The state of health in the European Union (EU-27) in 2019: a systematic analysis for the Global Burden of Disease study 2019.

BACKGROUND: The European Union (EU) faces many health-related challenges. Burden of diseases information and the resulting trends over time are essential for health planning. This paper reports estimates of disease burden in the EU and individual 27 EU countries in 2019, and compares them with those in 2010. METHODS: We used the Global Burden of Disease 2019 study estimates and 95% uncertainty intervals for the whole EU and each country to evaluate age-standardised death, years of life lost (YLLs), years lived with disability (YLDs) and disability-adjusted life years (DALYs) rates for Level 2 causes, as well as life expectancy and healthy life expectancy (HALE). RESULTS: In 2019, the age-standardised death and DALY rates in the EU were 465.8 deaths and 20,251.0 DALYs per 100,000 inhabitants, respectively. Between 2010 and 2019, there were significant decreases in age-standardised death and YLL rates across EU countries. However, YLD rates remained mainly unchanged. The largest decreases in age-standardised DALY rates were observed for "HIV/AIDS and sexually transmitted diseases" and "transport injuries" (each -19%). "Diabetes and kidney diseases" showed a significant increase for age-standardised DALY rates across the EU (3.5%). In addition, "mental disorders" showed an increasing age-standardised YLL rate (14.5%). CONCLUSIONS: There was a clear trend towards improvement in the overall health status of the EU but with differences between countries. EU health policymakers need to address the burden of diseases, paying specific attention to causes such as mental disorders. There are many opportunities for mutual learning among otherwise similar countries with different patterns of disease.

The impact of comorbid psychiatric disorders on chronic obstructive pulmonary disease (COPD) hospitalizations: a nationwide retrospective study.

AIMS: To characterize the register of a secondary diagnosis of mental illnesses in all chronic obstructive pulmonary disease (COPD) hospitalizations registered in Portugal from 2008 to 2015 and explore their impact on hospitalization outcomes. METHODS: A retrospective observational study was conducted. Hospitalizations of patients with at least 40 years old, discharged between 2008 and 2015 with a primary diagnosis of COPD (ICD-9-CM codes 491.x, 492.x and 496) were retrieved from a national administrative database. Comorbid psychiatric diagnoses were identified and defined by the HCUP Clinical Classification Software (CCS) category codes 650-670 (excluding 662). Length of hospital stay (LoS), admission type, in-hospital mortality, and estimated hospital charges were analyzed according to psychiatric diagnostic categories using sex and age-adjusted models. RESULTS: Of 66,661 COPD hospitalizations, 25,869 (38.8%) were episodes with a registered psychiatric comorbidity. These were more likely to correspond to younger inpatients (OR = 2.16, 95%CI 2.09-2.23; p < 0.001), to stay longer at the hospital (aOR = 1.08, 95%CI 1.05-1.12; p < 0.001), to incur in higher estimated hospital charges (aOR = 1.37, 95%CI 1.33-1.42; p < 0.001) and to be urgently admitted (aOR = 1.33, 95%CI 1.23-1.44; p < 0.001). After adjustment for age, in-hospital mortality was lower for episodes with psychiatric diagnoses (aOR = 0.90; 95%CI 0.84-0.96; p < 0.001), except for organic and neurodegenerative diseases category and developmental disorders, intellectual disabilities and disorders usually diagnosed in infancy, childhood, or adolescence category. DISCUSSION: These findings corroborate the additional burden placed by psychiatric disorders on COPD hospitalizations, highlighting the importance of individualizing care to address these comorbidities and minimize their impact on treatment outcomes.

Burden of infectious disease studies in Europe and the United Kingdom: a review of methodological design choices.

