A Randomized Controlled Trial of the Implementation of BREASTChoice, a Multilevel Breast Reconstruction Decision Support Tool With Personalized Risk Predictiona.

OBJECTIVE: To implement the BREASTChoice decision tool into the electronic health record and evaluate its effectiveness. BACKGROUND: BREASTChoice, is a multilevel decision tool that: 1) educates patients about breast reconstruction; 2) estimates personalized risk of complications; 3) clarifies patient preferences; and 4) informs clinicians about patients' risk and preferences. METHODS: A multisite randomized controlled trial enrolled adult women with stage 0-III breast malignancy undergoing mastectomy. Participants were randomized to BREASTChoice or a control website. A survey assessed knowledge, preferences, decisional conflict, shared decision-making, preferred treatment, and usability. We conducted intent-to-treat (ITT), per-protocol (PP) analyses (those randomized to BREASTChoice who accessed the tool), and stratified analyses. RESULTS: 23/25 eligible clinicians enrolled. 369/761 (48%) contacted patients enrolled and were randomized. Patients' average age was 51 years; 15% were older than 65. BREASTChoice participants had higher knowledge than control participants (ITT: mean 70.6 vs. 67.4, P=0.08; PP: mean 71.4 vs. 67.4, P=0.03), especially when stratified by site (ITT: P=0.04, PP: P=0.01), age (ITT: P=0.04, PP: P=0.02), and race (ITT: P=0.04, PP: P=0.01). BREASTChoice did not improve decisional conflict, match between preferences and treatment, or shared decision-making. In PP analyses, fewer high-risk patients using BREASTChoice chose reconstruction. BREASTChoice had high usability. CONCLUSIONS: BREASTChoice is a novel decision tool incorporating risk prediction, patient education, and clinician engagement. Patients using BREASTChoice had higher knowledge; older adults and those from racially minoritized backgrounds especially benefitted. There was no impact on other decision outcomes. Future studies should overcome implementation barriers and specifically examine decision outcomes among high-risk patients.

Self-Rated Health in Middle Age and Risk of Hospitalizations and Death: Recurrent Event Analysis of the ARIC Study.

BACKGROUND: Self-rated health is a simple measure that may identify individuals who are at a higher risk for hospitalization or death. OBJECTIVE: To quantify the association between a single measure of self-rated health and future risk of recurrent hospitalizations or death. PARTICIPANTS: Atherosclerosis Risk in Communities (ARIC) study, a community-based prospective cohort study of middle-aged men and women with follow-up beginning from 1987 to 1989. MAIN MEASURES: We quantified the associations between initial self-rated health with risk of recurrent hospitalizations and of death using a recurrent events survival model that allowed for dependency between the rates of hospitalization and hazards of death, adjusted for demographic and clinical factors. KEY RESULTS: Of the 14,937 ARIC cohort individuals with available self-rated health and covariate information, 34% of individuals reported "excellent" health, 47% "good," 16% "fair," and 3% "poor" at study baseline. After a median follow-up of 27.7 years, 1955 (39%), 3569 (51%), 1626 (67%), and 402 (83%) individuals with "excellent," "good," "fair," and "poor" health, respectively, had died. After adjusting for demographic factors and medical history, a less favorable self-rated health status was associated with increased rates of hospitalization and death. As compared to those reporting "excellent" health, adults with "good," "fair," and "poor" health had 1.22 (1.07 to 1.40), 2.01 (1.63 to 2.47), and 3.13 (2.39 to 4.09) times the rate of hospitalizations, respectively. The hazards of death also increased with worsening categories of self-rated health, with "good," "fair," and "poor" health individuals experiencing 1.30 (1.12 to 1.51), 2.15 (1.71 to 2.69), and 3.40 (2.54 to 4.56) times the hazard of death compared to "excellent," respectively. CONCLUSIONS: Even after adjusting for demographic and clinical factors, having a less favorable response on a single measure of self-rated health taken in middle age is a potent marker of future hospitalizations and death.

Cardiovascular health assessment in routine cancer follow-up in community settings: survivor risk awareness and perspectives.

