Geographical Pattern of COVID-19-Related Outcomes over the Pandemic Period in France: A Nationwide Socio-Environmental Study
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Health Data
- i.
- the total number of hospitalized persons due to COVID-19 infection,
- ii.
- the total number of severe COVID-19 cases in the intensive health care in the hospital,
- iii.
- the total number of deaths at the hospital caused by COVID-19 infection, and
- iv.
- the total number of hospitalized patients recovered and returned back home.
2.3. Air Pollution Data
2.4. Neighbourhood Deprivation Context
2.5. Descriptive Analysis
2.6. Spatial Analysis
2.7. Methodological Approach
- ▪
- c is the number of observed cases within the cluster and
- ▪
- C is the total number of cases in the data set.
- ▪
- Note that since the analysis is conditioned on the total number of cases observed, E[C] = C.
- ▪
- C is the total number of cases,
- ▪
- c is the observed number of cases within the window, and
- ▪
- E[c] is the covariate adjusted and expected number of cases within the window under the null-hypothesis.
- ▪
- Note that, since the analysis is conditioned on the total number of cases observed, C-E[c] is the expected number of cases outside the window.
- ▪
- I() is equal to 1 when the window has more cases than expected under the null-hypothesis, and 0 otherwise. Since this analysis is only interested in detecting clusters with higher than expected rates, I() was equal to 1.
2.8. Methodological Strategy
- i.
- Crude analysis (unadjusted) to identify and spatially localize the most likely cluster of high incidence of a COVID-19 related outcome.
- ii.
- Adjusted analysis for a living deprivation condition.
- iii.
- Adjusted analysis for long-term exposure to NO2.
- iv.
- Adjusted analysis for both the deprivation context and long-term exposure to NO2.
3. Results
3.1. Spatial Description
3.2. Descriptive Data
4. Spatial Distribution
Unadjusted Analysis
5. Discussion
- (i)
- (ii)
- the second one has a more direct effect by increasing susceptibility of people to COVID-19 infection.
- (i)
- Some studies suggest that people in lower income households are more likely experience overcrowding and live in overcrowded conditions. Therefore, deprived living conditions may constitute itself as an additional risk factor of the known underlying clinical risk factors that increase the severity and mortality of COVID-19 (including cardiovascular disease, obesity, diabetes, and hypertension [44]). It suggests that people living in deprived conditions have an increased susceptibility to COVID-19 mortality.
- (ii)
- The overcrowding combined with poor quality of housing conditions may increase the vulnerability of people for COVID-19 and the severity of its consequences. These deprived living conditions including damp housing and overcrowding may induce some health outcome respiratory disorders, such as asthma and other viral infections.
- -
- First, we performed an ecological study with data available at the French department level. Therefore, our results should be interpreted only in this design context and should not be interpreted at the individual level.
- -
- Second, our approach based on ecological data, has several limitations. One is the non-inclusion of gender and presence of pre-existing and background diseases and comorbidities in the analysis, which is known to be risk factors for COVID-19-related outcomes. Focusing in further studies on larger risk factors is recommended, and might produce clearer results to explain the cluster of the excess risk.
- -
- Third, to characterize the chronic exposure to NO2, as recent studies investigated this issue, we used mean values over five years (2014–2018) from all monitoring stations located in each French department, including several background, traffic, and industrial stations. The mean values from each station may vary according to the type: background, traffic, and industrial station, and to the area: rural, urban, and sub-urban. In our study, we used all data available at the French department level and carried out sensitivity analysis through three scenarios. However, in our study, using data from a different type of monitoring stations may misclassify the level of exposure of several French departments. At this time, these were the only available data for the study period, which covered all departments of France. However, the results revealed that the measure of associations as well as the statistical significance did not vary as much, according to the scenario used (data not shown). Next, studies need to assess the impact of air pollution on COVID-19 outcomes using modelled measures of NO2 exposure at a finer spatial scale.
