141 results found
Konstantinoudis G, Minelli C, Lam HCY, et al., 2023, Asthma hospitalisations and heat exposure in England: a case-crossover study during 2002-2019, Thorax, Vol: 78, Pages: 875-881, ISSN: 0040-6376
BACKGROUND: Previous studies have reported an association between warm temperature and asthma hospitalisation. They have reported different sex-related and age-related vulnerabilities; nevertheless, little is known about how this effect has changed over time and how it varies in space. This study aims to evaluate the association between asthma hospitalisation and warm temperature and investigate vulnerabilities by age, sex, time and space. METHODS: We retrieved individual-level data on summer asthma hospitalisation at high temporal (daily) and spatial (postcodes) resolutions during 2002-2019 in England from the NHS Digital. Daily mean temperature at 1 km×1 km resolution was retrieved from the UK Met Office. We focused on lag 0-3 days. We employed a case-crossover study design and fitted Bayesian hierarchical Poisson models accounting for possible confounders (rainfall, relative humidity, wind speed and national holidays). RESULTS: After accounting for confounding, we found an increase of 1.11% (95% credible interval: 0.88% to 1.34%) in the asthma hospitalisation risk for every 1°C increase in the ambient summer temperature. The effect was highest for males aged 16-64 (2.10%, 1.59% to 2.61%) and during the early years of our analysis. We also found evidence of a decreasing linear trend of the effect over time. Populations in Yorkshire and the Humber and East and West Midlands were the most vulnerable. CONCLUSION: This study provides evidence of an association between warm temperature and hospital admission for asthma. The effect has decreased over time with potential explanations including temporal differences in patterns of heat exposure, adaptive mechanisms, asthma management, lifestyle, comorbidities and occupation.
Itzkowitz N, Gong X, Atilola G, et al., 2023, Aircraft noise and cardiovascular morbidity and mortality near Heathrow Airport: a case-crossover study, Environment International, Vol: 177, Pages: 1-9, ISSN: 0160-4120
Aircraft noise causes annoyance and sleep disturbance and there is some evidence of associations between long-term exposures and cardiovascular disease (CVD). We investigated short-term associations between previous day aircraft noise and cardiovascular events in a population of 6.3 million residing near Heathrow Airport using a case-crossover design and exposure data for different times of day and night. We included all recorded hospitalisations (n=442,442) and deaths (n=49,443) in 2014-2018 due to CVD. Conditional logistic regression was used to estimate the ORs and adjusted for NO2 concentration, temperature, and holidays. We estimated an increase in risk for 10dB increment in noise during the previous evening (Leve OR = 1.007, 95% CI 0.999-1.015), particularly from 22:00-23:00h (OR= 1.007, 95% CI 1.000-1.013), and the early morning hours 04:30-06:00h (OR= 1.012, 95% CI 1.002-1.021) for all CVD admissions, but no significant associations with day-time noise. There was effect modification by age-sex, ethnicity, deprivation, and season, and some suggestion that high noise variability at night was associated with higher risks. Our findings are consistent with proposed mechanisms for short-term impacts of aircraft noise at night on CVD from experimental studies, including sleep disturbance, increases in blood pressure and stress hormone levels and impaired endothelial function.
Chiaravalloti-Neto F, Lorenz C, Lacerda AB, et al., 2023, Spatiotemporal bayesian modelling of scorpionism and its risk factors in the state of São Paulo, Brazil, PLoS Neglected Tropical Diseases, Vol: 17, Pages: 1-17, ISSN: 1935-2727
BackgroundScorpion stings in Brazil represent a major public health problem due to their incidence and their potential ability to lead to severe and often fatal clinical outcomes. A better understanding of scorpionism determinants is essential for a precise comprehension of accident dynamics and to guide public policy. Our study is the first to model the spatio-temporal variability of scorpionism across municipalities in São Paulo (SP) and to investigate its relationship with demographic, socioeconomic, environmental, and climatic variables.MethodologyThis ecological study analyzed secondary data on scorpion envenomation in SP from 2008 to 2021, using the Integrated Nested Laplace Approximation (INLA) to perform Bayesian inference for detection of areas and periods with the most suitable conditions for scorpionism.Principal findingsFrom the spring of 2008 to 2021, the relative risk (RR) increased eight times in SP, from 0.47 (95%CI 0.43–0.51) to 3.57 (95%CI 3.36–3.78), although there has been an apparent stabilization since 2019. The western, northern, and northwestern parts of SP showed higher risks; overall, there was a 13% decrease in scorpionism during winters. Among the covariates considered, an increase of one standard deviation in the Gini index, which captures income inequality, was associated with a 11% increase in scorpion envenomation. Maximum temperatures were also associated with scorpionism, with risks doubling for temperatures above 36°C. Relative humidity displayed a nonlinear association, with a 50% increase in risk for 30–32% humidity and reached a minimum of 0.63 RR for 75–76% humidity.ConclusionsHigher temperatures, lower humidity, and social inequalities were associated with a higher risk of scorpionism in SP municipalities. By capturing local and temporal relationships across space and time, authorities can design more effective strategies that adhere to local and temporal considerations.
