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Few European studies have investigated the effects of long-term exposure to both fine particulate matter (≤ 2.5 µm; PM2.5) and nitrogen dioxide (NO2) on mortality. We studied the association of exposure to NO2, PM2.5, and traffic indicators on cause-specific mortality to evaluate the form of the concentration-response relationship. We analyzed a population-based cohort enrolled at the 2001 Italian census with 9 years of follow-up. We selected all 1,265,058 subjects ≥ 30 years of age who had been living in Rome for at least 5 years at baseline. Residential exposures included annual NO2 (from a land use regression model) and annual PM2.5 (from a Eulerian dispersion model), as well as distance to roads with > 10,000 vehicles/day and traffic intensity. We used Cox regression models to estimate associations with cause-specific mortality adjusted for individual (sex, age, place of birth, residential history, marital status, education, occupation) and area (socioeconomic status, clustering) characteristics. Long-term exposures to both NO2 and PM2.5 were associated with an increase in nonaccidental mortality [hazard ratio (HR) = 1.03 (95% CI: 1.02, 1.03) per 10-µg/m3 NO2; HR = 1.04 (95% CI: 1.03, 1.05) per 10-µg/m3 PM2.5]. The strongest association was found for ischemic heart diseases (IHD) [HR = 1.10 (95% CI: 1.06, 1.13) per 10-µg/m3 PM2.5], followed by cardiovascular diseases and lung cancer. The only association showing some deviation from linearity was that between NO2 and IHD. In a bi-pollutant model, the estimated effect of NO2 on mortality was independent of PM2.5. This large study strongly supports an effect of long-term exposure to NO2 and PM2.5 on mortality, especially from cardiovascular causes. The results are relevant for the next European policy decisions regarding air quality.

Living in areas with higher levels of surrounding greenness and access to urban green areas have been associated with beneficial health outcomes. Some studies suggested a beneficial influence on mortality, but the evidence is still controversial. We used longitudinal data from a large cohort to estimate associations of two measures of residential greenness exposure with cause-specific mortality and stroke incidence. We studied a population-based cohort of 1,263,721 residents in Rome aged [Formula: see text], followed from 2001 to 2013. As greenness exposure, we utilized the leaf area index (LAI), which expresses the tree canopy as the leaf area per unit ground surface area, and the normalized difference vegetation index (NDVI) within 300- and [Formula: see text] buffers around home addresses. We estimated the association between the two measures of residential greenness and the outcomes using Cox models, after controlling for relevant individual covariates and contextual characteristics, and explored potential mediation by air pollution [fine particulate matter with aerodynamic diameter [Formula: see text] [Formula: see text] and [Formula: see text]] and road traffic noise. We observed 198,704 deaths from nonaccidental causes, 81,269 from cardiovascular diseases [CVDs; 29,654 from ischemic heart disease (IHD)], 18,090 from cerebrovascular diseases, and 29,033 incident cases of stroke. Residential greenness, expressed as interquartile range (IQR) increase in LAI within [Formula: see text], was inversely associated with stroke incidence {hazard ratio (HR) 0.977 [95% confidence interval (CI): 0.961, 0.994]} and mortality for nonaccidental [HR 0.988 (95% CI: 0.981, 0.994)], cardiovascular [HR 0.984 (95% CI: 0.974, 0.994)] and cerebrovascular diseases [HR 0.964 (95% CI: 0.943, 0.985)]. Similar results were obtained using NDVI with 300- or [Formula: see text] buffers. Living in greener areas was associated with better health outcomes in our study, which could be partly due to reduced exposure to environmental hazards. Further research is required to understand the underlying mechanisms. https://doi.org/10.1289/EHP2854.

