Particulate matter (PM) air pollution is one of the major causes of death worldwide, with demonstrated adverse effects from both short-term and long-term exposure. Most of the epidemiological studies ...have been conducted in cities because of the lack of reliable spatiotemporal estimates of particles exposure in nonurban settings. The objective of this study is to estimate daily PM10 (PM < 10 μm), fine (PM < 2.5 μm, PM2.5) and coarse particles (PM between 2.5 and 10 μm, PM2.5–10) at 1-km2 grid for 2013–2015 using a machine learning approach, the Random Forest (RF). Separate RF models were defined to: predict PM2.5 and PM2.5–10 concentrations in monitors where only PM10 data were available (stage 1); impute missing satellite Aerosol Optical Depth (AOD) data using estimates from atmospheric ensemble models (stage 2); establish a relationship between measured PM and satellite, land use and meteorological parameters (stage 3); predict stage 3 model over each 1-km2 grid cell of Italy (stage 4); and improve stage 3 predictions by using small-scale predictors computed at the monitor locations or within a small buffer (stage 5). Our models were able to capture most of PM variability, with mean cross-validation (CV) R2 of 0.75 and 0.80 (stage 3) and 0.84 and 0.86 (stage 5) for PM10 and PM2.5, respectively. Model fitting was less optimal for PM2.5–10, in summer months and in southern Italy. Finally, predictions were equally good in capturing annual and daily PM variability, therefore they can be used as reliable exposure estimates for investigating long-term and short-term health effects.
•Estimates of fine and coarse particles at fine spatiotemporal scale are lacking in Italy•We applied a multistage random forest model combining PM data with satellite, land-use and meteorology•We imputed missing satellite AOD data using ensemble atmospheric models•We estimated daily PM10, PM2.5 and PM2.5-10 at a 1-km2 grid over Italy for the years 2013-2015•Our model displayed good CV fitting (R2=0.75 for PM10, R2=0.80 for PM2.5, R2=0.64 for PM2.5-10) and negligible bias
Abstract
Background
Italy was the first country outside China to experience the impact of the COVID-19 pandemic, which resulted in a significant health burden. This study presents an analysis of the ...excess mortality across the 107 Italian provinces, stratified by sex, age group and period of the outbreak.
Methods
The analysis was performed using a two-stage interrupted time-series design using daily mortality data for the period January 2015–May 2020. In the first stage, we performed province-level quasi-Poisson regression models, with smooth functions to define a baseline risk while accounting for trends and weather conditions and to flexibly estimate the variation in excess risk during the outbreak. Estimates were pooled in the second stage using a mixed-effects multivariate meta-analysis.
Results
In the period 15 February–15 May 2020, we estimated an excess of 47 490 95% empirical confidence intervals (eCIs): 43 984 to 50 362 deaths in Italy, corresponding to an increase of 29.5% (95% eCI: 26.8 to 31.9%) from the expected mortality. The analysis indicates a strong geographical pattern, with the majority of excess deaths occurring in northern regions, where few provinces experienced increases up to 800% during the peak in late March. There were differences by sex, age and area both in the overall impact and in its temporal distribution.
Conclusion
This study offers a detailed picture of excess mortality during the first months of the COVID-19 pandemic in Italy. The strong geographical and temporal patterns can be related to the implementation of lockdown policies and multiple direct and indirect pathways in mortality risk.
Although the prevalence of preterm births ranges from 5 to 13% and represents the leading cause of perinatal mortality and morbidity in developed countries, the etiology of preterm birth remains ...uncertain. We aimed to evaluate the effect of short-term exposure to high and low temperatures and air pollution on preterm delivery and to identify socio-demographic and clinical maternal risk factors enhancing individual susceptibility.
We analyzed all singleton live births by natural delivery that occurred in Rome in 2001–2010. A time-series approach was used to estimate the effect of exposure to minimum temperature, maximum apparent temperature, heat waves, particulate matter with an aerodynamic diameter of 10μm or less (PM10), ozone, and nitrogen dioxide in the month preceding delivery; the analysis was conducted separately for cold and warm seasons. Socio-demographic and clinical risk factors were included as interaction terms.
