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 《-》

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》

Socio-economic inequalities in physical activity among Japanese adults during the COVID-19 pandemic.

This study aimed to explore the socio-economic inequalities in physical activity (PA) based on domains of daily life, such as work, transport, recreation and sedentary life, among Japanese adults during the COVID-19 pandemic. This was a cross-sectional study. This study used data from the 2020 National Sport and Lifestyle Survey, conducted by the Sasakawa Sports Foundation. Data of 2,296 (1,103 women) participants were analysed. PAs were assessed using the Global Physical Activity Questionnaire. Educational level and household income were used as indicators of socio-economic status. We calculated the slope index of inequality (SII) and relative index of inequality (RII). We detected absolute and relative inequalities for household income in all PA domains, except for work-related PA. The higher the participants' income, the longer they engaged in transport- and recreation-related PA and sedentary behaviour. Recreation-related PA had a larger disparity than other domains, with SII at 20.8% (95% confidence interval [CI] -28.4 to -13.1) and RII at 0.58 (95% CI 0.47-0.71). At the educational level, each inequality was observed in work- and recreation-related PA and sedentary behaviour. The higher the participants' educational level, the longer they engaged in recreation-related PA and sedentary behaviour. However, work-related PA was longer at lower educational levels, with RII at 1.90 (95% CI 1.48-2.44). The inequality in recreation-related PA was also relatively large (SII 23.3%, 95% CI -30.9 to -15.7; RII 0.54, 95% CI 0.45-0.66). Our study revealed significant socio-economic disparities in each PA domain, particularly in recreational PA. These results suggest a widening gap because of the COVID-19 pandemic.

Kyan A ，Takakura M 《-》

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 《-》

Socioeconomic inequalities in sport participation: pattern per sport and time trends - a repeated cross-sectional study.

Sport participation is an important component of a healthy lifestyle and is known to be more common among privileged individuals. However, few studies examined socio-demographic patterns of participation by type of activity. This study aims at quantifying socio-economic inequalities in sport participation by sport type, and to analyse their trend over 15 years. We used 2005-2019 data from the Bus Santé study, a yearly population-based cross-sectional survey of Geneva adults. Sport participation was defined as reporting at least one sporting activity over the previous week; educational level, household income and occupational position were used as indicators of socio-economic position. Socio-economic inequalities in sport participation, and their trend over time, were examined using the relative and slope indexes of inequality (RII/SII). Out of 7769 participants (50.8% women, mean age 46 years old), 60% participated in a sporting activity. Results showed that the higher the socioeconomic circumstances, the higher the sport participation (RII = 1.78; 95% Confidence Interval (CI): 1.64-1.92; SII = 0.33; 95%CI: 0.29-0.37 for education). Relative inequalities varied per sport e.g., 0.68 (95%CI: 0.44-1.07) for football and 4.25 (95%CI: 2.68-6.75) for tennis/badminton for education. Yearly absolute inequalities in sport participation tended to increase between 2005 and 2019 for household income, especially among women and older adults. We observed strong socio-economic inequalities in sport participation in Geneva, with different magnitude depending on the sport type. These inequalities seemed to increase over the 2005-2019 period. Our results call for tailored measures to promote the participation of socially disadvantaged populations in sporting activities.

Richard V ，Piumatti G ，Pullen N ，Lorthe E ，Guessous I ，Cantoreggi N ，Stringhini S ... -  《-》