Background:HPV vaccination coverage among 18-year-old women in Germany was 61% in 2024, despite the recommendation for girls since 2007. We assessed the association between HPV vaccination and risk of precancerous lesions and invasive cervical cancer in Bavaria, Germany's largest state. Methods:We conducted a retrospective cohort analysis of females born 1990-2005, covering 17 years of follow-up (2008-2024), using health claims and prescription data from the Bavarian Association of Statutory Health Insurance Physicians (KVB). Cumulative incidence of precancerous cervical lesions and invasive cervical cancer was estimated using the Kaplan-Meier method, and risks were estimated using Cox regression models. Analyses accounted for factors such as HPV vaccination status, age at vaccination, pre-vaccination contraceptive prescription, cervical cancer screening participation, and place of residence. Findings:Among 669,053 females, 33.76% (225,899/669,053) were fully vaccinated, 13.34% (89,268/669,053) partially vaccinated, and 52.89% (353,886/669,053) unvaccinated. 8965 precancerous lesions and 669 invasive cervical cancers were diagnosed in females aged 19-34 years. By age 34 years, cumulative incidence of precancerous lesions and invasive cervical cancer was 0.025 (95% CI: 0.020, 0.029) and 0.001 (95% CI: 0.001, 0.002) in fully vaccinated cohorts, and 0.041 (95% CI: 0.040, 0.043) and 0.004 (95% CI: 0.003, 0.004) in unvaccinated cohorts, respectively. Adjusted hazard ratios among fully vaccinated females were 0.48 (95% CI: 0.45, 0.52) for precancerous lesions and 0.35 (95% CI: 0.26, 0.47) for invasive cervical cancer. Interpretation:HPV vaccination is associated with a lower incidence of precancerous lesions and invasive cervical cancer. This is the first direct evidence of the effectiveness of the HPV vaccine in Germany. Funding:No external funding.

Type 1 Diabetes (T1D) is a common chronic autoimmune disorder in children and adolescents worldwide. We described the development of T1D incidence from 2012 to 2021 and compared the incidence of the pre-pandemic period (2012–2019) with the pandemic period (2020–2021) in Bavaria, Germany. Routinely collected health claims from the Bavarian Association of Statutory Health Insurance Physicians (KVB), covering a population of 2 million children and adolescents (aged ≤ 19 years), were used. All cases of newly diagnosed T1D (ICD-10-GM E10) were included. Sex-specific annual and quarterly crude incidence rates (CIR) and age-standardized incidence rates (ASIR) were calculated. Sex-specific CIRs were calculated by 5-year age groups. Interrupted time series analysis was used to analyze trend changes in the pandemic versus the pre-pandemic period. From 2012 to 2021, 5,762 incident cases were identified in Bavaria. Overall, an increasing incidence was observed with an average annual increase of 3.7

Emotional intelligence is a key factor for success in sporting competitions, arousing great interest in the psychological assessment of athletes. When the evaluation of psychological behaviour relies on Likert-type psychometric scales, individuals could tend to respond to items regardless of their content or by selecting the extremes or the middle part of the response scale, compromising the measurement process. In this vein, the present paper aims to address measurement issues regarding uncertainty and response style during the assessment of emotional intelligence of elite swimmers by exploiting latent trait models. Results provide evidence in favour of models accounting for specific response behaviour compared to simple item response theory models.

