitigated, at least in part, by social relationships. Modernization was not associated with higher psychological distress.