Examining factors associated with poor metered-dose inhaler technique in a cohort of bronx patients with uncontrolled asthma.

OBJECTIVE: Most studies investigating at-risk groups for poor inhaler technique (PT) have been in adolescents. However, evidence suggests older age correlates with PT. This study aimed to correlate patient characteristics with PT in an adult asthma cohort in the Bronx. METHODS: We categorized 237 patients with uncontrolled asthma by demonstration of good inhaler technique (GT) (n = 112) or PT (n = 58) at their initial visit. Independent variables included age, sex, ethnicity, language, insurance status, BMI, depression severity, and socioeconomic data. Two logistic regression models were created to assess odds of PT among independent variables at initial visit and odds of improvement in technique at follow-up. RESULTS: At the initial visit, patients with PT had a mean age of 53.74 (±13.54) versus 45.12 (±13.26) among those with GT (p= <0.001). The PT group also had a lower percentage of patients with private insurance (52.53% versus 71.15%, p = 0.037). When controlling for language, ethnicity, insurance status, and educational attainment, the odds of PT increased with age (OR, 1.051; CI, 1.017-1.087, p = 0.003) and BMI (OR, 1.065; CI, 1.010-1.123, p = 0.020). Males had lower odds of PT (OR, 0.379; CI, 0.144-0.997; p = 0.049). While insurance status did not affect odds of PT, Medicaid users had lower odds of improving technique (OR, 0.184; CI, 0.040-0.854; p = 0.031). CONCLUSIONS: At baseline, individuals with PT were younger and more likely to be on a public health insurance plan. Increasing age, increasing BMI, and female sex were associated with higher odds of PT at the baseline visit, but were not associated with improvements in technique.

Special Issue on Informatics Education: ChatGPT Performs Worse on USMLE-Style Ethics Questions Compared to Medical Knowledge Questions.

OBJECTIVES: The main objective of this study is to evaluate the ability of the Large Language Model ChatGPT to accurately answer USMLE board style medical ethics questions compared to medical knowledge based questions. This study has the additional objectives of comparing the overall accuracy of GPT-3.5 to GPT-4 and to assess the variability of responses given by each version. MATERIALS AND METHODS: Using AMBOSS, a third party USMLE Step Exam test prep service, we selected one group of 27 medical ethics questions and a second group of 27 medical knowledge questions matched on question difficulty for medical students. We ran 30 trials asking these questions on GPT-3.5 and GPT-4, and recorded the output. A random-effects linear probability regression model evaluated accuracy, and a Shannon entropy calculation evaluated response variation. RESULTS: Both versions of ChatGPT demonstrated a worse performance on medical ethics questions compared to medical knowledge questions. GPT-4 performed 18% points (P < 0.05) worse on medical ethics questions compared to medical knowledge questions and GPT-3.5 performed 7% points (P = 0.41) worse. GPT-4 outperformed GPT-3.5 by 22% points (P < 0.001) on medical ethics and 33% points (P < 0.001) on medical knowledge. GPT-4 also exhibited an overall lower Shannon entropy for medical ethics and medical knowledge questions (0.21 and 0.11, respectively) than GPT-3.5 (0.59 and 0.55) which indicates lower variability in response. CONCLUSION: Both versions of ChatGPT performed more poorly on medical ethics questions compared to medical knowledge questions. GPT-4 significantly outperformed GPT-3.5 on overall accuracy and exhibited a significantly lower response variability in answer choices. This underscores the need for ongoing assessment of ChatGPT versions for medical education. KEY WORDS: ChatGPT, Large Language Model, Artificial Intelligence, Medical Education, USMLE, Ethics.

Developing and Evaluating the DiabetesXcel Mobile Application for Adult Patients With Type 2 Diabetes.

The authors trialed a mobile application, DiabetesXcel, which included type 2 diabetes-focused educational videos and modules, in 50 adults of Bronx, NY, a region with a high prevalence of diabetes and diabetes complications. From baseline to 4 months and from baseline to 6 months, there was significantly improved quality of life, self-management, knowledge, self-efficacy, depression, A1C, and LDL cholesterol among those who used DiabetesXcel. There was also a significant decrease in diabetes-related emergency department visits and hospital admissions from baseline to 6 months. This study demonstrates that DiabetesXcel could be beneficial for type 2 diabetes management.

ASTHMAXcel PRO patient satisfaction and usability field testing.

