1
|
Ravelli E, Gonzales Martinez R. Environmental risk factors of airborne viral transmission: Humidity, Influenza and SARS-CoV-2 in the Netherlands. Spat Spatiotemporal Epidemiol 2022; 41:100432. [PMID: 35691642 DOI: 10.1101/2020.08.18.20177444] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2020] [Revised: 03/16/2021] [Accepted: 05/10/2021] [Indexed: 05/20/2023]
Abstract
OBJECTIVE The relationship between specific humidity and influenza/SARS-CoV-2 in the Netherlands is evaluated over time and at regional level. DESIGN Parametric and non-parametric correlation coefficients are calculated to quantify the relationship between humidity and influenza, using five years of weekly data. Bayesian spatio-temporal models-with a Poisson and a Gaussian likelihood-are estimated to find the relationship between regional humidity and the daily cases of SARS-CoV-2 in the municipalities and provinces of the Netherlands. RESULTS An inverse (negative) relationship is observed between specific humidity and the incidence of influenza between 2015 and 2019. The space-time analysis indicates that an increase of specific humidity of one gram of water vapor per kilogram of air (1 g/kg) is related to a reduction of approximately 5% in the risk of COVID-19 infections. CONCLUSIONS The increase in humidity during the outbreak of the SARS-CoV-2 in the Netherlands may have helped to reduce the risk of regional COVID-19 infections. Policies that lead to an increase in household specific humidity to over 6g/Kg will help reduce the spread of respiratory viruses such as influenza and SARS-CoV-2.
Collapse
Affiliation(s)
| | - Rolando Gonzales Martinez
- Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Center for Advanced Systems Understanding (CASUS), Germany.
| |
Collapse
|
2
|
Boniardi L, Nobile F, Stafoggia M, Michelozzi P, Ancona C. A multi-step machine learning approach to assess the impact of COVID-19 lockdown on NO 2 attributable deaths in Milan and Rome, Italy. Environ Health 2022; 21:17. [PMID: 35034644 PMCID: PMC8761378 DOI: 10.1186/s12940-021-00825-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 12/23/2021] [Indexed: 06/14/2023]
Abstract
BACKGROUND Air pollution is one of the main concerns for the health of European citizens, and cities are currently striving to accomplish EU air pollution regulation. The 2020 COVID-19 lockdown measures can be seen as an unintended but effective experiment to assess the impact of traffic restriction policies on air pollution. Our objective was to estimate the impact of the lockdown measures on NO2 concentrations and health in the two largest Italian cities. METHODS NO2 concentration datasets were built using data deriving from a 1-month citizen science monitoring campaign that took place in Milan and Rome just before the Italian lockdown period. Annual mean NO2 concentrations were estimated for a lockdown scenario (Scenario 1) and a scenario without lockdown (Scenario 2), by applying city-specific annual adjustment factors to the 1-month data. The latter were estimated deriving data from Air Quality Network stations and by applying a machine learning approach. NO2 spatial distribution was estimated at a neighbourhood scale by applying Land Use Random Forest models for the two scenarios. Finally, the impact of lockdown on health was estimated by subtracting attributable deaths for Scenario 1 and those for Scenario 2, both estimated by applying literature-based dose-response function on the counterfactual concentrations of 10 μg/m3. RESULTS The Land Use Random Forest models were able to capture 41-42% of the total NO2 variability. Passing from Scenario 2 (annual NO2 without lockdown) to Scenario 1 (annual NO2 with lockdown), the population-weighted exposure to NO2 for Milan and Rome decreased by 15.1% and 15.3% on an annual basis. Considering the 10 μg/m3 counterfactual, prevented deaths were respectively 213 and 604. CONCLUSIONS Our results show that the lockdown had a beneficial impact on air quality and human health. However, compliance with the current EU legal limit is not enough to avoid a high number of NO2 attributable deaths. This contribution reaffirms the potentiality of the citizen science approach and calls for more ambitious traffic calming policies and a re-evaluation of the legal annual limit value for NO2 for the protection of human health.
Collapse
Affiliation(s)
- Luca Boniardi
- EPIGET - Epidemiology, Epigenetics, and Toxicology Lab, Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy.
