Application of a user experience design approach for an EHR-based clinical decision support system.

Objective: We applied a user experience (UX) design approach to clinical decision support (CDS) tool development for the specific use case of pediatric asthma. Our objective was to understand physicians' workflows, decision-making processes, barriers (ie, pain points), and facilitators to increase usability of the tool. Materials and methods: We used a mixed-methods approach with semi-structured interviews and surveys. The coded interviews were synthesized into physician-user journey maps (ie, visualization of a process to accomplish goals) and personas (ie, user types). Interviews were conducted via video. We developed physician journey maps and user personas informed by their goals, systems interactions, and experiences with pediatric asthma management. Results: The physician end-user personas identified were: efficiency, relationship, and learning. Features of a potential asthma CDS tool sought varied by physician practice type and persona. It was important to the physician end-user that the asthma CDS tool demonstrate value by lowering workflow friction (ie, difficulty or obstacles), improving the environment surrounding physicians and patients, and using it as a teaching tool. Customizability versus standardization were important considerations for uptake. Discussion: Different values and motivations of physicians influence their use and interaction with the EHR and CDS tools. These different perspectives can be captured by applying a UX design approach to the development process. For example, with the importance of customizability, one approach may be to build a core module with variations depending on end-user preference. Conclusion: A UX approach can drive design to help understand physician-users and meet their needs; ultimately with the goal of increased uptake.

Trends in telemedicine visits among pediatric asthma patients during COVID-19.

Background: Environmental and social factors, including lack of access to asthma care, contribute to persistent inequities in asthma outcomes among children from historically marginalized ethnoracial groups. Telemedicine, which expanded rapidly during the coronavirus disease 2019 (COVID-19) pandemic, may be an approach to augment access to pediatric asthma care. Objectives: We sought to describe characteristics of pediatric (0-17 years) telemedicine users with asthma and characterize use trends throughout the COVID-19 pandemic. Methods: We conducted a retrospective analysis using electronic health record data of pediatric patients with asthma seen at University of California, Los Angeles, Medical Center between March 2019 to March 2022 describing telemedicine user characteristics, trends of asthma-related telemedicine use, and associations between user characteristics and having a telemedicine visit. Results: Among 6,777 patients with asthma, the percentage of asthma-related telemedicine visits peaked early in the pandemic, comprising 74.3% of visits, before decreasing to 13.6% in 2022. Compared to White patients, Black patients had lower odds of an asthma telemedicine visit (odds ratio [OR], 0.49; 95% confidence interval [CI], 0.26, 0.94). Those with public insurance (OR, 1.7; 95% CI, 1.19, 2.43), severe persistent asthma (OR, 3.03; 95% CI, 1.70, 5.42), or comorbidities (OR, 1.59; 95% CI, 1.08, 2.33) had higher odds. Time to first emergency department visit and hospitalization comparing those with at least one telemedicine visit to those with none were similar. Conclusions: More pediatric asthma patients are using telemedicine since the COVID-19 pandemic, particularly those with medical complexity and comorbidities, and outcomes appear similar. However, Black patients at our institution have lower odds of using telemedicine.

Text vs Patient Portal Messaging to Improve Influenza Vaccination Coverage: A Health System-Wide Randomized Clinical Trial.

Importance: Increasing influenza vaccination rates is a public health priority. One method recommended by the US Centers for Disease Control and Prevention and others is for health systems to send reminders nudging patients to be vaccinated. Objective: To evaluate and compare the effect of electronic health record (EHR)-based patient portal reminders vs text message reminders on influenza vaccination rates across a health system. Design, Setting, and Participants: This 3-arm randomized clinical trial was conducted from September 7, 2022, to April 30, 2023, among primary care patients within the University of California, Los Angeles (UCLA) health system. Interventions: Arm 1 received standard of care. The health system sent monthly reminder messages to patients due for an influenza vaccine by portal (arm 2) or text (arm 3). Arm 2 had a 2 × 2 nested design, with fixed vs responsive monthly reminders and preappointment vs no preappointment reminders. Arm 3 had 1 × 2 design, with preappointment vs no preappointment reminders. Preappointment reminders for eligible patients were sent 24 and 48 hours before scheduled primary care visits. Fixed reminders (in October, November, and December) involved identical messages via portal or text. Responsive portal reminders involved a September message asking patients about their plans for vaccination, with a follow-up reminder if the response was affirmative but the patient was not yet vaccinated. Main Outcomes and Measures: The primary outcome was influenza vaccination by April 30, 2023, obtained from the UCLA EHR, including vaccination from pharmacies and other sources. Results: A total of 262 085 patients (mean [SD] age, 45.1 [20.7] years; 237 404 [90.6%] adults; 24 681 [9.4%] children; 149 349 [57.0%] women) in 79 primary care practices were included (87 257 in arm 1, 87 478 in arm 2, and 87 350 in arm 3). At the entire primary care population level, none of the interventions improved influenza vaccination rates. All groups had rates of approximately 47%. There was no statistical or clinically significant improvement following portal vs text, preappointment reminders vs no preappointment reminders (portal and text reminders combined), or responsive vs fixed monthly portal reminders. Conclusions and Relevance: At the population level, neither portal nor text reminders for influenza vaccination were effective. Given that vaccine hesitancy may be a major reason for the lack of impact of portal or text reminders, more intensive interventions by health systems are needed to raise influenza vaccination coverage levels. Trial Registration: ClinicalTrials.gov Identifier: NCT05525494.