Background: ASTHMAXcel PRO, an enhanced version of the ASTHMAXcel mobile application, has been developed to deliver comprehensive, guideline-based asthma education while also facilitating the collection of patient-reported outcomes (PROs) and enhancing user experience.Objective: To perform field testing and conduct formative and summative evaluation of the ASTHMAXcel PRO application to assess its impact on patient satisfaction, usability, and usage.Methods: Twenty-eight adult patients completed a baseline visit during which ASTHMAXcel PRO was introduced, health literacy was assessed, and demographic data were collected. They were instructed to use the app for 4 weeks. The Questionnaire for User Interface Satisfaction (QUIS) and the Unified Theory of Acceptance and Use of Technology (UTAUT) questionnaire were administered at baseline and 4 weeks to assess user satisfaction and technology acceptance, respectively. Semi-structured interviews were conducted to gather feedback regarding the application from patients.Results: The baseline total scores were high for both UTAUT and QUIS (mean (SD): 64.2 (10.1), 6.8 (2.2) respectively) indicating that user satisfaction and acceptance began at high levels. UTAUT total score, as well as all domain scores, improved significantly from baseline to 4 weeks (p < 0.02). QUIS total score along with several domain scores (screen, system capabilities, usability) also increased from baseline to 4-weeks (p = 0.03, 0.01, 0.03, 0.01, respectively). These improvements remained significant when adjusting for age, gender, education, and health literacy. Patients reported that the application was helpful, informative, and easy to understand and use.Conclusion: The significant increases in satisfaction and technology adoption observed among ASTHMAXcel PRO users demonstrate that the application is viable and has the potential to improve upon usability challenges faced by existing mobile health applications.

Enhancing asthma research and improving health equity through decentralized clinical trials (DCTs) and mHealth technology.

The COVID-19 pandemic led to widespread disruption and termination of clinical research and a prompt adoption of mobile health (mHealth) technologies in the healthcare space. As the United States' healthcare system has rapidly become reliant on remotely conducted activities, the implementation of decentralized methods using mHealth technology in research investigation has become a necessary alternative to traditional in-person cohort studies. The aim of this article is to: report successful and unsuccessful examples of remote asthma clinical studies, explore the benefits and potential drawbacks of virtual clinical investigation, discuss the potential impact on equity and representation in asthma research, and provide suggestions through which investigators can implement decentralized clinical trials. Enhanced study accessibility, participant diversity, safety measures, and research efficacy are some of the benefits identified with a focused discussion on the impact on equity that decentralized clinical trials renders. Furthermore, potential concerns regarding regulatory compliance, data privacy, and effective mHealth design and solutions are discussed. Despite the setbacks and interruptions faced by the study participants and investigators due to the pandemic, the transition to decentralized clinical studies using mHealth technology is a positive, feasible step toward innovation and equity in the allergy and immunology field.

Using K-Means Clustering to Identify Physician Clusters by Electronic Health Record Burden and Efficiency.

Objectives: Electronic health records (EHRs) have transformed the way modern medicine is practiced, but they remain a major source of documentation burden among physicians. This study aims to use data from Signal, a tool provided by the Epic EHR, to analyze physician metadata in the Montefiore Health System via cluster analysis to assess EHR burden and efficiency. Methods: Data were obtained for a one-month period (July 2020) representing a return to normal operation post-telemedicine implementation. Six metrics from Signal were used to phenotype physicians: time on unscheduled days, pajama time, time outside of 7 AM to 7 PM, turnaround time, proficiency score, and visits closed the same day. k-Means clustering was employed to group physicians, and the clusters were assessed overall and by sex and specialty. Results: Our results demonstrate the partitioning of physicians into a higher-efficiency, lower-time outside of scheduled hours (TOSH) cluster and a lower-efficiency, higher-TOSH cluster even when stratified by sex and specialty. Intra-cluster comparisons showed general homogeneity of physician metrics with the exception of the higher-efficiency, lower-TOSH cluster when stratified by sex. Conclusions: Taken together, the clusters uniquely reflect the EHR efficiency-burden of the Montefiore Health System. Applying k-means clustering to readily available EHR data allows for a scalable, efficient, and adaptable approach of assessing physician EHR burden and efficiency, allowing health systems to examine documentation trends and target wellness interventions.

Electronic Health Record Usage Patterns Across Surgical Subspecialties.

