Disparities in Influenza, Pneumococcal, COVID-19 Vaccine Coverage in High-Risk Adults 19 to 64 Years of Age in Southeastern Minnesota, 2010-2021.

BACKGROUND: Despite effective vaccines against influenza, pneumococcus, and COVID-19, uptake has been suboptimal. RESEARCH QUESTION: Although disparities in vaccination by race and ethnicity have been observed, what is the role of other sociodemographic in US vaccine uptake? STUDY DESIGN AND METHODS: We conducted a population-based study using the Rochester Epidemiology Project (REP), a comprehensive medical records linkage system, to assess effects of sociodemographic factors including race, ethnicity, individual-level socioeconomic status (SES) via the housing-based socioeconomic status index, education, population density (urban or nonurban), and marital status with uptake of influenza, pneumococcal, and COVID-19 vaccination in high-risk adults. Adults at high risk of invasive pneumococcal disease residing in four counties in southeastern Minnesota who were 19 to 64 years of age were identified. Vaccination data were obtained from the Minnesota Immunization Information Connection and REP from January 1, 2010, through December 31, 2021. RESULTS: We identified 45,755 residents. Most were White (82%), non-Hispanic (94%), married (56%), and living in an urban setting (81%), with three-quarters obtaining at least some college education (74%). Although 45.1% were up-to-date on pneumococcal vaccines, 60.1% had completed the primary COVID-19 series. For influenza and COVID-19, higher SES, living in an urban setting, older age, and higher education positively correlated with vaccination. Magnitude of differences in race, education, and SES widened with booster vaccines. INTERPRETATION: This high-risk population is undervaccinated against preventable respiratory diseases, especially influenza and pneumococcus. Although national data reported improvement of disparities in COVID-19 vaccination uptake observed early in the pandemic, our data demonstrated gaps related to race, education level, SES, and age that widened with booster vaccines. Communities with high social vulnerabilities often show increased risk of severe disease outcomes, yet demonstrate lower uptake of preventive services. This highlights the need to understand better vaccine compliance and access in rural, lower SES, less-educated, Black, Hispanic, and younger populations, each of which were associated independently with decreased vaccination.

Clinical Needs Assessment of a Machine Learning-Based Asthma Management Tool: User-Centered Design Approach.

BACKGROUND: Personalized asthma management depends on a clinician's ability to efficiently review patient's data and make timely clinical decisions. Unfortunately, efficient and effective review of these data is impeded by the varied format, location, and workflow of data acquisition, storage, and processing in the electronic health record. While machine learning (ML) and clinical decision support tools are well-positioned as potential solutions, the translation of such frameworks requires that barriers to implementation be addressed in the formative research stages. OBJECTIVE: We aimed to use a structured user-centered design approach (double-diamond design framework) to (1) qualitatively explore clinicians' experience with the current asthma management system, (2) identify user requirements to improve algorithm explainability and Asthma Guidance and Prediction System prototype, and (3) identify potential barriers to ML-based clinical decision support system use. METHODS: At the "discovery" phase, we first shadowed to understand the practice context. Then, semistructured interviews were conducted digitally with 14 clinicians who encountered pediatric asthma patients at 2 outpatient facilities. Participants were asked about their current difficulties in gathering information for patients with pediatric asthma, their expectations of ideal workflows and tools, and suggestions on user-centered interfaces and features. At the "define" phase, a synthesis analysis was conducted to converge key results from interviewees' insights into themes, eventually forming critical "how might we" research questions to guide model development and implementation. RESULTS: We identified user requirements and potential barriers associated with three overarching themes: (1) usability and workflow aspects of the ML system, (2) user expectations and algorithm explainability, and (3) barriers to implementation in context. Even though the responsibilities and workflows vary among different roles, the core asthma-related information and functions they requested were highly cohesive, which allows for a shared information view of the tool. Clinicians hope to perceive the usability of the model with the ability to note patients' high risks and take proactive actions to manage asthma efficiently and effectively. For optimal ML algorithm explainability, requirements included documentation to support the validity of algorithm development and output logic, and a request for increased transparency to build trust and validate how the algorithm arrived at the decision. Acceptability, adoption, and sustainability of the asthma management tool are implementation outcomes that are reliant on the proper design and training as suggested by participants. CONCLUSIONS: As part of our comprehensive informatics-based process centered on clinical usability, we approach the problem using a theoretical framework grounded in user experience research leveraging semistructured interviews. Our focus on meeting the needs of the practice with ML technology is emphasized by a user-centered approach to clinician engagement through upstream technology design.

