Using natural language processing in emergency medicine health service research: A systematic review and meta-analysis.

OBJECTIVES: Natural language processing (NLP) represents one of the adjunct technologies within artificial intelligence and machine learning, creating structure out of unstructured data. This study aims to assess the performance of employing NLP to identify and categorize unstructured data within the emergency medicine (EM) setting. METHODS: We systematically searched publications related to EM research and NLP across databases including MEDLINE, Embase, Scopus, CENTRAL, and ProQuest Dissertations & Theses Global. Independent reviewers screened, reviewed, and evaluated article quality and bias. NLP usage was categorized into syndromic surveillance, radiologic interpretation, and identification of specific diseases/events/syndromes, with respective sensitivity analysis reported. Performance metrics for NLP usage were calculated and the overall area under the summary of receiver operating characteristic curve (SROC) was determined. RESULTS: A total of 27 studies underwent meta-analysis. Findings indicated an overall mean sensitivity (recall) of 82%-87%, specificity of 95%, with the area under the SROC at 0.96 (95% CI 0.94-0.98). Optimal performance using NLP was observed in radiologic interpretation, demonstrating an overall mean sensitivity of 93% and specificity of 96%. CONCLUSIONS: Our analysis revealed a generally favorable performance accuracy in using NLP within EM research, particularly in the realm of radiologic interpretation. Consequently, we advocate for the adoption of NLP-based research to augment EM health care management.

Multimorbidity and chronic pain management with opioids and other therapies among adults in the United States: A cross-sectional study.

Background: Multimorbidity, defined as the concurrent presence of ≥ 2 chronic conditions, and chronic pain (i.e., pain lasting ≥3 months) often co-exist. Multimodal pain management that includes non-pharmacologic treatment and non-opioid therapy is recommended to prevent serious risks associated with opioids. Purpose: Estimate the prevalence of types of pain treatment and analyze their associations with multimorbidity using a nationally representative survey in the United States (US). Methods: Data was collected from the 2020 National Health Interview Survey among adults with chronic pain and chronic conditions (N= 12,028). Chronic pain management was grouped into four categories: opioid therapy; non-opioid multimodal pain treatment; pain treatment with monotherapy; and no pain treatment. Chi-square tests and multivariable multinomial logistic regressions were used to analyze the association of multimorbidity with types of pain treatment after controlling for age, sex, social determinants of health (SDoH), and lifestyle characteristics. Results: Among NHIS respondents, 68% had multimorbidity. In adjusted multinomial logistic regressions with "pain management with monotherapy" as the reference group, those with multimorbidity were more likely to utilize opioids (AOR=1.63, 95% CI=1.23, 2.17). Those with severe pain were also more likely to use opioid therapy (AOR=19.36, 95% CI=13.35, 28.06) than those with little pain. Those with low income and education were less likely to have multimodal pain management without opioids. Conclusion: Seven in 10 adults had multimorbidity. Those with multimorbidity reported severe pain and relied on opioids for pain control. Regardless of multimorbidity status, SDoH was associated with types of chronic pain management.

Stress Due to Inflation: Changes over Time, Correlates, and Coping Strategies among Working-Age Adults in the United States.

BACKGROUND: During the COVID-19 pandemic, the annual US inflation rate increased from 1.2% in July 2020 to 8% in July 2022. It has since declined to 3.4% (December 2023). This study examined the prevalence of stress due to inflation during a period when it declined from 8.2% in September 2022 to 3% in June 2023 and its association with demographic and social determinants of health (SDOH). METHODS: We conducted a cross-sectional analysis of the online Household Pulse Survey (HPS), which surveils the experiences of US households. Beginning September 2022, HPS initiated data collection on "stress due to inflation" through a question on how stressful the increase in prices in the last 2 months has been. Participants could respond: very, moderately, a little, or not stressful. We analyzed data on working-age adults (18-64 years) who responded to the above question of stress due to inflation during 14-26 September 2022 (N = 32,579) and 7-19 June 2023 (N = 36,229). We used replicate weights in chi-squared tests and ordinal logistic regression analyses controlling for gender, age, race and ethnicity, COVID-19, COVID-19 vaccination, health insurance, and SDOH, including education, lost employment income, poverty status, marital status, food affordability, and region. RESULTS: The prevalence of stress due to inflation (price increases being very or moderately stressful) significantly increased from 76.9% in September 2022 to 78.9% in June 2023. The odds of stress due to inflation were higher for individuals with the following characteristics: female, transgender, having income below 400% of the federal poverty line, having lost employment income, not being able to afford food, had long or acute COVID-19, and did not have a COVID-19 vaccine. CONCLUSIONS: More than three quarters of working-age adults in the US experienced stress due to inflation. Despite a declining national inflation rate in recent months, stress due to inflation has significantly increased among working-age adults. Inflation-related stress warrants further research and policy attention.