Background 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. Methods 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 2 ) and particulate matter (PM) and a chemical transport model for ozone (O 3 ). 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). Results 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 2 (10 μg/m 3 ) and dementia hospitalizations (HR = 0.97; 95% CI: 0.96–0.99) and a positive association between exposure to O 3 , NOx and dementia hospitalizations, (O 3 : HR = 1.06; 95% CI: 1.04–1.09 per 10 μg/m 3 ; NOx: HR = 1.01; 95% CI: 1.00–1.02 per 20 μg/m 3 ).H. Exposure to NOx, NO 2 , PM 2.5 , and PM 10 was positively associated with Vd and negatively associated with Ad. Hospitalization for Sd was positively associated with exposure to O 3 (HR = 1.20; 95% CI: 1.15–1.24 per 10 μg/m 3 ). Conclusions Our results showed a positive association between exposure to NOx and O 3 and hospitalization for dementia and a negative association between NO 2 exposure and hospitalization for dementia. In the analysis by subtype, exposure to each pollutants (except O 3 ) demonstrated a positive association with vascular dementia, while O 3 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.

Migrants may be susceptible to vaccine barriers and hesitancy. We evaluated the association between migrant status, as measured by the citizenship from a High Migratory Pressure Country (HMPC), and COVID-19 vaccination uptake in the resident population in Rome, Italy. We also investigated sex differences. We followed participants for vaccination against COVID-19 in 2021. We calculated crude- and adjusted-vaccination rates and Cox hazard ratios of vaccination for migrants compared to Italians. Among migrants from HMPCs, we estimated HRs for females compared to males, stratifying by geographical area of origin. Models were adjusted for age and deprivation index and stratified by infection history. In 2021, among 1,731,832 18–64-year-olds, migrants were 55% less likely to uptake at least one COVID-19 vaccine dose than their Italian counterpart. Past SARS-CoV-2 infection reduced the difference between migrants and Italians to 27%. Among migrants from HMPCs, we observed a slight excess of vaccination uptake among females compared to males. Focusing on geographical areas, we observed that only females from central-western Asia were 9% less likely to uptake vaccination than males. Health communication strategies oriented to migrants and considering their different languages, cultures, and health literacy should be adopted for prevention before emergencies.

Background A relationship between quality of primary health care and preventable hospitalizations has been described in the US, especially among the elderly. In Europe, there has been a recent increase in the evaluation of Ambulatory Care Sensitive Conditions (ACSC) as an indicator of health care quality, but evidence is still limited. The aim of this study was to determine whether income level is associated with higher hospitalization rates for ACSC in adults in a country with universal health care coverage. Methods From the hospital registries in four Italian cities (Turin, Milan, Bologna, Rome), we identified 9384 hospital admissions for six chronic conditions (diabetes, hypertension, congestive heart failure, angina pectoris, chronic obstructive pulmonary disease, and asthma) among 20-64 year-olds in 2000. Case definition was based on the ICD-9-CM coding algorithm suggested by the Agency for Health Research and Quality - Prevention Quality Indicators . An area-based (census block) income index was used for each individual. All hospitalization rates were directly standardised for gender and age using the Italian population. Poisson regression analysis was performed to assess the relationship between income level (quintiles) and hospitalization rates (RR, 95% CI) separately for the selected conditions controlling for age, gender and city of residence. Results Overall, the ACSC age-standardized rate was 26.1 per 10.000 inhabitants. All conditions showed a statistically significant socioeconomic gradient, with low income people being more likely to be hospitalized than their well off counterparts. The association was particularly strong for chronic obstructive pulmonary disease (level V low income vs. level I high income RR = 4.23 95%CI 3.37-5.31) and for congestive heart failure (RR = 3.78, 95% CI = 3.09-4.62). With the exception of asthma, males were more vulnerable to ACSC hospitalizations than females. The risks were higher among 45-64 year olds than in younger people. Conclusions The socioeconomic gradient in ACSC hospitalization rates confirms the gap in health status between social groups in our country. Insufficient or ineffective primary care is suggested as a plausible additional factor aggravating inequality. This finding highlights the need for improving outpatient care programmes to reduce the excess of unnecessary hospitalizations among poor people.

