Ryu, Euijung

Background: The communities in which people live affect their health. We aimed to determine whether neighborhood characteristics are associated with outcomes in patients with atrial fibrillation (AF) in a Midwest community. Methods: Patients with incident AF from 2013-2017 from a 27-county region who survived >90 days after their diagnosis were identified (N=14,725). Patient addresses were linked to publicly-available data on population density (rural-urban commuting area; RUCA) and neighborhood socioeconomic status (area deprivation index; ADI). Patients were followed for ischemic stroke or transient ischemic attack (TIA), heart failure (HF), dementia/delirium, and all-cause death through 2021. Cox regression determined risk of outcomes by RUCA category (urban, micropolitan, small rural, isolated) and ADI quartile after adjustment for age, sex, race/ethnicity, and CHA2DS2-VASc score. Results: After excluding persons missing race/ethnicity (n=46), RUCA (n=1,465), and ADI (n=1,640), 13,233 patients with AF (mean age 73.8 years, 57% men) remained. After a median follow-up of 4.7 years, 5,989 patients died; 582 experienced a stroke/TIA, 4,122 (of 9,156 patients at risk) developed HF, and 1,758 (of 12,154 patients at risk) developed dementia/delirium. Persons living in isolated areas (HR 1.14, 95% CI 1.05-1.24) and the highest ADI quartile (HR 1.59, 95% CI 1.37-1.85) experienced the greatest risk of death (Figure). A trend toward an increased risk of HF was observed for the 2 highest ADI quartiles (p-trend <0.01), although the estimates did not reach statistical significance. The risk of other outcomes did not differ by RUCA or ADI category. Conclusion: Patients with AF residing in isolated areas and neighborhoods with greater area deprivation experience increased risks of death. Additional research is needed to better understand how neighborhood characteristics influence treatment, co-morbid conditions, and other outcomes in patients with AF.

Introduction: Nitrogen dioxide (NO 2 ) is known to be a trigger for asthma exacerbation. However, little is known about the role of seasonal variation in indoor and outdoor NO 2 levels in childhood asthma in a mixed rural-urban setting of North America. Methods: This prospective cohort study, as a feasibility study, included 62 families with children (5-17 years) that had diagnosed persistent asthma residing in Olmsted County, Minnesota. Indoor and outdoor NO 2 concentrations were measured using passive air samples over 2 weeks in winter and 2 weeks in summer. We assessed seasonal variation in NO 2 levels in urban and rural residential areas and the association with asthma control status collected from participants’ asthma diaries during the study period. Results: Outdoor NO 2 levels were lower (median: 2.4 parts per billion (ppb) in summer, 3.9 ppb in winter) than the Environmental Protection Agency (EPA) annual standard (53 ppb). In winter, a higher level of outdoor NO 2 was significantly associated with urban residential living area ( P = .014) and lower socioeconomic status (SES) ( P = .027). For both seasons, indoor NO 2 was significantly higher ( P < .05) in rural versus urban areas and in homes with gas versus electric stoves ( P < .05). Asthma control status was not associated with level of indoor or outdoor NO 2 in this cohort. Conclusions: NO 2 levels were low in this mixed rural-urban community and not associated with asthma control status in this small feasibility study. Further research with a larger sample size is warranted for defining a lower threshold of NO 2 concentration with health effect on asthma in mixed rural-urban settings.

