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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
2.
BMJ Glob Health ; 6(5)2021 05.
Article in English | MEDLINE | ID: covidwho-1504118

ABSTRACT

BACKGROUND: Most of the deaths among neonates in low-income and middle-income countries (LMICs) can be prevented through universal access to basic high-quality health services including essential facility-based inpatient care. However, poor routine data undermines data-informed efforts to monitor and promote improvements in the quality of newborn care across hospitals. METHODS: Continuously collected routine patients' data from structured paper record forms for all admissions to newborn units (NBUs) from 16 purposively selected Kenyan public hospitals that are part of a clinical information network were analysed together with data from all paediatric admissions ages 0-13 years from 14 of these hospitals. Data are used to show the proportion of all admissions and deaths in the neonatal age group and examine morbidity and mortality patterns, stratified by birth weight, and their variation across hospitals. FINDINGS: During the 354 hospital months study period, 90 222 patients were admitted to the 14 hospitals contributing NBU and general paediatric ward data. 46% of all the admissions were neonates (aged 0-28 days), but they accounted for 66% of the deaths in the age group 0-13 years. 41 657 inborn neonates were admitted in the NBUs across the 16 hospitals during the study period. 4266/41 657 died giving a crude mortality rate of 10.2% (95% CI 9.97% to 10.55%), with 60% of these deaths occurring on the first-day of admission. Intrapartum-related complications was the single most common diagnosis among the neonates with birth weight of 2000 g or more who died. A threefold variation in mortality across hospitals was observed for birth weight categories 1000-1499 g and 1500-1999 g. INTERPRETATION: The high proportion of neonatal deaths in hospitals may reflect changing patterns of childhood mortality. Majority of newborns died of preventable causes (>95%). Despite availability of high-impact low-cost interventions, hospitals have high and very variable mortality proportions after stratification by birth weight.


Subject(s)
Hospitals , Infant Mortality , Adolescent , Child , Child, Preschool , Cohort Studies , Humans , Infant , Infant, Newborn , Kenya/epidemiology , Retrospective Studies
3.
BMJ Open ; 11(9): e050995, 2021 09 07.
Article in English | MEDLINE | ID: covidwho-1398696

ABSTRACT

OBJECTIVES: To characterise adoption and explore specific clinical and patient factors that might influence pulse oximetry and oxygen use in low-income and middle-income countries (LMICs) over time; to highlight useful considerations for entities working on programmes to improve access to pulse oximetry and oxygen. DESIGN: A multihospital retrospective cohort study. SETTINGS: All admissions (n=132 737) to paediatric wards of 18 purposely selected public hospitals in Kenya that joined a Clinical Information Network (CIN) between March 2014 and December 2020. OUTCOMES: Pulse oximetry use and oxygen prescription on admission; we performed growth-curve modelling to investigate the association of patient factors with study outcomes over time while adjusting for hospital factors. RESULTS: Overall, pulse oximetry was used in 48.8% (64 722/132 737) of all admission cases. Use rose on average with each month of participation in the CIN (OR: 1.11, 95% CI 1.05 to 1.18) but patterns of adoption were highly variable across hospitals suggesting important factors at hospital level influence use of pulse oximetry. Of those with pulse oximetry measurement, 7% (4510/64 722) had hypoxaemia (SpO2 <90%). Across the same period, 8.6% (11 428/132 737) had oxygen prescribed but in 87%, pulse oximetry was either not done or the hypoxaemia threshold (SpO2 <90%) was not met. Lower chest-wall indrawing and other respiratory symptoms were associated with pulse oximetry use at admission and were also associated with oxygen prescription in the absence of pulse oximetry or hypoxaemia. CONCLUSION: The adoption of pulse oximetry recommended in international guidelines for assessing children with severe illness has been slow and erratic, reflecting system and organisational weaknesses. Most oxygen orders at admission seem driven by clinical and situational factors other than the presence of hypoxaemia. Programmes aiming to implement pulse oximetry and oxygen systems will likely need a long-term vision to promote adoption, guideline development and adherence and continuously examine impact.


Subject(s)
Oximetry , Oxygen , Child , Humans , Hypoxia/diagnosis , Kenya , Prospective Studies , Retrospective Studies
4.
BMJ Glob Health ; 6(5)2021 05.
Article in English | MEDLINE | ID: covidwho-1223600