The association between short-term air pollution exposure and daily mortality has been widely investigated, but little is known about the temporal variability of the effect estimates. We examined the temporal relationship between exposure to particulate matter (PM) (PM , PM ) and gases (NO , SO , and CO) with mortality in a large metropolitan area over the last 17 y. Our analysis included 359,447 nonaccidental deaths among ≥35-y-old individuals in Rome, Italy, over the study period 1998–2014. We related daily concentrations to mortality counts with a time-series Poisson regression analysis adjusted for long-term trends, meteorology, and population dynamics. Annual average concentrations decreased over the study period for all pollutants (e.g., from 42.9 to 26.6 μg/m for PM ). Each pollutant was positively associated with mortality, with estimated percentage increases over the entire study period ranging from 0.19% (95% CI: 0.13, 0.26) for a 1-Mg/m increase in CO (0–1 d lag) to 3.03% (95% CI: 2.44, 3.63) for a 10-μg/m increase in NO (0–5 d lag). We did not observe clear temporal patterns in year- or period-specific effect estimates for any pollutant. For example, we estimated that a 10-μg/m increase in PM was associated with 1.16% (95% CI: 0.53, 1.79), 0.99% (95% CI: 0.23, 1.77), and 1.87% (95% CI: 1.00, 2.74) increases in mortality for the periods 2001–2005, 2006–2010, and 2011–2014, respectively, and corresponding estimates for a 10-μg/m increase in NO were 4.20% (95% CI: 3.15, 5.25), 1.78% (95% CI: 0.73, 2.85), and 3.32% (95% CI: 2.03, 4.63). Mean concentrations of air pollutants have decreased over the last two decades in Rome, but effect estimates for a fixed increment in each exposure were generally consistent. These findings suggest that there has been little or no change in the overall toxicity of the air pollution mixture over time. https://doi.org/10.1289/EHP19.

Socioeconomic inequalities have a strong impact on population health all over the world. Occupational status is a powerful determinant of health in rich societies. We aimed at investigating the association between occupation and mortality in a large metropolitan study. Cohort study. Rome, capital of Italy. We used the Rome Longitudinal Study, the administrative cohort of residents in Rome at the 2001 general census, followed until 2015. We selected residents aged 15-65 years at baseline. For each subject, we had information on sex, age and occupation (occupational status and type of job) according to the Italian General Census recognition. We investigated all-cause, cancer, cardiovascular and accidental mortality, major causes of death in the working-age population. We used Cox proportional hazards models to investigate the association between occupation and all-cause and cause-specific mortality in men and women. We selected 1 466 726 subjects (52.1% women). 42 715 men and 29 915 women died during the follow-up. In men, 47.8% of deaths were due to cancer, 26.7% to cardiovascular causes and 6.4% to accidents, whereas in women 57.8% of deaths were due to cancer, 19.3% to cardiovascular causes and 3.5% to accidents. We found an association between occupational variables and mortality, more evident in men than in women. Compared with employed, unemployed had a higher risk of mortality for all causes with an HR=1.99 (95% CI 1.92 to 2.06) in men and an HR=1.49 (95% CI 1.39 to 1.60) in women. Compared with high-qualified non-manual workers, non-specialised manual workers had a higher mortality risk (HR=1.68, 95% CI 1.59 to 1.77 and HR=1.30, 95% CI 1.20 to 1.40, for men and women, respectively). This study shows the importance of occupational variables as social health determinants and provides evidence for policy-makers on the necessity of integrated and preventive policies aimed at improving the safety of the living and the working environment.