Rural Health Disparities in Allergy, Asthma, and Immunologic Diseases: The Current State and Future Direction for Clinical Care and Research.

Rural health disparities are well documented and continue to jeopardize the long-term health and wellness for the millions of individuals who live in rural America. The disparities observed between urban and rural residents encompass numerous morbidity and mortality measures for several chronic diseases and have been referred to as the "rural mortality penalty." Although the unmet health needs of rural communities are widely acknowledged, little is known about rural health disparities in allergies, asthma, and immunologic diseases. Furthermore, the intersection between rural health disparities and social determinants of health has not been fully explored. To achieve a more complete understanding of the factors that perpetuate rural health disparities, greater research efforts followed by improved practice and policy are needed that account for the complex social context within rural communities rather than a general comparison between urban and rural environments or focusing on biomedical factors. Moreover, research efforts must prioritize community inclusion throughout rural areas through meaningful engagement of stakeholders in both clinical care and research. In this review, we examine the scope of health disparities in the rural United States and the impact of social determinants of health. We then detail the current state of rural health disparities in the field of allergy, asthma, and immunology. To close, we offer future considerations to address knowledge gaps and unmet needs for both clinical care and research in addressing rural health disparities.

Application of Innovative Subject Recruitment System for Batch Enrollment: A Pilot Study.

INTRODUCTION: Using a digital process that leverages electronic health records (EHRs) can ease many of the challenges presented by the traditional enrollment process for clinical trials. We tested if automated batch enrollment using a technology-enabled subject recruitment system (TESRS) enhances recruitment while preserving representation of research subjects for the study population in our study setting. METHODS: An ongoing community-based prospective adult cohort study was used to randomize 600 subjects who were eligible by age and residential address to TESRS (n = 300) and standard mailing method (n = 300), respectively, for 3 months. Then, TESRS was initiated and included automatic identification of patients' preference for being contacted (online patient portal vs postal mail) from EHRs and automatic sending out of invitation letters followed by completion of a short online survey for checking eligibility and the digital consent process if eligible. We compared (1) median time to consent from invitation sent out per subject and total subjects recruited after a 3-month recruitment period, (2) the estimated study staff's time, and (3) representation of sociodemographic characteristics (e.g., age, sex, race, SES measured by HOUSES index, and rural residence) between subjects recruited via TESRS and those via traditional mailing methods. RESULTS: Median age of randomized subjects (n = 600) was 63 years with 52.0% female and 89.2% non-Hispanic White. Over a 3-month period, results showed consent rate via TESRS was 13% (39/297) similar to 11% (31/295) via standard mailing. However, recruitment was significantly faster with the TESRS approach (median 7 vs 26 days) given the study staff's effort. Study staff's time saved by using TESRS compared to standard mailing approach was estimated at 40 min per subject (equivalent to 200 h for 300 subjects). No significant differences in characteristics of research subjects from the study population were found. CONCLUSION: Our study demonstrated the utility of TESRS as a subject recruitment digital technology which significantly enhanced the recruitment effort while reducing the study staff burden of recruitment while maintaining the consistency of characteristics of recruited subjects. The strategy and support for implementing and testing TESRS in other study settings should be considered.

Rural and urban residents' attitudes and preferences toward COVID-19 prevention behaviors in a midwestern community.