OBJECTIVES: This study aimed to utilize metrics from physician action logs to analyze surgeon clinical, volume, electronic health record (EHR) efficiency, EHR proficiency, and workload outside scheduled time as impacted by physician characteristics such as years of experience, gender, subspecialty, academic title, and administrative title. METHODS: We selected 30 metrics from Epic Signal, an analytic tool in Epic that extracts metrics related to clinician documentation. Metrics measuring appointments, messages, and scheduled hours per day were used as a correlate for volume. EHR efficiency, and proficiency were measured by scores built into Epic Signal. Metrics measuring time spent in the EHR outside working hours were used as a correlate for documentation burden. We analyzed these metrics among surgeons at our institution across 4 months and correlated them with physician characteristics. RESULTS: Analysis of 133 surgeons showed that, when stratified by gender, female surgeons had significantly higher EHR metrics for time per day, time per appointment, and documentation burden, and significantly lower EHR metrics for efficiency when compared to male surgeons. When stratified by experience, surgeons with 0 to 5 years of experience had significantly lower EHR metrics for volume, time per day, efficiency, and proficiency when compared to surgeons with 6 to 10 and more than 10 years of experience. On multivariate analysis, having over 10 years of experience was an independent predictor of more appointments per day, greater proficiency, and spending less time per completed message. Female gender was an independent predictor of spending more time in notes per appointment and time spent in the EHR outside working hours. CONCLUSION: The burden associated with volume, proficiency, efficiency, and workload outside scheduled time related to EHR use varies by gender and years of experience in our cohort of surgeons. Evaluation of physician action logs could help identify those at higher risk of burnout due to burdensome medical documentation.

Elaborate biologic approval process delays care of patients with moderate-to-severe asthma.

Background: mAbs (biologics) are indicated in patients with poorly controlled moderate-to-severe asthma. The process of prior authorization and administration of a biologic requires exceptional commitment from clinical teams. Objective: Our aim was to evaluate the process of approval and administration of biologics for asthma and determine the most common reasons associated with denials of biologics and delays in administration. Methods: We examined the records of patients with asthma who were prescribed biologics from January 2018 to January 2020 at 2 centers, Montefiore Medical Center (Bronx, NY) and Scripps Clinics (San Diego, Calif). Demographics, insurance information, and details on the approval process were collected. Results: After querying of electronic health records, the records of 352 and 70 patients with moderate-to-severe asthma were included from Montefiore and Scripps, respectively. Most patients at Montefiore (58.2%) were insured under Managed Care Medicaid (MC Medicaid), whereas most patients at Scripps (61.4%) had commercial insurance. The median times from prescription to administration of a biologic were similar: 34 days (interquartile range [IQR] = 18-63 days) and 34 days (IQR = 22.5-56.0 days) (P = .97) for Montefiore and Scripps, respectively. However, the median approval time for Montefiore was 6 days (IQR = 1-20 days) and that for Scripps was 22 days (IQR = 10-36 days) (P < .001). Approval times for prescriptions requiring appeals were significantly longer than for prescriptions approved after the initial submission: 23 days versus 2.5 days and 40.5 days versus 15.5 days (for Montefiore and Scripps, respectively [P < .001 for both]). Conclusions: Lengthy appeals contribute to delays between prescribing and administering a biologic. Site-specific practices and insurance coverage influence approval timing of the biologics for asthma.

Preference for and impact of telehealth vs in-person asthma visits among Black and Latinx adults.

BACKGROUND: Black and Latinx adults experience disproportionate asthma-related morbidity and limited specialty care access. The severe acute respiratory syndrome coronavirus 2 pandemic expanded telehealth use. OBJECTIVE: To evaluate visit type (telehealth [TH] vs in-person [IP]) preferences and the impact of visit type on asthma outcomes among Black and Latinx adults with moderate-to-severe asthma. METHODS: For this PREPARE trial ancillary study, visit type preference was surveyed by e-mail or telephone post-trial. Emergency medical record data on visit types and asthma outcomes were available for a subset (March 2020 to April 2021). Characteristics associated with visit type preferences, and relationships between visit type and asthma outcomes (control [Asthma Control Test] and asthma-related quality of life [Asthma Symptom Utility Index]), were tested using multivariable regression. RESULTS: A total of 866 participants consented to be surveyed, with 847 respondents. Among the participants with asthma care experience with both visit types, 42.0% preferred TH for regular checkups, which associated with employment (odds ratio [OR] = 1.61; 95% confidence interval [CI], 1.09-2.39; P = .02), lower asthma medication adherence (OR = 1.06; 95% CI, 1.01-1.11; P = .03), and having more historical emergency department and urgent care asthma visits (OR = 1.10 for each additional visit; 95% CI, 1.02-1.18; P = .02), after adjustment. Emergency medical record data were available for 98 participants (62 TH, 36 IP). Those with TH visits were more likely Latinx, from the Southwest, employed, using inhaled corticosteroid-only controller therapy, with lower body mass index, and lower self-reported asthma medication adherence vs those with IP visits only. Both groups had comparable Asthma Control Test (18.4 vs 18.9, P = .52) and Asthma Symptom Utility Index (0.79 vs 0.84, P = .16) scores after adjustment. CONCLUSION: TH may be similarly efficacious as and often preferred over IP among Black and Latinx adults with moderate-to-severe asthma, especially for regular checkups. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02995733.

