Vaccination currently appears to be the only strategy to contain the spread of COVID-19. At the same time, vaccine hesitancy (VH) could limit its efficacy and has, therefore, attracted the attention of Public Health Systems. This systematic review aimed at assessing anti-COVID-19 vaccine acceptance rates worldwide and at identifying populations more prone to vaccine hesitancy, for which specific interventions should be planned. PubMed database was searched using a purposely formulated string. One hundred out of the 9243 studies retrieved were considered pertinent and thus included in the analyses. VH rate was analyzed according to patient geographical origin, ethnicity, age, study setting, and method used for data collection; data from specific populations were separately analyzed. Overall, this study demonstrated significant differences in terms of VH in the general population and in the specific subgroups examined according to geographical, demographic factors, as well as associated comorbidities, underlining the need for purposely designed studies in specific populations from the different countries, to design targeted programs aimed at increasing awareness for confidence and complacency toward COVID-19 vaccines.

Background: Patient-reported outcome measures (PROMs) are an extensively used tool to assess and improve the quality of healthcare services. PROMs can be related to individual demographic and clinical characteristics in patients undergoing hip arthroplasty (HA). The aim of this study is to identify distinct subgroups of patients with unique trajectories of PROMS scores and to determine patients’ features associated with these subgroups. Methods: We conducted a prospective, cohort study in which PROMs questionnaires (Euro Quality 5 Dimensions 3L, EQ-5D-3L, Euro-Quality-Visual-Analytic-Score, EQ-VAS, Hip disability and Osteoarthritis Outcome Score, HOOS-PS) were administered to patients undergoing elective HA pre-operatively, and at 6 and 12 months after surgery. For each measure, latent class growth analysis and growth mixture models were used to identify subgroups of patients with distinct trajectories of scores. Demografic and clinical predictors of the latent classes in growth mixture model were identified using a 3-step approach. Results: We found three distinct trajectories for each PROM score. These trajectories indicated a response heterogeneity to the HA among the patients (n = 991). Patient’s gender, ASA score, and obesity were significantly associated with different PROMs trajectories. Conclusions: We identified three distinct trajectories for each of the three PROMs indicators. Several demographic and clinical characteristics are associated with the different trajectories of PROMs at 6 and 12 months after HA and could be used to identify groups of patients with different outcomes following HA surgery. These findings underline the importance of patient-centered care, supporting the usefulness of integrating PROMs data alongside routinely collected healthcare records for guiding clinical care and maximizing patients’ positive outcomes. Trial registration: Protocol version (1.0) and trial registration data are available on the platform www.clinicaltrial.gov with the identifier NCT03790267, posted on December 31, 2018.

The mortality of hip fracture (HF) patients is increased by concomitant COVID-19; however, evidence is limited to only short follow-up. A retrospective matched case–control study was designed with the aim to report the 90-day mortality and determine the hazard ratio (HR) of concomitant HF and COVID-19 infection. Cases were patients hospitalized for HF and diagnosed with COVID-19. Controls were patients hospitalized for HF not meeting the criteria for COVID-19 diagnosis and were individually matched with each case through a case–control (1:3) matching algorithm. A total of 89 HF patients were treated during the study period, and 14 of them were diagnosed as COVID-19 positive (overall 15.7%). Patients’ demographic, clinical, and surgical characteristics were similar between case and control groups. At 90 days after surgery, 5 deaths were registered among the 14 COVID-19 cases (35.7%) and 4 among the 42 HF controls (9.5%). COVID-19-positive cases had a higher risk of mortality at 30 days (HR = 4.51; p = 0.0490) and 90 days (HR = 4.50; p = 0.025) with respect to controls. Patients with concomitant HF and COVID-19 exhibit high perioperative mortality, which reaches a plateau of nearly 30–35% after 30 to 45 days and is stable up to 90 days. The mortality risk is more than four-fold higher in patients with COVID-19.

