Trends in cost of treatment of lung cancer patients in 2014-2019 in Finland - a descriptive register study.

AIM: The aim of this descriptive study is to analyze the cost for the treatment of NSCLC and SCLC patients (2014-2019) in Finland. The primary objective is to understand recent (2014-2019) cost developments. METHODS: The study is retrospective and based on hospital register data. The study population consists of NSCLC and SCLC patients diagnosed in four out of the five Finnish university hospitals. The final sample included 4047 NSCLC patients and 766 SCLC patients. RESULTS: Cost of the treatment in lung cancer is increasing. Both the average cost of the first 12 months as well as the first 24 months after diagnosis increases over time. For patients diagnosed in 2014, the average cost of the first 24 months was 19,000 €and for those diagnosed in 2015 22,000 €. The annual increase in the nominal 24-month costs was 10.4% for NSCLC and 7.3% for SCLC patients. CONCLUSION: The average cost per patient has increased annually for both NSCLC and SCLC. Possible explanations to the cost increase are increased medicine costs (especially in NSCLC), and the increased percentage of patients being actively treated.

Personalized Risk Analysis to Improve the Psychological Resilience of Women Undergoing Treatment for Breast Cancer: Development of a Machine Learning-Driven Clinical Decision Support Tool.

BACKGROUND: Health professionals are often faced with the need to identify women at risk of manifesting poor psychological resilience following the diagnosis and treatment of breast cancer. Machine learning algorithms are increasingly used to support clinical decision support (CDS) tools in helping health professionals identify women who are at risk of adverse well-being outcomes and plan customized psychological interventions for women at risk. Clinical flexibility, cross-validated performance accuracy, and model explainability permitting person-specific identification of risk factors are highly desirable features of such tools. OBJECTIVE: This study aimed to develop and cross-validate machine learning models designed to identify breast cancer survivors at risk of poor overall mental health and global quality of life and identify potential targets of personalized psychological interventions according to an extensive set of clinical recommendations. METHODS: A set of 12 alternative models was developed to improve the clinical flexibility of the CDS tool. All models were validated using longitudinal data from a prospective, multicenter clinical pilot at 5 major oncology centers in 4 countries (Italy, Finland, Israel, and Portugal; the Predicting Effective Adaptation to Breast Cancer to Help Women to BOUNCE Back [BOUNCE] project). A total of 706 patients with highly treatable breast cancer were enrolled shortly after diagnosis and before the onset of oncological treatments and were followed up for 18 months. An extensive set of demographic, lifestyle, clinical, psychological, and biological variables measured within 3 months after enrollment served as predictors. Rigorous feature selection isolated key psychological resilience outcomes that could be incorporated into future clinical practice. RESULTS: Balanced random forest classifiers were successful at predicting well-being outcomes, with accuracies ranging between 78% and 82% (for 12-month end points after diagnosis) and between 74% and 83% (for 18-month end points after diagnosis). Explainability and interpretability analyses built on the best-performing models were used to identify potentially modifiable psychological and lifestyle characteristics that, if addressed systematically in the context of personalized psychological interventions, would be most likely to promote resilience for a given patient. CONCLUSIONS: Our results highlight the clinical utility of the BOUNCE modeling approach by focusing on resilience predictors that can be readily available to practicing clinicians at major oncology centers. The BOUNCE CDS tool paves the way for personalized risk assessment methods to identify patients at high risk of adverse well-being outcomes and direct valuable resources toward those most in need of specialized psychological interventions.

Validation of the Lean Healthcare Implementation Self-Assessment Instrument (LHISI) in the finnish healthcare context.

