43 results found
Runge J, Nowack P, Kretschmer M, et al., 2019, Detecting and quantifying causal associations in large nonlinear time series datasets, Science Advances, Vol: 5, Pages: 1-15, ISSN: 2375-2548
Identifying causal relationships and quantifying their strength fromobservational time series data are key problems in disciplines deal-ing with complex dynamical systems such as the Earth system orthe human body. Data-driven causal inference in such systems ischallenging since datasets are often high-dimensional and nonlinear with limited sample sizes. Here we introduce a novel method thatflexibly combines linear or nonlinear conditional independence testswith a causal discovery algorithm to estimate causal networks fromlarge-scale time series datasets. We validate the method on timeseries of well-understood physical mechanisms in the climate sys-tem and the human heart and using large-scale synthetic datasetsmimicking the typical properties of real world data. The experi-ments demonstrate that our method outperforms state-of-the-arttechniques in detection power, which opens up entirely new possi-bilities to discover and quantify causal networks from time seriesacross a range of research fields.
Davis SPX, Kumar S, Alexandrov Y, et al., 2019, Convolutional neural networks for reconstruction of undersampled optical projection tomography data applied to in vivo imaging of zebrafish., Journal of Biophotonics, ISSN: 1864-063X
Optical projection tomography (OPT) is a 3D mesoscopic imaging modality that can utilize absorption or fluorescence contrast. 3D images can be rapidly reconstructed from tomographic data sets sampled with sufficient numbers of projection angles using the Radon transform, as is typically implemented with optically cleared samples of the mm-to-cm scale. For in vivo imaging, considerations of phototoxicity and the need to maintain animals under anesthesia typically preclude the acquisition of OPT data at a sufficient number of angles to avoid artifacts in the reconstructed images. For sparse samples, this can be addressed with iterative algorithms to reconstruct 3D images from undersampled OPT data, but the data processing times present a significant challenge for studies imaging multiple animals. We show here that convolutional neural networks (CNN) can be used in place of iterative algorithms to remove artifacts - reducing processing time for an undersampled in vivo zebrafish dataset from 77 to 15 minutes. We also show that using CNN produces reconstructions of equivalent quality to CS with 40% fewer projections. We further show that diverse training data classes, for example ex vivo mouse tissue data, can be used for CNN-based reconstructions of OPT data of other species including live zebrafish. This article is protected by copyright. All rights reserved.
Flaxman S, Chirico M, Pereira P, et al., Scalable high-resolution forecasting of sparse spatiotemporal events with kernel methods: a winning solution to the NIJ "Real-Time Crime Forecasting Challenge", Annals of Applied Statistics, ISSN: 1932-6157
We propose a generic spatiotemporal event forecasting method,which we developed for the National Institute of Justice’s (NIJ) RealTime Crime Forecasting Challenge (National Institute of Justice,2017). Our method is a spatiotemporal forecasting model combining scalable randomized Reproducing Kernel Hilbert Space (RKHS)methods for approximating Gaussian processes with autoregressivesmoothing kernels in a regularized supervised learning framework.While the smoothing kernels capture the two main approaches in current use in the field of crime forecasting, kernel density estimation(KDE) and self-exciting point process (SEPP) models, the RKHScomponent of the model can be understood as an approximation tothe popular log-Gaussian Cox Process model. For inference, we discretize the spatiotemporal point pattern and learn a log-intensityfunction using the Poisson likelihood and highly efficient gradientbased optimization methods. Model hyperparameters including quality of RKHS approximation, spatial and temporal kernel lengthscales,number of autoregressive lags, bandwidths for smoothing kernels, aswell as cell shape, size, and rotation, were learned using crossvalidation. Resulting predictions significantly exceeded baseline KDEestimates and SEPP models for sparse events.
