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1.
Lancet ; 398(10301): 685-697, 2021 08 21.
Article in English | MEDLINE | ID: covidwho-1815297

ABSTRACT

BACKGROUND: Associations between high and low temperatures and increases in mortality and morbidity have been previously reported, yet no comprehensive assessment of disease burden has been done. Therefore, we aimed to estimate the global and regional burden due to non-optimal temperature exposure. METHODS: In part 1 of this study, we linked deaths to daily temperature estimates from the ERA5 reanalysis dataset. We modelled the cause-specific relative risks for 176 individual causes of death along daily temperature and 23 mean temperature zones using a two-dimensional spline within a Bayesian meta-regression framework. We then calculated the cause-specific and total temperature-attributable burden for the countries for which daily mortality data were available. In part 2, we applied cause-specific relative risks from part 1 to all locations globally. We combined exposure-response curves with daily gridded temperature and calculated the cause-specific burden based on the underlying burden of disease from the Global Burden of Diseases, Injuries, and Risk Factors Study, for the years 1990-2019. Uncertainty from all components of the modelling chain, including risks, temperature exposure, and theoretical minimum risk exposure levels, defined as the temperature of minimum mortality across all included causes, was propagated using posterior simulation of 1000 draws. FINDINGS: We included 64·9 million individual International Classification of Diseases-coded deaths from nine different countries, occurring between Jan 1, 1980, and Dec 31, 2016. 17 causes of death met the inclusion criteria. Ischaemic heart disease, stroke, cardiomyopathy and myocarditis, hypertensive heart disease, diabetes, chronic kidney disease, lower respiratory infection, and chronic obstructive pulmonary disease showed J-shaped relationships with daily temperature, whereas the risk of external causes (eg, homicide, suicide, drowning, and related to disasters, mechanical, transport, and other unintentional injuries) increased monotonically with temperature. The theoretical minimum risk exposure levels varied by location and year as a function of the underlying cause of death composition. Estimates for non-optimal temperature ranged from 7·98 deaths (95% uncertainty interval 7·10-8·85) per 100 000 and a population attributable fraction (PAF) of 1·2% (1·1-1·4) in Brazil to 35·1 deaths (29·9-40·3) per 100 000 and a PAF of 4·7% (4·3-5·1) in China. In 2019, the average cold-attributable mortality exceeded heat-attributable mortality in all countries for which data were available. Cold effects were most pronounced in China with PAFs of 4·3% (3·9-4·7) and attributable rates of 32·0 deaths (27·2-36·8) per 100 000 and in New Zealand with 3·4% (2·9-3·9) and 26·4 deaths (22·1-30·2). Heat effects were most pronounced in China with PAFs of 0·4% (0·3-0·6) and attributable rates of 3·25 deaths (2·39-4·24) per 100 000 and in Brazil with 0·4% (0·3-0·5) and 2·71 deaths (2·15-3·37). When applying our framework to all countries globally, we estimated that 1·69 million (1·52-1·83) deaths were attributable to non-optimal temperature globally in 2019. The highest heat-attributable burdens were observed in south and southeast Asia, sub-Saharan Africa, and North Africa and the Middle East, and the highest cold-attributable burdens in eastern and central Europe, and central Asia. INTERPRETATION: Acute heat and cold exposure can increase or decrease the risk of mortality for a diverse set of causes of death. Although in most regions cold effects dominate, locations with high prevailing temperatures can exhibit substantial heat effects far exceeding cold-attributable burden. Particularly, a high burden of external causes of death contributed to strong heat impacts, but cardiorespiratory diseases and metabolic diseases could also be substantial contributors. Changes in both exposures and the composition of causes of death drove changes in risk over time. Steady increases in exposure to the risk of high temperature are of increasing concern for health. FUNDING: Bill & Melinda Gates Foundation.


Subject(s)
Cause of Death/trends , Cold Temperature/adverse effects , Global Burden of Disease/statistics & numerical data , Global Health/statistics & numerical data , Hot Temperature/adverse effects , Mortality/trends , Bayes Theorem , Heart Diseases/epidemiology , Humans , Metabolic Diseases/epidemiology
2.
Lancet ; 2022 Apr 08.
Article in English | MEDLINE | ID: covidwho-1778513

ABSTRACT

BACKGROUND: 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. METHODS: We produced a comprehensive set of global and location-specific estimates of daily and cumulative SARS-CoV-2 infections through Nov 14, 2021, using data largely from Johns Hopkins University (Baltimore, MD, USA) and national databases for reported cases, hospital admissions, and reported deaths, as well as seroprevalence surveys identified through previous reviews, SeroTracker, and governmental organisations. We corrected these data for known biases such as lags in reporting, accounted for under-reporting of deaths by use of a statistical model of the proportion of excess mortality attributable to SARS-CoV-2, and adjusted seroprevalence surveys for waning antibody sensitivity, vaccinations, and reinfection from SARS-CoV-2 escape variants. We then created an empirical database of infection-detection ratios (IDRs), infection-hospitalisation ratios (IHRs), and infection-fatality ratios (IFRs). To estimate a complete time series for each location, we developed statistical models to predict the IDR, IHR, and IFR by location and day, testing a set of predictors justified through published systematic reviews. Next, we combined three series of estimates of daily infections (cases divided by IDR, hospitalisations divided by IHR, and deaths divided by IFR), into a more robust estimate of daily infections. We then used daily infections to estimate cumulative infections and the cumulative proportion of the population with one or more infections, and we then calculated posterior estimates of cumulative IDR, IHR, and IFR using cumulative infections and the corrected data on reported cases, hospitalisations, and deaths. Finally, we converted daily infections into a historical time series of Reffective by location and day based on assumptions of duration from infection to infectiousness and time an individual spent being infectious. For each of these quantities, we estimated a distribution based on an ensemble framework that captured uncertainty in data sources, model design, and parameter assumptions. FINDINGS: Global daily SARS-CoV-2 infections fluctuated between 3 million and 17 million new infections per day between April, 2020, and October, 2021, peaking in mid-April, 2021, primarily as a result of surges in India. Between the start of the pandemic and Nov 14, 2021, there were an estimated 3·80 billion (95% uncertainty interval 3·44-4·08) total SARS-CoV-2 infections and reinfections combined, and an estimated 3·39 billion (3·08-3·63) individuals, or 43·9% (39·9-46·9) of the global population, had been infected one or more times. 1·34 billion (1·20-1·49) of these infections occurred in south Asia, the highest among the seven super-regions, although the sub-Saharan Africa super-region had the highest infection rate (79·3 per 100 population [69·0-86·4]). The high-income super-region had the fewest infections (239 million [226-252]), and southeast Asia, east Asia, and Oceania had the lowest infection rate (13·0 per 100 population [8·4-17·7]). The cumulative proportion of the population ever infected varied greatly between countries and territories, with rates higher than 70% in 40 countries and lower than 20% in 39 countries. There was no discernible relationship between Reffective and total immunity, and even at total immunity levels of 80%, we observed no indication of an abrupt drop in Reffective, indicating that there is not a clear herd immunity threshold observed in the data. INTERPRETATION: COVID-19 has already had a staggering impact on the world up to the beginning of the omicron (B.1.1.529) wave, with over 40% of the global population infected at least once by Nov 14, 2021. The vast differences in cumulative proportion of the population infected across locations could help policy makers identify the transmission-prevention strategies that have been most effective, as well as the populations at greatest risk for future infection. This information might also be useful for targeted transmission-prevention interventions, including vaccine prioritisation. Our statistical approach to estimating SARS-CoV-2 infection allows estimates to be updated and disseminated rapidly on the basis of newly available data, which has and will be crucially important for timely COVID-19 research, science, and policy responses. FUNDING: Bill & Melinda Gates Foundation, J Stanton, T Gillespie, and J and E Nordstrom.

