Using Power Analysis to Choose the Unit of Randomization, Outcome, and Approach for Subgroup Analysis for a Multilevel Randomized Controlled Clinical Trial to Reduce Disparities in Cardiovascular Health.

We give examples of three features in the design of randomized controlled clinical trials which can increase power and thus decrease sample size and costs. We consider an example multilevel trial with several levels of clustering. For a fixed number of independent sampling units, we show that power can vary widely with the choice of the level of randomization. We demonstrate that power and interpretability can improve by testing a multivariate outcome rather than an unweighted composite outcome. Finally, we show that using a pooled analytic approach, which analyzes data for all subgroups in a single model, improves power for testing the intervention effect compared to a stratified analysis, which analyzes data for each subgroup in a separate model. The power results are computed for a proposed prevention research study. The trial plans to randomize adults to either telehealth (intervention) or in-person treatment (control) to reduce cardiovascular risk factors. The trial outcomes will be measures of the Essential Eight, a set of scores for cardiovascular health developed by the American Heart Association which can be combined into a single composite score. The proposed trial is a multilevel study, with outcomes measured on participants, participants treated by the same provider, providers nested within clinics, and clinics nested within hospitals. Investigators suspect that the intervention effect will be greater in rural participants, who live farther from clinics than urban participants. The results use published, exact analytic methods for power calculations with continuous outcomes. We provide example code for power analyses using validated software.

Evaluating site-of-care-related racial disparities in kidney graft failure using a novel federated learning framework.

OBJECTIVES: Racial disparities in kidney transplant access and posttransplant outcomes exist between non-Hispanic Black (NHB) and non-Hispanic White (NHW) patients in the United States, with the site of care being a key contributor. Using multi-site data to examine the effect of site of care on racial disparities, the key challenge is the dilemma in sharing patient-level data due to regulations for protecting patients' privacy. MATERIALS AND METHODS: We developed a federated learning framework, named dGEM-disparity (decentralized algorithm for Generalized linear mixed Effect Model for disparity quantification). Consisting of 2 modules, dGEM-disparity first provides accurately estimated common effects and calibrated hospital-specific effects by requiring only aggregated data from each center and then adopts a counterfactual modeling approach to assess whether the graft failure rates differ if NHB patients had been admitted at transplant centers in the same distribution as NHW patients were admitted. RESULTS: Utilizing United States Renal Data System data from 39 043 adult patients across 73 transplant centers over 10 years, we found that if NHB patients had followed the distribution of NHW patients in admissions, there would be 38 fewer deaths or graft failures per 10 000 NHB patients (95% CI, 35-40) within 1 year of receiving a kidney transplant on average. DISCUSSION: The proposed framework facilitates efficient collaborations in clinical research networks. Additionally, the framework, by using counterfactual modeling to calculate the event rate, allows us to investigate contributions to racial disparities that may occur at the level of site of care. CONCLUSIONS: Our framework is broadly applicable to other decentralized datasets and disparities research related to differential access to care. Ultimately, our proposed framework will advance equity in human health by identifying and addressing hospital-level racial disparities.

Development and validation of a race-agnostic computable phenotype for kidney health in adult hospitalized patients.

Standard race adjustments for estimating glomerular filtration rate (GFR) and reference creatinine can yield a lower acute kidney injury (AKI) and chronic kidney disease (CKD) prevalence among African American patients than non-race adjusted estimates. We developed two race-agnostic computable phenotypes that assess kidney health among 139,152 subjects admitted to the University of Florida Health between 1/2012-8/2019 by removing the race modifier from the estimated GFR and estimated creatinine formula used by the race-adjusted algorithm (race-agnostic algorithm 1) and by utilizing 2021 CKD-EPI refit without race formula (race-agnostic algorithm 2) for calculations of the estimated GFR and estimated creatinine. We compared results using these algorithms to the race-adjusted algorithm in African American patients. Using clinical adjudication, we validated race-agnostic computable phenotypes developed for preadmission CKD and AKI presence on 300 cases. Race adjustment reclassified 2,113 (8%) to no CKD and 7,901 (29%) to a less severe CKD stage compared to race-agnostic algorithm 1 and reclassified 1,208 (5%) to no CKD and 4,606 (18%) to a less severe CKD stage compared to race-agnostic algorithm 2. Of 12,451 AKI encounters based on race-agnostic algorithm 1, race adjustment reclassified 591 to No AKI and 305 to a less severe AKI stage. Of 12,251 AKI encounters based on race-agnostic algorithm 2, race adjustment reclassified 382 to No AKI and 196 (1.6%) to a less severe AKI stage. The phenotyping algorithm based on refit without race formula performed well in identifying patients with CKD and AKI with a sensitivity of 100% (95% confidence interval [CI] 97%-100%) and 99% (95% CI 97%-100%) and a specificity of 88% (95% CI 82%-93%) and 98% (95% CI 93%-100%), respectively. Race-agnostic algorithms identified substantial proportions of additional patients with CKD and AKI compared to race-adjusted algorithm in African American patients. The phenotyping algorithm is promising in identifying patients with kidney disease and improving clinical decision-making.

