Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 6 de 6
Filter
1.
Sci Rep ; 12(1): 2214, 2022 02 09.
Article in English | MEDLINE | ID: covidwho-1677263

ABSTRACT

The impact of specific risk factors for SARS-CoV-2 infection spread was investigated among the 215 municipalities in north-eastern Italy. SARS-CoV-2 incidence was gathered fortnightly since April 1, 2020 (21 consecutive periods) to depict three indicators of virus spreading from hierarchical Bayesian maps. Eight explanatory features of the municipalities were obtained from official databases (urbanicity, population density, active population on total, hosting schools or nursing homes, proportion of commuting workers or students, and percent of > 75 years population on total). Multivariate Odds Ratios (ORs), and corresponding 95% Confidence Intervals (CIs), quantified the associations between municipality features and virus spreading. The municipalities hosting nursing homes showed an excess of positive tested cases (OR = 2.61, ever versus never, 95% CI 1.37;4.98), and displayed repeated significant excesses: OR = 5.43, 3-4 times versus 0 (95% CI 1.98;14.87) and OR = 6.10, > 5 times versus 0 (95% CI 1.60;23.30). Municipalities with an active population > 50% were linked to a unique statistical excess of cases (OR = 3.06, 1 time versus 0, 95% CI 1.43;6.57) and were inversely related to repeated statistically significant excesses (OR = 0.25, > 5 times versus 0; 95% CI 0.06;0.98). We highlighted specific municipality features that give clues about SARS-CoV-2 prevention.


Subject(s)
COVID-19/epidemiology , Bayes Theorem , COVID-19/prevention & control , COVID-19/virology , Cities , Humans , Incidence , Italy/epidemiology , Odds Ratio , Risk Factors , SARS-CoV-2/isolation & purification
2.
Tumori ; : 3008916211073771, 2022 Feb 08.
Article in English | MEDLINE | ID: covidwho-1673723

ABSTRACT

INTRODUCTION: This study assesses the risk of infection and clinical outcomes in a large consecutive population of cancer and non-cancer patients tested for SARS-CoV-2 status. METHODS: Study patients underwent SARS-CoV-2 molecular-testing between 22 February 2020 and 31 July 2020, and were found infected (CoV2+ve) or uninfected. History of malignancy was obtained from regional population-based cancer registries. Cancer-patients were distinguished by time between cancer diagnosis and SARS-CoV-2 testing (<12/⩾12 months). Comorbidities, hospitalization, and death at 15 September 2020 were retrieved from regional population-based databases. The impact of cancer history on SARS-CoV-2 infection and clinical outcomes was calculated by fitting a multivariable logistic regression model, adjusting for sex, age, and comorbidities. RESULTS: Among 552,362 individuals tested for SARS-CoV-2, 55,206 (10.0%) were cancer-patients and 22,564 (4.1%) tested CoV2+ve. Irrespective of time since cancer diagnosis, SARS-CoV-2 infection was significantly lower among cancer patients (1,787; 3.2%) than non-cancer individuals (20,777; 4.2% - Odds Ratio (OR)=0.60; 0.57-0.63). CoV2+ve cancer-patients were older than non-cancer individuals (median age: 77 versus 57 years; p<0.0001), were more frequently men and with comorbidities. Hospitalizations (39.9% versus 22.5%; OR=1.61; 1.44-1.80) and deaths (24.3% versus 9.7%; OR=1.51; 1.32-1.72) were more frequent in cancer-patients. CoV2+ve cancer-patients were at higher risk of death (lung OR=2.90; 1.58-5.24, blood OR=2.73; 1.88-3.93, breast OR=1.77; 1.32-2.35). CONCLUSIONS: The risks of hospitalization and death are significantly higher in CoV2+ve individuals with past or present cancer (particularly malignancies of the lung, hematologic or breast) than in those with no history of cancer.