3.
Misganaw, Awoke, Naghavi, Mohsen, Walker, Ally, Mirkuzie, Alemnesh H.; Giref, Ababi Zergaw, Berheto, Tezera Moshago, Waktola, Ebba Abate, Kempen, John H.; Eticha, Getachew Tollera, Wolde, Tsigereda Kifle, Deguma, Dereje, Abate, Kalkidan Hassen, Abegaz, Kedir Hussein, Ahmed, Muktar Beshir, Akalu, Yonas, Aklilu, Addis, Alemu, Biresaw Wassihun, Asemahagn, Mulusew A.; Awedew, Atalel Fentahun, Balakrishnan, Senthilkumar, Bekuma, Tariku Tesfaye, Beyene, Addisu Shunu, Beyene, Misrak Getnet, Bezabih, Yihienew Mequanint, Birhanu, Biruk Tesfaye, Chichiabellu, Tesfaye Yitna, Dachew, Berihun Assefa, Dagnew, Amare Belachew, Demeke, Feleke Mekonnen, Demissie, Getu Debalkie, Derbew Molla, Meseret, Dereje, Nebiyu, Deribe, Kebede, Desta, Abebaw Alemayehu, Eshetu, Munir Kassa, Ferede, Tomas Y.; Gebreyohannes, Eyob Alemayehu, Geremew, Abraham, Gesesew, Hailay Abrha, Getacher, Lemma, Glenn, Scott D.; Hafebo, Aregash Samuel, Hashi, Abdiwahab, Hassen, Hamid Yimam, Hay, Simon I.; Hordofa, Diriba Fufa, Huluko, Dawit Hoyiso, Kasa, Ayele Semachew, Kassahun Azene, Getinet, Kebede, Ermiyas Mulu, Kebede, Hafte Kahsay, Kelkay, Bayew, Kidane, Samuel Z.; Legesse, Samson Mideksa, Manamo, Wondimu Ayele, Melaku, Yohannes Adama A.; Mengesha, Endalkachew Worku, Mengesha, Sisay Derso, Merie, Hayimro Edemealem, Mersha, Abera M.; Mersha, Amanual Getnet, Mirutse, Mizan Kiros, Mohammed, Ammas Siraj, Mohammed, Hussen, Mohammed, Salahuddin, Netsere, Henok Biresaw, Nigatu, Dabere, Obsa, Mohammed Suleiman, Odo, Daniel Bogale, Omer, Muktar, Regassa, Lemma Demissie, Sahiledengle, Biniyam, Shaka, Mohammed Feyisso, Shiferaw, Wondimeneh Shibabaw, Sidemo, Negussie Boti, Sinke, Abiy H.; Sintayehu, Yitagesu, Sorrie, Muluken Bekele, Tadesse, Birkneh Tilahun, Tadesse, Eyayou Girma, Tamir, Zemenu, Tamiru, Animut Tagele, Tareke, Amare Abera, Tefera, Yonas Getaye, Tekalegn, Yohannes, Tesema, Ayenew Kassie, Tesema, Tefera Tadele, Tesfay, Fisaha Haile, Tessema, Zemenu Tadesse, Tilahun, Tadesse, Tsegaye, Gebiyaw Wudie, Tusa, Biruk Shalmeno, Weledesemayat, Geremew Tassew, Yazie, Taklo Simeneh, Yeshitila, Yordanos Gizachew, Yirdaw, Birhanu Wubale, Zegeye, Desalegn Tegabu, Murray, Christopher J. L.; Gebremedhin, Lia Tadesse.
Lancet ; 399(10332): 1322-1335, 2022 Apr 02.
Article in English | MEDLINE | ID: covidwho-1768603

