OBJECTIVE:This retrospective study of a Black cohort sought to create predictive models to calculate the probability of a coronary artery calcium score (CACS) about one decade after obtaining cardiovascular risk measures. STUDY DESIGN AND SETTING:Participants (n = 656) in GENOA had CV risk variables measured (1995-2000) and a CACS about one decade later (2009-2011). Using multivariate regression, computer models were written to calculate the probability of a future CACS of zero, ≥ 10, and ≥ 100. ROC values were 0.78, 0.77, and 0.76, respectively. Machine learning models did not perform any better than multivariate regression. RESULTS:Age, height, smoking duration, sex, and hypertension were significant for all three models in predicting a future CACS. Height was inversely related to future CACS, but weight and BMI were not contributory to the models. Lipid-lowering medications and exercise were associated with an increased CACS, the so-called CACS "paradox." CONCLUSION:Predictive models of a future CACS such as these may help in identifying important risk factors for a future CACS. By identifying these risk factors and implementing early modification of CV risk factors, the development of CV disease may be slowed.

Introduction:The urgency and scale of the COVID-19 pandemic demanded a coordinated response from public health agencies and the biomedical research community. The National COVID Cohort Collaborative (N3C) was established as a centralized enclave in 2020 to support the study of COVID-19 across the U.S. The Institutional Development Award for Clinical and Translational Research (IDeA-CTR) centers enhanced N3C's national response by bringing representation from rural and medically underserved communities. This improved the representation of our diverse populations in the N3C Enclave and its use for research by IDeA-state investigators. Methods:We developed an organizational structure across the IDeA-CTRs to improve research productivity in resource-challenged areas of the U.S. This socio-technical ecosystem, informed by community input, included a governance committee and two workstreams. The operations workstream focused on data management and regulatory compliance, while the navigation, education, analysis, and training (NEAT) workstream supported educational and analytical activities for the N3C Enclave. Results:Our collaborative approach led to participation by 12 IDeA-CTRs, representing over 400 investigators from 23 sites. The shared governance, investigator engagement, and resource pooling enhanced research productivity and engagement with researchers across IDeA states. Participation in this IDeA-CTR N3C consortium enhanced informatics research capacity and collaboration across the IDeA-CTRs for participating networks. Conclusions:This collaborative model provides a roadmap and framework for future efforts among IDeA-CTRs and other academic partnerships. The socio-technical ecosystem fostered collectivism and team science, enabling the consortium to achieve far more than isolated efforts could, offering valuable insights for interdisciplinary research across geographically dispersed communities.

Previous studies demonstrated higher short-term mortality among rural compared with urban residents infected with SARS-CoV-2. However, whether this difference persists remains uncertain. This retrospective cohort study analyzed two-year post-COVID-19 mortality by rurality using the National Clinical Cohort Collaborative COVID-19 Enclave, a United States-based longitudinal electronic health record repository. We analyzed mortality among patients infected with SARS-CoV-2 between April 2020 and December 2022, with follow-up until December 2024. Patients were categorized into urban, urban-adjacent rural (UAR), and nonurban-adjacent rural (NAR) groups based on residential ZIP Code. Mortality differences were assessed using Kaplan-Meier analysis and weighted multivariable Cox regression, with weights derived from demographic factors and models adjusted for background clinical risk and social vulnerability. Among 3,082,978 SARS-CoV-2-infected patients, we found a significant association between rurality and increased two-year all-cause mortality post-infection. Adjusted hazards for two-year mortality for UAR and NAR were 1.19 (95% CI 1.18-1.21) and 1.26 (1.22-1.29). A reference cohort of 4,153,216 COVID-19-negative patients showed a modest yet consistent rural mortality penalty, with a similar relative hazard across cohorts, an observed rurality-COVID-19 interaction, and a greater absolute number of deaths following SARS-CoV-2 infection. Our findings emphasize ongoing rural mortality disparities and the importance of public health efforts in rural communities.