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Zhu, N.; Zhang, D.; Wang, W.; Li, X.; Yang, B.; Song, J.; Zhao, X.; Huang, B.; Shi, W.; Lu, R.; et al. A Novel Coronavirus from Patients with Pneumonia in China, 2019. N. Engl. J. Med. 2020, 382, 727–733. [Google Scholar] [CrossRef]
- Sohrabi, C.; Alsafi, Z.; O’Neill, N.; Khan, M.; Kerwan, A.; Al-Jabir, A.; Iosifidis, C.; Agha, R. World Health Organization Declares Global Emergency: A Review of the 2019 Novel Coronavirus (COVID-19). Int. J. Surg. Lond. Engl. 2020, 76, 71–76. [Google Scholar] [CrossRef]
- Wang, D.; Hu, B.; Hu, C.; Zhu, F.; Liu, X.; Zhang, J.; Wang, B.; Xiang, H.; Cheng, Z.; Xiong, Y.; et al. Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China. JAMA 2020. [Google Scholar] [CrossRef]
- Murthy, S.; Gomersall, C.D.; Fowler, R.A. Care for Critically Ill Patients with COVID-19. JAMA 2020, 323, 1499–1500. [Google Scholar] [CrossRef] [Green Version]
- WHO. WHO Director-General’s Opening Remarks at the Media Briefing on COVID-19–25 May 2020. Available online: https://www.who.int/dg/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---25-may-2020 (accessed on 28 May 2020).
- Fattorini, D.; Regoli, F. Role of the Chronic Air Pollution Levels in the Covid-19 Outbreak Risk in Italy. Environ. Pollut. Barking Essex 1987 2020, 264, 114732. [Google Scholar] [CrossRef]
- Adresse aux Français, 12 mars 2020. Available online: https://www.elysee.fr/emmanuel-macron/2020/03/12/adresse-aux-francais (accessed on 28 May 2020).
- Coronavirus: Edouard Philippe Annonce La Fermeture de Tous Les Lieux Publics «Non Indispensables». Available online: https://www.lemonde.fr/politique/article/2020/03/14/edouard-philippe-annonce-la-fermeture-de-tous-les-lieux-publics-non-indispensables_6033110_823448.html (accessed on 28 May 2020).
- Ogen, Y. Assessing Nitrogen Dioxide (NO2) Levels as a Contributing Factor to Coronavirus (COVID-19) Fatality. Sci. Total Environ. 2020, 726, 138605. [Google Scholar] [CrossRef]
- Wu, X.; Nethery, R.C.; Sabath, B.M.; Braun, D.; Dominici, F. Exposure to Air Pollution and COVID-19 Mortality in the United States: A Nationwide Cross-Sectional Study. medRxiv 2020. [Google Scholar] [CrossRef] [Green Version]
- Zhu, Y.; Xie, J.; Huang, F.; Cao, L. Association between Short-Term Exposure to Air Pollution and COVID-19 Infection: Evidence from China. Sci. Total Environ. 2020, 727, 138704. [Google Scholar] [CrossRef] [PubMed]
- Jiang, Y.; Wu, X.-J.; Guan, Y.-J. Effect of Ambient Air Pollutants and Meteorological Variables on COVID-19 Incidence. Infect. Control Hosp. Epidemiol. 2020, 1–11. [Google Scholar] [CrossRef]
- Brandt, E.B.; Beck, A.F.; Mersha, T.B. Air Pollution, Racial Disparities and COVID-19 Mortality. J. Allergy Clin. Immunol. 2020, 146, 61–63. [Google Scholar] [CrossRef]
- Bashir, M.F.; Bilal, B.M.; Komal, B. Correlation between Environmental Pollution Indicators and COVID-19 Pandemic: A Brief Study in Californian Context. Environ. Res. 2020, 187, 109652. [Google Scholar] [CrossRef]
- Copat, C.; Cristaldi, A.; Fiore, M.; Grasso, A.; Zuccarello, P.; Signorelli, S.S.; Conti, G.O.; Ferrante, M. The Role of Air Pollution (PM and NO2) in COVID-19 Spread and Lethality: A Systematic Review. Environ. Res. 2020, 191, 110129. [Google Scholar] [CrossRef] [PubMed]
- Cui, Y.; Zhang, Z.-F.; Froines, J.; Zhao, J.; Wang, H.; Yu, S.-Z.; Detels, R. Air Pollution and Case Fatality of SARS in the People’s Republic of China: An Ecologic Study. Environ. Health 2003, 2, 15. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Ahmad, K.; Erqou, S.; Shah, N.; Nazir, U.; Morrison, A.R.; Choudhary, G.; Wu, W.-C. Association of Poor Housing Conditions with COVID-19 Incidence and Mortality across US Counties. PLoS ONE 2020, 15, e0241327. [Google Scholar] [CrossRef] [PubMed]
- Les données hospitalires relative à la COVID-19. Available online: https://www.Data.Gouv.Fr/Fr/Datasets/Donnees-Hospitalieres-Relatives-a-Lepidemie-de-Covid-19/ (accessed on 2 December 2020).