Food insecurity and COVID-19 vaccine hesitancy among adults in the United States (US).

BACKGROUND: Some documented barriers of vaccination behaviors include social determinants of health (SDoH). While there have been many devastations from COVID-19, food security has fluctuated during the pandemic. The purpose of this study is to examine the association with food insecurity and vaccine hesitancy among adults in the U.S. METHODS: In response to the COVID-19 pandemic, Federal agencies created the online Household Pulse Survey (HPS) to track social outcomes of the pandemic in the U.S. We performed cross-sectional analysis with data from the HPS collected between March 30, 2022, and April 11, 2022 (Week 44) on adults (N = 6449, weighted N = 37,687,910). Vaccine hesitancy was divided into two groups: 1) probably, not sure, probably not, and definitely not receiving the vaccine, and 2) received the vaccine and definitely will get the vaccine. Food sufficiency was a binary variable (Yes/no) based on the question that best described the food eaten in the household the last 7 days. Chi-square tests and multivariable logistic regressions were conducted using replicate weights with SAS. Logistic regressions adjusted for sex, age, race and ethnicity, income, education, COVID-19 infection, health insurance, food insecurity, children <17 years, remote work, health worker status, functional status, and mental health. RESULTS: During HPS Week 44, 11.2 % of represented Americans experienced food insecurity and 13.8 % were vaccine hesitant. The unadjusted odds ratio was 2.41 (95 % CI = 1.30, 4.50), suggesting adults with food insecurity were more likely to be vaccine hesitant than those with food security. After adjusting for age, sex, race and ethnicity, marital status, and COVID-19 history, the statistical significance remained (AOR = 2.14, 95 % CI = 1.15, 3.99). However, after adjusting for education, we no longer observed a significant association (AOR = 1.70, 95 % CI = 0.89, 3.34), and it remained insignificant in the fully adjusted model (AOR = 1.62, 95 % CI = 0.78, 3.34). CONCLUSION: Overall, adults with food insecurity were 2.41 times as likely as those with food security to also have vaccine hesitancy. Education mediated the relationship between food insufficiency and vaccine hesitancy. Programs to improve vaccination rates need to also focus on food sufficiency.

Ethnic Disparities in Patient-Centered Communication with Healthcare Providers: A Comparison of Non-Hispanic Asians and Non-Hispanic Whites.

BACKGROUND: Patient-centered communication (PCC) is an important metric related to patient health outcomes. Non-Hispanic Asian (NHA) individuals reported lower PCC scores with healthcare providers than individuals of other races and ethnicities. We aim to determine the ethnic disparities in PCC and further investigate the association between PCC, individual's confidence in obtaining health-related information (health literacy), and confidence in taking care of themselves (health self-efficacy) among NHA in comparison to the Non-Hispanic White (NHW) population. METHODS: This is a cross-sectional study. We used data from the Health Information National Trends Survey 5 (HINTS) cycles 1 and 3. PCC was measured using seven validated survey questions with scores ranging from 7 to 28. Individuals' health literacy and self-efficacy were accessed using two-item survey questions. Multivariable logistic regression analyses were performed to determine the association of independent factors (e.g., self-efficacy, health literacy, and race and ethnicity) with PCC. RESULTS: We included 3,831 participants. Fewer NHAs (35.43%) reported high PCC scores compared to NHWs (48.99%, p = 0.0184). In fully adjusted logistic regression model, although NHAs were less likely to have high PCC scores (aOR = 0.78, 95% CI 0.46-1.32) compared to NHWs, the association was not statistically significant (p = 0.350). However, the aOR of confidence in self-efficacy associated with high PCC scores was 2.27 (95% CI 1.68-3.07, p < 0.001) and the aOR of confidence in health literacy with high PCC scores was 2.13 (95% CI 1.64-2.76, p < 0.001). CONCLUSIONS: High PCC scores reported by NHA were not significantly different from those of NHW individuals in adjusted comparisons. Individual's confidence in self-efficacy and health literacy was positively associated with high PCC regardless of race and ethnicity.

