Repetitive Transcranial Magnetic Stimulation for Treatment Resistant Depression: How patient characteristics and elements of treatment impact post-treatment outcomes

Repetitive Transcranial Magnetic Stimulation for Treatment Resistant Depression: How patient characteristics and elements of treatment impact post-treatment outcomes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Repetitive Transcranial Magnetic Stimulation for Treatment Resistant Depression: How patient characteristics and elements of treatment impact post-treatment outcomes Peter J Clagnaz, Irina Tardif, Kitty K Leung, Amal A Bhullar, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8695870/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 13 You are reading this latest preprint version Abstract Introduction: The present study assessed the effectiveness of repetitive Transcranial Magnetic Stimulation (rTMS) in reducing depressive symptoms in patients with treatment-resistant depression. Methods and materials: A retrospective study conducted at an academic rTMS outpatient clinic. Data were collected from adult patients (≥ 18 years) with treatment-resistant, nonpsychotic unipolar depression. The Montgomery-Asberg Depression Rating Scale (MADRS) and Patient Health Questionnaire (PHQ-9) were administered at the initiation of treatment, at midpoint, and at completion. The Area Deprivation Index (ADI) was calculated to measure neighborhood disadvantage. Independent and paired t- tests were performed to assess the change in MADRS and PHQ-9 scores, for ages of less than or 65 and greater for baseline scores. Participants were assessed at the end of treatment as either full remission, partial response, or lack of response. The effectiveness of rTMS was analyzed with a multivariable general linear model (GLM) fitted to compare change in depression severity, while controlling for confounders. Results Sixty patients were included. Forty-five (75%) of the participants completed the 36 or more sessions of rTMS treatment. The end-of-treatment MADRS and PHQ-9 scores were statistically significantly different from the pre-treament scores ( P < 0.001 ). The effect sizes were ( d = 1.70 and 1.37), respectively. Regression modeling showed age, insurance status, Body Mass Index (BMI), number of sessions and duration of treatment were statistically significant. The MADRS demonstrated better model quality to discriminate good from poor responders than the PHQ-9. Conclusions The rTMS treatment was shown to be an effective modality for treatment-resistant depression. The analysis found that patient characteristics and treatment factors were predictive of the patient response. Future studies should investigate the link between overall functioning, health-related social needs and rTMS response. Transcranial magnetic stimulation Treatment-resistant depression Figures Figure 1 Figure 2 1. Introduction One-third of individuals with Major Depressive Disorder (MDD) fail to respond to first-line treatments (pharmaco- or psychotherapy) often leading to treatment-resistant depression (TRD) (Rush et al., 2006 ). Repetitive TMS has become part of the standard of care for TRD, where response rates of 40% to 60% are characteristic (Marder et al., 2022 ). Effective treatment, however, requires understanding predictors of treatment response in MDD. Since the effectiveness of rTMS may depend on various demographic, clinical, and treatment-related factors, it is important for providers to be able to identify optimal patient and treatment parameters. Repetitive transcranial magnetic stimulation (rTMS) is a widely regarded neuromodulation technique used to treat a variety of psychiatric disorders such as major depressive disorder, anxious depression, and obsessive compulsive disorder (Marder et al., 2022 ). Other indications for rTMS include migraine with aura and smoking cessation. In attempts to optimize treatment, a variety of different stimulation protocols have been studied, encompassing different brain targets, stimulation parameters, targeting methods, and treatment schedules. This algorithmic uncertainty leaves treating physicians on their own to decide when to consider rTMS as a treatment modality. Repetitive TMS involves the application of repetitive magnetic pulses through a coil that is positioned above the left dorsolateral prefrontal cortex (Trapp et al., 2025 ). This area is a common target for MDD that is responsible for mood regulation, cognitive control, executive functioning, and emotion processing (Fitzgerald et al., 2006 ). However, determining the exact positioning of rTMS within treatment algorithms for TRD remains largely unconfirmed. While Dunner et al. (2012) found approximately two-thirds of responders or remitters continued to show a treatment response over time. On the other hand, prolonged periods of inadequate treatment contributed to a higher risk of chronicity, comorbidity, and suicidality, affirming the need for more effective treatment options for patients with TRD (Eaton et al., 2008 ). Additionally, no unified criteria for the