Suchergebnisse

[Background] The time stratified case cross-over approach is a popular alternative to conventional time series regression for analysing associations between time series of environmental exposures (air pollution, weather) and counts of health outcomes. These are almost always analyzed using conditional logistic regression on data expanded to case–control (case crossover) format, but this has some limitations. In particular adjusting for overdispersion and auto-correlation in the counts is not possible. It has been established that a Poisson model for counts with stratum indicators gives identical estimates to those from conditional logistic regression and does not have these limitations, but it is little used, probably because of the overheads in estimating many stratum parameters. ; [Methods] The conditional Poisson model avoids estimating stratum parameters by conditioning on the total event count in each stratum, thus simplifying the computing and increasing the number of strata for which fitting is feasible compared with the standard unconditional Poisson model. Unlike the conditional logistic model, the conditional Poisson model does not require expanding the data, and can adjust for overdispersion and auto-correlation. It is available in Stata, R, and other packages. ; [Results] By applying to some real data and using simulations, we demonstrate that conditional Poisson models were simpler to code and shorter to run than are conditional logistic analyses and can be fitted to larger data sets than possible with standard Poisson models. Allowing for overdispersion or autocorrelation was possible with the conditional Poisson model but when not required this model gave identical estimates to those from conditional logistic regression. ; [Conclusions] Conditional Poisson regression models provide an alternative to case crossover analysis of stratified time series data with some advantages. The conditional Poisson model can also be used in other contexts in which primary control for confounding is by fine stratification. ; AG was funded by a Methodology Research Fellowship from Medical Research Council UK (grant ID G1002296). AT was supported by a Salvador Madariaga's grant of the Ministry of Education of the Spanish Government. ; Peer reviewed

Interrupted time series (ITS) analysis is a valuable study design for evaluating the effectiveness of population-level health interventions that have been implemented at a clearly defined point in time. It is increasingly being used to evaluate the effectiveness of interventions ranging from clinical therapy to national public health legislation. Whereas the design shares many properties of regression-based approaches in other epidemiological studies, there are a range of unique features of time series data that require additional methodological considerations. In this tutorial we use a worked example to demonstrate a robust approach to ITS analysis using segmented regression. We begin by describing the design and considering when ITS is an appropriate design choice. We then discuss the essential, yet often omitted, step of proposing the impact model a priori. Subsequently, we demonstrate the approach to statistical analysis including the main segmented regression model. Finally we describe the main methodological issues associated with ITS analysis: over-dispersion of time series data, autocorrelation, adjusting for seasonal trends and controlling for time-varying confounders, and we also outline some of the more complex design adaptations that can be used to strengthen the basic ITS design.

[Background] Periods of high temperature have been widely found to be associated with excess mortality but with variable relationships in different cities. How these specifics depend on climatic and other characteristics of cities is not well understood. We assess summer temperature-mortality relationships using data from 50 provincial capitals in Spain, during the period 1990–2004. ; [Methods] Poisson time series regression analyses were applied to daily temperature and mortality data, adjusting for potential confounding seasonal factors. Associations of heat with mortality were summarised for each city as the risk increments at the 99th compared to the 90th percentiles of the whole-year temperature distributions, as predicted from spline curves. ; [Results] Risk increments averaged 14.6% between both centiles, or 3.3% per 1 Celsius degree. Although risk increments varied substantially between cities, the range of temperature from the 90th to 99th centile was the only characteristic independently significantly associated with them. The heat increment did not depend on other city climatic, socio-demographic and geographic determinants. ; [Conclusions] Cities in Spain are partially adapted to high mean summer temperatures but not to high variation in summer temperatures. ; This work was supported by a Salvador Madariaga's grant of the Ministry of Education of the Spanish Government (PRX12/00515) and conducted while Aurelio Tobías was visiting the London School of Hygiene and Tropical Medicine (UK). We acknowledge support of the publication fee by the CSIC Open Access Publication Support Initiative through its Unit of Information Resources for Research (URICI). Antonio Gasparrini was supported by a Methodology Research fellowship awarded by Medical Research Council-UK (grant ID G1002296). Julio Diaz was funded by a grant from the Strategic Health Action ISCIII (FIS Project ENPY1001/13). ; Peer reviewed