The Burden of Cancer and Pre-cancerous Conditions Among Transgender Individuals in a Large Healthcare Network.

The current study aimed to examine the prevalence of and risk factors for cancer and pre-cancerous conditions, comparing transgender and cisgender individuals, using 2012-2023 electronic health record data from a large healthcare system. We identified 2,745 transgender individuals using a previously validated computable phenotype and 54,900 matched cisgender individuals. We calculated the prevalence of cancer and pre-cancer related to human papillomavirus (HPV), human immunodeficiency virus (HIV), tobacco, alcohol, lung, breast, colorectum, and built multivariable logistic models to examine the association between gender identity and the presence of cancer or pre-cancer. Results indicated similar odds of developing cancer across gender identities, but transgender individuals exhibited significantly higher risks for pre-cancerous conditions, including alcohol-related, breast, and colorectal pre-cancers compared to cisgender women, and HPV-related, tobacco-related, alcohol-related, and colorectal pre-cancers compared to cisgender men. These findings underscore the need for tailored interventions and policies addressing cancer health disparities affecting the transgender population.

Machine Learning Prediction of Autism Spectrum Disorder Through Linking Mothers' and Children's Electronic Health Record Data.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder typically diagnosed in children. Early detection of ASD, particularly in girls who are often diagnosed late, can aid long-term development for children. We aimed to develop machine learning models for predicting ASD diagnosis in children, both boys and girls, using child-mother linked electronic health records (EHRs) data from a large clinical research network. Model features were children and mothers' risk factors in EHRs, including maternal health factors. We tested XGBoost and logistic regression with Random Oversampling (ROS) and Random Undersampling (RUS) to address imbalanced data. Logistic regression with RUS considering a three-year observation window for children's risk factors achieved the best performance for predicting ASD among the overall study population (AUROC = 0.798), boys (AUROC = 0.786), and girls (AUROC = 0.791). We calculated SHAP values to quantify the impacts of important clinical and sociodemographic risk factors.

Acceptability of HPV self-collection: A qualitative study of Black women living with type II diabetes and social vulnerability.

Introduction: Human papillomavirus (HPV) causes 99.7% of cervical cancer cases. Cervical cancer is preventable through early detection via HPV testing. However, the number of women screened for cervical cancer has not increased in the last several years. Lower screening rates among women living in high poverty and social vulnerability areas, Black women, and women with chronic co-morbidities (e.g., type 2 diabetes (T2D)) are associated with their higher cervical cancer mortality rates. When screened, Black women are more likely to be diagnosed at later stages and die from cervical cancer. HPV self-collection decreases barriers to cervical cancer screening and can help lessen disparities among underserved women. This study aimed to examine the acceptability of HPV self-collection among Black women with T2D living in socially vulnerable communities. Methods: Qualitative semi-structured interviews were conducted with 29 Black women with T2D living in communities with high social vulnerability. The Health Belief Model informed the development of the interview guide to gather data on the acceptability of HPV self-collection. Results: Three main themes aligned with the Health Belief Model were identified: (1) HPV self-collection provides a comfortable alternative to in-clinic HPV testing (perceived benefits); (2) HPV self-collection would result in awareness of current HPV status (health motivation); and (3) Women were concerned about collecting their sample accurately (perceived barriers). Discussion/Conclusion: Black women with T2D living in communities with high social vulnerability identified multiple benefits of cervical cancer screening through HPV self-collection. Women are concerned about their ability to collect these samples correctly. Our findings call for future studies focusing on increasing self-efficacy and skills to collect HPV samples among Black women with chronic conditions like T2D who reside in underserved communities with high social vulnerability.