Smartphone-derived Virtual Keyboard Dynamics Coupled with Accelerometer Data as a Window into Understanding Brain Health: Smartphone Keyboard and Accelerometer as Window into Brain Health.

We examine the feasibility of using accelerometer data exclusively collected during typing on a custom smartphone keyboard to study whether typing dynamics are associated with daily variations in mood and cognition. As part of an ongoing digital mental health study involving mood disorders, we collected data from a well-characterized clinical sample (N = 85) and classified accelerometer data per typing session into orientation (upright vs. not) and motion (active vs. not). The mood disorder group showed lower cognitive performance despite mild symptoms (depression/mania). There were also diurnal pattern differences with respect to cognitive performance: individuals with higher cognitive performance typed faster and were less sensitive to time of day. They also exhibited more well-defined diurnal patterns in smartphone keyboard usage: they engaged with the keyboard more during the day and tapered their usage more at night compared to those with lower cognitive performance, suggesting a healthier usage of their phone.

Physicians differ in their perceptions of sensitive medical records: Survey and interview study.

Physician categorizations of electronic health record (EHR) data (e.g., depression) into sensitive data categories (e.g., Mental Health) and their perspectives on the adequacy of the categories to classify medical record data were assessed. One thousand data items from patient EHR were classified by 20 physicians (10 psychiatrists paired with ten non-psychiatrist physicians) into data categories via a survey. Cluster-adjusted chi square tests and mixed models were used for analysis. 10 items were selected per each physician pair (100 items in total) for discussion during 20 follow-up interviews. Interviews were thematically analyzed. Survey item categorization yielded 500 (50.0%) agreements, 175 (17.5%) disagreements, 325 (32.5%) partial agreements. Categorization disagreements were associated with physician specialty and implied patient history. Non-psychiatrists selected significantly (p = .016) more data categories than psychiatrists when classifying data items. The endorsement of Mental Health and Substance Use categories were significantly (p = .001) related for both provider types. During thematic analysis, Encounter Diagnosis (100%), Problems (95%), Health Concerns (90%), and Medications (85%) were discussed the most when deciding the sensitivity of medical information. Most (90.0%) interview participants suggested adding additional data categories. Study findings may guide the evolution of digital patient-controlled granular data sharing technology and processes.

Evaluation of behavioral economic strategies to raise influenza vaccination rates across a health system: Results from a randomized clinical trial.

Influenza vaccination rates are low. Working with a large US health system, we evaluated three health system-wide interventions using the electronic health record's patient portal to improve influenza vaccination rates. We performed a two-arm RCT with a nested factorial design within the treatment arm, randomizing patients to usual-care control (no portal interventions) or to one or more portal interventions. We included all patients within this health system during the 2020-2021 influenza vaccination season, which overlapped with the COVID-19 pandemic. Through the patient portal, we simultaneously tested: pre-commitment messages (sent September 2020, asking patients to commit to a vaccination); monthly portal reminders (October - December 2020), direct appointment scheduling (patients could self-schedule influenza vaccination at multiple sites); and pre-appointment reminder messages (sent before scheduled primary care appointments, reminding patients about influenza vaccination). The main outcome measure was receipt of influenza vaccine (10/01/2020-03/31/2021). We randomized 213,773 patients (196,070 adults ≥18 years, 17,703 children). Influenza vaccination rates overall were low (39.0%). Vaccination rates for study arms did not differ: Control (38.9%), pre-commitment vs no pre-commitment (39.2%/38.9%), direct appointment scheduling yes/no (39.1%/39.1%), pre-appointment reminders yes/no (39.1%/39.1%); p > 0.017 for all comparisons (p value cut-off adjusted for multiple comparisons). After adjusting for age, gender, insurance, race, ethnicity, and prior influenza vaccination, none of the interventions increased vaccination rates. We conclude that patient portal interventions to remind patients to receive influenza vaccine during the COVID-19 pandemic did not raise influenza immunization rates. More intensive or tailored interventions are needed beyond portal innovations to increase influenza vaccination.