3.
BioDrugs ; 35(6): 749-764, 2021 Nov.
Article in English | MEDLINE | ID: covidwho-1460522

ABSTRACT

BACKGROUND: Biological drugs have improved the management of immune-mediated inflammatory diseases (IMIDs) despite being associated with important safety issues such as immunogenicity, infections, and malignancies in real-world settings. OBJECTIVE: The aim of this study was to explore the potential of a large Italian multi-database distributed network for use in the postmarketing surveillance of biological drugs, including biosimilars, in patients with IMID. METHODS: A retrospective cohort study was conducted using 13 Italian regional claims databases during 2010-2019. A tailor-made R-based tool developed for distributed analysis of claims data using a study-specific common data model was customized for this study. We measured the yearly prevalence of biological drug users and the frequency of switches between originator and biosimilars for infliximab, etanercept, and adalimumab separately and stratified them by calendar year and region. We then calculated the cumulative number of users and person-years (PYs) of exposure to individual biological drugs approved for IMIDs. For a number of safety outcomes (e.g., severe acute respiratory syndrome coronavirus 2 [SARS-COV-2] infection), we conducted a sample power calculation to estimate the PYs of exposure required to investigate their association with individual biological drugs approved for IMIDs, considering different strengths of association. RESULTS: From a total underlying population of almost 50 million inhabitants from 13 Italian regions, we identified 143,602 (0.3%) biological drug users, with a cumulative exposure of 507,745 PYs during the entire follow-up. The mean age ± standard deviation of biological drug users was 49.3 ± 16.3, with a female-to-male ratio of 1.2. The age-adjusted yearly prevalence of biological drug users increased threefold from 0.7 per 1000 in 2010 to 2.1 per 1000 in 2019. Overall, we identified 40,996 users of biosimilars of tumor necrosis factor (TNF)-α inhibitors (i.e., etanercept, adalimumab, and infliximab) in the years 2015-2019. Of these, 46% (N = 18,845) switched at any time between originator and biosimilars or vice versa. To investigate a moderate association (incidence rate ratio 2) between biological drugs approved for IMIDs and safety events of interest, such as optic neuritis (lowest background incidence rate 10.4/100,000 PYs) or severe infection (highest background incidence rate 4312/100,000 PYs), a total of 43,311 PYs and 104 PYs of exposure to individual biological drugs, respectively, would be required. As such, using this network, of 15 individual biological drugs approved for IMIDs, the association with those adverse events could be investigated for four (27%) and 14 (93%), respectively. CONCLUSION: The VALORE project multi-database network has access to data on more than 140,000 biological drug users (and > 0.5 million PYs) from 13 Italian regions during the years 2010-2019, which will be further expanded with the inclusion of data from other regions and more recent calendar years. Overall, the cumulated amount of person-time of exposure to biological drugs approved for IMIDs provides enough statistical power to investigate weak/moderate associations of almost all individual compounds and the most relevant safety outcomes. Moreover, this network may offer the opportunity to investigate the interchangeability of originator and biosimilars of several TNFα inhibitors in different therapeutic areas in real-world settings.


Subject(s)
Biosimilar Pharmaceuticals , COVID-19 , Delivery of Health Care , Female , Humans , Infliximab/adverse effects , Italy/epidemiology , Male , Retrospective Studies , SARS-CoV-2
4.
Cancer Med ; 10(21): 7781-7792, 2021 11.
Article in English | MEDLINE | ID: covidwho-1432369