BACKGROUND: Lean management is growing in popularity in the healthcare sector worldwide, yet healthcare organizations are struggling with assessing the maturity of their Lean implementation and monitoring its change over time. Most existing methods for such assessments are time consuming, require site visits by external consultants, and lack frontline involvement. The original Lean Healthcare Implementation Self-Assessment Instrument (LHISI) was developed by the Center for Lean Engagement and Research (CLEAR), University of California, Berkeley as a Lean principles-based survey instrument that avoids the above problems. We validated the original LHISI in the context of Finnish healthcare. METHODS: The original HISI survey was sent over a secure organizational email system to the over 26,000 employees of the Hospital District of Helsinki and Uusimaa in March 2020. The data were randomly split with one part used to carry out an exploratory factor analysis (EFA), and the other for testing the resulting model using confirmatory factor analysis (CFA). RESULTS: A total of 6073 employees responded to the LHISI survey, for an overall response rate of 23%. The results indicated that the 43 items used in the original LHISI can be reduced to 25 items, and these items measure a five-dimensional model of the progress of Lean implementation: leadership, commitment, standard work, communication, and daily management system. In comparison with a single-factor model, the fit measures for the 5-factor model were better: smaller X2, larger comparative fit index (CFI), smaller root mean square error of approximation (RMSEA), and smaller standardized root mean square residual (SRMR). CONCLUSIONS: The 25 item LHISI is valid and feasible to use in the context of Finnish healthcare. The LHISI allows the organization to self-monitor the progress of its Lean implementation and provides the leadership with actionable knowledge to guide the path towards Lean maturity across the organization. Our findings encourage further studies on the adoption and validation of the LHISI in healthcare organizations worldwide.

Eosinophilia, asthma, NERD and the use of oral corticosteroids predict uncontrolled chronic rhinosinusitis with nasal polyps after surgery.

BACKGROUND: Severe uncontrolled chronic rhinosinusitis with nasal polyps (CRSwNP) is a challenging condition to treat. The European Position Paper on Rhinosinusitis and Nasal Polyps 2020 (EPOS2020) has the following criteria when considering biological therapy for severe uncontrolled CRSwNP: eosinophilia, need for oral corticosteroids (OCS), symptom score, loss of sense of smell and co-morbid asthma. OBJECTIVE: This study aimed at finding associations of baseline factors with uncontrolled CRSwNP after endoscopic sinus surgery (ESS). METHODS: Electronic health record data of CRSwNP patients (N = 137) undergoing ESS in 2002-17 were used. Endpoints of uncontrolled CRSwNP were revision ESS, purchased OCS and antibiotic courses during follow up. Baseline factors were chosen based on EPOS2020 and the data available: nasal polyp (NP) eosinophilia, peripheral blood eosinophilia, co-existing asthma and/or non-steroidal anti-inflammatory drug exacerbated respiratory disease (NERD), need for OCS during the previous year, previous ESS, endoscopic NP score, and Lund-Mackay score of sinus computed tomography scans. RESULTS: During the follow-up of 10.1 ± 3.1 (mean ± standard deviation) years, 35 (25.5%) individuals underwent revision ESS. The best predictive model was obtained by a sum of baseline (1) blood eosinophilia ≥ 250 cells/≥l and/or NP eosinophilia ≥ 30% (Eos), (2) asthma/NERD, and (3) ≥ 1 OCS/year. It was significantly associated with revision ESS, purchased doctor-prescribed OCS and antibiotic courses during follow-up. CONCLUSIONS: We identified similar predictive variables for uncontrolled CRSwNP that are used in the EPOS2020 indications of biological therapy, thus suggesting that these estimates are usable in clinical practice.

CFS MATLAB toolbox: An experiment builder for continuous flash suppression (CFS) task.

