Association between Economic Growth, Mortality, and Healthcare Spending in 31 High-Income Countries

: This study aims to investigate the association between gross domestic product (GDP), mortality rate (MR) and current healthcare expenditure (CHE) in 31 high-income countries. We used panel data from 2000 to 2017 collected from WHO and OECD databases. The association between CHE, GDP and MR was investigated through a random-effects model. To control for reverse causality, we adopted a test of Granger causality. The model shows that the MR has a statistically significant and negative effect on CHE and that an increase in GDP is associated with an increase of CHE (p < 0.001). The Granger causality analysis shows that all the variables exhibit a bidirectional causality. We found a two-way relationship between GDP and CHE. Our analysis highlights the economic multiplier effect of CHE. In the debate on the optimal allocation of resources, this evidence should be taken into due consideration

The impact of the SARS-CoV-2 pandemic on cause-specific mortality patterns: a systematic literature review

Background Understanding the effects of the COVID-19 pandemic on cause-specific mortality should be a priority, as this metric allows for a detailed analysis of the true burden of the pandemic. The aim of this systematic literature review is to estimate the impact of the pandemic on different causes of death, providing a quantitative and qualitative analysis of the phenomenon. Methods We searched MEDLINE, Scopus, and ProQuest for studies that reported cause-specific mortality during the COVID-19 pandemic, extracting relevant data. Results A total of 2413 articles were retrieved, and after screening 22 were selected for data extraction. Cause-specific mortality results were reported using different units of measurement. The most frequently analyzed cause of death was cardiovascular diseases (n = 16), followed by cancer (n = 14) and diabetes (n = 11). We reported heterogeneous patterns of cause-specific mortality, except for suicide and road accident. Conclusions Evidence on non-COVID-19 cause-specific deaths is not exhaustive. Reliable scientific evidence is needed by policymakers to make the best decisions in an unprecedented and extremely uncertain historical period. We advocate for the urgent need to find an international consensus to define reliable methodological approaches to establish the true burden of the COVID-19 pandemic on non-COVID-19 mortality

Distributed Solutions for a Reliable Data-Driven Transformation of Healthcare Management and Research

Modern healthcare management and clinical practice strongly rely on data and scientific evidence. Digital technologies, tools, and services are core components of Healthcare Management and scientific Research (HMR). Data interoperability, security, privacy, and ease of sharing represent fundamental conditions for guaranteeing quality HMR. Current data management solutions in HMR are mainly built on two technological infrastructures: cloud-based (CB) or distributed ledger systems (DLTs). DLTs offer alternative and reliable alternatives for the management and sharing of data in HMR. Their use can help increase confidence and trust in the integrity of data and the resulting evidence. The aim of this paper is to shed light on CB and DLT solutions, emphasizing the potential role of innovative digital solutions based on DLTs in creating a data-driven transformation of HMR, and to describe relevant examples and practical uses of DLT-based solutions for patients, healthcare management, and research activities. DLTs in particular can be increasingly useful for patients to truly have control over their health, for healthcare policymakers to increase the quality of organizational processes, and for research funders, editors and publishers to increase the return on investment, and the reuse and reproducibility of research. In conclusion, harnessing the potential of digital technologies is essential to transform healthcare management and research, by enhancing data quality, reliability, and trust

The Willingness toward Vaccination: A Focus on Non-Mandatory Vaccinations

The Special Issue "The Willingness toward Vaccination: A Focus on Non-mandatory Vaccinations", published in the journal Vaccines, has the main aim of gathering more data on vaccine hesitancy and the willingness of individuals to receive vaccinations, particularly in the context of non-mandatory vaccines. The aim is to address vaccine hesitancy and improve vaccine coverage rates, in addition to identifying the determinants of vaccine hesitancy itself. This Special Issue garners articles that examine the external and internal factors that can influence the decision-making process of individuals regarding vaccination. Given that vaccine hesitancy is present in a significant part of the general population, it is crucial to have a better analytical understanding of the areas where hesitancy arises to determine appropriate strategies to address this issue

Predict, diagnose, and treat chronic kidney disease with machine learning: a systematic literature review

Objectives In this systematic review we aimed at assessing how artificial intelligence (AI), including machine learning (ML) techniques have been deployed to predict, diagnose, and treat chronic kidney disease (CKD). We systematically reviewed the available evidence on these innovative techniques to improve CKD diagnosis and patient management.Methods We included English language studies retrieved from PubMed. The review is therefore to be classified as a "rapid review ", since it includes one database only, and has language restrictions; the novelty and importance of the issue make missing relevant papers unlikely. We extracted 16 variables, including: main aim, studied population, data source, sample size, problem type (regression, classification), predictors used, and performance metrics. We followed the Preferred Reporting Items for Systematic Reviews (PRISMA) approach; all main steps were done in duplicate. The review was registered on PROSPERO.ResultsFrom a total of 648 studies initially retrieved, 68 articles met the inclusion criteria.Models, as reported by authors, performed well, but the reported metrics were not homogeneous across articles and therefore direct comparison was not feasible. The most common aim was prediction of prognosis, followed by diagnosis of CKD. Algorithm generalizability, and testing on diverse populations was rarely taken into account. Furthermore, the clinical evaluation and validation of the models/algorithms was perused; only a fraction of the included studies, 6 out of 68, were performed in a clinical context.Conclusions Machine learning is a promising tool for the prediction of risk, diagnosis, and therapy management for CKD patients. Nonetheless, future work is needed to address the interpretability, generalizability, and fairness of the models to ensure the safe application of such technologies in routine clinical practice

Variations of the quality of care during the COVID-19 pandemic affected the mortality rate of non-COVID-19 patients with hip fracture