- INSEE. Available online: Https://Www.Insee.Fr/Fr/Statistiques/2012713 (accessed on 19 May 2020).
- Goodman, A.; Wilkinson, P.; Stafford, M.; Tonne, C. Characterising Socio-Economic Inequalities in Exposure to Air Pollution: A Comparison of Socio-Economic Markers and Scales of Measurement. Health Place 2011, 17, 767–774. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- ATMO France. Available online: Https://Atmo-France.Org/ (accessed on 19 May 2020).
- Les données socioéconomiques en France. Available online: Https://Www.Insee.Fr/Fr/Statistiques/4476914 (accessed on 17 May 2020).
- Sabel, C.E.; Wilson, J.G.; Kingham, S.; Tisch, C.; Epton, M. Spatial Implications of Covariate Adjustment on Patterns of Risk: Respiratory Hospital Admissions in Christchurch, New Zealand. Soc. Sci. Med. 1982 2007, 65, 43–59. [Google Scholar] [CrossRef] [PubMed]
- Kulldorff, M. Information Management Services, Inc. SaTScan: Software for the Spatial, Temporal, and Space-Time Scan Statistics, Version 6.0. Available online: https://www.satscan.org/ (accessed on 10 October 2020).
- Kihal-Talantikite, W.; Weber, C.; Pedrono, G.; Segala, C.; Arveiler, D.; Sabel, C.E.; Deguen, S.; Bard, D. Developing a Data-Driven Spatial Approach to Assessment of Neighbourhood Influences on the Spatial Distribution of Myocardial Infarction. Int. J. Health Geogr. 2017, 16, 22. [Google Scholar] [CrossRef] [Green Version]
- Kihal-Talantikite, W.; Padilla, C.M.; Lalloué, B.; Gelormini, M.; Zmirou-Navier, D.; Deguen, S. Green Space, Social Inequalities and Neonatal Mortality in France. BMC Pregnancy Childbirth 2013, 13, 191. [Google Scholar] [CrossRef] [Green Version]
- Kihal-Talantikite, W.; Padilla, C.M.; Lalloue, B.; Rougier, C.; Defrance, J.; Zmirou-Navier, D.; Deguen, S. An Exploratory Spatial Analysis to Assess the Relationship between Deprivation, Noise and Infant Mortality: An Ecological Study. Environ. Health Glob. Access Sci. Source 2013, 12, 109. [Google Scholar] [CrossRef] [Green Version]
- Kulldorff, M.; Feuer, E.J.; Miller, B.A.; Freedman, L.S. Breast Cancer Clusters in the Northeast United States: A Geographic Analysis. Am. J. Epidemiol. 1997, 146, 161–170. [Google Scholar] [CrossRef]
- Bambhroliya, A.B.; Burau, K.D.; Sexton, K. Spatial Analysis of County-Level Breast Cancer Mortality in Texas. J. Environ. Public Health 2012, 2012, 959343. [Google Scholar] [CrossRef] [Green Version]
- Kulldorff, M. Scan Statistics for Geographical Disease Surveillance: An Overview. Spat. Syndrom. Surveill. Public Health 2005, 115–131. [Google Scholar] [CrossRef]
- Rusk, A.; Highfield, L.; Wilkerson, J.M.; Harrell, M.; Obala, A.; Amick, B. Spatial Distribution and Cluster Analysis of Retail Drug Shop Characteristics and Antimalarial Behaviors as Reported by Private Medicine Retailers in Western Kenya: Informing Future Interventions. Int. J. Health Geogr. 2016, 15, 9. [Google Scholar] [CrossRef] [Green Version]