This systematic literature review aimed to provide an overview of the characteristics and methods used in studies applying the disability-adjusted life years (DALY) concept for infectious diseases within European Union (EU)/European Economic Area (EEA)/European Free Trade Association (EFTA) countries and the United Kingdom. Electronic databases and grey literature were searched for articles reporting the assessment of DALY and its components. We considered studies in which researchers performed DALY calculations using primary epidemiological data input sources. We screened 3053 studies of which 2948 were excluded and 105 studies met our inclusion criteria. Of these studies, 22 were multi-country and 83 were single-country studies, of which 46 were from the Netherlands. Food- and water-borne diseases were the most frequently studied infectious diseases. Between 2015 and 2022, the number of burden of infectious disease studies was 1.6 times higher compared to that published between 2000 and 2014. Almost all studies (97%) estimated DALYs based on the incidence- and pathogen-based approach and without social weighting functions; however, there was less methodological consensus with regards to the disability weights and life tables that were applied. The number of burden of infectious disease studies undertaken across Europe has increased over time. Development and use of guidelines will promote performing burden of infectious disease studies and facilitate comparability of the results.

Epilepsy hospitalizations and mental disorders: A Portuguese population-based observational retrospective study (2008-2015).

BACKGROUND: Psychiatric comorbidities are highly frequent in people with epilepsy and were found to be markers of poorer prognosis. These comorbidities increase the use of healthcare resources, including emergency department visits and inpatient care. Despite this, there is little information on healthcare utilization associated with a wide range of comorbid mental disorders in people with epilepsy (PWE). OBJECTIVE: To characterize registered mental disorders among all hospitalizations with a primary diagnosis of epilepsy and to analyze their association with crucial hospitalization outcomes. METHODS: An observational retrospective study was performed using administrative data from hospitalization episodes with epilepsy as the primary diagnosis discharged between 2008 and 2015. Mental disorder categories 650 to 670 from Clinical Classification Software were selected as secondary diagnoses. Mann-Whitney U, Kruskall-Wallis, and Chi-squared tests were used to establish comparisons. For each episode, data regarding hospitalization outcomes was retrieved, including length of stay (LoS), in-hospital mortality (IHM), 8-year period readmissions, and total estimated charges. RESULTS: Overall, 27,785 hospitalizations were analyzed and 33.9% had registered mental disorders, with alcohol-related disorders being the most prevalent (11.7%). For episodes with a concomitant register of a mental disorder, LoS was significantly longer (5.0 vs. 4.0 days, P <0.001), and IHM was higher (2.8% vs. 2.2%, P <0.001), as were readmissions (25.5% vs. 23.7%, P <0.001), and median episodes' charges (1,578.7 vs. 1,324.4 euros, P <0.001). CONCLUSION: Epilepsy-related hospitalizations with registered mental disorders heightened the utilization of healthcare resources, stressing the importance of diagnosing and treating mental disorders in PWE.

Impact of multimorbidity patterns in hospital admissions: the case study of asthma.

Background: Most previous studies assessing multimorbidity in asthma assessed the frequency of individual comorbid diseases. Objective: We aimed to assess the frequency and clinical and economic impact of co-occurring groups of comorbidities (comorbidity patterns using the Charlson Comorbidity Index) on asthma hospitalizations. Methods: We assessed the dataset containing a registration of all Portuguese hospitalizations between 2011-2015. We applied three different approaches (regression models, association rule mining, and decision trees) to assess both the frequency and impact of comorbidities patterns in the length-of-stay, in-hospital mortality and hospital charges. For each approach, separate analyses were performed for episodes with asthma as main and as secondary diagnosis. Separate analyses were performed by participants' age group. Results: We assessed 198340 hospitalizations in patients >18 years old. Both in hospitalizations with asthma as main or secondary diagnosis, combinations of diseases involving cancer, metastasis, cerebrovascular disease, hemiplegia/paraplegia, and liver disease displayed a relevant clinical and economic burden. In hospitalizations having asthma as a secondary diagnosis, we identified several comorbidity patterns involving asthma and associated with increased length-of-stay (average impact of 1.3 [95%CI=0.6-2.0]-3.2 [95%CI=1.8-4.6] additional days), in-hospital mortality (OR range=1.4 [95%CI=1.0-2.0]-7.9 [95%CI=2.6-23.5]) and hospital charges (average additional charges of 351.0 [95%CI=219.1-482.8] to 1470.8 [95%CI=1004.6-1937.0]) Euro compared with hospitalizations without any registered Charlson comorbidity). Consistent results were observed with association rules mining and decision tree approaches. Conclusions: Our findings highlight the importance not only of a complete assessment of patients with asthma, but also of considering the presence of asthma in patients admitted by other diseases, as it may have a relevant impact on clinical and health services outcomes.