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Environmental and Industrial Toxicology Unit, Milan, Italy.
| | - Federica Nobile
- Department of Epidemiology, Lazio Regional Health Service/ASL, Roma 1, Via C. Colombo 112, 00147, Rome, Italy
| | - Massimo Stafoggia
- Department of Epidemiology, Lazio Regional Health Service/ASL, Roma 1, Via C. Colombo 112, 00147, Rome, Italy
| | - Paola Michelozzi
- Department of Epidemiology, Lazio Regional Health Service/ASL, Roma 1, Via C. Colombo 112, 00147, Rome, Italy
| | - Carla Ancona
- Department of Epidemiology, Lazio Regional Health Service/ASL, Roma 1, Via C. Colombo 112, 00147, Rome, Italy
| |
Collapse
|
3
|
Luftverschmutzung als wichtiger Kofaktor bei COVID-19-Sterbefällen. DER KARDIOLOGE 2021. [PMCID: PMC8447892 DOI: 10.1007/s12181-021-00508-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Hintergrund Die Sterblichkeit bei COVID-19 ist in Anwesenheit kardiopulmonaler Komorbiditäten erhöht. Luftverschmutzung ist ebenfalls mit einer erhöhten Sterblichkeit assoziiert, v. a. vermittelt durch kardiopulmonale Erkrankungen. Beobachtungen zu Beginn der COVID-19-Pandemie zeigten, dass die Sterblichkeit bei COVID-19 v. a. in Regionen mit stärkerer Luftverschmutzung erhöht ist. Ungeklärt ist der Einfluss von Luftverschmutzung für den Krankheitsverlauf bei COVID-19. Methode Es wurde eine selektive Literaturrecherche von Studien bis Anfang April 2021 in PubMed zum Zusammenhang von Luftverschmutzung und der COVID-19-Mortalität mit den Suchbegriffen „air pollution AND/OR COVID-19/coronavirus/SARS-CoV‑2 AND/OR mortality“ durchgeführt. Ergebnisse Aktuelle Untersuchungen belegen, dass etwa 15 % der weltweiten COVID-19-Todesfälle auf Luftverschmutzung zurückzuführen sind. Der Anteil der luftverschmutzungsbedingten COVID-19-Todesfälle in Europa liegt bei 19 %, in Nordamerika bei 17 % und in Ostasien bei 27 %. Diese Beteiligung der Luftverschmutzung an COVID-19-Todesfällen wurde mittlerweile ebenfalls durch verschiedene Studien aus den USA, Italien und England bestätigt. Luftverschmutzung und COVID-19 führen zu ähnlichen Schäden für das kardiopulmonale System, die möglicherweise den Zusammenhang zwischen Luftverschmutzung und erhöhter COVID-19-Mortalität erklären. Schlussfolgerung Der hier gezeigte Umweltaspekt der COVID-19-Pandemie verlangt danach, dass man verstärkt nach wirksamen Maßnahmen zur Reduzierung anthropogener Emissionen, die sowohl Luftverschmutzung als auch den Klimawandel verursachen, streben sollte.
Collapse
|
4
|
Naqvi HR, Mutreja G, Hashim M, Singh A, Nawazuzzoha M, Naqvi DF, Siddiqui MA, Shakeel A, Chaudhary AA, Naqvi AR. Global assessment of tropospheric and ground air pollutants and its correlation with COVID-19. ATMOSPHERIC POLLUTION RESEARCH 2021; 12:101172. [PMID: 34421319 PMCID: PMC8372483 DOI: 10.1016/j.apr.2021.101172] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 08/13/2021] [Accepted: 08/15/2021] [Indexed: 05/06/2023]
Abstract
The declaration of COVID-19 pandemic by the WHO initiated a series of lockdowns globally that varied in stringency and duration; however, the spatiotemporal effects of these lockdowns on air quality remain understudied. This study evaluates the global impact of lockdowns on air pollutants using tropospheric and ground-level indicators over a five-month period. Moreover, the relationship between air pollution and COVID-19 cases and mortalities was examined. Changes in the global tropospheric (NO2, aerosols, and O3) and ground-level (PM2.5, PM10, NO2, and O3) pollutants were observed, and the maximum air quality improvement was observed immediately after lockdown. Except for a few countries, a decline in air pollutants correlated with a reduction in Land Surface Temperature (LST). Notably, regions with higher tropospheric NO2 and aerosol concentrations were also COVID-19 hotspots. Our analysis showed moderate positive correlation for NO2 with COVID-19 cases (R2 = 0.33; r = 0.57, P = 0.006) and mortalities (R2 = 0.40; r = 0.63, P = 0.015), while O3 showed a weak-moderate positive correlation with COVID-19 cases (R2 = 0.22; r = 0.47, P = 0.003) and mortalities (R2 = 0.12; r = 0.35, P = 0.012). However, PM2.5, and PM10 showed no significant correlation with either COVID-19 cases or mortality. This study reveals that humans living under adverse air pollution conditions are at higher risk of COVID-19 infection and mortality.