IntroductionAs COVID-19 roared through the world, governments worldwide enforced containment measures that affected various treatment pathways, including those for hip fractures (HFs). This study aimed to measure process and outcome indicators related to the quality of care provided to non-COVID-19 elderly patients affected by HF in Emilia-Romagna, a region of Italy severely hit by the pandemic.MethodsWe collected the hospital discharge records of all patients admitted to the hospitals of Emilia-Romagna with a diagnosis of HF from January to May in the years 2019 (pre-pandemic period) and 2020 (pandemic period). We analyzed surgery rate, surgery delays, length of hospital stay, timely rehabilitation, and 30-day mortality for each HF patient. We evaluated monthly data (2020 vs. 2019) with the chi-square and t-test, where appropriate. Logistic regression was used to investigate the differences in 30-day mortality.ResultsOur study included 5379 patients with HF. In April and May 2020, there was a significant increase in the proportion of HF patients that did not undergo timely surgery. In March 2020, we found a significant increase in mortality (OR = 2.22). Male sex (OR = 1.92), age >= 90 years (OR = 4.33), surgery after 48 hours (OR = 3.08) and not receiving surgery (OR = 6.19) were significantly associated with increased mortality. After adjusting for the aforementioned factors, patients hospitalized in March 2020 still suffered higher mortality (OR = 2.21).ConclusionsThere was a reduction in the overall quality of care provided to non-COVID-19 elderly patients affected by HF, whose mortality increased in March 2020. Patients' characteristics and variations in processes of care partially explained this increase. Policymakers and professionals involved in the management of COVID-19 patients should be aware of the needs of patients with other health needs, which should be carefully investigated and included in future emergency preparedness and response plans

Cross-sectional analysis of family factors associated with lifestyle habits in a sample of Italian Primary School Children : the I-MOVE Project

: The acquisition of healthy dietary and exercise habits during childhood is essential for maintaining these behaviors during adulthood. In early childhood, parents have a profound influence on a child's lifestyle pursuits, serving as both role models and decision-makers. The present study examines family factors as potential contributors to healthy lifestyle habits and their child's overall diet quality among a sample of primary school children. A secondary aim is to evaluate several aspects of diet quality using the Mediterranean adaptation of the Diet Quality Index-International (DQI-I). This cross-sectional study involved 106 children enrolled in a primary school located in Imola, Italy. Data were collected from October to December 2019 using an interactive tool used to assess parent characteristics, children's lifestyle, food frequency (ZOOM-8 questionnaire), and actigraph accelerometers to capture children's physical activity and sedentary behavior. Adherence to the Mediterranean Diet (expressed by KIDMED Index) was positively associated with fathers' educational level, parental sport participation, and the parent's overall nutritional knowledge. Higher mothers' educational level was inversely associated with children's leisure screen time. Parents' nutritional knowledge was positively related to children's average daily minutes of organized sport activities. The better score for DQI-I was for consumption adequacy, followed by variety and moderation. The lowest score was for overall balance. The present study reinforces the importance of family factors in young children's lifestyle choices, particularly their dietary, leisure time, and exercise habits

Regional and sex inequalities of avoidable mortality in Italy: A time trend analysis

Objectives: This study provides a comprehensive analysis of avoidable mortality (AM), treatable mortality (TM), and preventable mortality (PM) across Italy, focusing on region- and gender-specific inequalities over a 14-year period. Study design: Time-trend analysis (2006–2019). Methods: The study was conducted using mortality data from the Italian Institute of Statistics to evaluate the extent and patterns of AM, TM, and PM in Italy. Biennial age-standardized mortality rates were calculated by gender and region using the joint OECD/Eurostat list. Results: The overall AM rates showed a large reduction from 2006/7 (221.0 per 100,000) to 2018/9 (166.4 per 100,000). Notably, females consistently displayed lower AM rates than males. Furthermore, both gender differences and the North–South gap of AM decreased during the period studied. The regions with the highest AM rates fluctuated throughout the study period. The highest percentage decrease in AM from 2006/7 to 2018/9, for both males (−41.3 %) and females (−34.2 %), was registered in the autonomous province of Trento, while the lowest reduction was observed in Molise for males (−17.4 %) and in Marche for females (−10.0 %). Conclusions: Remarkable gender and regional differences in AM between 2006 and 2019 have been recorded in Italy, although they have decreased over years. Continuous monitoring of AM and the implementation of region- and gender-specific interventions is essential to provide valuable insights for both policy and public health practice. This study contributes to the efforts to improve health equity between Italian regions

Semi-Automatic Systematic Literature Reviews and Information Extraction of COVID-19 Scientific Evidence: Description and Preliminary Results of the COKE Project

The COVID-19 pandemic highlighted the importance of validated and updated scientific information to help policy makers, healthcare professionals, and the public. The speed in disseminating reliable information and the subsequent guidelines and policy implementation are also essential to save as many lives as possible. Trustworthy guidelines should be based on a systematic evidence review which uses reproducible analytical methods to collect secondary data and analyse them. However, the guidelines’ drafting process is time consuming and requires a great deal of resources. This paper aims to highlight the importance of accelerating and streamlining the extraction and synthesis of scientific evidence, specifically within the systematic review process. To do so, this paper describes the COKE (COVID-19 Knowledge Extraction framework for next generation discovery science) Project, which involves the use of machine reading and deep learning to design and implement a semi-automated system that supports and enhances the systematic literature review and guideline drafting processes. Specifically, we propose a framework for aiding in the literature selection and navigation process that employs natural language processing and clustering techniques for selecting and organizing the literature for human consultation, according to PICO (Population/Problem, Intervention, Comparison, and Outcome) elements. We show some preliminary results of the automatic classification of sentences on a dataset of abstracts related to COVID-19