Rural populations are more vulnerable to the impacts of COVID-19 compared to their urban counterparts as they are more likely to be older, uninsured, to have more underlying medical conditions, and live further from medical care facilities. We engaged the Southeastern MN (SEMN) community (N = 7,781, 51% rural) to conduct a survey of motivators and barriers to masking to prevent COVID-19. We also assessed preferences for types of and modalities to receive education/intervention, exploring both individual and environmental factors primarily consistent with Social Cognitive Theory. Our results indicated rural compared to urban residents performed fewer COVID-19 prevention behaviors (e.g. 62% rural vs. 77% urban residents reported wearing a mask all of the time in public, p<0.001), had more negative outcome expectations for wearing a mask (e.g. 50% rural vs. 66% urban residents thought wearing a mask would help businesses stay open, p<0.001), more concerns about wearing a mask (e.g. 23% rural vs. 14% urban were very concerned about being 'too hot', p<0.001) and lower levels of self-efficacy for masking (e.g. 13.9±3.4 vs. 14.9±2.8, p<0.001). It appears that masking has not become a social norm in rural SEMN, with almost 50% (vs. 24% in urban residents) disagreeing with the expectation 'others in my community will wear a mask to stop the spread of Coronavirus'. Except for people (both rural and urban) who reported not being at all willing to wear a mask (7%), all others expressed interest in future education/interventions to help reduce masking barriers that utilized email and social media for delivery. Creative public health messaging consistent with SCT tailored to rural culture and norms is needed, using emails and social media with pictures and videos from role models they trust, and emphasizing education about when masks are necessary.

Paired Indoor and Outdoor Nitrogen Dioxide Associated With Childhood Asthma Outcomes in a Mixed Rural-Urban Setting: A Feasibility Study.

INTRODUCTION: Nitrogen dioxide (NO2) is known to be a trigger for asthma exacerbation. However, little is known about the role of seasonal variation in indoor and outdoor NO2 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 NO2 concentrations were measured using passive air samples over 2 weeks in winter and 2 weeks in summer. We assessed seasonal variation in NO2 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 NO2 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 NO2 was significantly associated with urban residential living area (P = .014) and lower socioeconomic status (SES) (P = .027). For both seasons, indoor NO2 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 NO2 in this cohort. CONCLUSIONS: NO2 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 NO2 concentration with health effect on asthma in mixed rural-urban settings.

The Role of Individual-Level Socioeconomic Status on Nursing Home Placement Accounting for Neighborhood Characteristics.

OBJECTIVE: Independent living is desirable for many older adults. Although several factors such as physical and cognitive functions are important predictors for nursing home placement (NHP), it is also reported that socioeconomic status (SES) affects the risk of NHP. In this study, we aimed to examine whether an individual-level measure of SES is associated with the risk of NHP after accounting for neighborhood characteristics. DESIGN: A population-based study (Olmsted County, Minnesota, USA). SETTING AND PARTICIPANTS: Older adults (age 65+ years) with no prior history of NHP. METHODS: Electronic health records (EHR) were used to identify individuals with any NHP between April 1, 2012 (baseline date) and April 30, 2019. Association between the (HOUsing-based index of SocioEconomic Status (HOUSES) index, an individual-level SES measure based on housing characteristics of current residence, and risk of NHP was tested using random effects Cox proportional hazard model adjusting for area deprivation index (ADI), an aggregated SES measure that captures neighborhood characteristics, and other pertinent confounders such as age and chronic disease burden. RESULTS: Among 15,031 older adults, 3341 (22.2%) experienced NHP during follow-up period (median: 7.1 years). At baseline date, median age was 73 years old with 55% female persons, 91% non-Hispanic Whites, and median number of chronic conditions of 4. Accounting for pertinent confounders, the HOUSES index was strongly associated with risk of NHP (hazard ratio 1.89; 95% confidence interval 1.66‒2.15 for comparing the lowest vs highest quartiles), which was not influenced by further accounting for ADI. CONCLUSIONS AND IMPLICATIONS: This study demonstrates that an individual-level SES measure capturing current individual-specific socioeconomic circumstances plays a significant role for predicting NHP independent of neighborhood characteristics where they reside. This study suggests that older adults who are at higher risk of NHP can be identified by utilizing the HOUSES index and potential individual-level intervention strategies can be applied to reduce the risk for those with higher risk.

