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.

BACKGROUND: Most studies estimate hepatitis C virus (HCV) disease prevalence from convenience samples. Consequently, screening policies may not include those at the highest risk for a new diagnosis.

METHODS: Clalit Health Services members aged 25-74 as of 31 December 2009 were included in the study. Rates of testing and new diagnoses of HCV were calculated, and potential risk groups were examined.

RESULTS: Of the 2 029 501 included members, those aged 45-54 and immigrants had lower rates of testing (12.5% and 15.6%, respectively), higher rates of testing positive (0.8% and 1.1%, respectively), as well as the highest rates of testing positive among tested (6.1% and 6.9%, respectively).

DISCUSSION: In this population-level study, groups more likely to test positive for HCV also had lower rates of testing. Policy makers and clinicians worldwide should consider creating screening policies using on population-based data to maximize the ability to detect and treat incident cases.

Methods for identifying patients at high risk for osteoporotic fractures, including dual-energy X-ray absorptiometry (DXA)1,2 and risk predictors like the Fracture Risk Assessment Tool (FRAX)3-6, are underutilized. We assessed the feasibility of automatic, opportunistic fracture risk evaluation based on routine abdomen or chest computed tomography (CT) scans. A CT-based predictor was created using three automatically generated bone imaging biomarkers (vertebral compression fractures (VCFs), simulated DXA T-scores and lumbar trabecular density) and CT metadata of age and sex. A cohort of 48,227 individuals (51.8% women) aged 50-90 with available CTs before 2012 (index date) were assessed for 5-year fracture risk using FRAX with no bone mineral density (BMD) input (FRAXnb) and the CT-based predictor. Predictions were compared to outcomes of major osteoporotic fractures and hip fractures during 2012-2017 (follow-up period). Compared with FRAXnb, the major osteoporotic fracture CT-based predictor presented better receiver operating characteristic area under curve (AUC), sensitivity and positive predictive value (PPV) (+1.9%, +2.4% and +0.7%, respectively). The AUC, sensitivity and PPV measures of the hip fracture CT-based predictor were noninferior to FRAXnb at a noninferiority margin of 1%. When FRAXnb inputs are not available, the initial evaluation of fracture risk can be done completely automatically based on a single abdomen or chest CT, which is often available for screening candidates7,8.

At the COVID-19 pandemic onset, when individual-level data of COVID-19 patients were not yet available, there was already a need for risk predictors to support prevention and treatment decisions. Here, we report a hybrid strategy to create such a predictor, combining the development of a baseline severe respiratory infection risk predictor and a post-processing method to calibrate the predictions to reported COVID-19 case-fatality rates. With the accumulation of a COVID-19 patient cohort, this predictor is validated to have good discrimination (area under the receiver-operating characteristics curve of 0.943) and calibration (markedly improved compared to that of the baseline predictor). At a 5% risk threshold, 15% of patients are marked as high-risk, achieving a sensitivity of 88%. We thus demonstrate that even at the onset of a pandemic, shrouded in epidemiologic fog of war, it is possible to provide a useful risk predictor, now widely used in a large healthcare organization.

Methods for identifying patients at high risk for osteoporotic fractures, including dual-energy X-ray absorptiometry (DXA)1,2 and risk predictors like the Fracture Risk Assessment Tool (FRAX)3-6, are underutilized. We assessed the feasibility of automatic, opportunistic fracture risk evaluation based on routine abdomen or chest computed tomography (CT) scans. A CT-based predictor was created using three automatically generated bone imaging biomarkers (vertebral compression fractures (VCFs), simulated DXA T-scores and lumbar trabecular density) and CT metadata of age and sex. A cohort of 48,227 individuals (51.8% women) aged 50-90 with available CTs before 2012 (index date) were assessed for 5-year fracture risk using FRAX with no bone mineral density (BMD) input (FRAXnb) and the CT-based predictor. Predictions were compared to outcomes of major osteoporotic fractures and hip fractures during 2012-2017 (follow-up period). Compared with FRAXnb, the major osteoporotic fracture CT-based predictor presented better receiver operating characteristic area under curve (AUC), sensitivity and positive predictive value (PPV) (+1.9%, +2.4% and +0.7%, respectively). The AUC, sensitivity and PPV measures of the hip fracture CT-based predictor were noninferior to FRAXnb at a noninferiority margin of 1%. When FRAXnb inputs are not available, the initial evaluation of fracture risk can be done completely automatically based on a single abdomen or chest CT, which is often available for screening candidates7,8.

At the COVID-19 pandemic onset, when individual-level data of COVID-19 patients were not yet available, there was already a need for risk predictors to support prevention and treatment decisions. Here, we report a hybrid strategy to create such a predictor, combining the development of a baseline severe respiratory infection risk predictor and a post-processing method to calibrate the predictions to reported COVID-19 case-fatality rates. With the accumulation of a COVID-19 patient cohort, this predictor is validated to have good discrimination (area under the receiver-operating characteristics curve of 0.943) and calibration (markedly improved compared to that of the baseline predictor). At a 5% risk threshold, 15% of patients are marked as high-risk, achieving a sensitivity of 88%. We thus demonstrate that even at the onset of a pandemic, shrouded in epidemiologic fog of war, it is possible to provide a useful risk predictor, now widely used in a large healthcare organization.