The Legacy of Redlining: Increasing Childhood Asthma Disparities Through Neighborhood Poverty.

RATIONALE: Identifying the root causes of racial disparities in childhood asthma is critical for health equity. OBJECTIVES: To determine if the 1930's racist policy of redlining led to present-day disparities in childhood asthma by increasing community-level poverty and decreasing neighborhood socioeconomic position (SEP). METHODS: We categorized census tracts at birth of participants from the Children's Respiratory and Environmental Workgroup birth cohort consortium into A, B, C, or D categories as defined by the Home Owners Loan Corporation (HOLC), with D being the highest perceived risk. Surrogates of present-day neighborhood-level SEP were determined for each tract including the percentage of low-income households, the CDC's social vulnerability index (SVI), and other tract-level variables. We performed causal mediation analysis, which, under the assumption of no unmeasured confounding, estimates the direct and mediated pathways by which redlining may cause asthma disparities through census tract-level mediators adjusting for individual-level covariates. MEASUREMENTS AND MAIN RESULTS: Of 4,849 children, the cumulative incidence of asthma through age 11 was 26.6% and 13.2% resided in census tracts with a HOLC grade of D. In mediation analyses, residing in grade D tracts (aOR = 1.03 [95%CI 1.01,1.05]) was significantly associated with childhood asthma, with 79% of this increased risk mediated by percentage of low-income households; results were similar for SVI and other tract-level variables. CONCLUSIONS: The historical structural racist policy of redlining led to present-day asthma disparities in part through decreased neighborhood SEP. Policies aimed at reversing the effects of structural racism should be considered to create more just, equitable, and healthy communities.

A randomized double-blinded trial to assess recurrence of systemic allergic reactions following COVID-19 mRNA vaccination.

BACKGROUND: Systemic allergic reactions (sARs) following coronavirus disease 2019 (COVID-19) mRNA vaccines were initially reported at a higher rate than after traditional vaccines. OBJECTIVE: We aimed to evaluate the safety of revaccination in these individuals and to interrogate mechanisms underlying these reactions. METHODS: In this randomized, double-blinded, phase 2 trial, participants aged 16 to 69 years who previously reported a convincing sAR to their first dose of COVID-19 mRNA vaccine were randomly assigned to receive a second dose of BNT162b2 (Comirnaty) vaccine and placebo on consecutive days in a blinded, 1:1 crossover fashion at the National Institutes of Health. An open-label BNT162b2 booster was offered 5 months later if the second dose did not result in severe sAR. None of the participants received the mRNA-1273 (Spikevax) vaccine during the study. The primary end point was recurrence of sAR following second dose and booster vaccination; exploratory end points included biomarker measurements. RESULTS: Of 111 screened participants, 18 were randomly assigned to receive study interventions. Eight received BNT162b2 second dose followed by placebo; 8 received placebo followed by BNT162b2 second dose; 2 withdrew before receiving any study intervention. All 16 participants received the booster dose. Following second dose and booster vaccination, sARs recurred in 2 participants (12.5%; 95% CI, 1.6 to 38.3). No sAR occurred after placebo. An anaphylaxis mimic, immunization stress-related response (ISRR), occurred more commonly than sARs following both vaccine and placebo and was associated with higher predose anxiety scores, paresthesias, and distinct vital sign and biomarker changes. CONCLUSIONS: Our findings support revaccination of individuals who report sARs to COVID-19 mRNA vaccines. Distinct clinical and laboratory features may distinguish sARs from ISRRs.

Development and validation of a patient-level model to predict dementia across a network of observational databases.