CFS toolbox is an open-source collection of MATLAB functions that utilizes PsychToolbox-3 (PTB-3). It is designed to allow a researcher to create and run continuous flash suppression experiments using a variety of experimental parameters (i.e., stimulus types and locations, noise characteristics, and experiment window settings). In a CFS experiment, one of the eyes at a time is presented with a dynamically changing noise pattern, while the other eye is concurrently presented with a static target stimulus, such as a Gabor patch. Due to the strong interocular suppression created by the dominant noise pattern mask, the target stimulus is rendered invisible for an extended duration. Very little knowledge of MATLAB is required for using the toolbox; experiments are generated by modifying csv files with the required parameters, and result data are output to text files for further analysis. The open-source code is available on the project page under a Creative Commons License ( http://www.mikkonuutinen.arkku.net/CFS_toolbox/ and https://bitbucket.org/mikkonuutinen/cfs_toolbox ).

Development and validation of classifiers and variable subsets for predicting nursing home admission.

BACKGROUND: In previous years a substantial number of studies have identified statistically important predictors of nursing home admission (NHA). However, as far as we know, the analyses have been done at the population-level. No prior research has analysed the prediction accuracy of a NHA model for individuals. METHODS: This study is an analysis of 3056 longer-term home care customers in the city of Tampere, Finland. Data were collected from the records of social and health service usage and RAI-HC (Resident Assessment Instrument - Home Care) assessment system during January 2011 and September 2015. The aim was to find out the most efficient variable subsets to predict NHA for individuals and validate the accuracy. The variable subsets of predicting NHA were searched by sequential forward selection (SFS) method, a variable ranking metric and the classifiers of logistic regression (LR), support vector machine (SVM) and Gaussian naive Bayes (GNB). The validation of the results was guaranteed using randomly balanced data sets and cross-validation. The primary performance metrics for the classifiers were the prediction accuracy and AUC (average area under the curve). RESULTS: The LR and GNB classifiers achieved 78% accuracy for predicting NHA. The most important variables were RAI MAPLE (Method for Assigning Priority Levels), functional impairment (RAI IADL, Activities of Daily Living), cognitive impairment (RAI CPS, Cognitive Performance Scale), memory disorders (diagnoses G30-G32 and F00-F03) and the use of community-based health-service and prior hospital use (emergency visits and periods of care). CONCLUSION: The accuracy of the classifier for individuals was high enough to convince the officials of the city of Tampere to integrate the predictive model based on the findings of this study as a part of home care information system. Further work need to be done to evaluate variables that are modifiable and responsive to interventions.

VQone MATLAB toolbox: A graphical experiment builder for image and video quality evaluations: VQone MATLAB toolbox.

This article presents VQone, a graphical experiment builder, written as a MATLAB toolbox, developed for image and video quality ratings. VQone contains the main elements needed for the subjective image and video quality rating process. This includes building and conducting experiments and data analysis. All functions can be controlled through graphical user interfaces. The experiment builder includes many standardized image and video quality rating methods. Moreover, it enables the creation of new methods or modified versions from standard methods. VQone is distributed free of charge under the terms of the GNU general public license and allows code modifications to be made so that the program's functions can be adjusted according to a user's requirements. VQone is available for download from the project page (http://www.helsinki.fi/psychology/groups/visualcognition/).

Adult-onset asthma, allergy, and aspirin hypersensitivity associate with self-reported food avoidance.

Background: The adoption of avoidance diets by adult-onset asthmatics has not previously been studied. We hypothesized that avoidance diets would associate with adult-onset asthma, allergy, and aspirin-exacerbated respiratory disease (AERD). Methods: A total of 1247 subjects with adult-onset asthma (age range: 31-91) from the Finnish national registry, and age- and sex-matched controls (n = 1970) participated in a questionnaire study in 1997. We estimated the association between asthma/allergy/AERD and avoidance diets, adjusting for potential confounding factors and validated the results in two retrospective cohorts of 5080 rhinitis/rhinosinusitis patients and 167 AERD patients from 2019 to 2020. Results: The presence of asthma positively associated with adoption of any avoidance diet (adjusted OR [CI95%] 1.24 [1.02-1.51], p = 0.029) as did allergic disease and self-reported AERD within the asthmatic group (1.79 [1.29-2.48], p = 0.001 and 1.69 [1.15-2.49], p = 0.007, respectively). Asthmatics and allergic asthmatics were more likely to report avoidance of fish, fruits and vegetables, and spices (p ≤ 0.03) compared to controls and non-allergic asthmatics. The adjusted OR for multiple diets among AERD patients was 2.57 [1.34-4.95] p = 0.005. In the validation, 26.2% of the allergic asthmatics and 10.8% of AERD patients had documented avoidance diets. Conclusions: Our study shows a positive association between avoidance diets and adult-onset asthma, and with allergic disease or AERD within asthmatic patients. Although we lack information on the reason patients chose to observe a specific diet, our results reinforce the importance of asking patients about their diet and if needed, giving dietary advice for adult asthma patients to help them avoid the adoption of unnecessarily restrictive diets.