Exploring facilitators and barriers to patient-provider communication regarding diabetes self-management.

Objective: Long-term type 2 diabetes management requires open communication between a patient and their provider for self-care adherence. This study explored facilitators and barriers to diabetes-specific communication in West Virginia, a medically underserved state with the highest prevalence of diabetes (16.2%) in the U.S. Methods: A secondary qualitative analysis was conducted using data from semi-structured interviews (n = 34) and 4 focus groups (n = 23) with participants of a diabetes education program. Transcripts were analyzed using thematic analysis. Results: Three facilitators to patient-provider communication emerged: "Patient-Provider Partnership", "Provider Accessibility", and "Empowerment Through Education". Partnership with providers, especially those who were accessible outside of scheduled appointments, and empowerment obtained through diabetes education facilitated diabetes-specific patient-provider communication. However, barriers included "Providers' Focus on 'Numbers' Rather than Patient Concerns", "Patient Lack of Preparation for Appointments", and "Providers 'Talking Down to' Patients". Conclusion: The findings highlight patient- and provider-related factors that impact diabetes-specific communication. Innovation: Previous interventions have trained providers in communication skills. Despite some success, challenges in implementation remain considering providers' limited time and overwhelming demands. This study suggests that diabetes education programs incorporating communication and self-advocacy skills could be a promising alternative as they can empower patients to communicate their diabetes-specific needs/preferences with providers.

Community perspectives on AI/ML and health equity: AIM-AHEAD nationwide stakeholder listening sessions.

Artificial intelligence and machine learning (AI/ML) tools have the potential to improve health equity. However, many historically underrepresented communities have not been engaged in AI/ML training, research, and infrastructure development. Therefore, AIM-AHEAD (Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity) seeks to increase participation and engagement of researchers and communities through mutually beneficial partnerships. The purpose of this paper is to summarize feedback from listening sessions conducted by the AIM-AHEAD Coordinating Center in February 2022, titled the "AIM-AHEAD Community Building Convention (ACBC)." A total of six listening sessions were held over three days. A total of 977 people registered with AIM-AHEAD to attend ACBC and 557 individuals attended the listening sessions across stakeholder groups. Facilitators led the conversation based on a series of guiding questions, and responses were captured through voice and chat via the Slido platform. A professional third-party provider transcribed the audio. Qualitative analysis included data from transcripts and chat logs. Thematic analysis was then used to identify common and unique themes across all transcripts. Six main themes arose from the sessions. Attendees felt that storytelling would be a powerful tool in communicating the impact of AI/ML in promoting health equity, trust building is vital and can be fostered through existing trusted relationships, and diverse communities should be involved every step of the way. Attendees shared a wealth of information that will guide AIM-AHEAD's future activities. The sessions highlighted the need for researchers to translate AI/ML concepts into vignettes that are digestible to the larger public, the importance of diversity, and how open-science platforms can be used to encourage multi-disciplinary collaboration. While the sessions confirmed some of the existing barriers in applying AI/ML for health equity, they also offered new insights that were captured in the six themes.

The Association of Multimorbidity With Whole Health Activities Among Adults in the United States: Evidence From the NHIS and BRFSS.

Background: Whole health is a holistic approach encompassing integrative medicine, emotional, and spiritual health and is critical to improving health outcomes among individuals with multimorbidity. Objective: To examine the prevalence of Whole Health activities and the association of multimorbidity and Whole Health activities using nationally representative datasets. Methods: As no single dataset has information on Whole Health self-care activities, data from the 2017 National Health Interview Survey (n = 25 134) was used to measure participants' mind-body therapy usage, sleep, mental health, and physical activity. We used the 2017 Behavioral Risk Factor Surveillance System (n = 347 029) to assess regular vegetable and/or fruit consumption. Results: A significantly lower percentage of adults with multimorbidity had adequate sleep (58.2%vs.67.1%), no psychological distress (71.8%vs.82.1%), adequate physical activity (48.2%vs.62.1%), and regular vegetable and/or fruit consumption (54.2%vs.56.6%) compared to those without multimorbidity. Although lower percentages of adults with multimorbidity utilized mind-body therapies (22.9%vs.25.2%), the association was reversed when adjusted for socioeconomic factors. In the fully adjusted models, adults with multimorbidity were more likely to use mind-body therapies (AOR = 1.19, 95%CI = 1.09, 1.31). Furthermore, when adjusting for other independent variables, the associations of multimorbidity with sleep, psychological distress, and diet were exacerbated, and the association of multimorbidity with physical activity was attenuated. Conclusion: Adults with multimorbidity were less likely to engage in most of the Whole Health activities except mind-body therapies compared to the no multimorbidity group. Findings suggest that adjustment for other factors such as age and socioeconomic status changed the magnitude and direction of the association of multimorbidity with Whole Health activities.