Geographic variation and racial disparities in adoption of newer glucose-lowering drugs with cardiovascular benefits among US Medicare beneficiaries with type 2 diabetes.

BACKGROUND: Prior studies have shown disparities in the uptake of cardioprotective newer glucose-lowering drugs (GLDs), including sodium-glucose cotranwsporter-2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP1a). This study aimed to characterize geographic variation in the initiation of newer GLDs and the geographic variation in the disparities in initiating these medications. METHODS: Using 2017-2018 claims data from a 15% random nationwide sample of Medicare Part D beneficiaries, we identified individuals diagnosed with type 2 diabetes (T2D), who had ≥1 GLD prescriptions, and did not use SGLT2i or GLP1a in the year prior to the index date,1/1/2018. Patients were followed up for a year. The cohort was spatiotemporally linked to Dartmouth hospital-referral regions (HRRs), with each patient assigned to 1 of 306 HRRs. We performed multivariable Poisson regression to estimate adjusted initiation rates, and multivariable logistic regression to assess racial disparities in each HRR. RESULTS: Among 795,469 individuals with T2D included in the analyses, the mean (SD) age was 73 (10) y, 53.3% were women, 12.2% were non-Hispanic Black, and 7.2% initiated a newer GLD in the follow-up year. In the adjusted model including clinical factors, compared to non-Hispanic White patients, non-Hispanic Black (initiation rate ratio, IRR [95% CI]: 0.66 [0.64-0.68]), American Indian/Alaska Native (0.74 [0.66-0.82]), Hispanic (0.85 [0.82-0.87]), and Asian/Pacific islander (0.94 [0.89-0.98]) patients were less likely to initiate newer GLDs. Significant geographic variation was observed across HRRs, with an initiation rate spanning 2.7%-13.6%. CONCLUSIONS: This study uncovered substantial geographic variation and the racial disparities in initiating newer GLDs.

Telehealth Implementation Response to COVID-19 in the OneFlorida+ Clinical Research Network: Perspectives of Clinicians and Health Systems Leaders.

Introduction: The COVID-19 pandemic forced health systems worldwide to make rapid adjustments to patient care. Nationwide stay-at-home mandates and public health concerns increased demand for telehealth to maintain patients' continuity of care. These circumstances permitted observation of telehealth implementation in real-world settings at a large scale. This study aimed to understand clinician and health system leader (HSL) experiences in expanding, implementing, and sustaining telehealth during COVID-19 in the OneFlorida+ clinical research network. Methods: We conducted semistructured videoconference interviews with 5 primary care providers, 7 specialist providers, and 12 HSLs across 7 OneFlorida+ health systems and settings. Interviews were audiorecorded, transcribed, and summarized using deductive team-based template coding. We then used matrix analysis to organize the qualitative data and identify inductive themes. Results: Rapid telehealth implementation occurred even among sites with low readiness, facilitated by responsive planning, shifts in resource allocation, and training. Common hurdles in routine telehealth use, including technical and reimbursement issues, were also barriers to telehealth implementation. Acceptability of telehealth was influenced by benefits such as the providers' ability to view a patient's home environment and the availability of tools to enhance patient education. Lower acceptability stemmed from the inability to conduct physical examinations during the shutdown. Conclusions: This study identified a broad range of barriers, facilitators, and strategies for implementing telehealth within large clinical research networks. The findings can contribute to optimizing the effectiveness of telehealth implementation in similar settings, and point toward promising directions for telehealth provider training to improve acceptability and promote sustainability.

A point-of-care pilot randomized intervention to connect patients with cancer-induced financial toxicity to telehealth financial counseling.

