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Measuring and forecasting progress towards the education-related SDG targets.

ABSTRACT: Education is a key dimension of well-being and a crucial indicator of development^1-4. The Sustainable Development Goals (SDGs) prioritize progress in education, with a new focus on inequality^5-7. Here we model the within-country distribution of years of schooling, and use this model to explore educational inequality since 1970 and to forecast progress towards the education-related 2030 SDG targets. We show that although the world is largely on track to achieve near-universal primary education by 2030, substantial challenges remain in the completion rates for secondary and tertiary education. Globally, the gender gap in schooling had nearly closed by 2018 but gender disparities remained acute in parts of sub-Saharan Africa, and North Africa and the Middle East. It is predicted that, by 2030, females will have achieved significantly higher educational attainment than males in 18 countries. Inequality in education reached a peak globally in 2017 and is projected to decrease steadily up to 2030. The distributions and inequality metrics presented here represent a framework that can be used to track the progress of each country towards the SDG targets and the level of inequality over time. Reducing educational inequality is one way to promote a fairer distribution of human capital and the development of more equitable human societies.

SUBMITTER: Friedman J

PROVIDER: S-EPMC7332421 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:INTRODUCTION:Nepal has made considerable progress on improving child survival during the Millennium Development Goal period, however, further progress will require accelerated reduction in neonatal mortality. Neonatal survival is one of the priorities for Sustainable Development Goals 2030. This paper examines the trends, equity gaps and factors associated with neonatal mortality between 2001 and 2016 to assess the likelihood of Every Newborn Action Plan (ENAP) target being reached in Nepal by 2030. METHODS:This study used data from the 2001, 2006, 2011 and 2016 Nepal Demographic and Health Surveys. We examined neonatal mortality rate (NMR) across the socioeconomic strata and the annual rate of reduction (ARR) between 2001 and 2016. We assessed association of socio-demographic, maternal, obstetric and neonatal factors associated with neonatal mortality. Based on the ARR among the wealth quintile between 2001 and 2016, we made projection of NMR to achieve the ENAP target. Using the Lorenz curve, we calculated the inequity distribution among the wealth quintiles between 2001 and 2016. RESULTS:In NDHS of 2001, 2006, 2011 and 2016, a total of 8400, 8600, 13,485 and 13,089 women were interviewed respectively. There were significant disparities between wealth quintiles that widened over the 15 years. The ARR for NMR declined with an average of 4.0% between 2001 and 2016. Multivariate analysis of the 2016 data showed that women who had not been vaccinated against tetanus had the highest risk of neonatal mortality (adjusted odds ratio [AOR] 3.38; 95% confidence interval [CI] 1.20-9.55), followed by women who had no education (AOR 1.87; 95% CI 1.62-2.16). Further factors significantly associated with neonatal mortality were the mother giving birth before the age of 20 (AOR 1.76; CI 95% 1.17-2.59), household air pollution (AOR 1.37; CI 95% 1.59-1.62), belonging to a poorest quintile (AOR 1.37; CI 95% 1.21-1.54), residing in a rural area (AOR 1.28; CI 95% 1.13-1.44), and having no toilet at home (AOR 1.21; CI 95% 1.06-1.40). If the trend of neonatal mortality rate of 2016 continues, it is projected that the poorest family will reach the ENAP target in 2067. CONCLUSIONS:Although neonatal mortality is declining in Nepal, if the current trend continues it will take another 50 years for families in the poorest group to attain the 2030 ENAP target. There are different factors associated with neonatal mortality, reducing the disparities for maternal and neonatal care will reduce mortality among the poorest families.

Project description:Malaria presents public health challenge despite extensive intervention campaigns. A 30-year hindcast of the climatic suitability for malaria transmission in India is presented, using meteorological variables from a state of the art seasonal forecast model to drive a process-based, dynamic disease model.The spatial distribution and seasonal cycles of temperature and precipitation from the forecast model are compared to three observationally-based meteorological datasets. These time series are then used to drive the disease model, producing a simulated forecast of malaria and three synthetic malaria time series that are qualitatively compared to contemporary and pre-intervention malaria estimates. The area under the Relative Operator Characteristic (ROC) curve is calculated as a quantitative metric of forecast skill, comparing the forecast to the meteorologically-driven synthetic malaria time series.The forecast shows probabilistic skill in predicting the spatial distribution of Plasmodium falciparum incidence when compared to the simulated meteorologically-driven malaria time series, particularly where modelled incidence shows high seasonal and interannual variability such as in Orissa, West Bengal, and Jharkhand (North-east India), and Gujarat, Rajastan, Madhya Pradesh and Maharashtra (North-west India). Focusing on these two regions, the malaria forecast is able to distinguish between years of "high", "above average" and "low" malaria incidence in the peak malaria transmission seasons, with more than 70% sensitivity and a statistically significant area under the ROC curve. These results are encouraging given that the three month forecast lead time used is well in excess of the target for early warning systems adopted by the World Health Organization. This approach could form the basis of an operational system to identify the probability of regional malaria epidemics, allowing advanced and targeted allocation of resources for combatting malaria in India.

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Measuring and forecasting progress towards the education-related SDG targets.

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