Browsing by Author "Patel, Jay S."
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Item Associations between immigrant status and pharmacological treatments for diabetes in U.S. adults(APA, 2018) Hsueh, Loretta; Vrany, Elizabeth A.; Patel, Jay S.; Hollingshead, Nicole A.; Hirsh, Adam T.; de Groot, Mary; Stewart, Jesse C.; Psychology, School of ScienceObjectives: Although treatment disparities in diabetes have been documented along racial/ethnic lines, it is unclear if immigrant groups in the United States experience similar treatment disparities. Our objective was to determine whether immigrant status is associated with differences in pharmacological treatment of diabetes in a nationally representative sample of adults with diabetes. We were specifically interested in differences in treatment with oral hypoglycemic agents (OHA) and insulin. Method: Respondents were 2,260 adults from National Health and Nutritional Examination Survey (NHANES) 2003–2012 with a self-reported diabetes diagnosis. Immigrant status was indicated by birth within (U.S.-born) or outside (foreign-born) the 50 U.S. States or Washington, DC. Multinomial logistic regression analyses examined associations between immigrant status and (a) treatment with OHAs only and (b) treatment with insulin only or insulin and OHA combination therapy, using no treatment as the reference group. Results: Adjusting for demographics, diabetes severity and duration, cardiovascular disease (CVD), and CVD risk factors, being foreign-born versus U.S.-born was not associated with treatment with OHAs only (odds ratio [OR] = 1.59; 95% confidence interval [CI] [0.97, 2.60]). However, being foreign-born was associated with decreased odds (OR = 0.53; 95% CI [0.28, 0.99]) of treatment with insulin. Conclusions: Pharmacological treatment of diabetes differs along immigrant status lines. To understand these findings, studies capturing the processes underlying treatment differences in diabetes among immigrants are needed. Findings raise the possibility that integrating information about a patient’s immigrant status, in addition to racial/ethnic identity, may be an important component of culturally sensitive diabetes care.Item Associations of somatic depressive symptoms with food attentional bias and eating behaviors(Elsevier, 2021-12) Shell, Aubrey L.; Jackson, Rachel A.; Patel, Jay S.; Hirsh, Adam T.; Cyders, Melissa A.; Stewart, Jesse C.; Psychology, School of ScienceRecent evidence suggests that atypical major depressive disorder (MDD) – whose key features include the reversed somatic symptoms of hyperphagia (increased appetite) and hypersomnia (increased sleep) – is a stronger predictor of future obesity than other MDD subtypes. The mechanisms underlying this relationship are unclear. The present study sought to elucidate whether the individual symptoms of hyperphagia, hypersomnia, poor appetite, and disturbed sleep have differential relationships with food attentional bias, emotional eating, external eating, and restrained eating. This cross-sectional laboratory study involved 103 young adults without obesity (mean age = 20 years, 79% female, 26% non-White, mean BMI = 23.4 kg/m2). We measured total depressive symptom severity and individual symptoms of hyperphagia, poor appetite, and disturbed sleep using the Hopkins Symptom Checklist-20 (SCL-20) and added an item to assess hypersomnia; food attentional bias using a Food Stroop task; and self-reported eating behaviors using the Dutch Eating Behavior Questionnaire. Hyperphagia was positively associated with emotional eating but negatively associated with food attentional bias. Hypersomnia was negatively associated with emotional eating. Poor appetite was negatively associated with emotional eating. Disturbed sleep was positively associated with food attentional bias and emotional eating. An aggregate of the remaining 15 depressive symptoms (SCL-15) was positively associated with emotional and restrained eating. Our findings highlight the importance of examining the direction of somatic depressive symptoms, and they set the stage for future research to identify subgroups of people with depression at greatest risk for obesity (e.g., those with hyperphagia and/or disturbed sleep) and the mechanisms responsible for this elevated risk (e.g., emotional eating).Item Cardiovascular Risk Factors as Differential Predictors of Incident Atypical and Typical Major Depressive Disorder in U.S. Adults(Wolters Kluwer, 2018-03) Patel, Jay S.; Bernston, Jessica; Polanka, Brittanny M.; Stewart, Jesse C.; Psychology, School of ScienceObjectives While the association between major depressive disorder (MDD) and future cardiovascular disease (CVD) is established, less is known about the relationship between CVD risk factors and future depression, and no studies have examined MDD subtypes. Our objective was to determine whether hypertension, tobacco use, and body mass index (BMI) differentially predict atypical and typical MDD in a national sample of U.S. adults. Methods We examined prospective data from 22,915 adults with no depressive disorder history at baseline who participated in Wave 1 (2001-2002) and Wave 2 (2004-2005) of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC). CVD risk factors (Wave 1) and incident MDD subtypes (Wave 2) were determined by structured interviews. Results There were 252 