Mapping development and health effects of cooking with solid fuels in low-income and middle-income countries, 2000–18: a geospatial modelling study

Background More than 3 billion people do not have access to clean energy and primarily use solid fuels to cook. Use of solid fuels generates household air pollution, which was associated with more than 2 million deaths in 2019. Although local patterns in cooking vary systematically, subnational trends in use of solid fuels have yet to be comprehensively analysed. We estimated the prevalence of solid-fuel use with high spatial resolution to explore subnational inequalities, assess local progress, and assess the effects on health in low-income and middle-income countries (LMICs) without universal access to clean fuels. Methods We did a geospatial modelling study to map the prevalence of solid-fuel use for cooking at a 5 km × 5 km resolution in 98 LMICs based on 2·1 million household observations of the primary cooking fuel used from 663 population-based household surveys over the years 2000 to 2018. We use observed temporal patterns to forecast household air pollution in 2030 and to assess the probability of attaining the Sustainable Development Goal (SDG) target indicator for clean cooking. We aligned our estimates of household air pollution to geospatial estimates of ambient air pollution to establish the risk transition occurring in LMICs. Finally, we quantified the effect of residual primary solid-fuel use for cooking on child health by doing a counterfactual risk assessment to estimate the proportion of deaths from lower respiratory tract infections in children younger than 5 years that could be associated with household air pollution. Findings Although primary reliance on solid-fuel use for cooking has declined globally, it remains widespread. 593 million people live in districts where the prevalence of solid-fuel use for cooking exceeds 95%. 66% of people in LMICs live in districts that are not on track to meet the SDG target for universal access to clean energy by 2030. Household air pollution continues to be a major contributor to particulate exposure in LMICs, and rising ambient air pollution is undermining potential gains from reductions in the prevalence of solid-fuel use for cooking in many countries. We estimated that, in 2018, 205 000 (95% uncertainty interval 147 000–257 000) children younger than 5 years died from lower respiratory tract infections that could be attributed to household air pollution. Interpretation Efforts to accelerate the adoption of clean cooking fuels need to be substantially increased and recalibrated to account for subnational inequalities, because there are substantial opportunities to improve air quality and avert child mortality associated with household air pollution.This study was funded by the Bill & Melinda Gates Foundation. L G Abreu acknowledges support from Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (Capes; finance Code 001), Conselho Nacional de Desenvolvimento Científico e Tecnológico, and Fundação de Amparo à Pesquisa do Estado de Minas Gerais. D A Bennett acknowledges support from the Oxford National Institute for Health Research (NIHR) Biomedical Research Centre (BRC). The views expressed are those of the author and not necessarily those of the NHS, the NIHR, or the UK Department of Health and Social Care. Z A Bhutta acknowledges support from the Institute for Global Health & Development at the Aga Khan University. F Carvalho acknowledges UID/MULTI/04378/2019 and UID/QUI/50006/2019 support with funding from FCT/MCTES through national funds. J-W De Neve is supported by the Alexander von Humboldt Foundation. S Dey acknowledges the support from the Centre of Excellence for Research on Clean Air, IIT Delhi. M Ausloos and C Herteliu are partly supported by a grant of the Romanian National Authority for Scientific Research and Innovation (project number PN-III-P4-ID-PCCF-2016-0084). C Herteliu is partly supported by a grant of the Romanian National Authority for Scientific Research and Innovation (project number PN-III-P2-2.1-SOL-2020-2-0351), the Romanian Ministry of Research Innovation and Digitalization (project number ID-585-CTR-42-PFE-2021), and the Romanian Ministry of Labour and Social Justice (30/PSCD/2018). M Jakovljevic acknowledges partial support through Grant OI 175 014 of the Ministry of Science Education and Technological Development of the Republic of Serbia. J S John acknowledges support from the Kunshan Government and China Center for Disease Control and Prevention. W Mendoza is a program analyst in population and development at the United Nations Population Fund country office in Peru, an institution that does not necessarily endorse this study. M N Khan acknowledges the support of Jatiya Kabi Kazi Nazrul Islam University, Bangladesh. K Krishan is supported by UGC Centre of Advanced Study (CAS II), awarded to the Department of Anthropology, Panjab University, Chandigarh, India. M Kumar acknowledges support (FIC/NIH funded K43 TW010716-04 study). B Lacey acknowledges support from UK Biobank, the NIHR Oxford Biomedical Research Centre, and the British Heart Foundation Oxford Centre of Research Excellence. B R Nascimento acknowledges support in part by CNPq (Bolsa de produtividade em pesquisa, 312382/2019-7), by the Edwards Lifesciences Foundation (Every Heartbeat Matters Program 2020) and by FAPEMIG (grant APQ-000627-20). A M Samy acknowledges the support from the Egyptian Fulbright Mission Program. M M Santric-Milicevic acknowledges the support of the Ministry of Education, Science and Technological Development of Serbia (contract 175087). A Sheikh acknowledges the support of Health Data Research UK. I N Soyiri acknowledges support from the University of Hull internal QR Global Challenges Research Fund. S B Zaman acknowledges receiving a scholarship from the Australian Government research training program in support of his academic career. Y Zhang was supported by Science and Technology Research Project of Hubei Provincial Department of Education (grant Q20201104) and Outstanding Young and Middle Aged Technology Innovation Team Project of Hubei Provincial Department of Education (grant T2020003).publishedVersio