Under-perception of airflow limitation, self-efficacy, and beliefs in older adults with asthma.

OBJECTIVE: Under-perception of airflow limitation is more common in older adults with asthma and may lead to under-reporting of asthma symptoms. Asthma management self-efficacy is linked with better asthma control and quality of life (QoL). We sought to examine asthma and medication beliefs as a mediator in the relationship between both under-perception and self-efficacy with asthma outcomes. METHODS: This cross-sectional study recruited participants with asthma ≥60 years from hospital-affiliated practices in East Harlem and the Bronx, New York. Perception of airflow limitation was measured for 6 weeks by having participants enter peak expiratory flow (PEF) estimates into an electronic peak flow meter followed by PEF blows. We used validated instruments to assess asthma and medication beliefs, asthma management self-efficacy, asthma control, and QoL. Asthma self-management behaviors (SMB) were quantified by electronic and self-report measures of inhaled corticosteroid (ICS) adherence and observation of inhaler technique. RESULTS: The sample comprised 331 participants (51% Hispanic, 27% Black, 84% female). Beliefs mediated the relationship between greater under-perception and better self-reported asthma control (ß = -0.08, p = .02) and better asthma QoL (ß =0.12, p = .02). Higher self-efficacy was also associated with better reported asthma control (ß = -0.10, p = .006) and better asthma QoL (ß =0.13, p = .01) in this indirect effect through beliefs. Accurate perception of airflow limitation was associated with higher adherence to SMB (ß = 0.29, p = .003). CONCLUSIONS: Less threatening asthma beliefs may be maladaptive in under-perception of airflow limitation by contributing to under-reporting of asthma symptoms, but adaptive in the context of higher self-efficacy and better asthma control.

Development and Validation of a Respiratory-Responsive Vocal Biomarker-Based Tool for Generalizable Detection of Respiratory Impairment: Independent Case-Control Studies in Multiple Respiratory Conditions Including Asthma, Chronic Obstructive Pulmonary Disease, and COVID-19.

BACKGROUND: Vocal biomarker-based machine learning approaches have shown promising results in the detection of various health conditions, including respiratory diseases, such as asthma. OBJECTIVE: This study aimed to determine whether a respiratory-responsive vocal biomarker (RRVB) model platform initially trained on an asthma and healthy volunteer (HV) data set can differentiate patients with active COVID-19 infection from asymptomatic HVs by assessing its sensitivity, specificity, and odds ratio (OR). METHODS: A logistic regression model using a weighted sum of voice acoustic features was previously trained and validated on a data set of approximately 1700 patients with a confirmed asthma diagnosis and a similar number of healthy controls. The same model has shown generalizability to patients with chronic obstructive pulmonary disease, interstitial lung disease, and cough. In this study, 497 participants (female: n=268, 53.9%; <65 years old: n=467, 94%; Marathi speakers: n=253, 50.9%; English speakers: n=223, 44.9%; Spanish speakers: n=25, 5%) were enrolled across 4 clinical sites in the United States and India and provided voice samples and symptom reports on their personal smartphones. The participants included patients who are symptomatic COVID-19 positive and negative as well as asymptomatic HVs. The RRVB model performance was assessed by comparing it with the clinical diagnosis of COVID-19 confirmed by reverse transcriptase-polymerase chain reaction. RESULTS: The ability of the RRVB model to differentiate patients with respiratory conditions from healthy controls was previously demonstrated on validation data in asthma, chronic obstructive pulmonary disease, interstitial lung disease, and cough, with ORs of 4.3, 9.1, 3.1, and 3.9, respectively. The same RRVB model in this study in COVID-19 performed with a sensitivity of 73.2%, specificity of 62.9%, and OR of 4.64 (P<.001). Patients who experienced respiratory symptoms were detected more frequently than those who did not experience respiratory symptoms and completely asymptomatic patients (sensitivity: 78.4% vs 67.4% vs 68%, respectively). CONCLUSIONS: The RRVB model has shown good generalizability across respiratory conditions, geographies, and languages. Results using data set of patients with COVID-19 demonstrate its meaningful potential to serve as a prescreening tool for identifying individuals at risk for COVID-19 infection in combination with temperature and symptom reports. Although not a COVID-19 test, these results suggest that the RRVB model can encourage targeted testing. Moreover, the generalizability of this model for detecting respiratory symptoms across different linguistic and geographic contexts suggests a potential path for the development and validation of voice-based tools for broader disease surveillance and monitoring applications in the future.