PURPOSE: Elevated costs of cancer treatment can result in economic and psychological "financial toxicity" distress. This pilot study assessed the feasibility of a point-of-care intervention to connect adult patients with cancer-induced financial toxicity to telehealth-delivered financial counseling. METHODS: We conducted a three-armed parallel randomized pilot study, allocating newly referred patients with cancer and financial toxicity to individual, group accredited telehealth financial counseling, or usual care with educational material (1:1:1). We assessed the feasibility of recruitment, randomization, retention, baseline and post-intervention COmprehensive Score for Financial Toxicity (COST), and Telehealth Usability Questionnaire (TUQ) scores. RESULTS: Of 382 patients screened, 121 were eligible and enrolled. 58 (48%) completed the intervention (9 individual, 9 group counseling, 40 educational booklet). 29 completed follow-up surveys: 45% female, 17% African American, 79% white, 7% Hispanic, 55% 45-64 years old, 31% over 64, 34% lived in rural areas, 24% had cancer stage I, 21% II, 7% III, 31% IV. Baseline characteristics were balanced across arms, retention status, surveys completion. Mean (SD) COST was 12.4 (6.1) at baseline and 16.0 (8.4) post-intervention. Mean (SD) COST score differences were 6.3 (11.6) after individual counseling, 5.8 (8.5) after group counseling, and 2.5 (6.4) after usual care. Mean TUQ score among nine counseling participants was 5.5 (0.9) over 7.0. Non-parametric comparisons were not statistically meaningful. CONCLUSION: Recruitment and randomization were feasible, while study retention presented challenges. Nine participants reported good usability and satisfaction with telehealth counseling. Larger-scale trials focused on improving participation, retention, and impact of financial counseling among patients with cancer are justified.

Developing a computable phenotype for glioblastoma.

BACKGROUND: Glioblastoma (GBM) is the most common malignant brain tumor, and thus it is important to be able to identify patients with this diagnosis for population studies. However, this can be challenging as diagnostic codes are non-specific. The aim of this study was to create a computable phenotype (CP) for GBM from structured and unstructured data to identify patients with this condition in a large electronic health record (EHR). METHODS: We used the UF Health Integrated Data Repository, a centralized clinical data warehouse that stores clinical and research data from various sources within the UF Health system, including the EHR system. We performed multiple iterations to refine the GBM-relevant diagnosis codes, procedure codes, medication codes, and keywords through manual chart review of patient data. We then evaluated the performances of various possible proposed CPs constructed from the relevant codes and keywords. RESULTS: We underwent six rounds of manual chart reviews to refine the CP elements. The final CP algorithm for identifying GBM patients was selected based on the best F1-score. Overall, the CP rule "if the patient had at least 1 relevant diagnosis code and at least 1 relevant keyword" demonstrated the highest F1-score using both structured and unstructured data. Thus, it was selected as the best-performing CP rule. CONCLUSIONS: We developed a CP algorithm for identifying patients with GBM using both structured and unstructured EHR data from a large tertiary care center. The final algorithm achieved an F1-score of 0.817, indicating a high performance which minimizes possible biases from misclassification errors.

A Fair Individualized Polysocial Risk Score for Identifying Increased Social Risk in Type 2 Diabetes.

Background: Racial and ethnic minority groups and individuals facing social disadvantages, which often stem from their social determinants of health (SDoH), bear a disproportionate burden of type 2 diabetes (T2D) and its complications. It is crucial to implement effective social risk management strategies at the point of care. Objective: To develop an electronic health records (EHR)-based machine learning (ML) analytical pipeline to address unmet social needs associated with hospitalization risk in patients with T2D. Methods: We identified real-world patients with T2D from the EHR data from University of Florida (UF) Health Integrated Data Repository (IDR), incorporating both contextual SDoH (e.g., neighborhood deprivation) and individual-level SDoH (e.g., housing instability). The 2015-2020 data were used for training and validation and 2021-2022 data for independent testing. We developed a machine learning analytic pipeline, namely individualized polysocial risk score (iPsRS), to identify high social risk associated with hospitalizations in T2D patients, along with explainable AI (XAI) and fairness optimization. Results: The study cohort included 10,192 real-world patients with T2D, with a mean age of 59 years and 58% female. Of the cohort, 50% were non-Hispanic White, 39% were non-Hispanic Black, 6% were Hispanic, and 5% were other races/ethnicities. Our iPsRS, including both contextual and individual-level SDoH as input factors, achieved a C statistic of 0.72 in predicting 1-year hospitalization after fairness optimization across racial and ethnic groups. The iPsRS showed excellent utility for capturing individuals at high hospitalization risk because of SDoH, that is, the actual 1-year hospitalization rate in the top 5% of iPsRS was 28.1%, ~13 times as high as the bottom decile (2.2% for 1-year hospitalization rate). Conclusion: Our ML pipeline iPsRS can fairly and accurately screen for patients who have increased social risk leading to hospitalization in real word patients with T2D.

Disparities Contributing to Late-Stage Diagnosis of Lung, Colorectal, Breast, and Cervical Cancers: Rural and Urban Poverty in Florida.