definition of TRD has affected the prior authorization process by various insurance carriers (Smith et al., 2025 ). Weissman et al. ( 2023 ) reported that insurance status was likely to create such barriers as access to care. Minoritized groups are often overrepresented in lower socioeconomic categories, which potentially explains their limited ability to undertake such a time- and resource-intensive treatment. As a result, this has led to disparate impact on racial and ethnic minorities who were shown to be underrepresented in treatment (Smith et al., 2025 ). Work also remains to be done in overcoming stigma, particularly historical mistrust with the mental health care system in minoritized populations. This study represented a retrospective, observational analysis to investigate predictors of treatment response-remission specific characteristics of the participating patients, treatment settings, and treatment factors. The objective of our study was to evaluate rTMS at the initiation and after completion of treatment at a newly formed rTMS outpatient clinic. The aim was to examine the effect of rTMS in a sample of patients with nonpsychotic severe unipolar depression recurrent or single episode and severe treatment resistance. We investigated rTMS based on identified patient characteristics, duration of symptoms associated with depression, number of failed medication trials, and the state Area Deprivation Index (ADI). In addition, when used with concurrent psychotherapy, the responsiveness to rTMS was examined. Thus, the knowledge gained can inform the development of more personalized elements of treatments that are based on individual patient and clinical characteristics. 2. Methods 2.1 Study Design This retrospective, observational study consisted of data from patients diagnosed with MDD who received care at the UF Health Jacksonville rTMS clinic between December 2021 and February 2025 and extracted from the EHR. The study was approved by the University of Florida Institutional Review Board (IRB202500308). 2.2 Participants The study included encounters of adult patients (≥ 18 years) with treatment-resistant depression. Patient information included age, sex, race/ethnicity, Body Mass Index (BMI), insurance status, and state ADI. Inclusion criteria were patients diagnosed with MDD, severe, single episode or recurrent episode without psychotic symptoms. Major Depressive Disorder was diagnosed based on the DSM-5 symptoms during a structured clinical interview fashioned to match insurance preauthorization. The Maudsley Staging Method (MSM) was also utilized to stage treatment-resistant depression that incorporates three factors: medication failures, severity of illness, and duration of depressive episode (Fekadu et al., 2009 ). Participants demonstrated an inadequate response to antidepressant trials, augmentation with a second antidepressant, anticonvulsant, Lithium, or an atypical neuroleptic during this depressive episode. The exclusion criteria included patients with seizure disorders or embedded ferromagnetic material that would be within 30 cm of the treatment coil. Additional exclusion criteria were previous failure of rTMS treatment and pregnancy. 2.3 Instruments The Montgomery-Asberg Depression Rating Scale (MADRS) consists of 10 items, each rated on a scale from 0 to 6, with higher scores indicating more severe symptoms (Montgomery & Asberg, 1979 ; Bondolfi et al., 2008). The total score, ranging from 0 to 60, indicates depression severity (0–6 normal, 7–19 mild, 20–34 moderate, 35 + severe). The Generalized Anxiety Disorder-7 (GAD-7) is a 7-item questionnaire used to screen for and assess the severity of anxiety symptoms with well established validity and reliability (Löwe et al., 2008 ). Scores are categorized as: 0–4 (minimal), 5–9 (mild), 10–14 (moderate), and 15–21 (severe). The GAD-7 was used to screen for the presence of anxiety symptoms. The Patient Health Questionnaire (PHQ-9) is a widely recognized and well validated test of depression that has been used across many conditions (Kroenke et al., 2001 ). The test consists of 9 items. The scores range from 0 to 27 with cutpoints of 5, 10, 15 and 20 that represent mild, moderate, moderately severe and severe levels of depressive symptoms, respectively. The Maudsley Staging Method (MSM) was developed to define and stage treatment-resistant depression. The MSM is calculated based on duration and severity of illness, medication failures, augentation, and ECT usage (Fekadu et al., 2009 ). The overall maximum score for MSM is 15. Fekadu et al. ( 2018 ) standardized the MSM by adding severity score categories of Mild (3–6), Moderate (7–10), and Severe (11–15). The Area Deprivation Index (ADI) measures neighborhood disadvantage using a combination of 17 variables such as income, education, employment, and housing quality, and used to inform health delivery and policy, especially for the most disadvantaged neighborhood groups (Knighton et al., 2016 ). Neighborhoods are ranked relative to others within a specific state then grouped into deciles (1 to 10), where higher numbers indicate greater disadvantage. The Centers for Medicare and Medicaid Services have used this validated measure to measure neighborhood disadvantage. 