Parkes B, Stafoggia M, Fecht D, et al., 2023, Community factors and excess mortality in the COVID-19 pandemic in England, Italy and Sweden, European Journal of Public Health, Vol: 33, Pages: 695-703, ISSN: 1101-1262
Background:Analyses of COVID-19 suggest specific risk factors make communities more or less vulnerable to pandemic related deaths within countries. What is unclear is whether the characteristics affecting vulnerability of small communities within countries produce similar patterns of excess mortality across countries with different demographics and public health responses to the pandemic. Our aim is to quantify community-level variations in excess mortality within England, Italy and Sweden and identify how such spatial variability was driven by community-level characteristics.Methods: We applied a two-stage Bayesian model to quantify inequalities in excess mortality in people aged 40 years and older at the community level in England, Italy and Sweden during the first year of the pandemic (March 2020–February 2021). We used community characteristics measuring deprivation, air pollution, living conditions, population density and movement of people as covariates to quantify their associations with excess mortality. Results:We found just under half of communities in England (48.1%) and Italy (45.8%) had an excess mortality of over 300 per 100,000 males over the age of 40, while for Sweden that covered 23.1% of communities. We showed that deprivation is a strong predictor of excess mortality across the three countries, and communities with high levels of overcrowding were associated with higher excess mortality in England and Sweden. Conclusion:These results highlight some international similarities in factors affecting mortality that will help policy makers target public health measures to increase resilience to the mortality impacts of this and future pandemics.
Shoari N, Heydari S, Blangiardo M, 2023, A decade of child pedestrian safety in England: a Bayesian spatio-temporal analysis, BMC Public Health, Vol: 23, Pages: 1-13, ISSN: 1471-2458
BackgroundChild pedestrian injury is a public health and health equality challenge worldwide, including in high-income countries. However, child pedestrian safety is less-understood, especially over long time spans. The intent of this study is to understand factors affecting child pedestrian safety in England over the period 2011–2020.MethodsWe conducted an area-level study using a Bayesian space-time interaction model to understand the association between the number of road crashes involving child pedestrians in English Local Authorities and a host of socio-economic, transport-related and built-environment variables. We investigated spatio-temporal trends in child pedestrian safety in England over the study period and identified high-crash local authorities.ResultsWe found that child pedestrian crash frequencies increase as child population, unemployment-related claimants, road density, and the number of schools increase. Nevertheless, as the number of licensed vehicles per capita and zonal-level walking/cycling increase, child pedestrian safety increases. Generally, child pedestrian safety has improved in England since 2011. However, the socio-economic inequality gap in child pedestrian safety has not narrowed down. In addition, we found that after adjusting for the effect of covariates, the rate of decline in crashes varies between local authorities. The presence of localised risk factors/mitigation measures contributes to variation in the spatio-temporal patterns of child pedestrian safety.ConclusionsOverall, southern England has experienced more improvement in child pedestrian safety over the last decade than the northern regions. Our study revealed socio-economic inequality in child pedestrian safety in England. To better inform safety and public health policy, our findings support the importance of a targeted system approach, considering the identification of high-crash areas while keeping track of how child pedestrian safety evolves over time.