Multidisciplinary Development and Initial Validation of a Clinical Knowledge Base on Chronic Respiratory Diseases for mHealth Decision Support Systems.

Most mobile health (mHealth) decision support systems currently available for chronic obstructive respiratory diseases (CORDs) are not supported by clinical evidence or lack clinical validation. The development of the knowledge base that will feed the clinical decision support system is a crucial step that involves the collection and systematization of clinical knowledge from relevant scientific sources and its representation in a human-understandable and computer-interpretable way. This work describes the development and initial validation of a clinical knowledge base that can be integrated into mHealth decision support systems developed for patients with CORDs. A multidisciplinary team of health care professionals with clinical experience in respiratory diseases, together with data science and IT professionals, defined a new framework that can be used in other evidence-based systems. The knowledge base development began with a thorough review of the relevant scientific sources (eg, disease guidelines) to identify the recommendations to be implemented in the decision support system based on a consensus process. Recommendations were selected according to predefined inclusion criteria: (1) applicable to individuals with CORDs or to prevent CORDs, (2) directed toward patient self-management, (3) targeting adults, and (4) within the scope of the knowledge domains and subdomains defined. Then, the selected recommendations were prioritized according to (1) a harmonized level of evidence (reconciled from different sources); (2) the scope of the source document (international was preferred); (3) the entity that issued the source document; (4) the operability of the recommendation; and (5) health care professionals' perceptions of the relevance, potential impact, and reach of the recommendation. A total of 358 recommendations were selected. Next, the variables required to trigger those recommendations were defined (n=116) and operationalized into logical rules using Boolean logical operators (n=405). Finally, the knowledge base was implemented in an intelligent individualized coaching component and pretested with an asthma use case. Initial validation of the knowledge base was conducted internally using data from a population-based observational study of individuals with or without asthma or rhinitis. External validation of the appropriateness of the recommendations with the highest priority level was conducted independently by 4 physicians. In addition, a strategy for knowledge base updates, including an easy-to-use rules editor, was defined. Using this process, based on consensus and iterative improvement, we developed and conducted preliminary validation of a clinical knowledge base for CORDs that translates disease guidelines into personalized patient recommendations. The knowledge base can be used as part of mHealth decision support systems. This process could be replicated in other clinical areas.

Dementia-related agitation: a 6-year nationwide characterization and analysis of hospitalization outcomes.

OBJECTIVES: To characterize all hospitalizations held in mainland Portugal (2010-2015) with dementia-related agitation based on International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) coding, and to investigate whether there is a relationship between agitation and hospitalization outcomes. METHODS: A retrospective observational study was conducted using an administrative dataset containing data from all mainland Portuguese public hospitals. Only hospitalization episodes for patients aged over 65 years who have received a dementia diagnosis ascertained by an ICD-9-CM code of dementia with behavioral disturbance (294.11 and 294.21) and dementia without behavioral disturbance (294.10 and 294.20) were selected. Episodes were further grouped according to the presence of an agitation code. For each episode, demographic data and hospitalization outcomes, including length of stay (LoS), in-hospital mortality, discharge destination and all-cause hospital readmissions, were sourced from the dataset. Comparative analyses were performed and multivariable logistic methods were used to estimate the adjusted associations between agitation (exposure) and outcomes. RESULTS: Overall, 53,156 episodes were selected, of which 6,586 had an agitation code. These were mostly related to male, younger inpatients (mean 81.19 vs. 83.29 years, p < 0.001), had a higher comorbidity burden, stayed longer at the hospital (median 9.00 vs. 8.00 days, p < 0.001) and frequently ended being transferred to another facility with inpatient care. Agitation was shown to independently increase LoS (aOR = 1.385; 95%CI:1.314-1.461), but not the risk of a fatal outcome (aOR = 0.648; 95%CI:0.600-0.700). CONCLUSION: These results support the importance of detecting and managing agitation early on admission, since its prompt management may prevent lengthy disruptive hospitalizations.

Validity of the Paediatric Canadian Triage Acuity Scale in a Tertiary Hospital: An Analysis of Severity Markers' Variability.