BACKGROUND: Guidelines recommend cardiovascular risk assessment and counseling for cancer survivors. For effective implementation, it is critical to understand survivor cardiovascular health (CVH) profiles and perspectives in community settings. We aimed to (1) Assess survivor CVH profiles, (2) compare self-reported and EHR-based categorization of CVH factors, and (3) describe perceptions regarding addressing CVH during oncology encounters. METHODS: This cross-sectional analysis utilized data from an ongoing NCI Community Oncology Research Program trial of an EHR heart health tool for cancer survivors (WF-1804CD). Survivors presenting for routine care after potentially curative treatment recruited from 8 oncology practices completed a pre-visit survey, including American Heart Association Simple 7 CVH factors (classified as ideal, intermediate, or poor). Medical record abstraction ascertained CVD risk factors and cancer characteristics. Likert-type questions assessed desired discussion during oncology care. RESULTS: Of 502 enrolled survivors (95.6% female; mean time since diagnosis = 4.2 years), most had breast cancer (79.7%). Many survivors had common cardiovascular comorbidities, including high cholesterol (48.3%), hypertension or high BP (47.8%) obesity (33.1%), and diabetes (20.5%); 30.5% of survivors received high cardiotoxicity potential cancer treatment. Less than half had ideal/non-missing levels for physical activity (48.0%), BMI (18.9%), cholesterol (17.9%), blood pressure (14.1%), healthy diet (11.0%), and glucose/ HbA1c (6.0%). While > 50% of survivors had concordant EHR-self-report categorization for smoking, BMI, and blood pressure; cholesterol, glucose, and A1C were unknown by survivors and/or missing in the EHR for most. Most survivors agreed oncology providers should talk about heart health (78.9%). CONCLUSIONS: Tools to promote CVH discussion can fill gaps in CVH knowledge and are likely to be well-received by survivors in community settings. TRIAL REGISTRATION: NCT03935282, Registered 10/01/2020.

Incidence of and Factors Associated With Recurrent Firearm Injury Among Patients Presenting to St. Louis Trauma Centers, 2010 to 2019 : A Cohort Study.

BACKGROUND: Firearm injuries are a public health crisis in the United States. OBJECTIVE: To examine the incidence and factors associated with recurrent firearm injuries and death among patients presenting with an acute (index), nonfatal firearm injury. DESIGN: Multicenter, observational, cohort study. SETTING: Four adult and pediatric level I trauma hospitals in St. Louis, Missouri, 2010 to 2019. PARTICIPANTS: Consecutive adult and pediatric patients (n = 9553) presenting to a participating hospital with a nonfatal acute firearm injury. MEASUREMENTS: Data on firearm-injured patient demographics, hospital and diagnostic information, health insurance status, and death were collected from the St. Louis Region-Wide Hospital-Based Violence Intervention Program Data Repository. The Centers for Disease Control and Prevention (CDC) Social Vulnerability Index was used to characterize the social vulnerability of the census tracts of patients' residences. Analysis included descriptive statistics and time-to-event analyses estimating the probability of experiencing a recurrent firearm injury. RESULTS: We identified 10 293 acutely firearm-injured patients of whom 9553 survived the injury and comprised the analytic sample. Over a median follow-up of 3.5 years (IQR, 1.5 to 6.4 years), 1155 patients experienced a recurrent firearm injury including 5 firearm suicides and 149 fatal firearm injuries. Persons experiencing recurrent firearm injury were young (25.3 ± 9.5 years), predominantly male (93%), Black (96%), and uninsured (50%), and resided in high social vulnerability regions (65%). The estimated risk for firearm reinjury was 7% at 1 year and 17% at 8 years. LIMITATIONS: Limited data on comorbidities and patient-level social determinants of health. Inability to account for recurrent injuries presenting to nonstudy hospitals. CONCLUSION: Recurrent injury and death are frequent among survivors of firearm injury, particularly among patients from socially vulnerable areas. Our findings highlight the need for interventions to prevent recurrence. PRIMARY FUNDING SOURCE: Emergency Medicine Foundation-AFFIRM and Missouri Foundation for Health.

Life's Essential 8: Updating and Enhancing the American Heart Association's Construct of Cardiovascular Health: A Presidential Advisory From the American Heart Association.