Objective: This study aimed to evaluate the joint associations of maternal hyperglycemic and hypertensive disorders with adverse pregnancy outcomes across the coronavirus disease 2019 (COVID-19) pandemic. Methods: This retrospective study included 110,447 Louisiana Medicaid pregnant women with first-time delivery from January 1, 2016, to December 31, 2021. Associations between hyperglycemic as well as hypertensive disorders and adverse pregnancy outcomes in pregnancy during prepandemic, early pandemic, and late pandemic were assessed by binary logistic regression. Results: The odds ratios of above adverse pregnancy outcomes were significantly higher during the early and late COVID-19 pandemic than those before the pandemic. Maternal gestational diabetes mellitus and diabetes before pregnancy were associated with higher risks of preterm birth, primary cesarean section, large for gestational age (LGA), macrosomia, neonatal hypoglycemia, neonatal jaundice, and neonatal respiratory distress syndrome (NRDS; all p < 0.05), respectively, compared with women with normal glucose during pregnancy. Maternal gestational hypertension, preeclampsia or eclampsia, and pre-existing hypertension were associated with higher risks of preterm birth, primary cesarean section, low birth weight (exception for gestational hypertension), small for gestational age, LGA (exception for preeclampsia or eclampsia), macrosomia (exception for preeclampsia or eclampsia), neonatal hypoglycemia, neonatal jaundice, and NRDS (all p < 0.05), respectively, compared with women with normal blood pressure during pregnancy. Most of these associations during the early and late pandemic were consistent with those before the COVID-19 pandemic. Conclusions: Maternal hyperglycemic and hypertensive disorders during pregnancy, compared with maternal normal glucose or blood pressure during pregnancy, were associated with higher risks of adverse maternal and neonatal outcomes. Interventions should be taken to help individuals achieve glycemic and blood pressure control to decrease the risk of adverse perinatal outcomes regardless of the COVID-19 pandemic.

Objectives/Goals: The primary objective of this study is to investigate the relationship between human leukocyte antigen (HLA) alleles to COVID-19 clinical severity, specifically: hospitalization, mortality, pneumonia by COVID-19, post-acute sequelae of SARS-CoV-2 infection (PASC), and clinical lab values. Methods/Study Population: We are conducting a retrospective cohort study utilizing the All of Us controlled tier dataset. The base population was defined as any patients with a COVID-19 diagnosis code (ICD-10: U07.1 or SNOMED: 840539006) and genomic sequencing data. PASC definitions were developed by the N3C consortium and refined in house. A total of 15,252 patients (64.5% female; 50.4% self-reported European ancestry; 18.8% self-reported African ancestry; 34.5% > 65 years old) are included in this study. HLA Class I and Class II alleles will be imputed from a global diversity reference panel utilizing the HIBAG “R” package. Results/Anticipated Results: Controlling for age, sex, race, and COVID-19 vaccination status, we anticipate determining the HLA alleles associated with severe clinical outcomes, such as Pneumonia by COVID (n = 1,436) and PASC (ICD-10:U09.9 or SNOMED:119303003 or OMOP:OMOP5160861 [n = 498]). We will assess which HLA alleles are associated with markedly different IgM and IgG COVID-19 serum antibody levels (n = 1,024). Coexisting conditions, i.e., type 2 diabetes, chronic obstructive pulmonary disease, and hypertension, will be controlled for with the Charlson comorbidity index. The accuracy of HLA allelic imputation will be validated in patients with long-read whole genome sequences. Discussion/Significance of Impact: Our findings can help identify patients who may be at risk of severe COVID-19 infection, particularly those undergoing bone marrow or organ transplantation. We hope this study will accelerate personalized care of COVID-19 in vulnerable populations.

INTRODUCTION:The objective of this study was to determine the burden of influenza disease in patients with or without diabetes in a population of American adults to understand the benefits of seasonal vaccination. RESEARCH DESIGN AND METHODS:We performed a retrospective cohort study using electronic medical records totaling 1,117,263 from two Louisiana healthcare providers spanning January 2012 through December 2017. Adults 18 years or older with two or more records within the study period were included. The primary outcome quantified was influenza-related diagnosis during inpatient (IP) or emergency room (ER) visits and risk reduction with the timing of immunization. RESULTS:Influenza-related IP or ER visits totaled 0.0122-0.0169 events per person within the 2013-2016 influenza seasons. Subjects with diabetes had a 5.6-fold more frequent influenza diagnosis for IP or ER visits than in subjects without diabetes or 3.7-fold more frequent when adjusted for demographics. Early immunization reduced the risk of influenza healthcare utilization by 66% for subjects with diabetes or 67% for subjects without diabetes when compared with later vaccination for the 2013-2016 influenza seasons. Older age and female sex were associated with a higher incidence of influenza, but not a significant change in risk reduction from vaccination. CONCLUSIONS:The risk for influenza-related healthcare utilization was 3.7-fold higher if patients had diabetes during 2013-2016 influenza seasons. Early immunization provides a significant benefit to adults irrespective of a diabetes diagnosis. All adults, but particularly patients with diabetes, should be encouraged to get the influenza vaccine at the start of the influenza season.

