Experience on postmortem minimally invasive tissue sampling to ascertain the cause of death determination in South African children: A case for implementing as standard of care.

Determining the death burden for prioritising public health interventions necessitates detailed data on the causal pathways to death. Postmortem minimally invasive tissue sampling (MITS), incorporating histology, molecular and microbial culture diagnostics, enhances cause-of-death attribution, particularly for infectious deaths. MITS proves a valid alternative to full diagnostic autopsies, especially in low- and middle-income countries. In Soweto, South Africa (SA), the Child Health and Mortality Prevention Surveillance (CHAMPS) programme has delineated over 1 000 child and stillbirth deaths since 2017. This SA CHAMPS site supports advocating for the use of postmortem MITS as routine practice, for more granular insights into under-5 mortality causes. This knowledge is crucial for SA's pursuit of Sustainable Development Goal 3.2, targeting reduced neonatal and under-5 mortality rates. This commentary explores the public health advantages and ethicolegal considerations surrounding implementing MITS as standard of care for stillbirths, neonatal and paediatric deaths in SA. Furthermore, based on the data from CHAMPS, we present three pragmatic algorithmic approaches to the wide array of testing options for cost-effectiveness and scalability of postmortem MITS in South African state facilities.

Bacterial vaginosis testing gaps for transmasculine patients may exacerbate health disparities.

Introduction: Bacterial vaginosis (BV) is associated with non-optimal changes in the vaginal microbiome and increased susceptibility to STIs and HIV in cisgender women. Much less is known about the sexual health of transmasculine people and susceptibility to BV, STIs, and HIV. This study's objective was to assess BV testing and outcomes of transmasculine and cisgender women patient populations at a large, LGBTQ + federally qualified health center. Methods: Retrospective electronic health record data were extracted for eligible patients having at least one primary care visit between January 1, 2021, and December 31, 2021. Transmasculine patients were limited to those with a testosterone prescription in 2021. We conducted log binomial regression analysis to determine the probability of receiving a BV test based on gender identity, adjusting for sociodemographic characteristics. Results: During 2021, 4,903 cisgender women patients and 1,867 transmasculine patients had at least one primary care visit. Compared to cisgender women, transmasculine patients were disproportionately young, White, queer, privately insured, living outside Chicago, and had a lower rate of BV testing (1.9% v. 17.3%, p < 0.001). Controlling for sociodemographics, transmasculine patients were less likely to receive a BV test [Prevalence Ratio = 0.19 (95% CI 0.13-0.27)]. Discussion: The low rate of BV testing among transmasculine patients may contribute to disparities in reproductive health outcomes. Prospective community- and provider-engaged research is needed to better understand the multifactorial determinants for sexual healthcare and gender-affirming care for transmasculine patients. In particular, the impact of exogenous testosterone on the vaginal microbiome should also be determined.

First They Came for Us All: Responding to Anti-Transgender Structural Violence With Collective, Community-Engaged, and Intersectional Health Equity Research and Advocacy.

This article is a call for collective action across health equity researchers and advocates to build a more just world. We attempt to make sense of senseless structural and interpersonal brutality in the context of the current political climate across the United States, whereby the spectrum of gender nonconformity has been and continues to be stigmatized. From drag performance to transgender identities to gender-affirming health care, extremists have instrumentalized primary levers of democracy-the courts, legislatures, and social media-to attempt to outlaw and eradicate gender expansiveness and those who provide forms of support and care, including gender-affirming medical care, to transgender, nonbinary, and gender-expansive (TNBGE) individuals.

An Implementation of a Community-Engaged, Group-Level Mental Health Pilot for Black and Latina Transgender Women.

