METHODS: The data was extracted from the Bangladesh Demographic and Health Survey (BDHS)-2014. A total of 4,092 married non-pregnant Bangladeshi mothers who had at least one child aged 2 years or younger were included in this study. A two-level logistic regression model was used to remove the clustering effect for finding the impact of socio-economic and demographic factors on EIBF.
RESULTS: The prevalence of EIBF among Bangladeshi mothers was 51.4% (urban: 47.1% and rural: 53.4%). A two -level logistic regression model showed that mothers living in the Sylhet division (p<0.01) and rural environment (p<0.05) were more likely to practice EIBF. Mothers who were obese or overweight (p<0.01), had secondary (p<0.05) or higher education (p<0.01) were less likely to provide early breastfeeding to their newborn babies compared to their counterparts. Those who delivered by caesarian-section (p<0.01) were less likely to perform EIBF while those who attended an antenatal care clinic more than 3 times (p<0.05) were more likely to do so.
CONCLUSIONS: About half of the Bangladeshi mothers did not start breast-feeding within one hour after birth. This study identified several geographical and socio-demographic factors that were associated with EIBF, and hope that this information will help the government to focus their resources to promote early breastfeeding.
OBJECTIVE: The study aimed to derive dietary patterns empirically and to examine the consistency and generalizability of patterns across sex, ethnicity, and urban status in a working population.
DESIGN: This was a cross-sectional study using data from the Clustering of Lifestyle Risk Factors and Understanding its Association with Stress on Health and Well-Being among School Teachers in Malaysia study collected between August 2014 and November 2015. Dietary intake was assessed using a food frequency questionnaire, and dietary patterns were derived using factor analysis.
PARTICIPANTS/SETTING: Participants were teachers from selected public schools from three states in Peninsular Malaysia (n=4,618).
MAIN OUTCOME MEASURES: Dietary patterns derived using factor analysis.
STATISTICAL ANALYSES PERFORMED: Separate factor analysis was conducted by sex, ethnicity, and urban status to identify dietary patterns. Eigenvalue >2, scree plot, Velicer's minimum average partial analysis, and Horn's parallel analysis were used to determine the number of factors to retain. The interpretability of each dietary pattern was evaluated. The consistency and generalizability of dietary patterns across subgroups were assessed using the Tucker congruence coefficient.
RESULTS: There was no subgroup-specific dietary pattern found. Thus, dietary patterns were derived using the pooled sample in the final model. Two dietary patterns (Western and Prudent) were derived. The Western dietary pattern explained 15.4% of total variance, characterized by high intakes of refined grains, animal-based foods, added fat, and sugar-sweetened beverages as well as fast food. The Prudent dietary pattern explained 11.1% of total variance and was loaded with pulses, legumes, vegetables, and fruits.
CONCLUSIONS: The derived Western and Prudent dietary patterns were consistent and generalizable across subgroups of sex, ethnicity, and urban status. Further research is needed to explore associations between these dietary patterns and chronic diseases.
DESIGN: Cross-sectional analysis was carried out using the first wave data from MELoR which is a longitudinal study.
SETTING: Urban community dwellers in a middle-income South East Asian country.
PARTICIPANTS: 1565 participants aged ≥55 years were selected by simple random sampling from the electoral rolls of three parliamentary constituencies.
OUTCOME MEASURES: Consenting participants from the MELoR study were asked the question 'Have you fallen down in the past 12 months?' during their computer-assisted home-based interviews. Logistic regression analyses were conducted to compare the prevalence of falls among various ethnic groups.
RESULTS: The overall estimated prevalence of falls for individuals aged 55 years and over adjusted to the population of Kuala Lumpur was 18.9%. The estimated prevalence of falls for the three ethnic populations of Malays, Chinese and Indian aged 55 years and over was 16.2%, 19.4% and 23.8%, respectively. Following adjustment for ethnic discrepancies in age, gender, marital status and education attainment, the Indian ethnicity remained an independent predictor of falls in our population (relative risk=1.45, 95% CI 1.08 to 1.85).
CONCLUSION: The prevalence of falls in this study is comparable to other previous Asian studies, but appears lower than Western studies. The predisposition of the Indian ethnic group to falls has not been previously reported. Further studies may be needed to elucidate the causes for the ethnic differences in fall prevalence.
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: An analysis was conducted among 2237 older adults who participated in a longitudinal study on aging (LRGS TUA). This study involved four states in Malaysia, with 49.4% from urban areas. Respondents were divided into three categories of SES based on percentile, stratified according to urban and rural settings. SES was measured using household income.
