Publications
13 results found
Rashid T, Bennett JE, Muller DC, et al., 2024, Mortality from leading cancers in districts of England from 2002 to 2019: a population-based, spatiotemporal study, The Lancet Oncology, Vol: 25, Pages: 86-98, ISSN: 1213-9432
BACKGROUND: Cancers are the leading cause of death in England. We aimed to estimate trends in mortality from leading cancers from 2002 to 2019 for the 314 districts in England. METHODS: We did a high-resolution spatiotemporal analysis of vital registration data from the UK Office for National Statistics using data on all deaths from the ten leading cancers in England from 2002 to 2019. We used a Bayesian hierarchical model to obtain robust estimates of age-specific and cause-specific death rates. We used life table methods to calculate the primary outcome, the unconditional probability of dying between birth and age 80 years by sex, cancer cause of death, local district, and year. We reported Spearman rank correlations between the probability of dying from a cancer and district-level poverty in 2019. FINDINGS: In 2019, the probability of dying from a cancer before age 80 years ranged from 0·10 (95% credible interval [CrI] 0·10-0·11) to 0·17 (0·16-0·18) for women and from 0·12 (0·12-0·13) to 0·22 (0·21-0·23) for men. Variation in the probability of dying was largest for lung cancer among women, being 3·7 times (95% CrI 3·2-4·4) higher in the district with the highest probability than in the district with the lowest probability; and for stomach cancer for men, being 3·2 times (2·6-4·1) higher in the district with the highest probability than in the one with the lowest probability. The variation in the probability of dying was smallest across districts for lymphoma and multiple myeloma (95% CrI 1·2 times [1·1-1·4] higher in the district with the highest probability than the lowest probability for women and 1·2 times [1·0-1·4] for men), and leukaemia (1·1 times [1·0-1·4] for women and 1·2 times [1·0-1·5] for men). The Spearman rank correlation between probability
Nathvani R, Vishwanath D, Clark SN, et al., 2023, Beyond here and now: evaluating pollution estimation across space and time from street view images with deep learning, Science of the Total Environment, Vol: 903, ISSN: 0048-9697
Advances in computer vision, driven by deep learning, allows for the inference of environmental pollution and its potential sources from images. The spatial and temporal generalisability of image-based pollution models is crucial in their real-world application, but is currently understudied, particularly in low-income countries where infrastructure for measuring the complex patterns of pollution is limited and modelling may therefore provide the most utility. We employed convolutional neural networks (CNNs) for two complementary classification models, in both an end-to-end approach and as an interpretable feature extractor (object detection), to estimate spatially and temporally resolved fine particulate matter (PM2.5) and noise levels in Accra, Ghana. Data used for training the models were from a unique dataset of over 1.6 million images collected over 15 months at 145 representative locations across the city, paired with air and noise measurements. Both end-to-end CNN and object-based approaches surpassed null model benchmarks for predicting PM2.5 and noise at single locations, but performance deteriorated when applied to other locations. Model accuracy diminished when tested on images from locations unseen during training, but improved by sampling a greater number of locations during model training, even if the total quantity of data was reduced. The end-to-end models used characteristics of images associated with atmospheric visibility for predicting PM2.5, and specific objects such as vehicles and people for noise. The results demonstrate the potential and challenges of image-based, spatiotemporal air pollution and noise estimation, and that robust, environmental modelling with images requires integration with traditional sensor networks.