With the increasing influx of patients and frequent overcrowding, the adoption of a valid triage system, capable of distinguishing patients who need urgent care, from those who can wait safely is paramount. Hence, the aim of this study is to evaluate the validity of the Paediatric Canadian Triage and Acuity Scale (PaedCTAS) in a Portuguese tertiary hospital. Furthermore, we aim to study the performance and appropriateness of the different surrogate severity markers to validate triage. This is a retrospective study considering all visits to the hospital's Paediatric Emergency Department (PED) between 2014 and 2019. This study considers cut-offs on all triage levels for dichotomization in order to calculate validity measures e.g. sensitivity, specificity and likelihood ratios, ROC curves; using hospital admission, admission to intensive care and the use of resources as outcomes/markers of severity. Over the study period there were 0.2% visits triaged as Level 1, 5.7% as Level 2, 39.4% as Level 3, 50.5% as Level 4, 4.2% as Level 5, from a total of 452,815 PED visits. The area under ROC curve was 0.96, 0.71, 0.76, 0.78, 0.59 for the surrogate markers: "Admitted to intensive care"; "Admitted to intermediate care"; "Admitted to hospital"; "Investigations performed in the PED" and "Uses PED resources", respectively. The association found between triage levels and the surrogate markers of severity suggests that the PedCTAS is highly valid. Different surrogate outcome markers convey different degrees of severity, hence different degrees of urgency. Therefore, the cut-offs to calculate validation measures and the thresholds of such measures should be chosen accordingly.

Multisource and temporal variability in Portuguese hospital administrative datasets: Data quality implications.

BACKGROUND: Unexpected variability across healthcare datasets may indicate data quality issues and thereby affect the credibility of these data for reutilization. No gold-standard reference dataset or methods for variability assessment are usually available for these datasets. In this study, we aim to describe the process of discovering data quality implications by applying a set of methods for assessing variability between sources and over time in a large hospital database. METHODS: We described and applied a set of multisource and temporal variability assessment methods in a large Portuguese hospitalization database, in which variation in condition-specific hospitalization ratios derived from clinically coded data were assessed between hospitals (sources) and over time. We identified condition-specific admissions using the Clinical Classification Software (CCS), developed by the Agency of Health Care Research and Quality. A Statistical Process Control (SPC) approach based on funnel plots of condition-specific standardized hospitalization ratios (SHR) was used to assess multisource variability, whereas temporal heat maps and Information-Geometric Temporal (IGT) plots were used to assess temporal variability by displaying temporal abrupt changes in data distributions. Results were presented for the 15 most common inpatient conditions (CCS) in Portugal. MAIN FINDINGS: Funnel plot assessment allowed the detection of several outlying hospitals whose SHRs were much lower or higher than expected. Adjusting SHR for hospital characteristics, beyond age and sex, considerably affected the degree of multisource variability for most diseases. Overall, probability distributions changed over time for most diseases, although heterogeneously. Abrupt temporal changes in data distributions for acute myocardial infarction and congestive heart failure coincided with the periods comprising the transition to the International Classification of Diseases, 10th revision, Clinical Modification, whereas changes in the Diagnosis-Related Groups software seem to have driven changes in data distributions for both acute myocardial infarction and liveborn admissions. The analysis of heat maps also allowed the detection of several discontinuities at hospital level over time, in some cases also coinciding with the aforementioned factors. CONCLUSIONS: This paper described the successful application of a set of reproducible, generalizable and systematic methods for variability assessment, including visualization tools that can be useful for detecting abnormal patterns in healthcare data, also addressing some limitations of common approaches. The presented method for multisource variability assessment is based on SPC, which is an advantage considering the lack of gold standard for such process. Properly controlling for hospital characteristics and differences in case-mix for estimating SHR is critical for isolating data quality-related variability among data sources. The use of IGT plots provides an advantage over common methods for temporal variability assessment due its suitability for multitype and multimodal data, which are common characteristics of healthcare data. The novelty of this work is the use of a set of methods to discover new data quality insights in healthcare data.

Methodological considerations in injury burden of disease studies across Europe: a systematic literature review.