ABSTRACT

BACKGROUND: It is well established that cancer patients infected with SARS-CoV-2 are at particularly elevated risk of adverse outcomes, but the comparison of SARS-CoV-2 infection risk between cancer patients and cancer-free individuals has been poorly investigated on a population-basis. METHODS: A population-based study was thus conducted in Friuli Venezia Giulia region, northeastern Italy, to estimate prevalence and determinants of SARS-CoV-2 infection among cancer patients, as compared to cancer-free individuals, and to evaluate adverse outcomes of SARS-CoV-2 infection. The study included 263,042 individuals tested for SARS-CoV-2 in February-December 2020 with cancer history retrieved through the regional cancer registry. Odds ratios (ORs) of SARS-CoV-2 positivity, with corresponding 95% confidence intervals (CIs), were calculated using multivariable logistic regression models, adjusted for sex and age. Hazard ratios (HRs) adjusted for sex and age for intensive care unit (ICU) admission and all-cause death were estimated using Cox models. RESULTS: Among 26,394 cancer patients tested for SARS-CoV-2, the prevalence of infection was 11.7% versus 16.2% among 236,648 cancer-free individuals, with a corresponding OR = 0.59 (95% CI: 0.57-0.62). The prevalence was much higher (29% in both groups) during the second pandemic wave (October-December 2020). Among cancer patients, age ≥80 years and cancer diagnosis ≥13 months before SARS-CoV-2 testing were the major risk factors of infection. Among 3098 infected cancer patients, the fatality rate was 17.4% versus 15.8% among 23,296 negative ones (HR = 1.63, 95% CI: 1.49-1.78), and versus 5.0% among 38,268 infected cancer-free individuals (HR = 1.23, 95% CI: 1.12-1.36). No significant differences emerged when considering ICU admission risk. CONCLUSION: Albeit cancer patients reported reduced SARS-CoV-2 infection risk, those infected showed higher mortality than uninfected ones and infected cancer-free population. Study findings claim for continuing to protect cancer patients from SARS-CoV-2, without reducing the level of oncologic care.


Subject(s)
COVID-19/epidemiology , Neoplasms/virology , Aged , Aged, 80 and over , COVID-19/mortality , Female , Humans , Intensive Care Units/statistics & numerical data , Italy/epidemiology , Male , Middle Aged , Mortality , Neoplasms/epidemiology , Prevalence , Retrospective Studies
5.
Epidemiol Prev ; 44(5-6 Suppl 2): 323-329, 2020.
Article in English | MEDLINE | ID: covidwho-1068154

ABSTRACT

OBJECTIVES: to study the cumulative incidence, the demographics and health conditions of the population tested for COVID-19, and to map the evolving distribution of individual cases in the population of the Friuli Venezia Giulia Region (North-Eastern Italy). DESIGN: population-based observational study based on a record linkage procedure of databases included in the electronic health information system of the Friuli Venezia Giulia Region. SETTING AND PARTICIPANTS: the study group consisted of individuals who resided in the Friuli Venezia Giulia Region and who underwent COVID-19 testing from 01.03 to 24.04.2020. The study group was identified from the laboratory database, which contains all the microbiological testing performed in regional facilities. Tested people were categorized into positive or negative cases, based on test results. MAIN OUTCOME MEASURES: probability of being tested for and cumulative incidence of COVID-19. RESULTS: the cumulative probability of being tested for COVID-19 was 278/10,000 inhabitants, while the cumulative incidence was 22 cases/10,000. Out of 33,853 tested people, 2,744 (8.1%) turned out to be positive for COVID-19. Women were tested more often than men (337 vs 216/10,000), and they showed a higher incidence of infection than men (25 and 19 infected cases/10,000 residents, respectively). Both cumulative incidence and cumulative probability of being tested were higher in the elderly population. About 25% of infected people was hosted in retirement homes and 9% was represented by healthcare workers. Thirty seven percent of positive cases had hypertension, 15% cardiologic diseases, while diabetes and cancer characterized 11.7% and 10% of the infected population, respectively. The geographic distribution of positive cases showed a faster spread of the infection in the city of Trieste, an urban area with the highest regional population density. CONCLUSIONS: the COVID-19 pandemic did not hit the Friuli Venezia Giulia Region as hard as other Northern Italian Regions. In the early phase, as documented in this study, the COVID-19 pandemic particularly affected women and elderly people, especially those living in retirement homes in Trieste.