Bennett J, Rashid T, Zolfaghari A, et al., 2023, Changes in life expectancy and house prices in London from 2002 to 2019: Hyper-resolution spatiotemporal analysis of death registration and real estate data, The Lancet Regional Health Europe, Vol: 27, Pages: 1-13, ISSN: 2666-7762
Background:London has outperformed smaller towns and rural areas in terms of life expectancy increase. Our aim was to investigate life expectancy change at very-small-area level, and its relationship with house prices and their change.Methods:We performed a hyper-resolution spatiotemporal analysis from 2002 to 2019 for 4835 London Lower-layer Super Output Areas (LSOAs). We used population and death counts in a Bayesian hierarchical model to estimate age- and sex-specific death rates for each LSOA, converted to life expectancy at birth using life table methods. We used data from the Land Registry via the real estate website Rightmove (www.rightmove.co.uk), with information on property size, type and land tenure in a hierarchical model to estimate house prices at LSOA level. We used linear regressions to summarise how much life expectancy changed in relation to the combination of house prices in 2002 and their change from 2002 to 2019. We calculated the correlation between change in price and change in sociodemographic characteristics of the resident population of LSOAs and population turnover.Findings:In 134 (2.8%) of London's LSOAs for women and 32 (0.7%) for men, life expectancy may have declined from 2002 to 2019, with a posterior probability of a decline >80% in 41 (0.8%, women) and 14 (0.3%, men) LSOAs. The life expectancy increase in other LSOAs ranged from <2 years in 537 (11.1%) LSOAs for women and 214 (4.4%) for men to >10 years in 220 (4.6%) for women and 211 (4.4%) for men. The 2.5th-97.5th-percentile life expectancy difference across LSOAs increased from 11.1 (10.7–11.5) years in 2002 to 19.1 (18.4–19.7) years for women in 2019, and from 11.6 (11.3–12.0) years to 17.2 (16.7–17.8) years for men. In the 20% (men) and 30% (women) of LSOAs where house prices had been lowest in 2002, mainly in east and outer west London, life expectancy increased only in proportion to the rise in house prices. In contrast, in the 30% (men) and
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
Nathvani R, Clark S, Muller E, et al., 2022, Characterisation of urban environment and activity across space and time using street images and deep learning in Accra, Scientific Reports, Vol: 12, ISSN: 2045-2322
The urban environment influences human health, safety and wellbeing. Cities in Africa are growing faster than other regions but have limited data to guide urban planning and policies. Our aim was to use smart sensing and analytics to characterise the spatial patterns and temporal dynamics of features of the urban environment relevant for health, liveability, safety and sustainability. We collected a novel dataset of 2.1 million time-lapsed day and night images at 145 representative locations throughout the Metropolis of Accra, Ghana. We manually labelled a subset of 1,250 images for 20 contextually relevant objects and used transfer learning with data augmentation to retrain a convolutional neural network to detect them in the remaining images. We identified 23.5 million instances of these objects including 9.66 million instances of persons (41% of all objects), followed by cars (4.19 million, 18%), umbrellas (3.00 million, 13%), and informally operated minibuses known as tro tros (2.94 million, 13%). People, large vehicles and market-related objects were most common in the commercial core and densely populated informal neighbourhoods, while refuse and animals were most observed in the peripheries. The daily variability of objects was smallest in densely populated settlements and largest in the commercial centre. Our novel data and methodology shows that smart sensing and analytics can inform planning and policy decisions for making cities more liveable, equitable, sustainable and healthy.
Asaria P, Bennett J, Elliott P, et al., 2022, Contributions of event rates, pre-hospital deaths and hospital case fatality to variations in myocardial infarction mortality in 326 districts in England: spatial analysis of linked hospitalisation and mortality data, The Lancet Public Health, Vol: 7, Pages: e813-e824, ISSN: 2468-2667
Background: Myocardial infarction (MI) mortality varies substantially within high-income countries. There is limited guidance on what interventions – primary and secondary prevention and/or improving care pathways and quality – can reduce and equalise MI mortality. Our aimwas to understand the contribution of incidence (event rate), pre-hospital deaths and hospital case-fatality, to how MI mortality varies within England.Methods: We used linked data on hospitalisation and deaths from 2015-2018 with geographical identifiers to estimate MI death and event rates, pre-hospital deaths and hospital case fatality for men and women aged 45 years and older in 326 districts in England. Data were analysed in a Bayesian spatial model that accounted for similarities and differences inspatial patterns of fatal and non-fatal MI. Results: The 99th to 1st percentile ratio of age-standardised MI death rate was 2.63 (95% credible interval 2.45-2.83) in women and 2.56 (2.37-2.76) in men across districts, with death rate highest in north of England. The main contributor to this variation was MI event rate, with a 99th to 1st percentile ratio of 2.55 (2.39-2.72) (women) and 2.17 (2.08-2.27) (men) across districts. Pre-hospital mortality was greater than hospital case fatality in every district. Prehospital mortality had a 99th to 1st percentile ratio 1.60 (1.50-1.70) in women and 1.75 (1.66-1.86) in men across districts and made a greater contribution to case-fatality variation thanhospital case fatality which had a 99th to 1st percentile ratio of 1.39 (1.29-1.49) (women) and1.49 (1.39-1.60) (men). The contribution of case fatality to variation in deaths across districtswas largest in middle ages. Pre-hospital mortality was slightly higher in men than women inmost districts and age groups, whereas hospital case fatality was higher in women in virtuallyall districts at ages up to and including 65-74 years; after this age, it became similar betweenthe sexes.3Interpretation: Mos
Semenova E, Xu Y, Howes A, et al., 2022, PriorVAE: encoding spatial priors with variational autoencoders for small-area estimation., Journal of the Royal Society Interface, Vol: 19, Pages: 1-11, ISSN: 1742-5662
Gaussian processes (GPs), implemented through multivariate Gaussian distributions for a finite collection of data, are the most popular approach in small-area spatial statistical modelling. In this context, they are used to encode correlation structures over space and can generalize well in interpolation tasks. Despite their flexibility, off-the-shelf GPs present serious computational challenges which limit their scalability and practical usefulness in applied settings. Here, we propose a novel, deep generative modelling approach to tackle this challenge, termed PriorVAE: for a particular spatial setting, we approximate a class of GP priors through prior sampling and subsequent fitting of a variational autoencoder (VAE). Given a trained VAE, the resultant decoder allows spatial inference to become incredibly efficient due to the low dimensional, independently distributed latent Gaussian space representation of the VAE. Once trained, inference using the VAE decoder replaces the GP within a Bayesian sampling framework. This approach provides tractable and easy-to-implement means of approximately encoding spatial priors and facilitates efficient statistical inference. We demonstrate the utility of our VAE two-stage approach on Bayesian, small-area estimation tasks.