BACKGROUND: Calculating the disease burden due to injury is complex, as it requires many methodological choices. Until now, an overview of the methodological design choices that have been made in burden of disease (BoD) studies in injury populations is not available. The aim of this systematic literature review was to identify existing injury BoD studies undertaken across Europe and to comprehensively review the methodological design choices and assumption parameters that have been made to calculate years of life lost (YLL) and years lived with disability (YLD) in these studies. METHODS: We searched EMBASE, MEDLINE, Cochrane Central, Google Scholar, and Web of Science, and the grey literature supplemented by handsearching, for BoD studies. We included injury BoD studies that quantified the BoD expressed in YLL, YLD, and disability-adjusted life years (DALY) in countries within the European Region between early-1990 and mid-2021. RESULTS: We retrieved 2,914 results of which 48 performed an injury-specific BoD assessment. Single-country independent and Global Burden of Disease (GBD)-linked injury BoD studies were performed in 11 European countries. Approximately 79% of injury BoD studies reported the BoD by external cause-of-injury. Most independent studies used the incidence-based approach to calculate YLDs. About half of the injury disease burden studies applied disability weights (DWs) developed by the GBD study. Almost all independent injury studies have determined YLL using national life tables. CONCLUSIONS: Considerable methodological variation across independent injury BoD assessments was observed; differences were mainly apparent in the design choices and assumption parameters towards injury YLD calculations, implementation of DWs, and the choice of life table for YLL calculations. Development and use of guidelines for performing and reporting of injury BoD studies is crucial to enhance transparency and comparability of injury BoD estimates across Europe and beyond.

All Patient Refined-Diagnosis Related Groups' (APR-DRGs) Severity of Illness and Risk of Mortality as predictors of in-hospital mortality.

The aims of this study were to assess All-Patient Refined Diagnosis-Related Groups' (APR-DRG) Severity of Illness (SOI) and Risk of Mortality (ROM) as predictors of in-hospital mortality, comparing with Charlson Comorbidity Index (CCI) and Elixhauser Comorbidity Index (ECI) scores. We performed a retrospective observational study using mainland Portuguese public hospitalizations of adult patients from 2011 to 2016. Model discrimination (C-statistic/ area under the curve) and goodness-of-fit (R-squared) were calculated. Our results comprised 4,176,142 hospitalizations with 5.9% in-hospital deaths. Compared to the CCI and ECI models, the model considering SOI, age and sex showed a statistically significantly higher discrimination in 49.6% (132 out of 266) of APR-DRGs, while in the model with ROM that happened in 33.5% of APR-DRGs. Between these two models, SOI was the best performer for nearly 20% of APR-DRGs. Some particular APR-DRGs have showed good discrimination (e.g. related to burns, viral meningitis or specific transplants). In conclusion, SOI or ROM, combined with age and sex, perform better than more widely used comorbidity indices. Despite ROM being the only score specifically designed for in-hospital mortality prediction, SOI performed better. These findings can be helpful for hospital or organizational models benchmarking or epidemiological analysis.

Depressive Disorder Related Hospitalizations in Portugal Between 2008-2015: a Nationwide Observational Study.

Depression is a prevalent disease, being one of the most relevant contributors of disability in the overall global burden of diseases. Hospitalization episodes are important quality indicators in psychiatric care. The primary aim of this study is to analyse depressive disorder related hospitalizations in Portuguese public hospitals and to detail clinical and sociodemographic differences among various subtypes of depression. Admissions with a primary diagnosis of depression in adult patients(> = 18 years) were selected from a national mainland hospitalization database. ICD-9-CM codes were used to select the diagnoses of interest: 296.2 × to 296.3x (Major depressive disorder), 300.4 (Dysthymic disorder) and 311 (Depressive disorder, not elsewhere classified). Birth date, sex, residence address, primary and secondary diagnoses, admission date, discharge date, length of stay (LoS), discharge status, and hospital estimated charges were obtained. A total of 28,569 hospitalizations (22,387 patients) with a primary diagnosis of depression were analysed. In the 8-year period of the study, 19.1% of all hospitalizations with a primary diagnosis of psychiatric disorder were linked to Depression. Major Depressive episodes were the most common (n = 15,384; 53.8%), followed by Depression unspecified episodes (n = 6,793; 23.8%), and Dysthymia (n = 6,392; 22.4%). Most episodes occurred in female patients (70.2%; n = 20,052), with a mean age of 50.6 years, and 37.0% (n = 10,564) of the episodes were associated to other psychiatric comorbidities. Depressive disorders are one of the leading causes of hospitalization in Portuguese psychiatric departments, being responsible for approximately 1 in 5 hospitalizations with a psychiatric diagnosis.

