Objectives: To evaluate work expectations of radiologists at different career levels, their fulfillment, prevalence of exhaustion, and exhaustion-associated factors. Methods: A standardized digital questionnaire was distributed internationally to radiologists of all career levels in the hospital and in ambulatory care via radiological societies and sent manually to 4500 radiologists of the largest German hospitals between December 2020 and April 2021. Statistics were based on age- and gender-adjusted regression analyses of respondents working in Germany (510 out of 594 total respondents). Results: The most frequent expectations were “joy at work” (97%) and a “good working atmosphere” (97%), which were considered fulfilled by at least 78%. The expectation of a “structured residency within the regular time interval” (79%) was more frequently judged fulfilled by senior physicians (83%, odds ratio (OR) 4.31 [95% confidence interval (95% CI) 1.95–9.52]), chief physicians (85%, 6.81 [95% CI 1.91–24.29]), and radiologists outside the hospital (88%, 7.59 [95% CI 2.40–24.03]) than by residents (68%). Exhaustion was most common among residents (physical exhaustion: 38%; emotional exhaustion: 36%), in-hospital specialists (29%; 38%), and senior physicians (30%; 29%). In contrast to paid extra hours, unpaid extra hours were associated with physical exhaustion (5–10 extra hours: OR 2.54 [95% CI 1.54–4.19]). Fewer opportunities to shape the work environment were related to a higher probability of physical (2.03 [95% CI 1.32–3.13]) and emotional (2.15 [95% CI 1.39–3.33]) exhaustion. Conclusions: While most radiologists enjoy their work, residents wish for more training structure. Ensuring payment of extra hours and employee empowerment may help preventing burnout in high-risk groups. Key Points: • Most important work expectations of radiologists who work in Germany are “joy at work,” a “good working atmosphere,” “support for further qualification,” and a “structured residency within the regular time interval,” with the latter containing potential for improvement according to residents. • Physical and emotional exhaustion are common at all career levels except for chief physicians and for radiologists who work outside the hospital in ambulatory care. • Exhaustion as a major burnout criterion is associated with unpaid extra hours and reduced opportunities to shape the work environment.

Most diagnostic studies exclude missing values and inconclusive results from the analysis or apply simple methods resulting in biased accuracy estimates. This may be due to the lack of availability or awareness of appropriate methods. This scoping review aimed to provide an overview of strategies to handle missing values and inconclusive results in the reference standard or index test in diagnostic accuracy studies. Conducting a systematic literature search in MEDLINE, Cochrane Library, and Web of Science, we could identify many articles proposing methods for addressing missing values in the reference standard. There are also several articles describing methods regarding missing values or inconclusive results in the index test. The latter encompass imputation, frequentist and Bayesian likelihood, model-based, and latent class methods. While methods for missing values in the reference standard are regularly applied in practice, this is not true for methods addressing missing values and inconclusive results in the index test. Our comprehensive overview and description of available methods may raise further awareness of these methods and will enhance their application. Future research is needed to compare the performance of these methods under different conditions to give valid and robust recommendations for their usage in various diagnostic accuracy research scenarios.

The mental health (MH) of especially children and adolescents with low socioeconomic status (SES) benefits from access to greenspaces. This study aimed at investigating social inequalities in the association between several types of social infrastructure (SI) and MH in children and adolescents. The sample comprised 12,624 children and adolescents of the KiGGS Wave 2 study (2014–2017). KiGGS provided information on SI (access to playgrounds, sports fields, swimming pools, parks) for all children and the environmental module (GerES V) within KiGGS on the walking time to SI for a subsample. Social inequality was measured by parental SES and the German Index of Socioeconomic deprivation and MH by the Strengths and Difficulties Questionnaire. Ordinal logistic regression analyses showed that access to fewer SI places was associated with higher odds of MH problems. Children and adolescents experiencing high (but not medium or low) socioeconomic deprivation at the municipal level were more likely to have MH problems when having less access to SI places. At the individual level, MH problems in high- and low-SES, but not medium-SES children and adolescents were associated with no access to SI places. Children and adolescents from high socioeconomically deprived areas and with low and high SES might benefit from high-availability SI.