Keeffe J, Casson R, Pesudovs K, et al., 2019, Prevalence and causes of vision loss in South-east Asia and Oceania in 2015: magnitude, temporal trends, and projections, British Journal of Ophthalmology, Vol: 103, Pages: 878-884, ISSN: 0007-1161
Background:To assess prevalence and causes of vision impairment in South-east Asia and Oceania regions from 1990 to 2015 and to forecast the figures for 2020. Methods:Based on a systematic review of medical literature, prevalence of blindness (presenting visual acuity (PVA)<3/60 in the better eye), moderate and severe vision impairment (MSVI; PVA<6/18 but ≥3/60), mild vision impairment (PVA<6/12 but ≥6/18) and near vision impairment (>N5 or N8 in the presence of normal vision) were estimated for 1990, 2010, 2015, and 2020. Results: The age-standardized prevalence of blindness for all ages and both genders was higher in the Oceania region but lower for MSVI when comparing the sub-regions. The prevalence of near vision impairment in people ≥50 years was 41% (UI 18.8-65.9). Comparison of the data for 2015 with 2020 predicts a small increase in the numbers of people affected by blindness, MSVI and mild VI in both sub-regions. The numbers predicted for near VI in South-east Asia are from 90.68 million in 2015 to 102.88 million in 2020. The main causes of blindness and MSVI in both sub-regions in 2015 were cataract, uncorrected refractive error, glaucoma, corneal disease and age-related macular degeneration. There was no trachoma in Oceania from 1990 and decreasing prevalence in South-east Asia with elimination predicted by 2020. Conclusions:Inboth regions the main challenges for eye care come from cataract which remains the main cause of blindness with uncorrected refractive error the main cause of MSVI. The trend between 1990 and 2015 is for a lower revalence of blindness and MSVI in both regions.
Nangia V, Jonas J, George R, et al., 2019, Prevalence and causes of blindness and vision impairment magnitude, Temporal Trends, and Projections in South and Central Asia, British Journal of Ophthalmology, Vol: 103, Pages: 871-877, ISSN: 0007-1161
Background: To assess prevalence and causes of vision loss in Central 68 and South Asia.Methods: A systematic review of medical literature assessed the prevalence of blindness (presenting visual acuity<3/60 in the better eye), moderate and severe vision impairment (MSVI; presenting visual acuity <6/18 but ≥3/60) and mild vision impairment (MVI; presenting visual acuity <6/12 and ≥6/18) in Central and South Asia for 1990, 2010, 2015, and 2020.Results: In Central and South Asia combined, age-standardized prevalences of blindness, MSVI and MVI in 2015 were for men and women 2.80% (80%uncertainty interval (UI):1.14-4.91) and 3.47% (80%UI:1.45-5.99),16.75% (80%UI:5.60-30.84) and 20.06% (80%UI:7.15-36.12), 11.49% (80%UI:3.43-21.44) and 12.77% (80%UI:4.04-23.48), respectively, with a significant decrease in the study period for both gender. In South Asia in 2015, 11.76 million individuals (32.65% of the global blindness figure) were blind and 61.19 million individuals (28.3% of the global total) had MSVI. From 1990 to 2015, cataract (accounting for 36.58% of all cases with blindness in 2015) was the most common cause of blindness, followed by undercorrected refractive error (36.43%), glaucoma (5.81%), age-related macular degeneration (2.44%), corneal diseases (2.43%), diabetic retinopathy (0.16%) and trachoma (0.04%). For MSVI in South Asia 2015, most common causes were under corrected refractive error (accounting for 66.39% of all cases with MSVI), followed by cataract (23.62%), age related macular degeneration (1.31%) and glaucoma (1.09%).Conclusions: One third of the global blind resided in South Asia in 2015, although the age-standardized prevalence of blindness and MSVI decreased significantly between 1990 and 2015.
Crawford L, Flaxman SR, Runcie DE, et al., 2019, Variable Prioritization in Nonlinear Black Box Methods: A Genetic Association Case Study, Annals of Applied Statistics, Vol: 13, Pages: 958-989, ISSN: 1932-6157
The central aim in this paper is to address variable selection questions innonlinear and nonparametric regression. Motivated by statistical genetics,where nonlinear interactions are of particular interest, we introduce a noveland interpretable way to summarize the relative importance of predictorvariables. Methodologically, we develop the "RelATive cEntrality" (RATE)measure to prioritize candidate genetic variants that are not just marginallyimportant, but whose associations also stem from significant covaryingrelationships with other variants in the data. We illustrate RATE throughBayesian Gaussian process regression, but the methodological innovations applyto other "black box" methods. It is known that nonlinear models often exhibitgreater predictive accuracy than linear models, particularly for phenotypesgenerated by complex genetic architectures. With detailed simulations and tworeal data association mapping studies, we show that applying RATE enables anexplanation for this improved performance.