The relationship between social support, self-efficacy, and asthma outcomes in older adults.

OBJECTIVE: There has been a call for research examining factors that influence asthma outcomes in older adults because of the notable disparities observed in this age group. Social support and self-efficacy are resources that factor into asthma outcomes. The current study aimed to examine the relationship between these resources (independently and jointly) and asthma control and quality of life. METHODS: Older adults with moderate-severe asthma were recruited from NYC. Data were obtained during in-person interviews via validated measures of social support, asthma self-efficacy, asthma control, and asthma quality of life. Linear regression evaluated self-efficacy in the relationship between social support and asthma outcomes. RESULTS: In a sample of 359 older adults (M = 68.04, 47.9% Hispanic, 26.5% Black, and 25.6% other), social support had an inverse association with asthma control. As social support increased, asthma control decreased (ß = 0.95, t(356) = -3.13, p = .002). Self-efficacy significantly moderated this relationship (ß = 0.01, t(356) = 2.37, p = .018). For individuals with low or moderate asthma self-efficacy, more received social support was associated with worse asthma control (ß = -0.33, t(356) = -4.66, p < .0001; ß = -0.20, t(356) = -3.21, p = .0014, respectively). For individuals with high self-efficacy, no relationship was found between received social support and asthma control (ß = -0.10, t(356)= -1.20, p =.23). For asthma quality of life, higher levels of received social support were associated with worse quality of life (ß = -0.88, t(356) = -2.64, p = .009), but this association was not significantly moderated by self-efficacy (ß = 0.01, t(356) = 1.90, p = .0582). CONCLUSIONS: For older adults with asthma, receiving more social support is associated with worse asthma outcomes, especially for older adults with lower asthma self-efficacy.

Effect of Telemedicine and the COVID-19 Pandemic on Medical Trainees' Usage of the Electronic Health Record in the Outpatient Setting.

OBJECTIVES: This study aimed to (1) determine the impact of COVID-19 (coronavirus disease 2019) and the corresponding increase in use of telemedicine on volume, efficiency, and burden of electronic health record (EHR) usage by residents and fellows; and (2) to compare these metrics with those of attending physicians. METHODS: We analyzed 11 metrics from Epic's Signal database of outpatient physician user logs for active residents/fellows at our institution across three 1-month time periods: August 2019 (prepandemic/pre-telehealth), May 2020 (mid-pandemic/post-telehealth implementation), and July 2020 (follow-up period) and compared these metrics between trainees and attending physicians. We also assessed how the metrics varied for medical trainees in primary care as compared with subspecialties. RESULTS: Analysis of 141 residents/fellows and 495 attendings showed that after telehealth implementation, overall patient volume, Time in In Basket per day, Time outside of 7 a.m. to 7 p.m., and Time in notes decreased significantly compared with the pre-telehealth period. Female residents, fellows, and attendings had a lower same day note closure rate before and during the post-telehealth implementation period and spent greater time working outside of 7 a.m. to 7 p.m. compared with male residents, fellows, and attendings (p < 0.01) compared with the pre-telehealth period. Attending physicians had a greater patient volume, spent more time, and were more efficient in the EHR compared with trainees (p < 0.01) in both the post-telehealth and follow-up periods as compared with the pre-telehealth period. CONCLUSION: The dramatic change in clinical operations during the pandemic serves as an inflection point to study changes in physician practice patterns in the EHR. We observed that (1) female physicians closed fewer notes the same day and spent more time in the EHR outside of normal working hours compared with male physicians, and (2) attending physicians had higher patient volumes and also higher efficiency in the EHR compared with resident physicians.

The Relationship Between Depressive Symptoms, eHealth Literacy, and Asthma Outcomes in the Context of a Mobile Health Intervention.