In 2010, the American Heart Association defined a novel construct of cardiovascular health to promote a paradigm shift from a focus solely on disease treatment to one inclusive of positive health promotion and preservation across the life course in populations and individuals. Extensive subsequent evidence has provided insights into strengths and limitations of the original approach to defining and quantifying cardiovascular health. In response, the American Heart Association convened a writing group to recommend enhancements and updates. The definition and quantification of each of the original metrics (Life's Simple 7) were evaluated for responsiveness to interindividual variation and intraindividual change. New metrics were considered, and the age spectrum was expanded to include the entire life course. The foundational contexts of social determinants of health and psychological health were addressed as crucial factors in optimizing and preserving cardiovascular health. This presidential advisory introduces an enhanced approach to assessing cardiovascular health: Life's Essential 8. The components of Life's Essential 8 include diet (updated), physical activity, nicotine exposure (updated), sleep health (new), body mass index, blood lipids (updated), blood glucose (updated), and blood pressure. Each metric has a new scoring algorithm ranging from 0 to 100 points, allowing generation of a new composite cardiovascular health score (the unweighted average of all components) that also varies from 0 to 100 points. Methods for implementing cardiovascular health assessment and longitudinal monitoring are discussed, as are potential data sources and tools to promote widespread adoption in policy, public health, clinical, institutional, and community settings.

Status of Cardiovascular Health in US Adults and Children Using the American Heart Association's New "Life's Essential 8" Metrics: Prevalence Estimates From the National Health and Nutrition Examination Survey (NHANES), 2013 Through 2018.

BACKGROUND: The American Heart Association recently published an updated algorithm for quantifying cardiovascular health (CVH)-the Life's Essential 8 score. We quantified US levels of CVH using the new score. METHODS: We included individuals ages 2 through 79 years (not pregnant or institutionalized) who were free of cardiovascular disease from the National Health and Nutrition Examination Surveys in 2013 through 2018. For all participants, we calculated the overall CVH score (range, 0 [lowest] to 100 [highest]), as well as the score for each component of diet, physical activity, nicotine exposure, sleep duration, body mass index, blood lipids, blood glucose, and blood pressure, using published American Heart Association definitions. Sample weights and design were incorporated in calculating prevalence estimates and standard errors using standard survey procedures. CVH scores were assessed across strata of age, sex, race and ethnicity, family income, and depression. RESULTS: There were 23 409 participants, representing 201 728 000 adults and 74 435 000 children. The overall mean CVH score was 64.7 (95% CI, 63.9-65.6) among adults using all 8 metrics and 65.5 (95% CI, 64.4-66.6) for the 3 metrics available (diet, physical activity, and body mass index) among children and adolescents ages 2 through 19 years. For adults, there were significant differences in mean overall CVH scores by sex (women, 67.0; men, 62.5), age (range of mean values, 62.2-68.7), and racial and ethnic group (range, 59.7-68.5). Mean scores were lowest for diet, physical activity, and body mass index metrics. There were large differences in mean scores across demographic groups for diet (range, 23.8-47.7), nicotine exposure (range, 63.1-85.0), blood glucose (range, 65.7-88.1), and blood pressure (range, 49.5-84.0). In children, diet scores were low (mean 40.6) and were progressively lower in higher age groups (from 61.1 at ages 2 through 5 to 28.5 at ages 12 through 19); large differences were also noted in mean physical activity (range, 63.1-88.3) and body mass index (range, 74.4-89.4) scores by sociodemographic group. CONCLUSIONS: The new Life's Essential 8 score helps identify large group and individual differences in CVH. Overall CVH in the US population remains well below optimal levels and there are both broad and targeted opportunities to monitor, preserve, and improve CVH across the life course in individuals and the population.

Endometrial cancer survivors' perceptions of their cardiovascular disease risk (results from WF-1804CD AH-HA).

OBJECTIVE: Despite considerable burden of cardiovascular disease (CVD), data on endometrial cancer survivors' CVD perceptions are lacking. We assessed survivors' perspectives on addressing CVD risk during oncology care. METHODS: This cross-sectional analysis utilized data from an ongoing trial of an EHR heart health tool (R01CA226078 & UG1CA189824) conducted through the NCI Community Oncology Research Program (NCORP, WF-1804CD). Endometrial cancer survivors post-potentially curative treatment were recruited from community practices and completed a pre-visit baseline survey, including American Heart Association Simple 7 CVD factors. Likert-type questions assessed confidence in understanding CVD risk, CVD risk perception, and desired discussion during oncology care. Medical record abstraction ascertained data on CVD and cancer characteristics. RESULTS: Survivors (N = 55, median age = 62; 62% 0-2 years post-diagnosis) were predominately white, non-Hispanic (87%). Most agreed/strongly agreed heart disease poses a risk to their health (87%) and oncology providers should talk to patients about heart health (76%). Few survivors reported smoking (12%) but many had poor/intermediate values for blood pressure (95%), body mass index (93%), fasting glucose/A1c (60%), diet (60%), exercise (47%) and total cholesterol (53%). 16% had not seen a PCP in the last year; these survivors were more likely to report financial hardship (22% vs 0%; p = 0.02). Most reported readiness to take steps to maintain or improve heart health (84%). CONCLUSIONS: Discussions of CVD risk during routine oncology care are likely to be well received by endometrial cancer survivors. Strategies are needed to implement CVD risk assessment guidelines and to enhance communication and referrals with primary care. Clinical Trials #: NCT03935282.