Background : Inflammation plays a complex, incompletely understood role in the pathogenesis of acute COVID-19 and Post-Acute Sequelae of SARS-CoV-2 infection (PASC or “Long COVID”). Systemic acute inflammation resulting in cytokine storm, hypercoagulability and endothelial damage is thought to be a central mechanism for severe morbidity and mortality in acute COVID-19. Anti-inflammatory medications taken routinely for chronic conditions prior to contracting COVID-19 (“background medications”) may modulate acute COVID-19 outcomes.  Methods : Using data from the National COVID Cohort Collaborative (N3C) enclave, we estimated effects of six classes of background medications on acute COVID outcomes. Medication classes included aspirin, celecoxib, other NSAIDS, steroids, immune suppressants, and antidepressants. Acute COVID outcomes included probability of hospital admission, inpatient mortality, and mortality among diagnosed COVID patients. Each medication class was compared to benzodiazepines (excluding midazolam) which served as a comparator/control. Only adult COVID patients with pre-existing osteoarthritis and without any diagnosed autoimmune disease were included in the analyses. Random effects logistic regression models were used to adjust for covariates and data contributing organization. Medication effects also were estimated for COVID negative cases.  Results : Non-aspirin NSAIDS were associated with mortality among diagnosed COVID-19 patients: adjusted Odds Ratio (aOR)=0.32 (p=.032) for celecoxib; aOR=0.51 (p<.001) for NSAIDS other than aspirin and celecoxib. For inpatient mortality: aOR=0.34 (p=.060) for celecoxib and aOR=0.74 (p=.200) for other non-aspirin NSAIDS. Similar effects were observed for COVID negative cases, including for inpatient mortality: aOR=0.21 (p<.001) for celecoxib and aOR=0.34 (p<.001) for other non-aspirin NSAIDS. Secondary analyses examined alternative explanations for results.  Discussion : Protective effects were observed for non-aspirin NSAIDS, especially celecoxib. However, those estimated effects implicitly assume the medication classes did not differ on the probability a true COVID-19 case was diagnosed. The similarity of COVID positive and COVID negative results suggests possible missing covariates. However, such similarity plausibly could stem from a medication having both “direct” and “indirect” effects on COVID outcomes. Adjudicating among the alternative interpretations would require data beyond those available. However, the effects observed for non-aspirin NSAIDS, while possibly biased, rationalize further investigation using study designs constructed to overcome the limitations of existing datasets. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement Yes ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The Pennington Biomedical Research Center’s IRB (20211-016-PBRC) and the N3C Data Access Committee (RP-504BA5) approved the study COVID-19 Treatments Associated with Lower Mortality. N3C operates under the authority of the National Institutes of Health IRB, with Johns Hopkins University serving as the central IRB (IRB00249128). No informed consent was obtained from individual patients because the study used a limited data set already stripped of direct identifiers in compliance with the HIPAA Privacy Rule. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data cannot be shared because The N3C Data Enclave is managed under the authority of the NIH information can be found at https://ncats.nih.gov/n3c/resources.

AbstractRationaleShort‐term weight loss is possible in a variety of settings. However, long‐term, free‐living weight loss maintenance following structured weight loss interventions remains elusive.ObjectiveThe purpose was to study body weight trajectories over 2 years of intensive lifestyle intervention (ILI) and up to 4 years of follow‐up versus usual care (UC).MethodsData were obtained from electronic medical records (EMRs) from participating clinics. Baseline (Day 0) was established as the EMR data point closest but prior to the baseline date of the trial. The sample included 111 ILI and 196 UC patients. The primary statistical analysis focused on differentiating weight loss trajectories between ILI and UC.ResultsThe ILI group experienced significantly greater weight loss compared with the UC group from Day 100 to Day 700, beyond which there were no significant differences. Intensive lifestyle intervention patients who maintained ≥5% and ≥10% weight loss at 24 months demonstrated significantly greater weight loss (p < 0.001) across the active intervention and follow‐up.ConclusionsFollowing 24 months of active intervention, patients with ILI regained weight toward their baseline to the point where ILI versus UC differences were no longer statistically or clinically significant. However, patients in the ILI who experienced ≥5% or ≥10% weight loss at the cessation of the active intervention maintained greater weight loss at the end of the follow‐up phase.Clinical Trial RegistrationClinicalTrials.gov: NCT02561221.