BACKGROUND: Periods of high temperature have been widely found to be associated with excess mortality but with variable relationships in different cities. How these specifics depend on climatic and other characteristics of cities is not well understood. We assess summer temperature-mortality relationships using data from 50 provincial capitals in Spain, during the period 1990-2004. METHODS: Poisson time series regression analyses were applied to daily temperature and mortality data, adjusting for potential confounding seasonal factors. Associations of heat with mortality were summarised for each city as the risk increments at the 99th compared to the 90th percentiles of the whole-year temperature distributions, as predicted from spline curves. RESULTS: Risk increments averaged 14.6% between both centiles, or 3.3% per 1 Celsius degree. Although risk increments varied substantially between cities, the range of temperature from the 90th to 99th centile was the only characteristic independently significantly associated with them. The heat increment did not depend on other city climatic, socio-demographic and geographic determinants. CONCLUSIONS: Cities in Spain are partially adapted to high mean summer temperatures but not to high variation in summer temperatures. ; This work was supported by a Salvador Madariaga's grant of the Ministry of Education of the Spanish Government (PRX12/00515) and conducted while Aurelio Tobías was visiting the London School of Hygiene and Tropical Medicine (UK). We acknowledge support of the publication fee by the CSIC Open Access Publication Support Initiative through its Unit of Information Resources for Research (URICI). Antonio Gasparrini was supported by a Methodology Research fellowship awarded by Medical Research Council-UK (grant ID G1002296). Julio Diaz was funded by a grant from the Strategic Health Action ISCIII (FIS Project ENPY1001/13). To Fernando Simon, from the Alert and Emergency Unit of the National Centre of Epidemiology, to provide data. ; Sí

Objective To evaluate the short term associations between nitrogen dioxide (NO2) and total, cardiovascular, and respiratory mortality across multiple countries/regions worldwide, using a uniform analytical protocol. Design Two stage, time series approach, with overdispersed generalised linear models and multilevel meta-analysis. Setting 398 cities in 22 low to high income countries/regions. Main outcome measures Daily deaths from total (62.8 million), cardiovascular (19.7 million), and respiratory (5.5 million) causes between 1973 and 2018. Results On average, a 10 μg/m3 increase in NO2 concentration on lag 1 day (previous day) was associated with 0.46% (95% confidence interval 0.36% to 0.57%), 0.37% (0.22% to 0.51%), and 0.47% (0.21% to 0.72%) increases in total, cardiovascular, and respiratory mortality, respectively. These associations remained robust after adjusting for co-pollutants (particulate matter with aerodynamic diameter ≤10 μm or ≤2.5 μm (PM10 and PM2.5, respectively), ozone, sulfur dioxide, and carbon monoxide). The pooled concentration-response curves for all three causes were almost linear without discernible thresholds. The proportion of deaths attributable to NO2 concentration above the counterfactual zero level was 1.23% (95% confidence interval 0.96% to 1.51%) across the 398 cities. Conclusions This multilocation study provides key evidence on the independent and linear associations between short term exposure to NO2 and increased risk of total, cardiovascular, and respiratory mortality, suggesting that health benefits would be achieved by tightening the guidelines and regulatory limits of NO2. ; HaK was supported by the National Natural Science Foundation of China (92043301, 82030103, and 91843302) and China Medical Board Collaborating Program (16-250). AG and FS were supported by the Medical Research Council, UK (MR/M022625/1), the Natural Environment Research Council, UK (NE/R009384/1), and the European Union's Horizon 2020 Project Exhaustion (820655). VH was supported by the Spanish Ministry of Science and Innovation (PCIN-2017-046), and the German Federal Ministry of Education and Research (01LS1201A2). YH and MH were supported by the Environment Research and Technology Development Fund (JPMEERF15S11412) of the Environmental Restoration and Conservation Agency, Japan. JK and AU were supported by the Czech Science Foundation (18-22125S). ST was supported by the Shanghai Municipal Science and Technology Commission (18411951600). Y-LLG was supported by a Career Development Fellowship of the Australian National Health and Medical Research Council (APP1163693). SL was supported by an Early Career Fellowship of the Australian National Health and Medical Research Council (APP1109193). JJKJJ and NR were supported by the Academy of Finland (310372). The funders had no role in considering the study design or in the collection, analysis, interpretation of data, writing of the report, or decision to submit the article for publication. ; Peer reviewed