A Novel Approach to Clustering Accelerometer Data for Application in Passive Predictions of Changes in Depression Severity.

The treatment of mood disorders, which can become a lifelong process, varies widely in efficacy between individuals. Most options to monitor mood rely on subjective self-reports and clinical visits, which can be burdensome and may not portray an accurate representation of what the individual is experiencing. A passive method to monitor mood could be a useful tool for those with these disorders. Some previously proposed models utilized sensors from smartphones and wearables, such as the accelerometer. This study examined a novel approach of processing accelerometer data collected from smartphones only while participants of the open-science branch of the BiAffect study were typing. The data were modeled by von Mises-Fisher distributions and weighted networks to identify clusters relating to different typing positions unique for each participant. Longitudinal features were derived from the clustered data and used in machine learning models to predict clinically relevant changes in depression from clinical and typing measures. Model accuracy was approximately 95%, with 97% area under the ROC curve (AUC). The accelerometer features outperformed the vast majority of clinical and typing features, which suggested that this new approach to analyzing accelerometer data could contribute towards unobtrusive detection of changes in depression severity without the need for clinical input.

Barriers and facilitators of electronic patient portal uptake for asthma management.

OBJECTIVE: An active patient-practitioner partnership is a key aspect of asthma management and patient-reported data helps with shared decision making. Technological advances such as the electronic patient portal can facilitate partnership, with the goal of improved asthma outcomes. However, uptake of portals by end-users for asthma management has been low. We studied portal-based asthma interventions to understand barriers and facilitators to its use. DATA SOURCES: We searched within the PubMed, Web of Science, Scopus, MEDLINE, and Google Scholar databases. STUDY SELECTIONS: We used the PRISMA extension for scoping reviews to guide our analysis of studies related to asthma and patient portals. We summarized relevant studies in terms of barriers and facilitators as well as study characteristics. RESULTS: Sixteen studies were included in our final analysis. Common barriers to patient portal use for asthma management were lack of perceived value by the end-user, low end-user technological literacy, and limited resources. Facilitators of portal use included ease of use, personalization, and adequate technical support. Patient portals in these studies were used for a variety of applications related to core asthma management concepts of assessment and monitoring, education for a partnership in asthma care, environmental factors, co-morbidities, and medications. CONCLUSIONS: Patient portal use for asthma management can be encouraged by ensuring the portal is easy to access and navigate, demonstrates values, as well as has readily available technical support. Involving end-users closely in the design process and implementation may help address barriers. Special attention is needed for groups with technological resource limitations.

Smartphone accelerometer data as a proxy for clinical data in modeling of bipolar disorder symptom trajectory.

Being able to track and predict fluctuations in symptoms of mental health disorders such as bipolar disorder outside the clinic walls is critical for expanding access to care for the global population. To that end, we analyze a dataset of 291 individuals from a smartphone app targeted at bipolar disorder, which contains rich details about their smartphone interactions (including typing dynamics and accelerometer motion) collected everyday over several months, along with more traditional clinical features. The aim is to evaluate whether smartphone accelerometer data could serve as a proxy for traditional clinical data, either by itself or in combination with typing dynamics. Results show that accelerometer data improves the predictive performance of machine learning models by nearly 5% over those previously reported in the literature based only on clinical data and typing dynamics. This suggests it is possible to elicit essentially the same "information" about bipolar symptomology using different data sources, in a variety of settings.

Associations between smartphone keystroke dynamics and cognition in MS.