Comorbidities of chronic rhinosinusitis in children and adults.

BACKGROUND: Chronic rhinosinusitis (CRS) is a chronic inflammatory disease of the nose and paranasal sinuses lasting ≥12 weeks. CRS may exist with (CRSwNP) or without (CRSsNP) nasal polyps. The aim was to evaluate conditions associated with CRS in a randomized hospital cohort. We hypothesized that comorbidities and surgical procedures differ between pediatric and adult patients. METHODS: This study consisted of hospital registry data of a random sample of rhinosinusitis patients (age range 0-89 years) with the diagnosis of J32 or J33, correspondingly, registered during outpatient visits from 2005 to 2019 (n = 1461). The covariates of interest were collected from electronic health records based on ICD-10 codes and keyword searches. RESULTS: Among pediatric patients (n = 104), the relative proportions of CRSsNP and CRSwNP were 86% and 14% respectively. The relative proportions of adult patients (n = 1357) with CRSsNP and CRSwNP were 60% and 40%, respectively. The following comorbidities significantly differed (p < 0.05) between pediatric and adult populations: allergy, chronic otitis media, and tonsillar diseases. In total, 41 % of the children and 46% of the adults underwent baseline endoscopic sinus surgery (ESS). Additional surgeries of the ear, nose and pharynx were significantly more common among children compared with adults. Risk of revision after baseline ESS was associated (p < 0.05) with allergy, asthma, eosinophilia, CRSwNP, immunodeficiency or its suspicion, non-steroidal anti-inflammatory drug exacerbated respiratory disease, and number of any diseases ≥2. CONCLUSIONS: Our study showed that comorbidities differ between pediatric and adult rhinosinusitis patients, as allergy, asthma and allergy, chronic otitis media, mental health disorders, and tonsils disease were significantly more prevalent among pediatric patients. Children and adults were equally treated with ESS. Notably, children underwent additional surgery on adenoids and tonsils more frequently. The effectiveness of ESS in multimorbid adults should be assessed at an individual level.

Systematic review of the performance evaluation of clinicians with or without the aid of machine learning clinical decision support system.

Background: For the adoption of machine learning clinical decision support systems (ML-CDSS) it is critical to understand the performance aid of the ML-CDSS. However, it is not trivial, how the performance aid should be evaluated. To design reliable performance evaluation study, both the knowledge from the practical framework of experimental study design and the understanding of domain specific design factors are required. Objective: The aim of this review study was to form a practical framework and identify key design factors for experimental design in evaluating the performance of clinicians with or without the aid of ML-CDSS. Methods: The study was based on published ML-CDSS performance evaluation studies. We systematically searched articles published between January 2016 and December 2022. From the articles we collected a set of design factors. Only the articles comparing the performance of clinicians with or without the aid of ML-CDSS using experimental study methods were considered. Results: The identified key design factors for the practical framework of ML-CDSS experimental study design were performance measures, user interface, ground truth data and the selection of samples and participants. In addition, we identified the importance of randomization, crossover design and training and practice rounds. Previous studies had shortcomings in the rationale and documentation of choices regarding the number of participants and the duration of the experiment. Conclusion: The design factors of ML-CDSS experimental study are interdependent and all factors must be considered in individual choices. Supplementary Information: The online version contains supplementary material available at 10.1007/s12553-023-00763-1.