Collapse
Affiliation(s)
- H R Naqvi
- Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, India
| | - G Mutreja
- Environmental Systems Research Institute, R & D Center, New Delhi, India
| | - M Hashim
- Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, India
| | - A Singh
- Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, India
| | - M Nawazuzzoha
- Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, India
| | - D F Naqvi
- ZiMetrics Technologies Pvt. Ltd., Pune, India
| | - M A Siddiqui
- Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, India
| | - A Shakeel
- Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, India
| | - A A Chaudhary
- Department of Biology, College of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh, 13317-7544, Saudi Arabia
| | - A R Naqvi
- Department of Periodontics, College of Dentistry, University of Illinois at Chicago, Chicago, IL, USA
| |
Collapse
|
5
|
Sanchez-Piedra C, Cruz-Cruz C, Gamiño-Arroyo AE, Prado-Galbarro FJ. Effects of air pollution and climatology on COVID-19 mortality in Spain. AIR QUALITY, ATMOSPHERE, & HEALTH 2021; 14:1869-1875. [PMID: 34335996 PMCID: PMC8310774 DOI: 10.1007/s11869-021-01062-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/26/2020] [Accepted: 06/29/2021] [Indexed: 05/02/2023]
Abstract
The health, economic, and social impact of COVID-19 has been significant across the world. Our objective was to evaluate the association between air pollution (through NO2 and PM2.5 levels) and COVID-19 mortality in Spanish provinces from February 3, 2020, to July 14, 2020, adjusting for climatic parameters. An observational and ecological study was conducted with information extracted from Datadista repository (Datadista, 2020). Air pollutants (NO2 and PM2.5 levels) were analyzed as potential determinants of COVID-19 mortality. Multilevel Poisson regression models were used to analyze the risk of mortality after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Models were adjusted by four climatic variables (hours of solar radiation, precipitation, daily temperature and wind speed) and population size. The mean levels of PM2.5 and NO2 across all provinces and time in Spain were 8.7 μg/m3 (SD 9.7) and 8.7 μg/m3 (SD 6.2), respectively. High levels of PM2.5 (IRR = 1.016, 95% CI: 1.007-1.026), NO2 (IRR = 1.066, 95% CI: 1.058-1.075) and precipitation (IRRNO2 = 0.989, 95% CI: 0.981-0.997) were positively associated with COVID-19 mortality, whereas temperature (IRRPM2.5 = 0.988, 95% CI: 0.976-1.000; and IRRNO2 = 0.771, 95% CI: 0.761-0.782, respectively) and wind speed (IRRNO2 = 1.095, 95% CI: 1.061-1.131) were negatively associated with COVID-19 mortality. Air pollution can be a key factor to understand the mortality rate for COVID-19 in Spain. Furthermore, climatic variables could be influencing COVID-19 progression. Thus, air pollution and climatology ought to be taken into consideration in order to control the pandemic. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s11869-021-01062-2.