Importance:Hypertensive disorders of pregnancy (HDP) are a group of high blood pressure disorders during pregnancy that are a leading cause of maternal and infant morbidity and mortality. Data on the trend in the incidence of HDP among the Medicaid population during coronavirus disease of 2019 (COVID-19) are lacking. Objective:To determine the trends in the annual incidence of HDP among pregnant Medicaid-insured women in Louisiana before and during the COVID-19 pandemic (2016-2021). Methods:A total of 113,776 pregnant women aged 15-50 years were included in this study. For multiparous individuals, only the first pregnancy was used in the analyses. Women with a diagnosis of each type-specific HDP were identified by using the International Classification of Diseases, 10th revision (ICD-10) codes. The annual incidence of HDP was calculated for each race and age subgroup. For each type-specific HDP, the annual age-specific incidence was calculated. Results:The incidence of HDP increased from 10.5% in 2016 to 17.7% in 2021. The highest race/ethnicity-specific incidence of HDP was seen in African American women (19.2%), then White women (13.1%), followed by other women (10.7%). Conclusion and Relevance:HDP remains a very prevalent and significant global health issue, especially in African American women and during the COVID-19 pandemic. Severe HDP substantially increases the risk of mortality in offspring and poses long-term issues for both mother and infant. HDP prevention holds particular relevance for the Medicaid population, given the health care disparities and barriers that impact quality of care, leading to an increased risk for HDP.

Importance: Although there are many regional and national studies on the trends in the incidence of gestational diabetes mellitus (GDM), the trends in the incidence of GDM among the Medicaid population are lacking, especially before and during coronavirus disease of 2019 (COVID-19). Objective: To investigate the trends in the incidence of GDM before and during COVID-19 pandemic (2016-2021) among the Louisiana Medicaid population. Design, Setting, and Participants: This study included 111,936, Louisiana Medicaid pregnant women of age 18-50 between January 1, 2016, to December 31, 2021. Main Outcomes and Measures: Pregnancies, GDM, and pre-pregnancy diabetes cases were identified by using the Tenth Revisions of the International Classification of Disease code. The annual incidence of GDM and annual prevalence of pre-pregnancy diabetes were calculated for each age and race subgroup. Results: The age-standardized incidence of GDM increased from 10.2% in 2016 to 14.8 in 2020 and decreased to 14.0% in 2021. The age-standardized prevalence of pre-pregnancy diabetes increased from 2.8% in 2016 to 3.4% in 2018 and decreased to 2.3% in 2021. The age-standardized rate of GDM was the highest among Asian women (23.0%), then White women (15.5%), and African American women (13.9%) (p for difference <0.001). The COVID-19 pandemic saw an increase in the incidence of GDM, with a rise in prominent GDM risk factors, such as obesity and sedentary behaviors, suggesting an association. Conclusion and Relevance: The incidence of GDM significantly increased during the COVID-19 pandemic. Potential reasons might include increased sedentary behavior and increased prevalence of obesity. GDM is a major public health issue, and the prevention of GDM is particularly essential for the Louisiana Medicaid population owing to the high prevalence of GDM-related risk factors in this population.

PurposeTo investigate the enduring disparities in adverse COVID-19 events between urban and rural communities in the United States, focusing on the effects of SARS-CoV-2 vaccination and therapeutic advances on patient outcomes.MethodsUsing National COVID Cohort Collaborative (N3C) data from 2021 to 2023, this retrospective cohort study examined COVID-19 hospitalization, inpatient death, and other adverse events. Populations were categorized into urban, urban-adjacent rural (UAR), and nonurban-adjacent rural (NAR). Adjustments included demographics, variant-dominant waves, comorbidities, region, and SARS-CoV-2 treatment and vaccination. Statistical methods included Kaplan-Meier survival estimates, multivariable logistic, and Cox regression.FindingsThe study included 3,018,646 patients, with rural residents constituting 506,204. These rural dwellers were older, had more comorbidities, and were less vaccinated than their urban counterparts. Adjusted analyses revealed higher hospitalization odds in UAR and NAR (aOR 1.07 [1.05-1.08] and 1.06 [1.03-1.08]), greater inpatient death hazard (aHR 1.30 [1.26-1.35] UAR and 1.37 [1.30-1.45] NAR), and greater risk of other adverse events compared to urban dwellers. Delta increased, while Omicron decreased, inpatient adverse events relative to pre-Delta, with rural disparities persisting throughout. Treatment effectiveness and vaccination were similarly protective across all cohorts, but dexamethasone post-ventilation was effective only in urban areas. Nirmatrelvir/ritonavir and molnupiravir better protected rural residents against hospitalization.ConclusionsDespite advancements in treatment and vaccinations, disparities in adverse COVID-19 outcomes persist between urban and rural communities. The effectiveness of some therapeutic agents appears to vary based on rurality, suggesting a nuanced relationship between treatment and geographic location while highlighting the need for targeted rural health care strategies.