BACKGROUND: Numerous studies have examined the association between air pollution and preterm birth (< 37 weeks gestation) but findings have been inconsistent. These associations may be more difficult to detect than associations with other adverse birth outcomes because of the different duration of exposure in preterm vs. term births, and the existence of seasonal cycles in incidence of preterm birth. METHODS: We analyzed data pertaining to 1,001,700 singleton births occurring between 1999 and 2008 in 24 Canadian cities where daily air pollution data were available from government monitoring sites. In the first stage, data were analyzed in each city employing Cox proportional hazards models using gestational age in days as the time scale, obtaining city-specific hazard ratios (HRs) with their 95% confidence intervals (CIs) expressed per interquartile range (IQR) of each air pollutant. Effects were examined using distributed lag functions for lags of 0-6 days prior to delivery, as well as cumulative lags from two to six days. We accounted for the potential nonlinear effect of daily mean ambient temperature using a cubic B-spline with three internal knots. In the second stage, we pooled the estimated city-specific hazard ratios using a random effects model. RESULTS: Pooled estimates across 24 cities indicated that an IQR increase in ozone (O3, 13.3 ppb) 0-3 days prior to delivery was associated with a hazard ratio of 1.036 (95% CI 1.005, 1.067) for preterm birth, adjusting for infant sex, maternal age, marital status and country of birth, neighbourhood socioeconomic status (SES) and visible minority, temperature, year and season of birth, and a natural spline function of day of year. There was some evidence of effect modification by gestational age and season. Associations with carbon monoxide, nitrogen dioxide, particulate matter, and sulphur dioxide were inconsistent. CONCLUSIONS: We observed associations between daily O3 in the week before delivery and preterm birth in an analysis of approximately 1 million births in 24 Canadian cities between 1999 and 2008. Our analysis is one of a limited number which have examined these short term associations employing Cox proportional hazards models to account for the different exposure durations of preterm vs. term births.

BACKGROUND: Until 2011, stockouts of family planning commodities were common in Senegalese public health facilities. Recognizing the importance of addressing this problem, the Government of Senegal implemented the Informed Push Model (IPM) supply system, which involves logisticians to collect facility-level stock turnover data once a month and provide contraceptive supplies accordingly. The aims of this paper were to evaluate the impact of IPM on contraceptive availability and on stockout duration. METHODS AND FINDINGS: To estimate the impact of the IPM on contraceptive availability, stock card data were obtained from health facilities selected through multistage sampling. A total number of 103 health facilities pertaining to 27 districts and nine regions across the country participated in this project. We compared the odds of contraceptive stockouts within the health facilities on the 23 months after the intervention with the 18 months before. The analysis was performed with a logistic model of the monthly time-series. The odds of stockout for any of the five contraceptive products decreased during the 23 months post-intervention compared to the 18 months pre-intervention (odds ratio, 95%CI: 0.34, 0.22-0.51). To evaluate the impact of the IPM on duration of stockouts, a mixed negative binomial zero-truncated regression analysis was performed. The IPM was not effective in reducing the duration of contraceptive stockouts (incidence rate ratio, 95%CI: 0.81, 0.24-2.7), except for the two long-acting contraceptives (intrauterine devices and implants). Our model predicted a decrease in stockout median duration from 23 pre- to 4 days post-intervention for intrauterine devices; and from 19 to 14 days for implants. CONCLUSIONS: We conclude that the IPM has resulted in greater efficiency in contraceptive stock management, increasing the availability of contraceptive methods in health facilities in Senegal. The IPM also resulted in decreased duration of stockouts for intrauterine devices and implants, but not for any of the short-acting contraception (pills and injectables).

AbstractExtreme temperature could affect traffic crashes by influencing road safety, vehicle performance, and drivers' behavior and abilities. Studies evaluating the impacts of extreme temperatures on the risk of traffic crashes have mainly overlooked the potential role of vehicle air conditioners. The aim of this study, therefore, was to evaluate the effect of exposure to extreme cold and hot temperatures on seeking medical attention due to motorcycle crashes. The study was conducted in Iran by using medical attendance for motorcycle crashes from March 2011 to June 2017. Data on daily minimum, mean and maximum temperature (°C), relative humidity (%), wind velocity (km/h), and precipitation (mm/day) were collected. We developed semi-parametric generalized additive models following a quasi-Poisson distribution with the distributed nonlinear lag model to estimate the immediate and lagged associations (reported as relative risk [RR], and 95% confidence interval [CI]). Between March 2011 and June 2017, 36,079 medical attendances due to motorcycle road traffic crashes were recorded (15.8 ± 5.92 victims per day). In this time period, the recorded temperature ranged from −11.2 to 45.4 °C (average: 25.5 ± 11.0 °C). We found an increased risk of medical attendance for motorcycle crashes (based on maximum daily temperature) at both extremely cold (1st percentile) and hot (99th percentile) temperatures and also hot (75th percentile) temperatures, mainly during lags 0 to 3 days (e.g., RR: 1.12 [95% CI: 1.05: 1.20]; RR: 1.08 [95% CI: 1.01: 1.16]; RR: 1.20 [95% CI: 1.09: 1.32] at lag0 for extremely cold, hot, and extremely hot conditions, respectively). The risk estimates for extremely hot temperatures were larger than hot and extremely cold temperatures. We estimated that 11.01% (95% CI: 7.77:14.06) of the medical attendance for motorcycle crashes is estimated to be attributable to non-optimal temperature (using mean temperature as exposure variable). Our findings have important public health messaging, given the considerable burden associated with road traffic injury, particularly in low- and middle-income countries.

