Publications
252 results found
Nielsen PY, Jensen MK, Mitarai N, et al., 2024, The Gompertz Law emerges naturally from the inter-dependencies between sub-components in complex organisms., Sci Rep, Vol: 14
Understanding and facilitating healthy aging has become a major goal in medical research and it is becoming increasingly acknowledged that there is a need for understanding the aging phenotype as a whole rather than focusing on individual factors. Here, we provide a universal explanation for the emergence of Gompertzian mortality patterns using a systems approach to describe aging in complex organisms that consist of many inter-dependent subsystems. Our model relates to the Sufficient-Component Cause Model, widely used within the field of epidemiology, and we show that including inter-dependencies between subsystems and modeling the temporal evolution of subsystem failure results in Gompertizan mortality on the population level. Our model also provides temporal trajectories of mortality-risk for the individual. These results may give insight into understanding how biological age evolves stochastically within the individual, and how this in turn leads to a natural heterogeneity of biological age in a population.
Khurana MP, Scheidwasser-Clow N, Penn MJ, et al., 2023, The limits of the constant-rate birth-death prior for phylogenetic tree topology inference., Syst Biol
Birth-death models are stochastic processes describing speciation and extinction through time and across taxa, and are widely used in biology for inference of evolutionary timescales. Previous research has highlighted how the expected trees under the constant-rate birth-death (crBD) model tend to differ from empirical trees, for example with respect to the amount of phylogenetic imbalance. However, our understanding of how trees differ between the crBD model and the signal in empirical data remains incomplete. In this Point of View, we aim to expose the degree to which the crBD model differs from empirically inferred phylogenies and test the limits of the model in practice. Using a wide range of topology indices to compare crBD expectations against a comprehensive dataset of 1189 empirically estimated trees, we confirm that crBD model trees frequently differ topologically compared with empirical trees. To place this in the context of standard practice in the field, we conducted a meta-analysis for a subset of the empirical studies. When comparing studies that used Bayesian methods and crBD priors with those that used other non-crBD priors and non-Bayesian methods (i.e., maximum likelihood methods), we do not find any significant differences in tree topology inferences. To scrutinize this finding for the case of highly imbalanced trees, we selected the 100 trees with the greatest imbalance from our dataset, simulated sequence data for these tree topologies under various evolutionary rates, and re-inferred the trees under maximum likelihood and using the crBD model in a Bayesian setting. We find that when the substitution rate is low, the crBD prior results in overly balanced trees, but the tendency is negligible when substitution rates are sufficiently high. Overall, our findings demonstrate the general robustness of crBD priors across a broad range of phylogenetic inference scenarios, but also highlights that empirically observed phylogenetic imbalance is highly imp
Bhatt S, Ferguson N, Flaxman S, et al., 2023, Semi-mechanistic Bayesian modelling of COVID-19 with renewal processes, Journal of the Royal Statistical Society Series A: Statistics in Society, Vol: 186, Pages: 601-615, ISSN: 0964-1998
<jats:title>Abstract</jats:title> <jats:p>We propose a general Bayesian approach to modelling epidemics such as COVID-19. The approach grew out of specific analyses conducted during the pandemic, in particular, an analysis concerning the effects of non-pharmaceutical interventions (NPIs) in reducing COVID-19 transmission in 11 European countries. The model parameterises the time-varying reproduction number Rt through a multilevel regression framework in which covariates can be governmental interventions, changes in mobility patterns, or other behavioural measures. Bayesian multilevel modelling allows a joint fit across regions, with partial pooling to share strength. This innovation was critical to our timely estimates of the impact of lockdown and other NPIs in the European epidemics: estimates from countries at later stages in their epidemics informed those of countries at earlier stages. Originally released as Imperial College Reports, the validity of this approach was borne out by the subsequent course of the epidemic. Our framework provides a fully generative model for latent infections and derived observations, including deaths, cases, hospitalizations, ICU admissions, and seroprevalence surveys. In this article, we additionally explore the confounded nature of NPIs and mobility. Versions of our model were used by New York State, Tennessee, and Scotland to estimate the current epidemic situation and make policy decisions.</jats:p>
Penn MJ, Scheidwasser N, Penn J, et al., 2023, Leaping through Tree Space: Continuous Phylogenetic Inference for Rooted and Unrooted Trees., Genome Biol Evol, Vol: 15
Phylogenetics is now fundamental in life sciences, providing insights into the earliest branches of life and the origins and spread of epidemics. However, finding suitable phylogenies from the vast space of possible trees remains challenging. To address this problem, for the first time, we perform both tree exploration and inference in a continuous space where the computation of gradients is possible. This continuous relaxation allows for major leaps across tree space in both rooted and unrooted trees, and is less susceptible to convergence to local minima. Our approach outperforms the current best methods for inference on unrooted trees and, in simulation, accurately infers the tree and root in ultrametric cases. The approach is effective in cases of empirical data with negligible amounts of data, which we demonstrate on the phylogeny of jawed vertebrates. Indeed, only a few genes with an ultrametric signal were generally sufficient for resolving the major lineages of vertebrates. Optimization is possible via automatic differentiation and our method presents an effective way forward for exploring the most difficult, data-deficient phylogenetic questions.