Association of multimorbidity with the use of health information technology.

Objective: To examine the association of multimorbidity with health information technology use among adults in the USA. Methods: We used cross-sectional study design and data from the Health Information National Trends Survey 5 Cycle 4. Health information technology use was measured with ten variables comprising access, recent use, and healthcare management. Unadjusted and adjusted logistic and multinomial logistic regressions were used to model the associations of multimorbidity with health information technology use. Results: Among adults with multimorbidity, health information technology use for specific purposes ranged from 37.8% for helping make medical decisions to 51.7% for communicating with healthcare providers. In multivariable regressions, individuals with multimorbidity were more likely to report general use of health information technology (adjusted odds ratios = 1.48, 95% confidence intervals = 1.01-2.15) and more likely to use health information technology to check test results (adjusted odds ratios = 1.85, 95% confidence intervals = 1.33-2.58) compared to adults with only one chronic condition, however, there were no significant differences in other forms of health information technology use. We also observed interactive associations of multimorbidity and age on various components of health information technology use. Compared to younger adults with multimorbidity, older adults (≥ 65 years of age) with multimorbidity were less likely to use almost all aspects of health information technology. Conclusion: Health information technology use disparities by age and multimorbidity were observed. Education and interventions are needed to promote health information technology use among older adults in general and specifically among older adults with multimorbidity.

Assessment of Joint and Interactive Effects of Multimorbidity and Chronic Pain on ADRD Risk in the Elder Population.

Objective: Multimorbidity and non-cancer chronic pain conditions (NCPC) are independently linked to elevated risk for cognitive impairment and incident Alzheimer's Disease and Related Dementias (ADRD)-both - We present the study of potential joint and interactive effects of these conditions on the risk of incident ADRD in older population. Methods: This retrospective-cohort study drew baseline and 2-year follow-up data from linked Medicare claims and Medicare Current Beneficiary Survey (MCBS). Baseline multimorbidity and NCPC were ascertained using claims data. ADRD was ascertained at baseline and follow-up. Results: NCPC accompanied by multimorbidity (vs. absence of NCPC or multimorbidity) had a significant and upward association with incident ADRD (adjusted odds ratio (AOR): 1.72, 95% CI 1.38, 2.13, p < 0.0001). Secondary analysis by number of comorbid conditions suggested that the joint effects of NCPC and multimorbidity on ADRD risk may increase with rising number contributing chronic conditions. Interaction analyses indicated significantly elevated excess risk for incident ADRD.

Prescription Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) and Incidence of Depression Among Older Cancer Survivors With Osteoarthritis: A Machine Learning Analysis.

ObjectiveS: This study examined prescription NSAIDs as one of the leading predictors of incident depression and assessed the direction of the association among older cancer survivors with osteoarthritis. Methods: This study used a retrospective cohort (N = 14, 992) of older adults with incident cancer (breast, prostate, colorectal cancers, or non-Hodgkin's lymphoma) and osteoarthritis. We used the longitudinal data from the linked Surveillance, Epidemiology, and End Results -Medicare data for the study period from 2006 through 2016, with a 12-month baseline and 12-month follow-up period. Cumulative NSAIDs days was assessed during the baseline period and incident depression was assessed during the follow-up period. An eXtreme Gradient Boosting (XGBoost) model was built with 10-fold repeated stratified cross-validation and hyperparameter tuning using the training dataset. The final model selected from the training data demonstrated high performance (Accuracy: 0.82, Recall: 0.75, Precision: 0.75) when applied to the test data. SHapley Additive exPlanations (SHAP) was used to interpret the output from the XGBoost model. Results: Over 50% of the study cohort had at least one prescption of NSAIDs. Nearly 13% of the cohort were diagnosed with incident depression, with the rates ranging between 7.4% for prostate cancer and 17.0% for colorectal cancer. The highest incident depression rate of 25% was observed at 90 and 120 cumulative NSAIDs days thresholds. Cumulative NSAIDs days was the sixth leading predictor of incident depression among older adults with OA and cancer. Age, education, care fragmentation, polypharmacy, and zip code level poverty were the top 5 predictors of incident depression. Conclusion: Overall, 1 in 8 older adults with cancer and OA were diagnosed with incident depression. Cumulative NSAIDs days was the sixth leading predictor with an overall positive association with incident depression. However, the association was complex and varied by the cumulative NSAIDs days.