Preterm births comprised nearly 6% of our cohort. An increase of 1.9% (95% confidence interval (CI) 0.86–2.87) in daily preterm births per 1°C increase in maximum apparent temperature in the 2days preceding delivery was estimated for the warm season. Older women, women with higher education levels, and women with obstetric or chronic pathologies reported during delivery had a lower effect of temperature on the risk of preterm birth, while women with a chronic disease in the two years before delivery and mothers<20years showed a higher effect. A +19% (95% CI 7.91–31.69) increase in preterm births was observed during heat waves. Temperature had no effect during the cold season. We detected a significant effect of PM10 on preterm-birth risk at a lag period of 12–22days during the warm season (+0.69%; 95% CI 0.23–1.15, for 1μg/m3 increase of pollutant); women with obstetric pathologies and with a higher education level showed a higher risk.
Our results suggest a possible short-term effect of heat and a more delayed and prolonged effect of PM10 exposure on preterm-birth risk, as well as the existence of more susceptible subgroups of women. Our observations support the few reported investigations, and may help to increase awareness among public-health stakeholders and clinicians regarding the role of these environmental exposures as risk factors for premature birth and health consequences for children later in life.
•The short-term effect of temperature and air pollution on preterm birth was studied.•A large 10-year birth cohort was analyzed via a time-series approach.•Average daily preterm births increased with temperature and PM10 during warm season.•Temperature effect was higher in women<20years or with previous chronic conditions.•PM10 effect was greater in women with a university degree or with obstetrical disease.
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.
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.
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.
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.
Animal agriculture has exponentially grown in recent decades in response to the rise in global demand for meat, even in countries like Italy that traditionally eat a Mediterranean, plant-based diet. ...Globalization related dietary changes are contributing to the epidemic of non-communicable diseases and to the global climate crisis, and are associated with huge carbon and water footprints. The objective of the study is to assess inequalities in health impacts and in attributable greenhouse gases-GHG emissions in Italy by hypothesizing different scenarios of reduction in red and processed meat consumption towards healthier consumption patterns more compliant with the recommendations of the Mediterranean food pyramid.
We used demographic and food consumption patterns from national surveys and risk relationships between meat intake and cardiovascular and colorectal cancer mortality from IARC and other meta-analyses. From the baseline data (year 2005-2006, average 406 gr/week beef and 245 gr/week processed meat), we considered hypothetical meat reduction scenarios according to international dietary guidelines such as the Mediterranean pyramid targets. For each geographical area (Northwest, Northeast, Centre, and South) and gender, we calculated the number of avoidable deaths from colorectal cancer, and cardiovascular disease among the adult population. Moreover, years of life gained by the adult population from 2012 to 2030 and changes in life expectancy of the 2012 birth cohort were quantified using gender-specific life tables. GHG emission reductions under Mediterranean scenario were estimated only for beef by applying the Global Warming Potential (GWP) coefficient to total consumption and to a low carbon food substitution in adult diet.
The deaths avoidable (as percentage change compared to baseline) according to the three reduction scenarios for beef consumption were between 2.3% and 4.5% for colorectal cancer, and between 2.1% and 4.0% for cardiovascular disease; higher benefits would be observed in Northwestern areas and among males. In parallel, 5% and 6.4% of colorectal cancer and CVD deaths would be avoided if the Italian population ate the advised quantity of processed meat. Life table analysis suggests that the scenario that is fully compliant with the Mediterranean diet model would save 5 million years of life lost prematurely among men and women over the next 18 years and would increase average life expectancy of future generations by over 7 months. Considering the environmental impact, emissions associated with the actual total intake of beef range from 12,900 to 21,800 Gg CO2 eq; emissions saved according to the Mediterranean scenario are in the range 8000-14000 Gg CO2 eq per year. The per capita reduction is 263 KgCO2eq/year/person with higher reductions in Northwestern and Central areas.
In Italy, scenarios for reducing beef consumption are consistent with significant health and environmental co-benefits on current and future generations. Results support introducing policies to promote healthier behavior towards red and processed meat in the adult population within an overall balanced and healthy dietary pattern. Interventions should address gender, vulnerable population groups, and geographical differences in order to be more effective.