A systematic review of recurrent firearm injury rates in the United States.

OBJECTIVES: To conduct a systematic review of methodologies, data sources, and best practices for identifying, calculating, and reporting recurrent firearm injury rates in the United States. METHODS: In accordance with PRISMA guidelines, we searched seven electronic databases on December 16, 2021, for peer-reviewed articles that calculated recurrent firearm injury in generalizable populations. Two reviewers independently assessed the risk of bias, screened the studies, extracted data, and a third resolved conflicts. FINDINGS: Of the 918 unique articles identified, 14 met our inclusion criteria and reported recurrent firearm injury rates from 1% to 9.5%. We observed heterogeneity in study methodologies, including data sources utilized, identification of subsequent injury, follow-up times, and the types of firearm injuries studied. Data sources ranged from single-site hospital medical records to comprehensive statewide records comprising medical, law enforcement, and social security death index data. Some studies applied machine learning to electronic health records to differentiate subsequent new firearm injuries from the index injury, while others classified all repeat firearm-related hospital admissions after variably defined cut-off times as a new injury. Some studies required a minimum follow-up observation period after the index injury while others did not. Four studies conducted survival analyses, albeit using different methodologies. CONCLUSIONS: Variability in both the data sources and methods used to evaluate and report recurrent firearm injury limits individual study generalizability of individual and societal factors that influence recurrent firearm injury. Our systematic review highlights the need for development, dissemination, and implementation of standard practices for calculating and reporting recurrent firearm injury.

The Evolution of Primary Care Telehealth Disparities During COVID-19: Retrospective Cohort Study.

BACKGROUND: Telehealth has become widely used as a novel way to provide outpatient care during the COVID-19 pandemic, but data about telehealth use in primary care remain limited. Studies in other specialties raise concerns that telehealth may be widening existing health care disparities, requiring further scrutiny of trends in telehealth use. OBJECTIVE: Our study aims to further characterize sociodemographic differences in primary care via telehealth compared to in-person office visits before and during the COVID-19 pandemic and determine if these disparities changed throughout 2020. METHODS: We conducted a retrospective cohort study in a large US academic center with 46 primary care practices from April-December 2019 to April-December 2020. Data were subdivided into calendar quarters and compared to determine evolving disparities throughout the year. We queried and compared billed outpatient encounters in General Internal Medicine and Family Medicine via binary logic mixed effects regression model and estimated odds ratios (ORs) with 95% CIs. We used sex, race, and ethnicity of the patient attending each encounter as fixed effects. We analyzed socioeconomic status of patients in the institution's primary county based on the patient's residence zip code. RESULTS: A total of 81,822 encounters in the pre-COVID-19 time frame and 47,994 encounters in the intra-COVID-19 time frame were analyzed; in the intra-COVID-19 time frame, a total of 5322 (11.1%) of encounters were telehealth encounters. Patients living in zip code areas with high utilization rate of supplemental nutrition assistance were less likely to use primary care in the intra-COVID-19 time frame (OR 0.94, 95% CI 0.90-0.98; P=.006). Encounters with the following patients were less likely to be via telehealth compared to in-person office visits: patients who self-identified as Asian (OR 0.74, 95% CI 0.63-0.86) and Nepali (OR 0.37, 95% CI 0.19-0.72), patients insured by Medicare (OR 0.77, 95% CI 0.68-0.88), and patients living in zip code areas with high utilization rate of supplemental nutrition assistance (OR 0.84, 95% CI 0.71-0.99). Many of these disparities persisted throughout the year. Although there was no statistically significant difference in telehealth use for patients insured by Medicaid throughout the whole year, subanalysis of quarter 4 found encounters with patients insured by Medicaid were less likely to be via telehealth (OR 0.73, 95% CI 0.55-0.97; P=.03). CONCLUSIONS: Telehealth was not used equally by all patients within primary care throughout the first year of the COVID-19 pandemic, specifically by patients who self-identified as Asian and Nepali, insured by Medicare, and living in zip code areas with low socioeconomic status. As the COVID-19 pandemic and telehealth infrastructure change, it is critical we continue to reassess the use of telehealth. Institutions should continue to monitor disparities in telehealth access and advocate for policy changes that may improve equity.