Subject(s)
COVID-19/epidemiology , Pandemics , SARS-CoV-2 , Adolescent , Adult , Age Distribution , Aged , Aged, 80 and over , Angiotensin II Type 1 Receptor Blockers/therapeutic use , Angiotensin Receptor Antagonists/therapeutic use , COVID-19/diagnosis , COVID-19 Testing/statistics & numerical data , Child , Child, Preschool , Comorbidity , Databases, Factual , Female , Geography, Medical , Homes for the Aged/statistics & numerical data , Hospitalization/statistics & numerical data , Humans , Incidence , Infant , Infant, Newborn , Italy/epidemiology , Male , Middle Aged , Procedures and Techniques Utilization , Retrospective Studies , Sex Distribution , Young Adult
6.
Epidemiol Prev ; 44(5-6 Suppl 2): 226-234, 2020.
Article in English | MEDLINE | ID: covidwho-1068143

ABSTRACT

OBJECTIVES: to describe the clinical and demographical characteristics of COVID-19 infected people in the Friuli Venezia Giulia Region (FVG, Northern Italy). DESIGN: retrospective cohort study with an individual level record linkage procedure of different administrative databases. SETTING AND PARTICIPANTS: the cohort included 3,010 patients residing in FVG who tested positive for COVID-19 between 1 March and 15 May 2020, 2020. Regional hospital admissions and deaths without hospital admissions up to June 1st, 2020 were analysed. Determinants of the probability of a highly severe illness were investigated in terms of hospitalisations or death without hospital admission. MAIN OUTCOME MEASURES: COVID-19 patients were identified from regional epidemiological data warehouse. Demographical and clinical variables such as gender, age, patient's comorbidities, vaccinations, ARBs/sartans prescriptions, and geographical residence variables were collected by linking different databases. Descriptive analyses were performed. Logistic multivariate regressions were used to estimate the probability of hospitalisation or death, whichever came first. Model coefficients and odds ratios (OR) were reported. RESULTS: COVID-19 population in FVG had a mean age of 60 years and 59% were females. The study found that 37% had hypertension while patients with cardiologic diseases, diabetes, and cancer were around 15%; 22% of the cases were residing in retirement homes. Approximately 30% received flu or pneumococcal vaccination and a similar proportion of patients had at least one prescription of ARBs /sartans in the previous 6 months. Statistical models showed a higher probability of a worst course of disease for males, elderly, highly complicated (in terms of resource use) subjects, in the presence of cardiologic diseases, diabetes, and pneumococcal vaccination. People living in retirement homes had a lower probability of hospitalisation/death without hospital admission. The cohort was divided into two groups: COVID-19 patients infected in the territory and infected in retirement homes. Among COVID-19 patients infected in the territory, the probability of hospitalisation/death was higher for males, for older individuals, and for those with comorbidities. Diabetes resulted to be a risk factor (OR 1.79; 95%CI 1.23-2.62), as well as pneumococcal vaccination (OR 1.64; 95%CI: 1.18-2.29), which is a likely proxy of fragile patients with pulmonary disease. The flu vaccination showed a potential protective effect with a 40% lower probability of hospitalisation or death (OR 0.62; 95%CI 0.44-0.85). Among the retirement homes cohort group, a higher probability of a bad course of disease emerged for males and for more complex patients. CONCLUSIONS: the greatest risk of hospitalisation/death as a measure of more severe illness was confirmed for males, elderly, and for individuals with comorbidities. Flu vaccination seemed to have had a protective effect while pneumococcal vaccination likely identified a group of high-risk patients to be actively monitored. For patients infected in the territory, different hospitalisation strategies were implemented by the regional health districts.


Subject(s)
COVID-19/epidemiology , Pandemics , Age Distribution , Aged , Angiotensin II Type 1 Receptor Blockers/pharmacology , Angiotensin Receptor Antagonists/pharmacology , Catchment Area, Health , Comorbidity , Databases, Factual , Female , Homes for the Aged/statistics & numerical data , Hospital Mortality , Hospitalization/statistics & numerical data , Humans , Italy/epidemiology , Male , Medical Record Linkage , Middle Aged , Multivariate Analysis , Pneumococcal Vaccines , Residence Characteristics , Retrospective Studies , Sex Distribution , Vaccination/statistics & numerical data
SELECTION OF CITATIONS
SEARCH DETAIL