Disparities in vaccination rates in solid organ transplant patients.

INTRODUCTION: Vaccinations against preventable respiratory infections such as Streptococcus pneumoniae and influenza are important in immunosuppressed solid organ transplant (SOT) recipients. Little is known about the role of age, race, ethnicity, sex, and sociodemographic factors including rurality, or socioeconomic status (SES) associated with vaccine uptake in this population. METHODS: We conducted a population-based study using the Rochester Epidemiology Project, a medical records linkage system, to assess socioeconomic and demographic factors associated with influenza and pneumococcal vaccination rates among adult recipients of solid organ transplantation (aged 19-64 years) living in four counties in southeastern Minnesota. Vaccination data were obtained from the Minnesota Immunization Information Connection from June 1, 2010 to June 30, 2020. Vaccination rate was assessed with Poisson and logistic regression models. RESULTS: A total of 468 SOT recipients were identified with an overall vaccination rate of 57%-63% for influenza and 56% for pneumococcal vaccines. As expected, vaccination for pneumococcal vaccine positively correlated with influenza vaccination. Rural patients had decreased vaccination in both compared to urban patients, even after adjusting for age, sex, race, ethnicity, and SES. Although the population was mostly White and non-Hispanic, neither vaccination differed by race or ethnicity, but influenza vaccination did by SES. Among organ transplant groups, liver and lung recipients were least vaccinated for influenza, and heart recipients were least up-to-date on pneumococcal vaccines. CONCLUSIONS: Rates of vaccination were below national goals. Rurality was associated with undervaccination. Further investigation is needed to understand and address barriers to vaccination among transplant recipients.

Incidence of Respiratory Syncytial Virus Infection in Older Adults Before and During the COVID-19 Pandemic.

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.

Rates of Asymptomatic COVID-19 Infection and Associated Factors in Olmsted County, Minnesota, in the Prevaccination Era.

Objective: To estimate rates and identify factors associated with asymptomatic COVID-19 in the population of Olmsted County during the prevaccination era. Patients and Methods: We screened first responders (n=191) and Olmsted County employees (n=564) for antibodies to SARS-CoV-2 from November 1, 2020 to February 28, 2021 to estimate seroprevalence and asymptomatic infection. Second, we retrieved all polymerase chain reaction (PCR)-confirmed COVID-19 diagnoses in Olmsted County from March 2020 through January 2021, abstracted symptom information, estimated rates of asymptomatic infection and examined related factors. Results: Twenty (10.5%; 95% CI, 6.9%-15.6%) first responders and 38 (6.7%; 95% CI, 5.0%-9.1%) county employees had positive antibodies; an additional 5 (2.6%) and 10 (1.8%) had prior positive PCR tests per self-report or medical record, but no antibodies detected. Of persons with symptom information, 4 of 20 (20%; 95% CI, 3.0%-37.0%) first responders and 10 of 39 (26%; 95% CI, 12.6%-40.0%) county employees were asymptomatic. Of 6020 positive PCR tests in Olmsted County with symptom information between March 1, 2020, and January 31, 2021, 6% (n=385; 95% CI, 5.8%-7.1%) were asymptomatic. Factors associated with asymptomatic disease included age (0-18 years [odds ratio {OR}, 2.3; 95% CI, 1.7-3.1] and >65 years [OR, 1.40; 95% CI, 1.0-2.0] compared with ages 19-44 years), body mass index (overweight [OR, 0.58; 95% CI, 0.44-0.77] or obese [OR, 0.48; 95% CI, 0.57-0.62] compared with normal or underweight) and tests after November 20, 2020 ([OR, 1.35; 95% CI, 1.13-1.71] compared with prior dates). Conclusion: Asymptomatic rates in Olmsted County before COVID-19 vaccine rollout ranged from 6% to 25%, and younger age, normal weight, and later tests dates were associated with asymptomatic infection.