Kontis V, Bennett JE, Parks RM, et al., 2022, Lessons learned and lessons missed: impact of the coronavirus disease 2019 (COVID-19) pandemic on all-cause mortality in 40 industrialised countries and US states prior to mass vaccination [version 2; peer review: 2 approved], Wellcome Open Research, Vol: 6, ISSN: 2398-502X
Background: Industrialised countries had varied responses to the COVID-19 pandemic, which may lead to different death tolls from COVID-19 and other diseases. Methods: We applied an ensemble of 16 Bayesian probabilistic models to vital statistics data to estimate the number of weekly deaths if the pandemic had not occurred for 40 industrialised countries and US states from mid-February 2020 through mid-February 2021. We subtracted these estimates from the actual number of deaths to calculate the impacts of the pandemic on all-cause mortality. Results: Over this year, there were 1,410,300 (95% credible interval 1,267,600-1,579,200) excess deaths in these countries, equivalent to a 15% (14-17) increase, and 141 (127-158) additional deaths per 100,000 people. In Iceland, Australia and New Zealand, mortality was lower than would be expected in the absence of the pandemic, while South Korea and Norway experienced no detectable change. The USA, Czechia, Slovakia and Poland experienced >20% higher mortality. Within the USA, Hawaii experienced no detectable change in mortality and Maine a 5% increase, contrasting with New Jersey, Arizona, Mississippi, Texas, California, Louisiana and New York which experienced >25% higher mortality. Mid-February to the end of May 2020 accounted for over half of excess deaths in Scotland, Spain, England and Wales, Canada, Sweden, Belgium, the Netherlands and Cyprus, whereas mid-September 2020 to mid-February 2021 accounted for >90% of excess deaths in Bulgaria, Croatia, Czechia, Hungary, Latvia, Montenegro, Poland, Slovakia and Slovenia. In USA, excess deaths in the northeast were driven mainly by the first wave, in southern and southwestern states by the summer wave, and in the northern plains by the post-September period. Conclusions: Prior to widespread vaccine-acquired immunity, minimising the overall death toll of the pandemic requires policies and non-pharmaceutical interventions that delay and reduce infections, effective trea
Rashid T, Bennett J, Paciorek C, et al., 2021, Life expectancy and risk of death in 6,791 English communities from 2002 to 2019: high-resolution spatiotemporal analysis of civil registration data, The Lancet Public Health, Vol: 6, Pages: e805-e816, ISSN: 2468-2667
Background: There is limited data with high spatial granularity on how mortality and longevity have changed in English communities. We estimated trends from 2002 to 2019 in life expectancy and probabilities of death at different ages for all 6,791 English middle-layer super output areas (MSOAs).Methods: We used de-identified data for all deaths in England from 2002 to 2019 with information on age, sex and MSOA of residence, and population counts by age, sex and MSOA. We used a Bayesian hierarchical model to obtain estimates of age-specific death rates by sharing information across age groups, MSOAs and years. We used life table methods to calculate life expectancy at birth and probabilities of death in different ages by sex and MSOA.Results: In 2002-2006 and 2006-2010, the vast majority of MSOAs experienced a life expectancy increase for both sexes. In 2010-2014, female life expectancy decreased in 351 (5%) of MSOAs. By 2014-2019, the number of MSOAs with declining life expectancy was 1,270 (19%) for women and 784 (12%) for men. The life expectancy increase from 2002 to 2019 was smaller where life expectancy had been lower in 2002, mostly northern urban MSOAs, and larger where life expectancy had been higher in 2002, mostly MSOAs in and around London. As a result of these trends, the gap between the 1st and 99th percentiles of MSOA life expectancy for women increased from 10.7 (95% credible interval 10.4-10.9) in 2002 to reach 14.2 (13.9-14.5) years in 2019, and from 11.5 (11.3-11.7) years to 13.6 (13.4-13.9) years for men. Interpretation: In many English communities, life expectancy declined in the years prior to the Covid-19 pandemic. To ensure that this trend does not continue there is a need for pro-equity economic and social policies, and greater investment on public health and healthcare.
