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2.
The European respiratory journal ; 2022.
Article in English | EuropePMC | ID: covidwho-1958253

ABSTRACT

Chronic exposure to ambient air pollution has been related to increased mortality in the general population [1]. After the outbreak of SARS-CoV-2 pandemic in 2019, there has been a fast proliferation of epidemiological studies linking ambient air pollution to COVID-19 incidence or adverse prognosis [2]. It has been hypothesised that ambient air pollution might increase human vulnerability to viruses by reducing immune defences, promoting a low-level chronic inflammatory state, or leading to chronic diseases [3]. Most studies applied ecological designs, and failed to account for key individual-level or area-level determinants of COVID-19 spread or severity, such as demographic characteristics of the studied populations, socioeconomic or clinical susceptibility, and area-level proxies of disease spread such as mobility or population density [4].

3.
EuropePMC; 2022.
Preprint in English | EuropePMC | ID: ppcovidwho-335206

ABSTRACT

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 COVID-19 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 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 model covariates to quantify their associations with excess mortality. We found that 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 the community-level areas. 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. These results highlight some international similarities in factors affecting mortality that will help policy makers target public health measures to increase the resilience of communities to the mortality impacts of this and future pandemics.

4.
JAMA Netw Open ; 5(4): e228109, 2022 04 01.
Article in English | MEDLINE | ID: covidwho-1801986

ABSTRACT

Importance: Mounting ecological evidence shows an association between short-term air pollution exposure and COVID-19, yet no study has examined this association on an individual level. Objective: To estimate the association between short-term exposure to ambient air pollution and SARS-CoV-2 infection among Swedish young adults. Design, Setting, and Participants: This time-stratified case-crossover study linked the prospective BAMSE (Children, Allergy Milieu, Stockholm, Epidemiology [in Swedish]) birth cohort to the Swedish national infectious disease registry to identify cases with positive results for SARS-CoV-2 polymerase chain reaction (PCR) testing from May 5, 2020, to March 31, 2021. Case day was defined as the date of the PCR test, whereas the dates with the same day of the week within the same calendar month and year were selected as control days. Data analysis was conducted from September 1 to December 31, 2021. Exposures: Daily air pollutant levels (particulate matter with diameter ≤2.5 µm [PM2.5], particulate matter with diameter ≤10 µm [PM10], black carbon [BC], and nitrogen oxides [NOx]) at residential addresses were estimated using dispersion models with high spatiotemporal resolution. Main Outcomes and Measures: Confirmed SARS-CoV-2 infection among participants within the BAMSE cohort. Distributed-lag models combined with conditional logistic regression models were used to estimate the association. Results: A total of 425 cases were identified, of whom 229 (53.9%) were women, and the median age was 25.6 (IQR, 24.9-26.3) years. The median exposure level for PM2.5 was 4.4 [IQR, 2.6-6.8] µg/m3 on case days; for PM10, 7.7 [IQR, 4.6-11.3] µg/m3 on case days; for BC, 0.3 [IQR, 0.2-0.5] µg/m3 on case days; and for NOx, 8.2 [5.6-14.1] µg/m3 on case days. Median exposure levels on control days were 3.8 [IQR, 2.4-5.9] µg/m3 for PM2.5, 6.6 [IQR, 4.5-10.4] µg/m3 for PM10, 0.2 [IQR, 0.2-0.4] µg/m3 for BC, and 7.7 [IQR, 5.3-12.8] µg/m3 for NOx. Each IQR increase in short-term exposure to PM2.5 on lag 2 was associated with a relative increase in positive results of SARS-CoV-2 PCR testing of 6.8% (95% CI, 2.1%-11.8%); exposure to PM10 on lag 2, 6.9% (95% CI, 2.0%-12.1%); and exposure to BC on lag 1, 5.8% (95% CI, 0.3%-11.6%). These findings were not associated with NOx, nor were they modified by sex, smoking, or having asthma, overweight, or self-reported COVID-19 respiratory symptoms. Conclusions and Relevance: The findings of this case-crossover study of Swedish young adults suggest that short-term exposure to particulate matter and BC was associated with increased risk of positive PRC test results for SARS-CoV-2, supporting the broad public health benefits of reducing ambient air pollution levels.


Subject(s)
Air Pollution , COVID-19 , Adult , Air Pollution/adverse effects , Air Pollution/analysis , COVID-19/epidemiology , Child , Cross-Over Studies , Environmental Exposure/adverse effects , Environmental Exposure/analysis , Female , Humans , Male , Nitrogen Oxides/analysis , Particulate Matter/adverse effects , Particulate Matter/analysis , Prospective Studies , SARS-CoV-2 , Sweden/epidemiology , Young Adult
5.
Int J Environ Res Public Health ; 19(5)2022 Mar 01.
Article in English | MEDLINE | ID: covidwho-1715371