The primary aim is to assess the implementation of an eight-session, group therapy pilot for Black and Latina transgender women in Chicago in terms of implementation outcomes regarding intervention effectiveness, acceptability, appropriateness, and feasibility. The Exploration Preparation Implementation Sustainment (EPIS) framework guided implementation processes, including community engagement as an implementation strategy, and an implementation taxonomy was used to evaluate outcomes of acceptability, appropriateness, and feasibility, in addition to intervention effectiveness regarding anxiety and community connectedness. Two rounds of the pilot were completed in 2020, during the COVID-19 pandemic, at a community-based organization serving LGBTQ+ (lesbian, gay, bisexual, transgender, queer/questioning) youth on Chicago's West Side. Participants (N = 14) completed a baseline and postintervention assessment and evaluations after each of eight intervention modules. Descriptive statistics show improvement across measures of anxiety and community connectedness, and high mean scores across domains of acceptability, appropriateness, and feasibility. Pilot findings indicate intervention effectiveness, acceptability, appropriateness, and feasibility to address mental health and social support of Black and Latina transgender women. Additional resources are needed for transgender community-engaged mental health programs and research to establish core and adaptable intervention elements, scaled-up evidence for clinical effectiveness, and, most importantly, to improve mental health outcomes and the sustainability of such interventions.

Temporary changes in STI & HIV testing & diagnoses across different phases of the COVID-19 pandemic, Chicago IL.

Introduction: While the U.S. has seen a sustained rise in STI cases over the past decade, the impact of the COVID-19 on STIs and HIV is unclear. Methods: To examine the short- and medium-term impacts of COVID-19 and HIV and STI testing and diagnosis, we compared pre-pandemic trends to three periods of the pandemic: early- pandemic, March-May 2020; mid-pandemic June 2020-May 2021; and late-pandemic, June 2021-May 2022. We compared average number of monthly tests and diagnoses, overall and by gender, as well as the monthly change (slope) in testing and diagnoses. Results: We find that after decreases in average monthly STI and HIV testing and diagnoses during the early- and mid-pandemic, cases were largely back to pre-pandemic levels by the late-pandemic, with some variation by gender. Conclusion: Changes in testing and diagnoses varied by phase of the pandemic. Some key populations may require additional outreach efforts to attain pre-pandemic testing levels.

The Trans Accountability Project: Community Engagement to Address Structural Marginalization and Health Inequities.

BACKGROUND: Black and Latina Transgender women face systemic marginalization and harm, increasing vulnerability to social stress and poor health outcomes. These communities have limited access to resources to mobilize and create paths toward health equity. OBJECTIVES: In this paper we report on the results of a community partnership to engage Black and Latina transgender communities on the South and West Sides of Chicago and establish service priorities for collective empowerment. METHODS: The Trans Accountability Project (TAP), a steering committee of racially diverse transgender and nonbinary representatives from four partner organizations, was established and led the design, recruitment, implementation, and analysis of a community needs assessment. World café and human-centered design methods, guided two community conversations/listening sessions around four activities: the perfect provider, my dream job, safety planning, and a stake-holder reflection. RESULTS: Sixty-three participants completed three activities and envisioned innovations for 1) accessible and holistic gender-affirming health care, 2) autonomous, flexible, and community-focused jobs in the arts, nonprofit/business, and care professions, and 3) safer social interactions and spaces. Ten stakeholders attended to listen and inform their organizational and clinical practices to empower Black and Latina transgender women. CONCLUSIONS: TAP prioritized accountability, connectedness, and centering the voices of Black and Latina transgender women as a starting point to intervene upon structural marginalization. Five insights emerged and have directed TAP's focus toward employment and collective care. Although further structural change remains a priority, TAP represents a mechanism for sharing power, improving communication and collaboration, and increasing transparency across relevant Chicago community-based organizations.

The Evaluation of a Clinical Decision Support Tool Using Natural Language Processing to Screen Hospitalized Adults for Unhealthy Substance Use: Protocol for a Quasi-Experimental Design.