Using the sociotechnical model to conduct a focused usability assessment of a breast reconstruction decision tool.

INTRODUCTION: BREASTChoice is a web-based breast reconstruction decision aid. The previous clinical trial-prior to the adaptation of this refined tool in which we explored usability-measured decision quality, quality of life, patient activation, shared decision making, and treatment choice. The current usability study was designed to elicit patients' and clinicians' perspectives on barriers and facilitators for implementing BREASTChoice into the clinical workflow. METHODS: We conducted qualitative interviews with patients and clinicians from two Midwestern medical specialty centers from August 2020 to April 2021. Interviews were first double coded until coders achieved a kappa > 0.8 and percent agreement > 95%, then were coded independently. We used a sociotechnical framework to evaluate BREASTChoice's implementation and sustainability potential according to end-users, human-computer interaction, and contextual factors. RESULTS: Twelve clinicians and ten patients completed interviews. Using the sociotechnical framework we determined the following. People Using the Tool: Patients and clinicians agreed that BREASTChoice could help patients make more informed decisions about their reconstruction and prepare better for their first plastic surgery appointment. Workflow and Communications: They felt that BREASTChoice could improve communication and process if the patient could view the tool at home and/or in the waiting room. Clinicians suggested the information from BREASTChoice about patients' risks and preferences be included in the patient's chart or the clinician electronic health record (EHR) inbox for accessibility during the consultation. Human Computer Interface: Patients and clinicians stated that the tool contains helpful information, does not require much time for the patient to use, and efficiently fills gaps in knowledge. Although patients found the risk profile information helpful, they reported needing time to read and digest. CONCLUSION: BREASTChoice was perceived as highly usable by patients and clinicians and has the potential for sustainability. Future research will implement and test the tool after integrating the stakeholder-suggested changes to its delivery process and content. It is critical to conduct usability assessments such as these prior to decision aid implementation to ensure success of the tool to improve risk communication.

A Regional Approach to Hospital-Based Violence Intervention Programs Through LOV.

CONTEXT: Community violence is an underaddressed public health threat. Hospital-based violence intervention programs (HVIPs) have been used to address the root causes of violence and prevent reinjury. OBJECTIVE: In this article, we describe the methodology of the St Louis Region-wide HVIP, Life Outside Violence (LOV) program, and provide preliminary process outcomes. DESIGN: Life Outside Violence mentors intervene following a violent injury to decrease risk of subsequent victimization and achieve goals unique to each participant by providing therapeutic counseling and case management services to patients and their families. PARTICIPANTS AND SETTING: Eligible patients are victims of violent injury between the ages of 8 and 24 years, who are residents of St Louis, Missouri, and present for care at a LOV partner adult or pediatric level I trauma hospital. INTERVENTION: Enrolled participants receive program services for 6 to 12 months and complete an individual treatment plan. MAIN OUTCOME MEASURES: In this article, we report LOV operational methodology, as well as process metrics, including program enrollment, graduation, and qualitative data on program implementation. RESULTS: From August 15, 2018, through April 30, 2022, 1750 LOV-eligible violently injured patients presented to a partner hospital, 349 were approached for program enrollment, and 206 consented to enroll in the program. During this pilot phase, 91 participants graduated from the LOV program and have process output data available for analysis. CONCLUSIONS: Life Outside Violence has been implemented into clinical practice as the first HVIP to influence across an entire region through partnership with multiple university and hospital systems. It is our hope that methods shared in this article will serve as a primer for organizations hoping to implement and expand HVIPs to interrupt community violence at the regional level.

Implementing a hospital-based violence intervention program for assault-injured youth: implications for social work practice.

