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

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 x 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, 205000 (95% uncertainty interval 147000-257000) 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. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd.Peer reviewe

Cloudy with a Chance of Cardiovascular Disease: Spatial Interpolation of Mortality Attributable to Air Pollution in China

Thesis (Master's)--University of Washington, 2015Ambient air pollution, especially particulate matter smaller than 2.5 microns (PM2.5), is a significant contributor to premature mortality. This issue is particularly relevant in China due to rapid urbanization and population growth over the past several decades. Increased pollution in major population centers has resulted in massive loss of life via respiratory and cardiovascular disease. For the Global Burden of Disease (GBD) 2013 Risk Factors analysis, grid-level exposure estimates of annual PM2.5 and cause-specific integrated exposure response (IER) curves were used to predict the relative risk of mortality from a related cause for every pixel. These gridded relative risks were combined with cause-specific provincial mortality results in order to quantify the mortality attributable to air pollution for every province. To provide increased spatial granularity that can further inform the literature and policy-makers, cause-specific mortality was interpolated in order to generate predictions at the pixel level. This analysis demonstrated spatial and temporal trends in mortality attributable to air pollution in China over the GBD time series from 1990 to 2013. Mortality rates were shown to rise over time as a function of PM2.5 exposure, while total mortality increases were most prominent in several key population centers. The cause composition of total attributable mortality was found to have changed over the time series as well, with background rates of LRI and COPD decreasing so that cardiovascular disease makes up the bulk of mortality. As demonstrated by the non-linear concave shape of the IER curve, the distribution of mortality across levels of PM2.5 indicates that the majority of deaths are occurring in areas with moderate levels of air pollution and high population. Future interventions must focus on generating significant decreases in PM2.5 exposure across the Eastern seaboard, where Chinese urbanization is concentrated. This work is limited by the assumption of constant underlying mortality rates within provinces. As the spatial granularity of GBD mortality estimates continues to improve, these estimates will become more accurate and have increased utility to environmental health policy

Global Metrics, Local Estimation: Magnifying the Health Impact of Environmental Justice

Thesis (Ph.D.)--University of Washington, 2023In the first chapter, Mapping development and health effects of cooking with solid fuels in low-income and middle-income countries, 2000–18: a geospatial modeling study, the prevalence of solid-fuel use for cooking is mapped at a 5 km à  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. We found that while 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. The second chapter, Scales of environmental justice: global sensitivity analysis of the Washington Environmental Health Disparities Map, is focused on understanding the factors that drive environmental health inequalities more locally, within the context of the Washington State Environmental Health Disparities (EHD) Map, a composite indicator of environmental justice that synthesizes 19 different environmental and population health indicators to generate cumulative impact rankings by census tract. We conducted a global sensitivity analysis of the EHD mapping methodology by permuting across alternative methods derived from the composite indicator construction literature, generating estimates of the uncertainty that results from analyst decisions in the development process. We estimated first, second and total order sensitivity statistics to quantify the relative influence of parameter choices on the tract rankings and on the accuracy of classifying communities in the top 20% of impact. On average, census tracts changed by more than one hundred ranks across these permutations. The observed deviations from the baseline EHD index were largest in the middle of the impact spectrum and smallest for tracts in the top 10% of impact. The formula used in aggregation and the method of data normalization were the most sensitive parameter decisions for both tract ranking and impact classification. We demonstrate that the EHD rankings were more robust in the highest impact tracts and relatively uncertain throughout the rest of the index, suggesting that this data is better suited for classifying hotspots than for estimating an ordinal impact. There are strong assumptions underlying the baseline EHD ranking methodology and these assumptions substantially drive the results In the third chapter, Validating the structure of an environmental justice index in Washington State, a multivariate case study, we explore the mechanisms through which the most sensitive parameter choices in the EHD methodology impact the final index. The statistical characteristics of the raw indicator data are analyzed in order to understand the influence of various ranking transformations on the data distributions, and case studies that exhibit large changes in ranking and impact classification between linear and nonlinear transformations are analyzed to assess the bias introduced by normalization methodology. Nonlinear transformations are observed to favor the high-impact classification of tracts with above average values for the majority of indicators while reducing the effect of outliers. A variance-weighted Principal Component Analysis (PCA) is employed to compare the results of inductive aggregation to the more theoretically derived baseline index. A PCA based index is observed to agree generally with the baseline index and classify the impacted tracts with high accuracy using only the first two principal components. The loading structure of individual indicators within these first two components suggests that twin gradients of urban environmental degradation and socioeconomic deprivation are driving the index rankings in the current formulation of the EHD map

