Browsing by Author "Shen, Chan"
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Item A Multi-Level Analysis of Individual and Neighborhood Factors Associated with Patient Portal Use among Adult Emergency Department Patients with Multimorbidity(MDPI, 2023-01-22) Wang, Hao; Shen, Chan; Barbaro, Michael; Ho, Amy F.; Pathak, Mona; Dunn, Cita; Sambamoorthi, UshaBACKGROUND: 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.Item Association of multimorbidity with the use of health information technology(Sage Publications, 2023-05-01) Manning, Sydney E.; Wang, Hao; Dwibedi, Nilanjana; Shen, Chan; Wiener, R. Constance; Findley, Patricia A.; Mitra, Sophie; Sambamoorthi, UshaOBJECTIVE: 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.Item Evaluating Equity in XGBoost Predictions of High Healthcare Expenditures for Older Women with Osteoarthritis in the United States(2024-03-21) Elchehabi, Sahar; Dehghan, Arshama; Pathak, Mona; Sambamoorthi, Nethra; Park, Chanhyun; Shen, Chan; Sambamoorthi, UshaPurpose: Osteoarthritis (OA) is a highly prevalent and debilitating condition among older adults. Studies suggest that women are more prone to develop symptomatic disease than men. OA is associated with high direct healthcare costs, attributable to its complex disease management. Furthermore, a small segment of this population may incur very high costs. Identifying these high-cost users is important for allocation of resources, cost containment, quality improvement, and population health management. However, current research in the prediction of high-cost users in OA using machine learning (ML) models is limited. Furthermore, ML model predictions of high-cost users must be equitable across sensitive attributes such as race and ethnicity and socio-economic status. This study investigated the leading predictors of high-cost users among older women with OA utilizing ML methods and the fairness of the ML algorithm in its predictions across subgroups of race and ethnicity, poverty, and education. Methods: A cross-sectional study was conducted using data from older women (age>65 years) with OA from the 2021 Medical Expenditure Panel Survey, a nationally representative survey of the non-institutionalized civilian households in the US. High-cost users were identified as having higher than the 90th percentile (>$39,388) in total healthcare expenditures. Key predictors were identified using interpretable ML model eXtreme Gradient Boosting (XGBoost) Classification and SHapley Additive exPlanations (SHAP). Overall model fit was evaluated with AUC, recall, and precision. Fairness was measured with demographic parity (equalization of odds, disparate impact, and equal opportunity) across racial and ethnic groups (Non-Hispanic White (NHW), Non-Hispanic Black (NHB), Hispanic ethnicity), education (no college and college) and poverty status (low income and high income). Counterfactual fairness was evaluated to ensure consistency in high-cost predictions between actual scenarios and counterfactual situations where individuals belong to different groups. Results: A higher percentage of Hispanic (12.2%) and NHB (14.4%) were high-cost users compared to NHW (9.0%). A higher percentage of older women without college education (10.7%) and with low income (11.2%) compared to those with college education (2.5%) and high income (5.2%) were high-cost users. The overall model fit was acceptable with AUC 0.81, recall 0.62, and precision 0.91. Multimorbidity, high school education level, and anxiety were the top 3 predictors of high-cost users. Prediction was lower among older women without college education (AUC = 0.80) and low income compared (AUC = 0.77) compared to overall prediction (AUC = 0.81). Demographic parity revealed little to no differences across racial and ethnic, education, and income groups. Conclusion: The fairness metrics indicated no bias in the predictions, likely attributable to the nationally representative nature of the survey sample and its large size. These findings need to be confirmed with other data that contain diverse populations. Leading predictors indicated that effective management of multimorbidity may reduce the risk of high-cost use in older women with OA.Item Increase in body mass index during the COVID-19 pandemic among people who smoke: An analysis of multi-site electronic health records(PLOS, 2023-04-12) Wiener, R. Constance; Waters, Christopher; Morgan, Emily; Findley, Patricia A.; Shen, Chan; Wang, Hao; Sambamoorthi, UshaThe 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.Item Leading Predictors of Economic Burden Among Postmenopausal Women with Heart Failure: An Application of Machine Learning with XGBoost and SHapley Additive exPlanations(2023) Dehghan, Arshama; Park, Chanhyun; Sambamoorthi, Nethra; Shen, Chan; Shara, Nawar; Sambamoorthi, UshaObjective: Heart Failure is associated with high direct healthcare costs, including out-of-pocket spending by the patients. However, there are knowledge gaps in HF research among postmenopausal women. Therefore, this study uses machine learning methods to identify leading predictors and their associations with economic burden among postmenopausal women (age > 50 years) with heart failure. Methods: This cross-sectional study used data from postmenopausal women with heart failure from the 2020 Medical Expenditure Panel Survey (MEPS: weighted N= 600,742). The economic burden was measured with total healthcare expenditures by the payors (third-party expenditures) and out-of-pocket expenditures by the patients and their families. We employed eXtreme Gradient Boosting (XGBoost) regression to determine key predictors. Global and local interpretations of associations were performed using SHapley Additive exPlanations (SHAP). Our predictive model used 21 features such as age, health status including comorbidities (anxiety, arthritis, asthma, cancer, COPD, depression, diabetes, high cholesterol, hypertension, and thyroid disease), perceived physical and mental health status, and polypharmacy. Social determinants of health (SDoH) consisted of marital status, health insurance coverage, prescription drug coverage, education, poverty status, and region. The model building included 70% training and 30% testing split of