Murphy C, Lim WW, Mills C, et al., 2023, Effectiveness of social distancing measures and lockdowns for reducing transmission of COVID-19 in non-healthcare, community-based settings, PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, Vol: 381, ISSN: 1364-503X
- Author Web Link
- Cite
- Citations: 1
Bhatt S, Ferguson N, Flaxman S, et al., 2023, Semi-mechanistic Bayesian modelling of COVID-19 with renewal processes, Journal of the Royal Statistical Society. Series A: Statistics in Society, Vol: 186, Pages: 633-636, ISSN: 0964-1998
We propose a general Bayesian approach to modelling epidemics such as COVID-19. The approach grew out of specific analyses conducted during the pandemic, in particular, an analysis concerning the effects of non-pharmaceutical interventions (NPIs) in reducing COVID-19 transmission in 11 European countries. The model parameterises the time-varying reproduction number Rt through a multilevel regression framework in which covariates can be governmental interventions, changes in mobility patterns, or other behavioural measures. Bayesian multilevel modelling allows a joint fit across regions, with partial pooling to share strength. This innovation was critical to our timely estimates of the impact of lockdown and other NPIs in the European epidemics: estimates from countries at later stages in their epidemics informed those of countries at earlier stages. Originally released as Imperial College Reports, the validity of this approach was borne out by the subsequent course of the epidemic. Our framework provides a fully generative model for latent infections and derived observations, including deaths, cases, hospitalizations, ICU admissions, and seroprevalence surveys. In this article, we additionally explore the confounded nature of NPIs and mobility. Versions of our model were used by New York State, Tennessee, and Scotland to estimate the current epidemic situation and make policy decisions.
Monod M, Blenkinsop A, Brizzi A, et al., 2023, Regularised B-splines projected Gaussian Process priors to estimate time-trends in age-specific COVID-19 deaths, Bayesian Analysis, Vol: 18, Pages: 957-987, ISSN: 1931-6690
The COVID-19 pandemic has caused severe public health consequences in the United States. In this study, we use a hierarchical Bayesian model to estimate the age-specific COVID-19 attributable deaths over time in the United States. The model is specified by a novel non-parametric spatial approach over time and age, a low-rank Gaussian Process (GP) projected by regularised B-splines. We show that this projection defines a new GP with attractive smoothness and computational efficiency properties, derive its kernel function, and discuss the penalty terms induced by the projected GP. Simulation analyses and benchmark results show that the B-splines projected GP may perform better than standard B-splines and Bayesian P-splines, and equivalently well as a standard GP at considerably lowerruntimes. We apply the model to weekly, age-stratified COVID-19 attributabledeaths reported by the US Centers for Disease Control, which are subject to censoring and reporting biases. Using the B-splines projected GP, we can estimate longitudinal trends in COVID-19 associated deaths across the US by 1-year age bands. These estimates are instrumental to calculate age-specific mortality rates, describe variation in age-specific deaths across the US, and for fitting epidemic models. Here, we couple the model with age-specific vaccination rates to show that vaccination rates were significantly associated with the magnitude of resurgences in COVID-19 deaths during the summer 2021. With counterfactual analyses, we quantify the avoided COVID-19 deaths under lower vaccination rates and avoidable COVID-19 deaths under higher vaccination rates. The B-splines projected GP priors that we develop are likely an appealing addition to the arsenal of Bayesianregularising priors.