A Technical Performance Study and Proposed Systematic and Comprehensive Evaluation of an ML-based CDS Solution for Pediatric Asthma.

Achieving optimal care for pediatric asthma patients depends on giving clinicians efficient access to pertinent patient information. Unfortunately, adherence to guidelines or best practices has shown to be challenging, as relevant information is often scattered throughout the patient record in both structured data and unstructured clinical notes. Furthermore, in the absence of supporting tools, the onus of consolidating this information generally falls upon the clinician. In this study, we propose a machine learning-based clinical decision support (CDS) system focused on pediatric asthma care to alleviate some of this burden. This framework aims to incorporate a machine learning model capable of predicting asthma exacerbation risk into the clinical workflow, emphasizing contextual data, supporting information, and model transparency and explainability. We show that this asthma exacerbation model is capable of predicting exacerbation with an 0.8 AUC-ROC. This model, paired with a comprehensive informatics-based process centered on clinical usability, emphasizes our focus on meeting the needs of the clinical practice with machine learning technology.

Role of geographic risk factors and social determinants of health in COVID-19 epidemiology: Longitudinal geospatial analysis in a midwest rural region.

Background: Studies examining the role of geographic factors in coronavirus disease-2019 (COVID-19) epidemiology among rural populations are lacking. Methods: Our study is a population-based longitudinal study based on rural residents in four southeast Minnesota counties from March through October 2020. We used a kernel density estimation approach to identify hotspots for COVID-19 cases. Temporal trends of cases and testing were examined by generating a series of hotspot maps during the study period. Household/individual-level socioeconomic status (SES) was measured using the HOUSES index and examined for association between identified hotspots and SES. Results: During the study period, 24,243 of 90,975 residents (26.6%) were tested for COVID-19 at least once; 1498 (6.2%) of these tested positive. Compared to other rural residents, hotspot residents were overall younger (median age: 40.5 vs 43.2), more likely to be minorities (10.7% vs 9.7%), and of higher SES (lowest HOUSES [SES] quadrant: 14.6% vs 18.7%). Hotspots accounted for 30.1% of cases (14.5% of population) for rural cities and 60.8% of cases (27.1% of population) for townships. Lower SES and minority households were primarily affected early in the pandemic and higher SES and non-minority households affected later. Conclusion: In rural areas of these four counties in Minnesota, geographic factors (hotspots) play a significant role in the overall burden of COVID-19 with associated racial/ethnic and SES disparities, of which pattern differed by the timing of the pandemic (earlier in pandemic vs later). The study results could more precisely guide community outreach efforts (e.g., public health education, testing/tracing, and vaccine roll out) to those residing in hotspots.

Assessing socioeconomic bias in machine learning algorithms in health care: a case study of the HOUSES index.

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.

Atopic asthma as a potentially significant but unrecognized risk factor for Kawasaki disease in children.

OBJECTIVES: Childhood asthma is known to be associated with risks of both respiratory and non-respiratory infections. Little is known about the relationship between asthma and the risk of Kawasaki disease (KD). We assessed associations of asthma status and asthma phenotype (e.g. atopic asthma) with KD. METHODS: We performed a population-based retrospective case-control study, using KD cases between January 1, 1979, and December 31, 2016, and two matched controls per case. KD cases were defined by the American Heart Association diagnostic criteria. Asthma status prior to KD (or control) index dates was ascertained by the two asthma criteria, Predetermined Asthma Criteria (PAC) and Asthma Predictive Index (API, a surrogate phenotype of atopic asthma). We assessed whether 4 phenotypes (both PAC + and API+; PAC + only; API + only, and non-asthmatics) were associated with KD. RESULTS: There were 124 KD cases during the study period. The group having both PAC + and API + was significantly associated with the increased odds of KD, compared to non-asthmatics (odds ratio [OR] 4.3; 95% CI: 1.3 - 14.3). While asthma defined by PAC was not associated with KD, asthma defined by PAC positive with eosinophilia (≥4%) was significantly associated with the increased odds of KD (OR: 6.7; 95% CI: 1.6 - 28.6) compared to non-asthmatics. Asthma status defined by API was associated with KD (OR = 4.7; 95% CI: 1.4-15.1). CONCLUSIONS: Atopic asthma may be associated with increased odds of KD. Further prospective studies are needed to determine biological mechanisms underlying the association between atopic asthma and increased odds of KD.