Tusting LS, Bisanzio D, Alabaster G, et al., 2019, Mapping changes in housing in sub-Saharan Africa from 2000 to 2015, Nature, Vol: 568, Pages: 391-394, ISSN: 0028-0836
Access to adequate housing is a fundamental human right, essential to human security, nutrition and health, and a core objective of the United Nations Sustainable Development Goals1,2. Globally, the housing need is most acute in Africa, where the population will more than double by 2050. However, existing data on housing quality across Africa are limited primarily to urban areas and are mostly recorded at the national level. Here we quantify changes in housing in sub-Saharan Africa from 2000 to 2015 by combining national survey data within a geostatistical framework. We show a marked transformation of housing in urban and rural sub-Saharan Africa between 2000 and 2015, with the prevalence of improved housing (with improved water and sanitation, sufficient living area and durable construction) doubling from 11% (95% confidence interval, 10-12%) to 23% (21-25%). However, 53 (50-57) million urban Africans (47% (44-50%) of the urban population analysed) were living in unimproved housing in 2015. We provide high-resolution, standardized estimates of housing conditions across sub-Saharan Africa. Our maps provide a baseline for measuring change and a mechanism to guide interventions during the era of the Sustainable Development Goals.
Ton J-F, Flaxman S, Sejdinovic D, et al., 2018, Spatial mapping with Gaussian processes and nonstationary Fourier features, Spatial Statistics, Vol: 28, Pages: 59-78, ISSN: 2211-6753
The use of covariance kernels is ubiquitous in the field of spatial statistics. Kernels allow data to be mapped into high-dimensional feature spaces and can thus extend simple linear additive methods to nonlinear methods with higher order interactions. However, until recently, there has been a strong reliance on a limited class of stationary kernels such as the Matérn or squared exponential, limiting the expressiveness of these modelling approaches. Recent machine learning research has focused on spectral representations to model arbitrary stationary kernels and introduced more general representations that include classes of nonstationary kernels. In this paper, we exploit the connections between Fourier feature representations, Gaussian processes and neural networks to generalise previous approaches and develop a simple and efficient framework to learn arbitrarily complex nonstationary kernel functions directly from the data, while taking care to avoid overfitting using state-of-the-art methods from deep learning. We highlight the very broad array of kernel classes that could be created within this framework. We apply this to a time series dataset and a remote sensing problem involving land surface temperature in Eastern Africa. We show that without increasing the computational or storage complexity, nonstationary kernels can be used to improve generalisation performance and provide more interpretable results.
Law HC, Sejdinovic D, Cameron E, et al., Variational Learning on Aggregate Outputs with Gaussian Processes, NIPS
Hu A, Flaxman S, 2018, Multimodal sentiment analysis to explore the structure of emotions, KDD 2018, Publisher: ACM, Pages: 350-358
We propose a novel approach to multimodal sentiment analysis using deep neural networks combining visual analysis and natural language processing. Our goal is different than the standard sentiment analysis goal of predicting whether a sentence expresses positive or negative sentiment; instead, we aim to infer the latent emotional state of the user. Thus, we focus on predicting the emotion word tags attached by users to their Tumblr posts, treating these as "self-reported emotions." We demonstrate that our multimodal model combining both text and image features outperforms separate models based solely on either images or text. Our model's results are interpretable, automatically yielding sensible word lists associated with emotions. We explore the structure of emotions implied by our model and compare it to what has been posited in the psychology literature, and validate our model on a set of images that have been used in psychology studies. Finally, our work also provides a useful tool for the growing academic study of images - both photographs and memes - on social networks.