BACKGROUND: The eastern provinces of the Democratic Republic of the Congo have been identified as endemic areas for cholera transmission, and despite continuous control efforts, they continue to experience regular cholera outbreaks that occasionally spread to the rest of the country. In a region where access to improved water sources is particularly poor, the question of which improvements in water access should be prioritized to address cholera transmission remains unresolved. This study aimed at investigating the temporal association between water supply interruptions and Cholera Treatment Centre (CTC) admissions in a medium-sized town. METHODS AND FINDINGS: Time-series patterns of daily incidence of suspected cholera cases admitted to the Cholera Treatment Centre in Uvira in South Kivu Province between 2009 and 2014 were examined in relation to the daily variations in volume of water supplied by the town water treatment plant. Quasi-poisson regression and distributed lag nonlinear models up to 12 d were used, adjusting for daily precipitation rates, day of the week, and seasonal variations. A total of 5,745 patients over 5 y of age with acute watery diarrhoea symptoms were admitted to the CTC over the study period of 1,946 d. Following a day without tap water supply, the suspected cholera incidence rate increased on average by 155% over the next 12 d, corresponding to a rate ratio of 2.55 (95% CI: 1.54-4.24), compared to the incidence experienced after a day with optimal production (defined as the 95th percentile-4,794 m3). Suspected cholera cases attributable to a suboptimal tap water supply reached 23.2% of total admissions (95% CI 11.4%-33.2%). Although generally reporting less admissions to the CTC, neighbourhoods with a higher consumption of tap water were more affected by water supply interruptions, with a rate ratio of 3.71 (95% CI: 1.91-7.20) and an attributable fraction of cases of 31.4% (95% CI: 17.3%-42.5%). The analysis did not suggest any association between levels of residual chlorine in the water fed to the distribution network and suspected cholera incidence. Laboratory confirmation of cholera was not available for this analysis. CONCLUSIONS: A clear association is observed between reduced availability of tap water and increased incidence of suspected cholera in the entire town of Uvira in Eastern Democratic Republic of the Congo. Even though access to piped water supplies is low in Uvira, improving the reliability of tap water supply may substantially reduce the incidence of suspected cholera, in particular in neighbourhoods having a higher access to tap water. These results argue in favour of water supply investments that focus on the delivery of a reliable and sustainable water supply, and not only on point-of-use water quality improvements, as is often seen during cholera outbreaks.

Background Ambient temperature may contribute to seasonality of mortality; in particular, a warming climate is likely to influence the seasonality of mortality. However, few studies have investigated seasonality of mortality under a warming climate. Methods Daily mean temperature, daily counts for all-cause, circulatory, and respiratory mortality, and annual data on prefecture-specific characteristics were collected for 47 prefectures in Japan between 1972 and 2015. A quasi-Poisson regression model was used to assess the seasonal variation of mortality with a focus on its amplitude, which was quantified as the ratio of mortality estimates between the peak and trough days (peak-to-trough ratio (PTR)). We quantified the contribution of temperature to seasonality by comparing PTR before and after temperature adjustment. Associations between annual mean temperature and annual estimates of the temperature-unadjusted PTR were examined using multilevel multivariate meta-regression models controlling for prefecture-specific characteristics. Results The temperature-unadjusted PTRs for all-cause, circulatory, and respiratory mortality were 1.28 (95% confidence interval (CI): 1.27–1.30), 1.53 (95% CI: 1.50–1.55), and 1.46 (95% CI: 1.44–1.48), respectively; adjusting for temperature reduced these PTRs to 1.08 (95% CI: 1.08–1.10), 1.10 (95% CI: 1.08–1.11), and 1.35 (95% CI: 1.32–1.39), respectively. During the period of rising temperature (1.3 °C on average), decreases in the temperature-unadjusted PTRs were observed for all mortality causes except circulatory mortality. For each 1 °C increase in annual mean temperature, the temperature-unadjusted PTR for all-cause, circulatory, and respiratory mortality decreased by 0.98% (95% CI: 0.54–1.42), 1.39% (95% CI: 0.82–1.97), and 0.13% (95% CI: − 1.24 to 1.48), respectively. Conclusion Seasonality of mortality is driven partly by temperature, and its amplitude may be decreasing under a warming climate. ; YC was supported by a Senior Research grant (2019R1A2C1086194) from the National Research Foundation of Korea (NRF), funded by the Ministry of Science, ICT (Information and Communication Technologies). YK was supported by JSPS KAKENHI Grant Number JP19K17104. AT was supported by the JSPS Invitational Fellowships for Research in Japan (Grant S18149). YG was supported by the Career Development Fellowship of the Australian National Health and Medical Research Council (Grants APP1107107 and APP1163693). AG was supported by the Medical Research Council UK (Grants MR/M022625/1 and MR/R013349/1), by the Natural Environment Research Council UK (Grant NE/R009384/1), and by the European Union's Horizon 2020 Project Exhaustion (Grant ID: 820655). YH was supported by the Environment Research and Technology Development Fund (S-14) of the Environmental Restoration and Conservation Agency, Japan. ; Peer reviewed