atypical and 991 typical MDD cases. In fully-adjusted models, baseline hypertension (OR=0.58, 95% CI: 0.43-0.76), former tobacco use (OR=1.46, 95% CI: 1.20-1.78), and BMI (OR=1.32, 95% CI: 1.25-1.40; all p’s<0.001) predicted incident atypical MDD versus no MDD, whereas no CVD risk factor predicted incident typical MDD. Baseline hypertension (OR=0.52, 95% CI: 0.39-0.70), former tobacco use (OR=1.53, 95% CI: 1.22-1.93), and BMI (OR=1.26, 95% CI: 1.18-1.36; all p’s<0.001) also predicted incident atypical MDD versus typical MDD. Conclusions Our study is the first to report that CVD risk factors differentially predict MDD subtypes, with hypertension (protective factor), former tobacco use (risk factor), and BMI (risk factor) being stronger predictors of incident atypical versus typical MDD. Such evidence could provide insights into the etiologies of MDD subtypes and inform interventions tailored to MDD subtype.Item Characterizing clinical findings of Sjögren's Disease patients in community practices using matched electronic dental-health record data(Public Library of Science, 2023-07-31) Felix Gomez, Grace Gomez; Hugenberg, Steven T.; Zunt, Susan; Patel, Jay S.; Wang, Mei; Rajapuri, Anushri Singh; Lembcke, Lauren R.; Rajendran, Divya; Smith, Jonas C.; Cheriyan, Biju; Boyd, LaKeisha J.; Eckert, George J.; Grannis, Shaun J.; Srinivasan, Mythily; Zero, Domenick T.; Thyvalikakath, Thankam P.; Cariology, Operative Dentistry and Dental Public Health, School of DentistryEstablished classifications exist to confirm Sjögren's Disease (SD) (previously referred as Sjögren's Syndrome) and recruit patients for research. However, no established classification exists for diagnosis in clinical settings causing delayed diagnosis. SD patients experience a huge dental disease burden impairing their quality of life. This study established criteria to characterize Indiana University School of Dentistry (IUSD) patients' SD based on symptoms and signs in the electronic health record (EHR) data available through the state-wide Indiana health information exchange (IHIE). Association between SD diagnosis, and comorbidities including other autoimmune conditions, and documentation of SD diagnosis in electronic dental record (EDR) were also determined. The IUSD patients' EDR were linked with their EHR data in the IHIE and queried for SD diagnostic ICD9/10 codes. The resulting cohorts' EHR clinical findings were characterized and classified using diagnostic criteria based on clinical experts' recommendations. Descriptive statistics were performed, and Chi-square tests determined the association between the different SD presentations and comorbidities including other autoimmune conditions. Eighty-three percent of IUSD patients had an EHR of which 377 patients had a SD diagnosis. They were characterized as positive (24%), uncertain (20%) and negative (56%) based on EHR clinical findings. Dry eyes and mouth were reported for 51% and positive Anti-Ro/SSA antibodies and anti-nuclear antibody (ANA) for 17% of this study cohort. One comorbidity was present in 98% and other autoimmune condition/s were present in 53% respectively. Significant differences were observed between the three SD clinical characteristics/classifications and certain medical and autoimmune conditions (p<0.05). Sixty-nine percent of patients' EDR did not mention SD, highlighting the huge gap in reporting SD during dental care. This study of SD patients diagnosed in community practices characterized three different SD clinical presentations, which can be used to generate SD study cohorts for longitudinal studies using EHR data. The results emphasize the heterogenous SD clinical presentations and the need for further research to diagnose SD early in community practice settings where most people seek care.Item Comparing gingivitis diagnoses by bleeding on probing (BOP) exclusively versus BOP combined with visual signs using large electronic dental records(Springer, 2023-10-10) Patel, Jay S.; Shin, Daniel; Willis, Lisa; Zai, Ahad; Kumar, Krishna; Thyvalikakath, Thankam P.; Cariology, Operative Dentistry and Dental Public Health, School of DentistryThe major significance of the 2018 gingivitis classification criteria is utilizing a simple, objective, and reliable clinical sign, bleeding on probing score (BOP%), to diagnose gingivitis. However, studies report variations in gingivitis diagnoses with the potential to under- or over-estimating disease occurrence. This study determined the agreement between gingivitis diagnoses generated using the 2018 criteria (BOP%) versus diagnoses using BOP% and other gingival visual assessments. We conducted a retrospective study of 28,908 patients' electronic dental records (EDR) from January-2009 to December-2014, at the Indiana University School of Dentistry. Computational and natural language processing (NLP) approaches were developed to diagnose gingivitis cases from BOP% and retrieve diagnoses from clinical notes. Subsequently, we determined the agreement between BOP%-generated diagnoses and clinician-recorded diagnoses. A thirty-four percent agreement was present between BOP%-generated diagnoses and clinician-recorded diagnoses for disease status (no gingivitis/gingivitis) and a 9% agreement for the disease extent (localized/generalized gingivitis). The computational program and NLP performed excellently with 99.5% and 