Objective: Examine the associations between smartphone keystroke dynamics and cognitive functioning among persons with multiple sclerosis (MS). Methods: Sixteen persons with MS with no self-reported upper extremity or typing difficulties and 10 healthy controls (HCs) completed six weeks of remote monitoring of their keystroke dynamics (i.e., how they typed on their smartphone keyboards). They also completed a comprehensive neuropsychological assessment and symptom ratings about fatigue, depression, and anxiety at baseline. Results: A total of 1,335,787 keystrokes were collected, which were part of 30,968 typing sessions. The MS group typed slower (P < .001) and more variably (P = .032) than the HC group. Faster typing speed was associated with better performance on measures of processing speed (P = .016), attention (P = .022), and executive functioning (cognitive flexibility: P = .029; behavioral inhibition: P = .002; verbal fluency: P = .039), as well as less severe impact from fatigue (P < .001) and less severe anxiety symptoms (P = .007). Those with better cognitive functioning and less severe symptoms showed a stronger correlation between the use of backspace and autocorrection events (P < .001). Conclusion: Typing speed may be sensitive to cognitive functions subserved by the frontal-subcortical brain circuits. Individuals with better cognitive functioning and less severe symptoms may be better at monitoring their typing errors. Keystroke dynamics have the potential to be used as an unobtrusive remote monitoring method for real-life cognitive functioning among persons with MS, which may improve the detection of relapses, evaluate treatment efficacy, and track disability progression.

Daily Associations of Air Pollution and Pediatric Asthma Risk Using the Biomedical REAI-Time Health Evaluation (BREATHE) Kit.

Background: Exposure to air pollution is associated with acute pediatric asthma exacerbations, including reduced lung function, rescue medication usage, and increased symptoms; however, most studies are limited in investigating longitudinal changes in these acute effects. This study aims to investigate the effects of daily air pollution exposure on acute pediatric asthma exacerbation risk using a repeated-measures design. Methods: We conducted a panel study of 40 children aged 8−16 years with moderate-to-severe asthma. We deployed the Biomedical REAI-Time Health Evaluation (BREATHE) Kit developed in the Los Angeles PRISMS Center to continuously monitor personal exposure to particulate matter of aerodynamic diameter < 2.5 µm (PM2.5), relative humidity and temperature, geolocation (GPS), and asthma outcomes including lung function, medication use, and symptoms for 14 days. Hourly ambient (PM2.5, nitrogen dioxide (NO2), ozone (O3)) and traffic-related (nitrogen oxides (NOx) and PM2.5) air pollution exposures were modeled based on location. We used mixed-effects models to examine the association of same day and lagged (up to 2 days) exposures with daily changes in % predicted forced expiratory volume in 1 s (FEV1) and % predicted peak expiratory flow (PEF), count of rescue inhaler puffs, and symptoms. Results: Participants were on average 12.0 years old (range: 8.4−16.8) with mean (SD) morning %predicted FEV1 of 67.9% (17.3%) and PEF of 69.1% (18.4%) and 1.4 (3.5) puffs per day of rescue inhaler use. Participants reported chest tightness, wheeze, trouble breathing, and cough symptoms on 36.4%, 17.5%, 32.3%, and 42.9%, respectively (n = 217 person-days). One SD increase in previous day O3 exposure was associated with reduced morning (beta [95% CI]: −4.11 [−6.86, −1.36]), evening (−2.65 [−5.19, −0.10]) and daily average %predicted FEV1 (−3.45 [−6.42, −0.47]). Daily (lag 0) exposure to traffic-related PM2.5 exposure was associated with reduced morning %predicted PEF (−3.97 [−7.69, −0.26]) and greater odds of "feeling scared of trouble breathing" symptom (odds ratio [95% CI]: 1.83 [1.03, 3.24]). Exposure to ambient O3, NOx, and NO was significantly associated with increased rescue inhaler use (rate ratio [95% CI]: O3 1.52 [1.02, 2.27], NOx 1.61 [1.23, 2.11], NO 1.80 [1.37, 2.35]). Conclusions: We found significant associations of air pollution exposure with lung function, rescue inhaler use, and "feeling scared of trouble breathing." Our study demonstrates the potential of informatics and wearable sensor technologies at collecting highly resolved, contextual, and personal exposure data for understanding acute pediatric asthma triggers.

Interstitial lung disease in children with Rubinstein-Taybi syndrome.