Youth in the U.S. experience a high rate of assault-related injuries resulting in physical, psychological and social sequelae that require a wide range of services after discharge from the hospital. Hospital-based violence intervention programs (HVIP's) have been developed to engage youth in services designed to reduce the incidence of violent injury in young people. HVIP's combine the efforts of medical staff with community-based partners to provide trauma-informed care to violently-injured people and have been found to be a cost-effective means to reduce re-injury rates and improve social and behavioral health outcomes. Few studies have explored the organizational and community level factors that impact implementation of these important and complex interventions. The objective of this study was to develop an in-depth understanding of the factors that impact HVIP implementation from the perspectives of 41 stakeholders through qualitative interviews. Thematic analysis generated three themes that included the importance of integrated, collaborative care, the need for providers who can perform multiple service roles and deploy a range of skills, and the importance of engaging clients through extended contact. In this article we explore these themes and their implications for healthcare social work.

Achieving Optimal Population Cardiovascular Health Requires an Interdisciplinary Team and a Learning Healthcare System: A Scientific Statement From the American Heart Association.

Population cardiovascular health, or improving cardiovascular health among patients and the population at large, requires a redoubling of primordial and primary prevention efforts as declines in cardiovascular disease mortality have decelerated over the past decade. Great potential exists for healthcare systems-based approaches to aid in reversing these trends. A learning healthcare system, in which population cardiovascular health metrics are measured, evaluated, intervened on, and re-evaluated, can serve as a model for developing the evidence base for developing, deploying, and disseminating interventions. This scientific statement on optimizing population cardiovascular health summarizes the current evidence for such an approach; reviews contemporary sources for relevant performance and clinical metrics; highlights the role of implementation science strategies; and advocates for an interdisciplinary team approach to enhance the impact of this work.

Development and Structure of an Accurate Machine Learning Algorithm to Predict Inpatient Mortality and Hospice Outcomes in the Coronavirus Disease 2019 Era.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has challenged the accuracy and racial biases present in traditional mortality scores. An accurate prognostic model that can be applied to hospitalized patients irrespective of race or COVID-19 status may benefit patient care. RESEARCH DESIGN: This cohort study utilized historical and ongoing electronic health record features to develop and validate a deep-learning model applied on the second day of admission predicting a composite outcome of in-hospital mortality, discharge to hospice, or death within 30 days of admission. Model features included patient demographics, diagnoses, procedures, inpatient medications, laboratory values, vital signs, and substance use history. Conventional performance metrics were assessed, and subgroup analysis was performed based on race, COVID-19 status, and intensive care unit admission. SUBJECTS: A total of 35,521 patients hospitalized between April 2020 and October 2020 at a single health care system including a tertiary academic referral center and 9 community hospitals. RESULTS: Of 35,521 patients, including 9831 non-White patients and 2020 COVID-19 patients, 2838 (8.0%) met the composite outcome. Patients who experienced the composite outcome were older (73 vs. 61 y old) with similar sex and race distributions between groups. The model achieved an area under the receiver operating characteristic curve of 0.89 (95% confidence interval: 0.88, 0.91) and an average positive predictive value of 0.46 (0.40, 0.52). Model performance did not differ significantly in White (0.89) and non-White (0.90) subgroups or when grouping by COVID-19 status and intensive care unit admission. CONCLUSION: A deep-learning model using large-volume, structured electronic health record data can effectively predict short-term mortality or hospice outcomes on the second day of admission in the general inpatient population without significant racial bias.

The Use of the Consolidated Framework for Implementation Research to Understand Facilitators and Barriers to Sexually Transmitted Infection Screening in Primary Care.

BACKGROUND: Adolescents bear a disproportionate burden of sexually transmitted infections (STIs) and the sequelae of delayed treatment, yet STI screening is infrequently performed in pediatric primary care clinics with many of those at-risk not administered testing. This study aims to understand contextual factors influencing STI screening and testing among adolescents in pediatric primary care. METHODS: We used the Consolidated Framework for Implementation Research (CFIR) as part of a stepwise approach to facilitate a deep understanding the pediatric primary care environment. We conducted semistructured interviews of physicians, nurses, and patient-parent dyads from 4 pediatric primary care practices in the St. Louis metropolitan area about STI screening practices and common concerns regarding STI screening. Qualitative analysis was conducted using a categorical coding technique informed by the CFIR followed by a thematic coding technique. RESULTS: We interviewed 23 physicians/nurses and 12 patient-parent dyads. Individual-level barriers to STI screening and testing included wide variability in clinicians' practice patterns and their perception of STI risk in the patient population. Structural barriers included a lack of capacity to perform testing in clinic and time constraints during patient visits. Confidentiality issues also created significant barriers to screening and testing on both individual and structural levels. Adopting confidential methods for testing and educating providers on patients' recommendations for STI testing were discussed as ways to potentially improve STI care in pediatric patients. CONCLUSIONS: Our use of the CFIR facilitated a systematic approach to identify gaps in STI care for adolescents and identified opportunities to close those gaps. An integrated, systematic approach that enhances patient confidentiality and improves clinicians' knowledge could address gaps in STI care in pediatric primary care settings.

