The genetic contribution to the comorbidity of depression and anxiety: a multi-site electronic health records study of almost 178 000 people.

BACKGROUND: Depression and anxiety are common and highly comorbid, and their comorbidity is associated with poorer outcomes posing clinical and public health concerns. We evaluated the polygenic contribution to comorbid depression and anxiety, and to each in isolation. METHODS: Diagnostic codes were extracted from electronic health records for four biobanks [N = 177 865 including 138 632 European (77.9%), 25 612 African (14.4%), and 13 621 Hispanic (7.7%) ancestry participants]. The outcome was a four-level variable representing the depression/anxiety diagnosis group: neither, depression-only, anxiety-only, and comorbid. Multinomial regression was used to test for association of depression and anxiety polygenic risk scores (PRSs) with the outcome while adjusting for principal components of ancestry. RESULTS: In total, 132 960 patients had neither diagnosis (74.8%), 16 092 depression-only (9.0%), 13 098 anxiety-only (7.4%), and 16 584 comorbid (9.3%). In the European meta-analysis across biobanks, both PRSs were higher in each diagnosis group compared to controls. Notably, depression-PRS (OR 1.20 per s.d. increase in PRS; 95% CI 1.18-1.23) and anxiety-PRS (OR 1.07; 95% CI 1.05-1.09) had the largest effect when the comorbid group was compared with controls. Furthermore, the depression-PRS was significantly higher in the comorbid group than the depression-only group (OR 1.09; 95% CI 1.06-1.12) and the anxiety-only group (OR 1.15; 95% CI 1.11-1.19) and was significantly higher in the depression-only group than the anxiety-only group (OR 1.06; 95% CI 1.02-1.09), showing a genetic risk gradient across the conditions and the comorbidity. CONCLUSIONS: This study suggests that depression and anxiety have partially independent genetic liabilities and the genetic vulnerabilities to depression and anxiety make distinct contributions to comorbid depression and anxiety.

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.

Phenotypic distinctions in depression and anxiety: a comparative analysis of comorbid and isolated cases.

BACKGROUND: Anxiety and depression are frequently comorbid yet phenotypically distinct. This study identifies differences in the clinically observable phenome across a wide variety of physical and mental disorders comparing patients with diagnoses of depression without anxiety, anxiety without depression, or both depression and anxiety. METHODS: Using electronic health records for 14 994 participants with depression and/or anxiety in the Mayo Clinic Biobank, a phenotype-based phenome-wide association study (Phe2WAS) was performed to test for differences between these groups across a broad range of clinical diagnoses observed in the electronic health record. Additional analyses were performed to determine the temporal sequencing of diagnoses. RESULTS: Compared to patients diagnosed only with anxiety, those diagnosed only with depression were more likely to have diagnoses of obesity (OR 1.75; p = 1 × 10-27), sleep apnea (OR 1.71; p = 1 × 10-22), and type II diabetes (OR 1.74; p = 9 × 10-18). Compared to those diagnosed only with depression, those diagnosed only with anxiety were more likely to have diagnoses of palpitations (OR 1.91; p = 2 × 10-25), benign skin neoplasms (OR 1.61; p = 2 × 10-17), and cardiac dysrhythmias (OR 1.45; p = 2 × 10-12). Patients with comorbid depression and anxiety were more likely to have diagnoses of other mental health disorders, substance use disorders, sleep problems, and gastroesophageal reflux relative to isolated depression. CONCLUSIONS: While depression and anxiety are closely related, this study suggests that phenotypic distinctions exist between depression and anxiety. Improving phenotypic characterization within the broad categories of depression and anxiety could improve the clinical assessment of depression and anxiety.

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.

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.

Predictors of Metformin Failure: Repurposing Electronic Health Record Data to Identify High-Risk Patients.

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) (<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 < .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.

Comorbidity and healthcare utilization in patients with treatment resistant depression: A large-scale retrospective cohort analysis using electronic health records.