ABSTRACT

BACKGROUND: The COVID-19 pandemic has led to an unprecedented global research effort to build a body of knowledge that can inform mitigation strategies. We carried out a bibliometric analysis to describe the COVID-19 research output in Africa in terms of setting, study design, research themes and author affiliation. METHODS: We searched for articles published between 1 December 2019 and 3 January 2021 from various databases including PubMed, African Journals Online, medRxiv, Collabovid, the WHO global research database and Google. All article types and study design were included. RESULTS: A total of 1296 articles were retrieved. 46.6% were primary research articles, 48.6% were editorial-type articles while 4.6% were secondary research articles. 20.3% articles used the entire continent of Africa as their study setting while South Africa (15.4%) was the most common country-focused setting. The most common research topics include 'country preparedness and response' (24.9%) and 'the direct and indirect health impacts of the pandemic' (21.6%). However, only 1.0% of articles focus on therapeutics and vaccines. 90.3% of the articles had at least one African researcher as author, 78.5% had an African researcher as first author, while 63.5% had an African researcher as last author. The University of Cape Town leads with the greatest number of first and last authors. 13% of the articles were published in medRxiv and of the studies that declared funding, the Wellcome Trust was the top funding body. CONCLUSIONS: This study highlights Africa's COVID-19 research and the continent's existing capacity to carry out research that addresses local problems. However, more studies focused on vaccines and therapeutics are needed to inform local development. In addition, the uneven distribution of research productivity among African countries emphasises the need for increased investment where needed.


Subject(s)
Bibliometrics , Biomedical Research , COVID-19 , Africa/epidemiology , COVID-19/epidemiology , Humans
5.
BMJ Glob Health ; 6(4)2021 04.
Article in English | MEDLINE | ID: covidwho-1186288

ABSTRACT

INTRODUCTION: We estimated unit costs for COVID-19 case management for patients with asymptomatic, mild-to-moderate, severe and critical COVID-19 disease in Kenya. METHODS: We estimated per-day unit costs of COVID-19 case management for patients. We used a bottom-up approach to estimate full economic costs and adopted a health system perspective and patient episode of care as our time horizon. We obtained data on inputs and their quantities from data provided by three public COVID-19 treatment hospitals in Kenya and augmented this with guidelines. We obtained input prices from a recent costing survey of 20 hospitals in Kenya and from market prices for Kenya. RESULTS: Per-day, per-patient unit costs for asymptomatic patients and patients with mild-to-moderate COVID-19 disease under home-based care are 1993.01 Kenyan shilling (KES) (US$18.89) and 1995.17 KES (US$18.991), respectively. When these patients are managed in an isolation centre or hospital, the same unit costs for asymptomatic patients and patients with mild-to-moderate disease are 6717.74 KES (US$63.68) and 6719.90 KES (US$63.70), respectively. Per-day unit costs for patients with severe COVID-19 disease managed in general hospital wards and those with critical COVID-19 disease admitted in intensive care units are 13 137.07 KES (US$124.53) and 63 243.11 KES (US$599.51). CONCLUSION: COVID-19 case management costs are substantial, ranging between two and four times the average claims value reported by Kenya's public health insurer. Kenya will need to mobilise substantial resources and explore service delivery adaptations that will reduce unit costs.


Subject(s)
COVID-19/economics , COVID-19/therapy , Case Management , Health Care Costs , Humans , Kenya/epidemiology , Pandemics
6.
BMJ Glob Health ; 6(3)2021 03.
Article in English | MEDLINE | ID: covidwho-1148159

ABSTRACT

We have worked to develop a Clinical Information Network (CIN) in Kenya as an early form of learning health systems (LHS) focused on paediatric and neonatal care that now spans 22 hospitals. CIN's aim was to examine important outcomes of hospitalisation at scale, identify and ultimately solve practical problems of service delivery, drive improvements in quality and test interventions. By including multiple routine settings in research, we aimed to promote generalisability of findings and demonstrate potential efficiencies derived from LHS. We illustrate the nature and range of research CIN has supported over the past 7 years as a form of LHS. Clinically, this has largely focused on common, serious paediatric illnesses such as pneumonia, malaria and diarrhoea with dehydration with recent extensions to neonatal illnesses. CIN also enables examination of the quality of care, for example that provided to children with severe malnutrition and the challenges encountered in routine settings in adopting simple technologies (pulse oximetry) and more advanced diagnostics (eg, Xpert MTB/RIF). Although regular feedback to hospitals has been associated with some improvements in quality data continue to highlight system challenges that undermine provision of basic, quality care (eg, poor access to blood glucose testing and routine microbiology). These challenges include those associated with increased mortality risk (eg, delays in blood transfusion). Using the same data the CIN platform has enabled conduct of randomised trials and supports malaria vaccine and most recently COVID-19 surveillance. Employing LHS principles has meant engaging front-line workers, clinical managers and national stakeholders throughout. Our experience suggests LHS can be developed in low and middle-income countries that efficiently enable contextually appropriate research and contribute to strengthening of health services and research systems.


Subject(s)
Child Health Services/standards , Delivery of Health Care/standards , Health Services Accessibility/standards , Health Services Research , Quality Improvement , COVID-19/epidemiology , COVID-19/prevention & control , Child , Child, Preschool , Developing Countries , Diarrhea/epidemiology , Diarrhea/prevention & control , Humans , Infant , Infant, Newborn , Kenya/epidemiology , Malaria/epidemiology , Malaria/prevention & control , Pandemics , Pneumonia/epidemiology , Pneumonia/prevention & control , SARS-CoV-2
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