INTRODUCTION: Rubinstein-Taybi syndrome (RSTS) is a rare genetic syndrome caused primarily by a mutation in the CREBBP gene found on chromosome 16. Patients with RSTS are at greater risk for a variety of medical problems, including upper airway obstruction and aspiration. Childhood interstitial lung disease (ILD) thus far has not been definitively linked to RSTS. Here we present three patients with RSTS who developed ILD and discuss possible mechanisms by which a mutation in CREBBP may be involved in the development of ILD. METHODS: Routine hematoxylin and eosin staining was performed on lung biopsy tissue for histological analysis. Immunofluorescent staining was performed on lung biopsy tissue for markers of fibrosis, surfactant deficiency and histone acetylation. Cases 1 and 2 had standard clinical microarray analysis. Case 3 had whole exome sequencing. Bioinformatics analyses were performed to identify possible causative genes using ToppGene. RESULTS: Computed tomography images in all cases showed consolidated densities overlying ground glass opacities. Lung histopathology revealed accumulation of proteinaceous material within alveolar spaces, evidence of fibrosis, and increased alveolar macrophages. Immunofluorescent staining showed increase in surfactant protein C staining, patchy areas of increased anti-smooth muscle antibody staining, and increased staining for acetylated histone 2 and histone 3 lysine 9. DISCUSSION: Clinical characteristics, radiographic imaging, lung histopathology, and immunofluorescent staining results shared by all cases demonstrated findings consistent with ILD. Immunofluorescent staining suggests two possible mechanisms for the development of ILD: abnormal surfactant metabolism and/or persistent activation of myofibroblasts. These two pathways could be related to dysfunctional CREBBP protein.

Partnered decision support: Parental perspectives of completing a pre-visit pediatric asthma questionnaire via the patient portal.

BACKGROUND: Collection of patient-reported data has been demonstrated to improve asthma outcomes. One method to collect information is through the electronic patient portal. In practice, patient portal use in pediatrics and, specifically for asthma management, has had low uptake. OBJECTIVE: To understand parental/caregiver experience of pediatric asthma care management, and perceptions of the use of patient portal questionnaires before the clinic visit. METHODS: We conducted semi-structured interviews with caregivers of children 5-11 years old with asthma in the University of California, Los Angeles (UCLA) Health System. We included patient portal "users" (n = 20) and "non-users" (n = 5). Interview questions were developed based on clinic visit workflow with a focus on perceived usefulness and ease of use to complete pediatric asthma questionnaires in the patient portal before the visit. Interviews were audio-recorded, transcribed, and codes were generated from themes using constant comparative analysis. RESULTS: We identified eight themes related to caregiver-physician communication, perception of portal questionnaires, facilitators, and barriers to portal questionnaire use. A salient finding was that caregivers considered the portal questionnaire as a tool to be integrated into the visit to facilitate a conversation about their child's asthma. Caregiver portal-based questionnaire use was more likely if the ongoing data entered was accessible to caregivers to track and update, and if caregivers were reassured the clinicians would use questionnaire responses during the visit. CONCLUSION: Caregivers of children with asthma are more likely to complete a patient portal intake questionnaire before the visit if they trust their responses will be used during the visit to inform care.

Asthma clustering methods: a literature-informed application to the children's health study data.

OBJECTIVE: The heterogeneity of asthma has inspired widespread application of statistical clustering algorithms to a variety of datasets for identification of potentially clinically meaningful phenotypes. There has not been a standardized data analysis approach for asthma clustering, which can affect reproducibility and clinical translation of results. Our objective was to identify common and effective data analysis practices in the asthma clustering literature and apply them to data from a Southern California population-based cohort of schoolchildren with asthma. METHODS: As of January 1, 2020, we reviewed key statistical elements of 77 asthma clustering studies. Guided by the literature, we used 12 input variables and three clustering methods (hierarchical clustering, k-medoids, and latent class analysis) to identify clusters in 598 schoolchildren with asthma from the Southern California Children's Health Study (CHS). RESULTS: Clusters of children identified by latent class analysis were characterized by exhaled nitric oxide, FEV1/FVC, FEV1 percent predicted, asthma control and allergy score; and were predictive of control at two year follow up. Clusters from the other two methods were less clinically remarkable, primarily differentiated by sex and race/ethnicity and less predictive of asthma control over time. CONCLUSION: Upon review of the asthma phenotyping literature, common approaches of data clustering emerged. When applying these elements to the Children's Health Study data, latent class analysis clusters-represented by exhaled nitric oxide and spirometry measures-had clinical relevance over time.

Real-time associations among MS symptoms and cognitive dysfunction using ecological momentary assessment.