2.4 Procedure Participants completed the MADRS, PHQ-9, and GAD-7 at the initiation of treatment, at the treatment midpoint, and at the completion of treatment. Psychiatrists supervising rTMS administered the clinician-rated MADRS. The PHQ-9 scores were provided by self-report. The GAD-7 appraised anxiety level during the clinical interview. Information also collected included duration of depression measured in years, antidepressant medication trials, type of insurance (commercial, Medicare, Medicaid), additional psychiatric diagnoses, number of Emergency Department (ED) visits, readmissions to a psychiatric hospital, and concomitant psychotherapy. The primary outcomes were change in depression severity based on the MADRS and PHQ-9 depression inventories. Secondary outcomes included the responses to rTMS according to age, sex, race, insurance status, MSM, GAD-7, number of medications, psychotherapy, and state ADI. To manage missing data, analyses were performed with data from the intention-to-treat sample. 2.5 Interventions The study participants completed the UF Health psychiatry rTMS protocol (see Table S1 for the delivery protocol). The rTMS device, the MagStim, uses high frequency (10 Hz) transcranial magnetic stimulation over the left dorsolateral prefrontal cortex (DLPFC), which has been shown to be clinically effective for depression. The trial of rTMS consisted of a course of 36 rTMS sessions over 9 weeks of treatment. Study participants were assessed at the end of treatment as either full remission, partial response, or no response. Pharmacological therapy for MDD consisted of continuing the current antidepressant medication prescribed that included SSRI, SNRI, DNRI, and TCA. A participant’s treatment may have also been augmented with lithium, an anticonvulsant or with a second-generation antipsychotic. Those with additional comorbid psychiatric diagnoses such as ADD, GAD, or PTSD, were to continue their current medication regime according to standard of care. Participants who received individual psychotherapy which might have consisted of cognitive-behavioral therapy, behavioral activation therapy, or supportive therapy, followed up as during usual care. 2.6 Statistical Analysis For continuous variables, the data was summarized with means and standard deviations. For categorical variables, data was summarized using the sample size (n) and percentage (%). To evaluate concordance between the MADRS clinician ratings and the self-reported PHQ-9, the correlation between these scores was computed using Pearson r statistic. The chi square test was used to determine differences among the classification percentages. Independent and paired t- tests were performed to assess the change in MADRS and PHQ-9 scores, for ages less than or 65 and greater and baseline scores. A paired-sample t- test was utilized to assess the effectiveness of rTMS treatments in reducing depressive symptoms by comparing the initial treatment MADRS and PHQ-9 with the endpoint MADRS and PHQ-9 scores at treatment termination. Cohen’s d were calculated to determine the effect size of the difference. A clinically relevant effect for treatment of depression was defined by the minimal important difference from a patient perspective, which corresponds to a Cohen’s d of 0.24 (Cuijpers et al., 2014 ). Using a data-driven approach, optimal cutpoints were identified to classify patients into three distinct categories. The definition of treatment non-response was a reduction of less than 50% in the endpoint compared to baseline total score on the MADRS. A partial response rate was defined as a minimum of 50% or more reduction in post-treatment scores on the MADRS. Full remission was defined as the cut-off value on the MADRS of < 10. Nonresponders on the PHQ-9 was a reduction of less than 50% in the endpoint compared to baseline total score on the PHQ-9. Partial response was defined as ≥ 50% reduction in PHQ-9 scores at final assessment relative to pre-TMS baseline. Full remission was defined as a final PHQ-9 score of less than 5. A multivariable general linear model (GLM) fitted to compare change in depression severity and adjusted for all covariates, was used to determine the contribution of the predictor variables with the change in depressive symptom severity over the course of treatment. This analysis was performed with scores on the MADRS and PHQ-9 as the dependent variables to determine the between-subjects main effects. Age, sex, race/ethnicity, BMI, insurance status, GAD-7, MSM, number of medications, number of sessions, duration of treatment, psychotherapy, and state ADI were added to the regression model as covariates to control for confounding. To assess the predictive performance of our rTMS response model, discrimination was evaluated using area under the receiver operating characteristic curve (AUC). Statistical analyses were performed with SPSS, version 30.0, and R 4.5.1. Tests were two-tailed with an alpha level of 0.05 for the primary analysis. 