The impact of neighborhood socioeconomic status on retention in care and viral suppression among people living with HIV.

Our study combined publicly available neighborhood socioeconomic status (nSES) data from the U.S. Census and clinical data to investigate the relationships between nSES, retention in care (RIC) and viral suppression (VS). Data from 2275 patients were extracted from 2009 to 2015 from a midwestern infectious diseases clinic. RIC was defined as patients who kept ≥ 3 visits and VS as an average viral load <200 copies/mL during their index year of study. Logistic regression models provided estimates for neighborhood-level and patient-level variables. In multivariable models, patients living in zip codes with low disability rates (1.50, 1.30-1.70), who wereolder (1.02, 1.01-1.03), and receiving antiretroviral therapy (ART; 3.81, 3.56-4.05) were more likely to have RIC, while those who were unemployed (0.72, 0.45-0.98) and self-reported as BIPOC (0.79, 0.64-0.97) were less likely to have RIC. None of the nSES variables were significantly associated with VS in multivariable models, yet older age (1.05, 1.04-1.05) and self-reported as BIPOC (1.68, 1.36-2.09) were modestly associated with VS, and receiving ART (6.14, 5.86-6.42) was a strong predictor of VS. In multivariable models, nSES variables were independently predictive more than of patient-level variables, for RIC but not VS.

Linking out-of-hospital deaths with a regional hospital-based firearm injury database: a clinical researcher's guide to accessing data from the National Death Index.

IntroductionFirearm injuries are a public health crisis in the US. The National Death Index (NDI) is a well-established, comprehensive database managed by the National Center for Health Statistics at the CDC. In this methodology paper we describe our experience accessing and linking data from the NDI to our regional, hospital-based violent injury database to identify out-of-hospital deaths from firearms. METHODS: We outline the key steps of our submission to the NDI. Data were collected from research team meeting notes, team member emails with NDI staff, and information provided from the NDI website and supplementary guides. Few of our collaborators or university partner investigators had accessed or used data from the NDI. We discuss the online NDI Processing Portal data request, data preparation and receipt from the NDI, troubleshooting tips, and a timeline of events. RESULTS: Our query to the NDI returned 12 034 records of 12 219 firearm-injured patient records from 2010 and 2019. The record match rate was 98.5%. DISCUSSION: Linking hospital-based data sets with NDI data can provide valuable information on out-of-hospital deaths. This has the potential to improve the quality of longitudinal morbidity and mortality calculations in hospital-based patient cohorts. We encountered logistic and administrative challenges in completing the online NDI Processing Portal and in preparing and receiving data from the NDI. It is our hope that the lessons learnt presented herein will help facilitate easy and streamlined acquisition of valuable NDI data for other clinical researchers. WHAT THIS STUDY ADDS: - A step-by-step guide for clinical researchers of how to apply to access data from the National Death Index (NDI).- Advice and lessons learned on how to efficiently and effectively access data from the NDI.- A well-described methodology to improve the quality of longitudinal morbdity and mortality calculations in hospital-based cohorts of firearm injured patients.What is already known on this subject:- There is a need for robust, longitudinal data sources that reliably track morbidity and mortality among firearm injured patients in the United States.- The NDI is a well-established, comprehensive database that holds death records for all 50 states, which provides valuable mortality data to the public health and medical research community.

Enabling Hotspot Detection and Public Health Response to the COVID-19 Pandemic.