98% f-1 measures, respectively. Sixty-six percent of patients diagnosed with gingivitis were reclassified as having healthy gingiva based on the 2018 diagnostic classification. The results indicate potential challenges with clinicians adopting the new diagnostic criterion as they transition to using the BOP% alone and not considering the visual signs of inflammation. Periodic training and calibration could facilitate clinicians' and researchers' adoption of the 2018 diagnostic system. The informatics approaches developed could be utilized to automate diagnostic findings from EDR charting and clinical notes.Item Developing Automated Computer Algorithms to Track Periodontal Disease Change from Longitudinal Electronic Dental Records(MDPI, 2023-03-08) Patel, Jay S.; Kumar, Krishna; Zai, Ahad; Shin, Daniel; Willis, Lisa; Thyvalikakath, Thankam P.Objective: To develop two automated computer algorithms to extract information from clinical notes, and to generate three cohorts of patients (disease improvement, disease progression, and no disease change) to track periodontal disease (PD) change over time using longitudinal electronic dental records (EDR). Methods: We conducted a retrospective study of 28,908 patients who received a comprehensive oral evaluation between 1 January 2009, and 31 December 2014, at Indiana University School of Dentistry (IUSD) clinics. We utilized various Python libraries, such as Pandas, TensorFlow, and PyTorch, and a natural language tool kit to develop and test computer algorithms. We tested the performance through a manual review process by generating a confusion matrix. We calculated precision, recall, sensitivity, specificity, and accuracy to evaluate the performances of the algorithms. Finally, we evaluated the density of longitudinal EDR data for the following follow-up times: (1) None; (2) Up to 5 years; (3) > 5 and ≤ 10 years; and (4) >10 and ≤ 15 years. Results: Thirty-four percent (n = 9954) of the study cohort had up to five years of follow-up visits, with an average of 2.78 visits with periodontal charting information. For clinician-documented diagnoses from clinical notes, 42% of patients (n = 5562) had at least two PD diagnoses to determine their disease change. In this cohort, with clinician-documented diagnoses, 72% percent of patients (n = 3919) did not have a disease status change between their first and last visits, 669 (13%) patients’ disease status progressed, and 589 (11%) patients’ disease improved. Conclusions: This study demonstrated the feasibility of utilizing longitudinal EDR data to track disease changes over 15 years during the observation study period. We provided detailed steps and computer algorithms to clean and preprocess the EDR data and generated three cohorts of patients. This information can now be utilized for studying clinical courses using artificial intelligence and machine learning methods.Item Effect of Modernized Collaborative Care for Depression on Depressive Symptoms and Cardiovascular Disease Risk Biomarkers: eIMPACT Randomized Controlled Trial(Elsevier, 2023) Stewart, Jesse C.; Patel, Jay S.; Polanka, Brittanny M.; Gao, Sujuan; Nurnberger, John I., Jr.; MacDonald, Krysha L.; Gupta, Samir K.; Considine, Robert V.; Kovacs, Richard J.; Vrany, Elizabeth A.; Berntson, Jessica; Hsueh, Loretta; Shell, Aubrey L.; Rollman, Bruce L.; Callahan, Christopher M.; Psychology, School of ScienceAlthough depression is a risk and prognostic factor for cardiovascular disease (CVD), clinical trials treating depression in patients with CVD have not demonstrated cardiovascular benefits. We proposed a novel explanation for the null results for CVD-related outcomes: the late timing of depression treatment in the natural history of CVD. Our objective was to determine whether successful depression treatment before, versus after, clinical CVD onset reduces CVD risk in depression. We conducted a single-center, parallel-group, assessor-blinded randomized controlled trial. Primary care patients with depression and elevated CVD risk from a safety net healthcare system (N = 216, Mage = 59 years, 78% female, 50% Black, 46% with income <$10,000/year) were randomized to 12 months of the eIMPACT intervention (modernized collaborative care involving internet cognitive-behavioral therapy [CBT], telephonic CBT, and/or select antidepressants) or usual primary care for depression (primary care providers supported by embedded behavioral health clinicians and psychiatrists). Outcomes were depressive symptoms and CVD risk biomarkers at 12 months. Intervention participants, versus usual care participants, exhibited moderate-to-large (Hedges' g = -0.65, p < 0.01) improvements in depressive symptoms. Clinical response data yielded similar results - 43% of intervention participants, versus 17% of usual care participants, had a ≥ 50% reduction in depressive symptoms (OR = 3.73, 95% CI: 1.93-7.21, p < 0.01). However, no treatment group differences were observed for the CVD risk biomarkers - i.e., brachial flow-mediated dilation, high-frequency heart rate variability, interleukin-6, high-sensitivity C-reactive protein, β-thromboglobulin, and platelet factor 4 (Hedges' gs = -0.23 to 0.02, ps ≥ 0.09). Our modernized collaborative care intervention - which harnessed technology to maximize access and minimize resources - produced clinically meaningful improvements