BACKGROUND: A prediction model can be a useful tool to quantify the risk of a patient developing dementia in the next years and take risk-factor-targeted intervention. Numerous dementia prediction models have been developed, but few have been externally validated, likely limiting their clinical uptake. In our previous work, we had limited success in externally validating some of these existing models due to inadequate reporting. As a result, we are compelled to develop and externally validate novel models to predict dementia in the general population across a network of observational databases. We assess regularization methods to obtain parsimonious models that are of lower complexity and easier to implement. METHODS: Logistic regression models were developed across a network of five observational databases with electronic health records (EHRs) and claims data to predict 5-year dementia risk in persons aged 55-84. The regularization methods L1 and Broken Adaptive Ridge (BAR) as well as three candidate predictor sets to optimize prediction performance were assessed. The predictor sets include a baseline set using only age and sex, a full set including all available candidate predictors, and a phenotype set which includes a limited number of clinically relevant predictors. RESULTS: BAR can be used for variable selection, outperforming L1 when a parsimonious model is desired. Adding candidate predictors for disease diagnosis and drug exposure generally improves the performance of baseline models using only age and sex. While a model trained on German EHR data saw an increase in AUROC from 0.74 to 0.83 with additional predictors, a model trained on US EHR data showed only minimal improvement from 0.79 to 0.81 AUROC. Nevertheless, the latter model developed using BAR regularization on the clinically relevant predictor set was ultimately chosen as best performing model as it demonstrated more consistent external validation performance and improved calibration. CONCLUSIONS: We developed and externally validated patient-level models to predict dementia. Our results show that although dementia prediction is highly driven by demographic age, adding predictors based on condition diagnoses and drug exposures further improves prediction performance. BAR regularization outperforms L1 regularization to yield the most parsimonious yet still well-performing prediction model for dementia.

Bayesian safety surveillance with adaptive bias correction.

Postmarket safety surveillance is an integral part of mass vaccination programs. Typically relying on sequential analysis of real-world health data as they accrue, safety surveillance is challenged by sequential multiple testing and by biases induced by residual confounding in observational data. The current standard approach based on the maximized sequential probability ratio test (MaxSPRT) fails to satisfactorily address these practical challenges and it remains a rigid framework that requires prespecification of the surveillance schedule. We develop an alternative Bayesian surveillance procedure that addresses both aforementioned challenges using a more flexible framework. To mitigate bias, we jointly analyze a large set of negative control outcomes that are adverse events with no known association with the vaccines in order to inform an empirical bias distribution, which we then incorporate into estimating the effect of vaccine exposure on the adverse event of interest through a Bayesian hierarchical model. To address multiple testing and improve on flexibility, at each analysis timepoint, we update a posterior probability in favor of the alternative hypothesis that vaccination induces higher risks of adverse events, and then use it for sequential detection of safety signals. Through an empirical evaluation using six US observational healthcare databases covering more than 360 million patients, we benchmark the proposed procedure against MaxSPRT on testing errors and estimation accuracy, under two epidemiological designs, the historical comparator and the self-controlled case series. We demonstrate that our procedure substantially reduces Type 1 error rates, maintains high statistical power and fast signal detection, and provides considerably more accurate estimation than MaxSPRT. Given the extensiveness of the empirical study which yields more than 7 million sets of results, we present all results in a public R ShinyApp. As an effort to promote open science, we provide full implementation of our method in the open-source R package EvidenceSynthesis.

Fear of Reinjury, Psychological Factors, and Sport Played Have Negative Impact on Return to Sport Following Medial Patellofemoral Ligament Reconstruction for Patellar Instability.

PURPOSE: To analyze factors that affect return to sport after medial patellofemoral ligament reconstruction (MPFLR), such as psychological factors, sport played, and a positive apprehension test following surgery, and to determine the average return to sport rates and time to return to sport. METHODS: A literature search was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Included studies met the following criteria: patients underwent MPFLR for patellar instability, return to sport was recorded, and a factor that affected return to sport was mentioned. Search terms included medial patellofemoral ligament, tibial tubercle osteotomy, tibial tubercle transfer, return to play, and return to sport. RESULTS: Eighteen of 632 identified studies met inclusion criteria, and 1,072 patients who underwent MFPLR were recorded. Return-to-sport rates and mean/median time ranged from 60.0% to 100% and 3 to 10.4 months, respectively. Of the patients, 55.6% to 84.0% returned to sport without decreasing the level of competition. Six of 12 studies (50.0%) reported fear of reinjury as the top reason for patients not returning or returning at a lower level of sport. Volleyball/handball had the lowest return to the same level following surgery (18.2%-50.0%). CONCLUSIONS: Athletes who underwent MPFLR following recurrent patellar instability returned to sport at a range of 60.0% to 100%. Return to sport at the same level or higher was found to have a lower maximum rate at 55.6% to 84.0%. Fear of reinjury and sport played were found to have a substantial impact on ability to return to sport. Surgeons can use this information to advise patients on expectations following surgery. LEVEL OF EVIDENCE: Level IV, systematic review of Level III and IV studies.