Background While COVID-19 vaccines reduce adverse outcomes, post-vaccination SARS-CoV-2 infection remains problematic. We sought to identify community factors impacting risk for breakthrough infections (BTI) among fully vaccinated persons by rurality. Methods We conducted a retrospective cohort study of US adults sampled between January 1 and December 20, 2021, from the National COVID Cohort Collaborative (N3C). Using Kaplan-Meier and Cox-Proportional Hazards models adjusted for demographic differences and comorbid conditions, we assessed impact of rurality, county vaccine hesitancy, and county vaccination rates on risk of BTI over 180 days following two mRNA COVID-19 vaccinations between January 1 and September 21, 2021. Additionally, Cox Proportional Hazards models assessed the risk of infection among adults without documented vaccinations. We secondarily assessed the odds of hospitalization and adverse COVID-19 events based on vaccination status using multivariable logistic regression during the study period. Results Our study population included 566,128 vaccinated and 1,724,546 adults without documented vaccination. Among vaccinated persons, rurality was associated with an increased risk of BTI (adjusted hazard ratio [aHR] 1.53, 95% confidence interval [CI] 1.42-1.64, for urban-adjacent rural and 1.65, 1.42-1.91, for nonurban-adjacent rural) compared to urban dwellers. Compared to low vaccine-hesitant counties, higher risks of BTI were associated with medium (1.07, 1.02-1.12) and high (1.33, 1.23-1.43) vaccine-hesitant counties. Compared to counties with high vaccination rates, a higher risk of BTI was associated with dwelling in counties with low vaccination rates (1.34, 1.27-1.43) but not medium vaccination rates (1.00, 0.95-1.07). Community factors were also associated with higher odds of SARS-CoV-2 infection among persons without a documented vaccination. Vaccinated persons with SARS-CoV-2 infection during the study period had significantly lower odds of hospitalization and adverse events across all geographic areas and community exposures. Conclusions Our findings suggest that community factors are associated with an increased risk of BTI, particularly in rural areas and counties with high vaccine hesitancy. Communities, such as those in rural and disproportionately vaccine hesitant areas, and certain groups at high risk for adverse breakthrough events, including immunosuppressed/compromised persons, should continue to receive public health focus, targeted interventions, and consistent guidance to help manage community spread as vaccination protection wanes.

Purpose Rural communities are among the most underserved and resource-scarce populations in the United States. However, there are limited data on COVID-19 outcomes in rural America. This study aims to compare hospitalization rates and inpatient mortality among SARS-CoV-2-infected persons stratified by residential rurality. Methods This retrospective cohort study from the National COVID Cohort Collaborative (N3C) assesses 1,033,229 patients from 44 US hospital systems diagnosed with SARS-CoV-2 infection between January 2020 and June 2021. Primary outcomes were hospitalization and all-cause inpatient mortality. Secondary outcomes were utilization of supplemental oxygen, invasive mechanical ventilation, vasopressor support, extracorporeal membrane oxygenation, and incidence of major adverse cardiovascular events or hospital readmission. The analytic approach estimates 90-day survival in hospitalized patients and associations between rurality, hospitalization, and inpatient adverse events while controlling for major risk factors using Kaplan-Meier survival estimates and mixed-effects logistic regression. Findings Of 1,033,229 diagnosed COVID-19 patients included, 186,882 required hospitalization. After adjusting for demographic differences and comorbidities, urban-adjacent and nonurban-adjacent rural dwellers with COVID-19 were more likely to be hospitalized (adjusted odds ratio [aOR] 1.18, 95% confidence interval [CI], 1.16-1.21 and aOR 1.29, CI 1.24-1.1.34) and to die or be transferred to hospice (aOR 1.36, CI 1.29-1.43 and 1.37, CI 1.26-1.50), respectively. All secondary outcomes were more likely among rural patients. Conclusions Hospitalization, inpatient mortality, and other adverse outcomes are higher among rural persons with COVID-19, even after adjusting for demographic differences and comorbidities. Further research is needed to understand the factors that drive health disparities in rural populations.