Despite advances in cancer screening, late-stage cancer diagnosis is still a major cause of morbidity and mortality in the United States. In this study, we aim to understand demographic and geographic factors associated with receiving a late-stage diagnosis (LSD) of lung, colorectal, breast, or cervical cancer. (1) Methods: We analyzed data of patients with a cancer diagnosis between 2016 and 2020 from the Florida Cancer Data System (FCDS), a statewide population-based registry. To investigate correlates of LSD, we estimated multi-variable logistic regression models for each cancer while controlling for age, sex, race, insurance, and census tract rurality and poverty. (2) Results: Patients from high-poverty rural areas had higher odds for LSD of lung (OR = 1.23, 95% CI (1.10, 1.37)) and breast cancer (OR = 1.31, 95% CI (1.17,1.47)) than patients from low-poverty urban areas. Patients in high-poverty urban areas saw higher odds of LSD for lung (OR = 1.05 95% CI (1.00, 1.09)), breast (OR = 1.10, 95% CI (1.06, 1.14)), and cervical cancer (OR = 1.19, 95% CI (1.03, 1.37)). (3) Conclusions: Financial barriers contributing to decreased access to care likely drive LSD for cancer in rural and urban communities of Florida.

Avenues for Strengthening PCORnet's Capacity to Advance Patient-Centered Economic Outcomes in Patient-Centered Outcomes Research (PCOR).

PCORnet, the National Patient-Centered Clinical Research Network, provides the ability to conduct prospective and observational pragmatic research by leveraging standardized, curated electronic health records data together with patient and stakeholder engagement. PCORnet is funded by the Patient-Centered Outcomes Research Institute (PCORI) and is composed of 8 Clinical Research Networks that incorporate at total of 79 health system "sites." As the network developed, linkage to commercial health plans, federal insurance claims, disease registries, and other data resources demonstrated the value in extending the networks infrastructure to provide a more complete representation of patient's health and lived experiences. Initially, PCORnet studies avoided direct economic comparative effectiveness as a topic. However, PCORI's authorizing law was amended in 2019 to allow studies to incorporate patient-centered economic outcomes in primary research aims. With PCORI's expanded scope and PCORnet's phase 3 beginning in January 2022, there are opportunities to strengthen the network's ability to support economic patient-centered outcomes research. This commentary will discuss approaches that have been incorporated to date by the network and point to opportunities for the network to incorporate economic variables for analysis, informed by patient and stakeholder perspectives. Topics addressed include: (1) data linkage infrastructure; (2) commercial health plan partnerships; (3) Medicare and Medicaid linkage; (4) health system billing-based benchmarking; (5) area-level measures; (6) individual-level measures; (7) pharmacy benefits and retail pharmacy data; and (8) the importance of transparency and engagement while addressing the biases inherent in linking real-world data sources.

A study of generative large language model for medical research and healthcare.

There are enormous enthusiasm and concerns in applying large language models (LLMs) to healthcare. Yet current assumptions are based on general-purpose LLMs such as ChatGPT, which are not developed for medical use. This study develops a generative clinical LLM, GatorTronGPT, using 277 billion words of text including (1) 82 billion words of clinical text from 126 clinical departments and approximately 2 million patients at the University of Florida Health and (2) 195 billion words of diverse general English text. We train GatorTronGPT using a GPT-3 architecture with up to 20 billion parameters and evaluate its utility for biomedical natural language processing (NLP) and healthcare text generation. GatorTronGPT improves biomedical natural language processing. We apply GatorTronGPT to generate 20 billion words of synthetic text. Synthetic NLP models trained using synthetic text generated by GatorTronGPT outperform models trained using real-world clinical text. Physicians' Turing test using 1 (worst) to 9 (best) scale shows that there are no significant differences in linguistic readability (p = 0.22; 6.57 of GatorTronGPT compared with 6.93 of human) and clinical relevance (p = 0.91; 7.0 of GatorTronGPT compared with 6.97 of human) and that physicians cannot differentiate them (p < 0.001). This study provides insights into the opportunities and challenges of LLMs for medical research and healthcare.

The role of health system penetration rate in estimating the prevalence of type 1 diabetes in children and adolescents using electronic health records.