OBJECTIVE: The ASTHMAXcel PRO mobile app provides asthma education and collects asthma outcome data. The objective of this study was to evaluate the associations between health/electronic health literacy (eHealth literacy) and depressive symptoms with app usage and clinical outcomes. METHODS: Adults with persistent asthma were recruited to use the app. Participants completed the Patient Health Questionnaire-9 to assess for depressive symptoms, Asthma Control Test, Mini Asthma Quality of Life (QOL) Questionnaire, and the Newest Vital Sign tool to measure health literacy. Data on a subset of participants were available on eHealth literacy ( n = 24) and average number of app logins across 2 months ( n = 40). RESULTS: The total study sample included 96 participants (46% non-Hispanic Black, 44.4% Hispanic). The average participant age was 44.0 (standard deviation = 14.9) years, with 74% identifying as female. Increased depressive symptoms were associated with worse asthma control ( ß = -0.46, p < .001) and asthma QOL ( ß = -0.38, p < .001), but not eHealth literacy. Higher eHealth literacy was associated with worse asthma QOL ( ß = -0.48, p = .02) and more app logins ( ß = 0.59, p = .04). Newest Vital Sign scores were not associated with any of the other measures. CONCLUSIONS: Depressive symptoms were associated with worse asthma outcomes. eHealth literacy was associated with increased patient engagement with the app and worse asthma QOL, which may reflect patients with worse QOL seeking out health information on the Internet (although directionality could not be assessed). Digital health literacy may be key to increasing patient engagement with mobile health interventions.Trial Registration: National Clinical Trial No. 03847142, https://clinicaltrials.gov/ct2/show/NCT03847142 .

Assessing Electronic Health Literacy at an Urban Academic Hospital.

BACKGROUND: Residents of the Bronx suffer marked health disparities due to socioeconomic and other factors. The coronavirus disease 2019 pandemic worsened these health outcome disparities and health care access disparities, especially with the abrupt transition to online care. OBJECTIVES: This study classified electronic health literacy (EHL) among patients at an urban, academic hospital in the Bronx, and assessed for associations between EHL levels and various demographic characteristics. METHODS: We designed a cross-sectional, observational study in adults 18 years or older presenting to the Montefiore Einstein Center for Cancer Care (MECCC) Department of Radiation Oncology or the Montefiore Department of Medicine in the Bronx. We assessed EHL using the eHealth Literacy Scale (eHEALS) survey, a previously validated tool, and our newly developed eHealth Literacy Objective Scale-Scenario Based (eHeLiOS-SB) tool. RESULTS: A total of 97 patients recruited from the MECCC and Department of Medicine participated in this study. There was a statistically significant association between age and EHL as assessed by both eHEALS and eHeLiOS-SB, with older adults having lower EHL scores. Additionally, a question designed to assess general attitudes toward digital health technologies found that most participants had a positive attitude toward such applications. CONCLUSION: Many patients, especially older adults, may require additional support to effectively navigate telehealth. Further research is warranted to optimize telemedicine strategies in this potentially-marginalized population and ultimately to create telehealth practices accessible to patients of all ages and demographics.

Trends in seasonal pollen and asthma-related morbidity among adults and children in a U.S. high-density urban center, 2001-2020.

OBJECTIVE: To analyze the long-term trends in pollen counts and asthma-related emergency department visits (AREDV) in adult and pediatric populations in the Bronx. METHODS: Daily values of adult and pediatric AREDV were retrospectively obtained from three major Bronx hospitals using ICD-10 codes and pollen counts were obtained from the Armonk station from 2001-2020. Wilcoxon Ranked Sum was applied to compare median values, while Spearman correlation was employed to examine the association between these variables, for both decades and each season. RESULTS: The median value of pediatric AREDV increased by 200% from the 1st to 2nd decade (p < 0.001) and AREDV peak shifted from predominantly the spring season in the 1st decade to the fall and winter seasons in the 2nd decade. Seasonal patterns were consistent over 20 years with summer AREDV lower than all other seasons (9 vs. 17 per day) (p < 0.001). Spring tree pollen peaks were correlated with AREDV peaks (rho = 0.34) (p < 0.001). Tree pollen exceeding 100 grains/m3 corresponded to a median of 19.0 AREDVs while all other tree pollen (0 - 99 grains/m3) corresponded to a median of 15.0 AREDVs (p < 0.001). AREDVs sharply declined in 2020, coinciding with the emergence of COVID-19. CONCLUSIONS: Pollen and AREDVs peak earlier in the spring and are more strongly interconnected, while asthma rates among children are rapidly rising, particularly in the fall and winter. These findings can advise targeted awareness campaigns for better management of asthma related morbidity.