Hawryluk I, Mishra S, Flaxman S, et al., 2023, Application of referenced thermodynamic integration to Bayesian model selection, PLoS One, Vol: 18, Pages: 1-16, ISSN: 1932-6203
Evaluating normalising constants is important across a range of topics in statisticallearning, notably Bayesian model selection. However, in many realistic problems thisinvolves the integration of analytically intractable, high-dimensional distributions, andtherefore requires the use of stochastic methods such as thermodynamic integration(TI). In this paper we apply a simple but under-appreciated variation of the TI method,here referred to as referenced TI, which computes a single model’s normalising constantin an efficient way by using a judiciously chosen reference density. The advantages ofthe approach and theoretical considerations are set out, along with pedagogical 1 and2D examples. The approach is shown to be useful in practice when applied to a realproblem — to perform model selection for a semi-mechanistic hierarchical Bayesianmodel of COVID-19 transmission in South Korea involving the integration of a 200Ddensity.
Katsiferis A, Bhatt S, Mortensen LH, et al., 2023, Machine learning models of healthcare expenditures predicting mortality: A cohort study of spousal bereaved Danish individuals, PLOS ONE, Vol: 18, ISSN: 1932-6203
Pakkanen MS, Miscouridou X, Penn MJ, et al., 2023, Unifying incidence and prevalence under a time-varying general branching process, Journal of Mathematical Biology, Vol: 87, ISSN: 0303-6812
Renewal equations are a popular approach used in modelling the number of new infections, i.e., incidence, in an outbreak. We develop a stochastic model of an outbreak based on a time-varying variant of the Crump–Mode–Jagers branching process. This model accommodates a time-varying reproduction number and a time-varying distribution for the generation interval. We then derive renewal-like integral equations for incidence, cumulative incidence and prevalence under this model. We show that the equations for incidence and prevalence are consistent with the so-called back-calculation relationship. We analyse two particular cases of these integral equations, one that arises from a Bellman–Harris process and one that arises from an inhomogeneous Poisson process model of transmission.We also show that the incidence integral equations that arise from both of these specific models agree with the renewal equation used ubiquitously in infectious disease modelling. We present a numerical discretisation scheme to solve these equations, and use this scheme to estimate rates of transmission from serological prevalence of SARS-CoV-2 in the UK and historical incidence data on Influenza, Measles, SARS and Smallpox.
Katsiferis A, Mortensen LH, Khurana MP, et al., 2023, Predicting mortality risk after a fall in older adults using health care spending patterns: a population-based cohort study, AGE AND AGEING, Vol: 52, ISSN: 0002-0729
Nash RK, Bhatt S, Cori A, et al., 2023, Estimating the epidemic reproduction number from temporally aggregated incidence data: A statistical modelling approach and software tool, PLOS COMPUTATIONAL BIOLOGY, Vol: 19, ISSN: 1553-734X
Zhang J, Lim Y-H, So R, et al., 2023, Long-term exposure to air pollution and risk of SARS-CoV-2 infection and COVID-19 hospitalisation or death: Danish nationwide cohort study, EUROPEAN RESPIRATORY JOURNAL, Vol: 62, ISSN: 0903-1936
Charles G, Wolock TM, Winskill P, et al., 2023, Seq2Seq Surrogates of Epidemic Models to Facilitate Bayesian Inference, Pages: 14170-14177
Epidemic models are powerful tools in understanding infectious disease. However, as they increase in size and complexity, they can quickly become computationally intractable. Recent progress in modelling methodology has shown that surrogate models can be used to emulate complex epidemic models with a high-dimensional parameter space. We show that deep sequence-to-sequence (seq2seq) models can serve as accurate surrogates for complex epidemic models with sequence based model parameters, effectively replicating seasonal and long-term transmission dynamics. Once trained, our surrogate can predict scenarios a several thousand times faster than the original model, making them ideal for policy exploration. We demonstrate that replacing a traditional epidemic model with a learned simulator facilitates robust Bayesian inference.