Baerenbold O, Meis M, MartínezHernández I, et al., 2023, A dependent Bayesian Dirichlet process model for source apportionment of particle number size distribution, Environmetrics, Vol: 34, ISSN: 1180-4009
The relationship between particle exposure and health risks has been well established in recent years. Particulate matter (PM) is made up of different components coming from several sources, which might have different level of toxicity. Hence, identifying these sources is an important task in order to implement effective policies to improve air quality and population health. The problem of identifying sources of particulate pollution has already been studied in the literature. However, current methods require an a priori specification of the number of sources and do not include information on covariates in the source allocations. Here, we propose a novel Bayesian nonparametric approach to overcome these limitations. In particular, we model source contribution using a Dirichlet process as a prior for source profiles, which allows us to estimate the number of components that contribute to particle concentration rather than fixing this number beforehand. To better characterize them we also include meteorological variables (wind speed and direction) as covariates within the allocation process via a flexible Gaussian kernel. We apply the model to apportion particle number size distribution measured near London Gatwick Airport (UK) in 2019. When analyzing this data, we are able to identify the most common PM sources, as well as new sources that have not been identified with the commonly used methods.
Li G, Denise H, Diggle P, et al., 2023, A spatio-temporal framework for modelling wastewater concentration during the COVID-19 pandemic, Environment International, Vol: 172, Pages: 1-10, ISSN: 0160-4120
The potential utility of wastewater-based epidemiology as an early warning tool has been explored widely across the globe during the current COVID-19 pandemic. Methods to detect the presence of SARS-CoV-2 RNA in wastewater were developed early in the pandemic, and extensive work has been conducted to evaluate the relationship between viral concentration and COVID-19 case numbers at the catchment areas of sewage treatment works (STWs) over time. However, no attempt has been made to develop a model that predicts wastewater concentration at fine spatio-temporal resolutions covering an entire country, a necessary step towards using wastewater monitoring for the early detection of local outbreaks.We consider weekly averages of flow-normalised viral concentration, reported as the number of SARS-CoV-2N1 gene copies per litre (gc/L) of wastewater available at 303 STWs over the period between 1 June 2021 and 30 March 2022. We specify a spatially continuous statistical model that quantifies the relationship between weekly viral concentration and a collection of covariates covering socio-demographics, land cover and virus associated genomic characteristics at STW catchment areas while accounting for spatial and temporal correlation.We evaluate the model’s predictive performance at the catchment level through 10-fold cross-validation. We predict the weekly viral concentration at the population-weighted centroid of the 32,844 lower super output areas (LSOAs) in England, then aggregate these LSOA predictions to the Lower Tier Local Authority level (LTLA), a geography that is more relevant to public health policy-making. We also use the model outputs to quantify the probability of local changes of direction (increases or decreases) in viral concentration over short periods (e.g. two consecutive weeks).The proposed statistical framework can predict SARS-CoV-2 viral concentration in wastewater at high spatio-temporal resolution across England. Additionally, the probabilistic q
Konstantinoudis G, Cosetta M, Vicedo Cabrera AM, et al., 2022, Ambient heat exposure and COPD hospitalisations in England: a nationwide case-crossover study during 2007-2018, Thorax, Vol: 77, Pages: 1098-1104, ISSN: 0040-6376
Background: There is emerging evidence suggesting a link between ambient heat exposure and chronic obstructive pulmonary disease (COPD) hospitalisations. Individual and contextual characteristics can affect population vulnerabilities to COPD hospitalisation due to heat exposure. This study quantifies the effect of ambient heat on COPD hospitalisations and examines population vulnerabilities by age, sex and contextual characteristics.Methods: Individual data on COPD hospitalisation at high geographical resolution (postcodes) during 2007–2018 in England was retrieved from the small area health statistics unit. Maximum temperature at 1 km ×1 km resolution was available from the UK Met Office. We employed a case-crossover study design and fitted Bayesian conditional Poisson regression models. We adjusted for relative humidity and national holidays, and examined effect modification by age, sex, green space, average temperature, deprivation and urbanicity.Results: After accounting for confounding, we found 1.47% (95% Credible Interval (CrI) 1.19% to 1.73%) increase in the hospitalisation risk for every 1°C increase in temperatures above 23.2°C (lags 0–2 days). We reported weak evidence of an effect modification by sex and age. We found a strong spatial determinant of the COPD hospitalisation risk due to heat exposure, which was alleviated when we accounted for contextual characteristics. 1851 (95% CrI 1 576 to 2 079) COPD hospitalisations were associated with temperatures above 23.2°C annually.Conclusion: Our study suggests that resources should be allocated to support the public health systems, for instance, through developing or expanding heat-health alerts, to challenge the increasing future heat-related COPD hospitalisation burden.