BACKGROUND: Automated and data-driven methods for screening using natural language processing (NLP) and machine learning may replace resource-intensive manual approaches in the usual care of patients hospitalized with conditions related to unhealthy substance use. The rigorous evaluation of tools that use artificial intelligence (AI) is necessary to demonstrate effectiveness before system-wide implementation. An NLP tool to use routinely collected data in the electronic health record was previously validated for diagnostic accuracy in a retrospective study for screening unhealthy substance use. Our next step is a noninferiority design incorporated into a research protocol for clinical implementation with prospective evaluation of clinical effectiveness in a large health system. OBJECTIVE: This study aims to provide a study protocol to evaluate health outcomes and the costs and benefits of an AI-driven automated screener compared to manual human screening for unhealthy substance use. METHODS: A pre-post design is proposed to evaluate 12 months of manual screening followed by 12 months of automated screening across surgical and medical wards at a single medical center. The preintervention period consists of usual care with manual screening by nurses and social workers and referrals to a multidisciplinary Substance Use Intervention Team (SUIT). Facilitated by a NLP pipeline in the postintervention period, clinical notes from the first 24 hours of hospitalization will be processed and scored by a machine learning model, and the SUIT will be similarly alerted to patients who flagged positive for substance misuse. Flowsheets within the electronic health record have been updated to capture rates of interventions for the primary outcome (brief intervention/motivational interviewing, medication-assisted treatment, naloxone dispensing, and referral to outpatient care). Effectiveness in terms of patient outcomes will be determined by noninferior rates of interventions (primary outcome), as well as rates of readmission within 6 months, average time to consult, and discharge rates against medical advice (secondary outcomes) in the postintervention period by a SUIT compared to the preintervention period. A separate analysis will be performed to assess the costs and benefits to the health system by using automated screening. Changes from the pre- to postintervention period will be assessed in covariate-adjusted generalized linear mixed-effects models. RESULTS: The study will begin in September 2022. Monthly data monitoring and Data Safety Monitoring Board reporting are scheduled every 6 months throughout the study period. We anticipate reporting final results by June 2025. CONCLUSIONS: The use of augmented intelligence for clinical decision support is growing with an increasing number of AI tools. We provide a research protocol for prospective evaluation of an automated NLP system for screening unhealthy substance use using a noninferiority design to demonstrate comprehensive screening that may be as effective as manual screening but less costly via automated solutions. TRIAL REGISTRATION: ClinicalTrials.gov NCT03833804; https://clinicaltrials.gov/ct2/show/NCT03833804. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/42971.

Machine Learning Techniques to Explore Clinical Presentations of COVID-19 Severity and to Test the Association With Unhealthy Opioid Use: Retrospective Cross-sectional Cohort Study.

BACKGROUND: The COVID-19 pandemic has exacerbated health inequities in the United States. People with unhealthy opioid use (UOU) may face disproportionate challenges with COVID-19 precautions, and the pandemic has disrupted access to opioids and UOU treatments. UOU impairs the immunological, cardiovascular, pulmonary, renal, and neurological systems and may increase severity of outcomes for COVID-19. OBJECTIVE: We applied machine learning techniques to explore clinical presentations of hospitalized patients with UOU and COVID-19 and to test the association between UOU and COVID-19 disease severity. METHODS: This retrospective, cross-sectional cohort study was conducted based on data from 4110 electronic health record patient encounters at an academic health center in Chicago between January 1, 2020, and December 31, 2020. The inclusion criterion was an unplanned admission of a patient aged ≥18 years; encounters were counted as COVID-19-positive if there was a positive test for COVID-19 or 2 COVID-19 International Classification of Disease, Tenth Revision codes. Using a predefined cutoff with optimal sensitivity and specificity to identify UOU, we ran a machine learning UOU classifier on the data for patients with COVID-19 to estimate the subcohort of patients with UOU. Topic modeling was used to explore and compare the clinical presentations documented for 2 subgroups: encounters with UOU and COVID-19 and those with no UOU and COVID-19. Mixed effects logistic regression accounted for multiple encounters for some patients and tested the association between UOU and COVID-19 outcome severity. Severity was measured with 3 utilization metrics: low-severity unplanned admission, medium-severity unplanned admission and receiving mechanical ventilation, and high-severity unplanned admission with in-hospital death. All models controlled for age, sex, race/ethnicity, insurance status, and BMI. RESULTS: Topic modeling yielded 10 topics per subgroup and highlighted unique comorbidities associated with UOU and COVID-19 (eg, HIV) and no UOU and COVID-19 (eg, diabetes). In the regression analysis, each incremental increase in the classifier's predicted probability of UOU was associated with 1.16 higher odds of COVID-19 outcome severity (odds ratio 1.16, 95% CI 1.04-1.29; P=.009). CONCLUSIONS: Among patients hospitalized with COVID-19, UOU is an independent risk factor associated with greater outcome severity, including in-hospital death. Social determinants of health and opioid-related overdose are unique comorbidities in the clinical presentation of the UOU patient subgroup. Additional research is needed on the role of COVID-19 therapeutics and inpatient management of acute COVID-19 pneumonia for patients with UOU. Further research is needed to test associations between expanded evidence-based harm reduction strategies for UOU and vaccination rates, hospitalizations, and risks for overdose and death among people with UOU and COVID-19. Machine learning techniques may offer more exhaustive means for cohort discovery and a novel mixed methods approach to population health.

