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.

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.

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.

Currently, clinicians rely mostly on population-level treatment effects from RCTs, usually considering the treatment's benefits. This study proposes a process, focused on practical usability, for translating RCT data into personalized treatment recommendations that weighs benefits against harms and integrates subjective perceptions of relative severity. Intensive blood pressure treatment (IBPT) was selected as the test case to demonstrate the suggested process, which was divided into three phases: (1) Prediction models were developed using the Systolic Blood-Pressure Intervention Trial (SPRINT) data for benefits and adverse events of IBPT. The models were externally validated using retrospective Clalit Health Services (CHS) data; (2) Predicted risk reductions and increases from these models were used to create a yes/no IBPT recommendation by calculating a severity-weighted benefit-to-harm ratio; (3) Analysis outputs were summarized in a decision support tool. Based on the individual benefit-to-harm ratios, 62 and 84% of the SPRINT and CHS populations, respectively, would theoretically be recommended IBPT. The original SPRINT trial results of significant decrease in cardiovascular outcomes following IBPT persisted only in the group that received a "yes-treatment" recommendation by the suggested process, while the rate of serious adverse events was slightly higher in the "no-treatment" recommendation group. This process can be used to translate RCT data into individualized recommendations by identifying patients for whom the treatment's benefits outweigh the harms, while considering subjective views of perceived severity of the different outcomes. The proposed approach emphasizes clinical practicality by mimicking physicians' clinical decision-making process and integrating all recommendation outputs into a usable decision support tool.

BACKGROUND: Although pregnant women are believed to have elevated risks of severe influenza infection and are targeted for influenza vaccination, no study to date has examined influenza vaccine effectiveness (IVE) against laboratory-confirmed influenza-associated hospitalizations during pregnancy, primarily because this outcome poses many methodological challenges.

OBJECTIVE: The Pregnancy Influenza Vaccine Effectiveness Network (PREVENT) was formed in 2016 as an international collaboration with the Centers for Disease Control and Prevention; Abt Associates; and study sites in Australia, Canada, Israel, and the United States. The primary goal of this collaboration is to estimate IVE in preventing acute respiratory or febrile illness (ARFI) hospitalizations associated with laboratory-confirmed influenza virus infection during pregnancy. Secondary aims include (1) describing the incidence, clinical course, and severity of influenza-associated ARFI hospitalization during pregnancy; (2) comparing the characteristics of ARFI-hospitalized pregnant women who were tested for influenza with those who were not tested; (3) describing influenza vaccination coverage in pregnant women; and (4) comparing birth outcomes among women with laboratory-confirmed influenza-associated hospitalization versus other noninfluenza ARFI hospitalizations.

METHODS: For an initial assessment of IVE, sites identified a retrospective cohort of pregnant women aged from 18 to 50 years whose pregnancies overlapped with local influenza seasons from 2010 to 2016. Pregnancies were defined as those that ended in a live birth or stillbirth of at least 20 weeks gestation. The analytic sample for the primary IVE analysis was restricted to pregnant women who were hospitalized for ARFI during site-specific influenza seasons and clinically tested for influenza virus infection using real-time reverse transcription polymerase chain reaction.

RESULTS: We identified approximately 2 million women whose pregnancies overlapped with influenza seasons; 550,344 had at least one hospitalization during this time. After restricting to women who were hospitalized for ARFI and tested for influenza, the IVE analytic sample included 1005 women.

CONCLUSIONS: In addition to addressing the primary question about the effectiveness of influenza vaccination, PREVENT data will address other important knowledge gaps including understanding the incidence, clinical course, and severity of influenza-related hospitalizations during pregnancy. The data infrastructure and international partnerships created for these analyses may be useful and informative for future influenza studies.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/11333.

BACKGROUND: To date, no study has examined influenza vaccine effectiveness (IVE) against laboratory-confirmed influenza-associated hospitalizations during pregnancy.

METHODS: The Pregnancy Influenza Vaccine Effectiveness Network (PREVENT) consisted of public health or healthcare systems with integrated laboratory, medical, and vaccination records in Australia, Canada (Alberta and Ontario), Israel, and the United States (California, Oregon, and Washington). Sites identified pregnant women aged 18 through 50 years whose pregnancies overlapped with local influenza seasons from 2010 through 2016. Administrative data were used to identify hospitalizations with acute respiratory or febrile illness (ARFI) and clinician-ordered real-time reverse transcription polymerase chain reaction (rRT-PCR) testing for influenza viruses. Overall IVE was estimated using the test-negative design and adjusting for site, season, season timing, and high-risk medical conditions.

RESULTS: Among 19450 hospitalizations with an ARFI discharge diagnosis (across 25 site-specific study seasons), only 1030 (6%) of the pregnant women were tested for influenza viruses by rRT-PCR. Approximately half of these women had pneumonia or influenza discharge diagnoses (54%). Influenza A or B virus infections were detected in 598/1030 (58%) of the ARFI hospitalizations with influenza testing. Across sites and seasons, 13% of rRT-PCR-confirmed influenza-positive pregnant women were vaccinated compared with 22% of influenza-negative pregnant women; the adjusted overall IVE was 40% (95% confidence interval = 12%-59%) against influenza-associated hospitalization during pregnancy.

CONCLUSION: Between 2010 and 2016, influenza vaccines offered moderate protection against laboratory-confirmed influenza-associated hospitalizations during pregnancy, which may further inform the benefits of maternal influenza vaccination programs.

BACKGROUND: A recently published, large prospective study showed unexpectedly high prevalence of acute pulmonary embolism (APE) among patients hospitalized for syncope. In such a case, a high incidence of recurrent pulmonary embolism is expected among patients who were discharged without APE workup.

OBJECTIVES: To determine the incidence of symptomatic APE among patients hospitalized for a first episode of syncope and discharged without APE workup or anticoagulation.

METHODS: This retrospective cohort study included patients hospitalized at Rambam Health Care Campus between January 2006 and February 2017 with a primary admission diagnosis of syncope, who were not investigated for APE and were not taking anticoagulants. The patients were followed up for up to three years after discharge. The occurrence of venous thromboembolism (VTE) during the follow-up period was documented.

RESULTS: The median follow-up duration was 33 months. 1,126 subjects completed a three-year follow-up. During this period, 38 patients (3.38%) developed VTE, 17 (1.51%) of them had APE. The cumulative incidence of VTE and APE was 1.9% (95% CI 1.3%-2.5%) and 0.9% (95% CI 0.4%-1.3%) respectively. Only seven subjects developed APE during the first year of follow-up. The median times from the event of syncope to the development of APE and VTE were 18 and 19 months respectively.

CONCLUSIONS: The cumulative incidence of VTE during a three-year follow-up period after an episode of syncope is low. In the absence of clinical suspicion, a routine diagnostic workup for APE in patients with syncope cannot be recommended.