Incidence rates of childhood asthma with recurrent exacerbations in the US Environmental influences on Child Health Outcomes (ECHO) program.

BACKGROUND: Descriptive epidemiological data on incidence rates (IRs) of asthma with recurrent exacerbations (ARE) are sparse. OBJECTIVES: This study hypothesized that IRs for ARE would vary by time, geography, age, and race and ethnicity, irrespective of parental asthma history. METHODS: The investigators leveraged data from 17,246 children born after 1990 enrolled in 59 US with 1 Puerto Rican cohort in the Environmental Influences on Child Health Outcomes (ECHO) consortium to estimate IRs for ARE. RESULTS: The overall crude IR for ARE was 6.07 per 1000 person-years (95% CI: 5.63-6.51) and was highest for children aged 2-4 years, for Hispanic Black and non-Hispanic Black children, and for those with a parental history of asthma. ARE IRs were higher for 2- to 4-year-olds in each race and ethnicity category and for both sexes. Multivariable analysis confirmed higher adjusted ARE IRs (aIRRs) for children born 2000-2009 compared with those born 1990-1999 and 2010-2017, 2-4 versus 10-19 years old (aIRR = 15.36; 95% CI: 12.09-19.52), and for males versus females (aIRR = 1.34; 95% CI 1.16-1.55). Black children (non-Hispanic and Hispanic) had higher rates than non-Hispanic White children (aIRR = 2.51; 95% CI 2.10-2.99; and aIRR = 2.04; 95% CI: 1.22-3.39, respectively). Children born in the Midwest, Northeast and South had higher rates than those born in the West (P < .01 for each comparison). Children with a parental history of asthma had rates nearly 3 times higher than those without such history (aIRR = 2.90; 95% CI: 2.43-3.46). CONCLUSIONS: Factors associated with time, geography, age, race and ethnicity, sex, and parental history appear to influence the inception of ARE among children and adolescents.

Implementing Suicide Risk Screening in a Virtual Addiction Clinic.

The purpose of this study was to describe the feasibility of implementing suicide risk screening in a virtual addiction clinic. Suicide risk screening was implemented in a virtual addiction clinic serving individuals with substance use disorders (SUD) using a quality improvement framework. One-hundred percent (252/252) of eligible patients enrolled in the clinic were screened for suicide risk (44% female; M[SD] age = 45.0[11.0] years, range = 21-68 years). Nineteen patients (8%) screened positive for suicide risk. After screening, no patients required emergency suicide interventions (100% non-acute positive). Notably, 74% (14/19) of those who screened positive did so by endorsing at least one past suicide attempt with no recent ideation. Suicide risk screening in virtual addiction clinics yields important clinical information for high-risk SUD populations without overburdening workflow with emergency services. Given the high proportion of non-acute positive screens based on suicide attempt histories with no recent ideation, clinicians may utilize information on suicide attempt history to facilitate further mental healthcare.

[Clinical Pathway for Suicide Risk Screening in Adult Primary Care Settings:Special Recommendations]. / Klinikai útvonal az öngyilkossági kockázat szurésére a felnott alapellátásban: Speciális ajánlások.