Source influence on emission pathways and ambient PM2.5 pollution over India (2015–2050)

India currently experiences degraded air quality, with future economic development leading to challenges for air quality management. Scenarios of sectoral emissions of fine particulate matter and its precursors were developed and evaluated for 2015–2050, under specific pathways of diffusion of cleaner and more energy efficiency technologies. The impacts of individual source-sectors on PM2.5 concentrations were assessed through GEOS-Chem model simulations of spatially and temporally resolved particulate matter concentrations, followed by population-weighted aggregation to national and state levels. PM2.5 pollution is a pan-India problem, with a regional character, not limited to urban areas or megacities. Under present-day emissions, levels in most states exceeded the national PM2.5 standard (40 µg/m3). Future evolution of emissions under current regulation or under promulgated or proposed regulation, yield deterioration in future air-quality in 2030 and 2050. Only under a scenario where more ambitious measures are introduced, promoting a total shift away from traditional biomass technologies and a very large shift (80–85 %) to non-fossil electricity generation was an overall reduction in PM2.5 concentrations below 2015 levels achieved. In this scenario, concentrations in 20 states and six union territories would fall below the national standard. However, even under this ambitious scenario, 10 states (including Delhi) would fail to comply with the national standard through to 2050. Under present day (2015) emissions, residential biomass fuel use for cooking and heating is the largest single sector influencing outdoor air pollution across most of India. Agricultural residue burning is the next most important source, especially in north-west and north India, while in eastern and peninsular India, coal burning in thermal power plants and industry are important contributors. The relative influence of anthropogenic dust and total dust is projected to increase in all future scenarios, largely from decreases in the influence of other PM2.5 sources. Overall, the findings suggest a large regional background of PM2.5 pollution (from residential biomass, agricultural residue burning and power plant and industrial coal), underlying that from local sources (transportation, brick kiln, distributed diesel) in highly polluted areas

Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015

SummaryBackground The Global Burden of Diseases, Injuries, and Risk Factors Study 2015 provides an up-to-date synthesis of the evidence for risk factor exposure and the attributable burden of disease. By providing national and subnational assessments spanning the past 25 years, this study can inform debates on the importance of addressing risks in context. Methods We used the comparative risk assessment framework developed for previous iterations of the Global Burden of Disease Study to estimate attributable deaths, disability-adjusted life-years (DALYs), and trends in exposure by age group, sex, year, and geography for 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks from 1990 to 2015. This study included 388 risk-outcome pairs that met World Cancer Research Fund-defined criteria for convincing or probable evidence. We extracted relative risk and exposure estimates from randomised controlled trials, cohorts, pooled cohorts, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. We developed a metric that allows comparisons of exposure across risk factors—the summary exposure value. Using the counterfactual scenario of theoretical minimum risk level, we estimated the portion of deaths and DALYs that could be attributed to a given risk. We decomposed trends in attributable burden into contributions from population growth, population age structure, risk exposure, and risk-deleted cause-specific DALY rates. We characterised risk exposure in relation to a Socio-demographic Index (SDI). Findings Between 1990 and 2015, global exposure to unsafe sanitation, household air pollution, childhood underweight, childhood stunting, and smoking each decreased by more than 25%. Global exposure for several occupational risks, high body-mass index (BMI), and drug use increased by more than 25% over the same period. All risks jointly evaluated in 2015 accounted for 57·8% (95% CI 56·6–58·8) of global deaths and 41·2% (39·8–42·8) of DALYs. In 2015, the ten largest contributors to global DALYs among Level 3 risks were high systolic blood pressure (211·8 million [192·7 million to 231·1 million] global DALYs), smoking (148·6 million [134·2 million to 163·1 million]), high fasting plasma glucose (143·1 million [125·1 million to 163·5 million]), high BMI (120·1 million [83·8 million to 158·4 million]), childhood undernutrition (113·3 million [103·9 million to 123·4 million]), ambient particulate matter (103·1 million [90·8 million to 115·1 million]), high total cholesterol (88·7 million [74·6 million to 105·7 million]), household air pollution (85·6 million [66·7 million to 106·1 million]), alcohol use (85·0 million [77·2 million to 93·0 million]), and diets high in sodium (83·0 million [49·3 million to 127·5 million]). From 1990 to 2015, attributable DALYs declined for micronutrient deficiencies, childhood undernutrition, unsafe sanitation and water, and household air pollution; reductions in risk-deleted DALY rates rather than reductions in exposure drove these declines. Rising exposure contributed to notable increases in attributable DALYs from high BMI, high fasting plasma glucose, occupational carcinogens, and drug use. Environmental risks and childhood undernutrition declined steadily with SDI; low physical activity, high BMI, and high fasting plasma glucose increased with SDI. In 119 countries, metabolic risks, such as high BMI and fasting plasma glucose, contributed the most attributable DALYs in 2015. Regionally, smoking still ranked among the leading five risk factors for attributable DALYs in 109 countries; childhood underweight and unsafe sex remained primary drivers of early death and disability in much of sub-Saharan Africa. Interpretation Declines in some key environmental risks have contributed to declines in critical infectious diseases. Some risks appear to be invariant to SDI. Increasing risks, including high BMI, high fasting plasma glucose, drug use, and some occupational exposures, contribute to rising burden from some conditions, but also provide opportunities for intervention. Some highly preventable risks, such as smoking, remain major causes of attributable DALYs, even as exposure is declining. Public policy makers need to pay attention to the risks that are increasingly major contributors to global burden. Funding Bill & Melinda Gates Foundation

A MANBA mutation resulting in residual beta-mannosidase activity associated with severe leukoencephalopathy: a possible pseudodeficiency variant

<p>Abstract</p> <p>Background</p> <p>β-Mannosidosis (OMIM 248510) is a rare inborn lysosomal storage disorder caused by the deficient activity of β-mannosidase, an enzyme encoded by a single gene (<it>MANBA</it>) located on chromosome 4q22-25. To date, only 20 cases of this autosomal recessive disorder have been described and 14 different <it>MANBA </it>mutations were incriminated in the disease. These are all null mutations or missense mutations that abolish β-mannosidase activity. In this study, we characterized the molecular defect of a new case of β-mannosidosis, presenting with a severe neurological disorder.</p> <p>Methods</p> <p>Genomic DNA was isolated from peripheral blood leukocytes of the patient to allow <it>MANBA </it>sequencing. The identified mutation was engineered by site-directed mutagenesis and the mutant protein was expressed through transient transfection in HEK293T cells. The β-mannosidase expression and activity were respectively assessed by Western blot and fluorometric assay in both leukocytes and HEK293T cells.</p> <p>Results</p> <p>A missense disease-associated mutation, c.1922G>A (p.Arg641His), was identified for which the patient was homozygous. In contrast to previously described missense mutations, this substitution does not totally abrogate the enzyme activity but led to a residual activity of about 7% in the patient's leukocytes, 11% in lymphoblasts and 14% in plasma. Expression studies in transfected cells also resulted in 7% residual activity.</p> <p>Conclusion</p> <p>Correlations between MANBA mutations, residual activity of β-mannosidase and the severity of the ensuing neurological disorder are discussed. Whether the c.1922G>A mutation is responsible for a yet undescribed pseudodeficiency of β-mannosidase is also discussed.</p