ABSTRACT

BACKGROUND: Previous Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) studies have reported national health estimates for Ethiopia. Substantial regional variations in socioeconomic status, population, demography, and access to health care within Ethiopia require comparable estimates at the subnational level. The GBD 2019 Ethiopia subnational analysis aimed to measure the progress and disparities in health across nine regions and two chartered cities. METHODS: We gathered 1057 distinct data sources for Ethiopia and all regions and cities that included census, demographic surveillance, household surveys, disease registry, health service use, disease notifications, and other data for this analysis. Using all available data sources, we estimated the Socio-demographic Index (SDI), total fertility rate (TFR), life expectancy, years of life lost, years lived with disability, disability-adjusted life-years, and risk-factor-attributable health loss with 95% uncertainty intervals (UIs) for Ethiopia's nine regions and two chartered cities from 1990 to 2019. Spatiotemporal Gaussian process regression, cause of death ensemble model, Bayesian meta-regression tool, DisMod-MR 2.1, and other models were used to generate fertility, mortality, cause of death, and disability rates. The risk factor attribution estimations followed the general framework established for comparative risk assessment. FINDINGS: The SDI steadily improved in all regions and cities from 1990 to 2019, yet the disparity between the highest and lowest SDI increased by 54% during that period. The TFR declined from 6·91 (95% UI 6·59-7·20) in 1990 to 4·43 (4·01-4·92) in 2019, but the magnitude of decline also varied substantially among regions and cities. In 2019, TFR ranged from 6·41 (5·96-6·86) in Somali to 1·50 (1·26-1·80) in Addis Ababa. Life expectancy improved in Ethiopia by 21·93 years (21·79-22·07), from 46·91 years (45·71-48·11) in 1990 to 68·84 years (67·51-70·18) in 2019. Addis Ababa had the highest life expectancy at 70·86 years (68·91-72·65) in 2019; Afar and Benishangul-Gumuz had the lowest at 63·74 years (61·53-66·01) for Afar and 64.28 (61.99-66.63) for Benishangul-Gumuz. The overall increases in life expectancy were driven by declines in under-5 mortality and mortality from common infectious diseases, nutritional deficiency, and war and conflict. In 2019, the age-standardised all-cause death rate was the highest in Afar at 1353·38 per 100 000 population (1195·69-1526·19). The leading causes of premature mortality for all sexes in Ethiopia in 2019 were neonatal disorders, diarrhoeal diseases, lower respiratory infections, tuberculosis, stroke, HIV/AIDS, ischaemic heart disease, cirrhosis, congenital defects, and diabetes. With high SDIs and life expectancy for all sexes, Addis Ababa, Dire Dawa, and Harari had low rates of premature mortality from the five leading causes, whereas regions with low SDIs and life expectancy for all sexes (Afar and Somali) had high rates of premature mortality from the leading causes. In 2019, child and maternal malnutrition; unsafe water, sanitation, and handwashing; air pollution; high systolic blood pressure; alcohol use; and high fasting plasma glucose were the leading risk factors for health loss across regions and cities. INTERPRETATION: There were substantial improvements in health over the past three decades across regions and chartered cities in Ethiopia. However, the progress, measured in SDI, life expectancy, TFR, premature mortality, disability, and risk factors, was not uniform. Federal and regional health policy makers should match strategies, resources, and interventions to disease burden and risk factors across regions and cities to achieve national and regional plans, Sustainable Development Goals, and universal health coverage targets. FUNDING: Bill & Melinda Gates Foundation.


Subject(s)
Global Burden of Disease , Global Health , Life Expectancy , Adult , Aged , Bayes Theorem , Cause of Death , Child , Ethiopia/epidemiology , Humans , Infant, Newborn , Quality-Adjusted Life Years , Risk Factors
4.
Wang, Haidong, Paulson, Katherine R.; Pease, Spencer A.; Watson, Stefanie, Comfort, Haley, Zheng, Peng, Aravkin, Aleksandr Y.; Bisignano, Catherine, Barber, Ryan M.; Alam, Tahiya, Fuller, John E.; May, Erin A.; Jones, Darwin Phan, Frisch, Meghan E.; Abbafati, Cristiana, Adolph, Christopher, Allorant, Adrien, Amlag, Joanne O.; Bang-Jensen, Bree, Bertolacci, Gregory J.; Bloom, Sabina S.; Carter, Austin, Castro, Emma, Chakrabarti, Suman, Chattopadhyay, Jhilik, Cogen, Rebecca M.; Collins, James K.; Cooperrider, Kimberly, Dai, Xiaochen, Dangel, William James, Daoud, Farah, Dapper, Carolyn, Deen, Amanda, Duncan, Bruce B.; Erickson, Megan, Ewald, Samuel B.; Fedosseeva, Tatiana, Ferrari, Alize J.; Frostad, Joseph Jon, Fullman, Nancy, Gallagher, John, Gamkrelidze, Amiran, Guo, Gaorui, He, Jiawei, Helak, Monika, Henry, Nathaniel J.; Hulland, Erin N.; Huntley, Bethany M.; Kereselidze, Maia, Lazzar-Atwood, Alice, LeGrand, Kate E.; Lindstrom, Akiaja, Linebarger, Emily, Lotufo, Paulo A.; Lozano, Rafael, Magistro, Beatrice, Malta, Deborah Carvalho, Månsson, Johan, Mantilla Herrera, Ana M.; Marinho, Fatima, Mirkuzie, Alemnesh H.; Misganaw, Awoke Temesgen, Monasta, Lorenzo, Naik, Paulami, Nomura, Shuhei, O'Brien, Edward G.; O'Halloran, James Kevin, Olana, Latera Tesfaye, Ostroff, Samuel M.; Penberthy, Louise, Reiner Jr, Robert C.; Reinke, Grace, Ribeiro, Antonio Luiz P.; Santomauro, Damian Francesco, Schmidt, Maria Inês, Shaw, David H.; Sheena, Brittney S.; Sholokhov, Aleksei, Skhvitaridze, Natia, Sorensen, Reed J. D.; Spurlock, Emma Elizabeth, Syailendrawati, Ruri, Topor-Madry, Roman, Troeger, Christopher E.; Walcott, Rebecca, Walker, Ally, Wiysonge, Charles Shey, Worku, Nahom Alemseged, Zigler, Bethany, Pigott, David M.; Naghavi, Mohsen, Mokdad, Ali H.; Lim, Stephen S.; Hay, Simon I.; Gakidou, Emmanuela, Murray, Christopher J. L..
Lancet ; 2022 Mar 10.
Article in English | MEDLINE | ID: covidwho-1735067

