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Summary: Background: Coverage levels for essential interventions aimed at reducing deaths of mothers and children are increasing steadily in most low-income and middle-income countries. We assessed how much poor and rural populations in these countries are benefiting from national-level progress. Methods: We analysed trends in a composite coverage indicator (CCI) based on eight reproductive, maternal, newborn, and child health interventions in 209 national surveys in 64 countries, from Jan 1, 1994, to Dec 31, 2014. Trends by wealth quintile and urban or rural residence were fitted with multilevel modelling. We used an approach akin to the calculation of population attributable risk to quantify the contribution of poor and rural populations to national trends. Findings: From 1994 to 2014, the CCI increased by 0·82 percent points a year across all countries; households in the two poorest quintiles had an increase of 0·99 percent points a year, which was faster than that for the three wealthiest quintiles (0·68 percent points). Gains among poor populations were faster in lower-middle-income and upper-middle-income countries than in low-income countries. Globally, national level increases in CCI were 17·5% faster than they would have been without the contribution of the two poorest quintiles. Coverage increased more rapidly annually in rural (0·93 percent points) than urban (0·52 percent points) areas. Interpretation: National coverage gains were accelerated by important increases among poor and rural mothers and children. Despite progress, important inequalities persist, and need to be addressed to achieve the Sustainable Development Goals. Funding: UNICEF, Wellcome Trust.

BACKGROUND:There is rapidly-emerging evidence on the harmful health effects of sedentary behaviors. The aim of this paper was to quantify time in sedentary behaviors and document socio-demographic variations in different life domains among adults. METHODS:A population-based survey was carried out in 2012 through face-to-face interviews with Brazilian adults aged 20+ years (N = 2,927). Information about time spent sedentary in a typical weekday was collected for five different domains (workplace, commuting, school/university, watching TV, and computer use at home). Descriptive and bivariate analyses examined variations in overall and domain-specific sedentary time by gender, age, educational attainment and socioeconomic position. RESULTS:On average, participants reported spending 5.8 (SD 4.5) hours per day sitting. The median value was 4.5 (interquartile range: 2.5-8) hours. Men, younger adults, those with higher schooling and from the wealthiest socioeconomic groups had higher overall sedentary scores. TV time was higher in women, older adults and among those with low schooling and socioeconomic position. Sedentary time in transport was higher in men, younger adults, and participants with high schooling and high socioeconomic position. Computer use at home was more frequent among young adults and those from high socioeconomic groups. Sitting at work was higher in those with higher schooling and from the wealthiest socioeconomic groups. Sedentary behavior at school was related inversely to age and directly to schooling. CONCLUSION:Patterns of sedentary behavior are different by life domains. Initiatives to reduce prolonged sitting among Brazilian adults will be required on multiple levels for different life domains.

Abstract Background Having high-quality data available by 2020, disaggregated by income, is one of the Sustainable Development Goals (SGD). We explored how well coverage with skilled birth attendance (SBA) is predicted by asset-based wealth quintiles and by absolute income. Methods We used data from 293 national surveys conducted in 100 low and middle-income countries (LMICs) from 1991 to 2014. Data on household income were computed using national income levels and income inequality data available from the World Bank and the Standardized World Income Inequality Database. Multivariate regression was used to explore the predictive capacity of absolute income compared to the traditional measure of quintiles of wealth index. Results The mean SBA coverage was 68.9% (SD: 24.2), compared to 64.7% (SD: 26.6) for institutional delivery coverage. Median daily family income in the same period was US$ 6.4 (IQR: 3.5–14.0). In cross-country analyses, log absolute income predicts 51.5% of the variability in SBA coverage compared to 22.0% predicted by the wealth index. For within-country analysis, use of absolute income improved the understanding of the gap in SBA coverage among the richest and poorest families. Information on income allowed identification of countries – such as Burkina Faso, Cambodia, Egypt, Nepal and Rwanda – which were well above what would be expected solely from changes in income. Conclusion Absolute income is a better predictor of SBA and institutional delivery coverage than the relative measure of quintiles of wealth index and may help identify countries where increased coverage is likely due to interventions other than increased income.