Humanitarian action in the health sector has played a crucial role in mitigating the impact of crises - conflicts, famines, and natural disasters - on morbidity and mortality, while also highlighting structural and operational limitations. This contribution reviews the evolution of humanitarian system reforms, from the initiatives following the failures observed in Rwanda, Darfur, Haiti, and Pakistan to the more recent strategies for renewal. Particular attention is given to the report of the Johns Hopkins-Lancet Commission on Health, Conflicts, and Forced Displacement (2026), which proposes a profound transformation of humanitarian architecture and governance, centred on strengthening the role of affected communities, increasing emphasis on accountability and monitoring of humanitarian law violations, achieving more equitable funding, and ensuring effective safeguarding of the right to health.The current convergence of protracted conflicts, attacks against civilians, personnel, and health infrastructures, forced displacements, and drastic reductions in funding has brought the humanitarian system to the brink of collapse. In this context, marked by increasing humanitarian needs, the Commission's recommendations, while not new, provide a platform to promote a structural change in the humanitarian system, oriented towards greater equity and the transfer of resources and decision-making responsibilities to the local level.
OBJECTIVES:to describe the garbage codes (GCs) identified in the Global Burden of Disease Study (GBD) 2023 and their redistribution to underlying causes of death in Italy in 2021. Specifically, the study aims to: 1. compare temporal trends in the proportion of GCs in Italy with those of other Western European countries with similar population sizes; 2. identify the most frequent GC packages, analyze their geographic distribution, and determine the causes to which they are reassigned; 3. examine the relationship between the proportion of GCs and contextual factors related to death certification, including the type of certifier, place of death, and requests for autopsy. DESIGN:descriptive epidemiological study based on GBD 2023 estimates. SETTING AND PARTICIPANTS:the analysis focuses on the Italian population in 2021, stratified by 21 subnational units, including 19 regions and 2 autonomous provinces. MAIN OUTCOME MEASURES:total number of deaths; number of GCs, defined as deaths attributed to causes that do not represent underlying causes of death; percentage of GCs, calculated as the number of GCs divided by total deaths and multiplied by 100. RESULTS:the proportion of GCs in Italy gradually decreased over time, from 34.6% in 1990 to 28.8% in 2021. In 2021, the three most frequent GC packages at the national level were 'unspecified type of stroke' (4.28% of total deaths), 'unspecified type of diabetes' (2.44%), and 'heart failure, right or left' (2.38%). In the same year, the proportion of GCs was positively correlated with the share of deaths occurring at home (r 0.71; p <0.001), with missing data on the type of certifying physician (r 0.54, p=0.020), on place of death (r 0.77, p <0.001), and on autopsy requests (r 0.76, p <0.001). CONCLUSIONS:misreporting of causes of death arises from multiple mechanisms, reflecting errors of different nature and severity, with important implications for public health policies and health information systems. While redistribution methods are essential to produce comparable and policy-relevant estimates, improving data quality at the source remains a critical priority.
OBJECTIVES:to explore the experiences and training needs of healthcare workers involved in cancer screening programmes in Italy during the COVID-19 pandemic. DESIGN:an anonymous questionnaire was developed by the Institute for Cancer Research, Prevention, and Clinical Network (ISPRO) of Florence (Italy), distributed using Google Forms between May and June 2023. Responses were collected using 5-point Likert scales, multiple-choice questions, and open-ended prompts. Descriptive analyses were performed using Stata/SE version 16.1 for quantitative data; thematic analysis was applied to open-ended questions. SETTING AND PARTICIPANTS:the Italian Ministry of Health's CCM 2021 multiregional programme aimed to develop a training programme for cancer screening professionals to address COVID-19-related issues and maintain a high level of preparedness. This exploratory survey guided the design of the training programme which targeted cancer screening workers in 3 Regions: Toscana, Emilia-Romagna, and Basilicata. MAIN OUTCOME MEASURES:the questionnaire comprised 36 items in 3 sections: experiences during the pandemic, organizational support, and sociodemographic characteristics. RESULTS:the responses were 317. Participants were predominantly female (82.3%), mostly from Emilia-Romagna (65.0%) and Toscana (33.1%). The most frequently reported negative consequence concerned psychological well-being (63.1%), yet only 3.5% of the participants received psychological counselling, and nearly half (49.8%) reported no organizational support. However, teamwork and collaboration among colleagues were generally high (87.1%); 65.9% reported developing new competencies. Many opposed suspending first-level screening in future emergencies (46.1%) and strongly supported reaffirming screening importance within healthcare (89.3%) and society (92.8%). A structured pandemic plan was considered the most valuable measure (63.1%). CONCLUSIONS:enhancing cancer screening programme resilience requires a systems-level approach integrating psychological support and proactive governance. Future policies should use these insights to safeguard users and professionals.