Increase in body mass index during the COVID-19 pandemic among people who smoke: An analysis of multi-site electronic health records.

The effects of the COVID-19 period among people who smoke (compared by sex) are largely unknown. The purpose of this study was to compare body mass index (BMI) increase among men and women who smoked during the pandemic. We used a retrospective longitudinal, observational study design of secondary data. We used electronic health records from TriNetX network (n = 486,072) from April 13, 2020-May 5, 2022 among adults aged 18-64 who smoked and had a normal BMI prior to the pandemic. The main measure was a change of BMI from < 25 to ≥25. Risk ratio was determined between men and women with propensity score matching. Overall, 15.8% increased BMI to ≥25; 44,540 (18.3%) were women and 32,341 (13.3%) were men (Risk Ratio = 1.38, 95% CI: 1.36, 1.40; p < .0001). Adults with diabetes, hypertension, asthma, COPD or emphysema or who were women, were more likely to develop BMI≥25 during the pandemic. Women who smoked were more likely to have an increase in BMI than men who smoked during the COVID-19 period.

Prevalence and factors associated with diagnosed diabetes mellitus among Asian Indian adults in the United States.

Higher prevalence of diabetes mellitus (DM) has been documented among South Asians living in the United States. However, combining the south Asian subgroups into one category masks the heterogeneity in the diagnosed DM, after controlling for known protective and risk factors. We assessed the association of Asian Indian ethnicity to diagnosed DM using a nationally representative sample of 1,986 Asian Indian adults in the US compared to 109,072 Non-Hispanic Whites (NHWs) using disaggregated data from the National Health Interview Survey (2012-2016) (NHIS). 2010 US census figures were used for age-sex standardization. Age-sex adjusted prevalence of DM was 8.3% in Asian Indians as compared to 5.8% in NHW. In adjusted multivariable logistic regression models, Asian Indians had higher odds ratios of reporting diagnosed DM compared to NHWs (AOR = 1.39, 95% CI: 1.12, 1.71). This association remained strong and significant even after controlling for other risk factors in the model (AOR = 1.47, 95% CI: 1.16, 1.85). Results suggest a favorable socio-economic profile of Asian Indians was not protective on diagnosed DM. In addition, they were more likely to have diagnosed DM due to higher prevalence of obesity despite healthier behaviors of smoking and exercise.

Whole Health in Parts: Omissions from National Data Sets.

Background: The Whole Health model is a holistic approach to facilitate whole health practices by addressing (1) the physical, mental, and social health of individuals and (2) associated support systems. Several national organizations such as the Institute for Healthcare Improvement's (IHI) Age-Friendly Health Systems (AFHS) movement and, the U.S. Department of Veterans Affairs have implemented whole health frameworks with many common elements and promoted whole health practice and skills. However, implementing a Whole Health model across communities and health systems will require evidence of effectiveness. Generating evidence on the effectiveness of the Whole Health model's effect on health outcomes requires data-driven intelligence. Methods: We identified the national public-use data sets that are most often used in health research with a machine-assisted literature search of PubMed and Scopus for peer-reviewed journal articles published from 2010 through the end of 2021, including preprints, using Python [3.7]. We then assessed if the 8 most commonly used datasets include variables associated with whole health. Results: The number of publications examining whole health has increased annually in the last decade, with more than 2800 publications in 2020 alone. Since 2010, 24,811 articles have been published using 1 of these data sets. However, we also found a lack of data (ie, data set includes all of the whole health variables) to examine whole health in national data sets. Conclusions: We support a call to expand data collection and standardization of critical measures of whole health.