Suicide is a serious public health concern. On average, 80% of suicide decedents had contact with primary care within one year of their suicide. This and other research underscore the importance of screening for suicide risk within primary care settings, and implementation of suicide risk screening is already underway in many practices. However, while primary care practices may be familiar with screening for other mental health concerns (e.g., depression), many feel uncomfortable or unprepared for suicide risk screening. To meet the increasing demand for evidence-based suicide-risk screening guidance, we provide a clinical pathway for adult primary care practices (to include family medicine, internal medicine, women's health). The pathway was developed by experts with research, clinical expertise and experience in suicide risk screening and primary care. We also provide detailed guidance to aid primary care practices in their decisions about how to implement the clinical pathway.

Development and evaluation of an algorithm to link mothers and infants in two US commercial healthcare claims databases for pharmacoepidemiology research.

BACKGROUND: Administrative healthcare claims databases are used in drug safety research but are limited for investigating the impacts of prenatal exposures on neonatal and pediatric outcomes without mother-infant pair identification. Further, existing algorithms are not transportable across data sources. We developed a transportable mother-infant linkage algorithm and evaluated it in two, large US commercially insured populations. METHODS: We used two US commercial health insurance claims databases during the years 2000 to 2021. Mother-infant links were constructed where persons of female sex 12-55 years of age with a pregnancy episode ending in live birth were associated with a person who was 0 years of age at database entry, who shared a common insurance plan ID, had overlapping insurance coverage time, and whose date of birth was within ± 60-days of the mother's pregnancy episode live birth date. We compared the characteristics of linked vs. non-linked mothers and infants to assess similarity. RESULTS: The algorithm linked 3,477,960 mothers to 4,160,284 infants in the two databases. Linked mothers and linked infants comprised 73.6% of all mothers and 49.1% of all infants, respectively. 94.9% of linked infants' dates of birth were within ± 30-days of the associated mother's pregnancy episode end dates. Characteristics were largely similar in linked vs. non-linked mothers and infants. Differences included that linked mothers were older, had longer pregnancy episodes, and had greater post-pregnancy observation time than mothers with live births who were not linked. Linked infants had less observation time and greater healthcare utilization than non-linked infants. CONCLUSIONS: We developed a mother-infant linkage algorithm and applied it to two US commercial healthcare claims databases that achieved a high linkage proportion and demonstrated that linked and non-linked mother and infant cohorts were similar. Transparent, reusable algorithms applied to large databases enable large-scale research on exposures during pregnancy and pediatric outcomes with relevance to drug safety. These features suggest studies using this algorithm can produce valid and generalizable evidence to inform clinical, policy, and regulatory decisions.

Well-differentiated chrondrosarcoma in a cynomolgus macaque (Macaca fascicularis).

A cynomolgus macaque presented with an osteolytic lesion of the left femur. Histopathology was consistent with well-differentiated chondrosarcoma. No metastasis was found in chest radiographs out to 12 months. This case suggests survival out to 1 year without metastasis following amputation may be possible in NHPs with this condition.

Padé approximant meets federated learning: A nearly lossless, one-shot algorithm for evidence synthesis in distributed research networks with rare outcomes.

OBJECTIVE: We developed and evaluated a novel one-shot distributed algorithm for evidence synthesis in distributed research networks with rare outcomes. MATERIALS AND METHODS: Fed-Padé, motivated by a classic mathematical tool, Padé approximants, reconstructs the multi-site data likelihood via Padé approximant whose key parameters can be computed distributively. Thanks to the simplicity of [2,2] Padé approximant, Fed-Padé requests an extremely simple task and low communication cost for data partners. Specifically, each data partner only needs to compute and share the log-likelihood and its first 4 gradients evaluated at an initial estimator. We evaluated the performance of our algorithm with extensive simulation studies and four observational healthcare databases. RESULTS: Our simulation studies revealed that a [2,2]-Padé approximant can well reconstruct the multi-site likelihood so that Fed-Padé produces nearly identical estimates to the pooled analysis. Across all simulation scenarios considered, the median of relative bias and rate of instability of our Fed-Padé are both <0.1%, whereas meta-analysis estimates have bias up to 50% and instability up to 75%. Furthermore, the confidence intervals derived from the Fed-Padé algorithm showed better coverage of the truth than confidence intervals based on the meta-analysis. In real data analysis, the Fed-Padé has a relative bias of <1% for all three comparisons for risks of acute liver injury and decreased libido, whereas the meta-analysis estimates have a substantially higher bias (around 10%). CONCLUSION: The Fed-Padé algorithm is nearly lossless, stable, communication-efficient, and easy to implement for models with rare outcomes. It provides an extremely suitable and convenient approach for synthesizing evidence in distributed research networks with rare outcomes.