ABSTRACT

BACKGROUND: Mortality statistics are fundamental to public health decision making. Mortality varies by time and location, and its measurement is affected by well known biases that have been exacerbated during the COVID-19 pandemic. This paper aims to estimate excess mortality from the COVID-19 pandemic in 191 countries and territories, and 252 subnational units for selected countries, from Jan 1, 2020, to Dec 31, 2021. METHODS: All-cause mortality reports were collected for 74 countries and territories and 266 subnational locations (including 31 locations in low-income and middle-income countries) that had reported either weekly or monthly deaths from all causes during the pandemic in 2020 and 2021, and for up to 11 year previously. In addition, we obtained excess mortality data for 12 states in India. Excess mortality over time was calculated as observed mortality, after excluding data from periods affected by late registration and anomalies such as heat waves, minus expected mortality. Six models were used to estimate expected mortality; final estimates of expected mortality were based on an ensemble of these models. Ensemble weights were based on root mean squared errors derived from an out-of-sample predictive validity test. As mortality records are incomplete worldwide, we built a statistical model that predicted the excess mortality rate for locations and periods where all-cause mortality data were not available. We used least absolute shrinkage and selection operator (LASSO) regression as a variable selection mechanism and selected 15 covariates, including both covariates pertaining to the COVID-19 pandemic, such as seroprevalence, and to background population health metrics, such as the Healthcare Access and Quality Index, with direction of effects on excess mortality concordant with a meta-analysis by the US Centers for Disease Control and Prevention. With the selected best model, we ran a prediction process using 100 draws for each covariate and 100 draws of estimated coefficients and residuals, estimated from the regressions run at the draw level using draw-level input data on both excess mortality and covariates. Mean values and 95% uncertainty intervals were then generated at national, regional, and global levels. Out-of-sample predictive validity testing was done on the basis of our final model specification. FINDINGS: Although reported COVID-19 deaths between Jan 1, 2020, and Dec 31, 2021, totalled 5·94 million worldwide, we estimate that 18·2 million (95% uncertainty interval 17·1-19·6) people died worldwide because of the COVID-19 pandemic (as measured by excess mortality) over that period. The global all-age rate of excess mortality due to the COVID-19 pandemic was 120·3 deaths (113·1-129·3) per 100 000 of the population, and excess mortality rate exceeded 300 deaths per 100 000 of the population in 21 countries. The number of excess deaths due to COVID-19 was largest in the regions of south Asia, north Africa and the Middle East, and eastern Europe. At the country level, the highest numbers of cumulative excess deaths due to COVID-19 were estimated in India (4·07 million [3·71-4·36]), the USA (1·13 million [1·08-1·18]), Russia (1·07 million [1·06-1·08]), Mexico (798 000 [741 000-867 000]), Brazil (792 000 [730 000-847 000]), Indonesia (736 000 [594 000-955 000]), and Pakistan (664 000 [498 000-847 000]). Among these countries, the excess mortality rate was highest in Russia (374·6 deaths [369·7-378·4] per 100 000) and Mexico (325·1 [301·6-353·3] per 100 000), and was similar in Brazil (186·9 [172·2-199·8] per 100 000) and the USA (179·3 [170·7-187·5] per 100 000). INTERPRETATION: 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. FUNDING: Bill & Melinda Gates Foundation, J Stanton, T Gillespie, and J and E Nordstrom.

5.
Lancet ; 2022 Mar 02.
Article in English | MEDLINE | ID: covidwho-1713034

ABSTRACT

BACKGROUND: Gender is emerging as a significant factor in the social, economic, and health effects of COVID-19. However, most existing studies have focused on its direct impact on health. Here, we aimed to explore the indirect effects of COVID-19 on gender disparities globally. METHODS: We reviewed publicly available datasets with information on indicators related to vaccine hesitancy and uptake, health care services, economic and work-related concerns, education, and safety at home and in the community. We used mixed effects regression, Gaussian process regression, and bootstrapping to synthesise all data sources. We accounted for uncertainty in the underlying data and modelling process. We then used mixed effects logistic regression to explore gender gaps globally and by region. FINDINGS: Between March, 2020, and September, 2021, women were more likely to report employment loss (26·0% [95% uncertainty interval 23·8-28·8, by September, 2021) than men (20·4% [18·2-22·9], by September, 2021), as well as forgoing work to care for others (ratio of women to men: 1·8 by March, 2020, and 2·4 by September, 2021). Women and girls were 1·21 times (1·20-1·21) more likely than men and boys to report dropping out of school for reasons other than school closures. Women were also 1·23 (1·22-1·23) times more likely than men to report that gender-based violence had increased during the pandemic. By September 2021, women and men did not differ significantly in vaccine hesitancy or uptake. INTERPRETATION: The most significant gender gaps identified in our study show intensified levels of pre-existing widespread inequalities between women and men during the COVID-19 pandemic. Political and social leaders should prioritise policies that enable and encourage women to participate in the labour force and continue their education, thereby equipping and enabling them with greater ability to overcome the barriers they face. FUNDING: The Bill & Melinda Gates Foundation.

6.
Murray, Christopher J. L.; Ikuta, Kevin Shunji, Sharara, Fablina, Swetschinski, Lucien, Robles Aguilar, Gisela, Gray, Authia, Han, Chieh, Bisignano, Catherine, Rao, Puja, Wool, Eve, Johnson, Sarah C.; Browne, Annie J.; Chipeta, Michael Give, Fell, Frederick, Hackett, Sean, Haines-Woodhouse, Georgina, Kashef Hamadani, Bahar H.; Kumaran, Emmanuelle A. P.; McManigal, Barney, Agarwal, Ramesh, Akech, Samuel, Albertson, Samuel, Amuasi, John, Andrews, Jason, Aravkin, Aleskandr, Ashley, Elizabeth, Bailey, Freddie, Baker, Stephen, Basnyat, Buddha, Bekker, Adrie, Bender, Rose, Bethou, Adhisivam, Bielicki, Julia, Boonkasidecha, Suppawat, Bukosia, James, Carvalheiro, Cristina, Castañeda-Orjuela, Carlos, Chansamouth, Vilada, Chaurasia, Suman, Chiurchiù, Sara, Chowdhury, Fazle, Cook, Aislinn J.; Cooper, Ben, Cressey, Tim R.; Criollo-Mora, Elia, Cunningham, Matthew, Darboe, Saffiatou, Day, Nicholas P. J.; De Luca, Maia, Dokova, Klara, Dramowski, Angela, Dunachie, Susanna J.; Eckmanns, Tim, Eibach, Daniel, Emami, Amir, Feasey, Nicholas, Fisher-Pearson, Natasha, Forrest, Karen, Garrett, Denise, Gastmeier, Petra, Giref, Ababi Zergaw, Greer, Rachel Claire, Gupta, Vikas, Haller, Sebastian, Haselbeck, Andrea, Hay, Simon I.; Holm, Marianne, Hopkins, Susan, Iregbu, Kenneth C.; Jacobs, Jan, Jarovsky, Daniel, Javanmardi, Fatemeh, Khorana, Meera, Kissoon, Niranjan, Kobeissi, Elsa, Kostyanev, Tomislav, Krapp, Fiorella, Krumkamp, Ralf, Kumar, Ajay, Kyu, Hmwe Hmwe, Lim, Cherry, Limmathurotsakul, Direk, Loftus, Michael James, Lunn, Miles, Ma, Jianing, Mturi, Neema, Munera-Huertas, Tatiana, Musicha, Patrick, Mussi-Pinhata, Marisa Marcia, Nakamura, Tomoka, Nanavati, Ruchi, Nangia, Sushma, Newton, Paul, Ngoun, Chanpheaktra, Novotney, Amanda, Nwakanma, Davis, Obiero, Christina W.; Olivas-Martinez, Antonio, Olliaro, Piero, Ooko, Ednah, Ortiz-Brizuela, Edgar, Peleg, Anton Yariv, Perrone, Carlo, Plakkal, Nishad, Ponce-de-Leon, Alfredo, Raad, Mathieu, Ramdin, Tanusha, Riddell, Amy, Roberts, Tamalee, Robotham, Julie Victoria, Roca, Anna, Rudd, Kristina E.; Russell, Neal, Schnall, Jesse, Scott, John Anthony Gerard, Shivamallappa, Madhusudhan, Sifuentes-Osornio, Jose, Steenkeste, Nicolas, Stewardson, Andrew James, Stoeva, Temenuga, Tasak, Nidanuch, Thaiprakong, Areerat, Thwaites, Guy, Turner, Claudia, Turner, Paul, van Doorn, H. Rogier, Velaphi, Sithembiso, Vongpradith, Avina, Vu, Huong, Walsh, Timothy, Waner, Seymour, Wangrangsimakul, Tri, Wozniak, Teresa, Zheng, Peng, Sartorius, Benn, Lopez, Alan D.; Stergachis, Andy, Moore, Catrin, Dolecek, Christiane, Naghavi, Mohsen.
Lancet ; 399(10325): 629-655, 2022 02 12.
Article in English | MEDLINE | ID: covidwho-1624565