Interactive association of chronic illness and food insecurity with emergency department utilization among school-age children in the United States: A cross-sectional study.

Background and Aims: Food insecurity combined with chronic disease conditions is a risk factor for Emergency Department (ED) utilization, an indicator of poor quality of care. However, such an association is not certain among school-age children with chronic conditions. Therefore, we aim to determine the association of food insecurity, chronic conditions, and ED utilization among school-age children in the United States. Methods: We analyzed the data from the 2017 Medical expenditure panel survey (MEPS) among children aged 6-17 years (N = 5518). MEPS data was released electronically by the Agency for Healthcare Research and Quality (AHRQ). We identified four groups of school-age children based on the presence of food security and chronic conditions: 1) with food insecurity and chronic conditions; 2) no food insecurity and chronic conditions; 3) with food insecurity and no chronic conditions; and 4) no food insecurity and no chronic conditions. We compared ED utilization among these four groups using incidence rate ratios (IRR) after adjusting children's age, sex, race and ethnicity, household income, insurance coverage, obesity, and geographic region using count data model, specifically multivariable Poison regression. We used SAS 9.4 and STATA 14.2 for all the data analyses. Results: There were unweighted 5518 school-age children who represented weighted 50,479,419 school-age children in the final analysis. Overall, 6.0% had food insecurity with chronic conditions. These children had higher ED utilization (19.7%) than the other three groups (13.3%, 8.8%, and 7.2%, p < 0.001). The adjusted IRR of ED utilization among school-age children with food insecurity and chronic conditions was 1.90 (95% confidence interval 1.20-3.01, p = 0.007) compared with those with food security and chronic conditions. Conclusion: One in 16 school-age children has both food insecurity and chronic conditions. Food insecurity was positively associated with frequent ED visits in the presence of chronic conditions. Therefore, addressing food insecurity may reduce the risk of ED visits.

A Multi-Level Analysis of Individual and Neighborhood Factors Associated with Patient Portal Use among Adult Emergency Department Patients with Multimorbidity.

Background: Patient portals tethered to electronic health records (EHR) have become vital to patient engagement and better disease management, specifically among adults with multimorbidity. We determined individual and neighborhood factors associated with patient portal use (MyChart) among adult patients with multimorbidity seen in an Emergency Department (ED). Methods: This study adopted a cross-sectional study design and used a linked database of EHR from a single ED site to patients' neighborhood characteristics (i.e., zip code level) from the American Community Survey. The study population included all adults (age > 18 years), with at least one visit to an ED and multimorbidity between 1 January 2019 to 31 December 2020 (N = 40,544). Patient and neighborhood characteristics were compared among patients with and without MyChart use. Random-intercept multi-level logistic regressions were used to analyze the associations of patient and neighborhood factors with MyChart use. Results: Only 19% (N = 7757) of adults with multimorbidity used the patient portal. In the fully adjusted multi-level model, at the patient level, having a primary care physician (AOR = 5.55, 95% CI 5.07−6.07, p < 0.001) and health insurance coverage (AOR = 2.41, 95% CI 2.23−2.61, p < 0.001) were associated with MyChart use. At the neighborhood level, 4.73% of the variation in MyChart use was due to differences in neighborhood factors. However, significant heterogeneity existed in patient portal use when neighborhood characteristics were included in the model. Conclusions: Among ED patients with multimorbidity, one in five adults used patient portals. Patient-level factors, such as having primary care physicians and insurance, may promote patient portal use.

Factors associated with COVID-19-related mental health among Asian Indians in the United States.

Background: In the United States, the COVID-19 pandemic has caused increased mental health symptoms and mental illness. Specific subgroups such as Asian Indians in the US have also been subject to additional stressors due to unprecedented loss of lives in their home country and increased Asian hate due to the misperception that Asians are to be blamed for the spread of the SARS-CoV-2. Objective: We examined the various factors including discrimination associated with COVID-19-related mental health symptoms among Asian Indians. Methods: We administered an online survey between May 2021 and July 2021 using convenient and snowball sampling methods to recruit Asian Indian adults (age > 18 years, N = 289). The survey included questions on mental health and the experience with unfair treatment in day-to-day life. Descriptive analysis and logistic regressions were performed. Results: Overall, 46.0% reported feeling down, depressed, or lonely and feeling nervous, tense, or worried due to the COVID-19 pandemic; 90.0% had received at least one dose of vaccination and 74.7% reported some form of discrimination. In the fully-adjusted logistic regression, age (AOR = 0.95; 95%CI- 0.92, 0.97;p < 0.01) and general health (AOR=0.84; 95%CI- 0.73, 0.97; p < 0.015) were negatively associated with mental health symptoms. Participants who experienced discrimination were more likely (AOR=1.26; 95%CI- 1.08, 1.46; p < 0.01) to report mental health symptoms. Conclusion: In this highly vaccinated group of Asian Indians discriminatory behaviors were associated with mental health symptoms suggesting the need for novel institutional level policy responses to reduce anti-Asian racism.