Representing and utilizing clinical textual data for real world studies: An OHDSI approach.

Clinical documentation in electronic health records contains crucial narratives and details about patients and their care. Natural language processing (NLP) can unlock the information conveyed in clinical notes and reports, and thus plays a critical role in real-world studies. The NLP Working Group at the Observational Health Data Sciences and Informatics (OHDSI) consortium was established to develop methods and tools to promote the use of textual data and NLP in real-world observational studies. In this paper, we describe a framework for representing and utilizing textual data in real-world evidence generation, including representations of information from clinical text in the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), the workflow and tools that were developed to extract, transform and load (ETL) data from clinical notes into tables in OMOP CDM, as well as current applications and specific use cases of the proposed OHDSI NLP solution at large consortia and individual institutions with English textual data. Challenges faced and lessons learned during the process are also discussed to provide valuable insights for researchers who are planning to implement NLP solutions in real-world studies.

Characteristics and outcomes of COVID-19 patients with and without asthma from the United States, South Korea, and Europe.

Objective: Large international comparisons describing the clinical characteristics of patients with COVID-19 are limited. The aim of the study was to perform a large-scale descriptive characterization of COVID-19 patients with asthma.Methods: We included nine databases contributing data from January to June 2020 from the US, South Korea (KR), Spain, UK and the Netherlands. We defined two cohorts of COVID-19 patients ('diagnosed' and 'hospitalized') based on COVID-19 disease codes. We followed patients from COVID-19 index date to 30 days or death. We performed descriptive analysis and reported the frequency of characteristics and outcomes in people with asthma defined by codes and prescriptions.Results: The diagnosed and hospitalized cohorts contained 666,933 and 159,552 COVID-19 patients respectively. Exacerbation in people with asthma was recorded in 1.6-8.6% of patients at presentation. Asthma prevalence ranged from 6.2% (95% CI 5.7-6.8) to 18.5% (95% CI 18.2-18.8) in the diagnosed cohort and 5.2% (95% CI 4.0-6.8) to 20.5% (95% CI 18.6-22.6) in the hospitalized cohort. Asthma patients with COVID-19 had high prevalence of comorbidity including hypertension, heart disease, diabetes and obesity. Mortality ranged from 2.1% (95% CI 1.8-2.4) to 16.9% (95% CI 13.8-20.5) and similar or lower compared to COVID-19 patients without asthma. Acute respiratory distress syndrome occurred in 15-30% of hospitalized COVID-19 asthma patients.Conclusion: The prevalence of asthma among COVID-19 patients varies internationally. Asthma patients with COVID-19 have high comorbidity. The prevalence of asthma exacerbation at presentation was low. Whilst mortality was similar among COVID-19 patients with and without asthma, this could be confounded by differences in clinical characteristics. Further research could help identify high-risk asthma patients.[Box: see text]Supplemental data for this article is available online at https://doi.org/10.1080/02770903.2021.2025392 .

Understanding the ongoing learning needs of Australian metropolitan, rural and remote paediatricians: Evaluation of a neurology outreach programme.