'Cloudbuster': a Python-based open source application for three-dimensional reconstruction and quantification of stacked biological imaging samples.

Three-dimensional (3D) spheroid cultures are generating increasing interest in cancer research, e.g. for the evaluation of pharmacological effects of novel small molecule inhibitors. This is mainly due to the fact that such 3D structures reflect physiological characteristics of tumours and the cellular microenvironments they reside in more faithfully than two-dimensional (2D) cell cultures; in addition, they allow the reduction of animal experiments while providing significantly relevant human-based models. Quantification of such organoid structures as well as the mainly slice-based acquisition and thus forced 2D representation of 3D spheroids provide a challenge for the interpretation of the associated generated data. Here, we provide a novel open-source workflow to reconstruct a 3D entity from slice-recorded microscopical images with or without treatment with anti-migratory small molecule inhibitors. This reconstruction produces distinct point clouds as basis for subsequent comparison of basic readout parameters using average computer processor, memory and graphics resources within an acceptable time frame. We were able to validate the usefulness of this workflow using 3D data generated by various imaging techniques, including z-stacks from confocal microscopy and histochemically labelled spheroid sectioning, and demonstrate the possibility to accurately characterize inhibitor effects in great detail.

Community engagement to improve access to healthcare: a comparative case study to advance implementation science for transgender health equity.

BACKGROUND: Recent calls to action have been made for Implementation Science to attend to health inequities at the intersections of race, gender, and social injustice in the United States. Transgender people, particularly Black and Latina transgender women, experience a range of health inequities and social injustices. In this study, we compared two processes of transgender community engagement in Los Angeles and in Chicago as an implementation strategy to address inequitable access to care; we adapted and extended the Exploration Planning Implementation and Sustainment (EPIS) framework for transgender health equity. METHODS: A comparative case method and the EPIS framework were used to examine parallel implementation strategies of transgender community engagement to expand access to care. To foster conceptual development and adaptation of EPIS for trans health equity, the comparative case method required detailed description, exploration, and analyses of the community-engagement processes that led to different interventions to expand access. In both cities, the unit of analysis was a steering committee made up of local transgender and cisgender stakeholders. RESULTS: Both steering committees initiated their exploration processes with World Café-style, transgender community-engaged events in order to assess community needs and structural barriers to healthcare. The steering committees curated activities that amplified the voices of transgender community members among stakeholders, encouraging more effective and collaborative ways to advance transgender health equity. Based on analysis and findings from the Los Angeles town hall, the steering committee worked with a local medical school, extending the transgender medicine curriculum, and incorporating elements of transgender community-engagement. The Chicago steering committee determined from their findings that the most impactful intervention on structural racism and barriers to healthcare access would be to design and pilot an employment program for Black and Latina transgender women. CONCLUSION: In Los Angeles and Chicago, transgender community engagement guided implementation processes and led to critical insights regarding specific, local barriers to healthcare. The steering committee itself represented an important vehicle for individual-, organizational-, and community-level relationship and capacity building. This comparative case study highlights key adaptations of EPIS toward the formation of an implementation science framework for transgender health equity.