ABSTRACT

The large availability of both air pollution and COVID-19 data, and the simplicity to make geographical correlations between them, led to a proliferation of ecological studies relating the levels of pollution in administrative areas to COVID-19 incidence, mortality or lethality rates. However, the major drawback of these studies is the ecological fallacy that can lead to spurious associations. In this frame, an increasing concern has been addressed to clarify the possible role of contextual variables such as municipalities' characteristics (including urban, rural, semi-rural settings), those of the resident communities, the network of social relations, the mobility of people, and the responsiveness of the National Health Service (NHS), to better clarify the dynamics of the phenomenon. The objective of this paper is to identify and collect the municipalities' and community contextual factors and to synthesize their information content to produce suitable indicators in national environmental epidemiological studies, with specific emphasis on assessing the possible role of air pollution on the incidence and severity of the COVID-19 disease. A first step was to synthesize the content of spatial information, available at the municipal level, in a smaller set of "summary indexes" that can be more easily viewed and analyzed. For the 7903 Italian municipalities (1 January 2020-ISTAT), 44 variables were identified, collected, and grouped into five information dimensions a priori defined: (i) geographic characteristics of the municipality, (ii) demographic and anthropogenic characteristics, (iii) mobility, (iv) socio-economic-health area, and (v) healthcare offer (source: ISTAT, EUROSTAT or Ministry of Health, and further ad hoc elaborations (e.g., OpenStreetMaps)). Principal component analysis (PCA) was carried out for the five identified dimensions, with the aim of reducing the large number of initial variables into a smaller number of components, limiting as much as possible the loss of information content (variability). We also included in the analysis PM2.5, PM10 and NO2 population weighted exposure (PWE) values obtained using a four-stage approach based on the machine learning method, "random forest", which uses space-time predictors, satellite data, and air quality monitoring data estimated at the national level. Overall, the PCA made it possible to extract twelve components: three for the territorial characteristics dimension of the municipality (variance explained 72%), two for the demographic and anthropogenic characteristics dimension (variance explained 62%), three for the mobility dimension (variance explained 83%), two for the socio-economic-health sector (variance explained 58%) and two for the health offer dimension (variance explained 72%). All the components of the different dimensions are only marginally correlated with each other, demonstrating their potential ability to grasp different aspects of the spatial distribution of the COVID-19 pathology. This work provides a national repository of contextual variables at the municipality level collapsed into twelve informative factors suitable to be used in studies on the association between chronic exposure to air pollution and COVID-19 pathology, as well as for investigations on the role of air pollution on the health of the Italian population.


Subject(s)
Air Pollution , COVID-19 , Air Pollution/adverse effects , Air Pollution/analysis , COVID-19/epidemiology , Environmental Exposure/analysis , Epidemiologic Studies , Humans , SARS-CoV-2 , State Medicine
6.
J Clin Med ; 11(3)2022 Feb 07.
Article in English | MEDLINE | ID: covidwho-1674685

ABSTRACT

Evidence on social determinants of health on the risk of SARS-CoV-2 infection and adverse outcomes is still limited. Therefore, this work investigates educational disparities in the incidence of infection and mortality within 30 days of the onset of infection during 2020 in Rome, with particular attention to changes in socioeconomic inequalities over time. A cohort of 1,538,231 residents in Rome on 1 January 2020, aged 35+, followed from 1 March to 31 December 2020, were considered. Cumulative incidence and mortality rates by education were estimated. Multivariable log-binomial and Cox regression models were used to investigate educational disparities in the incidence of SARS-CoV-2 infection and mortality during the entire study period and in three phases of the pandemic. During 2020, there were 47,736 incident cases and 2281 deaths. The association between education and the incidence of infection changed over time. Till May 2020, low- and medium-educated individuals had a lower risk of infection than that of the highly educated. However, there was no evidence of an association between education and the incidence of SARS-CoV-2 infection during the summer. Lastly, low-educated adults had a 25% higher risk of infection from September to December than that of the highly educated. Similarly, there was substantial evidence of educational inequalities in mortality within 30 days of the onset of infection in the last term of 2020. In Rome, social inequalities in COVID-19 appeared in the last term of 2020, and they strengthen the need for monitoring inequalities emerging from this pandemic.

7.
Environ Health ; 21(1): 17, 2022 01 16.
Article in English | MEDLINE | ID: covidwho-1630544

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.


Subject(s)
Air Pollutants , Air Pollution , COVID-19 , Air Pollutants/analysis , Air Pollution/analysis , Cities , Communicable Disease Control , Environmental Monitoring , Humans , Italy/epidemiology , Machine Learning , Nitrogen Dioxide , Particulate Matter/analysis , Rome/epidemiology , SARS-CoV-2
8.
Epidemiol Prev ; 44(5-6 Suppl 2): 236-243, 2020.
Article in Italian | MEDLINE | ID: covidwho-1068144

ABSTRACT

OBJECTIVES: to assess the temporal variation in excess total mortality and the portion of excess explained by COVID-19 deaths by geographical area, gender, and age during the COVID-19 epidemic. DESIGN: descriptive analysis of temporal variations of total excess deaths and COVID-19 deaths in the phase 1 and phase 2 of the epidemic in Italy. SETTING AND PARTICIPANTS: 12 Northern cities and 20 Central-Southern cities from December 2019 to June 2020: daily mortality from the National Surveillance System of Daily Mortality (SiSMG) and COVID-19 deaths from the integrated COVID-19 surveillance system. MAIN OUTCOME MEASURES: total mortality excess and COVID-19 deaths, defined as deaths in microbiologically confirmed cases of SARS-CoV-2, by gender and age groups. RESULTS: the largest excess mortality was observed in the North and during the first phase of the epidemic. The portion of excess mortality explained by COVID-19 decreases with age, decreasing to 51% among the very old (>=85 years). In phase 2 (until June 2020), the impact was more contained and totally attributable to COVID-19 deaths and this suggests an effectiveness of social distancing measures. CONCLUSIONS: mortality surveillance is a sensible information basis for the monitoring of health impact of the different phases of the epidemic and supporting decision making at the local and national level on containment measures to put in place in coming months.