BACKGROUNDRecent epidemiological evidence suggests associations between air pollution exposure and major depressive disorders, but the literature is inconsistent for other mental illnesses. We ...investigated the associations of several air pollutants and road traffic noise with the incidence of different categories of mental disorders in a large population-based cohort.METHODSWe enrolled 1,739,277 individuals 30 + years from the 2011 census in Rome, Italy, and followed them up until 2019. In detail, we analyzed 1,733,331 participants (mean age 56.43 +/- 15.85 years; 54.96 % female) with complete information on covariates of interest. We excluded subjects with prevalent mental disorders at baseline to evaluate the incidence (first hospitalization or co-pay exemption) of schizophrenia spectrum disorders, bipolar, anxiety, personality, or substance use disorders. In addition, we studied subjects with first prescriptions of antipsychotics, antidepressants, and mood stabilizers. Annual average concentrations of fine particulate matter (PM2.5), nitrogen dioxide (NO₂), Black Carbon (BC), ultrafine particles (UFP), and road traffic noise were assigned to baseline residential addresses. We applied Cox regression models adjusted for individual and area-level covariates.RESULTSEach interquartile range (1.13 µg/m3) increase in PM2.5 was associated with a hazard ratio (HR) of 1.070 (95 % confidence interval CI: 1.017, 1.127) for schizophrenia spectrum disorder, 1.135 (CI: 1.086, 1.186) for depression, 1.097 (CI: 1.030, 1.168) for anxiety disorders. Positive associations were also detected for BC and UFP, and with the three categories of drug prescriptions. Bipolar, personality, and substance use disorders did not show clear associations. The effects were highest in the age group 30-64 years, except for depression.CONCLUSIONSLong-term exposure to ambient air pollution, especially fine and ultrafine particles, was associated with increased risks of schizophrenia spectrum disorder, depression, and anxiety disorders. The association of the pollutants with the prescriptions of specific drugs increases the credibility of the results.
In several volcanic areas of Italy, arsenic levels exceed European regulatory limits (10 μg/L in drinking water). There is still uncertainty about health risks from arsenic at low-medium doses (<100 ...μg/L).
A large population-based study using an administrative cohort of residents in the Viterbo province (Central Italy), chronically exposed to low-medium arsenic levels via drinking water, was investigated to evaluate the effects of a lifetime exposure to arsenic on mortality from cancers and chronic diseases.
The study population consisted of 165,609 residents of 17 municipalities, followed from 1990 until 2010. Average individual arsenic exposure at the first residence (AsI) was estimated through a space-time modeling approach using residential history and arsenic concentrations from water supply. A time-dependent Cumulative Arsenic dose Indicator (CAI) was calculated, accounting for daily water intake and exposure duration. Mortality Hazard Ratios (HR) were estimated by gender for different diseases using Cox proportional models, adjusting for individual and area-level confounders. A flexible non-parametric approach was used to investigate dose-response relationships.
Mean AsI exposure was 19.3 μg/L, and average exposure duration was 39.5 years. Associations of AsI and CAI indicators with several diseases were found, with greatest risks found for lung cancer in both sexes (HR = 2.61 males; HR = 2.09 females), myocardial infarction, peripheral arterial disease and COPD in males (HR = 2.94; HR = 2.44; HR = 2.54 respectively) and diabetes in females (HR = 2.56). For lung cancer and cardiovascular diseases dose-response relationship is modelled by piecewise linear functions revealing effects even for doses lower than 10 μg/L, and no threshold dose value was identified as safe for health.
Results provide new evidence for risk assessment of low-medium concentrations of arsenic and contribute to the ongoing debate about the threshold-dose of effect, suggesting that even concentrations below 10 μg/L carry a mortality risk. Policy actions are urgently needed in areas exposed to arsenic like in the Viterbo province, to comply with current EU regulations.
Few studies have explored the role of air pollution in neurodegenerative processes, especially various types of dementia. Our aim was to evaluate the association between long-term exposure to air ...pollution and first hospitalization for dementia subtypes in a large administrative cohort.
We selected 350,844 subjects (free of dementia) aged 65-100 years at inclusion (21/10/2001) and followed them until 31/12/2013. We selected all subjects hospitalized for the first time with primary or secondary diagnoses of various forms of dementia. We estimated the exposure at residence using land use regression models for nitrogen oxides (NOx, NO
) and particulate matter (PM) and a chemical transport model for ozone (O
). We used Cox models to estimate the association between exposure and first hospitalization for dementia and its subtypes: vascular dementia (Vd), Alzheimer's disease (Ad) and senile dementia (Sd).