Antimicrobial resistance is a global health issue, and the WHO has made significant efforts in the development of tools for its monitoring. However, such tools are underutilized, due to limited knowledge, technical capacity, and scarcity of economic resources. AMR surveillance can be conducted using WHOnet and R, two free-of-charge software tools widely adopted in both clinical practice and scientific research. WHOnet is designed for managing laboratory data and antimicrobial susceptibility test results, while R is a programming language dedicated to statistical computing and data visualization. The combined use of these tools enables a reproducible workflow for retrospective AMR trend analysis. This paper provides step-by-step instructions on how to perform such analysis and also provides the respective R code. The described code and software results are shown using real-world data from an Italian hospital as an example. The standardization of the analysis process and the rapid availability of data on antimicrobial resistance are critical for both clinicians and public health professionals. They would allow for empirical decisions on antimicrobial treatment based on the specific epidemiological characteristics of the hospital or community setting.

Clinical research is a pivotal element for the Emilia-Romagna (Italy) healthcare system. Since 2009, several regional legislative acts established that clinical research was one of healthcare workers’ institutional activity as well as healthcare assistance and recommended each public Hospitals and Local Health Trusts (LHTs) to set up a research infrastructure (RI) to support clinical researchers. The goal of this intervention was both to assess the regional RI organization and to design a policy fostering the development of a RIs’ regional network. Our work, that took place in one year, consists of three-steps conducting: (I) a survey on all 15 RIs to assess their organizational model; (II) an interview to the key informants of each RIs to assess their strengths and weaknesses; (III) a three-round Delphi survey to evaluate the stratification of the activities in terms of complexity. The survey showed a high level of heterogeneity among RIs in terms of organizational structure, activities, human and instrumental resources and strengths and weaknesses. For example technological, economic, and human resources were considered opportunities, although difficulty at hiring data managers emerged as a potential threat. The Delphi panel assigned 3 levels of complexity (low, medium, high) to each activity carried out by RIs: 14 (47%) were considered low (mandatory for all RIs); 11 (37%) were considered medium (needed in RIs with medium volume/complexity of research activities) and, lastly, only 1 (3%) was considered high (needed in RIs with high complexity of research activities); for 4 (13%) activities no consensus was reached. The survey showed high level of heterogeneity and fragmentation among regional RIs in terms of organization, activities, resources and support offered to researchers. It also identified which level of complexity each RI should have and proposed possible solutions fostering a regional network of RIs to improve research activities in our Region. Key messages: • Research is a key element of any innovative healthcare system. Public research needs a strong coordination. • This work enabled policymakers to draft a policy based on evidence coming from stakeholders.

Background: Fear of contracting COVID infection may cause delay or reduction in hospital admissions for acute cerebrovascular events. The main objective of this study was to measure the indirect impact of the pandemic on the volume of hospitalizations for ischemic stroke (IS), in-hospital and 30-day mortality, 30-day readmissions and out-of-hospital IS mortality. Methods: Study design was a quasi-experimental population-based interrupted time-series. Data were drawn from the Health Information System of the Emilia-Romagna region (Italy). To evaluate volume of hospitalizations and inpatient mortality, we included all inpatient care hospitalizations with a principal diagnosis of IS (ICD-9-CM: 433.x1, 434.x1, 436) between 2015-2021. To evaluate out-of-hospital mortality, we included all patients who died at home for IS (ICD-10 cause of death: H34.1, I63.x, I64.x). Results: We observed in 2020 a 13% decrease in IS hospitalizations and an increase in the proportion of in-hospital mortality (+20%), out-of-hospital mortality (2 more deaths per day on average), and 30-day mortality (+4%). Compared to 2020, 2021 showed a 16% lower in-hospital mortality and a 7% lower 30-day mortality. Change in out-of-hospital mortality was not statistically significant. Comparing 2021 to 2015-2019, almost all outcomes did not significantly differ (in-hospital mortality: +0.62%; 30-day mortality: -3.18%; 30-day readmissions: +2.34%). Wee found a significant 12% decrease in hospital admissions and an increase in out-of-hospital mortality by 1.37 deaths per day on average, in 2021 compared to 2015-2019. Conclusions: COVID pandemic caused a reduction in hospital admissions, resulting in worse outcomes, mostly in 2020 and to a lesser extent in 2021. The improvement in 2021 for most outcomes may have benefitted by the vaccine indirect effect. Key messages: • Reduction in IS admissions due to COVID had a negative impact on patient outcomes. • This lesson needs to be acknowledged and considered in a preparedness framework for future pandemics.