Subject(s)
COVID-19/epidemiology , Mortality/trends , SARS-CoV-2 , Adolescent , Adult , Aged , Aged, 80 and over , COVID-19/mortality , COVID-19/prevention & control , Cause of Death , Female , Humans , Italy/epidemiology , Male , Middle Aged , Population Surveillance , Quarantine , Time Factors , Urban Population/statistics & numerical data , Young Adult
9.
Epidemiol Prev ; 44(5-6 Suppl 2): 161-168, 2020.
Article in Italian | MEDLINE | ID: covidwho-1068136

ABSTRACT

Air pollution is one of the leading causes of death worldwide, with adverse effects related both to short-term and long-term exposure. It has also recently been linked to COVID-19 pandemic. To analyze this possible association in Italy, studies on the entire area of the peninsula are necessary, both urban and non-urban areas. Therefore, there is a need for a homogeneous and applicable exposure assessment tool throughout the country.Experiences of high spatio-temporal resolution models for Italian territory already exist for PM estimation, using space-time predictors, satellite data, air quality monitoring data.This work completes the availability of these estimations for the most recent years (2016-2019) and is also applied to nitrogen oxides and ozone. The spatial resolution is 1x1 km.The model confirms its capability of capturing most of PM variability (R2=0.78 and 0.74 for PM10 e PM2.5, respectively), and provides reliable estimates also for ozone (R2=0.76); for NO2 the model performance is lower (R2=0.57). The model estimations were used to calculate the PWE (population-weighted exposure) as the annual mean, weighted on the resident population in each individual cell, which represents the estimation of the Italian population's chronic exposure to air pollution.These estimates are ready to be used in studies on the association between chronic exposure to air pollution and COVID-19 pathology, as well as for investigations on the role of air pollution on the health of the Italian population.


Subject(s)
Air Microbiology , Air Pollutants/analysis , Air Pollution/adverse effects , COVID-19/epidemiology , Environmental Exposure , Models, Theoretical , Pandemics , SARS-CoV-2/isolation & purification , Air Pollutants/adverse effects , Air Pollution/statistics & numerical data , Environmental Monitoring , Geography, Medical , Global Burden of Disease , Humans , Italy/epidemiology , Machine Learning , Particulate Matter/adverse effects , Particulate Matter/analysis
11.
BMC Public Health ; 20(1): 1238, 2020 Aug 14.
Article in English | MEDLINE | ID: covidwho-713857

ABSTRACT

BACKGROUND: Standardized mortality surveillance data, capable of detecting variations in total mortality at population level and not only among the infected, provide an unbiased insight into the impact of epidemics, like COVID-19 (Coronavirus disease). We analysed the temporal trend in total excess mortality and deaths among positive cases of SARS-CoV-2 by geographical area (north and centre-south), age and sex, taking into account the deficit in mortality in previous months. METHODS: Data from the Italian rapid mortality surveillance system was used to quantify excess deaths during the epidemic, to estimate the mortality deficit during the previous months and to compare total excess mortality with deaths among positive cases of SARS-CoV-2. Data were stratified by geographical area (north vs centre and south), age and sex. RESULTS: COVID-19 had a greater impact in northern Italian cities among subjects aged 75-84 and 85+ years. COVID-19 deaths accounted for half of total excess mortality in both areas, with differences by age: almost all excess deaths were from COVID-19 among adults, while among the elderly only one third of the excess was coded as COVID-19. When taking into account the mortality deficit in the pre-pandemic period, different trends were observed by area: all excess mortality during COVID-19 was explained by deficit mortality in the centre and south, while only a 16% overlap was estimated in northern cities, with quotas decreasing by age, from 67% in the 15-64 years old to 1% only among subjects 85+ years old. CONCLUSIONS: An underestimation of COVID-19 deaths is particularly evident among the elderly. When quantifying the burden in mortality related to COVID-19, it is important to consider seasonal dynamics in mortality. Surveillance data provides an impartial indicator for monitoring the following phases of the epidemic, and may help in the evaluation of mitigation measures adopted.


Subject(s)
Coronavirus Infections/mortality , Mortality/trends , Pneumonia, Viral/mortality , Adolescent , Adult , Aged , Aged, 80 and over , COVID-19 , Cities/epidemiology , Female , Humans , Italy/epidemiology , Male , Middle Aged , Pandemics , Spatio-Temporal Analysis , Young Adult
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