Introduction Surgical care is essential for addressing acute and chronic conditions in children and adolescents. While national-level data on pediatric surgical procedures in Mexico exist, analysis of such data remain limited. This study aimed to describe geographic variations and temporal trends in pediatric surgical procedures performed at public hospitals from 2010 to 2022, including the impact of the COVID-19 pandemic, and to identify factors associated with in-hospital mortality. Methods This retrospective registry-based analysis used hospital discharge data from Mexico’s Ministry of Health (MoH) public hospitals (2010–2022), including all surgical procedures in patients aged 0–17. Descriptive statistics summarized demographic and clinical characteristics. Surgical specialties stratified by age and sex were visualized using sankey flow diagrams. Age-standardized incidence rates (ASIR) were calculated per 100,000 children and adolescents by state and year using the WHO world standard population. Interrupted time series (ITS) analysis with Poisson regression evaluated trends of surgical procedure volume. Logistic regression identified factors associated with in-hospital mortality. Results Among 752,654 pediatric surgical patients, 58.2% were male and 41.8% female. The most common age group was 10–14 years (27.7%). Overall, 16.6% of patients were underweight, 21.6% overweight, 19.4% obese. Additionally, 2.1% identified as indigenous. General surgery (46.8%) and orthopedic surgery (23.4%) were the most frequent surgical procedures. The ASIR of surgical procedures was 141.8 per 100,000 children and adolescents nationwide (2010–2022). Guanajuato reported the highest overall ASIR (333.8 per 100,000), while Nuevo León had the lowest (18.5 per 100,000). Surgical procedure volumes increased until 2015, declined thereafter, and dropped sharply by 35.0% in April 2020 at the onset of the nationwide lockdown (exp(β): 0.65, 95% CI: 0.59–0.70), with volumes gradually recovering by 2022. Hospital-acquired infections (HAIs) (aOR: 2.71, 95% CI: 2.46–2.98) and prolonged length of stay (LOS) (aOR: 2.27, 95% CI: 2.13–2.41) were associated with increased in-hospital mortality. Conclusion This national analysis demonstrates pronounced geographic and temporal disparities in pediatric surgical care across Mexico’s public hospitals, including substantial declines during the COVID-19 pandemic. Coordinated investments in pediatric surgical infrastructure, state-level health information systems, and referral networks are critical to ensuring equitable, evidence-based pediatric surgical services.

Screening tests are widely used for disease detection, but test users often struggle to interpret negative or positive test results correctly, in particular in the presence of symptoms. This study proposes a framework for adaptive predictive values (APV) that personalises test interpretation by incorporating individual symptoms. The APV framework integrates individual symptom data into the calculation of predictive values, modifying the individual’s prior probability of being diseased. The discriminatory power of different symptoms can be determined via Bayes Factors. The framework is illustrated by a web application (ShinyApp) for the estimation of predictive values for SARS-CoV-2 infection based on the presence of typical symptoms, by reusing the symptom profile data from the REACT-1 study, which allows users to adjust their test result interpretation. By incorporating individual symptoms, the APV framework personalises the predictive values of screening tests. The higher the discriminatory power of a symptom, the more the individualised risk estimations differ from standard values based on population-wide prior risks. The ShinyApp demonstrates how users can input their test result, test type, residence region, and recent symptoms to obtain a personalised interpretation of their infection risk. The APV framework enhances the interpretation of screening test results by integrating personalised symptom data, improving risk estimation for individual test users. The approach can be extended beyond symptoms to include other individual characteristics.

Aims/hypothesis The aim of the study is to describe the time trend of type 2 diabetes incidence in the largest state of Germany, Bavaria, from 2012 to 2021, and to compare the incidence rates during the pandemic period (2020–2021) to the pre-pandemic period (2012–2019). Methods This secondary data analysis uses health claims data provided by the Bavarian Association of Statutory Health Insurance Physicians (KVB), covering approximately 11 million insurees, accounting for 85% of the total population of Bavaria, Germany. Newly diagnosed type 2 diabetes cases in adults (≥20 years) coded as E11 (Diabetes mellitus, Type 2) or E14 (Unspecified diabetes mellitus) under ICD-10, German modification (ICD-10-GM) for the study period 2012 to 2021 were included. Annual and quarterly age-standardised incidence rates (ASIR) stratified by sex, age and region were calculated using the European standard population. Sex-specific crude incidence rates (CIR) were calculated using 10-year age groups. Regression analyses adjusted for time trends, seasonal effects, and pandemic effects were used to analyse the incidence trend and to assess the effect of the pandemic. Results Overall, 745,861 new cases of type 2 diabetes were diagnosed between 2012 and 2021: 50.4% (376,193 cases) in women. The male/female ratio remained stable over the observation period, while the median age at diagnosis decreased from 61 to 58 years in men and from 66 years to 61 years in women. ASIR were consistently higher for men compared with women, with the yearly difference remaining stable over time (2012: 18%; 2021: 20%). An overall decreasing trend in ASIR was observed during the study period, with a strong decrease from 2012 to 2017, followed by a less pronounced decline from 2018 to 2021 for both sexes. For men, ASIR decreased from 1514 per 100,000 person-years in 2012 to 995 per 100,000 person-years in 2021 (4.6% average annual reduction), and for women from 1238 per 100,000 person-years in 2012 to 796 per 100,000 person-years in 2021 (4.8% average annual reduction). This downward trend was also observed for age groups above 50 years. Regression analyses showed no significant change in incidence rates during the pandemic period (2020 and 2021) compared with the pre-pandemic period. Conclusions/interpretation For the first time, a 10-year incidence trend of type 2 diabetes is reported for Germany, showing a strong decline from 2012 to 2017, followed by a less pronounced decline from 2018 to 2021. The incidence trend of type 2 diabetes appears not to have been affected by the first 2 years of the COVID-19 pandemic. Despite an overall increasing prevalence, the incidence is decreasing, potentially resulting from robust screening by family physicians, reducing the median age at diagnosis by 3 to 5 years. However, further investigation is needed to fully identify the reasons for the declining incidence trend. Continued incidence monitoring is necessary to identify the long-term trend and the potential effect of the pandemic on diagnoses of type 2 diabetes. Graphical Abstract