Cluster Analysis of Finnish Population-Based Adult-Onset Asthma Patients.

BACKGROUND: Phenotypes of adult asthma have been identified in previous studies but rarely in population-based settings. OBJECTIVE: To identify clusters of adult-onset asthma in a Finnish population-based study on subjects born before 1967. METHODS: We used population-based data from 1350 asthmatics with adult-onset asthma (Adult Asthma in Finland) from Finnish national registers. Twenty-eight covariates were selected based on literature. The number of covariates was reduced by using factor analysis before cluster analysis. RESULTS: Five clusters (CLU1-CLU5) were identified, 3 clusters with late-onset adult asthma (onset ≥40 years) and 2 clusters with onset at earlier adulthood (<40 years). Subjects in CLU1 (n = 666) had late-onset asthma and were nonobese, symptomatic, and predominantly female with few respiratory infections during childhood. CLU2 (n = 36) consisted of subjects who had earlier-onset asthma, were predominantly female, obese with allergic asthma, and had recurrent respiratory infections. Subjects in CLU3 (n = 75) were nonobese, older, and predominantly men with late-onset asthma, smoking history, comorbidities, severe asthma, least allergic diseases, low education, many siblings, and childhood in rural areas. CLU4 (n = 218) was a late-onset cluster consisting of obese females with comorbidities, asthma symptoms, and low education level. Subjects in CLU5 (n = 260) had earlier onset asthma, were nonobese, and predominantly allergic females. CONCLUSIONS: Our population-based adult-onset asthma clusters take into account several critical factors such as obesity and smoking, and identified clusters that partially overlap with clusters identified in clinical settings. Results give us a more profound understanding of adult-onset asthma phenotypes and support personalized management.

Risk factors of NSAID-exacerbated respiratory disease: A population-based study.

BACKGROUND: Asthma with NSAID-exacerbated respiratory disease (NERD) is associated with uncontrolled or severe asthma. NERD patients are more prone to severe allergic reactions and their asthma exacerbations lead to hospitalisations twice as often compared to patients with non-NERD-asthma. NERD patients are prone to recurrent nasal polyposis requiring frequent endoscopic sinus surgeries. However, the early risk factors of NERD are not fully understood. The aim was to identify risk factors of NERD among patients with adult-onset asthma. METHODS: We used data from 1350 population-based adult asthmatics with adult-onset asthma from Finnish national registers. NERD was defined as self-reported wheeze or other typical respiratory symptoms after ingestion of NSAIDs. Thirty-six covariates covering several domains (personal characteristics, life-style, early life factors, asthma characteristics and multimorbidities) were selected based on literature and were studied in association with NERD using logistic regressions. RESULTS: The study population included 153 (11.3%) asthmatics with NERD. Thirty-six covariates were entered in univariate logistic regression analysis, in which 23 were associated with NERD (p < 0.05). These variables were entered in a multivariable logistic regression model in which allergic respiratory symptoms, female sex, osteoarthritis, difficult asthma, nasal polyps, second-hand smoke exposure at home, having 3 or more older siblings and being overweight were significantly associated with asthma with NERD (p < 0.05). Overweight decreased the risk of NERD, other factors increased it. CONCLUSION: According to our study, risk factors of NERD in part are associated with female sex, BMI, exposure to tobacco smoke, allergy, orthopaedic disorders and infection history, and their early recognition might thus be important to manage the burden of NERD.

Personalized prediction of one-year mental health deterioration using adaptive learning algorithms: a multicenter breast cancer prospective study.