The Identification of Subphenotypes and Associations with Health Outcomes in Patients with Opioid-Related Emergency Department Encounters Using Latent Class Analysis.

The emergency department (ED) is a critical setting for the treatment of patients with opioid misuse. Detecting relevant clinical profiles allows for tailored treatment approaches. We sought to identify and characterize subphenotypes of ED patients with opioid-related encounters. A latent class analysis was conducted using 14,057,302 opioid-related encounters from 2016 through 2017 using the National Emergency Department Sample (NEDS), the largest all-payer ED database in the United States. The optimal model was determined by face validity and information criteria-based metrics. A three-step approach assessed class structure, assigned individuals to classes, and examined characteristics between classes. Class associations were determined for hospitalization, in-hospital death, and ED charges. The final five-class model consisted of the following subphenotypes: Chronic pain (class 1); Alcohol use (class 2); Depression and pain (class 3); Psychosis, liver disease, and polysubstance use (class 4); and Pregnancy (class 5). Using class 1 as the reference, the greatest odds for hospitalization occurred in classes 3 and 4 (Ors 5.24 and 5.33, p < 0.001) and for in-hospital death in class 4 (OR 3.44, p < 0.001). Median ED charges ranged from USD 2177 (class 1) to USD 2881 (class 4). These subphenotypes provide a basis for examining patient-tailored approaches for this patient population.

Development and multimodal validation of a substance misuse algorithm for referral to treatment using artificial intelligence (SMART-AI): a retrospective deep learning study.

BACKGROUND: Substance misuse is a heterogeneous and complex set of behavioural conditions that are highly prevalent in hospital settings and frequently co-occur. Few hospital-wide solutions exist to comprehensively and reliably identify these conditions to prioritise care and guide treatment. The aim of this study was to apply natural language processing (NLP) to clinical notes collected in the electronic health record (EHR) to accurately screen for substance misuse. METHODS: The model was trained and developed on a reference dataset derived from a hospital-wide programme at Rush University Medical Center (RUMC), Chicago, IL, USA, that used structured diagnostic interviews to manually screen admitted patients over 27 months (between Oct 1, 2017, and Dec 31, 2019; n=54 915). The Alcohol Use Disorder Identification Test and Drug Abuse Screening Tool served as reference standards. The first 24 h of notes in the EHR were mapped to standardised medical vocabulary and fed into single-label, multilabel, and multilabel with auxillary-task neural network models. Temporal validation of the model was done using data from the subsequent 12 months on a subset of RUMC patients (n=16 917). External validation was done using data from Loyola University Medical Center, Chicago, IL, USA between Jan 1, 2007, and Sept 30, 2017 (n=1991 adult patients). The primary outcome was discrimination for alcohol misuse, opioid misuse, or non-opioid drug misuse. Discrimination was assessed by the area under the receiver operating characteristic curve (AUROC). Calibration slope and intercept were measured with the unreliability index. Bias assessments were performed across demographic subgroups. FINDINGS: The model was trained on a cohort that had 3·5% misuse (n=1 921) with any type of substance. 220 (11%) of 1921 patients with substance misuse had more than one type of misuse. The multilabel convolutional neural network classifier had a mean AUROC of 0·97 (95% CI 0·96-0·98) during temporal validation for all types of substance misuse. The model was well calibrated and showed good face validity with model features containing explicit mentions of aberrant drug-taking behaviour. A false-negative rate of 0·18-0·19 and a false-positive rate of 0·03 between non-Hispanic Black and non-Hispanic White groups occurred. In external validation, the AUROCs for alcohol and opioid misuse were 0·88 (95% CI 0·86-0·90) and 0·94 (0·92-0·95), respectively. INTERPRETATION: We developed a novel and accurate approach to leveraging the first 24 h of EHR notes for screening multiple types of substance misuse. FUNDING: National Institute On Drug Abuse, National Institutes of Health.

The effect of estrogen on brown adipose tissue activity in male rats.