OBJECTIVE: Having sufficient population coverage from the electronic health records (EHRs)-connected health system is essential for building a comprehensive EHR-based diabetes surveillance system. This study aimed to establish an EHR-based type 1 diabetes (T1D) surveillance system for children and adolescents across racial and ethnic groups by identifying the minimum population coverage from EHR-connected health systems to accurately estimate T1D prevalence. MATERIALS AND METHODS: We conducted a retrospective, cross-sectional analysis involving children and adolescents <20 years old identified from the OneFlorida+ Clinical Research Network (2018-2020). T1D cases were identified using a previously validated computable phenotyping algorithm. The T1D prevalence for each ZIP Code Tabulation Area (ZCTA, 5 digits), defined as the number of T1D cases divided by the total number of residents in the corresponding ZCTA, was calculated. Population coverage for each ZCTA was measured using observed health system penetration rates (HSPR), which was calculated as the ratio of residents in the corresponding ZTCA and captured by OneFlorida+ to the overall population in the same ZCTA reported by the Census. We used a recursive partitioning algorithm to identify the minimum required observed HSPR to estimate T1D prevalence and compare our estimate with the reported T1D prevalence from the SEARCH study. RESULTS: Observed HSPRs of 55%, 55%, and 60% were identified as the minimum thresholds for the non-Hispanic White, non-Hispanic Black, and Hispanic populations. The estimated T1D prevalence for non-Hispanic White and non-Hispanic Black were 2.87 and 2.29 per 1000 youth, which are comparable to the reference study's estimation. The estimated prevalence of T1D for Hispanics (2.76 per 1000 youth) was higher than the reference study's estimation (1.48-1.64 per 1000 youth). The standardized T1D prevalence in the overall Florida population was 2.81 per 1000 youth in 2019. CONCLUSION: Our study provides a method to estimate T1D prevalence in children and adolescents using EHRs and reports the estimated HSPRs and prevalence of T1D for different race and ethnicity groups to facilitate EHR-based diabetes surveillance.

Pediatric primary care provider and staff perspectives on the implementation of electronic health record-based social needs interventions: A mixed-methods study.

Introduction: Interventions to address social needs in clinical settings can improve child and family health outcomes. Electronic health record (EHR) tools are available to support these interventions but are infrequently used. This mixed-methods study sought to identify approaches for implementing social needs interventions using an existing EHR module in pediatric primary care. Methods: We conducted focus groups and interviews with providers and staff (n = 30) and workflow assessments (n = 48) at four pediatric clinics. Providers and staff completed measures assessing the acceptability, appropriateness, and feasibility of social needs interventions. The Consolidated Framework for Implementation Research guided the study. A hybrid deductive-inductive approach was used to analyze qualitative data. Results: Median scores (range 1-5) for acceptability (4.9) and appropriateness (5.0) were higher than feasibility (3.9). Perceived barriers to implementation related to duplicative processes, parent disclosure, and staffing limitations. Facilitators included the relative advantage of the EHR module compared to existing documentation practices, importance of addressing social needs, and compatibility with clinic culture and workflow. Self-administered screening was seen as inappropriate for sensitive topics. Strategies identified included providing resource lists, integrating social needs assessments with existing screening questionnaires, and reducing duplicative documentation. Conclusions: This study offers insight into the implementation of EHR-based social needs interventions and identifies strategies to promote intervention uptake. Findings highlight the need to design interventions that are feasible to implement in real-world settings. Future work should focus on integrating multiple stakeholder perspectives to inform the development of EHR tools and clinical workflows to support social needs interventions.

Does the Intersectionality of Race/Ethnicity and Type 2 Diabetes Increase the Odds of a Cervical Cancer Diagnosis? A Nested Case-Control Study of a Florida Statewide Multisite EHR Database.

Cervical cancer and Type 2 Diabetes (T2D) share common demographic risk factors. Despite this, scarce research has examined the relationship between race/ethnicity, having T2D, and cervical cancer incidence. We analyzed statewide electronic health records data between 2012 and 2019 from the OneFlorida+ Data Trust. We created a 1:4 nested case-control dataset. Each case (patient with cervical cancer) was matched with four controls (patients without cervical cancer) without replacement by year of encounter, diagnosis, and age. We used conditional logistic regression to estimate the unadjusted and adjusted odds ratios (ORs) and 95% confidence intervals (CIs) to examine the association between race/ethnicity, T2D, and cervical cancer incidence. A total of 100,739 cases and 402,956 matched controls were identified. After adjusting for sociodemographic characteristics, non-Hispanic Black women with T2D had higher odds of cervical cancer compared with non-Hispanic White women with T2D (OR: 1.58, 95% CI 1.41-1.77). Living in a rural area, having Medicaid/Medicare insurance, and having high social vulnerability were associated with higher odds of having a cervical cancer diagnosis. Our findings imply the need to address the higher burden of cervical cancer diagnosis among non-Hispanic Black women with T2D and in underserved populations.