ABSTRACT

BACKGROUND: Antimicrobial resistance (AMR) poses a major threat to human health around the world. Previous publications have estimated the effect of AMR on incidence, deaths, hospital length of stay, and health-care costs for specific pathogen-drug combinations in select locations. To our knowledge, this study presents the most comprehensive estimates of AMR burden to date. METHODS: We estimated deaths and disability-adjusted life-years (DALYs) attributable to and associated with bacterial AMR for 23 pathogens and 88 pathogen-drug combinations in 204 countries and territories in 2019. We obtained data from systematic literature reviews, hospital systems, surveillance systems, and other sources, covering 471 million individual records or isolates and 7585 study-location-years. We used predictive statistical modelling to produce estimates of AMR burden for all locations, including for locations with no data. Our approach can be divided into five broad components: number of deaths where infection played a role, proportion of infectious deaths attributable to a given infectious syndrome, proportion of infectious syndrome deaths attributable to a given pathogen, the percentage of a given pathogen resistant to an antibiotic of interest, and the excess risk of death or duration of an infection associated with this resistance. Using these components, we estimated disease burden based on two counterfactuals: deaths attributable to AMR (based on an alternative scenario in which all drug-resistant infections were replaced by drug-susceptible infections), and deaths associated with AMR (based on an alternative scenario in which all drug-resistant infections were replaced by no infection). We generated 95% uncertainty intervals (UIs) for final estimates as the 25th and 975th ordered values across 1000 posterior draws, and models were cross-validated for out-of-sample predictive validity. We present final estimates aggregated to the global and regional level. FINDINGS: On the basis of our predictive statistical models, there were an estimated 4·95 million (3·62-6·57) deaths associated with bacterial AMR in 2019, including 1·27 million (95% UI 0·911-1·71) deaths attributable to bacterial AMR. At the regional level, we estimated the all-age death rate attributable to resistance to be highest in western sub-Saharan Africa, at 27·3 deaths per 100 000 (20·9-35·3), and lowest in Australasia, at 6·5 deaths (4·3-9·4) per 100 000. Lower respiratory infections accounted for more than 1·5 million deaths associated with resistance in 2019, making it the most burdensome infectious syndrome. The six leading pathogens for deaths associated with resistance (Escherichia coli, followed by Staphylococcus aureus, Klebsiella pneumoniae, Streptococcus pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa) were responsible for 929 000 (660 000-1 270 000) deaths attributable to AMR and 3·57 million (2·62-4·78) deaths associated with AMR in 2019. One pathogen-drug combination, meticillin-resistant S aureus, caused more than 100 000 deaths attributable to AMR in 2019, while six more each caused 50 000-100 000 deaths: multidrug-resistant excluding extensively drug-resistant tuberculosis, third-generation cephalosporin-resistant E coli, carbapenem-resistant A baumannii, fluoroquinolone-resistant E coli, carbapenem-resistant K pneumoniae, and third-generation cephalosporin-resistant K pneumoniae. INTERPRETATION: To our knowledge, this study provides the first comprehensive assessment of the global burden of AMR, as well as an evaluation of the availability of data. AMR is a leading cause of death around the world, with the highest burdens in low-resource settings. Understanding the burden of AMR and the leading pathogen-drug combinations contributing to it is crucial to making informed and location-specific policy decisions, particularly about infection prevention and control programmes, access to essential antibiotics, and research and development of new vaccines and antibiotics. There are serious data gaps in many low-income settings, emphasising the need to expand microbiology laboratory capacity and data collection systems to improve our understanding of this important human health threat. FUNDING: Bill & Melinda Gates Foundation, Wellcome Trust, and Department of Health and Social Care using UK aid funding managed by the Fleming Fund.


Subject(s)
Anti-Bacterial Agents/pharmacology , Bacterial Infections/epidemiology , Drug Resistance, Bacterial , Global Burden of Disease , Anti-Bacterial Agents/therapeutic use , Bacterial Infections/drug therapy , Bacterial Infections/microbiology , Global Health , Humans , Models, Statistical
7.
Bollyky, Thomas J.; Hulland, Erin N.; Barber, Ryan M.; Collins, James K.; Kiernan, Samantha, Moses, Mark, Pigott, David M.; Reiner Jr, Robert C.; Sorensen, Reed J. D.; Abbafati, Cristiana, Adolph, Christopher, Allorant, Adrien, Amlag, Joanne O.; Aravkin, Aleksandr Y.; Bang-Jensen, Bree, Carter, Austin, Castellano, Rachel, Castro, Emma, Chakrabarti, Suman, Combs, Emily, Dai, Xiaochen, Dangel, William James, Dapper, Carolyn, Deen, Amanda, Duncan, Bruce B.; Earl, Lucas, Erickson, Megan, Ewald, Samuel B.; Fedosseeva, Tatiana, Ferrari, Alize J.; Flaxman, Abraham D.; Fullman, Nancy, Gakidou, Emmanuela, Galal, Bayan, Gallagher, John, Giles, John R.; Guo, Gaorui, He, Jiawei, Helak, Monika, Huntley, Bethany M.; Idrisov, Bulat, Johanns, Casey, LeGrand, Kate E.; Letourneau, Ian D.; Lindstrom, Akiaja, Linebarger, Emily, Lotufo, Paulo A.; Lozano, Rafael, Magistro, Beatrice, Malta, Deborah Carvalho, Månsson, Johan, Mantilla Herrera, Ana M.; Marinho, Fatima, Mirkuzie, Alemnesh H.; Mokdad, Ali H.; Monasta, Lorenzo, Naik, Paulami, Nomura, Shuhei, O'Halloran, James Kevin, Odell, Christopher M.; Olana, Latera Tesfaye, Ostroff, Samuel M.; Pasovic, Maja, Passos, Valeria Maria de Azeredo, Penberthy, Louise, Reinke, Grace, Santomauro, Damian Francesco, Schmidt, Maria Inês, Sholokhov, Aleksei, Spurlock, Emma, Troeger, Christopher E.; Varavikova, Elena, Vo, Anh T.; Vos, Theo, Walcott, Rebecca, Walker, Ally, Wigley, Simon D.; Wiysonge, Charles Shey, Worku, Nahom Alemseged, Wu, Yifan, Wulf Hanson, Sarah, Zheng, Peng, Hay, Simon I.; Murray, Christopher J. L.; Dieleman, Joseph L..
Lancet ; 2022 Feb 01.
Article in English | MEDLINE | ID: covidwho-1665567