Participation of rural patients in clinical trials at a multisite academic medical center.

OBJECTIVE: Clinical trials, which are mainly conducted in urban medical centers, may be less accessible to rural residents. Our aims were to assess participation and the factors associated with participation of rural residents in clinical trials. METHODS: Using geocoding, the residential address of participants enrolled into clinical trials at Mayo Clinic locations in Arizona, Florida, and the Midwest between January 1, 2016, and December 31, 2017, was categorized as urban or rural. The distance travelled by participants and trial characteristics was compared between urban and rural participants. Ordinal logistic regression analyses were used to evaluate whether study location and risks were associated with rural participation in trials. RESULTS: Among 292 trials, including 136 (47%) cancer trials, there were 2313 participants. Of these, 731 (32%) were rural participants, which is greater than the rural population in these 9 states (19%, P < 0.001). Compared to urban participants, rural participants were older (65 ± 12 years vs 64 ± 12 years, P = 0.004) and travelled further to the medical center (103 ± 104 vs 68 ± 88 miles, P < 0.001). The proportion of urban and rural participants who were remunerated was comparable. In the multivariable analysis, the proportion of rural participants was lower (P < 0.001) in Arizona (10%) and Florida (18%) than the Midwest (38%) but not significantly associated with the study-related risks. CONCLUSIONS: Approximately one in three clinical trial participants were rural residents versus one in five in the population. Rural residents travelled further to access clinical trials. The study-associated risks were not associated with the distribution of rural and urban participants in trials.

Establishing an expert consensus for the operational definitions of asthma-associated infectious and inflammatory multimorbidities for computational algorithms through a modified Delphi technique.

BACKGROUND: A subgroup of patients with asthma has been reported to have an increased risk for asthma-associated infectious and inflammatory multimorbidities (AIMs). To systematically investigate the association of asthma with AIMs using a large patient cohort, it is desired to leverage a broad range of electronic health record (EHR) data sources to automatically identify AIMs accurately and efficiently. METHODS: We established an expert consensus for an operational definition for each AIM from EHR through a modified Delphi technique. A series of questions about the operational definition of 19 AIMS (11 infectious diseases and 8 inflammatory diseases) was generated by a core team of experts who considered feasibility, balance between sensitivity and specificity, and generalizability. Eight internal and 5 external expert panelists were invited to individually complete a series of online questionnaires and provide judgement and feedback throughout three sequential internal rounds and two external rounds. Panelists' responses were collected, descriptive statistics tabulated, and results reported back to the entire group. Following each round the core team of experts made iterative edits to the operational definitions until a moderate (≥ 60%) or strong (≥ 80%) level of consensus among the panel was achieved. RESULTS: Response rates for each Delphi round were 100% in all 5 rounds with the achievement of the following consensus levels: (1) Internal panel consensus: 100% for 8 definitions, 88% for 10 definitions, and 75% for 1 definition, (2) External panel consensus: 100% for 12 definitions and 80% for 7 definitions. CONCLUSIONS: The final operational definitions of AIMs established through a modified Delphi technique can serve as a foundation for developing computational algorithms to automatically identify AIMs from EHRs to enable large scale research studies on patient's multimorbidities associated with asthma.

