Risultati per: Le cause della polmonite nei bambini

Annals of Internal Medicine, Ahead of Print.

This Swedish nationwide cohort study investigates all-cause and cause-specific mortality among a large cohort of individuals with hypochondriasis and a demographically matched cohort of individuals without hypochondriasis.

Nato in Italia e basato sul genoma, nel mirino il neuroblastoma

Previous research on the mortality risk of people with obsessive-compulsive disorder (OCD) has shown mixed results and hasn’t gone into specific causes of death. Now, results from a large cohort study published in The BMJ suggests they have an 82% higher risk of dying from all causes.

I consigli della Società italiana di pediatria

Legato a problemi di salute mentale; disponibile anche in Italia

Un nuovo polo in Veneto. I giovani pazienti aumentano del 5% l’anno

Se ben trattata ha un decorso benigno ma possibili le recidive

A hormone that is produced largely by the placenta and fetus likely contributes to maternal symptoms.

Objectives
The fragmentation of the response to the COVID-19 pandemic at national, regional and local levels is a possible source of variability in the impact of the pandemic on society. This study aims to assess how much of this variability affected the burden of COVID-19, measured in terms of all-cause 2020 excess mortality.

Design
Ecological retrospective study.

Setting
Lombardy region of Italy, 2015–2020.

Outcome measures
We evaluated the relationship between the intensity of the epidemics and excess mortality, assessing the heterogeneity of this relationship across the 91 districts after adjusting for relevant confounders.

Results
The epidemic intensity was quantified as the COVID-19 hospitalisations per 1000 inhabitants. Five confounders were identified through a directed acyclic graph: age distribution, population density, pro-capita gross domestic product, restriction policy and population mobility.
Analyses were based on a negative binomial regression model with district-specific random effects. We found a strong, positive association between COVID-19 hospitalisations and 2020 excess mortality (p

Comunicato del 05/02/2024 n°8

Comunicato del 03/02/2024 n°6

Stroke, Volume 55, Issue Suppl_1, Page AWMP47-AWMP47, February 1, 2024. Background:Previous studies have shown that neighborhood socioeconomic status may be associated with post-stroke mortality. However, these studies did not adjust for stroke severity. We aim to determine the association between neighborhood of residence on long-term mortality after stroke.Methods:Within our population of 1.3 million in the Greater Cincinnati Northern Kentucky area, incident strokes among adult residents were ascertained at all area hospitals during calendar years 2005, 2010 and 2015. Participants addresses were used to geocode and determine their census tract and then used a validated index to determine neighborhood socio-economic status. We used national death index data to determine mortality status at 5 years post stroke. Individuals who survived at least 30 days were included in the analysis. We used cox proportional hazards to determine the association between neighborhood of residence deprivation and case fatality rate after stroke.Results:Among 4514 incident ischemic strokes, there were 1719 deaths over 5 years; for an all-cause post-stroke mortality rate of 38.08% among survivors of 30 days post-stroke. Similarly, there were 552 intracerebral hemorrhages with 224 deaths over 5 years: yielding an all-cause post-stroke mortality rate of 40.58%. Individuals in the quartile of neighborhoods with lowest neighborhood socio-economic status had 1.3 times the all-cause post-stroke mortality rate (HR 1.29 95% CI 1.08-1.54); after adjustment for age sex, race, and year of study (2005, 2010 and 2015). After adjustment for age, race, sex, year of study and NIH stroke scale score, individuals living in the quartile of neighborhoods with the lowest neighborhood socio-economic status had 1.29 times the risk of dying (HR 1.29 95% CI 1.09-1.53). We found insufficient evidence of an association between neighborhood socio-economic status and 5-year case fatality rate among intracerebral hemorrhages.Conclusion:Rates of all-cause post-stroke long-term mortality were higher among patients with ischemic stroke who lived in neighborhoods with lowest quartile of socio-economic status. This effect persisted even after adjusting for stroke severity.

Stroke, Volume 55, Issue Suppl_1, Page AWP108-AWP108, February 1, 2024. Introduction:Hospital readmissions are often used as an indicator of quality of care. However, identifying patients at risk of readmission after stroke is challenging and predictive models have historically not performed well, in part because they often rely on single data sources. Data linkage might offer a solution.Methods:We probabilistically linked data from the Michigan’s Get With The Guidelines Stroke registry and Michigan Value Collaborative multipayer claims database from Medicare and Blue Cross Blue Shield beneficiaries discharged alive following acute stroke (ICD-10 I61-I63) between 2016-2020. The registry dataset included 64 variables covering demographics, stroke presentation, medical history, procedures, and complications. The hospital dataset included 20 variables from the American Hospital Association’s database. The claims dataset included payer and 79 HCC comorbidity codes. Using combinations of the 3 data sources, we examined the performance of multivariable LASSO logistic regression models to predict all cause readmission at 30-days and 1-year post discharge. We generated hospital-specific testing and training models and reported the mean model discrimination (AUC) of all combinations of the 3 datasets.Results:Of 19,382 linked stroke discharges, 2,724 (14.1%) and 8,169 (42.2%) were readmitted within 30-days and 1-year, respectively. For 30-day readmission, the model based on only registry data produced the best performance (M1, Table). However, for prediction of 1-year readmission, the combination of registry and claims data produced the best performing model (M13, Table). Hospital level characteristics did not have any significant impact on prediction accuracy.Conclusions:Clinical registry data was the best data source for predicting 30-day readmission. However, claims based data were additive when predicting readmission within 1-year, probably because HCC codes add information about total comorbidity burden.