Identifying individual patient characteristics that contribute to long-term mental health deterioration following diagnosis of breast cancer (BC) is critical in clinical practice. The present study employed a supervised machine learning pipeline to address this issue in a subset of data from a prospective, multinational cohort of women diagnosed with stage I-III BC with a curative treatment intention. Patients were classified as displaying stable HADS scores (Stable Group; n = 328) or reporting a significant increase in symptomatology between BC diagnosis and 12 months later (Deteriorated Group; n = 50). Sociodemographic, life-style, psychosocial, and medical variables collected on the first visit to their oncologist and three months later served as potential predictors of patient risk stratification. The flexible and comprehensive machine learning (ML) pipeline used entailed feature selection, model training, validation and testing. Model-agnostic analyses aided interpretation of model results at the variable- and patient-level. The two groups were discriminated with a high degree of accuracy (Area Under the Curve = 0.864) and a fair balance of sensitivity (0.85) and specificity (0.87). Both psychological (negative affect, certain coping with cancer reactions, lack of sense of control/positive expectations, and difficulties in regulating negative emotions) and biological variables (baseline percentage of neutrophils, thrombocyte count) emerged as important predictors of mental health deterioration in the long run. Personalized break-down profiles revealed the relative impact of specific variables toward successful model predictions for each patient. Identifying key risk factors for mental health deterioration is an essential first step toward prevention. Supervised ML models may guide clinical recommendations toward successful illness adaptation.

Are preparedness indices reflective of pandemic preparedness? A COVID-19 reality check.

The paper contributes to the body of knowledge working towards enhancing the understanding of crisis and disaster preparedness and effective response, via the lens of the ongoing global pandemic and responding to the questions: do the current measures for pandemic preparedness reflect preparedness adequately, and what does pandemic preparedness mean? We analysed how the reported cumulative mortality rates, during the spring of 2020 and in the 60 days after the date of a country's first COVID-19 related death, compared to the expected preparedness rank according to the existing global preparedness indices (IHR and GHSI) on a country level. We found, at country level, that the health-related outcomes from the first wave of the pandemic were primarily negatively correlated with the expected preparedness. We contend that our results indicate a need to investigate further development and enhancement of the preparedness indices.

Using machine learning for the personalised prediction of revision endoscopic sinus surgery.

BACKGROUND: Revision endoscopic sinus surgery (ESS) is often considered for chronic rhinosinusitis (CRS) if maximal conservative treatment and baseline ESS prove insufficient. Emerging research outlines the risk factors of revision ESS. However, accurately predicting revision ESS at the individual level remains uncertain. This study aims to examine the prediction accuracy of revision ESS and to identify the effects of risk factors at the individual level. METHODS: We collected demographic and clinical variables from the electronic health records of 767 surgical CRS patients ≥16 years of age. Revision ESS was performed on 111 (14.5%) patients. The prediction accuracy of revision ESS was examined by training and validating different machine learning models, while the effects of variables were analysed using the Shapley values and partial dependence plots. RESULTS: The logistic regression, gradient boosting and random forest classifiers performed similarly in predicting revision ESS. Area under the receiving operating characteristic curve (AUROC) values were 0.744, 0.741 and 0.730, respectively, using data collected from the baseline visit until six months after baseline ESS. The length of time during which data were collected improved the prediction performance. For data collection times of 0, 3, 6 and 12 months after baseline ESS, AUROC values for the logistic regression were 0.682, 0.715, 0.744 and 0.784, respectively. The number of visits before or after baseline ESS, the number of days from the baseline visit to the baseline ESS, patient age, CRS with nasal polyps (CRSwNP), asthma, non-steroidal anti-inflammatory drug exacerbated respiratory disease and immunodeficiency or suspicion of it all associated with revision ESS. Patient age and number of visits before baseline ESS carried non-linear effects for predictions. CONCLUSIONS: Intelligent data analysis identified important predictors of revision ESS at the individual level, such as the frequency of clinical visits, patient age, Type 2 high diseases and immunodeficiency or a suspicion of it.

