Inequalities in the prevalence of diabetes mellitus and its risk factors in Sri Lanka: a lower middle income country.

De Silva AP ，De Silva SHP ，Haniffa R ，Liyanage IK ，Jayasinghe S ，Katulanda P ，Wijeratne CN ，Wijeratne S ，Rajapaksa LC ... -  《International Journal for Equity in Health》

Educational and wealth inequalities in tobacco use among men and women in 54 low-income and middle-income countries.

Socioeconomic differentials of tobacco smoking in high-income countries are well described. However, studies to support health policies and place monitoring systems to tackle socioeconomic inequalities in smoking and smokeless tobacco use common in low-and-middle-income countries (LMICs) are seldom reported. We aimed to describe, sex-wise, educational and wealth-related inequalities in tobacco use in LMICs. 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. 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). 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.

Sreeramareddy CT ，Harper S ，Ernstsen L 《-》

Social, cultural and economical determinants of diabetes mellitus in Kalutara district, Sri Lanka: a cross sectional descriptive study.

Pubudu De Silva A ，Padmal De Silva SH ，Liyanage IK ，Rajapakse LC ，Jayasinghe KS ，Katulanda P ，Wijeratne CN ，Wijeratne S ... -  《International Journal for Equity in Health》

Cardiovascular risk factors-using repeated cross-sectional surveys to assess time trends in socioeconomic inequalities in neighbouring countries.

This study compares trends in socioeconomic inequalities related to key cardiovascular risk factors in neighbouring countries Northern Ireland (NI) and the Republic of Ireland (RoI). Repeated cross-sectional studies. Population based. 3500-4000 in national surveys in NI and 5000-9000 in RoI, aged 20-69 years. Educational attainment was used as a socioeconomic indicator by which the magnitude and direction of trends in inequalities for smoking, diabetes, obesity and physical inactivity in NI and RoI were examined between 1997/1998 and 2007/2011. Gender-specific relative and absolute inequalities were calculated using the Relative Index of Inequality (RII) and Slope Index of Inequality (SII) for both countries. In both countries, the prevalence of diabetes and obesity increased whereas levels of smoking and physical inactivity decreased over time. In NI relative inequalities increased for obesity (RII 1.1 in males and 2.1 in females in 2010/2011) and smoking (RII 4.5 in males and 4.2 in females in 2010/2011) for both genders and absolute inequalities increased for all risk factors in men and increased for diabetes and obesity in women. In RoI greater inequality was observed in women, particularly for smoking (RII 2.8 in 2007) and obesity (RII 8.2 in 2002) and in men for diabetes (RII 3.2 in 2002). Interventions to reduce inequalities in risk factors, particularly smoking, obesity and diabetes are encouraged across both countries.

Hughes J ，Kabir Z ，Kee F ，Bennett K ... -  《BMJ Open》

Socio-economic inequalities in health among older adults in China.

This study aimed to explore socio-economic inequalities in the health status of older people in China using the most recent data available. This was a cross-sectional study. Data for this study were obtained from the 2018 China Health and Retirement Longitudinal Study, which included 9831 subjects aged 60 years and older. We assessed differences in the prevalence of self-reported health, functional limitations, and chronic conditions by education level and household income level, and then estimated the Slope of Inequality Index (SII) and the Relative Inequality Index (RII) - indexes of the relative magnitude of socio-economic inequalities in health. We found inequalities in all dimensions of health (self-assessed health status, reported chronic conditions, and physical functional limitations) at the household income level. Physical functional limitations, particularly the ability to perform instrumental activities of daily living, produced greater inequality than other domains, with an adjusted SII of 0.495 (95% CI, 0.467-0.524) and an adjusted RII of 2.129 (95% CI, 1.604-2.653). ADL limitations (adjusted SII, 0.524, 95% CI, 0.473-0.575, adjusted RII, 1.527, 95% CI, 1.027-2.027) and self-measured health (adjusted SII, 0.523, 95% CI, 0.258-0.789, adjusted RII, 1.531, 95% CI, 0.551-2.512) were also clearly different. Inequalities were also found across all health domains in terms of educational attainment. Consistent with inequalities in household income, inequalities were greatest for limitations in the ability to perform instrumental activities of daily living (adjusted SII, 0.581, 95% CI, 0.424-0.739, adjusted RII, 3.699, 95% CI, 3.642-3.757). Relative inequalities in limitations in activities of daily living (adjusted SII, 0.676, 95% CI, 0.560-0.792, adjusted RII, 2.587, 95% CI, 2.392-2.784) and self-rated health (poor/very poor) (adjusted SII, 0.647, 95% CI, 0.617-0.677, adjusted RII, 2.406, 95% CI, 2.224-2.587) were also higher. Our study shows significant socio-economic differences in the areas of self-rated health, functional limitations, and reported chronic diseases, particularly in the area of IADL limitations. These inequalities need to be explicitly addressed and vulnerable subgroups should be targeted to reduce the socio-economic disparities.

Shang XT ，Wei ZH 《-》