Penn MJJ, Laydon DJJ, Penn J, et al., 2023, Intrinsic randomness in epidemic modelling beyond statistical uncertainty, Communications Physics, Vol: 6, ISSN: 2399-3650
Uncertainty can be classified as either aleatoric (intrinsic randomness) or epistemic (imperfect knowledge of parameters). The majority of frameworks assessing infectious disease risk consider only epistemic uncertainty. We only ever observe a single epidemic, and therefore cannot empirically determine aleatoric uncertainty. Here, we characterise both epistemic and aleatoric uncertainty using a time-varying general branching process. Our framework explicitly decomposes aleatoric variance into mechanistic components, quantifying the contribution to uncertainty produced by each factor in the epidemic process, and how these contributions vary over time. The aleatoric variance of an outbreak is itself a renewal equation where past variance affects future variance. We find that, superspreading is not necessary for substantial uncertainty, and profound variation in outbreak size can occur even without overdispersion in the offspring distribution (i.e. the distribution of the number of secondary infections an infected person produces). Aleatoric forecasting uncertainty grows dynamically and rapidly, and so forecasting using only epistemic uncertainty is a significant underestimate. Therefore, failure to account for aleatoric uncertainty will ensure that policymakers are misled about the substantially higher true extent of potential risk. We demonstrate our method, and the extent to which potential risk is underestimated, using two historical examples.
Fornace KM, Topazian HM, Routledge I, et al., 2023, No evidence of sustained nonzoonotic <i>Plasmodium knowlesi</i> transmission in Malaysia from modelling malaria case data, NATURE COMMUNICATIONS, Vol: 14
- Author Web Link
- Cite
- Citations: 1
Dan SK, Chen Y, Chen Y, et al., 2023, Estimating fine age structure and time trends in human contact patterns from coarse contact data: the Bayesian rate consistency model, PLoS Computational Biology, Vol: 19, ISSN: 1553-734X
Since the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), large-scale social contact surveys are now longitudinally measuring the fundamental changes in human interactions in the face of the pandemic and non-pharmaceutical interventions. Here, we present a model-based Bayesian approach that can reconstruct contact patterns at 1-year resolution even when the age of the contacts is reported coarsely by 5 or 10-year age bands. This innovation is rooted in population-level consistency constraints in how contacts between groups must add up, which prompts us to call the approach presented here the Bayesian rate consistency model. The model can also quantify time trends and adjust for reporting fatigue emerging in longitudinal surveys through the use of computationally efficient Hilbert Space Gaussian process priors. We illustrate estimation accuracy on simulated data as well as social contact data from Europe and Africa for which the exact age of contacts is reported, and then apply the model to social contact data with coarse information on the age of contacts that were collected in Germany during the COVID-19 pandemic from April to June 2020 across five longitudinal survey waves. We estimate the fine age structure in social contacts during the early stages of the pandemic and demonstrate that social contact intensities rebounded in an age-structured, non-homogeneous manner. The Bayesian rate consistency model provides a model-based, non-parametric, computationally tractable approach for estimating the fine structure and longitudinal trends in social contacts and is applicable to contemporary survey data with coarsely reported age of contacts as long as the exact age of survey participants is reported.
Laydon D, Cauchemez S, Hinsley W, et al., 2023, Impact of proactive and reactive vaccination strategies for health-care workers against MERS-CoV: a mathematical modelling study, The Lancet Global Health, Vol: 11, Pages: e759-e769, ISSN: 2214-109X
BackgroundSeveral vaccine candidates are in development against MERS-CoV, which remains a major public health concern. In anticipation of available MERS-CoV vaccines, we examine strategies for their optimal deployment among health-care workers.MethodsUsing data from the 2013–14 Saudi Arabia epidemic, we use a counterfactual analysis on inferred transmission trees (who-infected-whom analysis) to assess the potential impact of vaccination campaigns targeting health-care workers, as quantified by the proportion of cases or deaths averted. We investigate the conditions under which proactive campaigns (ie vaccinating in anticipation of the next outbreak) would outperform reactive campaigns (ie vaccinating in response to an unfolding outbreak), considering vaccine efficacy, duration of vaccine protection, effectiveness of animal reservoir control measures, wait (time between vaccination and next outbreak, for proactive campaigns), reaction time (for reactive campaigns), and