Bourne RRA, Jonas JB, Bron AM, et al., 2018, Prevalence and causes of vision loss in high-income countries and in Eastern and Central Europe in 2015: magnitude, temporal trends and projections, BRITISH JOURNAL OF OPHTHALMOLOGY, Vol: 102, Pages: 575-585, ISSN: 0007-1161
Background Within a surveillance of the prevalence and causes of vision impairment in high-income regions and Central/Eastern Europe, we update figures through 2015 and forecast expected values in 2020.Methods Based on a systematic review of medical literature, prevalence of blindness, moderate and severe vision impairment (MSVI), mild vision impairment and presbyopia was estimated for 1990, 2010, 2015, and 2020.Results Age-standardised prevalence of blindness and MSVI for all ages decreased from 1990 to 2015 from 0.26% (0.10–0.46) to 0.15% (0.06–0.26) and from 1.74% (0.76–2.94) to 1.27% (0.55–2.17), respectively. In 2015, the number of individuals affected by blindness, MSVI and mild vision impairment ranged from 70 000, 630 000 and 610 000, respectively, in Australasia to 980 000, 7.46 million and 7.25 million, respectively, in North America and 1.16 million, 9.61 million and 9.47 million, respectively, in Western Europe. In 2015, cataract was the most common cause for blindness, followed by age-related macular degeneration (AMD), glaucoma, uncorrected refractive error, diabetic retinopathy and cornea-related disorders, with declining burden from cataract and AMD over time. Uncorrected refractive error was the leading cause of MSVI.Conclusions While continuing to advance control of cataract and AMD as the leading causes of blindness remains a high priority, overcoming barriers to uptake of refractive error services would address approximately half of the MSVI burden. New data on burden of presbyopia identify this entity as an important public health problem in this population. Additional research on better treatments, better implementation with existing tools and ongoing surveillance of the problem is needed.
Law HCL, Sutherland D, Sejdinovic D, et al., 2018, Bayesian approaches to distribution regression, The 21st International Conference on Artificial Intelligence and Statistics: AISTATS 2018, Publisher: AISTATS
Distribution regression has recently attractedmuch interest as a generic solution to the problem of supervised learning where labels are available at the group level, rather than at the individual level. Current approaches, however, do notpropagate the uncertainty in observations due tosampling variability in the groups. This effectively assumes that small and large groups areestimated equally well, and should have equalweight in the final regression. We account forthis uncertainty with a Bayesian distribution regression formalism, improving the robustnessand performance of the model when group sizesvary. We frame our models in a neural networkstyle, allowing for simple MAP inference usingbackpropagation to learn the parameters, as wellas MCMC-based inference which can fully propagate uncertainty. We demonstrate our approachon illustrative toy datasets, as well as on a challenging problem of predicting age from images.
Abbati G, Tosi A, Osborne M, et al., 2018, AdaGeo: adaptive geometric learning for optimization and sampling, 21 st International Conference on Artificial Intelligence and Statistics (AISTAT)S, Publisher: PMLR, Pages: 226-234
Gradient-based optimization and Markov Chain Monte Carlo sampling can be found at the heart of several machine learning methods. In high-dimensional settings, well-known issues such as slow-mixing, non-convexity and correlations can hinder the algorithms’ efficiency. In order to overcome these difficulties, we propose AdaGeo, a preconditioning framework for adaptively learning the geometry of the parameter space during optimization or sampling. In particular, we use the Gaussian process latent variable model (GP-LVM) to represent a lower-dimensional embedding of the parameters, identifying the underlying Riemannian manifold on which the optimization or sampling is taking place. Samples or optimization steps are consequently proposed based on the geometry of the manifold. We apply our framework to stochastic gradient descent, stochastic gradient Langevin dynamics, and stochastic gradient Riemannian Langevin dynamics, and show performance improvements for both optimization and sampling.