Risk, Mechanisms and Implications of Asthma-Associated Infectious and Inflammatory Multimorbidities (AIMs) among Individuals With Asthma: a Systematic Review and a Case Study.

Our prior work and the work of others have demonstrated that asthma increases the risk of a broad range of both respiratory (e.g., pneumonia and pertussis) and non-respiratory (e.g., zoster and appendicitis) infectious diseases as well as inflammatory diseases (e.g., celiac disease and myocardial infarction [MI]), suggesting the systemic disease nature of asthma and its impact beyond the airways. We call these conditions asthma-associated infectious and inflammatory multimorbidities (AIMs). At present, little is known about why some people with asthma are at high-risk of AIMs, and others are not, to the extent to which controlling asthma reduces the risk of AIMs and which specific therapies mitigate the risk of AIMs. These questions represent a significant knowledge gap in asthma research and unmet needs in asthma care, because there are no guidelines addressing the identification and management of AIMs. This is a systematic review on the association of asthma with the risk of AIMs and a case study to highlight that 1) AIMs are relatively under-recognized conditions, but pose major health threats to people with asthma; 2) AIMs provide insights into immunological and clinical features of asthma as a systemic inflammatory disease beyond a solely chronic airway disease; and 3) it is time to recognize AIMs as a distinctive asthma phenotype in order to advance asthma research and improve asthma care. An improved understanding of AIMs and their underlying mechanisms will bring valuable and new perspectives improving the practice, research, and public health related to asthma.

Factors associated with willingness to wear a mask to prevent the spread of COVID-19 in a Midwestern Community.

OBJECTIVE: To identify motivators and barriers to wearing a mask to prevent COVID-19. PARTICIPANTS AND METHODS: An anonymous, online survey of adults from Southeastern Minnesota conducted August 2020. We assessed willingness to wear a mask and its associations with socio-demographics, COVID-19-related factors and prevention behaviors using multivariable ordinal logistic regression. RESULTS: Of 7,786 respondents (78% women, 51% rural), 9% reported 'not at all willing', 27% 'willing', and 64% 'very willing' to wear a mask. Factors independently associated with willingness to wear a mask were: urban residence (OR = 1.23, 95% CI 1.05-1.44, p = 0.009); college degree or greater (OR 1.42, CI 1.05-1.93, p = 0.025); age (18-29 years OR 1.29, CI 01.02-1.64, p = 0.038; 30-39 OR = 1.37, CI 1.12-1.69, p = 0.003; 60-69 OR = 1.44, CI 1.09-1.91, p = 0.011; 70-89 OR 2.09, CI 1.32-3.37, p = 0.002; 40-49 reference group); and (all p < 0.001) democratic party affiliation (OR 1.79, CI 1.40-2.29), correct COVID-19 knowledge (OR 1.50, CI 1.28-1.75), 5 + COVID-19 prevention behaviors (OR 2.74, CI 1.98-3.81), positive perceived impacts for wearing a mask (OR 1.55, 1.52-1.59), perceived COVID-19 severity (OR 2.1, CI 1.44-3.1), and greater stress (OR 1.03, CI 1.02-1.04), and trust in the Centers for Disease Control (CDC) (OR 1.78, CI 1.45 -2.19). CONCLUSION: Results from this sample of SEMN residents suggest interventions to enhance COVID-19 knowledge, positive expectations for mask wearing, and trust in the CDC are warranted. Research is needed to understand cultural and other barriers and facilitators among sub-populations, e.g., rural residents less willing to wear a mask.

Artificial intelligence-assisted clinical decision support for childhood asthma management: A randomized clinical trial.

