High levels of hospital readmission (rehospitalisation rates) is widely used as indicator of a poor quality of care. This is sometimes also referred to as recidivism or heavy utilization. Previous studies have examined a number of factors likely to influence readmission, although a systematic review of research on post-discharge factors and readmissions has not been conducted so far. The main objective of this review was to identify frequently reported post-discharge factors and their effects on readmission rates. Studies on the association between post-discharge variables and readmission after an index discharge with a main psychiatric diagnosis were searched in the bibliographic databases Ovid Medline, PsycINFO, ProQuest Health Management, OpenGrey and Google Scholar. Relevant articles published between January 1990 and June 2014 were included. A systematic approach was used to extract and organize in categories the information about post-discharge factors associated with readmission rates. Of the 760 articles identified by the initial search, 80 were selected for this review which included a total number of 59 different predictors of psychiatric readmission. Subsequently these were grouped into four categories: 1) individual vulnerability factors, 2) aftercare related factors, 3) community care and service responsiveness, and 4) contextual factors and social support. Individual factors were addressed in 58 papers and were found to be significant in 37 of these, aftercare factors were significant in 30 out of the 45 papers, community care and social support factors were significant in 21 out of 31 papers addressing these while contextual factors and social support were significant in all seven papers which studied them. This review represents a first attempt at providing an overview of post-discharge factors previously studied in association with readmission. Hence, by mapping out the current research in the area, it highlights the gaps in research and it provides guidance future studies in the area.

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.

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.

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.

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 epidemiology of neglected tropical diseases (NTD) is persistently underprioritized, despite NTD being widespread among the poorest populations and in the least developed countries on earth. This situation necessitates thorough and efficient public health intervention. Romania is at the brink of becoming a developed country. However, this South-Eastern European country appears to be a region that is susceptible to an underestimated burden of parasitic diseases despite recent public health reforms. Moreover, there is an evident lack of new epidemiologic data on NTD after Romania’s accession to the European Union (EU) in 2007. Using the national ICD-10 dataset for hospitalized patients in Romania, we generated time series datasets for 2008–2018. The objective was to gain deep understanding of the epidemiological distribution of three selected and highly endemic parasitic diseases, namely, ascariasis, enterobiasis and cystic echinococcosis (CE), during this period and forecast their courses for the ensuing two years. Through descriptive and inferential analysis, we observed a decline in case numbers for all three NTD. Several distributional particularities at regional level emerged. Furthermore, we performed predictions using a novel automated time series (AutoTS) machine learning tool and could interestingly show a stable course for these parasitic NTD. Such predictions can help public health officials and medical organizations to implement targeted disease prevention and control. To our knowledge, this is the first study involving a retrospective analysis of ascariasis, enterobiasis and CE on a nationwide scale in Romania. It is also the first to use AutoTS technology for parasitic NTD.