Collapse
Affiliation(s)
| | - Copytzy Cruz-Cruz
- Orphan Drug Laboratory, Biologic System Department, Metropolitan Autonomous University, Calzada del Hueso 1100, Coapa, Villaquietud, Coyoacán, 04960 Mexico City, Mexico
| | | | - Francisco-Javier Prado-Galbarro
- Orphan Drug Laboratory, Biologic System Department, Metropolitan Autonomous University, Calzada del Hueso 1100, Coapa, Villaquietud, Coyoacán, 04960 Mexico City, Mexico
| |
Collapse
|
6
|
Davies B, Parkes BL, Bennett J, Fecht D, Blangiardo M, Ezzati M, Elliott P. Community factors and excess mortality in first wave of the COVID-19 pandemic in England. Nat Commun 2021; 12:3755. [PMID: 34145260 DOI: 10.1101/2020.11.19.20234849] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Accepted: 05/19/2021] [Indexed: 05/26/2023] Open
Abstract
Risk factors for increased risk of death from 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 in people aged 40 years and older at the community level during the first wave of the pandemic in England, March-May 2020 compared with 2015-2019. 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 with a non-white ethnicity. We found no association between population density or air pollution and excess mortality. Effective and timely public health and healthcare measures that target the communities at greatest risk are urgently needed to avoid further widening of inequalities in mortality patterns as the pandemic progresses.
Collapse
Affiliation(s)
- Bethan Davies
- UK Small Area Health Statistics Unit, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, Norfolk Place, London, UK
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, Norfolk Place, London, UK
- National Institute for Health Research Health Protection Research Unit in Chemical and Radiation Threats and Hazards, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, Norfolk Place, London, UK
- National Institute for Health Research Health Protection Research Unit in Environmental Exposures and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, Norfolk Place, London, UK
| | - Brandon L Parkes
- UK Small Area Health Statistics Unit, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, Norfolk Place, London, UK
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, Norfolk Place, London, UK
| | - James Bennett
- UK Small Area Health Statistics Unit, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, Norfolk Place, London, UK
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, Norfolk Place, London, UK
| | - Daniela Fecht
- UK Small Area Health Statistics Unit, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, Norfolk Place, London, UK
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, Norfolk Place, London, UK
| | - Marta Blangiardo
- UK Small Area Health Statistics Unit, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, Norfolk Place, London, UK
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, Norfolk Place, London, UK
- National Institute for Health Research Health Protection Research Unit in Chemical and Radiation Threats and Hazards, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, Norfolk Place, London, UK
| | - Majid Ezzati
- UK Small Area Health Statistics Unit, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, Norfolk Place, London, UK
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, Norfolk Place, London, UK
- Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
| | - Paul Elliott
- UK Small Area Health Statistics Unit, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, Norfolk Place, London, UK.
- MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, Norfolk Place, London, UK.
- National Institute for Health Research Health Protection Research Unit in Chemical and Radiation Threats and Hazards, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, Norfolk Place, London, UK.
- National Institute for Health Research Health Protection Research Unit in Environmental Exposures and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, Norfolk Place, London, UK.
- UK Dementia Research Institute at Imperial College, Imperial College London, London, UK.
| |
Collapse
|
7
|
COVID-19 and air pollution in Vienna-a time series approach. Wien Klin Wochenschr 2021; 133:951-957. [PMID: 33959810 PMCID: PMC8101341 DOI: 10.1007/s00508-021-01881-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 04/20/2021] [Indexed: 11/12/2022]
Abstract
We performed a time series analysis in Vienna, Austria, investigating the temporal association between daily air pollution (nitrogen dioxide, NO2 and particulate matter smaller than 10 µm, PM10) concentration and risk of coronavirus disease 2019 (COVID-19) infection and death. Data covering about 2 months (March–April 2020) were retrieved from public databases. Infection risk was defined as the ratio between infected and infectious. In a separate sensitivity analysis different models were applied to estimate the number of infectious people per day. The impact of air pollution was assessed through a linear regression on the natural logarithm of infection risk. Risk of COVID-19 mortality was estimated by Poisson regression. Both pollutants were positively correlated with the risk of infection with the coefficient for NO2 being 0.032 and for PM10 0.014. That association was significant for the irritant gas (p = 0.012) but not for particles (p = 0.22). Pollutants did not affect COVID-19-related mortality. The study findings might have wider implications on an interaction between air pollution and infectious agents.