Bucyibaruta G, Blangiardo M, Konstantinoudis G, 2022, Community-level characteristics of COVID-19 vaccine hesitancy in England: A nationwide cross-sectional study, European Journal of Epidemiology, Vol: 37, Pages: 1071-1081, ISSN: 0393-2990
One year after the start of the COVID-19 vaccination programme in England, more than 43 million people older than 12 years old had received at least a first dose. Nevertheless, geographical differences persist, and vaccine hesitancy is still a major public health concern; understanding its determinants is crucial to managing the COVID-19 pandemic and preparing for future ones. In this cross-sectional population-based study we used cumulative data on the first dose of vaccine received by 01-01-2022 at Middle Super Output Area level in England. We used Bayesian hierarchical spatial models and investigated if the geographical differences in vaccination uptake can be explained by a range of community-level characteristics covering socio-demographics, political view, COVID-19 health risk awareness and targeting of high risk groups and accessibility. Deprivation is the covariate most strongly associated with vaccine uptake (Odds Ratio 0.55, 95%CI 0.54-0.57; most versus least deprived areas). The most ethnically diverse areas have a 38% (95%CI 36-40%) lower odds of vaccine uptake compared with those least diverse. Areas with the highest proportion of population between 12 and 24 years old had lower odds of vaccination (0.87, 95%CI 0.85-0.89). Finally increase in vaccine accessibility is associated with COVID-19 vaccine coverage (OR 1.07, 95%CI 1.03-1.12). Our results suggest that one year after the start of the vaccination programme, there is still evidence of inequalities in uptake, affecting particularly minorities and marginalised groups. Strategies including prioritising active outreach across communities and removing practical barriers and factors that make vaccines less accessible are needed to level up the differences.
Gomez-Rubio V, Cameletti M, Blangiardo M, 2022, Missing data analysis and imputation via latent Gaussian Markov random fields, SORT-STATISTICS AND OPERATIONS RESEARCH TRANSACTIONS, Vol: 46, Pages: 217-244, ISSN: 1696-2281
Gomez-Rubio V, Cameletti M, Blangiardo M, 2022, Missing data analysis and imputation via latent Gaussian Markov random fields, SORT-STATISTICS AND OPERATIONS RESEARCH TRANSACTIONS, Vol: 46, ISSN: 1696-2281
Shoari N, Beevers S, Brauer M, et al., 2022, Towards healthy school neighbourhoods: a baseline analysis in Greater London, Environment International, Vol: 165, ISSN: 0160-4120
Creating healthy environments around schools is important to promote healthy childhood development and is a critical component of public health. In this paper we present a tool to characterize exposure to multiple urban environment features within 400 m (5-10 minutes walking distance) of schools in Greater London. We modelled joint exposure to air pollution (NO2 and PM2.5), access to public greenspace, food environment, and road safety for 2,929 schools, employing a Bayesian non-parametric approach based on the Dirichlet Process Mixture modelling. We identified 12 latent clusters of schools with similar exposure profiles and observed some spatial clustering patterns. Socioeconomic and ethnicity disparities were manifested with respect to exposure profiles. Specifically, three clusters (containing 645 schools) showed the highest joint exposure to air pollution, poor food environment, and unsafe roads and were characterized with high deprivation. The most deprived cluster of schools had a median of 2.5 ha greenspace, 29.0 µg/m3 of NO2, 19.3 µg/m3 of PM2.5, 20 fast food retailers, and five child pedestrian crashes over a three-year period. The least deprived cluster of schools had a median of 21.8 ha greenspace, 15.6 µg/m3 of NO2, 15.1 µg/m3 of PM2.5, 2 fast food retailers, and one child pedestrian crash over a three-year period. To have a school-level understanding of exposure levels, we then benchmarked schools based on the probability of exceeding the median exposure to various features of interest. Our study accounts for multiple exposures, enabling us to highlight spatial distribution of exposure profile clusters, and to identify predominant exposure to urban environment features for each cluster of schools. Our findings can help relevant stakeholders, such as schools and public health authorities, to compare schools based on their exposure levels, prioritize interventions, and design local policies that target the schools most in need.
Nicholson G, Blangiardo M, Briers M, et al., 2022, Interoperability of statistical models in pandemic preparedness: principles and reality, Statistical Science: a review journal, Vol: 37, Pages: 183-206, ISSN: 0883-4237
We present interoperability as a guiding framework for statistical modelling to assist policy makers asking multiple questions using diverse datasets in the face of an evolving pandemic response. Interoperability provides an important set of principles for future pandemic preparedness, through the joint design and deployment of adaptable systems of statistical models for disease surveillance using probabilistic reasoning. We illustrate this through case studies for inferring and characterising spatial-temporal prevalence and reproduction numbers of SARS-CoV-2 infections in England.