Flaxman SR, Bourne RA, Resnikoff S, et al., 2017, Global causes of blindness and distance vision impairment 1990–2020: a systematic review and meta-analysis, The Lancet Global Health, Vol: 5, Pages: e1221-e1234, ISSN: 2214-109X
BackgroundContemporary data for causes of vision impairment and blindness form an important basis of recommendations in public health policies. Refreshment of the Global Vision Database with recently published data sources permitted modelling of cause of vision loss data from 1990 to 2015, further disaggregation by cause, and forecasts to 2020.MethodsIn this systematic review and meta-analysis, we analysed published and unpublished population-based data for the causes of vision impairment and blindness from 1980 to 2014. We identified population-based studies published before July 8, 2014, by searching online databases with no language restrictions (MEDLINE from Jan 1, 1946, and Embase from Jan 1, 1974, and the WHO Library Database). We fitted a series of regression models to estimate the proportion of moderate or severe vision impairment (defined as presenting visual acuity of <6/18 but ≥3/60 in the better eye) and blindness (presenting visual acuity of <3/60 in the better eye) by cause, age, region, and year.FindingsWe identified 288 studies of 3 983 541 participants contributing data from 98 countries. Among the global population with moderate or severe vision impairment in 2015 (216·6 million [80% uncertainty interval 98·5 million to 359·1 million]), the leading causes were uncorrected refractive error (116·3 million [49·4 million to 202·1 million]), cataract (52·6 million [18·2 million to 109·6 million]), age-related macular degeneration (8·4 million [0·9 million to 29·5 million]), glaucoma (4·0 million [0·6 million to 13·3 million]), and diabetic retinopathy (2·6 million [0·2 million to 9·9 million]). Among the global population who were blind in 2015 (36·0 million [12·9 million to 65·4 million]), the leading causes were cataract (12·6 million [3·4 million to 28·7 million]), uncorrected
Goodman B, Flaxman S, 2017, European Union regulations on algorithmic decision-making and a “Right to Explanation”, AI Magazine, Vol: 38, ISSN: 0738-4602
We summarize the potential impact that the European Union’s new General Data Protection Regulation will have on the routine use of machine learning algorithms. Slated to take effect as law across the EU in 2018, it will restrict automated individual decision-making (that is, algorithms that make decisions based on user-level predictors) which “significantly affect” users. The law will also effectively create a “right to explanation,” whereby a user can ask for an explanation of an algorithmic decision that was made about them. We argue that while this law will pose large challenges for industry, it highlights opportunities for computer scientists to take the lead in designing algorithms and evaluation frameworks which avoid discrimination and enable explanation.
Flaxman SR, Teh YW, Sejdinovic D, Poisson Intensity Estimation with Reproducing Kernels, Electronic Journal of Statistics, ISSN: 1935-7524
Bhatt S, Cameron E, Flaxman SR, et al., 2017, Improved prediction accuracy for disease risk mapping using Gaussian process stacked generalization., Interface, Vol: 14, ISSN: 1742-5662
Maps of infectious disease-charting spatial variations in the force of infection, degree of endemicity and the burden on human health-provide an essential evidence base to support planning towards global health targets. Contemporary disease mapping efforts have embraced statistical modelling approaches to properly acknowledge uncertainties in both the available measurements and their spatial interpolation. The most common such approach is Gaussian process regression, a mathematical framework composed of two components: a mean function harnessing the predictive power of multiple independent variables, and a covariance function yielding spatio-temporal shrinkage against residual variation from the mean. Though many techniques have been developed to improve the flexibility and fitting of the covariance function, models for the mean function have typically been restricted to simple linear terms. For infectious diseases, known to be driven by complex interactions between environmental and socio-economic factors, improved modelling of the mean function can greatly boost predictive power. Here, we present an ensemble approach based on stacked generalization that allows for multiple nonlinear algorithmic mean functions to be jointly embedded within the Gaussian process framework. We apply this method to mapping Plasmodium falciparum prevalence data in sub-Saharan Africa and show that the generalized ensemble approach markedly outperforms any individual method.