Suicide Mortality Rate as a Sustainable Development Goal (SDG): A Bibliometric Analysis.

Suicidal behaviors are a serious but potentially preventable cause of premature death. Increased awareness of the importance of mental health for global health has led to new initiatives, supported by the World Health Organization (WHO) and the United Nations (UN). The suicide mortality rate is one of the indicators covered in the UN's Sustainable Development Goal (SDG) 3. The aim of this study is to identify the scientific production and its temporal evolution related to the suicide mortality rate indicator in the context of mental disorders and as one of the SDG. A bibliometric analysis was performed in Scopus to assess the related research on suicide mortality rate, including on the context of the third SDG, from inception to September 2, 2020. The set of articles were analyzed for bibliometric measures. A total of 3126 documents about mental health and suicide mortality rate on the context of SDG were collected. Articles were the predominant type of literature on this area (78.3%), with significant expression on the last years, more evident around 2015, the year of adoption of SDGs. Despite a large volume of evidence, the debate about suicide mortality rate as an indicator of SDG is still very sparse suggesting a need for better consensus on its evaluation methods. This study presents useful characteristics for the formulation of new studies and provides specific targets for the construction or improvement of public policies on the context of the SDGs for further discussion on this strategy proposed by the UN.

Bipolar Disorder Related Hospitalizations - a Descriptive Nationwide Study Using a Big Data Approach.

Bipolar Disorder (BD) is a mental disorder which frequently requires long hospitalizations and need for acute psychiatric care. The aim of this study was to describe a nationwide perspective of BD related hospitalizations and to use a BigData based approach in mental health research. We performed a retrospective observational study using a nationwide hospitalization database containing all hospitalizations registered in Portuguese public hospitals from 2008-2015. Hospitalizations with a primary diagnosis of BD were selected based on International Classification of Diseases version 9, Clinical Modification (ICD-9-CM) codes of diagnosis 296.xx (excluding 296.2x; 296.3x and 296.9x). From 20,807 hospitalizations belonging to 13,300 patients, around 33.4% occurred in male patients with a median length of stay of 16.0 days and a mean age of 47.9 years. The most common hospitalization diagnosis in BD has the code 296.4x (manic episode) representing 34.3% of all hospitalizations, followed by the code 296.5x (depressed episode) with 21.4%. The mean estimated hospitalization charge was 3,508.5€ per episode, with a total charge of 73M€ in the 8-year period of this study.This is a nationwide study giving a broad perspective of the BD hospitalization panorama at a national level. We found important differences in hospitalization characteristics by sex, age and primary diagnosis.

Body composition in women with premature ovarian insufficiency using hormone therapy and the relation to cardiovascular risk markers: A case-control study.

OBJECTIVE: To compare body composition between women with premature ovarian insufficiency (POI) using hormone therapy and controls with normal ovarian function, and to correlate body composition with cardiovascular risk markers in the POI group. PATIENTS AND DESIGN: A case-control study of 70 women with POI matched by age and body mass index with 70 controls. MEASUREMENTS: All were submitted to whole-body dual-energy X-ray absorptiometry (DXA) to analyse body composition. In the POI group, total cholesterol, HDL-cholesterol, LDL-cholesterol, VLDL-cholesterol, triglycerides, glucose, insulin, transaminases and C-reactive protein levels were measured, as well as the thickness of the carotid artery intima-media complex. RESULTS: Total mass, fat mass, lean mass (total, percentage and index) and the android/gynoid (A/G) ratio were similar in both groups; however, bone mineral content was lower (P < .001) in the POI group. Lean and fat mass indexes were 14.19 ± 1.63 and 11.04 ± 3.58, respectively, and the percentage of gynoid to android fat was higher (51.17 ± 6.71% versus 47.74 ± 9.19%; A/G ratio = 0.93 ± 0.15) in the POI group. In addition, the increase in total mass correlated positively with glucose and ALT levels and negatively with HDL-cholesterol. Increased A/G ratio was the measurement most frequently associated with cardiovascular risk markers. CONCLUSION: The body composition of women with POI using hormone therapy is similar to that of women with normal ovarian function with regard to lean and fat mass content and fat distribution. In women with POI, the higher the A/G ratio, the worse the cardiovascular risk markers.