Padellini T, Jersakova R, Diggle PJ, et al., 2022, Time varying association between deprivation, ethnicity and SARS-CoV-2 infections in England: A population-based ecological study, LANCET REGIONAL HEALTH-EUROPE, Vol: 15, ISSN: 2666-7762
Schneider R, Masselot P, Vicedo-Cabrera AM, et al., 2022, Differential impact of government lockdown policies on reducing air pollution levels and related mortality in Europe, Scientific Reports, Vol: 12, ISSN: 2045-2322
Previous studies have reported a decrease in air pollution levels following the enforcement of lockdown measures during the first wave of the COVID-19 pandemic. However, these investigations were mostly based on simple pre-post comparisons using past years as a reference and did not assess the role of different policy interventions. This study contributes to knowledge by quantifying the association between specific lockdown measures and the decrease in NO2, O3, PM2.5, and PM10 levels across 47 European cities. It also estimated the number of avoided deaths during the period. This paper used new modelled data from the Copernicus Atmosphere Monitoring Service (CAMS) to define business-as-usual and lockdown scenarios of daily air pollution trends. This study applies a spatio-temporal Bayesian non-linear mixed effect model to quantify the changes in pollutant concentrations associated with the stringency indices of individual policy measures. The results indicated non-linear associations with a stronger decrease in NO2 compared to PM2.5 and PM10 concentrations at very strict policy levels. Differences across interventions were also identified, specifically the strong effects of actions linked to school/workplace closure, limitations on gatherings, and stay-at-home requirements. Finally, the observed decrease in pollution potentially resulted in hundreds of avoided deaths across Europe.
Konstantinoudis G, Cameletti M, Gómez-Rubio V, et al., 2022, Regional excess mortality during the 2020 COVID-19 pandemic in five European countries, Nature Communications, Vol: 13, Pages: 1-11, ISSN: 2041-1723
The impact of the COVID-19 pandemic on excess mortality from all causes in 2020 varied across and within European countries. Using data for 2015-2019, we applied Bayesian spatio-temporal models to quantify the expected weekly deaths at the regional level had the pandemic not occurred in England, Greece, Italy, Spain, and Switzerland. With around 30%, Madrid, Castile-La Mancha, Castile-Leon (Spain) and Lombardia (Italy) were the regions with the highest excess mortality. In England, Greece and Switzerland, the regions most affected were Outer London and the West Midlands (England), Eastern, Western and Central Macedonia (Greece), and Ticino (Switzerland), with 15-20% excess mortality in 2020. Our study highlights the importance of the large transportation hubs for establishing community transmission in thefirst stages of the pandemic. Here, we show that acting promptly to limit transmission around these hubs is essential to prevent spread to other regions and countries.
Konstantinoudis G, Gómez-Rubio V, Cameletti M, et al., 2022, A framework for estimating and visualising excess mortality during the COVID-19 pandemic., Publisher: arXiv
COVID-19 related deaths underestimate the pandemic burden on mortality because they suffer from completeness and accuracy issues. Excess mortality is a popular alternative, as it compares observed with expected deaths based on the assumption that the pandemic did not occur. Expected deaths had the pandemic not occurred depend on population trends, temperature, and spatio-temporal patterns. In addition to this, high geographical resolution is required to examine within country trends and the effectiveness of the different public health policies. In this tutorial, we propose a framework using R to estimate and visualise excess mortality at high geographical resolution. We show a case study estimating excess deaths during 2020 in Italy. The proposed framework is fast to implement and allows combining different models and presenting the results in any age, sex, spatial and temporal aggregation desired. This makes it particularly powerful and appealing for online monitoring of the pandemic burden and timely policy making.