spatial level (hospital, regional, or national, for reactive campaigns). We also examine the relative efficiency (cases averted per thousand doses) of different strategies.FindingsThe spatial scale of reactive campaigns is crucial. Proactive campaigns outperform campaigns that vaccinate health-care workers in response to outbreaks at their hospital, unless vaccine efficacy has waned significantly. However, reactive campaigns at the regional or national levels consistently outperform proactive campaigns, regardless of vaccine efficacy. When considering the number of cases averted per vaccine dose administered, the rank order is reversed: hospital-level reactive campaigns are most efficient, followed by regional-level reactive campaigns, with national-level and proactive campaigns being least efficient. If the number of cases required to trigger reactive vaccination increases, the performance of hospital-level campaigns is greatly reduced; the impact of regional-level campaigns is variable, but tha
Epstein A, Namuganga JF, Nabende I, et al., 2023, Mapping malaria incidence using routine health facility surveillance data in Uganda, BMJ GLOBAL HEALTH, Vol: 8, ISSN: 2059-7908
Bertozzi-Villa A, Bever CAA, Gerardin J, et al., 2023, An archetypes approach to malaria intervention impact mapping: a new framework and example application, MALARIA JOURNAL, Vol: 22
Lison A, Banholzer N, Sharma M, et al., 2023, Effectiveness assessment of non-pharmaceutical interventions: lessons learned from the COVID-19 pandemic, The Lancet Public Health, Vol: 8, Pages: e311-e317, ISSN: 2468-2667
Numerous studies have assessed the effectiveness of non-pharmaceutical interventions (NPIs), such as school closures and stay-at-home orders, during the COVID-19 pandemic. Such assessments can inform public health policy and contribute to evidence-based choices of NPIs during subsequentwaves or future epidemics. However, methodological issues and a lack of common standards have limited the practical value of the existing evidence. Based on our work and literature review, we discuss lessons learned from the COVID-19 pandemic and make recommendations for standardizing and improving assessment, data collection, and modeling. These recommendations can contribute to more reliable and policy-relevant assessments of NPI effectiveness during future epidemics.
Rod NH, Bengtsson J, Elsenburg LK, et al., 2023, Cancer burden among adolescents and young adults in relation to childhood adversity: a nationwide life-course cohort study of 1.2 million individuals, LANCET REGIONAL HEALTH-EUROPE, Vol: 27, ISSN: 2666-7762
Nielsen PY, Bartholdy A, Gjerdrum LMR, et al., 2023, Danish Pathology Life Course (PATHOLIFE) cohort: a register based cohort extending upon a national tissue biobank, BMJ OPEN, Vol: 13, ISSN: 2044-6055
Katsiferis A, Bhatt S, Mortensen LH, et al., 2023, Sex differences in health care expenditures and mortality after spousal bereavement: A register-based Danish cohort study, PLOS ONE, Vol: 18, ISSN: 1932-6203
- Author Web Link
- Cite
- Citations: 1
Whittaker C, Hamlet A, Sherrard-Smith E, et al., 2023, Seasonal dynamics of Anopheles stephensi and its implications for mosquito detection and emergent malaria control in the Horn of Africa, Proceedings of the National Academy of Sciences of USA, Vol: 120, Pages: 1-9, ISSN: 0027-8424
Invasion of the malaria vector Anopheles stephensi across the Horn of Africa threatens control efforts across the continent, particularly in urban settings where the vector is able to proliferate. Malaria transmission is primarily determined by the abundance of dominant vectors, which often varies seasonally with rainfall. However, it remains unclear how An. stephensi abundance changes throughout the year, despite this being a crucial input to surveillance and control activities. We collate longitudinal catch data from across its endemic range to better understand the vector's seasonal dynamics and explore the implications of this seasonality for malaria surveillance and control across the Horn of Africa. Our analyses reveal pronounced variation in seasonal dynamics, the timing and nature of which are poorly predicted by rainfall patterns. Instead, they are associated with temperature and patterns of land use; frequently differing between rural and urban settings. Our results show that timing entomological surveys to coincide with rainy periods is unlikely to improve the likelihood of detecting An. stephensi. Integrating these results into a malaria transmission model, we show that timing indoor residual spraying campaigns to coincide with peak rainfall offers little improvement in reducing disease burden compared to starting in a random month. Our results suggest that unlike other malaria vectors in Africa, rainfall may be a poor guide to predicting the timing of peaks in An. stephensi-driven malaria transmission. This highlights the urgent need for longitudinal entomological monitoring of the vector in its new environments given recent invasion and potential spread across the continent.
