ArticleBMC public health2025
Unmasking pandemic patterns: decoding the COVID-19's impact on mortality in Italy with Generalized Gamma overdispersion model.
Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundItaly has been significantly impacted by the COVID-19 pandemic, particularly in its early stages, resulting in healthcare strain, societal restrictions, and disruption. Understanding the long-term effects, notably excess mortality beyond the initial peaks, remains important. Prior studies have focused on the early phase, leaving out subsequent and updated mortality trends.
methodThis study analyzes Italian mortality rates from 2015 to 2023, employing Generalized Additive Models for Location, Scale, and Shape (GAMLSS), the Generalized Gamma Overdispersion model. Data analysis considered factors such as gender, age groups (under 65 and 65 or older), and geographical differences (Northern versus Central-Southern Italy) as key characteristics of the mortality trend.
resultsThe study identified several phases of the pandemic, characterized by a significant early 2020 mortality peak and subsequent smaller peaks. Mortality rates were higher in Northern Italy, with males and the elderly being the most affected. Overall, mortality rates increased during the pandemic, particularly among these groups, and then returned to normal levels in 2023. An increase in the overdispersion parameter, estimated via the GAMLSS model, is evident in the post-pandemic phase and persists until 2023.
conclusionThe findings highlight the complex nature of COVID-19's impact on mortality in Italy. They reveal the temporal phases, regional disparities, and demographic vulnerabilities that contribute to the overall mortality picture. The overdispersion component indicates more significant variability and unpredictability of mortality patterns until 2023. This highlights the intricate interplay of factors, including healthcare capacity, viral mutations, and the effectiveness of public health responses. This study emphasizes the need for targeted interventions and protective measures in the most affected groups.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.