Shoari N, Heydari S, Blangiardo M, 2022, School neighbourhood and compliance with WHO-recommended annual NO2 guideline: A case study of Greater London, Science of the Total Environment, Vol: 803, ISSN: 0048-9697
Despite several national and local policies towards cleaner air in England, many schools in London breach the WHO-recommended concentrations of air pollutants such as NO2 and PM2.5. This is while, previous studies highlight significant adverse health effects of air pollutants on children's health. In this paper we adopted a Bayesian spatial hierarchical model to investigate factors that affect the odds of schools exceeding the WHO-recommended concentration of NO2 (i.e., 40 μg/m3 annual mean) in Greater London (UK). We considered a host of variables including schools' characteristics as well as their neighbourhoods' attributes from household, socioeconomic, transport-related, land use, built and natural environment characteristics perspectives. The results indicated that transport-related factors including the number of traffic lights and bus stops in the immediate vicinity of schools, and borough-level bus fuel consumption are determinant factors that increase the likelihood of non-compliance with the WHO guideline. In contrast, distance from roads, river transport, and underground stations, vehicle speed (an indicator of traffic congestion), the proportion of borough-level green space, and the area of green space at schools reduce the likelihood of exceeding the WHO recommended concentration of NO2. We repeated our analysis under a hypothetical scenario in which the recommended concentration of NO2 is 35 μg/m3 - instead of 40 μg/m3. Our results underscore the importance of adopting clean fuel technologies on buses, installing green barriers, and reducing motorised traffic around schools in reducing exposure to NO2 concentrations in proximity to schools. Also, our findings highlight the presence of environmental inequalities in the Greater London area. This study would be useful for local authority decision making with the aim of improving air quality for school-aged children in urban settings.
Nicholson G, Lehmann B, Padellini T, et al., 2022, Improving local prevalence estimates of SARS-CoV-2 infections using a causal debiasing framework, Nature Microbiology, Vol: 7, Pages: 97-107, ISSN: 2058-5276
Global and national surveillance of SARS-CoV-2 epidemiology is mostly based on targeted schemes focused on testing individuals with symptoms. These tested groups are often unrepresentative of the wider population and exhibit test positivity rates that are biased upwards compared with the true population prevalence. Such data are routinely used to infer infection prevalence and the effective reproduction number, Rt, which affects public health policy. Here, we describe a causal framework that provides debiased fine-scale spatiotemporal estimates by combining targeted test counts with data from a randomized surveillance study in the United Kingdom called REACT. Our probabilistic model includes a bias parameter that captures the increased probability of an infected individual being tested, relative to a non-infected individual, and transforms observed test counts to debiased estimates of the true underlying local prevalence and Rt. We validated our approach on held-out REACT data over a 7-month period. Furthermore, our local estimates of Rt are indicative of 1-week- and 2-week-ahead changes in SARS-CoV-2-positive case numbers. We also observed increases in estimated local prevalence and Rt that reflect the spread of the Alpha and Delta variants. Our results illustrate how randomized surveys can augment targeted testing to improve statistical accuracy in monitoring the spread of emerging and ongoing infectious disease.
Gong X, Itzkowitz N, Atilola GO, et al., 2022, The association between aircraft noise levels and deprivation
There is limited evidence on deprivation distribution of noise exposure. Noise data (Lden, Laeq24, Lnight, Levening, and Lday) were available for London Heathrow airport for 2014-18. These were linked with different measures of deprivation: the Carstairs deprivation index (UK Census-derived), fuel poverty rate and the avoidable death rate. We used a random effects model, accounting for year and % ethnic minority to quantify the association. Our findings consistently indicate that areas with the least deprivation are the quietest. We found that quintiles 2 - 5 of all deprivation variables exhibit a positive association, indicating that the least deprived areas are consistently the quietest. There is some evidence of a dose-response relationship with aircraft noise in terms of avoidable death rate. Carstairs index quintiles are significantly associated with all four metrics. Fuel poverty has a significant but relatively weak relationship with aircraft noise, compared to Carstairs and avoidable death rate. There is less conclusive evidence of gradients for Carstairs index and fuel poverty rate. Results will be discussed with community groups near Heathrow prior to Internoise 2022. As air transport increases post-pandemic, information on noise exposures as well as views from community groups can inform future airport policies.