Examine COVID-19 knowledge, concerns, behaviors, stress, and sources of information among patients in a safety-net health system in Louisiana. Research assistants surveyed participants via structured telephone interviews from April to October 2020. The data presented in this study were obtained in the pre-vaccine availability period. Of 623 adult participants, 73.5% were female, 54.7% Black, and 44.8% lived in rural small towns; mean age was 48.69. Half (50.5%) had spoken to a healthcare provider about the virus, 25.8% had been tested for COVID-19; 11.4% tested positive. Small town residents were less likely to be tested than those in cities (21.1% vs 29.3%, p = 0.05). Knowledge of COVID-19 symptoms and ways to prevent the disease increased from (87.9% in the spring to 98.9% in the fall, p < 0.001). Participants indicating that the virus had 'changed their daily routine a lot' decreased from 56.9% to 39.3% (p < 0.001). The main source of COVID-19 information was TV, which increased over time, 66.1-83.6% (p < 0.001). Use of websites (34.2%) did not increase. Black adults were more likely than white adults (80.7% vs 65.6%, p < 0.001) to rely on TV for COVID-19 information. Participants under 30 were more likely to get COVID-19 information from websites and social media (58.2% and 35.8% respectively). This study provides information related to the understanding of COVID-19 in rural and underserved communities that can guide clinical and public health strategies.

Introduction: Breast cancer is a heterogeneous disease, consisting of multiple molecular subtypes. Obesity has been associated with an increased risk for postmenopausal breast cancer, but few studies have examined breast cancer subtypes separately. Obesity is often complicated by type 2 diabetes, but the possible association of diabetes with specific breast cancer subtypes remains poorly understood. Methods: In this retrospective case-control study, Louisiana Tumor Registry records of primary invasive breast cancer diagnosed in 2010-2015 were linked to electronic health records in the Louisiana Public Health Institute's Research Action for Health Network. Controls were selected from Research Action for Health Network and matched to cases by age and race. Conditional logistic regression was used to identify metabolic risk factors. Data analysis was conducted in 2020-2021. Results: There was a significant association between diabetes and breast cancer for Luminal A, Triple-Negative Breast Cancer, and human epidermal growth factor 2-positive subtypes. In multiple logistic regression, including both obesity status and diabetes as independent risk factors, Luminal A breast cancer was also associated with overweight status. Diabetes was associated with increased risk for Luminal A and Triple-Negative Breast Cancer in subgroup analyses, including women aged >= 50 years, Black women, and White women. Conclusions: Although research has identified obesity and diabetes as risk factors for breast cancer, these results underscore that comorbid risk is complex and may differ by molecular subtype. There was a significant association between diabetes and the incidence of Luminal A, Triple-Negative Breast Cancer, and human epidermal growth factor 2-positive breast cancer in Louisiana. (C) 2022 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.

ObjectiveAdvancements in fluoroscopy-assisted procedures have increased radiation exposure among cardiologists. Radiation has been linked to cardiovascular complications but its effect on cardiac rhythm, specifically, is underexplored.MethodsDemographic, social, occupational, and medical history information was collected from board-certified cardiologists via an electronic survey. Bivariate and multivariable logistic regression analyses were performed to assess the risk of atrial arrhythmias (AA).ResultsWe received 1,478 responses (8.8% response rate) from cardiologists, of whom 85.4% were male, and 66.1% were ≤65 years of age. Approximately 36% were interventional cardiologists and 16% were electrophysiologists. Cardiologists > 50 years of age, with > 10,000 hours (h) of radiation exposure, had a significantly lower prevalence of AA vs. those with ≤10,000 h (11.1% vs. 16.7%, p = 0.019). A multivariable logistic regression was performed and among cardiologists > 50 years of age, exposure to > 10,000 radiation hours was significantly associated with a lower likelihood of AA, after adjusting for age, sex, diabetes mellitus, hypertension, and obstructive sleep apnea (adjusted OR 0.57; 95% CI 0.38–0.85, p = 0.007). The traditional risk factors for AA (age, sex, hypertension, diabetes mellitus, and obstructive sleep apnea) correlated positively with AA in our data set. Cataracts, a well-established complication of radiation exposure, were more prevalent in those exposed to > 10,000 h of radiation vs. those exposed to ≤10,000 h of radiation, validating the dependent (AA) and independent variables (radiation exposure), respectively.ConclusionAA prevalence may be inversely associated with radiation exposure in Cardiologists based on self-reported data on diagnosis and radiation hours. Large-scale prospective studies are needed to validate these findings.