Collapse
|
8
|
Katoto PDMC, Brand AS, Bakan B, Obadia PM, Kuhangana C, Kayembe-Kitenge T, Kitenge JP, Nkulu CBL, Vanoirbeek J, Nawrot TS, Hoet P, Nemery B. Acute and chronic exposure to air pollution in relation with incidence, prevalence, severity and mortality of COVID-19: a rapid systematic review. Environ Health 2021; 20:41. [PMID: 33838685 PMCID: PMC8035877 DOI: 10.1186/s12940-021-00714-1] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 03/05/2021] [Indexed: 05/05/2023]
Abstract
BACKGROUND Air pollution is one of the world's leading mortality risk factors contributing to seven million deaths annually. COVID-19 pandemic has claimed about one million deaths in less than a year. However, it is unclear whether exposure to acute and chronic air pollution influences the COVID-19 epidemiologic curve. METHODS We searched for relevant studies listed in six electronic databases between December 2019 and September 2020. We applied no language or publication status limits. Studies presented as original articles, studies that assessed risk, incidence, prevalence, or lethality of COVID-19 in relation with exposure to either short-term or long-term exposure to ambient air pollution were included. All patients regardless of age, sex and location diagnosed as having COVID-19 of any severity were taken into consideration. We synthesised results using harvest plots based on effect direction. RESULTS Included studies were cross-sectional (n = 10), retrospective cohorts (n = 9), ecological (n = 6 of which two were time-series) and hypothesis (n = 1). Of these studies, 52 and 48% assessed the effect of short-term and long-term pollutant exposure, respectively and one evaluated both. Pollutants mostly studied were PM2.5 (64%), NO2 (50%), PM10 (43%) and O3 (29%) for acute effects and PM2.5 (85%), NO2 (39%) and O3 (23%) then PM10 (15%) for chronic effects. Most assessed COVID-19 outcomes were incidence and mortality rate. Acutely, pollutants independently associated with COVID-19 incidence and mortality were first PM2.5 then PM10, NO2 and O3 (only for incident cases). Chronically, similar relationships were found for PM2.5 and NO2. High overall risk of bias judgments (86 and 39% in short-term and long-term exposure studies, respectively) was predominantly due to a failure to adjust aggregated data for important confounders, and to a lesser extent because of a lack of comparative analysis. CONCLUSION The body of evidence indicates that both acute and chronic exposure to air pollution can affect COVID-19 epidemiology. The evidence is unclear for acute exposure due to a higher level of bias in existing studies as compared to moderate evidence with chronic exposure. Public health interventions that help minimize anthropogenic pollutant source and socio-economic injustice/disparities may reduce the planetary threat posed by both COVID-19 and air pollution pandemics.
Collapse
Affiliation(s)
- Patrick D. M. C. Katoto
- Department of Medicine and Centre for Infectious Diseases, Faculty of Medicine and Health Sciences, Stellenbosch University, Francie van Zijl Drive, Tygerberg, Cape Town, 7505 South Africa
- Department of Internal Medicine, Division of Respiratory Medicine & Centre for Global Health and Tropical Diseases, Catholic University of Bukavu, Bukavu, Democratic Republic of the Congo
| | - Amanda S. Brand
- Centre for Evidence-Based Health Care, Division of Epidemiology and Biostatistics, Department of Global Health, Stellenbosch University, Cape Town, South Africa
| | - Buket Bakan
- Department of Molecular Biology and Genetics, Faculty of Science, Ataturk University, 25240 Erzurum, Turkey
| | - Paul Musa Obadia
- Centre for Environment and Health, Department of Public Health and Primary Care, KU Leuven, Herestraat 49 (O&N 706), B-3000 Leuven, Belgium
- Unit of Toxicology and Environment, School of Public Health, University of Lubumbashi, Lubumbashi, Democratic Republic of Congo
| | - Carsi Kuhangana
- Unit of Toxicology and Environment, School of Public Health, University of Lubumbashi, Lubumbashi, Democratic Republic of Congo
- Department of Public Health, Faculty of Medicine and Public Health, University of Kolwezi, Kolwezi, Democratic Republic of the Congo
| | - Tony Kayembe-Kitenge
- Centre for Environment and Health, Department of Public Health and Primary Care, KU Leuven, Herestraat 49 (O&N 706), B-3000 Leuven, Belgium
- Unit of Toxicology and Environment, School of Public Health, University of Lubumbashi, Lubumbashi, Democratic Republic of Congo
| | - Joseph Pyana Kitenge
- Occupational Medicine and Environmental Health, Department of Public Health, Faculty of Medicine, University of Lubumbashi, Lubumbashi, Democratic Republic of the Congo