Statistical model for factors correlating with COVID-19 deaths.

Background: The COVID-19 pandemic has caused major disruption in societies globally. Our aim is to understand, what factors were associated with the impact of the pandemic on death rates. This will help countries to better prepare for and respond in future pandemics. Methods: We modeled with a linear mixed effect model the impact of COVID-19 with the dependent variable "Daily mortality change" (DMC) with country features variables and intervention (containment measurement) data. We tested both country characteristics consisting of demographic, societal, health related, healthcare system specific, environmental and cultural feature as well as COVID-19 specific response in the form of social distancing interventions. Results: A statistically significant country feature was Geert Hofstede's masculinity, i.e., the extent to which the use of force is endorsed socially, correlating positively with a higher DMC. The effects of different interventions were stronger that those of country features, particularly cancelling public events, controlling international travel and closing workplaces. Conclusion: Social distancing interventions and the country feature: Geert Hofstede's masculinity dimension had a significant impact on COVID-19 mortality change. However other country features, such as development and population health did not show significance. Thus, the crises responders and scholars could revisit the concept and understanding of preparedness for and response to pandemics.

Long-term surveillance of rotavirus vaccination after implementation of a national immunization program in Finland (2008-2018).

BACKGROUND: Rotavirus (RV) vaccination was included in the Finnish National immunization Program (NIP) in 2009. RotaTeq (RV5) has been used exclusively with a national average vaccination coverage rate (VCR) of > 90%. While previous studies have demonstrated that inpatient rotavirus gastroenteritis (RVGE) admissions declined by as much as 96% in Finnish children ≤ 5 years old following RV vaccination introduction, no study has evaluated long-term protection after vaccination in Finland. In this study, we analyze incidence of hospital outpatient visits and inpatient admissions of gastroenteritis in children up to 7 years of age. METHODS: We first describe the incidence of RVGE, viral gastroenteritis (VGE), and acute gastroenteritis (AGE) for all Finnish children born during 2008-2011. Children were stratified by the year of birth into not-eligible, partially eligible and rotavirus vaccine-eligible (born in 2008, 2009, 2010 and 2011, respectively). Hospital inpatient and outpatient data was collected from the National Care Register for all children from birth until December 31st, 2018. We also studied RVGE incidence during 2014-2017 for children<3 years of age in municipalities with VCRs of 90% and above and municipalities with VCRs below 90%. RESULTS: RVGE incidence decreased significantly soon after implementation of RV vaccination in the NIP. In vaccine-eligible cohorts, no clear peak incidence in the youngest age groups could be observed, and no RVGE cases were observed beyond 6 years after vaccination, in contrast to vaccine ineligible and partially eligible cohorts. Despite an overall high VCR in Finland, regions with high VCR had lower incidence of RVGE than regions with lower VCR. CONCLUSION: Incidence of RVGE has remained low in all age groups during the 10 years following introduction of RV vaccine in the Finnish NIP. Differences in RVGE incidence were observed in regions with high as compared with lower VCR, highlighting the importance of maintaining high vaccination coverage.

The relative proportion of comorbidities among rhinitis and rhinosinusitis patients and their impact on visit burden.