ABSTRACT

BACKGROUND: National rates of COVID-19 infection and fatality have varied dramatically since the onset of the pandemic. Understanding the conditions associated with this cross-country variation is essential to guiding investment in more effective preparedness and response for future pandemics. METHODS: Daily SARS-CoV-2 infections and COVID-19 deaths for 177 countries and territories and 181 subnational locations were extracted from the Institute for Health Metrics and Evaluation's modelling database. Cumulative infection rate and infection-fatality ratio (IFR) were estimated and standardised for environmental, demographic, biological, and economic factors. For infections, we included factors associated with environmental seasonality (measured as the relative risk of pneumonia), population density, gross domestic product (GDP) per capita, proportion of the population living below 100 m, and a proxy for previous exposure to other betacoronaviruses. For IFR, factors were age distribution of the population, mean body-mass index (BMI), exposure to air pollution, smoking rates, the proxy for previous exposure to other betacoronaviruses, population density, age-standardised prevalence of chronic obstructive pulmonary disease and cancer, and GDP per capita. These were standardised using indirect age standardisation and multivariate linear models. Standardised national cumulative infection rates and IFRs were tested for associations with 12 pandemic preparedness indices, seven health-care capacity indicators, and ten other demographic, social, and political conditions using linear regression. To investigate pathways by which important factors might affect infections with SARS-CoV-2, we also assessed the relationship between interpersonal and governmental trust and corruption and changes in mobility patterns and COVID-19 vaccination rates. FINDINGS: The factors that explained the most variation in cumulative rates of SARS-CoV-2 infection between Jan 1, 2020, and Sept 30, 2021, included the proportion of the population living below 100 m (5·4% [4·0-7·9] of variation), GDP per capita (4·2% [1·8-6·6] of variation), and the proportion of infections attributable to seasonality (2·1% [95% uncertainty interval 1·7-2·7] of variation). Most cross-country variation in cumulative infection rates could not be explained. The factors that explained the most variation in COVID-19 IFR over the same period were the age profile of the country (46·7% [18·4-67·6] of variation), GDP per capita (3·1% [0·3-8·6] of variation), and national mean BMI (1·1% [0·2-2·6] of variation). 44·4% (29·2-61·7) of cross-national variation in IFR could not be explained. Pandemic-preparedness indices, which aim to measure health security capacity, were not meaningfully associated with standardised infection rates or IFRs. Measures of trust in the government and interpersonal trust, as well as less government corruption, had larger, statistically significant associations with lower standardised infection rates. High levels of government and interpersonal trust, as well as less government corruption, were also associated with higher COVID-19 vaccine coverage among middle-income and high-income countries where vaccine availability was more widespread, and lower corruption was associated with greater reductions in mobility. If these modelled associations were to be causal, an increase in trust of governments such that all countries had societies that attained at least the amount of trust in government or interpersonal trust measured in Denmark, which is in the 75th percentile across these spectrums, might have reduced global infections by 12·9% (5·7-17·8) for government trust and 40·3% (24·3-51·4) for interpersonal trust. Similarly, if all countries had a national BMI equal to or less than that of the 25th percentile, our analysis suggests global standardised IFR would be reduced by 11·1%. INTERPRETATION: Efforts to improve pandemic preparedness and response for the next pandemic might benefit from greater investment in risk communication and community engagement strategies to boost the confidence that individuals have in public health guidance. Our results suggest that increasing health promotion for key modifiable risks is associated with a reduction of fatalities in such a scenario. FUNDING: Bill & Melinda Gates Foundation, J Stanton, T Gillespie, J and E Nordstrom, and Bloomberg Philanthropies.

8.
J Epidemiol Community Health ; 2022 Jan 19.
Article in English | MEDLINE | ID: covidwho-1629386

ABSTRACT

BACKGROUND: Over the last 30 years, South Africa has experienced four 'colliding epidemics' of HIV and tuberculosis, chronic illness and mental health, injury and violence, and maternal, neonatal, and child mortality, which have had substantial effects on health and well-being. Using data from the 2019 Global Burden of Diseases, Injuries and Risk Factors Study (GBD 2019), we evaluated national and provincial health trends and progress towards important Sustainable Development Goal targets from 1990 to 2019. METHODS: We analysed GBD 2019 estimates of mortality, non-fatal health loss, summary health measures and risk factor burden, comparing trends over 1990-2007 and 2007-2019. Additionally, we decomposed changes in life expectancy by cause of death and assessed healthcare system performance. RESULTS: Across the nine provinces, inequalities in mortality and life expectancy increased over 1990-2007, largely due to differences in HIV/AIDS, then decreased over 2007-2019. Demographic change and increases in non-communicable diseases nearly doubled the number of years lived with disability between 1990 and 2019. From 1990 to 2019, risk factor burdens generally shifted from communicable and nutritional disease risks to non-communicable disease and injury risks; unsafe sex remained the top risk factor. Despite widespread improvements in healthcare system performance, the greatest gains were generally in economically advantaged provinces. CONCLUSIONS: Reductions in HIV/AIDS and related conditions have led to improved health since 2007, though most provinces still lag in key areas. To achieve health targets, provincial governments should enhance health investments and exchange of knowledge, resources and best practices alongside populations that have been left behind, especially following the COVID-19 pandemic.

