RESEARCH DESIGN AND METHODS: The prevalence of diabetes, defined as self-reported or fasting glycemia ≥7 mmol/L, was documented in 119,666 adults from three high-income (HIC), seven upper-middle-income (UMIC), four lower-middle-income (LMIC), and four low-income (LIC) countries. Relationships between diabetes and its risk factors within these country groupings were assessed using multivariable analyses.

RESULTS: Age- and sex-adjusted diabetes prevalences were highest in the poorer countries and lowest in the wealthiest countries (LIC 12.3%, UMIC 11.1%, LMIC 8.7%, and HIC 6.6%; P < 0.0001). In the overall population, diabetes risk was higher with a 5-year increase in age (odds ratio 1.29 [95% CI 1.28-1.31]), male sex (1.19 [1.13-1.25]), urban residency (1.24 [1.11-1.38]), low versus high education level (1.10 [1.02-1.19]), low versus high physical activity (1.28 [1.20-1.38]), family history of diabetes (3.15 [3.00-3.31]), higher waist-to-hip ratio (highest vs. lowest quartile; 3.63 [3.33-3.96]), and BMI (≥35 vs. <25 kg/m(2); 2.76 [2.52-3.03]). The relationship between diabetes prevalence and both BMI and family history of diabetes differed in higher- versus lower-income country groups (P for interaction < 0.0001). After adjustment for all risk factors and ethnicity, diabetes prevalences continued to show a gradient (LIC 14.0%, LMIC 10.1%, UMIC 10.9%, and HIC 5.6%).

METHODS: A population-based cross-sectional study was conducted in Singapore. Participants wore an accelerometer for 7 days to measure physical activity (PA). Demographic, anthropometric and psychological data were also collected. Psychological variables included PA guideline knowledge, motivational profile for PA self-regulation (5 subscales), perceived barriers to PA (4 subscales) and perceived social support for PA. Regression models with adjustment for socio-demographic variables were fitted.

METHODS: In this large-scale prospective cohort study, we recruited adults aged between 35 years and 70 years from 367 urban and 302 rural communities in 20 countries. We collected data on families and households in two questionnaires, and data on cardiovascular risk factors in a third questionnaire, which was supplemented with physical examination. We assessed socioeconomic status using education and a household wealth index. Education was categorised as no or primary school education only, secondary school education, or higher education, defined as completion of trade school, college, or university. Household wealth, calculated at the household level and with household data, was defined by an index on the basis of ownership of assets and housing characteristics. Primary outcomes were major cardiovascular disease (a composite of cardiovascular deaths, strokes, myocardial infarction, and heart failure), cardiovascular mortality, and all-cause mortality. Information on specific events was obtained from participants or their family.

FINDINGS: Recruitment to the study began on Jan 12, 2001, with most participants enrolled between Jan 6, 2005, and Dec 4, 2014. 160 299 (87·9%) of 182 375 participants with baseline data had available follow-up event data and were eligible for inclusion. After exclusion of 6130 (3·8%) participants without complete baseline or follow-up data, 154 169 individuals remained for analysis, from five low-income, 11 middle-income, and four high-income countries. Participants were followed-up for a mean of 7·5 years. Major cardiovascular events were more common among those with low levels of education in all types of country studied, but much more so in low-income countries. After adjustment for wealth and other factors, the HR (low level of education vs high level of education) was 1·23 (95% CI 0·96-1·58) for high-income countries, 1·59 (1·42-1·78) in middle-income countries, and 2·23 (1·79-2·77) in low-income countries (pinteraction<0·0001). We observed similar results for all-cause mortality, with HRs of 1·50 (1·14-1·98) for high-income countries, 1·80 (1·58-2·06) in middle-income countries, and 2·76 (2·29-3·31) in low-income countries (pinteraction<0·0001). By contrast, we found no or weak associations between wealth and these two outcomes. Differences in outcomes between educational groups were not explained by differences in risk factors, which decreased as the level of education increased in high-income countries, but increased as the level of education increased in low-income countries (pinteraction<0·0001). Medical care (eg, management of hypertension, diabetes, and secondary prevention) seemed to play an important part in adverse cardiovascular disease outcomes because such care is likely to be poorer in people with the lowest levels of education compared to those with higher levels of education in low-income countries; however, we observed less marked differences in care based on level of education in middle-income countries and no or minor differences in high-income countries.

INTERPRETATION: Although people with a lower level of education in low-income and middle-income countries have higher incidence of and mortality from cardiovascular disease, they have better overall risk factor profiles. However, these individuals have markedly poorer health care. Policies to reduce health inequities globally must include strategies to overcome barriers to care, especially for those with lower levels of education.

METHODS: We undertook analysis on 11,821 subjects from six seroprevalence surveys conducted in Malaysia between 2001 and 2013, which composed of five urban and two rural series.

RESULTS: Prevalence of dengue increased with age in both urban and rural locations in Malaysia, which exceeded 90 % among those aged 70 years or beyond. The age-specific rates of the 5 urban surveys overlapped without clear separation among them, while prevalence was lower in younger subjects in rural series than in urban series, the trend reversed in older subjects. There were no differences in the seroprevalence by gender, ethnicity or region. Poisson regression model confirmed the prevalence have not changed in urban areas since 2001 but in rural areas, there was a significant positive time trend such that by year 2008, rural prevalence was as high as in urban areas.

METHODS: A cross-sectional study was conducted among 214 respondents in northeastern Malaysia using a multi-stage stratified random sampling method. The study population was divided into two groups based on geographical locations: urban and rural. All data were entered and analyzed using the IBM Statistics for Social Sciences (SPSS) version 22.0 software for Windows (IBM, Armonk, NY, USA). The continuous variables were presented using mean and standard deviation (SD), whereas the categorical variables were described using frequency and percentage. Multiple logistic regression was performed to determine the associated factors for good KABP toward leptospirosis among the respondents.

RESULTS: It was found that 52.8% of respondents had good knowledge, 84.6% had positive attitudes, 59.8% had positive beliefs, and 53.7% had satisfactory practices. There were no significant sociodemographic factors associated with knowledge and practice, except for educational status, which was significant in the attitude and belief domains. Those with higher education exhibited better attitudes (Odds Ratio (OR) 3.329; 95% Coefficient Interval (CI): 1.140, 9.723; p = 0.028) and beliefs (OR 3.748; 95% CI: 1.485, 9.459; p = 0.005). The communities in northeastern Malaysia generally have good knowledge and a high level of positive attitude; however, this attitude cannot be transformed into practice as the number of people with satisfactory practice habits is much lower compared to those with positive attitudes. As for the belief domain, the communities must have positive beliefs to perceive the threat of the disease.

METHODS: In this cross-sectional study, we include 196 homeless people aged above 18 years, Malaysian who were able to communicate with interviewers, and respondents who were not aggressive. These respondents were transits at Pusat Transit Gelandangan Kuala Lumpur and Anjung Singgah Kuala Lumpur and were available during interview sessions. They were selected via simple random sampling and were interviewed via face to face guided interviews using a validated structured questionnaire. Data were analysed descriptively, as well as using bivariate and multivariate analysis to explore the associated factors.