Padellini T, Jersakova R, Diggle PJ, et al., 2021, Time varying association between deprivation, ethnicity and SARS-CoV-2 infections in England: a space-time study., Publisher: MedRxiv
Background: Ethnically diverse and socio-economically deprived communities have been differentially affected by the COVID-19 pandemic in the UK. Method: Using a multilevel regression model we assess the time-varying association between SARS-CoV-2 infections and areal level deprivation and ethnicity. We separately consider weekly test positivity rate (number of positive tests over the total number of tests) and estimated unbiased prevalence (proportion of individuals in the population who would test positive) at the Lower Tier Local Authority (LTLA) level. The model also adjusts for age, urbanicity, vaccine uptake and spatio-temporal correlation structure. Findings: Comparing the least deprived and predominantly White areas with most deprived and predominantly non-White areas over the whole study period, the weekly positivity rate increases by 13% from 297% to 335%. Similarly, prevalence increases by 10% from 037% to 041%. Deprivation has a stronger effect until October 2020, while the effect of ethnicity becomes slightly more pronounced at the peak of the second wave and then again in May-June 2021. Not all BAME groups were equally affected: in the second wave of the pandemic, LTLAs with large South Asian populations were the most affected, whereas areas with large Black populations did not show increased values for either outcome during the entire period under analysis. Interpretation: At the area level, IMD and BAME% are both associated with an increased COVID-19 burden in terms of prevalence (disease spread) and test positivity (disease monitoring), and the strength of association varies over the course of the pandemic. The consistency of results across the two outcome measures suggests that community level characteristics such as deprivation and ethnicity have a differential impact on disease exposure or susceptibility rather than testing access and habits. Fundings: EPSRC, MRC, The Alan Turing Institute, NIH, UKHSA, DHSC, NIHR.
Huang G, Blangiardo M, Brown PE, et al., 2021, Long-term exposure to air pollution and COVID-19 incidence: A multi-country study, Spatial and Spatio-temporal Epidemiology, Vol: 39, Pages: 1-11, ISSN: 1877-5845
The study of the impacts of air pollution on COVID-19 has gained increasing attention. However, most of the existing studies are based on a single country, with a high degree of variation in the results reported in different papers. We attempt to inform the debate about the long-term effects of air pollution on COVID-19 by conducting a multi-country analysis using a spatial ecological design, including Canada, Italy, England and the United States. The model allows the residual spatial autocorrelation after accounting for covariates. It is concluded that the effects of PM2.5 and NO2 are inconsistent across countries. Specifically, NO2 was not found to be an important factor affecting COVID-19 infection, while a large effect for PM2.5 in the US is not found in the other three countries. The Population Attributable Fraction for COVID-19 incidence ranges from 3.4% in Canada to 45.9% in Italy, although with considerable uncertainty in these estimates.
Python A, Bender A, Blangiardo M, et al., 2021, A downscaling approach to compare COVID-19 count data from databases aggregated at different spatial scales, JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY, Vol: 185, Pages: 202-218, ISSN: 0964-1998
Elfadaly FG, Adamson A, Patel J, et al., 2021, BIMAM-a tool for imputing variables missing across datasets using a Bayesian imputation and analysis model, INTERNATIONAL JOURNAL OF EPIDEMIOLOGY, Vol: 50, Pages: 1419-1425, ISSN: 0300-5771
Davies B, Parkes B, Bennett J, et al., 2021, Community factors and excess mortality in first wave of the COVID-19 pandemic in England, Nature Communications, ISSN: 2041-1723
Risk factors for increased risk of death from Coronavirus Disease 19 (COVID-19) have been identified but less is known on characteristics that make communities resilient or vulnerable to the mortality impacts of the pandemic. We applied a two-stage Bayesian spatial model to quantify inequalities in excess mortality at the community level during the first wave of the pandemic in England. We used geocoded data on all deaths in people aged 40 years and older during March-May 2020 compared with 2015-2019 in 6,791 local communities. Here we show that communities with an increased risk of excess mortality had a high density of care homes, and/or high proportion of residents on income support, living in overcrowded homes and/or high percent of people with a non-White ethnicity (including Black, Asian and other minority ethnic groups). Conversely, after accounting for other community characteristics, we found no association between population density or air pollution and excess mortality. Overall, the social and environmental variables accounted for around 15% of the variation in mortality at community level. Effective and timely public health and healthcare measures that target the communities at greatest risk are urgently needed if England and other industrialised countries are to avoid further widening of inequalities in mortality patterns as the pandemic progresses.