9.
Lancet ; 398(10299): 522-534, 2021 08 07.
Article in English | MEDLINE | ID: covidwho-1592159

ABSTRACT

BACKGROUND: The COVID-19 pandemic and efforts to reduce SARS-CoV-2 transmission substantially affected health services worldwide. To better understand the impact of the pandemic on childhood routine immunisation, we estimated disruptions in vaccine coverage associated with the pandemic in 2020, globally and by Global Burden of Disease (GBD) super-region. METHODS: For this analysis we used a two-step hierarchical random spline modelling approach to estimate global and regional disruptions to routine immunisation using administrative data and reports from electronic immunisation systems, with mobility data as a model input. Paired with estimates of vaccine coverage expected in the absence of COVID-19, which were derived from vaccine coverage models from GBD 2020, Release 1 (GBD 2020 R1), we estimated the number of children who missed routinely delivered doses of the third-dose diphtheria-tetanus-pertussis (DTP3) vaccine and first-dose measles-containing vaccine (MCV1) in 2020. FINDINGS: Globally, in 2020, estimated vaccine coverage was 76·7% (95% uncertainty interval 74·3-78·6) for DTP3 and 78·9% (74·8-81·9) for MCV1, representing relative reductions of 7·7% (6·0-10·1) for DTP3 and 7·9% (5·2-11·7) for MCV1, compared to expected doses delivered in the absence of the COVID-19 pandemic. From January to December, 2020, we estimated that 30·0 million (27·6-33·1) children missed doses of DTP3 and 27·2 million (23·4-32·5) children missed MCV1 doses. Compared to expected gaps in coverage for eligible children in 2020, these estimates represented an additional 8·5 million (6·5-11·6) children not routinely vaccinated with DTP3 and an additional 8·9 million (5·7-13·7) children not routinely vaccinated with MCV1 attributable to the COVID-19 pandemic. Globally, monthly disruptions were highest in April, 2020, across all GBD super-regions, with 4·6 million (4·0-5·4) children missing doses of DTP3 and 4·4 million (3·7-5·2) children missing doses of MCV1. Every GBD super-region saw reductions in vaccine coverage in March and April, with the most severe annual impacts in north Africa and the Middle East, south Asia, and Latin America and the Caribbean. We estimated the lowest annual reductions in vaccine delivery in sub-Saharan Africa, where disruptions remained minimal throughout the year. For some super-regions, including southeast Asia, east Asia, and Oceania for both DTP3 and MCV1, the high-income super-region for DTP3, and south Asia for MCV1, estimates suggest that monthly doses were delivered at or above expected levels during the second half of 2020. INTERPRETATION: Routine immunisation services faced stark challenges in 2020, with the COVID-19 pandemic causing the most widespread and largest global disruption in recent history. Although the latest coverage trajectories point towards recovery in some regions, a combination of lagging catch-up immunisation services, continued SARS-CoV-2 transmission, and persistent gaps in vaccine coverage before the pandemic still left millions of children under-vaccinated or unvaccinated against preventable diseases at the end of 2020, and these gaps are likely to extend throughout 2021. Strengthening routine immunisation data systems and efforts to target resources and outreach will be essential to minimise the risk of vaccine-preventable disease outbreaks, reach children who missed routine vaccine doses during the pandemic, and accelerate progress towards higher and more equitable vaccination coverage over the next decade. FUNDING: Bill & Melinda Gates Foundation.


Subject(s)
COVID-19 , Diphtheria-Tetanus-Pertussis Vaccine , Measles Vaccine , Vaccination Coverage/statistics & numerical data , Child , Global Health , Humans , Models, Statistical
10.
Nat Commun ; 12(1): 6923, 2021 11 26.
Article in English | MEDLINE | ID: covidwho-1537314

ABSTRACT

Nationwide nonpharmaceutical interventions (NPIs) have been effective at mitigating the spread of the novel coronavirus disease (COVID-19), but their broad impact on other diseases remains under-investigated. Here we report an ecological analysis comparing the incidence of 31 major notifiable infectious diseases in China in 2020 to the average level during 2014-2019, controlling for temporal phases defined by NPI intensity levels. Respiratory diseases and gastrointestinal or enteroviral diseases declined more than sexually transmitted or bloodborne diseases and vector-borne or zoonotic diseases. Early pandemic phases with more stringent NPIs were associated with greater reductions in disease incidence. Non-respiratory diseases, such as hand, foot and mouth disease, rebounded substantially towards the end of the year 2020 as the NPIs were relaxed. Statistical modeling analyses confirm that strong NPIs were associated with a broad mitigation effect on communicable diseases, but resurgence of non-respiratory diseases should be expected when the NPIs, especially restrictions of human movement and gathering, become less stringent.


Subject(s)
Communicable Diseases/epidemiology , Disease Notification/statistics & numerical data , COVID-19/epidemiology , COVID-19/prevention & control , COVID-19/transmission , China/epidemiology , Communicable Disease Control , Communicable Diseases/classification , Communicable Diseases/transmission , Humans , Incidence , Models, Statistical , SARS-CoV-2
11.
Nat Commun ; 12(1): 5026, 2021 08 18.
Article in English | MEDLINE | ID: covidwho-1363491

ABSTRACT

Nationwide prospective surveillance of all-age patients with acute respiratory infections was conducted in China between 2009‒2019. Here we report the etiological and epidemiological features of the 231,107 eligible patients enrolled in this analysis. Children <5 years old and school-age children have the highest viral positivity rate (46.9%) and bacterial positivity rate (30.9%). Influenza virus, respiratory syncytial virus and human rhinovirus are the three leading viral pathogens with proportions of 28.5%, 16.8% and 16.7%, and Streptococcus pneumoniae, Mycoplasma pneumoniae and Klebsiella pneumoniae are the three leading bacterial pathogens (29.9%, 18.6% and 15.8%). Negative interactions between viruses and positive interactions between viral and bacterial pathogens are common. A Join-Point analysis reveals the age-specific positivity rate and how this varied for individual pathogens. These data indicate that differential priorities for diagnosis, prevention and control should be highlighted in terms of acute respiratory tract infection patients' demography, geographic locations and season of illness in China.