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

Modeling COVID-19 scenarios for the United States

We use COVID-19 case and mortality data from 1 February 2020 to 21 September 2020 and a deterministic SEIR (susceptible, exposed, infectious and recovered) compartmental framework to model possible trajectories of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and the effects of non-pharmaceutical interventions in the United States at the state level from 22 September 2020 through 28 February 2021. Using this SEIR model, and projections of critical driving covariates (pneumonia seasonality, mobility, testing rates and mask use per capita), we assessed scenarios of social distancing mandates and levels of mask use. Projections of current non-pharmaceutical intervention strategies by state—with social distancing mandates reinstated when a threshold of 8 deaths per million population is exceeded (reference scenario)—suggest that, cumulatively, 511,373 (469,578–578,347) lives could be lost to COVID-19 across the United States by 28 February 2021. We find that achieving universal mask use (95% mask use in public) could be sufficient to ameliorate the worst effects of epidemic resurgences in many states. Universal mask use could save an additional 129,574 (85,284–170,867) lives from September 22, 2020 through the end of February 2021, or an additional 95,814 (60,731–133,077) lives assuming a lesser adoption of mask wearing (85%), when compared to the reference scenario

Age–sex differences in the global burden of lower respiratory infections and risk factors, 1990–2019: results from the Global Burden of Disease Study 2019

Background: The global burden of lower respiratory infections (LRIs) and corresponding risk factors in children older than 5 years and adults has not been studied as comprehensively as it has been in children younger than 5 years. We assessed the burden and trends of LRIs and risk factors across all age groups by sex, for 204 countries and territories. Methods: In this analysis of data for the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, we used clinician-diagnosed pneumonia or bronchiolitis as our case definition for LRIs. We included International Classification of Diseases 9th edition codes 079.6, 466–469, 470.0, 480–482.8, 483.0–483.9, 484.1–484.2, 484.6–484.7, and 487–489 and International Classification of Diseases 10th edition codes A48.1, A70, B97.4–B97.6, J09–J15.8, J16–J16.9, J20–J21.9, J91.0, P23.0–P23.4, and U04–U04.9. We used the Cause of Death Ensemble modelling strategy to analyse 23 109 site-years of vital registration data, 825 site-years of sample vital registration data, 1766 site-years of verbal autopsy data, and 681 site-years of mortality surveillance data. We used DisMod-MR 2.1, a Bayesian meta-regression tool, to analyse age–sex-specific incidence and prevalence data identified via systematic reviews of the literature, population-based survey data, and claims and inpatient data. Additionally, we estimated age–sex-specific LRI mortality that is attributable to the independent effects of 14 risk factors. Findings: Globally, in 2019, we estimated that there were 257 million (95% uncertainty interval [UI] 240–275) LRI incident episodes in males and 232 million (217–248) in females. In the same year, LRIs accounted for 1·30 million (95% UI 1·18–1·42) male deaths and 1·20 million (1·07–1·33) female deaths. Age-standardised incidence and mortality rates were 1·17 times (95% UI 1·16–1·18) and 1·31 times (95% UI 1·23–1·41) greater in males than in females in 2019. Between 1990 and 2019, LRI incidence and mortality rates declined at different rates across age groups and an increase in LRI episodes and deaths was estimated among all adult age groups, with males aged 70 years and older having the highest increase in LRI episodes (126·0% [95% UI 121·4–131·1]) and deaths (100·0% [83·4–115·9]). During the same period, LRI episodes and deaths in children younger than 15 years were estimated to have decreased, and the greatest decline was observed for LRI deaths in males younger than 5 years (–70·7% [–77·2 to –61·8]). The leading risk factors for LRI mortality varied across age groups and sex. More than half of global LRI deaths in children younger than 5 years were attributable to child wasting (population attributable fraction [PAF] 53·0% [95% UI 37·7–61·8] in males and 56·4% [40·7–65·1] in females), and more than a quarter of LRI deaths among those aged 5–14 years were attributable to household air pollution (PAF 26·0% [95% UI 16·6–35·5] for males and PAF 25·8% [16·3–35·4] for females). PAFs of male LRI deaths attributed to smoking were 20·4% (95% UI 15·4–25·2) in those aged 15–49 years, 30·5% (24·1–36·9) in those aged 50–69 years, and 21·9% (16·8–27·3) in those aged 70 years and older. PAFs of female LRI deaths attributed to household air pollution were 21·1% (95% UI 14·5–27·9) in those aged 15–49 years and 18·2% (12·5–24·5) in those aged 50–69 years. For females aged 70 years and older, the leading risk factor, ambient particulate matter, was responsible for 11·7% (95% UI 8·2–15·8) of LRI deaths. Interpretation: The patterns and progress in reducing the burden of LRIs and key risk factors for mortality varied across age groups and sexes. The progress seen in children younger than 5 years was clearly a result of targeted interventions, such as vaccination and reduction of exposure to risk factors. Similar interventions for other age groups could contribute to the achievement of multiple Sustainable Development Goals targets, including promoting wellbeing at all ages and reducing health inequalities. Interventions, including addressing risk factors such as child wasting, smoking, ambient particulate matter pollution, and household air pollution, would prevent deaths and reduce health disparities. Funding: Bill & Melinda Gates Foundation