Risk factors and their contribution to population health in the European Union (EU-28) countries in 2007 and 2017.

BACKGROUND: The Global Burden of Disease (GBD) study has generated a wealth of data on death and disability outcomes in Europe. It is important to identify the disease burden that is attributable to risk factors and, therefore, amenable to interventions. This paper reports the burden attributable to risk factors, in deaths and disability-adjusted life years (DALYs), in the 28 European Union (EU) countries, comparing exposure to risks between them, from 2007 to 2017. METHODS: Retrospective descriptive study, using secondary data from the GBD 2017 Results Tool. For the EU-28 and each country, attributable (all-cause) age-standardized death and DALY rates, and summary exposure values are reported. RESULTS: In 2017, behavioural and metabolic risk factors showed a higher attributable burden compared with environmental risks, with tobacco, dietary risks and high systolic blood pressure standing out. While tobacco and air quality improved significantly between 2007 and 2017 in both exposure and attributable burden, others such as childhood maltreatment, drug use or alcohol use did not. Despite significant heterogeneity between EU countries, the EU-28 burden attributable to risk factors decreased in this period. CONCLUSION: Accompanying the improvement of population health in the EU-28, a comparable trend is visible for attributable burden due to risk factors. Besides opportunities for mutual learning across countries with different disease/risk factors patterns, good practices (i.e. tobacco control in Sweden, air pollution mitigation in Finland) might be followed. On the opposite side, some concerning cases must be highlighted (i.e. tobacco in Bulgaria, Latvia and Estonia or drug use in Czech Republic).

Prediction of Asthma Hospitalizations for the Common Cold Using Google Trends: Infodemiology Study.

BACKGROUND: In contrast to air pollution and pollen exposure, data on the occurrence of the common cold are difficult to incorporate in models predicting asthma hospitalizations. OBJECTIVE: This study aims to assess whether web-based searches on common cold would correlate with and help to predict asthma hospitalizations. METHODS: We analyzed all hospitalizations with a main diagnosis of asthma occurring in 5 different countries (Portugal, Spain, Finland, Norway, and Brazil) for a period of approximately 5 years (January 1, 2012-December 17, 2016). Data on web-based searches on common cold were retrieved from Google Trends (GT) using the pseudo-influenza syndrome topic and local language search terms for common cold for the same countries and periods. We applied time series analysis methods to estimate the correlation between GT and hospitalization data. In addition, we built autoregressive models to forecast the weekly number of asthma hospitalizations for a period of 1 year (June 2015-June 2016) based on admissions and GT data from the 3 previous years. RESULTS: In time series analyses, GT data on common cold displayed strong correlations with asthma hospitalizations occurring in Portugal (correlation coefficients ranging from 0.63 to 0.73), Spain (ρ=0.82-0.84), and Brazil (ρ=0.77-0.83) and moderate correlations with those occurring in Norway (ρ=0.32-0.35) and Finland (ρ=0.44-0.47). Similar patterns were observed in the correlation between forecasted and observed asthma hospitalizations from June 2015 to June 2016, with the number of forecasted hospitalizations differing on average between 12% (Spain) and 33% (Norway) from observed hospitalizations. CONCLUSIONS: Common cold-related web-based searches display moderate-to-strong correlations with asthma hospitalizations and may be useful in forecasting them.

Electroconvulsive Therapy Use in Psychiatric Hospitalizations in Portugal: A Nationwide Descriptive Study.