- Scherber, K.; Langner, M.; Endlicher, W. Spatial Analysis of Hospital Admissions for Respiratory Diseases during Summer Months in Berlin Taking Bioclimatic and Socio-Economic Aspects into Account. Erde 2013, 144, 217–237. [Google Scholar] [CrossRef]
- Dwass, M. Modified Randomization Tests for Nonparametric Hypotheses. Ann. Math. Stat. 1957, 28, 181–187. [Google Scholar] [CrossRef]
- Kulldorff, M. A Spatial Scan Statistic. Commun. Stat.-Theory Methods 1997, 26, 1481–1496. [Google Scholar] [CrossRef]
- Wong, W.; Lee, J. Statistical Analysis of Geographic Information with ArcView GIS and ArcGIS; Wiley: Hoboken, NJ, USA, 2005. [Google Scholar]
- Kihal-Talantikite, W.; Deguen, S.; Padilla, C.; Siebert, M.; Couchoud, C.; Vigneau, C.; Bayat, S. Spatial Distribution of End-Stage Renal Disease (ESRD) and Social Inequalities in Mixed Urban and Rural Areas: A Study in the Bretagne Administrative Region of France. Clin. Kidney J. 2015, 8, 7–13. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Pranata, R.; Vania, R.; Tondas, A.E.; Setianto, B.; Santoso, A. A Time-to-Event Analysis on Air Pollutants with the Risk of Cardiovascular Disease and Mortality: A Systematic Review and Meta-Analysis of 84 Cohort Studies. J. Evid.-Based Med. 2020. [Google Scholar] [CrossRef]
- Domingo, J.L.; Rovira, J. Effects of Air Pollutants on the Transmission and Severity of Respiratory Viral Infections. Environ. Res. 2020, 187, 109650. [Google Scholar] [CrossRef]
- Guan, W.; Liang, W.; Zhao, Y.; Liang, H.; Chen, Z.; Li, Y.; Liu, X.; Chen, R.; Tang, C.; Wang, T.; et al. Comorbidity and Its Impact on 1590 Patients with COVID-19 in China: A Nationwide Analysis. Eur. Respir. J. 2020, 55, 2000547. [Google Scholar] [CrossRef] [Green Version]
- Patel, J.A.; Nielsen, F.B.H.; Badiani, A.A.; Assi, S.; Unadkat, V.A.; Patel, B.; Ravindrane, R.; Wardle, H. Poverty, Inequality and COVID-19: The Forgotten Vulnerable. Public Health 2020, 183, 110–111. [Google Scholar] [CrossRef] [PubMed]
- Singer, M. Introduction to Syndemics: A Systems Approach to Public and Community Health; Jossey-Bass, Wiley.com: San Francisco, CA, USA, 2009. [Google Scholar]
- Dahlgren, G.; Whitehead, M. Policies and Strategies to Promote Social Equity in Health. Background Document to WHO—Strategy Paper for Europe; Arbetsrapport; Institute for Futures Studies: Stockholm, Sweden, 1991. [Google Scholar]
- Bambra, C.; Riordan, R.; Ford, J.; Matthews, F. The COVID-19 Pandemic and Health Inequalities. J. Epidemiol. Community Health 2020, 74, 964–968. [Google Scholar] [CrossRef] [PubMed]
- Bambra, C.; Joyce, K.E.; Maryon-Davies, A. Strategic Review of Health Inequalities in Englandpost-2010 (Marmot Review): Task Group 8: Priority Public Health Conditions; Final Report, Project Report; University College London, Department of Epidemiology and Public Health, the Global Health Equity Group: London, UK, 2010; Available online: https://www.researchgate.net/publication/41231868_Strategic_review_of_health_inequalitiesin_England_post-2010_Marmot_Review_Task_Group_8_priority_public_health_conditions_final_report (accessed on 12 February 2021).