in depressive symptoms. However, successful depression treatment did not lower CVD risk biomarkers. Our findings indicate that depression treatment alone may not be sufficient to reduce the excess CVD risk of people with depression and that alternative approaches are needed. In addition, our effective intervention highlights the utility of eHealth interventions and centralized, remote treatment delivery in safety net clinical settings and could inform contemporary integrated care approaches.Item Measurement invariance of the patient health questionnaire-9 (PHQ-9) depression screener in U.S. adults across sex, race/ethnicity, and education level: NHANES 2005–2016(Wiley, 2019-07-29) Patel, Jay S.; Oh, Youngha; Rand, Kevin L.; Wu, Wei; Cyders, Melissa A.; Kroenke, Kurt; Stewart, Jesse C.; Psychology, School of ScienceItem Number of Recent Stressful Life Events and Incident Cardiovascular Disease: Moderation by Lifetime Depressive Disorder(Elsevier, 2017) Berntson, Jessica; Patel, Jay S.; Stewart, Jesse C.; Department of Psychology, School of ScienceObjective We investigated whether number of recent stressful life events is associated with incident cardiovascular disease (CVD) and whether this relationship is stronger in adults with a history of clinical depression. Methods Prospective data from 28,583 U.S. adults (mean age = 45 years) initially free of CVD who participated in Waves 1 (2001–2002) and 2 (2004–2005) of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) were examined. Number of past-year stressful life events (Wave 1), lifetime depressive disorder (Wave 1), and incident CVD (Wave 2) were determined by structured interviews. Results There were 1069 cases of incident CVD. Each additional stressful life event was associated with a 15% increased odds of incident CVD [Odds Ratio (OR) = 1.15, 95% Confidence Interval (CI): 1.11, 1.19]. As hypothesized, a stressful life events by lifetime depressive disorder interaction was detected (P = 0.003). Stratified analyses indicated that stressful life events had a stronger association with incident CVD among adults with (OR = 1.18, 95% CI: 1.10, 1.27, n = 4908) versus without (OR = 1.10, 95% CI: 1.07, 1.14, n = 23,675) a lifetime depressive disorder. Conclusion Our findings suggest that a greater number of recent stressful life events elevate the risk of new-onset CVD and that this risk is potentiated in adults with a history of clinical depression.Item Retrospective Study of the Reasons and Time Involved for Dental Providers' Medical Consults(Frontiers, 2022-05) Li, Shuning; Williams, Karmen S.; Medam, Jayanth Kumar; Patel, Jay S.; Gonzalez, Theresa; Thyvalikakath, Thankam P.; Cariology, Operative Dentistry and Dental Public Health, School of DentistryBackground: Patient-reported medical histories and medical consults are primary approaches to obtaining patients' medical histories in dental settings. While patient-reported medical histories are reported to have inconsistencies, sparse information exists regarding the completeness of medical providers' responses to dental providers' medical consults. This study examined records from a predoctoral dental student clinic to determine the reasons for medical consults; the medical information requested, the completeness of returned responses, and the time taken to receive answers for medical consult requests. Methods: A random sample of 240 medical consult requests for 179 distinct patients were selected from patient encounters between 1 January 2015 and 31 December 2017. Descriptive statistics and summaries were calculated to determine the reasons for the consult, the type of information requested and returned, and the time interval for each consult. Results: The top two reasons for medical consults were to obtain more information (46.1%) and seek medical approval to proceed with treatment (30.3%). Laboratory and diagnostic reports (56.3%), recommendations/medical clearances (39.6%), medication information (38.3%), and current medical conditions (19.2%) were the frequent requests. However, medical providers responded fewer times to dental providers' laboratory and diagnostic report requests (41.3%), recommendations/medical clearances (19.2%), and current medical conditions (13.3%). While 86% of consults were returned in 30 days and 14% were completed after 30 days. Conclusions: The primary reasons for dental providers' medical consults are to obtain patient information and seek recommendations for dental care. Laboratory/diagnostic reports, current medical conditions, medication history, or modifications constituted the frequently requested information. Precautions for dental procedures, antibiotic prophylaxis, and contraindications included reasons to seek medical providers' recommendations. The results also highlight the challenges they experience, such as requiring multiple attempts to contact medical providers, the incompleteness of information shared, and the delays experienced in completing at least 25% of the consults. Practical Implications: The study results call attention to the importance of interdisciplinary care to provide optimum dental care and the necessity to establish systems such as integrated electronic dental record-electronic health record systems and health information exchanges to improve information sharing and communication between dental and medical providers.