BackgroundConditional logistic regression trees have been proposed as a flexible alternative to the standard method of conditional logistic regression for the analysis of matched case-control studies. While they allow to avoid the strict assumption of linearity and automatically incorporate interactions, conditional logistic regression trees may suffer from a relatively high variability. Further machine learning methods for the analysis of matched case-control studies are missing because conventional machine learning methods cannot handle the matched structure of the data.ResultsA random forest method for the analysis of matched case-control studies based on conditional logistic regression trees is proposed, which overcomes the issue of high variability. It provides an accurate estimation of exposure effects while being more flexible in the functional form of covariate effects. The efficacy of the method is illustrated in a simulation study and within an application to real-world data from a matched case-control study on the effect of regular participation in cervical cancer screening on the development of cervical cancer.ConclusionsThe proposed random forest method is a promising add-on to the toolbox for the analysis of matched case-control studies and addresses the need for machine-learning methods in this field. It provides a more flexible approach compared to the standard method of conditional logistic regression, but also compared to conditional logistic regression trees. It allows for non-linearity and the automatic inclusion of interaction effects and is suitable both for exploratory and explanatory analyses.

In this work, three fundamentally different machine learning models are combined to create a new, joint model for forecasting the UEFA EURO 2024. Therefore, a generalized linear model, a random forest model, and a extreme gradient boosting model are used to predict the number of goals a team scores in a match. The three models are trained on the match results of the UEFA EUROs 2004-2020, with additional covariates characterizing the teams for each tournament as well as three enhanced variables derived from different ranking methods for football teams. The first enhanced variable is based on historic match data from national teams, the second is based on the bookmakers' tournament winning odds of all participating teams, and the third is based on historic match data of individual players both for club and international matches, resulting in player ratings. Then, based on current covariate information of the participating teams, the final trained model is used to predict the UEFA EURO 2024. For this purpose, the tournament is simulated 100.000 times, based on the estimated expected number of goals for all possible matches, from which probabilities across the different tournament stages are derived. Our combined model identifies France as the clear favourite with a winning probability of 19.2 (13.7

SCOPE:Interindividual variations in postprandial metabolism and weight loss outcomes have been reported. The literature suggests links between postprandial metabolism and weight regulation. Therefore, the study aims to evaluate if postprandial glucose metabolism after a glucose load predicts anthropometric outcomes of a weight loss intervention. METHODS AND RESULTS:Anthropometric data from adults with obesity (18-65 years, body mass index [BMI] 30.0-39.9 kg m-2) are collected pre- and post an 8-week formula-based weight loss intervention. An oral glucose tolerance test (OGTT) is performed at baseline, from which postprandial parameters are derived from glucose and insulin concentrations. Linear regression models explored associations between these parameters and anthropometric changes (∆) postintervention. A random forest model is applied to identify predictive parameters for anthropometric outcomes after intervention. Postprandial parameters after an OGTT of 158 participants (63.3% women, age 45 ± 12, BMI 34.9 ± 2.9 kg m-2) reveal nonsignificant associations with changes in anthropometric parameters after weight loss (p > 0.05). Baseline fat-free mass (FFM) and sex are primary predictors for ∆ FFM [kg]. CONCLUSION:Postprandial glucose metabolism after a glucose load does not predict anthropometric outcomes after short-term weight loss via a formula-based low-calorie diet in adults with obesity.

