Gunther Schauberger

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% in females (from 23.5 in 2012 to 32.5 per 100,000 person-years (py) in 2021) and 5.0% in males (from 25.2 to 38.9 per 100,000 py). The increase was highest in age groups 0–4 (12.4%) in males and 15–19 (8.5%) in females. Regression analysis showed no clear level or slope change in T1D incidence for both sexes during the pandemic period. When accounting for non-linear trends, a statistically significant level change was observed for females during the pandemic period (ß=7.66, 95% CI 0.23, 15.09), suggesting a potential association for females. The increasing T1D incidence in Bavarian youth from 2012 to 2021 highlights the importance of incidence surveillance.

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.

Aim: 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. Subject and methods: 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. Results: 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. Conclusion: 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.

Background Inconsistent findings about the impact of the COVID-19 pandemic on cardiovascular disease diagnosis and consultations have been reported internationally. The objective of this study was to analyse the impact of the pandemic period (2020–2021) on the incidence rate of coronary heart disease (CHD) compared with the pre-pandemic period (2012–2019) in Bavaria, Germany. Methods We used health claims data of around 9 million statutorily insured residents (≥20 years) of Bavaria, Germany. We calculated quarterly age-standardised incidence rates for men and women diagnosed with CHD using the European Standard Population 2013. Interrupted time series regression models were used to analyse possible pandemic effects on the CHD incidence rates. Results Overall, 797 074 new CHD cases (47% women) were diagnosed from 2012 to 2021. Both pre-pandemic and pandemic incidence rates for women were lower than for men. Regression models showed decreasing incidence rates in the pre-pandemic period in men (−5.2% per year (p.a.), 95% CI: −5.7% to −4.7%) and in women (−6.6% p.a., 95% CI: −7.3% to −6.0%) and seasonal effects (higher in quarter 4 compared with Q1–Q3). During the pandemic period, there was no clear evidence of a level change in the incidence rates both in women and men. However, there are indications of a smaller decline in the incidence during the pandemic compared with the pre-pandemic period, in particular in women (−0.7% p.a., 95% CI: −6.0% to 4.8%) and less prominent in men (−1.7% p.a., 95% CI: −6.0% to 2.8%). Conclusions An overall decreasing CHD incidence rate was observed in men and women in the past decade but no clear impact of the pandemic was seen. These results show the importance of incidence monitoring beyond the pandemics to maintain chronic disease care.

Background: Conditional 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. Results: A 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. Conclusions: The 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.