A comparative analysis on serious adverse events reported for COVID-19 vaccines in adolescents and young adults.

This study aims to assess the safety profile of COVID-19 vaccines (mRNA and viral vector vaccines) in teenagers and young adults, as compared to Influenza and HPV vaccines, and to early data from Monkeypox vaccination in United States. Methods: We downloaded data from the Vaccine Adverse Event Reporting System (VAERS) and collected the following Serious Adverse Events (SAEs) reported for COVID-19, Influenza, HPV and Monkeypox vaccines: deaths, life-threatening illnesses, disabilities, hospitalizations. We restricted our analysis to the age groups 12-17 and 18-49, and to the periods December 2020 to July 2022 for COVID-19 vaccines, 2010-2019 for Influenza vaccines, 2006-2019 for HPV vaccines, June 1, 2022 to November 15, 2022 for Monkeypox vaccine. Rates were calculated in each age and sex group, based on an estimation of the number of administered doses. Results: Among adolescents the total number of reported SAEs per million doses for, respectively, COVID-19, Influenza and HPV vaccines were 60.73, 2.96, 14.62. Among young adults the reported SAEs rates for, respectively, COVID-19, Influenza, Monkeypox vaccines were 101.91, 5.35, 11.14. Overall, the rates of reported SAEs were significantly higher for COVID-19, resulting in a rate 19.60-fold higher than Influenza vaccines (95% C.I. 18.80-20.44), 4.15-fold higher than HPV vaccines (95% C.I. 3.91-4.41) and 7.89-fold higher than Monkeypox vaccine (95% C.I. 3.95-15.78). Similar trends were observed in teenagers and young adults with higher Relative Risks for male adolescents. Conclusion: The study identified a risk of SAEs following COVID-19 vaccination which was markedly higher compared to Influenza vaccination and substantially higher compared to HPV vaccination, both for teenagers and young adults, with an increased risk for the male adolescents group. Initial, early data for Monkeypox vaccination point to significantly lower rates of reported SAEs compared to those for COVID-19 vaccines. In conclusion these results stress the need of further studies to explore the bases for the above differences and the importance of accurate harm-benefit analyses, especially for adolescent males, to inform the COVID-19 vaccination campaign.

Robust inference for non-linear regression models from the Tsallis score: Application to coronavirus disease 2019 contagion in Italy.

We discuss an approach of robust fitting on non-linear regression models, in both frequentist and Bayesian approaches, which can be employed to model and predict the contagion dynamics of the coronavirus disease 2019 (COVID-19) in Italy. The focus is on the analysis of epidemic data using robust dose-response curves, but the functionality is applicable to arbitrary non-linear regression models.

Cognitive decline and depressive symptoms in late-life are associated with statin use: evidence from a population-based study of Sardinian old people living in their own home.

OBJECTIVES: This study was designed to provide further insights into the effects of dyslipidemia (Dys-y) and use of statins (St-y) on cognitive functions and mood in older people. METHODS: Three hundred and twenty-nine subjects aged > or = 65 years were screened for cognitive dysfunction using mini mental state examination (MMSE). The geriatric depression scale (GDS) was used to detect depression. Interview questionnaires surveyed activities of daily living (ADL) and instrumental ADL (IADL), as well as other functional disabilities. The presence of neutral lipids (NLs) in cytoplasm of peripheral blood mononuclear cells (PBMCs) was determined with the Oil red O (ORO) staining. RESULTS: There was no significant difference in MMSE and GDS scores between normal (Dys-n) and Dys-y. However, when Dys-y subjects were divided into St-y and non-statin users (St-n), significant differences emerged in the scores of MMSE and GDS: St-y had lower MMSE and higher GDS than St-n. Multiple correspondence analysis and logistic regression provided further evidence that elderly St-y were much more likely to suffer of cognitive impairment and depression than St-n. Another interesting finding was that the intensity of NL-PBMCs measured by ORO staining was greater in subjects with altered MMSE compared with cognitively normal subjects. In addition St-y had higher ORO score than St-n. DISCUSSION: This is an observational study and cannot, therefore, prove a causal relationship between St-y in the elderly and a higher cognitive decline, nevertheless it provides substantial indications that caution should be exercised in the provision of statins in elderly subjects to avoid accelerated memory loss.

Resources allocation in healthcare for cancer: a case study using generalised additive mixed models.

Our aim is to develop a method for helping resources re-allocation in healthcare linked to cancer, in order to replan the allocation of providers. Ageing of the population has a considerable impact on the use of health resources because aged people require more specialised medical care due notably to cancer. We propose a method useful to monitor changes of cancer incidence in space and time taking into account two age categories, according to healthcar general organisation. We use generalised additive mixed models with a Poisson response, according to the methodology presented in Wood, Generalised additive models: an introduction with R. Chapman and Hall/CRC, 2006. Besides one-dimensional smooth functions accounting for non-linear effects of covariates, the space-time interaction can be modelled using scale invariant smoothers. Incidence data collected by a general cancer registry between 1992 and 2007 in a specific area of France is studied. Our best model exhibits a strong increase of the incidence of cancer along time and an obvious spatial pattern for people more than 70 years with a higher incidence in the central band of the region. This is a strong argument for re-allocating resources for old people cancer care in this sub-region.

Bayesian semi-parametric ZIP models with space-time interactions: an application to cancer registry data.

We analyse lymphoid leukemia incidence data collected between 1988 and 2002 from the cancer registry of Haut-Rhin, a region in north-east France. For each patient, sex, area of residence, date of birth and date of diagnosis are available. Incidence summaries in the registry are grouped by 3-year periods. A disproportionately large frequency of zeros in the data leads to a lack of fit for Poisson models of relative risk. The aim of our analysis was to model the spatio-temporal variations of the disease taking into account some non-standard requirements, such as count data with many zeros and space-time interactions. For this purpose, we consider a flexible zero-inflated Poisson model for semi-parametric regression which incorporates space-time interactions (modelled by means of varying coefficient model) using an extension of the methodology proposed in Fahrmeir & Osuna (2006, Structured additive regression for overdispersed and zero-inflated count data. Stoc. Models Bus. Ind., 22, 351-369). Inference is carried out from a Bayesian perspective using Markov chain Monte Carlo methods by means of the BayesX software. Our analysis of the geographical distribution of the disease and its evolution in time may be considered as a starting point for further studies.