RATIONALE: Clinical decision support (CDS) tools leveraging electronic health records (EHRs) have been an approach for addressing challenges in asthma care but remain under-studied through clinical trials. OBJECTIVES: To assess the effectiveness and efficiency of Asthma-Guidance and Prediction System (A-GPS), an Artificial Intelligence (AI)-assisted CDS tool, in optimizing asthma management through a randomized clinical trial (RCT). METHODS: This was a single-center pragmatic RCT with a stratified randomization design conducted for one year in the primary care pediatric practice of the Mayo Clinic, MN. Children (<18 years) diagnosed with asthma receiving care at the study site were enrolled along with their 42 primary care providers. Study subjects were stratified into three strata (based on asthma severity, asthma care status, and asthma diagnosis) and were blinded to the assigned groups. MEASUREMENTS: Intervention was a quarterly A-GPS report to clinicians including relevant clinical information for asthma management from EHRs and machine learning-based prediction for risk of asthma exacerbation (AE). Primary endpoint was the occurrence of AE within 1 year and secondary outcomes included time required for clinicians to review EHRs for asthma management. MAIN RESULTS: Out of 555 participants invited to the study, 184 consented for the study and were randomized (90 in intervention and 94 in control group). Median age of 184 participants was 8.5 years. While the proportion of children with AE in both groups decreased from the baseline (P = 0.042), there was no difference in AE frequency between the two groups (12% for the intervention group vs. 15% for the control group, Odds Ratio: 0.82; 95%CI 0.374-1.96; P = 0.626) during the study period. For the secondary end points, A-GPS intervention, however, significantly reduced time for reviewing EHRs for asthma management of each participant (median: 3.5 min, IQR: 2-5), compared to usual care without A-GPS (median: 11.3 min, IQR: 6.3-15); p<0.001). Mean health care costs with 95%CI of children during the trial (compared to before the trial) in the intervention group were lower than those in the control group (-$1,036 [-$2177, $44] for the intervention group vs. +$80 [-$841, $1000] for the control group), though there was no significant difference (p = 0.12). Among those who experienced the first AE during the study period (n = 25), those in the intervention group had timelier follow up by the clinical care team compared to those in the control group but no significant difference was found (HR = 1.93; 95% CI: 0.82-1.45, P = 0.10). There was no difference in the proportion of duration when patients had well-controlled asthma during the study period between the intervention and the control groups. CONCLUSIONS: While A-GPS-based intervention showed similar reduction in AE events to usual care, it might reduce clinicians' burden for EHRs review resulting in efficient asthma management. A larger RCT is needed for further studying the findings. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02865967.

Role of Geographic Risk Factors in COVID-19 Epidemiology: Longitudinal Geospatial Analysis.

OBJECTIVE: To perform a geospatial and temporal trend analysis for coronavirus disease 2019 (COVID-19) in a Midwest community to identify and characterize hot spots for COVID-19. PARTICIPANTS AND METHODS: We conducted a population-based longitudinal surveillance assessing the semimonthly geospatial trends of the prevalence of test confirmed COVID-19 cases in Olmsted County, Minnesota, from March 11, 2020, through October 31, 2020. As urban areas accounted for 84% of the population and 86% of all COVID-19 cases in Olmsted County, MN, we determined hot spots for COVID-19 in urban areas (Rochester and other small cities) of Olmsted County, MN, during the study period by using kernel density analysis with a half-mile bandwidth. RESULTS: As of October 31, 2020, a total of 37,141 individuals (30%) were tested at least once, of whom 2433 (7%) tested positive. Testing rates among race groups were similar: 29% (black), 30% (Hispanic), 25% (Asian), and 31% (white). Ten urban hot spots accounted for 590 cases at 220 addresses (2.68 cases per address) as compared with 1843 cases at 1292 addresses in areas outside hot spots (1.43 cases per address). Overall, 12% of the population residing in hot spots accounted for 24% of all COVID-19 cases. Hot spots were concentrated in neighborhoods with low-income apartments and mobile home communities. People living in hot spots tended to be minorities and from a lower socioeconomic background. CONCLUSION: Geographic and residential risk factors might considerably account for the overall burden of COVID-19 and its associated racial/ethnic and socioeconomic disparities. Results could geospatially guide community outreach efforts (eg, testing/tracing and vaccine rollout) for populations at risk for COVID-19.