Pragmatic trials are increasingly used to study the implementation of weight loss interventions in real-world settings. This study compared researcher-measured body weights versus electronic medical record (EMR)-derived body weights from a pragmatic trial conducted in an underserved patient population. The PROPEL trial randomly allocated 18 clinics to usual care (UC) or to an intensive lifestyle intervention (ILI) designed to promote weight loss. Weight was measured by trained technicians at baseline and at 6, 12, 18, and 24 months. A total of 11 clinics (6 UC/5 ILI) with 577 enrolled patients also provided EMR data (n = 561), which included available body weights over the period of the trial. The total number of assessments were 2638 and 2048 for the researcher-measured and EMR-derived body weight values, respectively. The correlation between researcher-measured and EMR-derived body weights was 0.988 (n = 1 939; p < 0.0001). The mean difference between the EMR and researcher weights (EMR-researcher) was 0.63 (2.65 SD) kg, and a Bland-Altman graph showed good agreement between the two data collection methods; the upper and lower boundaries of the 95% limits of agreement are −4.65 kg and +5.91 kg, and 71 (3.7%) of the values were outside the limits of agreement. However, at 6 months, percent weight loss in the ILI compared to the UC group was 7.3% using researcher-measured data versus 5.5% using EMR-derived data. At 24 months, the weight loss maintenance was 4.6% using the technician-measured data versus 3.5% using EMR-derived data. At the group level, body weight data derived from researcher assessments and an EMR showed good agreement; however, the weight loss difference between ILI and UC was blunted when using EMR data. This suggests that weight loss studies that rely on EMR data may require larger sample sizes to detect significant effects. ClinicalTrials.gov number NCT02561221.

The aim of this study was to compute intra-class correlations (ICCs) for weight-related and patient-reported outcomes in a cluster randomized clinical trial (cRCT) for weight loss. Baseline and follow-up data from the Promoting Successful Weight Loss in Primary Care in Louisiana (PROPEL) cRCT were used in this analysis. ICCs were computed for baseline and follow-up measures, and changes in body weight, cardiometabolic risk factors and health-related and weight-related quality of life at 6, 12, 18 and 24 months. Baseline ICCs ranged from 0 for PROMIS measures of anxiety and fatigue to 0.055 for total cholesterol (median = 0.019). The ICCs were higher for changes and decreased over time during follow-up. The ICCs for changes were highest in the pooled sample (intervention and usual care combined) followed by the intervention and usual care groups, respectively. The results demonstrated significant ICCs for several outcomes in a weight loss cRCT. The ICCs differed in magnitude depending on whether baseline versus longitudinal data were used, whether data were combined across treatment arms or were considered separately, and varied across the follow-up period. All these factors must be considered when choosing an ICC to inform sample size estimates for future weight loss cRCTs conducted in primary care settings.