the data, 10-fold cross-validations, and up to six rounds of optimization using Python 3.9.12. Model performance metrics included absolute mean squared errors, root mean squared error and coefficient of determination; these were evaluated using the test dataset. Results: The model offered excellent accuracy as evidenced by its low mean absolute errors (0.442,0.310), root mean square errors (0.452,0.342), and high coefficients of determination (0.935,0.987) for third-party and out-of-pocket expenditures, respectively. The top 10 leading predictors of third-party expenditures included polypharmacy, age, resident of the Midwest region, asthma, perceived physical and mental health, anxiety, hypertension, white race, and low income. The SHAP plots from the third-party expenditures revealed complex relationships of age, physical, and mental health with the target variable. Polypharmacy, low income, anxiety, and asthma were associated with higher third-party expenditures. Non-Hispanic white Women and those with hypertension had lower third-party expenditures. The top 10 leading predictors of out-of-pocket expenditures included age, Latinx ethnicity, asthma, cancer, being poor, having middle income and high income, prescription drug coverage, private insurance, and polypharmacy. Out-of-pocket expenditure plots only highlighted age as the key complex factor. Being poor, having middle income, and reporting Latinx ethnicity were associated with lower out-of-pocket expenditures. High income, prescription drug coverage, private insurance, polypharmacy, and the presence of asthma and cancer were associated with higher out-of-pocket expenditures. Conclusion: The leading predictors differed by payor source. SDoH were associated with economic burden, suggesting that addressing SDoH may reduce healthcare costs. Cost-containment policies, programs, and interventions at the payor and patient levels need to include effective comorbidity management strategies. The limitations of this study include cross-sectional study design, self-reported data that may be subject to recall bias, and severity of comorbidities that may affect the economic burden. However, the study also has several strengths, such as nationally representative data, the inclusion of SDoH, validated information on expenditures, and robust interpretable machine learning methods.Item Prescription Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) and Incidence of Depression Among Older Cancer Survivors With Osteoarthritis: A Machine Learning Analysis(Sage Publications, 2023-04-27) Shaikh, Nazneen F.; Shen, Chan; LeMasters, Traci; Dwibedi, Nilanjana; Ladani, Amit; Sambamoorthi, UshaOBJECTIVES: 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.Item Relationship Between Pain and Total Healthcare Expenditure in Elderly Osteoarthritis Patients: An Interpretable Machine Learning (ML) Investigation with eXtreme Gradient Boosting(2024-03-21) Middleton, Claire; Dehghan, Arshama; Park, Chanhyun; Shen, Chan; Sambamoorthi, UshaObjective: Osteoarthritis (OA), a painful degenerative joint condition, affects over 32.5 million adults in the United States and one-fourth experience severe joint pain. In 2019, adults with OA had $45.4 billion more annual expenditures relative to those without OA. Although statistical methods have been utilized in studying the additional costs associated with pain among adults with OA, there is still a notable gap in understanding the relationship through the lens of ML methods. The objective of this study is to determine pain as a leading predictor of economic burden among older adults (age > 65 years) with OA using ML methods. Methods: We used data on older adults (age > 65 years) with OA (N = 1,640) from the 2021 Medical Expenditure Panel Survey (MEPS), a nationally representative survey of households in the US. Log-transformed total healthcare expenditures, which included payments by the insurers and the patients, represented the economic burden. We employed eXtreme Gradient Boosting (XGBoost) regression to determine key predictors. Global and local interpretations of associations were performed using a SHapley Additive exPlanation (SHAP), including a Partial Dependence Plot (PDP) for pain. Our predictive model utilized 24 features including biological (sex, age), race and ethnicity, clinical (pain, polypharmacy, physical and mental health status, and chronic conditions), and Social Determinants of Health (SDOH) such as marital status, education, poverty status, census region, insurance coverage, and prescription drug coverage. Chronic conditions included anxiety, depression, thyroid disease, diabetes, hypertension, coronary artery disease, cancer, hyperlipidemia, asthma, and chronic obstructive pulmonary disease. Pain interfering with regular work over the past four weeks was assessed using the Veterans Rand 12-item Health survey (VR-12), employing a Likert scale ranging from 0 (none) to 4 (extreme) to represent pain level. Missing values for pain level were imputed using K-Nearest Neighbors (KNN) Imputation. The model building included 70% training and 30% testing split of the data and 3-fold cross-validations using Python 3.10.12. Model performance was evaluated with R-square, mean absolute error, and Root Mean Square Error (RMSE) using the test dataset. Results: Approximately, one in 4 adults with OA reported moderate to extreme pain. The top 3 predictors of healthcare expenditures were: polypharmacy, physical health, and pain level. Higher pain levels and polypharmacy were associated with higher total expenditures. Excellent physical health was associated with lower total healthcare expenditures. Additionally, the SHAP PDP suggested a linear relationship between pain levels and total expenditures. Model performance was modest with a mean absolute error (1.086), RMSE (1.736), and R-square (0.452) for total expenditures. Conclusion: Higher pain levels predicted higher economic burden in older adults with OA. Effective management of pain may be a pathway to reduce the economic burden of OA. As polypharmacy was a leading predictor of healthcare expenditures, this model underscores the importance of reducing polypharmacy use in older adults with medication utilization review and management.