OBJECTIVES:to apply, at the regional level, the algorithm developed by the Health Protection Agency (ATS) of the Metropolitan City of Milan to identify people with disabilities through the integration of routinely collected health and social care databases; to produce annual prevalence estimates and describe their territorial distribution (across ATS jurisdictions and districts), as well as the main classification characteristics according to the International Classification of Functioning, Disability and Health (ICF-2018), a framework that characterizes an individual's health status through the coding of body functions and structures, activities, and participation. DESIGN:retrospective observational study based on administrative data. SETTING AND PARTICIPANTS:residents in Lombardy Region (Northern Italy), which is divided into 8 ATSs and 86 districts, during the period 2018-2024. MAIN OUTCOME MEASURES:annual period prevalence of disability; sex differences; contribution of data flows; ICF-2018 classification combinations. RESULTS:in 2024, 1,083,538 people with disability were identified (10.8% of the regional population). Prevalence ranged across ATSs from 9.6% to 16.9%, and across districts from 6.3% to 19.8%. The largest overall contributions came from outpatient claims (28-SAN), hospital discharge records (SDO), exemption registries, and major prosthetics, with heterogeneity across ATSs. The most frequent ICF-2018 combination was "Structures of the nervous system/Mental functions" identified exclusively in healthcare flows. CONCLUSIONS:integrating healthcare and sociohealthcare flows within a shared ICF framework enables comparable small-area disability estimates to support integrated planning. Clinical validation and systematic monitoring of data quality are needed to strengthen the algorithm use.
BACKGROUND:cancer registries (CRs) are essential tools for oncological surveillance. The accurate coding of topography and morphology (through ICD-O-3 coding), traditionally performed manually, is complex and time-consuming. Artificial Intelligence (AI) offers new opportunities to automate this process, overcoming the limitations of existing algorithms, which often focus only on topography. OBJECTIVES:to develop an AI-based algorithm capable of automatically assigning the combined topography-morphology (topo-morpho) code from a synthetic clinical pathway expressed in natural language (LN-PDTA). DESIGN:retrospective observational study based on integrated registry and healthcare administrative data. Deterministic record linkage was performed among the CR, administrative databases, and pathology reports (AP), considering clinical events within ±180 days from incidence date. Clinical information (diagnoses, pharmacological therapies, surgical procedures, causes of death, pathology codes) was transformed into chronological clinical tokens concatenated into a single string. The target variable was the combined topo-morpho code assigned by registry coders. An LSTM neural network (embedding=64, hidden=128) was trained to learn token sequences. SETTING AND PARTICIPANTS:incident cancer cases recorded by the Cancer Registry of the Agency for Health Protection of the Metropolitan Area of Milan in 2017-2018; multiple, benign, and uncertain tumors were excluded. Main outcome measures: accuracy in the prediction of topography, morphology, and combined topo-morpho. Precision, recall, and F1 score at different confidence thresholds. Secondary analysis for high-incidence cancer sites and identification of the most predictive tokens and information sources. RESULTS:the dataset included 34,168 cases, split 80:20 into training and test sets. On the test set, the model achieved 89% accuracy for topography prediction, 59% for morphology, and 56% for the combined topo-morpho classification. Performances were better for highly frequent sites (breast 73%; colorectal 61%). For lung and prostate cancers, accuracy for topography reached 94% and 98%, respectively. The most predictive tokens and information sources were identified: pathology reports, mortality data, and surgical procedures for topography; pathology reports, hospital discharge diagnoses, and mortality for morphology. CONCLUSIONS:the LN-PDTA-based neural network approach shows promising results for the most frequent topographies and morphologies, thus enabling automatic coding of a fair number of cases, reducing manual coding time and supporting more efficient cancer registry operations.