BACKGROUND: Medical comorbidity and healthcare utilization in patients with treatment resistant depression (TRD) is usually reported in convenience samples, making estimates unreliable. There is only limited large-scale clinical research on comorbidities and healthcare utilization in TRD patients. METHODS: Electronic Health Record data from over 3.3 million patients from the INSIGHT Clinical Research Network in New York City was used to define TRD as initiation of a third antidepressant regimen in a 12-month period among patients diagnosed with major depressive disorder (MDD). Age and sex matched TRD and non-TRD MDD patients were compared for anxiety disorder, 27 comorbid medical conditions, and healthcare utilization. RESULTS: Out of 30,218 individuals diagnosed with MDD, 15.2 % of patients met the criteria for TRD (n = 4605). Compared to MDD patients without TRD, the TRD patients had higher rates of anxiety disorder and physical comorbidities. They also had higher odds of ischemic heart disease (OR = 1.38), stroke/transient ischemic attack (OR = 1.57), chronic kidney diseases (OR = 1.53), arthritis (OR = 1.52), hip/pelvic fractures (OR = 2.14), and cancers (OR = 1.41). As compared to non-TRD MDD, TRD patients had higher rates of emergency room visits, and inpatient stays. In relation to patients without MDD, both TRD and non-TRD MDD patients had significantly higher levels of anxiety disorder and physical comorbidities. LIMITATIONS: The INSIGHT-CRN data lack information on depression severity and medication adherence. CONCLUSIONS: TRD patients compared to non-TRD MDD patients have a substantially higher prevalence of various psychiatric and medical comorbidities and higher health care utilization. These findings highlight the challenges of developing interventions and care coordination strategies to meet the complex clinical needs of TRD patients.

The importance of social activity to risk of major depression in older adults.

BACKGROUND: Several social determinants of health (SDoH) have been associated with the onset of major depressive disorder (MDD). However, prior studies largely focused on individual SDoH and thus less is known about the relative importance (RI) of SDoH variables, especially in older adults. Given that risk factors for MDD may differ across the lifespan, we aimed to identify the SDoH that was most strongly related to newly diagnosed MDD in a cohort of older adults. METHODS: We used self-reported health-related survey data from 41 174 older adults (50-89 years, median age = 67 years) who participated in the Mayo Clinic Biobank, and linked ICD codes for MDD in the participants' electronic health records. Participants with a history of clinically documented or self-reported MDD prior to survey completion were excluded from analysis (N = 10 938, 27%). We used Cox proportional hazards models with a gradient boosting machine approach to quantify the RI of 30 pre-selected SDoH variables on the risk of future MDD diagnosis. RESULTS: Following biobank enrollment, 2073 older participants were diagnosed with MDD during the follow-up period (median duration = 6.7 years). The most influential SDoH was perceived level of social activity (RI = 0.17). Lower level of social activity was associated with a higher risk of MDD [hazard ratio = 2.27 (95% CI 2.00-2.50) for highest v. lowest level]. CONCLUSION: Across a range of SDoH variables, perceived level of social activity is most strongly related to MDD in older adults. Monitoring changes in the level of social activity may help identify older adults at an increased risk of MDD.

Social connectedness as a determinant of mental health: A scoping review.

Public health and epidemiologic research have established that social connectedness promotes overall health. Yet there have been no recent reviews of findings from research examining social connectedness as a determinant of mental health. The goal of this review was to evaluate recent longitudinal research probing the effects of social connectedness on depression and anxiety symptoms and diagnoses in the general population. A scoping review was performed of PubMed and PsychInfo databases from January 2015 to December 2021 following PRISMA-ScR guidelines using a defined search strategy. The search yielded 66 unique studies. In research with other than pregnant women, 83% (19 of 23) studies reported that social support benefited symptoms of depression with the remaining 17% (5 of 23) reporting minimal or no evidence that lower levels of social support predict depression at follow-up. In research with pregnant women, 83% (24 of 29 studies) found that low social support increased postpartum depressive symptoms. Among 8 of 9 studies that focused on loneliness, feeling lonely at baseline was related to adverse outcomes at follow-up including higher risks of major depressive disorder, depressive symptom severity, generalized anxiety disorder, and lower levels of physical activity. In 5 of 8 reports, smaller social network size predicted depressive symptoms or disorder at follow-up. In summary, most recent relevant longitudinal studies have demonstrated that social connectedness protects adults in the general population from depressive symptoms and disorders. The results, which were largely consistent across settings, exposure measures, and populations, support efforts to improve clinical detection of high-risk patients, including adults with low social support and elevated loneliness.