Factors Associated with Reducing Disparities in Electronic Personal Heath Records Use Among Non-Hispanic White and Hispanic Adults.

OBJECTIVES: Personal health records (PHR) use has improved individuals' health outcomes. The adoption of PHR remains low with documented racial disparities. We aim to determine factors associated with reducing racial and ethnic disparities among Hispanic adults in PHR use. METHODS: Participants included non-Hispanic White (NHW) and Hispanic adults (age ≥ 18 years) enrolled in Health Information National Trends Survey in 2018 and 2019. We identified PHR use as online medical record access in the last 12 months. We considered three factors (1. accessing mHealth Apps on the phone, 2. having a usual source of care, and 3. electronically communicating (e-communication) with healthcare providers) as facilitating PHR use. Multivariable logistic regressions with replicate weights were analyzed to determine factors associated with racial/ethnic disparities in PHR use after controlling for general characteristics (i.e., sex, age, education, insurance status, and income). RESULTS: A lower percentage of Hispanics than NHWs used PHR (42.0% vs. 53.5%, P < .001). When adjusted for individual general characteristics, the adjusted odds ratio (AOR) of e-communication with healthcare providers associated with PHR use was 1.49 (1.19-1.86, P < .001), AOR was 2.06 (1.62-2.6, P < .001) on accessing to mHealth App, and 2.60 (1.86-3.63, P < .001) on having a usual source of care. However, the racial difference was not statistically significant after adjusting three factors promoting PHR use (AOR = 0.90, 95% CI = 0.66, 1.22, P = .48). CONCLUSIONS: Ethnic disparities were reduced when PHR use was facilitated by having a usual source of care, active e-communication, and having access to mHealth apps. Interventions focusing on these three factors may potentially reduce racial/ethnic disparities.

A Linear Decomposition Approach to Explain Excess Direct Healthcare Expenditures Associated with Pain Among Adults with Osteoarthritis.

Objective: Many patients with osteoarthritis experience pain which can lead to higher healthcare expenditures. It is important to understand the factors that drive the excess expenditures associated with pain in osteoarthritis. Design: Cross-sectional. Study sample: Our study sample consisted of adults (age ⩾ 18 years) from the Medical Expenditure Panel Survey (MEPS, 2018). Methods: Adults who were alive during the calendar year and had pain status were included in this study (N = 2804 weighted N = 32.03 million). Osteoarthritis was identified from the medical conditions file and household file. We used multivariable ordinary least squares regression to identify the statistically significant association of pain with direct healthcare expenditures. The Blinder-Oaxaca post-linear decomposition on log-transformed total direct healthcare expenditures was used to estimate the extent to which differences in characteristics contribute to the excess expenditures associated with pain. Results: Adults with osteoarthritis and pain had higher average expenditures ($21 814 vs $10 827, P < .001; 9.318 vs 8.538 in logtransformed expenditures) compared to those without pain. Pooled regression weights explained 62.9% of excess expenditures differences in characteristics between the 2 groups. The 2 main drivers of excess healthcare expenditures among adults with osteoarthritis and pain were (i) comorbidities (diabetes, asthma, chronic obstructive pulmonary disease, depression, heart diseases, cancer, and non-cancer pain conditions and (ii) prescription medications (NSAIDs, opioids, and polypharmacy). Conclusion: Need factors such as comorbid conditions, and prescription treatment explained the excess healthcare expenditures among adults with osteoarthritis and pain. The study findings suggest that reducing polypharmacy and appropriate management of comorbid conditions may be a pathway to reduce excess expenditures among adults with osteoarthritis and pain.