Summary: Background: Living in single parent and blended families or as an only child—compared to living in two‐parent biological families or with siblings, respectively—is associated with a higher body mass index (BMI) in cross‐sectional studies. However, longitudinal research addressing the children's BMI in this context is scarce. Further, little is known about the association between family structure and metabolic health.Objectives: This study aimed at investigating the association between both aspects of family structure with BMI and a metabolic score (MetS).Methods: Cross‐sectional data from 7804 children participating in the European multi‐center I.Family study (2013/2014) and longitudinal data from 5621 children who also participated previously in the IDEFICS study (2007–2010) were used. Family structure was assessed by a detailed interview. BMI z‐score and the MetS were based on measured anthropometry, blood pressure, high‐density lipoprotein, blood glucose, and triglycerides. Linear regressions were performed to model associations between family structure with BMI and MetS.Results: Children from single‐parent families had higher BMI z‐scores in the cross‐sectional (β = 0.09, 95% confidence interval [CI]: 0.001 to 0.18) and longitudinal analyses compared to those from two‐parent families. Cross‐sectionally, the number of siblings was associated with lower BMI z‐scores (β = −0.07, 95% CI: −0.10 to −0.03) and lower MetS (β = −0.14, 95% CI: −0.26 to −0.01). Longitudinally, only children between baseline and follow‐up had higher BMI z‐scores at follow‐up (β = 0.07, 95% CI: 0.01 to 0.14) compared to stable siblings.Conclusion: Obesity prevention measures should focus on single‐parent households and families with an only child.

To date, PHMR has often relied on male/female stratification, but rarely considers the complex, intersecting social positions of men and women in describing the prevalence of health and disease. Stratification on an Intersectional Gender-Score (IG-Score), which is based on a variety of social covariables, would allow comparison of the prevalence of individuals who share the same complex intersectional profile (IG-Score). The cross-sectional case study was based on the German Socio-Economic Panel 2017 (n = 23,269 age 18+). After stratification, covariable-balance within the total sample and IG-Score-subgroups was assessed by standardized mean differences. Prevalence of self-rated health, mental distress, depression and hypertension was compared in men and women. In the IG-Score-subgroup with highest proportion of males and lowest probability of falling into the ‘woman’-category, most individuals were in full-time employment. The IG-Score-subgroup with highest proportion of women and highest probability of falling into the ‘woman’-category was characterized by part-time/occasional employment, housewife/-husband, and maternity/parental leave. Gender differences in prevalence of health indicators remained within the male-dominated IG-Score-subgroup, whereas the same prevalence of depression and self-rated health was observed for men and women constituting the female-dominated IG-Score-subgroup. These results might indicate that sex/gender differences of depression and self-rated health could be interpreted against the background of gender associated processes. In summary, the proposed procedure allows comparison of prevalence of health indicators conditional on men and women sharing the same complex intersectional profile.

Background: Most diagnostic accuracy studies have applied a complete case analysis (CCA) or single imputation approach to address missing values in the index test, which may lead to biased results. Therefore, this simulation study aims to compare the performance of different methods in estimating the AUC of a continuous index test with missing values in a single-test diagnostic accuracy study. Methods: We simulated data for a reference standard, continuous index test, and three covariates using different sample sizes, prevalences of the target condition, correlations between index test and covariates, and true AUCs. Subsequently, missing values were induced for the continuous index test, assuming varying proportions of missing values and missingness mechanisms. Seven methods (multiple imputation (MI), empirical likelihood, and inverse probability weighting approaches) were compared to a CCA in terms of their performance to estimate the AUC given missing values in the index test. Results: Under missing completely at random (MCAR) and many missing values, CCA gives good results for a small sample size and all methods perform well for a large sample size. If missing values are missing at random (MAR), all methods are severely biased if the sample size and prevalence are small. An augmented inverse probability weighting method and standard MI methods perform well with higher prevalence and larger sample size, respectively. Most methods give biased results if missing values are missing not at random (MNAR) and the correlation or the sample size and prevalence are low. Methods using the covariates improve with increasing correlation. Conclusions: Most methods perform well if the proportion of missing values is small. Given a higher proportion of missing values and MCAR, we would recommend to conduct a CCA and standard MI methods for a small and large sample size, respectively. In the absence of better alternatives we recommend to conduct a CCA and to discuss its limitations, if the sample size is small, and missing values are M(N)AR. Standard MI methods and the augmented inverse probability approach may be a good alternative, if the sample size and/or correlation increases. All methods are biased under MNAR and a low correlation.