BACKGROUND:Vaccination is essential, especially in older adults whose immune system function declines with age. The COVID-19 pandemic and its associated lockdowns temporarily disrupted routine vaccination services. We aimed to assess vaccination coverage for Influenza, Pneumococcus, and Herpes zoster among older adults in Bavaria over time and investigate potential pandemic effects on these rates. METHODS:Based on health claims data from the Bavarian Association of Statutory Health Insurance Physicians (KVB), we estimated the percentage of adults aged 60 years and older vaccinated following the German Standing Committee on Vaccinations (STIKO) recommendation for Influenza (2012-2021), Pneumococcus (2017-2021) and Herpes zoster (2019-2021), stratified by sex and 10-year age groups. Using time series regression analysis, we estimated the effect of the pandemic period (2020-2021) on quarterly Influenza and Pneumococcal vaccination rates. RESULTS:In the first year of the pandemic (2020), Influenza, Pneumococcus and Herpes zoster coverage in both sexes increased by 9.9, 8.7, and 2.5 percentage points (pp), respectively. In 2021, Influenza coverage decreased by 4.7 pp., while Pneumococcus and Herpes zoster coverage increased by 2.7 and 3.8 pp., respectively. Influenza and Pneumococcal vaccinations showed a seasonal pattern, with vaccinations occurring mainly in the fourth quarter; this pattern was distorted for Pneumococcus during the pandemic. Per the time series regression analysis, Influenza vaccination rates in the fourth quarters of 2020 and 2021 were 7.86 (95 %CI: 5.10-10.62) and 8.87 (95 %CI: 5.80-11.54) pp. higher for males and females, respectively, compared to that of the pre-pandemic period. During the pandemic, the quarterly Pneumococcal vaccination rates increased by 0.68 (95 %CI: 0.19-1.18) pp. in males and 0.80 (95 %CI: 0.30-1.30) pp. in females. CONCLUSION:The heightened increase in vaccination rates observed in 2020 may have resulted from increased vaccination awareness during the pandemic. As the pandemic effect wanes, more efforts are needed to sustain and increase these vaccination rates.

Recent studies show declining trends in hysterectomy rates in several countries. The objective of this study was to analyse hysterectomy time trends in Germany over a fifteen-year period using an age-period-cohort approach. Using an ecological study design, inpatient data from Diagnoses Related Group on hysterectomies by subtype performed in Germany from 2005 to 2019 were retrieved from the German Statistical Office. Descriptive time trends and age-period-cohort analyses were then performed. A total of 1,974,836 hysterectomies were performed over the study period. The absolute number of hysterectomies reduced progressively from 155,680 (365 procedures/100,000 women) in 2005 to 101,046 (257 procedures/100,000 women) in 2019. Total and radical hysterectomy decreased by 49.7% and 44.2%, respectively, whilst subtotal hysterectomy increased five-fold. The age-period-cohort analysis revealed highest hysterectomy rates in women aged 45–49 for total and subtotal hysterectomy with 608.63 procedures/100,000 women (95% CI 565.70, 654.82) and 151.30 procedures/100,000 women (95% CI 138.38, 165.44) respectively. Radical hysterectomy peaked later at 65–69 years with a rate of 40.63 procedures/100,000 women (95% CI 38.84, 42.52). The risk of undergoing total or radical hysterectomy decreased over the study period but increased for subtotal hysterectomy. Although, overall hysterectomy rates have declined, subtotal hysterectomy rates have increased; reflecting changes in clinical practice largely influenced by the availability of uterus-sparing options, evolving guidelines and introduction of newer surgical approaches.

