Helena Manguerra

BACKGROUND: The Sustainable Development Goals (SDGs) mandate systematic monitoring of the health and wellbeing of all children to achieve optimal early childhood development. However, global epidemiological data on children with developmental disabilities are scarce. The Global Burden of Diseases, Injuries, and Risk Factors Study 2016 provides a comprehensive assessment of prevalence and years lived with disability (YLDs) for development disabilities among children younger than 5 years in 195 countries and territories from 1990 to 2016. METHODS: We estimated prevalence and YLDs for epilepsy, intellectual disability, hearing loss, vision loss, autism spectrum disorder, and attention deficit hyperactivity disorder. YLDs were estimated as the product of the prevalence estimate and the disability weight for each mutually exclusive disorder, corrected for comorbidity. We used DisMod-MR 2.1, a Bayesian meta-regression tool, on a pool of primary data derived from systematic reviews of the literature, health surveys, hospital and claims databases, cohort studies, and disease-specific registries. FINDINGS: Globally, 52·9 million (95% uncertainty interval [UI] 48·7-57·3; or 8·4% [7·7-9·1]) children younger than 5 years (54% males) had developmental disabilities in 2016 compared with 53·0 million (49·0-57·1; or 8·9% [8·2-9·5]) in 1990. About 95% of these children lived in low-income and middle-income countries. YLDs among these children increased from 3·8 million (95% UI 2·8-4·9) in 1990 to 3·9 million (2·9-5·2) in 2016. These disabilities accounted for 13·3% of the 29·3 million YLDs for all health conditions among children younger than 5 years in 2016. Vision loss was the most prevalent disability, followed by hearing loss, intellectual disability, and autism spectrum disorder. However, intellectual disability was the largest contributor to YLDs in both 1990 and 2016. Although the prevalence of developmental disabilities among children younger than 5 years decreased in all countries (except for North America) between 1990 and 2016, the number of children with developmental disabilities increased significantly in sub-Saharan Africa (71·3%) and in North Africa and the Middle East (7·6%). South Asia had the highest prevalence of children with developmental disabilities in 2016 and North America had the lowest. INTERPRETATION: The global burden of developmental disabilities has not significantly improved since 1990, suggesting inadequate global attention on the developmental potential of children who survived childhood as a result of child survival programmes, particularly in sub-Saharan Africa and south Asia. The SDGs provide a framework for policy and action to address the needs of children with or at risk of developmental disabilities, particularly in resource-poor countries. FUNDING: The Bill & Melinda Gates Foundation.

BACKGROUND: Malnutrition is a major contributor to disease burden in India. To inform subnational action, we aimed to assess the disease burden due to malnutrition and the trends in its indicators in every state of India in relation to Indian and global nutrition targets. METHODS: We analysed the disease burden attributable to child and maternal malnutrition, and the trends in the malnutrition indicators from 1990 to 2017 in every state of India using all accessible data from multiple sources, as part of Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017. The states were categorised into three groups using their Socio-demographic Index (SDI) calculated by GBD on the basis of per capita income, mean education, and fertility rate in women younger than 25 years. We projected the prevalence of malnutrition indicators for the states of India up to 2030 on the basis of the 1990-2017 trends for comparison with India National Nutrition Mission (NNM) 2022 and WHO and UNICEF 2030 targets. FINDINGS: Malnutrition was the predominant risk factor for death in children younger than 5 years of age in every state of India in 2017, accounting for 68·2% (95% UI 65·8-70·7) of the total under-5 deaths, and the leading risk factor for health loss for all ages, responsible for 17·3% (16·3-18·2) of the total disability-adjusted life years (DALYs). The malnutrition DALY rate was much higher in the low SDI than in the middle SDI and high SDI state groups. This rate varied 6·8 times between the states in 2017, and was highest in the states of Uttar Pradesh, Bihar, Assam, and Rajasthan. The prevalence of low birthweight in India in 2017 was 21·4% (20·8-21·9), child stunting 39·3% (38·7-40·1), child wasting 15·7% (15·6-15·9), child underweight 32·7% (32·3-33·1), anaemia in children 59·7% (56·2-63·8), anaemia in women 15-49 years of age 54·4% (53·7-55·2), exclusive breastfeeding 53·3% (51·5-54·9), and child overweight 11·5% (8·5-14·9). If the trends estimated up to 2017 for the indicators in the NNM 2022 continue in India, there would be 8·9% excess prevalence for low birthweight, 9·6% for stunting, 4·8% for underweight, 11·7% for anaemia in children, and 13·8% for anaemia in women relative to the 2022 targets. For the additional indicators in the WHO and UNICEF 2030 targets, the trends up to 2017 would lead to 10·4% excess prevalence for wasting, 14·5% excess prevalence for overweight, and 10·7% less exclusive breastfeeding in 2030. The prevalence of malnutrition indicators, their rates of improvement, and the gaps between projected prevalence and targets vary substantially between the states. INTERPRETATION: Malnutrition continues to be the leading risk factor for disease burden in India. It is encouraging that India has set ambitious targets to reduce malnutrition through NNM. The trends up to 2017 indicate that substantially higher rates of improvement will be needed for all malnutrition indicators in most states to achieve the Indian 2022 and the global 2030 targets. The state-specific findings in this report indicate the effort needed in each state, which will be useful in tracking and motivating further progress. Similar subnational analyses might be useful for other low-income and middle-income countries. FUNDING: Bill & Melinda Gates Foundation; Indian Council of Medical Research, Department of Health Research, Ministry of Health and Family Welfare, Government of India.