| | - Celestin Banza Lubaba Nkulu
- Unit of Toxicology and Environment, School of Public Health, University of Lubumbashi, Lubumbashi, Democratic Republic of Congo
| | - Jeroen Vanoirbeek
- Centre for Environment and Health, Department of Public Health and Primary Care, KU Leuven, Herestraat 49 (O&N 706), B-3000 Leuven, Belgium
| | - Tim S. Nawrot
- Centre for Environment and Health, Department of Public Health and Primary Care, KU Leuven, Herestraat 49 (O&N 706), B-3000 Leuven, Belgium
- Centre of Environmental Health, University of Hasselt, Hasselt, Belgium
| | - Peter Hoet
- Centre for Environment and Health, Department of Public Health and Primary Care, KU Leuven, Herestraat 49 (O&N 706), B-3000 Leuven, Belgium
| | - Benoit Nemery
- Centre for Environment and Health, Department of Public Health and Primary Care, KU Leuven, Herestraat 49 (O&N 706), B-3000 Leuven, Belgium
| |
Collapse
|
9
|
|
10
|
Abstract
The spatio-temporal dynamics of an outbreak provide important insights to help direct public health resources intended to control transmission. They also provide a focus for detailed epidemiological studies and allow the timing and impact of interventions to be assessed.A common approach is to aggregate case data to administrative regions. Whilst providing a good visual impression of change over space, this method masks spatial variation and assumes that disease risk is constant across space. Risk factors for COVID-19 (e.g. population density, deprivation and ethnicity) vary from place to place across England so it follows that risk will also vary spatially. Kernel density estimation compares the spatial distribution of cases relative to the underlying population, unfettered by arbitrary geographical boundaries, to produce a continuous estimate of spatially varying risk.Using test results from healthcare settings in England (Pillar 1 of the UK Government testing strategy) and freely available methods and software, we estimated the spatial and spatio-temporal risk of COVID-19 infection across England for the first 6 months of 2020. Widespread transmission was underway when partial lockdown measures were introduced on 23 March 2020 and the greatest risk erred towards large urban areas. The rapid growth phase of the outbreak coincided with multiple introductions to England from the European mainland. The spatio-temporal risk was highly labile throughout.In terms of controlling transmission, the most important practical application of our results is the accurate identification of areas within regions that may require tailored intervention strategies. We recommend that this approach is absorbed into routine surveillance outputs in England. Further risk characterisation using widespread community testing (Pillar 2) data is needed as is the increased use of predictive spatial models at fine spatial scales.
Collapse
|
11
|
Ali N, Islam F. The Effects of Air Pollution on COVID-19 Infection and Mortality-A Review on Recent Evidence. Front Public Health 2020; 8:580057. [PMID: 33324598 PMCID: PMC7725793 DOI: 10.3389/fpubh.2020.580057] [Citation(s) in RCA: 85] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Accepted: 10/23/2020] [Indexed: 11/24/2022] Open
Abstract
The outbreak of COVID-19 has created a serious public health concern worldwide. Although, most of the regions around the globe have been affected by COVID-19 infections; some regions are more badly affected in terms of infections and fatality rates than others. The exact reasons for such variations are not clear yet. This review discussed the possible effects of air pollution on COVID-19 infections and mortality based on some recent evidence. The findings of most studies reviewed here demonstrate that both short-term and long-term exposure to air pollution especially PM2.5 and nitrogen dioxide (NO2) may contribute significantly to higher rates of COVID-19 infections and mortalities with a lesser extent also PM10. A significant correlation has been found between air pollution and COVID-19 infections and mortality in some countries in the world. The available data also indicate that exposure to air pollution may influence COVID-19 transmission. Moreover, exposure to air pollution may increase vulnerability and have harmful effects on the prognosis of patients affected by COVID-19 infections. Further research should be conducted considering some potential confounders such as age and pre-existing medical conditions along with exposure to NO2, PM2.5 and other air pollutants to confirm their detrimental effects on mortalities from COVID-19.
Collapse
Affiliation(s)
- Nurshad Ali
- Department of Biochemistry and Molecular Biology, Shahjalal University of Science and Technology, Sylhet, Bangladesh
| | | |
Collapse
|