Rashid T, 2021, High-resolution spatiotemporal analysis in 6,791 English areas reveals declines in life expectancy, Publisher: OXFORD UNIV PRESS, Pages: 78-78, ISSN: 1101-1262
Pepperrell T, Rodgers F, Tandon P, et al., 2021, Making a COVID-19 vaccine that works for everyone: ensuring equity and inclusivity in clinical trials., Global Health Action, Vol: 14, Pages: 1-7, ISSN: 1654-9880
Coronavirus disease 2019 (COVID-19) mortality and morbidity have been shown to increase with deprivation and impact non-White ethnicities more severely. Despite the extra risk Black, Asian and Minority Ethnicity (BAME) groups face in the pandemic, our current medical research system seems to prioritise innovation aimed at people of European descent. We found significant difficulties in assessing baseline demographics in clinical trials for COVID-19 vaccines, displaying a lack of transparency in reporting. Further, we found that most of these trials take place in high-income countries, with only 25 of 219 trials (11.4%) taking place in lower middle- or low-income countries. Trials for the current best vaccine candidates (BNT162b2, ChadOx1, mRNA-173) recruited 80.0% White participants. Underrepresentation of BAME groups in medical research will perpetuate historical distrust in healthcare processes, and poses a risk of unknown differences in efficacy and safety of these vaccines by phenotype. Limiting trial demographics and settings will mean a lack of global applicability of the results of COVID-19 vaccine trials, which will slow progress towards ending the pandemic.
Kontis V, Bennett JE, Rashid T, et al., 2020, Magnitude, demographics and dynamics of the effect of the first wave of the COVID-19 pandemic on all-cause mortality in 21 industrialized countries, Nature Medicine, Vol: 26, Pages: 1919-1928, ISSN: 1078-8956
The Coronavirus Disease 2019 (COVID-19) pandemic has changed many social, economic, environmental and healthcare determinants of health. We applied an ensemble of 16 Bayesian models to vital statistics data to estimate the all-cause mortality effect of the pandemic for 21 industrialized countries. From mid-February through May 2020, 206,000 (95% credible interval, 178,100–231,000) more people died in these countries than would have had the pandemic not occurred. The number of excess deaths, excess deaths per 100,000 people and relative increase in deaths were similar between men and women in most countries. England and Wales and Spain experienced the largest effect: ~100 excess deaths per 100,000 people, equivalent to a 37% (30–44%) relative increase in England and Wales and 38% (31–45%) in Spain. Bulgaria, New Zealand, Slovakia, Australia, Czechia, Hungary, Poland, Norway, Denmark and Finland experienced mortality changes that ranged from possible small declines to increases of 5% or less in either sex. The heterogeneous mortality effects of the COVID-19 pandemic reflect differences in how well countries have managed the pandemic and the resilience and preparedness of the health and social care system.
Wang S, Rashid T, Thorp H, et al., 2020, A shortening of the life-cycle of major tropical cyclones, Geophysical Research Letters, Vol: 47, Pages: 28 Jul 2020-28 Jul 2020, ISSN: 0094-8276
In this study a comprehensive picture of the changing intensity life cycle of major (Category 3 and higher) tropical cyclones (TCs) is presented. Over the past decades, the lifetime maximum intensity has increased, but there has also been a significant decrease in duration of time spent at intensities greater than Category 1. These compensating effects have maintained a stable global mean‐accumulated cyclone energy of individual major TCs. The global mean duration of major TCs has shortened by about 1 day from 1982 to 2018. There has been both faster intensification (Categories 1 to 3) and weakening (Categories 3 to 1) by about 40%. The probabilities of rapid intensification and rapid weakening have both risen in the period 2000–2018 compared to 1982–1999. A statistically significant anticorrelation is found between the lifetime maximum intensity and the following duration of the final weakening. This suggests an element of self‐regulation of TC life cycles.
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