Although seasonal variations in mortality have been recognized for millennia, the role of temperature remains unclear. We aimed to assess seasonal variation in mortality and to examine the contribution of temperature. ; This work was primarily supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI [Grant Number 19K19461]. Y.C. was supported by a Senior Research grant [2019R1A2C1086194] from the National Research Foundation of Korea (NRF), funded by the Ministry of Science, ICT (Information and Communication Technologies). V.H. received support from the Spanish Ministry of Economy, Industry and Competitiveness [Grant ID: PCIN-2017-046]. J.K. and A.U. were supported by the Czech Science Foundation [project 18-22125S]. A.S. acknowledged funding from European Union's Horizon 2020 research and innovation programme under grant agreement No 820655 (EXHAUSTION). A.G. was supported by the Medical Research Council-UK [Grant ID: MR/R013349/1], the Natural Environment Research Council UK [Grant ID: NE/R009384/1] and the European Union's Horizon 2020 Project Exhaustion [Grant ID: 820655]. M.H. was supported by the Japan Science and Technology Agency (JST) as part of SICORP [Grant Number JPMJSC20E4]. ; Peer reviewed

There is conflicting evidence on the influence of weather on COVID-19 transmission. Our aim is to estimate weather-dependent signatures in the early phase of the pandemic, while controlling for socio-economic factors and non-pharmaceutical interventions. We identify a modest non-linear association between mean temperature and the effective reproduction number (Re) in 409 cities in 26 countries, with a decrease of 0.087 (95% CI: 0.025; 0.148) for a 10 °C increase. Early interventions have a greater effect on Re with a decrease of 0.285 (95% CI 0.223; 0.347) for a 5th - 95th percentile increase in the government response index. The variation in the effective reproduction number explained by government interventions is 6 times greater than for mean temperature. We find little evidence of meteorological conditions having influenced the early stages of local epidemics and conclude that population behaviour and government interventions are more important drivers of transmission. ; This work was generated using Copernicus Climate Change Service (C3S) and Copernicus Atmosphere Monitoring Service (CAMS) information [2020]. The authors would like to thank the European Centre for Medium-Range Weather Forecasts (ECMWF) that implements the C3S and CAMS on behalf of the European Union. D.R. was supported by a postdoctoral research fellowship of the Xunta de Galicia (Spain). A.G. was funded by the Medical Research Council-UK (Grant ID: MR/R013349/1), the Natural Environment Research Council UK (Grant ID: NE/R009384/1) and the European Union's Horizon 2020 Project Exhaustion (Grant ID: 820655). R.L. was supported by a Royal Society Dorothy Hodgkin Fellowship. S.A. and S.M. were funded by the Wellcome Trust (grant 210758/Z/18/Z210758/Z/18/Z). The following funding sources are acknowledged as providing funding for the MCC Collaborative Research Network authors: J.K. and A.U. were supported by the Czech Science Foundation, project 18-22125S. S.T. was supported by the Shanghai Municipal Science and Technology Commission (Grant 18411951600). N.S. is supported by the NIEHS-funded HERCULES Center (P30ES019776). H.K. was supported by the National Research Foundation of Korea (BK21 Center for Integrative Response to Health Disasters, Graduate School of Public Health, Seoul National University). A.S., F.D.R. and S.R. were funded by the European Union's Horizon 2020 Project Exhaustion (Grant ID: 820655). Each member of the CMMID COVID-19 Working Group contributed to processing, cleaning and interpretation of data, interpreted findings, contributed to the manuscript and approved the