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Background and Objectives : Groin hernia repair surgery (GHRS) is among the most common elective interventions. The aim of this three-year nationwide study on GHRS is to provide a thorough analysis of the impact that the COVID-19 pandemic had on the Romanian Health System in regard to elective procedures. Materials and Methods : 46,795 groin hernia cases obtained between 2019 and 2021 from the DRG database using ICD-10 diagnostic codes. The data were collected from all 261 GHRS performing hospitals nationwide, including 227 public hospitals (PbH) and 34 private hospitals (PvH). The 42 variables taken into account were processed using Microsoft Excel 2021, applying Chi square, F-Test Two-Sample for variances, and Two Sample t -Test. The significance threshold considered was p < 0.001. Results : Of the grand total of cases, 96.2% were inguinal hernias, 86.8% were performed on men, 15.2% were laparoscopic procedures, and 6.88% were in PvH. Overall, due to the pandemic, the total number of GHRS decreased with 44.45% in 2020 and with 29.72% in 2021 compared to pre-pandemic year 2019. April 2020 shows the steepest decrease in GHRS (91 procedures nationwide). In the private sector, there was an opposite trend with increases in the number of cases by 12.21% and a 70.22% in both pandemic years. The mean admission period (MAP) for all procedures was 5.5 days. There was a significant difference between PbH and PvH (5.75 vs. 2.8 days, p < 0.0001). During the pandemic, the MAP in PbH decreased (6.02 in 2019, 5.82 in 2020 and 5.3 in 2021), remaining stable for PvH (2.9 days in 2019, 2.85 days in 2020 and 2.74 days in 2021). Conclusions : The COVID-19 pandemic significantly reduced the overall number of GHRS performed in Romania in 2020 and 2021, compared to 2019. However, the private sector thrived with an actual increase in the number of cases. There was a significant lower MAP in the PvH compared to PbH throughout the three-year period.

Background: Health policies in transitioning health systems are rarely informed by the economic burden of disease due to scanty access to data. This study aimed to estimate direct and indirect costs for first-ever acute ischemic stroke (AIS) during the first year for patients residing in Cluj, Romania, and hospitalized in 2019 at the County Emergency Hospital (CEH)., Methods: The study was conducted using a mixed, retrospective costing methodology from a societal perspective to measure the cost of first-ever AIS in the first year after onset. Patient pathways for AIS were reconstructed to aid in mapping inpatient and outpatient cost items. We used anonymized administrative and clinical data at the hospital level and publicly available databases., Results: The average cost per patient in the first year after stroke onset was RON 25,297.83 (EUR 5226.82), out of which 80.87% were direct costs. The total cost in Cluj, Romania in 2019 was RON 17,455,502.7 (EUR 3,606,505.8)., Conclusions: Our costing exercise uncovered shortcomings of stroke management in Romania, particularly related to acute care and neurorehabilitation service provision. Romania spends significantly less on healthcare than other countries (5.5% of GDP vs. 9.8% European Union average), exposing stroke survivors to a disproportionately high risk for preventable and treatable post-stroke disability.

The application of machine learning (ML) for use in generating insights and making predictions on new records continues to expand within the medical community. Despite this progress to date, the application of time series analysis has remained underexplored due to complexity of the underlying techniques. In this study, we have deployed a novel ML, called automated time series (AutoTS) machine learning, to automate data processing and the application of a multitude of models to assess which best forecasts future values. This rapid experimentation allows for and enables the selection of the most accurate model in order to perform time series predictions. By using the nation-wide ICD-10 (International Classification of Diseases, Tenth Revision) dataset of hospitalized patients of Romania, we have generated time series datasets over the period of 2008-2018 and performed highly accurate AutoTS predictions for the ten deadliest diseases. Forecast results for the years 2019 and 2020 were generated on a NUTS 2 (Nomenclature of Territorial Units for Statistics) regional level. This is the first study to our knowledge to perform time series forecasting of multiple diseases at a regional level using automated time series machine learning on a national ICD-10 dataset. The deployment of AutoTS technology can help decision makers in implementing targeted national health policies more efficiently.

The aim of this paper is to examine aggressive behaviors among Romanian high school students between 15 and 16 years old, to compare data in two national representative samples and to identify factors associated with physical fighting. This study investigates the association of selected factors (social, school performance and substance use) with physical fighting. A total of 2289 Romanian students were included in the 2007 database and 2770 in the 2011 database. This study revealed that 35.87% of the teenagers have taken part in a physical fight during the previous 12 months, as compared with the European average of 31.5%. Romania has the highest prevalence of violent behavior by participating in a group bruising of an individual in both surveys, 2007 and 2011. A logistic regression analysis performed for the 2011 study revealed the following factors associated with physical fighting: binge drinking during the previous 30 days, male gender, serious problems with friends, parent(s) who do not know where and with whom the adolescents spend their evenings, poor parental caring, low school grades, and high truancy. A decrease in almost all aggressive behaviors was noticed in 2011, compared to 2007. These findings may be useful to support and guide policy makers regarding improvement and implementation of strategies to further prevent aggressive behaviors in teenagers., Competing Interests: The authors declare no conflict of interest.