Background: The aim was to evaluate the relative proportion of Non-steroidal anti-inflammatory drug exacerbated respiratory disease (NERD) and other comorbidities, and their impact on the burden of outpatient visits due to allergic rhinitis (AR), non-allergic rhinitis (NAR), acute rhinosinusitis (ARS), and chronic rhinosinusitis with nasal polyps (CRSwNP) and without (CRSsNP). Methods: We used hospital registry data of a random sample of 5080 rhinitis/rhinosinusitis patients diagnosed during 2005-2019. International Statistical Classification of Diseases and Related Health Problems (ICD10) diagnoses, visits, and other factors were collected from electronic health records by using information extraction and data processing methods. Cox's proportional hazards model was used for modeling the time to the next outpatient visit. Results: The mean (±standard deviation) age of the population was 33.6 (±20.7) years and 56.1% were female. The relative proportion of AR, NAR, ARS, CRSsNP and CRSwNP, were 33.5%, 27.5%, 27.2%, 20.7%, and 10.9%, respectively. The most common other comorbidities were asthma (44.4%), other chronic respiratory diseases (38.5%), musculoskeletal diseases (38.4%), and cardiovascular diseases (35.7%). Non-steroidal anti-inflammatory drug exacerbated respiratory disease existed in 3.9% of all patients, and 17.7% of the CRSwNP group. The relative proportion of subjects having 1, 2, 3 and ≥ 4 other diseases were 18.0%, 17.6%, 17.0%, 37.0%, respectively. All diseases except AR, ARS, and mouth breathing, were associated with a high frequency of outpatient visits. Conclusions: Our results revealed a high relative proportion of NERD and other comorbidities, which affect the burden of outpatient visits and hence confirm the socioeconomic impact of upper airway diseases.

Outcomes of lipid control in secondary prevention of coronary artery disease in Finland: A 24-month follow-up after acute coronary syndrome.

BACKGROUND AND AIMS: Ischemic heart diseases are the main cause of death worldwide, therefore secondary prevention and treatment of coronary artery disease (CAD) are highly significant for public health and mortality. The objective of this study is to evaluate LDL cholesterol (LDL-C) levels as outcomes of secondary prevention of CAD in Finland up to 24 months after being diagnosed with acute coronary syndrome (ACS). This retrospective analysis of patients with ACS was conducted in two areas of Finland that have a combined population of 400,000. METHODS: The data used in the study covered all outpatient visits, inpatient episodes, prescriptions and LDL-C results for ACS patients during 2011-2015. To evaluate the outcome of the prevention, three separate measurements of patients' LDL-C levels were considered: baseline, first follow-up and final follow-up. The factors associated with reaching treatment goal were identified using logistic regression analysis. RESULTS: 32% of ACS patients achieved the treatment goal (LDL-C <1.8 mmol/l) at the end of the 24-month follow-up period, but 21% of patients fluctuated between being on and above target. CONCLUSION: Two thirds of CAD patients with ACS and on statin therapy do not achieve LDL-C treatment target recommended by the guidelines. Since LDL-C levels fluctuate in the follow-up, a low level during the first 12 months after the acute event does not guarantee the maintenance of the results in the long term. Hence, LDL-C levels should be monitored at least on an yearly basis on follow-ups, and treatment adapted accordingly.

Upper nasal hemifield location and nonspatial auditory tones accelerate visual detection during dichoptic viewing.

Visual performance is asymmetric across the visual field, but locational biases that occur during dichoptic viewing are not well understood. In this study, we characterized horizontal, vertical and naso-temporal biases in visual target detection during dichoptic stimulation and explored whether the detection was facilitated by non-spatial auditory tones associated with the target's location. The detection time for single monocular targets that were suppressed from view with a 10 Hz dynamic noise mask presented to the other eye was measured at the 4° intercardinal location of each eye with the breaking Continuous Flash Suppression (b-CFS) technique. Each target was either combined with a sound (i.e., high or low pitch tone) that was congruent or incongruent with its vertical location (i.e., upper or lower visual field) or presented without a sound. The results indicated faster detection of targets in the upper rather than lower visual field and faster detection of targets in the nasal than temporal hemifield of each eye. Sounds generally accelerated target detection, but the tone pitch-elevation congruency did not further enhance performance. These findings suggest that visual detection during dichoptic viewing differs from standard viewing conditions with respect to location-related perceptual biases and crossmodal modulation of visual perception. These differences should be carefully considered in experimental designs employing dichoptic stimulation techniques and in display applications that utilize dichoptic viewing.