- Wu, R.; Ai, S.; Cai, J.; Zhang, S.; Qian, Z.; Zhang, Y.; Wu, Y.; Chen, L.; Tian, F.; Li, H.; et al. Predictive Model and Risk Factors for Case Fatality of COVID-19: A Cohort of 21,392 Cases in Hubei, China. Innovation 2020, 1, 100022. [Google Scholar] [CrossRef] [PubMed]
Health Event | Overcrowded Housing * | |
---|---|---|
Beta-Coefficient | p-Value | |
Cases hospitalized | 439.4 | <0.0001 |
Cases in intensive healthcare | 77.5 | <0.0001 |
Death | 71.8 | <0.0001 |
Recovered cases, returned back home | 330.6 | <0.0001 |
Health Event | Household Living in Overcrowded Housing * | Interaction Test p-Value ** | |||||
---|---|---|---|---|---|---|---|
Tertile 1 | Tertile 2 | Tertile 3 | |||||
Beta-Coefficient | p-Value | Beta-Coefficient | p-Value | Beta-Coefficient | p-Value | ||
Cases hospitalized | 39.6 | 0.078 | 61.3 | 0.032 | 183.7 | <0.0001 | 0.121 |
Cases in intensive healthcare | 4.79 | 0.0814 | 10.1 | 0.018 | 35.5 | <0.0001 | 0.008 |
Death | 7.04 | 0.090 | 11.4 | 0.06 | 32.2 | <0.0001 | 0.094 |
Recovered cases, returned back home | 24.0 | 0.119 | 41.7 | 0.031 | 136.9 | 0.002 | 0.312 |
Analysis | Cluster Radius b | Number of Departments c | No. of Observed Cases d | No of Expected Cases e | RR | LLr | Shift f | p-Value g |
---|---|---|---|---|---|---|---|---|
Unadjusted No adjustment (Crude) a | ||||||||
Hospitalization | 536.19 | 43 | 166,571 | 116,576.69 | 2.49 | 22,089.95 | 0.001 | |
Intensive healthcare at hospital | 80.27 | 6 | 10,549 | 4267.91 | 3.05 | 3906.31 | 0.001 | |
Death at hospital | 536.19 | 43 | 27,241 | 18,229.86 | 2.94 | 4642.46 | 0.001 | |
Recovered and returned back home | 536.19 | 43 | 117,321 | 82,005.68 | 2.51 | 15,673.73 | 0.001 | |
Adjusted analysis for long-term exposure to NO2 | ||||||||
Hospitalization | 285.12 | 22 | 99,987 | 81,646.92 | 1.39 | 3074.93 | Yes | 0.001 |
Intensive healthcare at hospital | 16.57 | 4 | 8461 | 5501.48 | 1.70 | 825.26 | Yes | 0.001 |
Death at hospital | 285.12 | 22 | 16,731 | 12,883.78 | 1.55 | 855.92 | Yes | 0.001 |
Recovered and returned back home | 317.61 | 25 | 77,346 | 63,957.82 | 1.40 | 2247.95 | Yes | 0.001 |
Adjusted analysis for long-term exposure to NO2 and deprived level (Occupation) | ||||||||
Hospitalization | 321.93 | 37 | 131,368 | 115,393.32 | 1.32 | 2189.38 | Yes | |
Intensive healthcare at hospital | 16.57 | 4 | 8461 | 5982.79 | 1.54 | 555.34 | Yes | 0.001 |
Death at hospital | 285.12 | 22 | 16,731 | 266.4 | 1.44 | 601.95 | Yes | 0.001 |
Recovered and returned back home | 285.12 | 22 | 71,241 | 60,053.16 | 1.33 | 1605.65 | Yes | 0.001 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Deguen, S.; Kihal-Talantikite, W. Geographical Pattern of COVID-19-Related Outcomes over the Pandemic Period in France: A Nationwide Socio-Environmental Study. Int. J. Environ. Res. Public Health 2021, 18, 1824. https://doi.org/10.3390/ijerph18041824
Deguen S, Kihal-Talantikite W. Geographical Pattern of COVID-19-Related Outcomes over the Pandemic Period in France: A Nationwide Socio-Environmental Study. International Journal of Environmental Research and Public Health. 2021; 18(4):1824. https://doi.org/10.3390/ijerph18041824
Chicago/Turabian StyleDeguen, Séverine, and Wahida Kihal-Talantikite. 2021. "Geographical Pattern of COVID-19-Related Outcomes over the Pandemic Period in France: A Nationwide Socio-Environmental Study" International Journal of Environmental Research and Public Health 18, no. 4: 1824. https://doi.org/10.3390/ijerph18041824