Subject(s)
Bacteria/isolation & purification , Bacterial Infections/microbiology , Respiratory Tract Infections/microbiology , Respiratory Tract Infections/virology , Virus Diseases/virology , Viruses/isolation & purification , Adolescent , Adult , Bacteria/classification , Bacteria/genetics , Bacterial Infections/epidemiology , Child , Child, Preschool , China/epidemiology , Female , Humans , Infant , Male , Prospective Studies , Respiratory Tract Infections/epidemiology , Seasons , Virus Diseases/epidemiology , Viruses/classification , Viruses/genetics , Young Adult
12.
Lancet Reg Health West Pac ; 16: 100268, 2021 Nov.
Article in English | MEDLINE | ID: covidwho-1415636

ABSTRACT

BACKGROUND: Non pharmaceutical interventions (NPI) including hand washing directives were implemented in China and worldwide to combat the COVID-19 pandemic, which are likely to have had impacted a broad spectrum of enteric pathogen infections. METHODS: Etiologically diagnostic data from 45 937 and 67 395 patients with acute diarrhea between 2012 and 2020, who were tested for seven viral pathogens and 13 bacteria respectively, were analyzed to assess the changes of enteric pathogen infections in China during the first COVID-19 pandemic year compared to pre-pandemic years. FINDINGS: Test positive rates of all enteric viruses decreased during 2020, compared to the average levels during 2012-2019, with a relative decrease of 71•75% for adenovirus, 58•76% for norovirus, 53•50% for rotavirus A, and 72•07% for the combination of other four uncommon viruses. In general, a larger reduction of positive rate in viruses was seen among adults than pediatric patients. A rebound of rotavirus A was seen after September 2020 in North China rather than South China. Test positive rates of bacteria decreased during 2020, compared to the average levels during 2012-2019, excepting for nontyphoidal Salmonella and Campylobacter coli with 66•53% and 90•48% increase respectively. This increase was larger for pediatric patients than for adult patients. INTERPRETATION: The activity of enteric pathogens changed profoundly alongside the NPIs implemented during the COVID-19 pandemic in China. Greater reductions of the test positive rates were found for almost all enteric viruses than for bacteria among acute diarrhea patients, with further large differences by age and geography. Lifting of NPIs will lead to resurgence of enteric pathogen infections, particularly in children whose immunity may not have been developed and/or waned. FUNDING: China Mega-Project on Infectious Disease Prevention; National Natural Science Funds.

13.
Nat Commun ; 12(1): 2609, 2021 05 10.
Article in English | MEDLINE | ID: covidwho-1223089

ABSTRACT

Forecasts and alternative scenarios of COVID-19 mortality have been critical inputs for pandemic response efforts, and decision-makers need information about predictive performance. We screen n = 386 public COVID-19 forecasting models, identifying n = 7 that are global in scope and provide public, date-versioned forecasts. We examine their predictive performance for mortality by weeks of extrapolation, world region, and estimation month. We additionally assess prediction of the timing of peak daily mortality. Globally, models released in October show a median absolute percent error (MAPE) of 7 to 13% at six weeks, reflecting surprisingly good performance despite the complexities of modelling human behavioural responses and government interventions. Median absolute error for peak timing increased from 8 days at one week of forecasting to 29 days at eight weeks and is similar for first and subsequent peaks. The framework and public codebase ( https://github.com/pyliu47/covidcompare ) can be used to compare predictions and evaluate predictive performance going forward.


Subject(s)
COVID-19/mortality , Models, Statistical , Forecasting , Humans , SARS-CoV-2 , Time Factors
14.
Lancet Infect Dis ; 21(1): 59-69, 2021 01.
Article in English | MEDLINE | ID: covidwho-1059565

ABSTRACT

BACKGROUND: Substantial progress has been made in reducing the burden of malaria in Africa since 2000, but those gains could be jeopardised if the COVID-19 pandemic affects the availability of key malaria control interventions. The aim of this study was to evaluate plausible effects on malaria incidence and mortality under different levels of disruption to malaria control. METHODS: Using an established set of spatiotemporal Bayesian geostatistical models, we generated geospatial estimates across malaria-endemic African countries of the clinical case incidence and mortality of malaria, incorporating an updated database of parasite rate surveys, insecticide-treated net (ITN) coverage, and effective treatment rates. We established a baseline estimate for the anticipated malaria burden in Africa in the absence of COVID-19-related disruptions, and repeated the analysis for nine hypothetical scenarios in which effective treatment with an antimalarial drug and distribution of ITNs (both through routine channels and mass campaigns) were reduced to varying extents. FINDINGS: We estimated 215·2 (95% uncertainty interval 143·7-311·6) million cases and 386·4 (307·8-497·8) thousand deaths across malaria-endemic African countries in 2020 in our baseline scenario of undisrupted intervention coverage. With greater reductions in access to effective antimalarial drug treatment, our model predicted increasing numbers of cases and deaths: 224·1 (148·7-326·8) million cases and 487·9 (385·3-634·6) thousand deaths with a 25% reduction in antimalarial drug coverage; 233·1 (153·7-342·5) million cases and 597·4 (468·0-784·4) thousand deaths with a 50% reduction; and 242·3 (158·7-358·8) million cases and 715·2 (556·4-947·9) thousand deaths with a 75% reduction. Halting planned 2020 ITN mass distribution campaigns and reducing routine ITN distributions by 25%-75% also increased malaria burden to a total of 230·5 (151·6-343·3) million cases and 411·7 (322·8-545·5) thousand deaths with a 25% reduction; 232·8 (152·3-345·9) million cases and 415·5 (324·3-549·4) thousand deaths with a 50% reduction; and 234·0 (152·9-348·4) million cases and 417·6 (325·5-553·1) thousand deaths with a 75% reduction. When ITN coverage and antimalarial drug coverage were synchronously reduced, malaria burden increased to 240·5 (156·5-358·2) million cases and 520·9 (404·1-691·9) thousand deaths with a 25% reduction; 251·0 (162·2-377·0) million cases and 640·2 (492·0-856·7) thousand deaths with a 50% reduction; and 261·6 (167·7-396·8) million cases and 768·6 (586·1-1038·7) thousand deaths with a 75% reduction. INTERPRETATION: Under pessimistic scenarios, COVID-19-related disruption to malaria control in Africa could almost double malaria mortality in 2020, and potentially lead to even greater increases in subsequent years. To avoid a reversal of two decades of progress against malaria, averting this public health disaster must remain an integrated priority alongside the response to COVID-19. FUNDING: Bill and Melinda Gates Foundation; Channel 7 Telethon Trust, Western Australia.


Subject(s)
COVID-19/epidemiology , Malaria/epidemiology , Malaria/mortality , SARS-CoV-2 , Africa/epidemiology , Antimalarials/therapeutic use , Bayes Theorem , Humans , Incidence , Insecticide-Treated Bednets , Malaria/drug therapy , Malaria/prevention & control , Models, Statistical , Morbidity
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