Abstract Background Rural communities are among the most vulnerable and resource-scarce populations in the United States. Rural data is rarely centralized, precluding comparability across regions, and no significant studies have studied this population at scale. The purpose of this study is to present findings from the National COVID Cohort Collaborative (N3C) to provide insight into future research and highlight the urgent need to address health disparities in rural populations. N3C Patient Distribution This figure shows the geospatial distribution of the N3C COVID-19 positive population. N3C contains data from 55 data contributors from across the United States, 40 of whom include sufficient location information to map by ZIP Code centroid spatially. Of those sites, we selected 27 whose data met our minimum robustness qualifications for inclusion in our study. This bubble map is to scale with larger bubbles representing more patients. A. shows all N3C patients. B. shows only urban N3C distribution. C. shows the urban-adjacent rural patient distribution. D. shows the nonurban-adjacent rural patient distribution, representing the most isolated patients in N3C. Methods This retrospective cohort of 573,018 patients from 27 hospital systems presenting with COVID-19 between January 2020 and March 2021, of whom 117,897 were admitted (see Data Analysis Plan diagram for inclusion/exclusion criteria), analyzes outcomes and 30-day survival for the hospitalized population by the degree of rurality. Multivariate Cox regression analysis and mixed-effects models were used to estimate the association between rurality, hospitalization, and all-cause mortality, controlling for major risk factors associated with rural-urban health discrepancies and differences in health system outcomes. The difference in distribution by rurality is described as well as supplemented by population-level statistics to confirm representativeness. Data Analysis Plan This data analysis plan includes an overview of study inclusion and exclusion criteria, the matrix for data robustness to determine potential sites to include, and our covariate selection, model building, and residual testing strategy. Results This study demonstrates a significant difference between hospital admissions and outcomes in urban versus urban-adjacent rural (UAR) and nonurban-adjacent rural (NAR) lines. Hospital admissions for UAR (OR 1.41, p< 0.001, 95% CI: 1.37 – 1.45) and NAR (OR 1.42, p< 0.001, 95% CI: 1.35 – 1.50) were significantly higher than their urban counterparts. Similar distributions were present for all-cause mortality for UAR (OR 1.39, p< 0.001, 95% CI: 1.30 – 1.49) and NAR (OR 1.38, p< 0.001, 95% CI: 1.22 – 1.55) compared to urban populations. These associations persisted despite adjustments for significant differences in BMI, Charlson Comorbidity index Score, gender, age, and the quarter of diagnosis for COVID-19. Baseline Characteristics Hospitalized COVID-19 Positive Population by Rurality Category, January 2020 – March 2021 Survival Curves in Hospitalized Patients Over 30 Days from Day of Admission This figure shows a survival plot of COVID-19 positive hospitalized patients in N3C by rural category (A), Charlson Comorbidity Index (B), Quarter of Diagnosis (C), and Age Group (D) from hospital admission through day 30. Events were censored at day 30 based on the incidence of death or transfer to hospice care. These four factors had the highest predictive power of the covariates evaluated in this study. Unadjusted and Adjusted Odds Ratios for Hospitalization and All-Cause Mortality by Rural Category, January 2020 – March 2021 This figure shows the adjusted and unadjusted odds ratios for being hospitalized or dying after hospitalization for the COVID-19 positive population in N3C. Risk is similar between adjusted and unadjusted models, suggesting a real impact of rurality on all-cause mortality. A shows the unadjusted odds ratios for admission to the hospital after a positive COVID-19 diagnosis for all N3C patients. B shows the unadjusted odds ratios for all-cause mortality at any point after hospitalization for COVID-19 positive patients. C shows the adjusted odds ratios for being admitted to the hospital after a positive COVID-19 diagnosis for all N3C patients. D shows the adjusted odds ratios for all-cause mortality for all-cause mortality at any point after hospitalization for COVID-19 positive patients. Adjusted models include adjustments for gender, race, ethnicity, BMI, age, Charlson Comorbidity Index (CCI) composite score, rurality, and quarter of diagnosis. The data provider is included as a random effect in all models. Conclusion In N3C, we found that hospitalizations and all-cause mortality were greater among rural populations when compared to urban populations after adjustment for several factors, including age and co-morbidities. This study also identified key demographic and clinical disparities among rural patients that require further investigation. Disclosures Sally L. Hodder, M.D., Gilead (Advisor or Review Panel member)Merck (Grant/Research Support, Advisor or Review Panel member)Viiv Healthcare (Grant/Research Support, Advisor or Review Panel member)

Background: Advancement of fluoroscopy-assisted procedures in the field of medicine has led to an increase in the frequency of their use among cardiologists, radiologists and surgeons. The personal health risk involved with radiation exposure is of concern and has come under the limelight in recent times. In addition to other consequences, radiation has been linked to cardiovascular disease, but its significance is not well established. Methods: Self-reported demographic, social, occupational, and medical data was collected from board-certified cardiologists via an electronic survey. Bivariate and multivariable logistic regression analyses were performed. Results: 1478 responses were collected from board-certified cardiologists; 85.4% were males, 79% were Caucasian and 66.1% were ≤65 yrs of age. 35.6% of respondents were interventional cardiologists and 16.4% were electrophysiologists. Of those who performed procedures, 92.2% wore lead apparel during all times of radiation exposure. Exposure hours, stratified by less or more than 20,000, correlated positively with the presence of hypertension, and remained significant when adjusted for common risk factors such as age, sex, race, DM, OSA, and alcohol/tobacco use (OR 1.63 CI 1.16 to 2.29, p = 0.005). Conclusion: This study captures self-reported data of just over 4% of cardiologists in the US, and demonstrates a positive correlation between hypertension and procedural radiation exposure hours even post-adjustment for traditional risk factors. As the use of fluoroscopy-assisted procedures continues to grow, further research is necessary to inform operators of the personal health risks of radiation exposure and drive progress in protective attire and risk mitigation strategies.