OBJECTIVE: Centrally administered estrogen can increase sympathetic nerve activity to brown adipose tissue, resulting in thermogenesis. The central thermogenic effects of estrogen have not been investigated in males. Therefore, this study sought to investigate the effects of peripherally and centrally administered estrogen on thermogenesis, heart rate and mean arterial pressure in male rats. Thermogenesis was assessed by monitoring brown adipose tissue temperature. RESULTS: Peripherally administered estrogen elicited no significant effect on brown adipose tissue temperature, heart rate or mean arterial pressure. Centrally administered estrogen elicited a coincident increase in both brown adipose tissue and core temperature. Centrally administered estrogen also resulted in a decrease in mean arterial pressure but had no effect on heart rate. With the present data it is not possible to elucidate whether changes in temperature were the result of thermogenic or thermoregulatory mechanisms.

External validation of a machine learning classifier to identify unhealthy alcohol use in hospitalized patients.

BACKGROUND AND AIMS: Unhealthy alcohol use (UAU) is one of the leading causes of global morbidity. A machine learning approach to alcohol screening could accelerate best practices when integrated into electronic health record (EHR) systems. This study aimed to validate externally a natural language processing (NLP) classifier developed at an independent medical center. DESIGN: Retrospective cohort study. SETTING: The site for validation was a midwestern United States tertiary-care, urban medical center that has an inpatient structured universal screening model for unhealthy substance use and an active addiction consult service. PARTICIPANTS/CASES: Unplanned admissions of adult patients between October 23, 2017 and December 31, 2019, with EHR documentation of manual alcohol screening were included in the cohort (n = 57 605). MEASUREMENTS: The Alcohol Use Disorders Identification Test (AUDIT) served as the reference standard. AUDIT scores ≥5 for females and ≥8 for males served as cases for UAU. To examine error in manual screening or under-reporting, a post hoc error analysis was conducted, reviewing discordance between the NLP classifier and AUDIT-derived reference. All clinical notes excluding the manual screening and AUDIT documentation from the EHR were included in the NLP analysis. FINDINGS: Using clinical notes from the first 24 hours of each encounter, the NLP classifier demonstrated an area under the receiver operating characteristic curve (AUCROC) and precision-recall area under the curve (PRAUC) of 0.91 (95% CI = 0.89-0.92) and 0.56 (95% CI = 0.53-0.60), respectively. At the optimal cut point of 0.5, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 0.66 (95% CI = 0.62-0.69), 0.98 (95% CI = 0.98-0.98), 0.35 (95% CI = 0.33-0.38), and 1.0 (95% CI = 1.0-1.0), respectively. CONCLUSIONS: External validation of a publicly available alcohol misuse classifier demonstrates adequate sensitivity and specificity for routine clinical use as an automated screening tool for identifying at-risk patients.

Transgender data collection in the electronic health record: Current concepts and issues.

There are over 1 million transgender people living in the United States, and 33% report negative experiences with a healthcare provider, many of which are connected to data representation in electronic health records (EHRs). We present recommendations and common pitfalls involving sex- and gender-related data collection in EHRs. Our recommendations leverage the needs of patients, medical providers, and researchers to optimize both individual patient experiences and the efficacy and reproducibility of EHR population-based studies. We also briefly discuss adequate additions to the EHR considering name and pronoun usage. We add the disclaimer that these questions are more complex than commonly assumed. We conclude that collaborations between local transgender and gender-diverse persons and medical providers as well as open inclusion of transgender and gender-diverse individuals on terminology and standards boards is crucial to shifting the paradigm in transgender and gender-diverse health.

Bias and fairness assessment of a natural language processing opioid misuse classifier: detection and mitigation of electronic health record data disadvantages across racial subgroups.