INTRODUCTION: Public-facing maps of COVID-19 cases, hospital admissions, and deaths are commonly displayed at the state, county, and zip code levels, and low case counts are suppressed to protect confidentiality. Public health authorities are tasked with case identification, contact tracing, and canvasing for educational purposes during a pandemic. Given limited resources, authorities would benefit from the ability to tailor their efforts to a particular neighborhood or congregate living facility. METHODS: We describe the methods of building a real-time visualization of patients with COVID-19-positive tests, which facilitates timely public health response to the pandemic. We developed an interactive street-level visualization that shows new cases developing over time and resolving after 14 days of infection. Our source data included patient demographics (ie, age, race and ethnicity, and sex), street address of residence, respiratory test results, and date of test. RESULTS: We used colored dots to represent infections. The resulting animation shows where new cases developed in the region and how patterns changed over the course of the pandemic. Users can enlarge specific areas of the map and see street-level detail on residential location of each case and can select from demographic overlays and contour mapping options to see high-level patterns and associations with demographics and chronic disease prevalence as they emerge. CONCLUSIONS: Before the development of this tool, local public health departments in our region did not have a means to map cases of disease to the street level and gain real-time insights into the underlying population where hotspots had developed. For privacy reasons, this tool is password-protected and not available to the public. We expect this tool to prove useful to public health departments as they navigate not only COVID-19 pandemic outcomes but also other public health threats, including chronic diseases and communicable disease outbreaks.

Association of STS database variables with repair durability in ischemic mitral regurgitation using machine learning.

BACKGROUND: Machine learning (ML) can identify nonintuitive clinical variable combinations that predict clinical outcomes. To assess the potential predictive contribution of standardized Society of Thoracic Surgeons (STS) Database clinical variables, we used ML to detect their association with repair durability in ischemic mitral regurgitation (IMR) patients in a single institution study. METHODS: STS Database variables (n = 53) served as predictors of repair durability in ML modeling of 224 patients who underwent surgical revascularization and mitral valve repair for IMR. Follow-up mortality and echocardiography data allowed 1-year outcome analysis in 173 patients. Supervised ML analyses were performed using recurrence (≥3+ IMR) or death versus nonrecurrence (<3+ IMR) as the binary outcome classification. RESULTS: We tested standard ML and deep learning algorithms, including support vector machines, logistic regression, and deep neural networks. Following training, final models were utilized to predict class labels for the patients in the test set, producing receiver operating characteristic (ROC) curves. The three models produced similar area under the curve (AUC), and predicted class labels with promising accuracy (AUC = 0.72-0.75). CONCLUSIONS: Readily-available STS Database variables have potential to play a significant role in the development of ML models to direct durable surgical therapy in IMR patients.

Cardiometabolic risk factors and survival after cancer in the Women's Health Initiative.

BACKGROUND: Cardiometabolic abnormalities are a leading cause of death among women, including women with cancer. METHODS: This study examined the association between prediagnosis cardiovascular health and total and cause-specific mortality among 12,076 postmenopausal women who developed local- or regional-stage invasive cancer in the Women's Health Initiative (WHI). Cardiovascular risk factors included waist circumference, hypertension, high cholesterol, and type 2 diabetes. Obesity-related cancers included breast cancer, colorectal cancer, endometrial cancer, kidney cancer, pancreatic cancer, ovarian cancer, stomach cancer, liver cancer, and non-Hodgkin lymphoma. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) adjusted for important predictors of survival. RESULTS: After a median follow-up of 10.0 years from the date of the cancer diagnosis, there were 3607 total deaths, with 1546 (43%) due to cancer. Most participants (62.9%) had 1 or 2 cardiometabolic risk factors, and 8.1% had 3 or 4. In adjusted models, women with 3 to 4 risk factors (vs none) had a higher risk of all-cause mortality (HR, 1.99; 95% CI, 1.73-2.30), death due to cardiovascular disease (CVD) (HR, 4.01; 95% CI, 2.88-5.57), cancer-specific mortality (HR, 1.37; 95% CI, 1.1-1.72), and other-cause mortality (HR, 2.14; 95% CI, 1.70-2.69). A higher waist circumference was associated with greater all-cause mortality (HR, 1.17; 95% CI, 1.06-1.30) and cancer-specific mortality (HR, 1.22; 95% CI, 1.04-1.42). CONCLUSIONS: Among postmenopausal women diagnosed with cancer in the WHI, cardiometabolic risk factors before the cancer diagnosis were associated with greater all-cause, CVD, cancer-specific, and other-cause mortality. These results raise hypotheses regarding potential clinical intervention strategies targeting cardiometabolic abnormalities that require future prospective studies for confirmation. LAY SUMMARY: This study uses information from the Women's Health Initiative (WHI) to find out whether cardiac risk factors are related to a greater risk of dying among older women with cancer. The WHI is the largest study of medical problems faced by older women in this country. The results show that women who have 3 or 4 risk factors are more likely to die of any cause, heart disease, or cancer in comparison with women with no risk factors. It is concluded that interventions to help to lower the burden of cardiac risk factors can have an important impact on survivorship among women with cancer.