Importance Little is known about the burden and outcomes of respiratory syncytial virus (RSV)–positive acute respiratory infection (ARI) in community-dwelling older adults. Objective To assess the incidence of RSV-positive ARI before and during the COVID-19 pandemic, and to assess outcomes for RSV-positive ARI in older adults. Design, Setting, and Participants This was a community-based cohort study of adults residing in southeast Minnesota that followed up with 2325 adults aged 50 years or older for 2 RSV seasons (2019-2021) to assess the incidence of RSV-positive ARI. The study assessed outcomes at 2 to 4 weeks, 6 to 7 months, and 12 to 13 months after RSV-positive ARI. Exposure RSV-positive and -negative ARI. Main Outcomes and Measures RSV status was the main study outcome. Incidence and attack rates of RSV-positive ARI were calculated during each RSV season, including before (October 2019 to April 2020) and during (October 2020 to April 2021) COVID-19 pandemic, and further calculated during non-RSV season (May to September 2021) for assessing impact of COVID-19. The self-reported quality of life (QOL) by Short-Form Health Survey-36 (SF-36) and physical functional measures (eg, 6-minute walk and spirometry) at each time point was assessed. Results In this study of 2325 participants, the median (range) age of study participants was 67 (50-98) years, 1380 (59%) were female, and 2240 (96%) were non-Hispanic White individuals. The prepandemic incidence rate of RSV-positive ARI was 48.6 (95% CI, 36.9-62.9) per 1000 person-years with a 2.50% (95% CI, 1.90%-3.21%) attack rate. No RSV-positive ARI case was identified during the COVID-19 pandemic RSV season. Incidence of 10.2 (95% CI, 4.1-21.1) per 1000 person-years and attack rate of 0.42%; (95% CI, 0.17%-0.86%) were observed during the summer of 2021. Based on prepandemic RSV season results, participants with RSV-positive ARI (vs matched RSV-negative ARI) reported significantly lower QOL adjusted mean difference (limitations due to physical health, −16.7 [95% CI, −31.8 to −1.8]; fatigue, −8.4 [95% CI, −14.3 to −2.4]; and difficulty in social functioning, −11.9 [95% CI, −19.8 to −4.0] within 2 to 4 weeks after RSV-positive ARI [ie, short-term outcome]). Compared with participants with RSV-negative ARI, those with RSV-positive ARI also had lower QOL (fatigue: −4.0 [95% CI, −8.5 to −1.3]; difficulty in social functioning, −5.8 [95% CI, −10.3 to −1.3]; and limitation due to emotional problem, −7.0 [95% CI, −12.7 to −1.3] at 6 to 7 months after RSV-positive ARI [intermediate-term outcome]; fatigue, −4.4 [95% CI, −7.3 to −1.5]; difficulty in social functioning, −5.2 [95% CI, −8.7 to −1.7] and limitation due to emotional problem, −5.7 [95% CI, −10.7 to −0.6] at 12-13 months after RSV-positive ARI [ie, long-term outcomes]) independent of age, sex, race and/or ethnicity, socioeconomic status, and high-risk comorbidities. Conclusions and Relevance In this cohort study, the burden of RSV-positive ARI in older adults during the pre-COVID-19 period was substantial. After a reduction of RSV-positive ARI incidence from October 2020 to April 2021, RSV-positive ARI re-emerged during the summer of 2021. RSV-positive ARI was associated with significant long-term lower QOL beyond the short-term lower QOL in older adults.

Abstract Context Metformin is the first-line drug for treating diabetes but has a high failure rate. Objective To identify demographic and clinical factors available in the electronic health record (EHR) that predict metformin failure. Methods A cohort of patients with at least 1 abnormal diabetes screening test that initiated metformin was identified at 3 sites (Arizona, Mississippi, and Minnesota). We identified 22 047 metformin initiators (48% female, mean age of 57 ± 14 years) including 2141 African Americans, 440 Asians, 962 Other/Multiracial, 1539 Hispanics, and 16 764 non-Hispanic White people. We defined metformin failure as either the lack of a target glycated hemoglobin (HbA1c) (&lt;7%) within 18 months of index or the start of dual therapy. We used tree-based extreme gradient boosting (XGBoost) models to assess overall risk prediction performance and relative contribution of individual factors when using EHR data for risk of metformin failure. Results In this large diverse population, we observed a high rate of metformin failure (43%). The XGBoost model that included baseline HbA1c, age, sex, and race/ethnicity corresponded to high discrimination performance (C-index of 0.731; 95% CI 0.722, 0.740) for risk of metformin failure. Baseline HbA1c corresponded to the largest feature performance with higher levels associated with metformin failure. The addition of other clinical factors improved model performance (0.745; 95% CI 0.737, 0.754, P &lt; .0001). Conclusion Baseline HbA1c was the strongest predictor of metformin failure and additional factors substantially improved performance suggesting that routinely available clinical data could be used to identify patients at high risk of metformin failure who might benefit from closer monitoring and earlier treatment intensification.