Bager P, Nielsen J, Bhatt S, et al., 2023, Conflicting COVID-19 excess mortality estimates., Lancet, Vol: 401, Pages: 432-433
Flaxman S, Whittaker C, Semenova E, et al., 2023, Assessment of COVID-19 as the underlying cause of death among children and young people aged 0 to 19 years in the US., Jama Network Open, Vol: 6, Pages: 1-9, ISSN: 2574-3805
IMPORTANCE: COVID-19 was the underlying cause of death for more than 940 000 individuals in the US, including at least 1289 children and young people (CYP) aged 0 to 19 years, with at least 821 CYP deaths occurring in the 1-year period from August 1, 2021, to July 31, 2022. Because deaths among US CYP are rare, the mortality burden of COVID-19 in CYP is best understood in the context of all other causes of CYP death. OBJECTIVE: To determine whether COVID-19 is a leading (top 10) cause of death in CYP in the US. DESIGN, SETTING, AND PARTICIPANTS: This national population-level cross-sectional epidemiological analysis for the years 2019 to 2022 used data from the US Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research (WONDER) database on underlying cause of death in the US to identify the ranking of COVID-19 relative to other causes of death among individuals aged 0 to 19 years. COVID-19 deaths were considered in 12-month periods between April 1, 2020, and August 31, 2022, compared with deaths from leading non-COVID-19 causes in 2019, 2020, and 2021. MAIN OUTCOMES AND MEASURES: Cause of death rankings by total number of deaths, crude rates per 100 000 population, and percentage of all causes of death, using the National Center for Health Statistics 113 Selected Causes of Death, for ages 0 to 19 and by age groupings (<1 year, 1-4 years, 5-9 years, 10-14 years, 15-19 years). RESULTS: There were 821 COVID-19 deaths among individuals aged 0 to 19 years during the study period, resulting in a crude death rate of 1.0 per 100 000 population overall; 4.3 per 100 000 for those younger than 1 year; 0.6 per 100 000 for those aged 1 to 4 years; 0.4 per 100 000 for those aged 5 to 9 years; 0.5 per 100 000 for those aged 10 to 14 years; and 1.8 per 100 000 for those aged 15 to 19 years. COVID-19 mortality in the time period of August 1, 2021, to July 31, 2022, was among the 10 leading causes of death in CYP aged 0 to 19 years in the US
Mehrjou A, Soleymani A, Abyaneh A, et al., 2023, Pyfectious: An individual-level simulator to discover optimal containment policies for epidemic diseases, PLOS COMPUTATIONAL BIOLOGY, Vol: 19, ISSN: 1553-734X
Zheng J, Zhang N, Shen G, et al., 2023, Spatiotemporal and Seasonal Trends of Class A and B Notifiable Infectious Diseases in China: Retrospective Analysis, JMIR PUBLIC HEALTH AND SURVEILLANCE, Vol: 9, ISSN: 2369-2960
- Author Web Link
- Cite
- Citations: 1
Nguyen M, Dzianach PA, Castle PECW, et al., 2023, Trends in treatment-seeking for fever in children under five years old in 151 countries from 1990 to 2020., PLOS Glob Public Health, Vol: 3
Access to medical treatment for fever is essential to prevent morbidity and mortality in individuals and to prevent transmission of communicable febrile illness in communities. Quantification of the rates at which treatment is accessed is critical for health system planning and a prerequisite for disease burden estimates. In this study, national data on the proportion of children under five years old with fever who were taken for medical treatment were collected from all available countries in Africa, Latin America, and Asia (n = 91). We used generalised additive mixed models to estimate 30-year trends in the treatment-seeking rates across the majority of countries in these regions (n = 151). Our results show that the proportions of febrile children brought for medical treatment increased steadily over the last 30 years, with the greatest increases occurring in areas where rates had originally been lowest, which includes Latin America and Caribbean, North Africa and the Middle East (51 and 50% increase, respectively), and Sub-Saharan Africa (23% increase). Overall, the aggregated and population-weighted estimate of children with fever taken for treatment at any type of facility rose from 61% (59-64 95% CI) in 1990 to 71% (69-72 95% CI) in 2020. The overall population-weighted average for fraction of treatment in the public sector was largely unchanged during the study period: 49% (42-58 95% CI) sought care at public facilities in 1990 and 47% (44-52 95% CI) in 2020. Overall, the findings indicate that improvements in access to care have been made where they were most needed, but that despite rapid initial gains, progress can plateau without substantial investment. In 2020 there remained significant gaps in care utilisation that must be factored in when developing control strategies and deriving disease burden estimates.
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.