RESULTS: The prevalence of low SES was higher among older adults in the rural area (50.6%) as compared to the urban area (49.4%). Factors associated with low SES among older adults in an urban setting were low dietary fibre intake (Adj OR:0.91),longer time for the Timed up and Go Test (Adj OR:1.09), greater disability (Adj OR:1.02), less frequent practice of caloric restriction (Adj OR:1.65), lower cognitive processing speed score (Adj OR:0.94) and lower protein intake (Adj OR:0.94). Whilst, among respondents from rural area, the factors associated with low SES were lack of dietary fibre intake (Adj OR:0.79), lower calf circumference (Adj OR: 0.91), lesser fresh fruits intake (Adj OR:0.91), greater disability (Adj OR:1.02) and having lower score in instrumental activities of daily living (Adj OR: 0.92).
CONCLUSION: Lower SES ismore prevalent in rural areas. Poor dietary intake, lower fitness and disability were common factors associated with low in SES, regardless of settings. Factors associated with low SES identifiedin both the urban and rural areas in our study may be useful inplanning strategies to combat low SES and its related problems among older adults.
METHODS: This prospective cross-sectional study aimed to identify rural-urban differences in risk factors for low birth weight among women in Malaysia. Pregnant women at ≥20 weeks of gestation in urban and rural Malaysia (n = 437) completed questionnaires on sociodemographic characteristics and physical activity. Weight and middle-upper arm circumference were measured. Infant birth outcomes were extracted from medical records.
RESULTS: The overall prevalence of low birth weight infants was 6.38%. Rural women had more low birth weight infants than urban women (9.8% vs 2.0%, p = 0.03). Findings showed rural women were less sedentary (p = 0.003) and participated in more household/caregiving activities (p = 0.036), sports activities (p = 0.01) and less occupational activity (p urban women. Logistic regression revealed that older age (OR = 1.395, 95% Cl = 1.053 to 1.846), low parity (OR = 0.256, 95% Cl = 0.088-0.747) and low middle-upper arm circumference (OR = 0.738, 95% Cl = 0.552 to 0.987) increased the risk of low birth weight infants in rural, but not in urban women.
CONCLUSIONS: We observed differences in risk factors for low birth weight between urban and rural pregnant women. Age, malnutrition and low parity were risk factors for low birth weight among rural pregnant women. Our findings suggest that rural pregnant women with low nutritional status should be encouraged to monitor their middle-upper arm circumference consistently throughout pregnancy. Improving nutritional status in rural pregnant women may reduce the risk of low birth weight infants in this population.
METHODS: Two cross-sectional studies were conducted in urban and rural areas of Yangon Region in 2013 and 2014 respectively, using the WHO STEPwise approach to surveillance of risk factors of NCDs. Through a multi-stage cluster sampling method, 1486 participants were recruited.
RESULTS: Age-standardized prevalence of the behavioral risk factors tended to be higher in the rural than urban areas for all included factors and significantly higher for alcohol drinking (19.9% vs. 13.9%; p = 0.040) and low fruit & vegetable consumption (96.7% vs. 85.1%; p = 0.001). For the metabolic risk factors, the tendency was opposite, with higher age-standardized prevalence estimates in urban than rural areas, significantly for overweight and obesity combined (40.9% vs. 31.2%; p = 0.023), obesity (12.3% vs.7.7%; p = 0.019) and diabetes (17.2% vs. 9.2%; p = 0.024). In sub-group analysis by gender, the prevalence of hypercholesterolemia and hypertriglyceridemia were significantly higher in urban than rural areas among males, 61.8% vs. 40.4%; p = 0.002 and 31.4% vs. 20.7%; p = 0.009, respectively. Mean values of age-standardized metabolic parameters showed higher values in urban than rural areas for both male and female. Based on WHO age-standardized Framingham risk scores, 33.0% (95% CI = 31.7-34.4) of urban dwellers and 27.0% (95% CI = 23.5-30.8) of rural dwellers had a moderate to high risk of developing CHD in the next 10 years.
CONCLUSION: The metabolic risk factors, as well as a moderate or high ten-year risk of CHD were more common among urban residents whereas behavioral risk factors levels were higher in among the rural people of Yangon Region. The high prevalences of NCD risk factors in both urban and rural areas call for preventive measures to reduce the future risk of NCDs in Myanmar.