International consensus for neuroblastoma molecular diagnostics: report from the International Neuroblastoma Risk Group (INRG) Biology Committee

Neuroblastoma serves as a paradigm for utilising tumour genomic data for determining patient prognosis and treatment allocation. However, before the establishment of the International Neuroblastoma Risk Group (INRG) Task Force in 2004, international consensus on markers, methodology, and data interpretation did not exist, compromising the reliability of decisive genetic markers and inhibiting translational research efforts. The objectives of the INRG Biology Committee were to identify highly prognostic genetic aberrations to be included in the new INRG risk classification schema and to develop precise definitions, decisive biomarkers, and technique standardisation. The review of the INRG database (n=8800 patients) by the INRG Task Force finally enabled the identification of the most significant neuroblastoma biomarkers. In addition, the Biology Committee compared the standard operating procedures of different cooperative groups to arrive at international consensus for methodology, nomenclature, and future directions. Consensus was reached to include MYCN status, 11q23 allelic status, and ploidy in the INRG classification system on the basis of an evidence-based review of the INRG database. Standardised operating procedures for analysing these genetic factors were adopted, and criteria for proper nomenclature were developed. Neuroblastoma treatment planning is highly dependant on tumour cell genomic features, and it is likely that a comprehensive panel of DNA-based biomarkers will be used in future risk assignment algorithms applying genome-wide techniques. Consensus on methodology and interpretation is essential for uniform INRG classification and will greatly facilitate international and cooperative clinical and translational research studies

Variation in the COVID-19 infection-fatality ratio by age, time, and geography during the pre-vaccine era: a systematic analysis