AIM: The purpose of this study was to evaluate whether a neurology outreach teaching programme delivered via video-teleconferencing (6 × 60 min live sessions every 6-8 weeks) is acceptable, contributes to understanding and meets the neurology learning needs of Australian paediatricians from metropolitan, rural and remote areas. METHODS: A sample of six NSW sites that joined the neurology outreach programme between 2017 and 2019 (Arm 1) and six interstate sites from QLD, WA and TAS who commenced the programme in 2020 (Arm 2) participated. A mixed-methods survey explored participants' learning needs and value of the programme. RESULTS: Forty-six participants submitted programme evaluation surveys (26 arm 1, 20 arm 2); 9 were removed due to insufficient data (n = 37). Quantitative and qualitative data showed the programme was acceptable in format, relevant to practice, appropriate for clinician learning needs, and engaging. Clinicians reported improvement in understanding and confidence. Participants felt more connected/less isolated and up-to-date. Participants reported a positive impact from the programme on approach to neurological problems and ensuing consults, and more differentiated and appropriate paediatric neurology referrals. CONCLUSION: This study validates the live video-teleconference outreach model as an acceptable, effective and important means of providing continuing neurology education for Australian paediatricians.

Concomitant Tibial Tubercle Osteotomy Reduces the Risk of Revision Surgery After Medial Patellofemoral Ligament Reconstruction for the Treatment of Patellar Instability.

PURPOSE: To compare postoperative complication rates between patients who underwent medial patellofemoral ligament reconstruction (MPFLR) and those who underwent MPFLR with tibial tubercle osteotomy (TTO) in a large-scale study. A secondary goal was to investigate demographic associations with outcomes. METHODS: Patients who underwent MPFLR (n = 3,480) or MPFLR-TTO (n = 615) for patellar instability were identified in the PearlDiver database. Rates of surgery for infection, procedures for knee stiffness, patellar fracture, and revision MPFLR within 2 years postoperatively were compared using multivariable logistic regression. Demographic associations with outcomes were also investigated. RESULTS: The MPFLR-TTO cohort exhibited a significantly lower rate of revision surgery at 2 years (0.8% vs 1.9%; odds ratio [OR], 0.33; 95% confidence interval [CI], 0.10-0.80; P = .036) when compared with the MPFLR group. Independent of index procedure, patients younger than 21 years had significantly lower rates of requiring procedures for knee stiffness (OR, 0.35; 95% CI, 0.22-0.54; P < .001) and any complication at 2 years (OR, 0.59; 95% CI, 0.45-0.78; P < .001) when compared with older patients. Male patients displayed a significantly lower rate of requiring procedures for knee stiffness at 2 years than female patients (OR, 0.46; 95% CI, 0.25-0.78; P = .007). Tobacco use was associated with a significantly higher rate of postoperative infection at 2 years (OR, 2.35; 95% CI, 1.00-5.38; P = .046). CONCLUSIONS: The MPFLR cohort exhibited higher rates of revision surgery at 2 years compared with the MPFLR-TTO cohort. Patient age under 21 years was associated with lower rates of any complication and requiring procedures for knee stiffness, male sex was associated with a lower rate of requiring procedures for knee stiffness, and tobacco use was associated with a higher rate of surgery for postoperative infection. This information can assist surgeons when counseling patients before these procedures are performed. LEVEL OF EVIDENCE: Level III, retrospective, comparative prognostic trial.

Estimating evaporation rates and contaminant air concentrations due to small spills of non-ideal aqueous organic solvent mixtures in a controlled environment.

Although small spills of non-ideal organic solvent mixtures are ubiquitous undesirable events in occupational settings, the potential risk of exposure associated with such scenarios remains insufficiently investigated. This study aimed to examine the impact of non-ideality on evaporation rates and contaminant air concentrations resulting from small spills of organic solvent mixtures. Evaporation rate constants alphas (α) were experimentally measured for five pure solvents using a gravimetric approach during solvent evaporation tests designed to simulate small spills of solvents. Two equations were used for estimating contaminants' evaporation rates from aqueous mixtures assuming either ideal or non-ideal behavior based on the pure-chemical alpha values. A spill model also known as the well-mixed room model with exponentially decreasing emission rate was used to predict air concentrations during various spill scenarios based on the two sets of estimated evaporation rates. Model predictive performance was evaluated by comparing the estimates against real-time concentrations measured for the same scenarios. Evaluations for 12 binary non-ideal aqueous mixtures found that the estimated evaporation rates accounting for the correction by the activity coefficients of the solvents (median = 0.0318 min-1) were higher than the evaporation rates estimated without the correction factor (median = 0.00632 min-1). Model estimates using the corrected evaporation rates reasonably agreed with the measured values, with a median predicted peak concentrations-to-measured peak concentrations ratio of 0.92 (0.81 to 1.32) and a median difference between the predicted and the measured peak times of -5 min. By contrast, when the non-corrected evaporation rates were used, the median predicted peak concentrations-to-measured peak concentrations ratio was 0.31 (0.08 to 0.75) and the median difference between the predicted and the measured peak times was +33 min. Results from this study demonstrate the importance of considering the non-ideality effect for accurately estimating evaporation rates and contaminant air concentrations generated by solvent mixtures. Moreover, this study is a step further in improving knowledge of modeling exposures related to small spills of organic solvent mixtures.