Burney P, Patel J, Minelli C, et al., 2021, Prevalence and population attributable risk for chronic airflow obstruction in a large multinational study, American Journal of Respiratory and Critical Care Medicine, Vol: 203, Pages: 1353-1365, ISSN: 1073-449X
Rationale: The Global Burden of Disease programme identified smoking, and ambient and household air pollution as the main drivers of death and disability from Chronic Obstructive Pulmonary Disease (COPD). Objective: To estimate the attributable risk of chronic airflow obstruction (CAO), a quantifiable characteristic of COPD, due to several risk factors. Methods: The Burden of Obstructive Lung Disease study is a cross-sectional study of adults, aged≥40, in a globally distributed sample of 41 urban and rural sites. Based on data from 28,459 participants, we estimated the prevalence of CAO, defined as a post-bronchodilator one-second forced expiratory volume to forced vital capacity ratio < lower limit of normal, and the relative risks associated with different risk factors. Local RR were estimated using a Bayesian hierarchical model borrowing information from across sites. From these RR and the prevalence of risk factors, we estimated local Population Attributable Risks (PAR). Measurements and Main Results: Mean prevalence of CAO was 11.2% in men and 8.6% in women. Mean PAR for smoking was 5.1% in men and 2.2% in women. The next most influential risk factors were poor education levels, working in a dusty job for ≥10 years, low body mass index (BMI), and a history of tuberculosis. The risk of CAO attributable to the different risk factors varied across sites. Conclusions: While smoking remains the most important risk factor for CAO, in some areas poor education, low BMI and passive smoking are of greater importance. Dusty occupations and tuberculosis are important risk factors at some sites.
Konstantinoudis G, Padellini T, Bennett J, et al., 2021, Response to "re: long-term exposure to air-pollution and COVID-19 mortality in England: a hierarchical spatial analysis", Environment International, Vol: 150, ISSN: 0160-4120
Lowe R, Lee SA, O'Reilly KM, et al., 2021, Combined effects of hydrometeorological hazards and urbanisation on dengue risk in Brazil: a spatiotemporal modelling study, LANCET PLANETARY HEALTH, Vol: 5, Pages: E209-E219
Konstantinoudis G, Padellini T, Bennett J, et al., 2021, Long-term exposure to air-pollution and COVID-19 mortality in England: a hierarchical spatial analysis, Environment International, Vol: 146, ISSN: 0160-4120
Recent studies suggested a link between long-term exposure to air-pollution and COVID-19 mortality. However, due to their ecological design based on large spatial units, they neglect the strong localised air-pollution patterns, and potentially lead to inadequate confounding adjustment. We investigated the effect of long-term exposure to NO2 and PM2.5 on COVID-19 mortality in England using high geographical resolution. In this nationwide cross-sectional study in England, we included 38,573 COVID-19 deaths up to June 30, 2020 at the Lower Layer Super Output Area level (n = 32,844 small areas). We retrieved averaged NO2 and PM2.5 concentration during 2014–2018 from the Pollution Climate Mapping. We used Bayesian hierarchical models to quantify the effect of air-pollution while adjusting for a series of confounding and spatial autocorrelation. We find a 0.5% (95% credible interval: −0.2%, 1.2%) and 1.4% (95% CrI: −2.1%, 5.1%) increase in COVID-19 mortality risk for every 1 μg/m3 increase in NO2 and PM2.5 respectively, after adjusting for confounding and spatial autocorrelation. This corresponds to a posterior probability of a positive effect equal to 0.93 and 0.78 respectively. The spatial relative risk at LSOA level revealed strong patterns, similar for the different pollutants. This potentially captures the spread of the disease during the first wave of the epidemic. Our study provides some evidence of an effect of long-term NO2 exposure on COVID-19 mortality, while the effect of PM2.5 remains more uncertain.
Boulieri A, Blangiardo M, 2020, A spatio-temporal model to estimate life expectancy and to detect unusual trends at the local authority level in England, BMJ Open, Vol: 10, ISSN: 2044-6055
Objectives To estimate life expectancy at the local authority level and detect those areas that have a substantially low life expectancy after accounting for deprivation.Design We used registration data from the Office for National Statistics on mortality and population in England, by local authority, age group and socioeconomic deprivation decile, for both men and women over the period 2001–2018. We used a statistical model within the Bayesian framework to produce robust mortality rates, which were then transformed to life expectancy estimates. A rule based on exceedance probabilities was used to detect local authorities characterised by a low life expectancy among areas with a similar deprivation level from 2012 onwards.Results We confirmed previous findings showing differences in the life expectancy gap between the most and least deprived areas from 2012 to 2018. We found variations in life expectancy trends across local authorities, and we detected a number of those with a low life expectancy when compared with others of a similar deprivation level.Conclusions There are factors other than deprivation that are responsible for low life expectancy in certain local authorities. Further investigation on the detected areas can help understand better the stalling of life expectancy which was observed from 2012 onwards and plan efficient public health policies.
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.