AbstractBackground and AimsInfluenza is a challenging infectious illness for older adults. It is not completely clear whether influenza is associated with frailty or functional decline. We sought to determine the association between incident influenza infection and frailty and prefrailty in community patients over 50 years of age. We also investigated the association between influenza vaccination and frailty and prefrailty as a secondary aim.MethodsThis was a prospective community cohort study from October 2019 to November 2020 in participants over 50 years. The primary outcome was the development of frailty as defined by three of five frailty criteria (slow gait speed, low grip strength, 5% weight loss, low energy, and low physical functioning). The primary predictor was a positive polymerase chain reaction (PCR) for influenza infection. Influenza vaccination was based on electronic health record reviewing 1 year before enrollment. We reported the relationship between influenza and frailty by calculating odds ratios (OR) with 95% confidence intervals (CI) after adjustment for age, sex, socioeconomic status, Charlson Comorbidity Index (CCI), influenza vaccine, and previous self‐rated frailty from multinomial logistic regression model comparing frail and prefrail to nonfrail subjects.ResultsIn 1135 participants, the median age was 67 years (interquartile range 60−74), with 41% men. Eighty‐one participants had PCR‐confirmed influenza (7.1%). Frailty was not associated with influenza, with an OR of 0.50 (95% CI 0.17−1.43) for frail participants compared to nonfrail participants. Influenza vaccination is associated with frailty, with an OR of 1.69 (95% CI 1.09−2.63) for frail compared to nonfrail. Frailty was associated with a higher CCI with an OR of 1.52 (95% CI 1.31−1.76).ConclusionWe did not find a relationship between influenza infection and frailty. We found higher vaccination rates in participants with frailty compared to nonfrail participants While influenza was not associated with frailty, future work may involve longer follow‐up.

Abstract Objective Artificial intelligence (AI) models may propagate harmful biases in performance and hence negatively affect the underserved. We aimed to assess the degree to which data quality of electronic health records (EHRs) affected by inequities related to low socioeconomic status (SES), results in differential performance of AI models across SES. Materials and Methods This study utilized existing machine learning models for predicting asthma exacerbation in children with asthma. We compared balanced error rate (BER) against different SES levels measured by HOUsing-based SocioEconomic Status measure (HOUSES) index. As a possible mechanism for differential performance, we also compared incompleteness of EHR information relevant to asthma care by SES. Results Asthmatic children with lower SES had larger BER than those with higher SES (eg, ratio = 1.35 for HOUSES Q1 vs Q2–Q4) and had a higher proportion of missing information relevant to asthma care (eg, 41% vs 24% for missing asthma severity and 12% vs 9.8% for undiagnosed asthma despite meeting asthma criteria). Discussion Our study suggests that lower SES is associated with worse predictive model performance. It also highlights the potential role of incomplete EHR data in this differential performance and suggests a way to mitigate this bias. Conclusion The HOUSES index allows AI researchers to assess bias in predictive model performance by SES. Although our case study was based on a small sample size and a single-site study, the study results highlight a potential strategy for identifying bias by using an innovative SES measure.

