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Objective: To illustrate the problem of subpopulation miscalibration, to adapt an algorithm for recalibration of the predictions, and to validate its performance.Materials and Methods: In this retrospective cohort study, we evaluated the calibration of predictions based on the Pooled Cohort Equations (PCE) and the fracture risk assessment tool (FRAX) in the overall population and in subpopulations defined by the intersection of age, sex, ethnicity, socioeconomic status, and immigration history. We next applied the recalibration algorithm and assessed the change in calibration metrics, including calibration-in-the-large.Results: 1 021 041 patients were included in the PCE population, and 1 116 324 patients were included in the FRAX population. Baseline overall model calibration of the 2 tested models was good, but calibration in a substantial portion of the subpopulations was poor. After applying the algorithm, subpopulation calibration statistics were greatly improved, with the variance of the calibration-in-the-large values across all subpopulations reduced by 98.8% and 94.3% in the PCE and FRAX models, respectively.Discussion: Prediction models in medicine are increasingly common. Calibration, the agreement between predicted and observed risks, is commonly poor for subpopulations that were underrepresented in the development set of the models, resulting in bias and reduced performance for these subpopulations. In this work, we empirically evaluated an adapted version of the fairness algorithm designed by Hebert-Johnson et al. (2017) and demonstrated its use in improving subpopulation miscalibration.Conclusion: A postprocessing and model-independent fairness algorithm for recalibration of predictive models greatly decreases the bias of subpopulation miscalibration and thus increases fairness and equality. [ABSTRACT FROM AUTHOR]

Objective: To evaluate whether a woman's age at first birth is associated with cardiovascular risk and metabolic health outcomes (cardiometabolic outcomes) by age 45.Methods: This is a retrospective, population-based cohort study that uses electronic health record data from the largest health fund in Israel. Women aged 34-39 at baseline (2004-2006) free of chronic diseases were identified as nulliparous at baseline and were followed up to 10 years (through 2016). The cohort was divided into three groups based on their age at first birth: younger parturients (ages 35-39), older parturients (ages 40-44), and never had children. The percentage of adverse pregnancy events and cardiometabolic outcomes at age 45 were compared across these three groups as well as to women in the general population. Cardiovascular risk and metabolic health outcomes were defined as: Type 2 diabetes, obesity, hypertension, cardiovascular disease, and Framingham risk score.Methods and results: Out of a group of 126,121 women aged 34-39 at baseline, 9979 were nulliparous and free of comorbidities. Over the course of the follow-up, there were 952 younger parturients and 673 older parturients who had their first birth, and 8354 women who remained persistent nulliparous. While older parturients had more adverse pregnancy events, there was no difference in rates of cardiometabolic outcomes between the two parturient groups, and they both had lower rates than the persistent nulliparous and the general population.Conclusions: Parturients free of major chronic diseases who give birth at a later age do not have increased cardiometabolic outcomes in midlife as compared to a general population of women in a large retrospective cohort. Our results may support clinicians when counseling healthy women who are seeking advice regarding delaying their first pregnancy without a tradeoff on health outcomes. [ABSTRACT FROM AUTHOR]

The article reflects author views on effectiveness of the BNT162b2 mRNA vaccine against coronavirus disease 2019 (Covid-19) in a real-world setting is presented. Topics include multiple inquiries requesting vaccine-effectiveness measures in specific subgroups of patients; and additional person-time allowed for less-common outcomes, including Covid-19-related hospitalization and severe disease.