Developing the Blueprint for Youth Suicide Prevention.

Suicide and suicidal behavior among youth and young adults are a major public health crisis, exacerbated by the COVID-19 pandemic and demonstrated by increases in suicidal ideation and attempts among youth. Supports are needed to identify youth at risk and intervene in safe and effective ways. To address this need, the American Academy of Pediatrics and the American Foundation for Suicide Prevention, in collaboration with experts from the National Institute of Mental Health, developed the Blueprint for Youth Suicide Prevention ( Blueprint ) to translate research into strategies that are feasible, pragmatic, and actionable across all contexts in which youth live, learn, work, and play. In this piece, we describe the process of developing and disseminating the Blueprint. Through a summit and focus meetings, cross-sectoral partners convened to discuss the context of suicide risk among youth; explore the landscape of science, practice, and policy; build partnerships; and identify strategies for clinics, communities, and schools-all with a focus on health disparities and equity. These meetings resulted in 5 major takeaways: (1) suicide is often preventable; (2) health equity is critical to suicide prevention; (3) individual and systems changes are needed; (4) resilience should be a key focus; and (5) cross-sectoral partnerships are critical. These meetings and takeaways then informed the content of the Blueprint , which discusses the epidemiology of youth and young adult suicide and suicide risk, including health disparities; the importance of a public health framework; risk factors, protective factors, and warning signs; strategies for clinical settings, strategies for community and school settings; and policy priorities. Following the process description, lessons learned are also discussed, followed by a call to action for the public health community and all who serve and support youth. Finally, key steps to establishing and sustaining partnerships and implications for policy and practice are discussed.

The Lived Experiences of Female Relatives of Child Sexual Abuse Material (CSAM) Offenders in Ireland and the United Kingdom.

There is a limited understanding about how an association with those that download Child Sexual Abuse Material (CSAM), a highly stigmatized crime, impacts the lives of their innocent family members. Non-offending family members are often considered a valuable protective resource for offender desistance and in safeguarding children from abuse. Therefore, the present study aimed to explore the lived experiences of female family members of CSAM offenders in Ireland and the United Kingdom to both identify and target areas for intervention thus ameliorating their ability to protect. A qualitative research design was adopted, and data analyzed via reflexive thematic analysis. Fifteen individuals self-selected for participation and interviews resulted in the identification of three key themes: Shattered Worldview, The Injured Self; Contamination by Association. The analysis highlighted how non-offending family members experienced considerable shame, trauma, and stigma with consequences that reached into every aspect of their lives. The findings are discussed in the context of the limited available literature along with research implications and recommendations for both policy and practice.

Seek COVER: using a disease proxy to rapidly develop and validate a personalized risk calculator for COVID-19 outcomes in an international network.