OBJECTIVES: The primary aim of this study was to describe a Portuguese nationwide epidemiological perspective on the use of electroconvulsive therapy (ECT) in hospitalized psychiatric patients. The secondary aims of the study were to characterize clinical and sociodemographic trends of hospitalized patients treated with ECT. METHODS: A retrospective-observational study was conducted using an administrative database gathering every registered Portuguese public hospital hospitalizations from 2008 to 2015. We selected all hospitalizations with a procedure code 94.27 (Other electroshock therapy) defined by the International Classification of Diseases version 9, Clinical Modification. Variables included birth date, sex, address, primary and secondary diagnoses, admission/discharge date, length of stay, and discharge status from each single hospitalization episode. We also calculated Portugal's inpatient ECT prevalence rate (iP%). RESULTS: There were a total of 879 registered hospitalizations with ECT within the 8-year period of this study. Most occurred in female patients (53.4%), belonging to the age group of 51 to 70 years old, with a mean age of 50.5 years. The median length of stay was 43.0 days with an interquartile range of 27.0 to 68.0. The most frequent primary diagnosis was major depressive disorder, recurrent episode, representing 19.6% of all hospitalizations. The iP% for the study period was 0.71%. CONCLUSIONS: In Portugal, most of the patients who received ECT were women above middle age, and depressive disorders were the most common indication. Portugal's iP% represents a low rate when compared with other European countries, which might indicate an underutilization of ECT in Portuguese psychiatric hospitals.

A Hybrid Model to Classify Patients with Chronic Obstructive Respiratory Diseases.

Over the last decades, an increase in the ageing population and age-related diseases has been observed, with the increase in healthcare costs. As so, new solutions to provide more efficient and affordable support to this group of patients are needed. Such solutions should never discard the user and instead should focus on promoting more healthy lifestyles and provide tools for patients' active participation in the treatment and management of their diseases. In this concern, the Personal Health Empowerment (PHE) project presented in this paper aims to empower patients to monitor and improve their health, using personal data and technology assisted coaching. The work described in this paper focuses on defining an approach for user modelling on patients with chronic obstructive respiratory diseases using a hybrid modelling approach to identify different groups of users. A classification model with 90.4% prediction accuracy was generated combining agglomerative hierarchical clustering and decision tree classification techniques. Furthermore, this model identified 5 clusters which describe characteristics of 5 different types of users according to 7 generated rules. With the modelling approach defined in this study, a personalized coaching solution will be built considering patients with different necessities and capabilities and adapting the support provided, enabling the recognition of early signs of exacerbations and objective self-monitoring and treatment of the disease. The novel factor of this approach resides in the possibility to integrate personalized coaching technologies adapted to each kind of user within a smartphone-based application resulting in a reliable and affordable alternative for patients to manage their disease.

Schizophrenia Related Hospitalizations - a Big Data Analysis of a National Hospitalization Database.

Schizophrenia is a mental disorder characterized by long hospitalizations and frequent need for chronic/acute psychiatric care. Hospitalizations represent a valuable quality of care indicator in schizophrenia patients. The aim of this study was to describe a nationwide perspective of schizophrenia related hospitalizations. We performed a retrospective observational study using a nationwide hospitalization database containing all hospitalizations registered in Portuguese public hospitals from 2008 to 2015. Hospitalizations with a primary diagnosis of schizophrenia were selected based on the definition by CCS - Clinical Classification Software diagnostic single-level 659. Schizophrenia subtypes were identified based on International Classification of Diseases version 9, Clinical Modification (ICD-9-CM) codes of diagnosis 295.xx. A total of 25,385 hospitalizations were registered belonging to 14,279 patients. 68.0% of the hospitalizations occurred in male patients and the median length of stay was 18.0 days. In male patients' hospitalizations, the most frequent age group was 31-50 years followed by the age group of 18-30 years (55.9 and 24.0% respectively). For female patients, the most frequent age group was 31-50 years followed by 51-70 years (54.1 and 22.6%, respectively). There were 73 hospitalization with a deadly outcome (0.29%). Paranoid type was the most frequent subtype of schizophrenia (50.5%). The mean hospitalization charges were 3509.7€ per episode, with a total charge of 89.1 M€ in the 8-year period. This is a nationwide study using Big Data analysis giving a broad perspective of schizophrenia hospitalization panorama at a nationwide level. We found differences in hospitalization characteristics according to patients' gender, age and primary diagnosis.