Parent Perspectives on Electronic Health Record-Based Social Needs Screening and Documentation: A Qualitative Study.

OBJECTIVE: Social needs interventions in clinical settings can improve child health outcomes; however, they are not routinely delivered in routine pediatric care. The electronic health record (EHR) can support these interventions, but parent engagement in the development of EHR-based social needs interventions is lacking. The aim of this study was to assess parent perspectives on EHR-based social needs screening and documentation and identify family-centered approaches for screening design and implementation. METHODS: We enrolled 20 parents from four pediatric primary care clinics. Parents completed a social risk questionnaire from an existing EHR module and participated in qualitative interviews. Parents were asked about the acceptability of EHR-based social needs screening and documentation and preferences for screening administration. A hybrid deductive-inductive approach was used to analyze qualitative data. RESULTS: Parents identified the benefits of social needs screening and documentation but expressed concerns related to privacy, fear of negative outcomes, and use of outdated documentation. Some felt self-administered electronic questionnaires would mitigate parent discomfort and encourage disclosure of social needs, while others felt face-to-face screening would be more effective. Parents stressed the importance of transparency on the purpose of social needs screening and the use of data. CONCLUSIONS: This work can inform the design and implementation of EHR-based social needs interventions that are acceptable and feasible for parents. Findings suggest strategies such as clear communication and multi-modal delivery methods may enhance intervention uptake. Future work should integrate feedback from multiple stakeholders to design and evaluate interventions that are family-centered and feasible to implement in clinical settings.

Stratified split sampling of electronic health records.

Although superficially similar to data from clinical research, data extracted from electronic health records may require fundamentally different approaches for model building and analysis. Because electronic health record data is designed for clinical, rather than scientific use, researchers must first provide clear definitions of outcome and predictor variables. Yet an iterative process of defining outcomes and predictors, assessing association, and then repeating the process may increase Type I error rates, and thus decrease the chance of replicability, defined by the National Academy of Sciences as the chance of "obtaining consistent results across studies aimed at answering the same scientific question, each of which has obtained its own data."[1] In addition, failure to account for subgroups may mask heterogeneous associations between predictor and outcome by subgroups, and decrease the generalizability of the findings. To increase chances of replicability and generalizability, we recommend using a stratified split sample approach for studies using electronic health records. A split sample approach divides the data randomly into an exploratory set for iterative variable definition, iterative analyses of association, and consideration of subgroups. The confirmatory set is used only to replicate results found in the first set. The addition of the word 'stratified' indicates that rare subgroups are oversampled randomly by including them in the exploratory sample at higher rates than appear in the population. The stratified sampling provides a sufficient sample size for assessing heterogeneity of association by testing for effect modification by group membership. An electronic health record study of the associations between socio-demographic factors and uptake of hepatic cancer screening, and potential heterogeneity of association in subgroups defined by gender, self-identified race and ethnicity, census-tract level poverty and insurance type illustrates the recommended approach.

Data-driven analysis to understand long COVID using electronic health records from the RECOVER initiative.

Recent studies have investigated post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) using real-world patient data such as electronic health records (EHR). Prior studies have typically been conducted on patient cohorts with specific patient populations which makes their generalizability unclear. This study aims to characterize PASC using the EHR data warehouses from two large Patient-Centered Clinical Research Networks (PCORnet), INSIGHT and OneFlorida+, which include 11 million patients in New York City (NYC) area and 16.8 million patients in Florida respectively. With a high-throughput screening pipeline based on propensity score and inverse probability of treatment weighting, we identified a broad list of diagnoses and medications which exhibited significantly higher incidence risk for patients 30-180 days after the laboratory-confirmed SARS-CoV-2 infection compared to non-infected patients. We identified more PASC diagnoses in NYC than in Florida regarding our screening criteria, and conditions including dementia, hair loss, pressure ulcers, pulmonary fibrosis, dyspnea, pulmonary embolism, chest pain, abnormal heartbeat, malaise, and fatigue, were replicated across both cohorts. Our analyses highlight potentially heterogeneous risks of PASC in different populations.