Employment Characteristics and Risk of Hospitalization Among Older Adults Participating in the Mayo Clinic Biobank.

Objective: To determine the relationship between characteristics of employment and future hospitalization in older adults. Patients and Methods: We conducted a survey of adults aged 65 years or older participating in the Mayo Clinic Biobank. Using a frequency-matched, case-control design, we compared patients who were hospitalized within 5 years of biobank enrollment (cases) with those who were not hospitalized (controls). We assessed the duration of work, age at first job, number of jobs, disability, retirement, and reasons for leaving work. We performed logistic regression analysis to assess the association of these factors with hospitalization, accounting for age, sex, comorbid conditions, and education level. Results: Among 3536 participants (1600 cases and 1936 controls; median age, 68.5 years; interquartile range, 63.4-73.9 years), cases were older, more likely to be male, and had lower education levels. Comorbid illnesses had the largest association with hospitalization (odds ratio [OR], 4.09; 95% CI, 3.37-4.97 [highest vs lowest quartile]). On adjusted analyses, odds of hospitalization increased with the presence of disability (OR, 1.31; 95% CI, 1.01-1.69) and decreased with having 1 or 2 lifetime jobs vs no employment (OR, 0.77; 95% CI, 0.60-1.00). The length of work, furlough, age of retirement, childcare issues, and reasons for leaving a job were not associated with hospitalization. Conclusion: This study reports an association between disability during work and hospitalization. On the basis of our findings, it may be important to obtain a more detailed work history from patients because it may provide further insight into their future health.

Influenza infection is not associated with phenotypical frailty in older patients, a prospective cohort study.

Background and Aims: Influenza 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. Methods: This 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. Results: In 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). Conclusion: We 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.

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.

Risk of pneumonia in asthmatic children using inhaled corticosteroids: a nested case-control study in a birth cohort.

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.

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.

Artificial Intelligence Assesses Clinicians' Adherence to Asthma Guidelines Using Electronic Health Records.

BACKGROUND: Clinicians' asthma guideline adherence in asthma care is suboptimal. The effort to improve adherence can be enhanced by assessing and monitoring clinicians' adherence to guidelines reflected in electronic health records (EHRs), which require costly manual chart review because many care elements cannot be identified by structured data. OBJECTIVE: This study was designed to demonstrate the feasibility of an artificial intelligence tool using natural language processing (NLP) leveraging the free text EHRs of pediatric patients to extract key components of the 2007 National Asthma Education and Prevention Program guidelines. METHODS: This is a retrospective cross-sectional study using a birth cohort with a diagnosis of asthma at Mayo Clinic between 2003 and 2016. We used 1,039 clinical notes with an asthma diagnosis from a random sample of 300 patients. Rule-based NLP algorithms were developed to identify asthma guideline-congruent elements by examining care description in EHR free text. RESULTS: Natural language processing algorithms demonstrated a sensitivity (0.82-1.0), specificity (0.95-1.0), positive predictive value (0.86-1.0), and negative predictive value (0.92-1.0) against manual chart review for asthma guideline-congruent elements. Assessing medication compliance and inhaler technique assessment were the most challenging elements to assess because of the complexity and wide variety of descriptions. CONCLUSIONS: Natural language processing technologies may enable the automated assessment of clinicians' documentation in EHRs regarding adherence to asthma guidelines and can be a useful population management and research tool to assess and monitor asthma care quality. Multisite studies with a larger sample size are needed to assess the generalizability of these NLP algorithms.

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.

Extracting social determinants of health from electronic health records using natural language processing: a systematic review.

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.

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.