Background Inhaled corticosteroids (ICSs) are important in asthma management, but there are concerns regarding associated risk of pneumonia. While studies in asthmatic adults have shown inconsistent results, this risk in asthmatic children is unclear. Objective Our aim was to determine the association of ICS use with pneumonia risk in asthmatic children. Methods A nested case-control study was performed in the Mayo Clinic Birth Cohort. Asthmatic children (<18 years) with a physician diagnosis of asthma were identified from electronic medical records of children born at Mayo Clinic from 1997 to 2016 and followed until 31 December 2017. Pneumonia cases defined by Infectious Disease Society of America were 1:1 matched with controls without pneumonia by age, sex and asthma index date. Exposure was defined as ICS prescription at least 90 days prior to pneumonia. Associations of ICS use, type and dose (low, medium and high) with pneumonia risk were analysed using conditional logistic regression. Results Of the 2108 asthmatic children eligible for the study (70% mild intermittent and 30% persistent asthma), 312 children developed pneumonia during the study period. ICS use overall was not associated with risk of pneumonia (adjusted OR: 0.94, 95% CI: 0.62 to 1.41). Poorly controlled asthma was significantly associated with the risk of pneumonia (OR: 2.03, 95% CI: 1.35 to 3.05; p<0.001). No ICS type or dose was associated with risk of pneumonia. Conclusion ICS use in asthmatic children was not associated with risk of pneumonia but poorly controlled asthma was. Future asthma studies may need to include pneumonia as a potential outcome of asthma management.

Abstract Work history is associated with long term health outcomes We hypothesize that characteristics of the first work experience, such as age at first job and length of work (hereafter job) are associated with future risk of hospitalization. We further hypothesize that the length of work will be associated with hospitalization. We conducted a survey of adults &gt;60 years using a nested case-control approach within the Mayo Clinic Biobank. We collected job related variables including age at job start, reason for ending, and length of time. To test associations between each variable and hospitalization, we used age and gender adjusted logistic regression models. Our study included 4,024 subjects: 1,801 cases and 2223 controls. The mean age at time of match was 77.3 years (SD 7.2 years) with 49.2% males. Older age at the first full-time job was associated with lower chance of hospitalization later in life (OR=0.81 [0.67, 0.97] for those who started the job over 22 compared to those started at 18 or less). Cases were more likely to have stopped working because of illness (OR=2.04 [95% CI 1.29,3.27]). Cases were less likely to have stopped working because of retirement (OR=0.82 [95% CI: 0.72, 0.93]). We found cases were employed with a slightly shorter time (20.5 yrs. (SD 16.6)) compared to controls (21.8 yrs. (SD 16.3)) (p=0.005). Cases started work earlier and stopped work more frequently because of illness/disability compared to controls. This could reflect educational attainment in controls. This study highlights work history as potential predictor of future hospitalization.

Abstract Objective Social determinants of health (SDoH) are nonclinical dispositions that impact patient health risks and clinical outcomes. Leveraging SDoH in clinical decision-making can potentially improve diagnosis, treatment planning, and patient outcomes. Despite increased interest in capturing SDoH in electronic health records (EHRs), such information is typically locked in unstructured clinical notes. Natural language processing (NLP) is the key technology to extract SDoH information from clinical text and expand its utility in patient care and research. This article presents a systematic review of the state-of-the-art NLP approaches and tools that focus on identifying and extracting SDoH data from unstructured clinical text in EHRs. Materials and Methods A broad literature search was conducted in February 2021 using 3 scholarly databases (ACL Anthology, PubMed, and Scopus) following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 6402 publications were initially identified, and after applying the study inclusion criteria, 82 publications were selected for the final review. Results Smoking status (n = 27), substance use (n = 21), homelessness (n = 20), and alcohol use (n = 15) are the most frequently studied SDoH categories. Homelessness (n = 7) and other less-studied SDoH (eg, education, financial problems, social isolation and support, family problems) are mostly identified using rule-based approaches. In contrast, machine learning approaches are popular for identifying smoking status (n = 13), substance use (n = 9), and alcohol use (n = 9). Conclusion NLP offers significant potential to extract SDoH data from narrative clinical notes, which in turn can aid in the development of screening tools, risk prediction models, and clinical decision support systems.