work for publication. The following funding sources are acknowledged as providing funding for the CMMID COVID-19 working group authors. This research was partly funded by the Bill & Melinda Gates Foundation (INV-001754: M.Q; INV-003174: K.P., M.J., Y.L., J.L.; NTD Modelling Consortium OPP1184344: C.A.B.P., G.M.; OPP1180644: S.R.P.; OPP1183986: E.S.N.). BMGF (OPP1157270: K.E.A.). DFID/Wellcome Trust (Epidemic Preparedness Coronavirus research programme 221303/Z/20/Z: C.A.B.P.). EDCTP2 (RIA2020EF-2983-CSIGN: H.P.G.). ERC Starting Grant (#757699: M.Q.). This project has received funding from the European Union's Horizon 2020 research and innovation programme—project EpiPose (101003688: K.P., M.J., P.K., R.C.B., W.J.E., Y.L.). This research was partly funded by the Global Challenges Research Fund (GCRF) project 'RECAP' managed through RCUK and ESRC (ES/P010873/1: A.G., C.I.J., T.J.). HDR UK (MR/S003975/1: R.M.E.). MRC (MR/N013638/1: N.R.W.; MR/V027956/1: W.W.). Nakajima Foundation (A.E.). This research was partly funded by the National Institute for Health Research (NIHR) using UK aid from the UK Government to support global health research. The views expressed in this publication are those of the author(s) and not necessarily those of the NIHR or the UK Department of Health and Social Care (16/136/46: B.J.Q.; 16/137/109: B.J.Q., F.Y.S., M.J., Y.L.; Health Protection Research Unit for Immunisation NIHR200929: N.G.D.; Health Protection Research Unit for Modelling Methodology HPRU-2012-10096: T.J.; NIHR200908: R.M.E.; NIHR200929: F.G.S., M.J.; PR-OD-1017-20002: A.R., W.J.E.). Royal Society (Dorothy Hodgkin Fellowship: R.L.; RP\EA\180004: P.K.). UK DHSC/UK Aid/NIHR (PR-OD-1017-20001: H.P.G.). UK MRC (MC_PC_19065—Covid 19: Understanding the dynamics and drivers of the COVID-19 epidemic using real-time outbreak analytics: A.G., N.G.D., R.M.E., S.C., T.J., W.J.E., Y.L.; MR/P014658/1: G.M.K.). Authors of this research receive funding from the UK Public Health Rapid Support Team funded by the United Kingdom Department of Health and Social Care (T.J.). Wellcome Trust (206250/Z/17/Z: A.J.K., T.W.R.; 206471/Z/17/Z: O.B.; 208812/Z/17/Z: S.C.; 210758/Z/18/Z: J.D.M., J.H., N.I.B.; UNS110424: F.K.). No funding (A.M.F., A.S., C.J.V.-A., D.C.T., J.W., K.E.A., Y.-W.D.C.). LSHTM, DHSC/UKRI COVID-19 Rapid Response Initiative (MR/V028456/1: Y.L.). Innovation Fund of the Joint Federal Committee (01VSF18015: F.K.). Foreign, Commonwealth and Development Office/Wellcome Trust (221303/Z/20/Z: M.K.). ; Peer reviewed

There is conflicting evidence on the influence of weather on COVID-19 transmission. Our aim is to estimate weather-dependent signatures in the early phase of the pandemic, while controlling for socio-economic factors and non-pharmaceutical interventions. We identify a modest non-linear association between mean temperature and the effective reproduction number (Re) in 409 cities in 26 countries, with a decrease of 0.087 (95% CI: 0.025; 0.148) for a 10 °C increase. Early interventions have a greater effect on Re with a decrease of 0.285 (95% CI 0.223; 0.347) for a 5th - 95th percentile increase in the government response index. The variation in the effective reproduction number explained by government interventions is 6 times greater than for mean temperature. We find little evidence of meteorological conditions having influenced the early stages of local epidemics and conclude that population behaviour and government interventions are more important drivers of transmission.

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.

International audience ; 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 NO 2 , O 3 , PM 2.5 , and PM 10 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 NO 2 compared to PM 2.5 and PM 10 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.