OBJECTIVES: To assess fairness and bias of a previously validated machine learning opioid misuse classifier. MATERIALS & METHODS: Two experiments were conducted with the classifier's original (n = 1000) and external validation (n = 53 974) datasets from 2 health systems. Bias was assessed via testing for differences in type II error rates across racial/ethnic subgroups (Black, Hispanic/Latinx, White, Other) using bootstrapped 95% confidence intervals. A local surrogate model was estimated to interpret the classifier's predictions by race and averaged globally from the datasets. Subgroup analyses and post-hoc recalibrations were conducted to attempt to mitigate biased metrics. RESULTS: We identified bias in the false negative rate (FNR = 0.32) of the Black subgroup compared to the FNR (0.17) of the White subgroup. Top features included "heroin" and "substance abuse" across subgroups. Post-hoc recalibrations eliminated bias in FNR with minimal changes in other subgroup error metrics. The Black FNR subgroup had higher risk scores for readmission and mortality than the White FNR subgroup, and a higher mortality risk score than the Black true positive subgroup (P < .05). DISCUSSION: The Black FNR subgroup had the greatest severity of disease and risk for poor outcomes. Similar features were present between subgroups for predicting opioid misuse, but inequities were present. Post-hoc mitigation techniques mitigated bias in type II error rate without creating substantial type I error rates. From model design through deployment, bias and data disadvantages should be systematically addressed. CONCLUSION: Standardized, transparent bias assessments are needed to improve trustworthiness in clinical machine learning models.

Implementation of Gender Identity and Assigned Sex at Birth Data Collection in Electronic Health Records: Where Are We Now?

In 2015, the United States Department of Health and Human Services instantiated rules mandating the inclusion of sexual orientation and gender identity (SO/GI) data fields for systems certified under Stage 3 of the Meaningful Use of Electronic Health Records (EHR) program. To date, no published assessments have benchmarked implementation penetration and data quality. To establish a benchmark for a U.S. health system collection of gender identity and sex assigned at birth, we analyzed one urban academic health center's EHR data; specifically, the records of patients with unplanned hospital admissions during 2020 (N = 49,314). Approximately one-quarter of patient records included gender identity data, and one percent of them indicated a transgender or nonbinary (TGNB) status. Data quality checks suggested limited provider literacy around gender identity as well as limited provider and patient comfort levels with gender identity disclosures. Improvements are needed in both provider and patient literacy and comfort around gender identity in clinical settings. To include TGNB populations in informatics-based research, additional novel approaches, such as natural language processing, may be needed for more comprehensive and representative TGNB cohort discovery. Community and stakeholder engagement around gender identity data collection and health research will likely improve these implementation efforts.

Pre-participation Vision Screening and Comprehensive Eye Care in National Collegiate Athletic Association Athletes.

SIGNIFICANCE: Pre-participation physical evaluation and its vision screenings have been the mainstay of medical clearance for competitive play for decades. The ability of screening to address athlete's sports-specific vision needs is unknown. METHODS: Fifty-eight intercollegiate football players consented to participate in a comprehensive, sports-specific eye examination in addition to the standard pre-participation vision screening. Sensitivity, specificity, and positive and negative predictive values were determined for screening's ability to detect athletes whose vision might improve with correction, athletes who had significant ocular findings that impact safety, and either of the two conditions together. The effect no recent eye examination added to pre-participation vision screening results was evaluated for change in screening yield. Descriptive statistics of the cohort and associations with no recent comprehensive eye examination were generated. RESULTS: The pre-participation vision screening was able to identify three athletes not meeting visual acuity requirements for medical clearance to play without a comprehensive assessment. A failed screening was poorly able to identify athletes who might benefit from improved acuity (sensitivity, 9.1%; specificity, 100%), have sports-specific significant ocular findings (sensitivity, 10.5%; specificity, 97.3%), or have either together (sensitivity, 7.5%; specificity, 100%). Sixty percent (33/55) of athletes reported never having a comprehensive examination or one within the last 10 years. Fifty-eight percent (34/58) had improved best-corrected visual acuity after comprehensive examination, and 81% (47/58) had improved acuity or a sports-specific significant finding. CONCLUSIONS: The pre-participation vision screening was largely able to identify athletes meeting the minimum visual acuity requirement for athlete clearance. It poorly identified those who might benefit from improved vision with refractive correction and those in whom sport-specific significant eye findings were noted. Comprehensive eye care had a clear benefit for the majority of athletes tested. This benefit needs to be balanced with the potential added costs and time constraints to players and athletic department staff.