BACKGROUND: India has made substantial progress in improving child survival over the past few decades, but a comprehensive understanding of child mortality trends at disaggregated geographical levels is not available. We present a detailed analysis of subnational trends of child mortality to inform efforts aimed at meeting the India National Health Policy (NHP) and Sustainable Development Goal (SDG) targets for child mortality. METHODS: We assessed the under-5 mortality rate (U5MR) and neonatal mortality rate (NMR) from 2000 to 2017 in 5 × 5 km grids across India, and for the districts and states of India, using all accessible data from various sources including surveys with subnational geographical information. The 31 states and groups of union territories were categorised into three groups using their Socio-demographic Index (SDI) level, calculated as part of the Global Burden of Diseases, Injuries, and Risk Factors Study on the basis of per-capita income, mean education, and total fertility rate in women younger than 25 years. Inequality between districts within the states was assessed using the coefficient of variation. We projected U5MR and NMR for the states and districts up to 2025 and 2030 on the basis of the trends from 2000 to 2017 and compared these projections with the NHP 2025 and SDG 2030 targets for U5MR (23 deaths and 25 deaths per 1000 livebirths, respectively) and NMR (16 deaths and 12 deaths per 1000 livebirths, respectively). We assessed the causes of child death and the contribution of risk factors to child deaths at the state level. FINDINGS: U5MR in India decreased from 83·1 (95% uncertainty interval [UI] 76·7-90·1) in 2000 to 42·4 (36·5-50·0) per 1000 livebirths in 2017, and NMR from 38·0 (34·2-41·6) to 23·5 (20·1-27·8) per 1000 livebirths. U5MR varied 5·7 times between the states of India and 10·5 times between the 723 districts of India in 2017, whereas NMR varied 4·5 times and 8·0 times, respectively. In the low SDI states, 275 (88%) districts had a U5MR of 40 or more per 1000 livebirths and 291 (93%) districts had an NMR of 20 or more per 1000 livebirths in 2017. The annual rate of change from 2010 to 2017 varied among the districts from a 9·02% (95% UI 6·30-11·63) reduction to no significant change for U5MR and from an 8·05% (95% UI 5·34-10·74) reduction to no significant change for NMR. Inequality between districts within the states increased from 2000 to 2017 in 23 of the 31 states for U5MR and in 24 states for NMR, with the largest increases in Odisha and Assam among the low SDI states. If the trends observed up to 2017 were to continue, India would meet the SDG 2030 U5MR target but not the SDG 2030 NMR target or either of the NHP 2025 targets. To reach the SDG 2030 targets individually, 246 (34%) districts for U5MR and 430 (59%) districts for NMR would need a higher rate of improvement than they had up to 2017. For all major causes of under-5 death in India, the death rate decreased between 2000 and 2017, with the highest decline for infectious diseases, intermediate decline for neonatal disorders, and the smallest decline for congenital birth defects, although the magnitude of decline varied widely between the states. Child and maternal malnutrition was the predominant risk factor, to which 68·2% (65·8-70·7) of under-5 deaths and 83·0% (80·6-85·0) of neonatal deaths in India could be attributed in 2017; 10·8% (9·1-12·4) of under-5 deaths could be attributed to unsafe water and sanitation and 8·8% (7·0-10·3) to air pollution. INTERPRETATION: India has made gains in child survival, but there are substantial variations between the states in the magnitude and rate of decline in mortality, and even higher variations between the districts of India. Inequality between districts within states has increased for the majority of the states. The district-level trends presented here can provide crucial guidance for targeted efforts needed in India to reduce child mortality to meet the Indian and global child survival targets. District-level mortality trends along with state-level trends in causes of under-5 and neonatal death and the risk factors in this Article provide a comprehensive reference for further planning of child mortality reduction in India. FUNDING: Bill & Melinda Gates Foundation; and Indian Council of Medical Research, Department of Health Research, Ministry of Health and Family Welfare, Government of India.