Global prevalence and burden of depressive and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic

Before 2020, mental disorders were leading causes of the global health-related burden, with depressive and anxiety disorders being leading contributors to this burden. The emergence of the COVID-19 pandemic has created an environment where many determinants of poor mental health are exacerbated. The need for up-to-date information on the mental health impacts of COVID-19 in a way that informs health system responses is imperative. In this study, we aimed to quantify the impact of the COVID-19 pandemic on the prevalence and burden of major depressive disorder and anxiety disorders globally in 2020. Through a systematic review of data reporting the prevalence of major depressive disorder and anxiety disorders during the COVID-19 pandemic and published between Jan 1, 2020, and Jan 29, 2021 and using the assembled data in a meta-regression to estimate change in the prevalence of major depressive disorder and anxiety disorders between pre-pandemic and mid-pandemic (using periods as defined by each study) via COVID-19 impact indicators (human mobility, daily SARS-CoV-2 infection rate, and daily excess mortality rate) by age, sex, and location. Final prevalence estimates and disability weights were used to estimate years lived with disability and disability-adjusted life-years (DALYs) for major depressive disorder and anxiety disorders

Estimating global, regional, and national daily and cumulative infections with SARS-CoV-2 through Nov 14, 2021: a statistical analysis

Timely, accurate, and comprehensive estimates of SARS-CoV-2 daily infection rates, cumulative infections, the proportion of the population that has been infected at least once, and the effective reproductive number (Reffective) are essential for understanding the determinants of past infection, current transmission patterns, and a population’s susceptibility to future infection with the same variant. Although several studies have estimated cumulative SARS-CoV-2 infections in select locations at specific points in time, all of these analyses have relied on biased data inputs that were not adequately corrected for. In this study, we aimed to provide a novel approach to estimating past SARS-CoV-2 daily infections, cumulative infections, and the proportion of the population infected, for 190 countries and territories from the start of the pandemic to Nov 14, 2021. This approach combines data from reported cases, reported deaths, excess deaths attributable to COVID-19, hospitalisations, and seroprevalence surveys to produce more robust estimates that minimise constituent biases

Estimating excess mortality due to the COVID-19 pandemic: a systematic analysis of COVID-19-related mortality, 2020???21

The full impact of the pandemic has been much greater than what is indicated by reported deaths due to COVID-19 alone. Strengthening death registration systems around the world, long understood to be crucial to global public health strategy, is necessary for improved monitoring of this pandemic and future pandemics. In addition, further research is warranted to help distinguish the proportion of excess mortality that was directly caused by SARS-CoV-2 infection and the changes in causes of death as an indirect consequence of the pandemic