BACKGROUND: We investigated whether we could use influenza data to develop prediction models for COVID-19 to increase the speed at which prediction models can reliably be developed and validated early in a pandemic. We developed COVID-19 Estimated Risk (COVER) scores that quantify a patient's risk of hospital admission with pneumonia (COVER-H), hospitalization with pneumonia requiring intensive services or death (COVER-I), or fatality (COVER-F) in the 30-days following COVID-19 diagnosis using historical data from patients with influenza or flu-like symptoms and tested this in COVID-19 patients. METHODS: We analyzed a federated network of electronic medical records and administrative claims data from 14 data sources and 6 countries containing data collected on or before 4/27/2020. We used a 2-step process to develop 3 scores using historical data from patients with influenza or flu-like symptoms any time prior to 2020. The first step was to create a data-driven model using LASSO regularized logistic regression, the covariates of which were used to develop aggregate covariates for the second step where the COVER scores were developed using a smaller set of features. These 3 COVER scores were then externally validated on patients with 1) influenza or flu-like symptoms and 2) confirmed or suspected COVID-19 diagnosis across 5 databases from South Korea, Spain, and the United States. Outcomes included i) hospitalization with pneumonia, ii) hospitalization with pneumonia requiring intensive services or death, and iii) death in the 30 days after index date. RESULTS: Overall, 44,507 COVID-19 patients were included for model validation. We identified 7 predictors (history of cancer, chronic obstructive pulmonary disease, diabetes, heart disease, hypertension, hyperlipidemia, kidney disease) which combined with age and sex discriminated which patients would experience any of our three outcomes. The models achieved good performance in influenza and COVID-19 cohorts. For COVID-19 the AUC ranges were, COVER-H: 0.69-0.81, COVER-I: 0.73-0.91, and COVER-F: 0.72-0.90. Calibration varied across the validations with some of the COVID-19 validations being less well calibrated than the influenza validations. CONCLUSIONS: This research demonstrated the utility of using a proxy disease to develop a prediction model. The 3 COVER models with 9-predictors that were developed using influenza data perform well for COVID-19 patients for predicting hospitalization, intensive services, and fatality. The scores showed good discriminatory performance which transferred well to the COVID-19 population. There was some miscalibration in the COVID-19 validations, which is potentially due to the difference in symptom severity between the two diseases. A possible solution for this is to recalibrate the models in each location before use.

PheValuator 2.0: Methodological improvements for the PheValuator approach to semi-automated phenotype algorithm evaluation.

PURPOSE: Phenotype algorithms are central to performing analyses using observational data. These algorithms translate the clinical idea of a health condition into an executable set of rules allowing for queries of data elements from a database. PheValuator, a software package in the Observational Health Data Sciences and Informatics (OHDSI) tool stack, provides a method to assess the performance characteristics of these algorithms, namely, sensitivity, specificity, and positive and negative predictive value. It uses machine learning to develop predictive models for determining a probabilistic gold standard of subjects for assessment of cases and non-cases of health conditions. PheValuator was developed to complement or even replace the traditional approach of algorithm validation, i.e., by expert assessment of subject records through chart review. Results in our first PheValuator paper suggest a systematic underestimation of the PPV compared to previous results using chart review. In this paper we evaluate modifications made to the method designed to improve its performance. METHODS: The major changes to PheValuator included allowing all diagnostic conditions, clinical observations, drug prescriptions, and laboratory measurements to be included as predictors within the modeling process whereas in the prior version there were significant restrictions on the included predictors. We also have allowed for the inclusion of the temporal relationships of the predictors in the model. To evaluate the performance of the new method, we compared the results from the new and original methods against results found from the literature using traditional validation of algorithms for 19 phenotypes. We performed these tests using data from five commercial databases. RESULTS: In the assessment aggregating all phenotype algorithms, the median difference between the PheValuator estimate and the gold standard estimate for PPV was reduced from -21 (IQR -34, -3) in Version 1.0 to 4 (IQR -3, 15) using Version 2.0. We found a median difference in specificity of 3 (IQR 1, 4.25) for Version 1.0 and 3 (IQR 1, 4) for Version 2.0. The median difference between the two versions of PheValuator and the gold standard for estimates of sensitivity was reduced from -39 (-51, -20) to -16 (-34, -6). CONCLUSION: PheValuator 2.0 produces estimates for the performance characteristics for phenotype algorithms that are significantly closer to estimates from traditional validation through chart review compared to version 1.0. With this tool in researcher's toolkits, methods, such as quantitative bias analysis, may now be used to improve the reliability and reproducibility of research studies using observational data.