Bourne RRA, Flaxman SR, Braithwaite T, et al., 2017, Magnitude, temporal trends, and projections of the global prevalence of blindness and distance and near vision impairment: a systematic review and meta-analysis, The Lancet Global Health, Vol: 5, Pages: E888-E897, ISSN: 2214-109X
Background Global and regional prevalence estimates for blindness and vision impairment are important for thedevelopment of public health policies. We aimed to provide global estimates, trends, and projections of globalblindness and vision impairment.Methods We did a systematic review and meta-analysis of population-based datasets relevant to global visionimpairment and blindness that were published between 1980 and 2015. We fitted hierarchical models to estimate theprevalence (by age, country, and sex), in 2015, of mild visual impairment (presenting visual acuity worse than 6/12 to6/18 inclusive), moderate to severe visual impairment (presenting visual acuity worse than 6/18 to 3/60 inclusive),blindness (presenting visual acuity worse than 3/60), and functional presbyopia (defined as presenting near visionworse than N6 or N8 at 40 cm when best-corrected distance visual acuity was better than 6/12).Findings Globally, of the 7·33 billion people alive in 2015, an estimated 36·0 million (80% uncertainty interval [UI]12·9–65·4) were blind (crude prevalence 0·48%; 80% UI 0·17–0·87; 56% female), 216·6 million (80% UI98·5–359·1) people had moderate to severe visual impairment (2·95%, 80% UI 1·34–4·89; 55% female), and188·5 million (80% UI 64·5–350·2) had mild visual impairment (2·57%, 80% UI 0·88–4·77; 54% female).Functional presbyopia affected an estimated 1094·7 million (80% UI 581·1–1686·5) people aged 35 years andolder, with 666·7 million (80% UI 364·9–997·6) being aged 50 years or older. The estimated number of blindpeople increased by 17·6%, from 30·6 million (80% UI 9·9–57·3) in 1990 to 36·0 million (80% UI 12·9–65·4)in 2015. This change was attributable to three factors, namely an incre
Leasher JL, Bourne RRA, Flaxman SR, et al., 2016, Global Estimates on the Number of People Blind or Visually Impaired by Diabetic Retinopathy: A Meta-analysis From 1990-2010 (vol 39, pg 1643, 2016), DIABETES CARE, Vol: 39, Pages: 2096-2096, ISSN: 0149-5992
Bourne RRA, Taylor HR, Flaxman SR, et al., 2016, Number of People Blind or Visually Impaired by Glaucoma Worldwide and in World Regions 1990-2010: A Meta-Analysis, PLOS ONE, Vol: 11, ISSN: 1932-6203
Leasher JL, Bourne RRA, Flaxman SR, et al., 2016, Global Estimates on the Number of People Blind or Visually Impaired by Diabetic Retinopathy: A Meta-analysis From 1990 to 2010, DIABETES CARE, Vol: 39, Pages: 1643-1649, ISSN: 0149-5992
Naidoo KS, Leasher J, Bourne RR, et al., 2016, Global Vision Impairment and Blindness Due to Uncorrected Refractive Error, 1990-2010, OPTOMETRY AND VISION SCIENCE, Vol: 93, Pages: 227-234, ISSN: 1040-5488
Flaxman SR, Neill DB, Smola AJ, 2016, Gaussian Processes for Independence Tests with Non-iid Data in Causal Inference, ACM Transactions on Intelligent Systems and Technology, Vol: 7, ISSN: 2157-6904
In applied fields, practitioners hoping to apply causal structure learning or causal orientation algorithms face an important question: which independence test is appropriate for my data? In the case of real-valued iid data, linear dependencies, and Gaussian error terms, partial correlation is sufficient. But once any of these assumptions is modified, the situation becomes more complex. Kernel-based tests of independence have gained popularity to deal with nonlinear dependencies in recent years, but testing for conditional independence remains a challenging problem. We highlight the important issue of non-iid observations: when data are observed in space, time, or on a network, “nearby” observations are likely to be similar. This fact biases estimates of dependence between variables. Inspired by the success of Gaussian process regression for handling non-iid observations in a wide variety of areas and by the usefulness of the Hilbert-Schmidt Independence Criterion (HSIC), a kernel-based independence test, we propose a simple framework to address all of these issues: first, use Gaussian process regression to control for certain variables and to obtain residuals. Second, use HSIC to test for independence. We illustrate this on two classic datasets, one spatial, the other temporal, that are usually treated as iid. We show how properly accounting for spatial and temporal variation can lead to more reasonable causal graphs. We also show how highly structured data, like images and text, can be used in a causal inference framework using a novel structured input/output Gaussian process formulation. We demonstrate this idea on a dataset of translated sentences, trying to predict the source language.