Background The infection-fatality ratio (IFR) is a metric that quantifies the likelihood of an individual dying once infected with a pathogen. Understanding the determinants of IFR variation for COVID-19, the disease caused by the SARS-CoV-2 virus, has direct implications for mitigation efforts with respect to clinical practice, non-pharmaceutical interventions, and the prioritisation of risk groups for targeted vaccine delivery. The IFR is also a crucial parameter in COVID-19 dynamic transmission models, providing a way to convert a population's mortality rate into an estimate of infections.Methods We estimated age-specific and all-age IFR by matching seroprevalence surveys to total COVID-19 mortality rates in a population. The term total COVID-19 mortality refers to an estimate of the total number of deaths directly attributable to COVID-19. After applying exclusion criteria to 5131 seroprevalence surveys, the IFR analyses were informed by 2073 all-age surveys and 718 age-specific surveys (3012 age-specific observations). When seroprevalence was reported by age group, we split total COVID-19 mortality into corresponding age groups using a Bayesian hierarchical model to characterise the non-linear age pattern of reported deaths for a given location. To remove the impact of vaccines on the estimated IFR age pattern, we excluded age-specific observations of seroprevalence and deaths that occurred after vaccines were introduced in a location. We estimated age-specific IFR with a non-linear meta-regression and used the resulting age pattern to standardise all-age IFR observations to the global age distribution. All IFR observations were adjusted for baseline and waning antibody-test sensitivity. We then modelled age-standardised IFR as a function of time, geography, and an ensemble of 100 of the top-performing covariate sets. The covariates included seven clinical predictors (eg, age-standardised obesity prevalence) and two measures of health system performance. Final estimates for 190 countries and territories, as well as subnational locations in 11 countries and territories, were obtained by predicting age-standardised IFR conditional on covariates and reversing the age standardisation.Findings We report IFR estimates for April 15, 2020, to January 1, 2021, the period before the introduction of vaccines and widespread evolution of variants. We found substantial heterogeneity in the IFR by age, location, and time. Age-specific IFR estimates form a J shape, with the lowest IFR occurring at age 7 years (0-0023%, 95% uncertainty interval [UI] 0-0015-0-0039) and increasing exponentially through ages 30 years (0-0573%, 0-0418-0-0870), 60 years (1-0035%, 0-7002-1-5727), and 90 years (20-3292%, 14-6888-28-9754). The countries with the highest IFR on July 15, 2020, were Portugal (2-085%, 0-946-4-395), Monaco (1-778%, 1-265-2-915), Japan (1-750%, 1-302-2-690), Spain (1-710%, 0-991-2-718), and Greece (1-637%, 1-155-2-678). All-age IFR varied by a factor of more than 30 among 190 countries and territories.After age standardisation, the countries with the highest IFR on July 15, 2020, were Peru (0-911%, 0-636-1-538), Portugal (0-850%, 0-386-1-793), Oman (0-762%, 0-381-1-399), Spain (0-751%, 0-435-1-193), and Mexico (0-717%, 0-426-1-404). Subnational locations with high IFRs also included hotspots in the UK and southern and eastern states of the USA. Sub-Saharan African countries and Asian countries generally had the lowest all-age and age-standardised IFRs. Population age structure accounted for 74% of logit-scale variation in IFRs estimated for 39 in-sample countries on July 15, 2020. A post-hoc analysis showed that high rates of transmission in the care home population might account for higher IFRs in some locations. Among all countries and territories, we found that the median IFR decreased from 0-466% (interquartile range 0-223-0-840) to 0-314% (0-143-0-551) between April 15, 2020, and Jan 1, 2021.Interpretation Estimating the IFR for global populations helps to identify relative vulnerabilities to COVID-19. Information about how IFR varies by age, time, and location informs clinical practice and non-pharmaceutical interventions like physical distancing measures, and underpins vaccine risk stratification. IFR and mortality risk form a J shape with respect to age, which previous research, such as that by Glynn and Moss in 2020, has identified to be a common pattern among infectious diseases. Understanding the experience of a population with COVID-19 mortality requires consideration for local factors; IFRs varied by a factor of more than 30 among 190 countries and territories in this analysis. In particular, the presence of elevated age-standardised IFRs in countries with well resourced health-care systems indicates that factors beyond health-care capacity are important. Potential extenuating circumstances include outbreaks among care home residents, variable burdens of severe cases, and the population prevalence of comorbid conditions that increase the severity of COVID-19 disease. During the pre-vaccine period, the estimated 33% decrease in median IFR over 8 months suggests that treatment for COVID-19 has improved over time. Estimating IFR for the pre-vaccine era provides an important baseline for describing the progression of COVID-19 mortality patterns.Funding Bill &amp; Melinda Gates Foundation, J Stanton, T Gillespie, and J and E Nordstrom Copyright (c) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license

Burden of cancer in the Eastern Mediterranean Region, 2005–2015: findings from the Global Burden of Disease 2015 Study

Objectives: To estimate incidence, mortality, and disability-adjusted life years (DALYs) caused by cancer in the Eastern Mediterranean Region (EMR) between 2005 and 2015. Methods: Vital registration system and cancer registry data from the EMR region were analyzed for 29 cancer groups in 22 EMR countries using the Global Burden of Disease Study 2015 methodology. Results: In 2015, cancer was responsible for 9.4% of all deaths and 5.1% of all DALYs. It accounted for 722,646 new cases, 379,093 deaths, and 11.7 million DALYs. Between 2005 and 2015, incident cases increased by 46%, deaths by 33%, and DALYs by 31%. The increase in cancer incidence was largely driven by population growth and population aging. Breast cancer, lung cancer, and leukemia were the most common cancers, while lung, breast, and stomach cancers caused most cancer deaths. Conclusions: Cancer is responsible for a substantial disease burden in the EMR, which is increasing. There is an urgent need to expand cancer prevention, screening, and awareness programs in EMR countries as well as to improve diagnosis, treatment, and palliative care services.The funding source played no role in the design of thestudy, the analysis and interpretation of data, and the writing of thepaper. GBD 2015 is funded by Bill & Melinda Gates Foundation

Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015

Background Non-fatal outcomes of disease and injury increasingly detract from the ability of the world's population to live in full health, a trend largely attributable to an epidemiological transition in many countries from causes affecting children, to non-communicable diseases (NCDs) more common in adults. For the Global Burden of Diseases, Injuries, and Risk Factors Study 2015 (GBD 2015), we estimated the incidence, prevalence, and years lived with disability for diseases and injuries at the global, regional, and national scale over the period of 1990 to 2015. Methods We estimated incidence and prevalence by age, sex, cause, year, and geography with a wide range of updated and standardised analytical procedures. Improvements from GBD 2013 included the addition of new data sources, updates to literature reviews for 85 causes, and the identification and inclusion of additional studies published up to November, 2015, to expand the database used for estimation of non-fatal outcomes to 60 900 unique data sources. Prevalence and incidence by cause and sequelae were determined with DisMod-MR 2.1, an improved version of the DisMod-MR Bayesian meta-regression tool first developed for GBD 2010 and GBD 2013. For some causes, we used alternative modelling strategies where the complexity of the disease was not suited to DisMod-MR 2.1 or where incidence and prevalence needed to be determined from other data. For GBD 2015 we created a summary indicator that combines measures of income per capita, educational attainment, and fertility (the Socio-demographic Index [SDI]) and used it to compare observed patterns of health loss to the expected pattern for countries or locations with similar SDI scores. Findings We generated 9·3 billion estimates from the various combinations of prevalence, incidence, and YLDs for causes, sequelae, and impairments by age, sex, geography, and year. In 2015, two causes had acute incidences in excess of 1 billion: upper respiratory infections (17·2 billion, 95% uncertainty interval [UI] 15·4–19·2 billion) and diarrhoeal diseases (2·39 billion, 2·30–2·50 billion). Eight causes of chronic disease and injury each affected more than 10% of the world's population in 2015: permanent caries, tension-type headache, iron-deficiency anaemia, age-related and other hearing loss, migraine, genital herpes, refraction and accommodation disorders, and ascariasis. The impairment that affected the greatest number of people in 2015 was anaemia, with 2·36 billion (2·35–2·37 billion) individuals affected. The second and third leading impairments by number of individuals affected were hearing loss and vision loss, respectively. Between 2005 and 2015, there was little change in the leading causes of years lived with disability (YLDs) on a global basis. NCDs accounted for 18 of the leading 20 causes of age-standardised YLDs on a global scale. Where rates were decreasing, the rate of decrease for YLDs was slower than that of years of life lost (YLLs) for nearly every cause included in our analysis. For low SDI geographies, Group 1 causes typically accounted for 20–30% of total disability, largely attributable to nutritional deficiencies, malaria, neglected tropical diseases, HIV/AIDS, and tuberculosis. Lower back and neck pain was the leading global cause of disability in 2015 in most countries. The leading cause was sense organ disorders in 22 countries in Asia and Africa and one in central Latin America; diabetes in four countries in Oceania; HIV/AIDS in three southern sub-Saharan African countries; collective violence and legal intervention in two north African and Middle Eastern countries; iron-deficiency anaemia in Somalia and Venezuela; depression in Uganda; onchoceriasis in Liberia; and other neglected tropical diseases in the Democratic Republic of the Congo. Interpretation Ageing of the world's population is increasing the number of people living with sequelae of diseases and injuries. Shifts in the epidemiological profile driven by socioeconomic change also contribute to the continued increase in years lived with disability (YLDs) as well as the rate of increase in YLDs. Despite limitations imposed by gaps in data availability and the variable quality of the data available, the standardised and comprehensive approach of the GBD study provides opportunities to examine broad trends, compare those trends between countries or subnational geographies, benchmark against locations at similar stages of development, and gauge the strength or weakness of the estimates available. Funding Bill &amp; Melinda Gates Foundation