Introduction We aimed to evaluate the diagnostic stability of a large spectrum of psychiatric diagnoses in adolescents and young adult patients attending specialised facilities in the Lazio Region (Italy). Methods A total of 3871 subjects (11–35 years) at their first hospitalisation with a psychiatric diagnosis were selected and followed up for 10 years on subsequent hospital admissions to psychiatric care. Results A total of 1145 patients were readmitted to a hospital for a psychiatric disorder in the following 10 years. Among these subjects, the concordance between the first and last diagnosis was 57.8% with a weighted kappa of 0.47 (95% CI: 0.42–0.51); the repeated stability (the same diagnosis in at least 75% of admissions) was 31.2%. The diagnostic categories with the highest values of prospective concordance and kappa were schizophrenia spectrum and other functional psychoses (70%, kappa 0.53), substance use disorders (54%, kappa 0.57), and eating disorders (80.9%, kappa 0.76). Conclusions In a population study, the stability over time of the first psychiatric diagnosis in a hospital varied according to the specific diagnostic categories, and overall, it was lower than previously reported. The trajectories were disorder‐specific, and the stability was influenced by several factors, including the individuals' characteristics, the disorder's severity, and the diagnostic setting.

ObjectivesSeveral physiological abnormalities that develop during COVID-19 are associated with increased mortality. In the present study, we aimed to develop a clinical risk score to predict the in-hospital mortality in COVID-19 patients, based on a set of variables available soon after the hospitalisation triage.SettingRetrospective cohort study of 516 patients consecutively admitted for COVID-19 to two Italian tertiary hospitals located in Northern and Central Italy were collected from 22 February 2020 (date of first admission) to 10 April 2020.ParticipantsConsecutive patients≥18 years admitted for COVID-19.Main outcome measuresSimple clinical and laboratory findings readily available after triage were compared by patients’ survival status (‘dead’ vs ‘alive’), with the objective of identifying baseline variables associated with mortality. These were used to build a COVID-19 in-hospital mortality risk score (COVID-19MRS).ResultsMean age was 67±13 years (mean±SD), and 66.9% were male. Using Cox regression analysis, tertiles of increasing age (≥75, upper vs <62 years, lower: HR 7.92; p<0.001) and number of chronic diseases (≥4 vs 0–1: HR 2.09; p=0.007), respiratory rate (HR 1.04 per unit increase; p=0.001), PaO2/FiO2 (HR 0.995 per unit increase; p<0.001), serum creatinine (HR 1.34 per unit increase; p<0.001) and platelet count (HR 0.995 per unit increase; p=0.001) were predictors of mortality. All six predictors were used to build the COVID-19MRS (Area Under the Curve 0.90, 95% CI 0.87 to 0.93), which proved to be highly accurate in stratifying patients at low, intermediate and high risk of in-hospital death (p<0.001).ConclusionsThe COVID-19MRS is a rapid, operator-independent and inexpensive clinical tool that objectively predicts mortality in patients with COVID-19. The score could be helpful from triage to guide earlier assignment of COVID-19 patients to the most appropriate level of care.

Background Land Use Regression models (LUR) are useful to estimate the spatial variability of air pollution in urban areas. Few studies have evaluated the stability of spatial contrasts in outdoor nitrogen dioxide (NO 2 ) concentration over several years. We aimed to compare measured and estimated NO 2 levels 12 years apart, the stability of the exposure estimates for members of a large cohort study, and the association of the exposure estimates with natural mortality within the cohort. Methods We measured NO 2 at 67 locations in Rome in 1995/96 and 78 sites in 2007, over three one-week-long periods. To develop LUR models, several land-use and traffic variables were used. NO 2 concentration at each residential address was estimated for a cohort of 684,000 adults. We used Cox regression to analyze the association between the two estimated exposures and mortality. Results The mean NO 2 measured concentrations were 45.4 μg/m 3 (SD 6.9) in 1995/96 and 44.6 μg/m 3 (SD 11.0) in 2007, respectively. The correlation of the two measurements was 0.79. The LUR models resulted in adjusted R 2 of 0.737 and 0.704, respectively. The correlation of the predicted exposure values for cohort members was 0.96. The association of each 10 μg/m 3 increase in NO 2 with mortality was 6 % for 1995/96 and 4 % for 2007 LUR models. The increased risk per an inter-quartile range change was identical (4 %, 95 % CI:3–6 %) for both estimates of NO 2 . Conclusions Measured and predicted NO 2 values from LUR models, from samples collected 12 years apart, had good agreement, and the exposure estimates were similarly associated with mortality in a large cohort study.