BACKGROUND: Following the economic crisis in Greece in 2010, the country's ongoing austerity measures include a substantial contraction of health-care expenditure, with reports of subsequent negative health consequences. A comprehensive evaluation of mortality and morbidity is required to understand the current challenges of public health in Greece. METHODS: We used the results of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2016 to describe the patterns of death and disability among those living in Greece from 2000 to 2010 (pre-austerity) and 2010 to 2016 (post-austerity), and compared trends in health outcomes and health expenditure to those in Cyprus and western Europe. We estimated all-cause mortality from vital registration data, and we calculated cause-specific deaths and years of life lost. Age-standardised mortality rates were compared using the annualised rate of change (ARC). Mortality risk factors were assessed using a comparative risk assessment framework for 84 risk factors and clusters to calculative summary exposure values and population attributable fraction statistics. We assessed the association between trends in total, government, out-of-pocket, and prepaid public health expenditure and all-cause mortality with a segmented correlation analysis. FINDINGS: All-age mortality in Greece increased from 944·5 (95% uncertainty interval [UI] 923·1-964·5) deaths per 100 000 in 2000 to 997·8 (975·4-1018) in 2010 and 1174·9 (1107·4-1243·2) in 2016, with a higher ARC after 2010 and the introduction of austerity (2·72% [1·65 to 3·74] for 2010-16) than before (0·55% [0·24 to 0·85] for 2000-10) or in western Europe during the same period (0·86% [0·54 to 1·17]). Age-standardised reduction in ARC approximately halved from 2000-10 (-1·61 [95% UI -1·91 to -1·30]) to 2010-16 (-0·87% [-2·03 to 0·20]), with post-2010 ARC similar to that in Cyprus (-0·86% [-1·4 to -0·36]) and lower than in western Europe (-1·14% [-1·48 to -0·81]). Mortality changes in Greece coincided with a rapid decrease in government health expenditure, but also with aggregate population ageing from 2010 to 2016 that was faster than observed in Cyprus. Causes of death that increased were largely those that are responsive to health care. Comparable temporal and age patterns were noted for non-fatal health outcomes, with a somewhat faster rise in years lived with disability since 2010 in Greece compared with Cyprus and western Europe. Risk factor exposures, especially high body-mass index, smoking, and alcohol use, explained much of the mortality increase in Greek adults aged 15-49 years, but only explained a minority of that in adults older than 70 years. INTERPRETATION: The findings of increases in total deaths and accelerated population ageing call for specific focus from health policy makers to ensure the health-care system is equipped to meet the needs of the people in Greece. FUNDING: Bill & Melinda Gates Foundation.

BACKGROUND: To inform actions at the district level under the National Nutrition Mission (NNM), we assessed the prevalence trends of child growth failure (CGF) indicators for all districts in India and inequality between districts within the states. METHODS: We assessed the trends of CGF indicators (stunting, wasting and underweight) from 2000 to 2017 across the districts of India, aggregated from 5 × 5 km grid estimates, using all accessible data from various surveys with subnational geographical information. The states were categorised into three groups using their Socio-demographic Index (SDI) levels calculated as part of the Global Burden of Disease Study based on per capita income, mean education and fertility rate in women younger than 25 years. Inequality between districts within the states was assessed using coefficient of variation (CV). We projected the prevalence of CGF indicators for the districts up to 2030 based on the trends from 2000 to 2017 to compare with the NNM 2022 targets for stunting and underweight, and the WHO/UNICEF 2030 targets for stunting and wasting. We assessed Pearson correlation coefficient between two major national surveys for district-level estimates of CGF indicators in the states. FINDINGS: The prevalence of stunting ranged 3.8-fold from 16.4% (95% UI 15.2-17.8) to 62.8% (95% UI 61.5-64.0) among the 723 districts of India in 2017, wasting ranged 5.4-fold from 5.5% (95% UI 5.1-6.1) to 30.0% (95% UI 28.2-31.8), and underweight ranged 4.6-fold from 11.0% (95% UI 10.5-11.9) to 51.0% (95% UI 49.9-52.1). 36.1% of the districts in India had stunting prevalence 40% or more, with 67.0% districts in the low SDI states group and only 1.1% districts in the high SDI states with this level of stunting. The prevalence of stunting declined significantly from 2010 to 2017 in 98.5% of the districts with a maximum decline of 41.2% (95% UI 40.3-42.5), wasting in 61.3% with a maximum decline of 44.0% (95% UI 42.3-46.7), and underweight in 95.0% with a maximum decline of 53.9% (95% UI 52.8-55.4). The CV varied 7.4-fold for stunting, 12.2-fold for wasting, and 8.6-fold for underweight between the states in 2017; the CV increased for stunting in 28 out of 31 states, for wasting in 16 states, and for underweight in 20 states from 2000 to 2017. In order to reach the NNM 2022 targets for stunting and underweight individually, 82.6% and 98.5% of the districts in India would need a rate of improvement higher than they had up to 2017, respectively. To achieve the WHO/UNICEF 2030 target for wasting, all districts in India would need a rate of improvement higher than they had up to 2017. The correlation between the two national surveys for district-level estimates was poor, with Pearson correlation coefficient of 0.7 only in Odisha and four small north-eastern states out of the 27 states covered by these surveys. INTERPRETATION: CGF indicators have improved in India, but there are substantial variations between the districts in their magnitude and rate of decline, and the inequality between districts has increased in a large proportion of the states. The poor correlation between the national surveys for CGF estimates highlights the need to standardise collection of anthropometric data in India. The district-level trends in this report provide a useful reference for targeting the efforts under NNM to reduce CGF across India and meet the Indian and global targets.