Introduction: Multiple sclerosis (MS) is characterized by a wide range of disabling symptoms, including cognitive dysfunction, fatigue, depression, anxiety, pain, and sleep difficulties. The current study aimed to examine real-time associations between non-cognitive and cognitive symptoms (latter measured both objectively and subjectively in real-time) using smartphone-administered ecological momentary assessment (EMA). Methods: Forty-five persons with MS completed EMA four times per day for 3 weeks. For each EMA, participants completed mobile versions of the Trail-Making Test part B (mTMT-B) and a finger tapping task, as well as surveys about symptom severity. Multilevel models were conducted to account for within-person and within-day clustering. Results: A total of 3,174 EMA sessions were collected; compliance rate was 84%. There was significant intra-day variability in mTMT-B performance (p < 0.001) and levels of self-reported fatigue (p < 0.001). When participants reported depressive symptoms that were worse than their usual levels, they also performed worse on the mTMT-B (p < 0.001), independent of upper extremity motor functioning. Other self-reported non-cognitive symptoms were not associated with real-time performance on the mTMT-B [p > 0.009 (Bonferroni-corrected)]. In contrast, when self-reported fatigue (p < 0.001), depression (p < 0.001), anxiety (p < 0.001), and pain (p < 0.001) were worse than the individual's typical levels, they also reported more severe cognitive dysfunction at the same time. Further, there was a statistical trend that self-reported cognitive dysfunction (not mTMT-B performance) predicted one's self-reported sense of accomplishment in real-time. Discussion: The current study was the first to identify divergent factors that influence subjectively and objectively measured cognitive functioning in real time among persons with MS. Notably, it is when symptom severity was worse than the individual's usual levels (and not absolute levels) that led to cognitive fluctuations, which supports the use of EMA in MS symptom monitoring.

Naturalistic smartphone keyboard typing reflects processing speed and executive function.

OBJECTIVE: The increase in smartphone usage has enabled the possibility of more accessible ways to conduct neuropsychological evaluations. The objective of this study was to determine the feasibility of using smartphone typing dynamics with mood scores to supplement cognitive assessment through trail making tests. METHODS: Using a custom-built keyboard, naturalistic keypress dynamics were unobtrusively recorded in individuals with bipolar disorder (n = 11) and nonbipolar controls (n = 8) on an Android smartphone. Keypresses were matched to digital trail making tests part B (dTMT-B) administered daily in two periods and weekly mood assessments. Following comparison of dTMT-Bs to the pencil-and-paper equivalent, longitudinal mixed-effects models were used to analyze daily dTMT-B performance as a function of typing and mood. RESULTS: Comparison of the first dTMT-B to paper TMT-B showed adequate reliability (intraclass correlations = 0.74). In our model, we observed that participants who typed slower took longer to complete dTMT-B (b = 0.189, p < .001). This trend was also seen in individual fluctuations in typing speed and dTMT-B performance (b = 0.032, p = .004). Moreover, participants who were more depressed completed the dTMT-B slower than less depressed participants (b = 0.189, p < .001). A practice effect was observed for the dTMT-Bs. CONCLUSION: Typing speed in combination with depression scores has the potential to infer aspects of cognition (visual attention, processing speed, and task switching) in people's natural environment to complement formal in-person neuropsychological assessments that commonly include the trail making test.

Adverse Childhood Experiences and Association With Pediatric Asthma Severity in the 2016-2017 National Survey of Children's Health.

OBJECTIVES: Prior studies have found that adverse childhood experiences (ACEs) are associated with asthma prevalence and onset, presumably related to stress and increased inflammation. We hypothesized that ACEs may be associated with asthma severity as well. We studied the 2016-2017 US National Survey of Children's Health dataset to explore the relationship between ACEs and pediatric asthma severity. METHODS: We analyzed children ages 0 to 17 years old who had current caregiver-reported asthma diagnosed by a healthcare provider. We reported descriptive characteristics using chi-square analysis of variance (ANOVA) and used multivariable regression analysis to assess the relationship of cumulative and individual ACEs with asthma severity. Survey sampling weights and SAS survey procedures were implemented to produce nationally representative results. RESULTS: Our analysis included 3691 children, representing a population of 5,465,926. Unadjusted analysis demonstrated that ACEs - particularly household economic hardship, parent/guardian served time in jail, witnessed household violence, or victim/witness of neighborhood violence - were each associated with higher odds of moderate/severe caregiver-reported asthma. After controlling for confounders possibly associated with both exposure (ACEs) and outcome (asthma severity), children who witnessed parent/adult violence had higher adjusted odds of caregiver-reported moderate/severe asthma. (1.67, confidence interval 1.05-2.64, P = .03) CONCLUSIONS: Intrafamilial witnessed household violence is significantly associated with caregiver-reported moderate/severe asthma.