This systematic literature review aims to summarize global research on parental acceptance, attitudes, and knowledge regarding human papillomavirus vaccinations. The literature search was conducted in PubMed, Web of Science and Scopus, and included publications from 2006 to 2023. Study quality was assessed using the Newcastle-Ottawa Scale. The Grading of Recommendations Assessment, Development, and Evaluation guidelines were used to assess the strength of evidence for the primary outcome. Meta-analyses were performed using random-effects models to estimate pooled parental acceptance of HPV vaccinations. Studies were stratified by study years, and a subgroup analysis was conducted to estimate vaccine acceptance rates by world regions. Additionally, sensitivity analyses examined the role of parents in accepting HPV vaccinations for children of different sexes. Based on 86 studies, we found that parents generally supported HPV vaccinations for their children, yet HPV vaccine acceptance rates showed high variation (12.0 to 97.5

BACKGROUND:Multiple risk factors contribute jointly to the development and progression of cardiometabolic diseases. Therefore, joint longitudinal trajectories of multiple risk factors might represent different degrees of cardiometabolic risk.METHODS:We analyzed population-based data comprising three examinations (Exam 1: 1999-2001, Exam 2: 2006-2008, Exam 3: 2013-2014) of 976 male and 1004 female participants of the KORA cohort (Southern Germany). Participants were followed up for cardiometabolic diseases, including cardiovascular mortality, myocardial infarction and stroke, or a diagnosis of type 2 diabetes, until 2016. Longitudinal multivariate k-means clustering identified sex-specific trajectory clusters based on nine cardiometabolic risk factors (age, systolic and diastolic blood pressure, body-mass-index, waist circumference, Hemoglobin-A1c, total cholesterol, high- and low-density lipoprotein cholesterol). Associations between clusters and cardiometabolic events were assessed by logistic regression models.RESULTS:We identified three trajectory clusters for men and women, respectively. Trajectory clusters reflected a distinct distribution of cardiometabolic risk burden and were associated with prevalent cardiometabolic disease at Exam 3 (men: odds ratio (OR)ClusterII = 2.0, 95% confidence interval: (0.9-4.5); ORClusterIII = 10.5 (4.8-22.9); women: ORClusterII = 1.7 (0.6-4.7); ORClusterIII = 5.8 (2.6-12.9)). Trajectory clusters were furthermore associated with incident cardiometabolic cases after Exam 3 (men: ORClusterII = 3.5 (1.1-15.6); ORClusterIII = 7.5 (2.4-32.7); women: ORClusterII = 5.0 (1.1-34.1); ORClusterIII = 8.0 (2.2-51.7)). Associations remained significant after adjusting for a single time point cardiovascular risk score (Framingham).CONCLUSIONS:On a population-based level, distinct longitudinal risk profiles over a 14-year time period are differentially associated with cardiometabolic events. Our results suggest that longitudinal data may provide additional information beyond single time-point measures. Their inclusion in cardiometabolic risk assessment might improve early identification of individuals at risk.

Background and Objectives: There is a high inter-individual variability in the postprandial response to an oral glucose tolerance test (OGTT). However, there is limited evidence on whether the individual postprandial response is associated with the success of a weight management intervention. This work examines postprandial glucose and insulin response to an OGTT as predictors for changes in anthropometric parameters after a standardized weight loss intervention. Methods: Adults (18–65 years) with a body mass index (BMI) between 30.0 and 39.9 kg/m2 were recruited for the Lifestyle Intervention (LION) study (NCT04023942). Blood samples were taken before the start of the 8-week formula diet and during an OGTT. Several parameters describing the postprandial glucose and insulin response (e.g., area under the curve, peak time, and concentration) were calculated. Anthropometric parameters (e.g., body weight, fat mass) were collected before and after the 8-week formula diet. Finally, regression analyses adjusted for age and sex were fitted. Results: A total of 272 participants (mean age 45 ± 11 years, BMI 34.5 ± 2.9 kg/m2, 64% women) were included in the analysis. The formula diet resulted in an average weight loss of 11.8 ± 3.5 kg body weight and 8.2 ± 2.5 kg (4.1 ± 2.2%) fat mass. Postprandial parameters describing the glucose or insulin response from a total of 161 OGTTs showed no significant associations with changes in anthropometric parameters. Discussion: The examined postprandial glucose or insulin responses are not associated with weight loss success after an 8-week formula diet.