We selected 21,548 first hospitalizations for dementia (7497 for Vd, 7669 for Ad and 7833 for Sd). Overall, we observed a negative association between exposure to NO
(10 μg/m
) and dementia hospitalizations (HR = 0.97; 95% CI: 0.96-0.99) and a positive association between exposure to O
, NOx and dementia hospitalizations, (O
: HR = 1.06; 95% CI: 1.04-1.09 per 10 μg/m
; NOx: HR = 1.01; 95% CI: 1.00-1.02 per 20 μg/m
).H. Exposure to NOx, NO
, PM
, and PM
was positively associated with Vd and negatively associated with Ad. Hospitalization for Sd was positively associated with exposure to O
(HR = 1.20; 95% CI: 1.15-1.24 per 10 μg/m
).
Our results showed a positive association between exposure to NOx and O
and hospitalization for dementia and a negative association between NO
exposure and hospitalization for dementia. In the analysis by subtype, exposure to each pollutants (except O
) demonstrated a positive association with vascular dementia, while O
exposure was associated with senile dementia. The results regarding vascular dementia are a clear indication that the brain effects of air pollution are linked with vascular damage.
The present study aimed at developing a standardized heat wave definition to estimate and compare the impact on mortality by gender, age and death causes in Europe during summers 1990-2004 and 2003, ...separately, accounting for heat wave duration and intensity.
Heat waves were defined considering both maximum apparent temperature and minimum temperature and classified by intensity, duration and timing during summer. The effect was estimated as percent increase in daily mortality during heat wave days compared to non heat wave days in people over 65 years. City specific and pooled estimates by gender, age and cause of death were calculated.
The effect of heat waves showed great geographical heterogeneity among cities. Considering all years, except 2003, the increase in mortality during heat wave days ranged from + 7.6% in Munich to + 33.6% in Milan. The increase was up to 3-times greater during episodes of long duration and high intensity. Pooled results showed a greater impact in Mediterranean (+ 21.8% for total mortality) than in North Continental (+ 12.4%) cities. The highest effect was observed for respiratory diseases and among women aged 75-84 years. In 2003 the highest impact was observed in cities where heat wave episode was characterized by unusual meteorological conditions.
Climate change scenarios indicate that extreme events are expected to increase in the future even in regions where heat waves are not frequent. Considering our results prevention programs should specifically target the elderly, women and those suffering from chronic respiratory disorders, thus reducing the impact on mortality.
Environmental exposures have been linked to length of gestation but the question as to during which weeks of gestation pregnancies are most susceptible still remains little explored.
We estimated the ...effect of maximum apparent temperature and air pollution levels on risk of birth by week of gestation.
We analyzed two cohorts of singleton live births in Rome (2001–2010) and Barcelona (2007–2012). Maximum apparent temperature (MAT), PM10, O3 and NO2 were analyzed in the warm period (1st April–31st October).
Gestational week-specific hazard ratios of giving birth associated to a 1-unit increase in exposure were estimated fitting Cox regression models adjusted for seasonality, and demographic and clinical characteristics of the mother.
We observed 78,633 births (5.5% preterm) in Rome and 27,255 (4.5% preterm) in Barcelona. The highest hazard ratios for 1°C increase in MAT were in the 22nd–26th weeks of gestation, 1.071, (95% CI 1.052–1.091) in Rome and 1.071 (95% CI 1.036–1.106) in Barcelona, and decreased to 1.032 (95% CI 1.026–1.038) and 1.033 (95% CI 1.020–1.045) at the 36th week of gestation, respectively. Similar associations and trends were observed for PM10 and NO2 after adjusting for MAT. O3 showed similar trends but weaker associations.
We found, consistently in Rome and Barcelona, an increased risk of delivery for a unit increase in MAT, PM10, NO2 and O3, especially in the second half of the second trimester, thus effectively increasing the risk of preterm and particularly early preterm birth. Results may help to increase awareness of these risks among public-health regulators and clinicians, leading to better preventive strategies.
•Potential triggering effects of temperature and air pollution on delivery were studied by week of gestation•Two large birth cohorts of two European cities were analyzed via survival analysis with time dependent variable.•The effects of both heat and pollutants on the probability of birth were modified by week of gestation•Effects were higher in the first half of the 2nd trimester of gestation, and decreas over the gestational period.•The highest triggering effects were observed for heat in both cities, followed by PM10 in Rome and NO2 in Barcelona.
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