Flaxman S, Goel S, Rao JM, 2016, Filter Bubbles, Echo Chambers, and Online News Consumption, Public Opinion Quarterly, Vol: 80, Pages: 298-320, ISSN: 0033-362X
Khairallah M, Kahloun R, Bourne R, et al., 2015, Number of People Blind or Visually Impaired by Cataract Worldwide and in World Regions, 1990 to 2010, INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, Vol: 56, Pages: 6762-6769, ISSN: 0146-0404
Stevens GA, Bennett JE, Hennocq Q, et al., 2015, Trends and mortality effects of vitamin A deficiency in children in 138 low-income and middle-income countries between 1991 and 2013: a pooled analysis of population-based surveys, Lancet Global Health, Vol: 3, Pages: e528-e536, ISSN: 2214-109X
Background: Vitamin A deficiency is a risk factor for blindness and for mortality from measles and diarrhoea in children aged 6–59 months. We aimed to estimate trends in the prevalence of vitamin A deficiency between 1991 and 2013 and its mortality burden in low-income and middle-income countries.Methods: We collated 134 population-representative data sources from 83 countries with measured serum retinol concentration data. We used a Bayesian hierarchical model to estimate the prevalence of vitamin A deficiency, defined as a serum retinol concentration lower than 0·70 μmol/L. We estimated the relative risks (RRs) for the effects of vitamin A deficiency on mortality from measles and diarrhoea by pooling effect sizes from randomised trials of vitamin A supplementation. We used information about prevalences of deficiency, RRs, and number of cause-specific child deaths to estimate deaths attributable to vitamin A deficiency. All analyses included a systematic quantification of uncertainty.Findings: In 1991, 39% (95% credible interval 27–52) of children aged 6–59 months in low-income and middle-income countries were vitamin A deficient. In 2013, the prevalence of deficiency was 29% (17–42; posterior probability [PP] of being a true decline=0·81). Vitamin A deficiency significantly declined in east and southeast Asia and Oceania from 42% (19–70) to 6% (1–16; PP>0·99); a decline in Latin America and the Caribbean from 21% (11–33) to 11% (4–23; PP=0·89) also occurred. In 2013, the prevalence of deficiency was highest in sub-Saharan Africa (48%; 25–75) and south Asia (44%; 13–79). 94 500 (54 200–146 800) deaths from diarrhoea and 11 200 (4300–20 500) deaths from measles were attributable to vitamin A deficiency in 2013, which accounted for 1·7% (1·0–2·6) of all deaths in children younger than 5 years in low-income and middle-income countries. M
Jonas JB, Bourne RRA, White RA, et al., 2014, Visual Impairment and Blindness Due to Macular Diseases Globally: A Systematic Review and Meta-Analysis, AMERICAN JOURNAL OF OPHTHALMOLOGY, Vol: 158, Pages: 808-815, ISSN: 0002-9394
Keeffe J, Taylor HR, Fotis K, et al., 2014, Prevalence and causes of vision loss in Southeast Asia and Oceania: 1990-2010, BRITISH JOURNAL OF OPHTHALMOLOGY, Vol: 98, Pages: 586-591, ISSN: 0007-1161
Bourne RRA, Jonas JB, Flaxman SR, et al., 2014, Prevalence and causes of vision loss in high-income countries and in Eastern and Central Europe: 1990-2010, BRITISH JOURNAL OF OPHTHALMOLOGY, Vol: 98, Pages: 629-638, ISSN: 0007-1161
Leasher JL, Lansingh V, Flaxman SR, et al., 2014, Prevalence and causes of vision loss in Latin America and the Caribbean: 1990-2010, BRITISH JOURNAL OF OPHTHALMOLOGY, Vol: 98, Pages: 619-628, ISSN: 0007-1161
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