3. Results 3.1 Demographic Characteristics A total of 60 participants were included. Forty-five (75%) of the participants completed the full 36 sessions of rTMS treatment per protocol. Thirteen patients received less than 36 treatments that ranged from 3 to 35 rTMS sessions. Three participants received greater than 36 treatments, specifically, 48, 51, and 64 sessions, respectively. The average age was 51.9 (SD = 17.7) years. Of the included participants, 37 (61.7%) were female. Regarding race/ethnicity, 54 participants (90.0%) were non-Hispanic White, 5 participants (8.3%) were Non-Hispanic Black, and one was Hispanic (1.7%). The average BMI was 31.0 (SD = 7.7). The insurance status was 42 (70.0%) had commercial insurance, 16 (26.7%) had Medicare, and 2 (3.3%) with Medicaid. The average GAD-7 anxiety score was 14.0 (SD = 5.5). The average MSM was 11.20 (1.3). The median number of psychiatric medications was three. There were 16 participants who received psychotherapy (26.7%). The average state ADI for the sample was 4.8 (SD = 2.7). The average baseline MADRS and PHQ-9 scores were (35.0, SD = 6.4; 20.1, SD = 3.9), respectively. Eight patients had at least one inpatient psychiatric hospitalization and six visited an Emergency Department during the study period. The percentages and chi square values of the patients based on the categorical rTMS response classification (nonresponse, partial response, full response) for the MADRS and PHQ-9 are summarized in Table 1 and Table 2 , respectively. Table S2 lists the prescribed psychiatric medication for each patient. 3.2 PHQ-9 and MADRS concordance There was strong agreement between the PHQ-9 and MADRS ratings, with a correlation coefficient across all paired ratings of ( r = 0.562, P < 0.001 , (95% CI: 0.359–0.714) at baseline and ( r = 0.924, P < 0.001 , (95% CI: 0.876–0.954) at the end of treatment. 3.3 Change in Depression Severity Independent t -tests for age and baseline MADRS and PHQ-9 were not statistically significant ( t =-0.21, P = 0.84; t =-0.26, P = 0.80, respectively). Paired-sample t -tests were utilized to assess the effectiveness of TMS treatments in reducing depressive symptoms. The end-of-treatment MADRS scores were statistically significantly different from the pre-treatment scores ( t = 13.13, P < 0.001 ). Furthermore, the effect size of the difference was ( d = 1.70, 95% CI: 1.30–2.09). The end-of-treatment PHQ-9 scores were statistically significantly different from the pre-treatment scores ( t = 10.64, P < 0.001 ). Furthermore, the effect size of the difference was ( d = 1.37, 95% CI: 1.02–1.73). Figure 1 shows the change in depression severity over time with rTMS at baseline, midpoint of treatment, and the endpoint. A multivariate GLM was fitted to compare change in depression severity with the outcome variables MADRS and PHQ-9. Predictor variables, age, sex, race/ethnicity, BMI, insurance status, GAD-7, MSM, number of medications, number of sessions, duration of treatment, psychotherapy, and state ADI were entered to control for confounding. The overall model fit for MADRS and PHQ-9 were statistically significant ( F = 21.06, P < 0.001; F = 11.53, P < 0.001 ), respecively. Age, insurance status, BMI, number of rTMS sessions, and treatment duration were statistically significant for MADRS, controlling for all other variables. Age and treatment duration were statistically significant for PHQ-9, controlling for all other variables (See Table 3 ). Insurance status, BMI, and number of sessions were not statistically significant. Baseline anxiety was not statistically significantly associated with the reduction of depressive symptoms ( P = 0.32 and P = 0.19), respectively. The state ADI was not statistically significant either ( P = 0.83 and P = 0.88), respectively. Table 3 displays the regression model results for both the MADRS and PHQ-9. 3.4 Model Performance Comparison Receiver Operator Characteristics curves for the rTMS response model compared the ability to discriminate poor from good responders and evaluate their predictive performance. The cut point was above or below the 50% reduction in MADRS and PHQ-9 scores. The ROC curve for MADRS demonstrated better discrimination for poor responders (AUC = 0.854), while the AUC for good responders was (AUC = 0.664). Area difference between the two curves was not statistically different ( z = 1.18, 95% CI: -0.13-0.51, P = 0.24). Overall model quality for the poor TMS responders substantially improved prediction (0.69), whereas for good responders (0.39), the ROC curve demonstrated less than ideal performance. The ROC curve for PHQ-9 demonstrated slightly better discrimination for good responders (AUC = 0.505), while the AUC for poor responders was (AUC = 0.496). Area difference between the two curves was not statistically different ( z =-0.045, 95% CI: -0.41-0.40, P = 0.96). Overall model quality for the poor TMS responders showed improved prediction (0.24), whereas for good responders (0.19), the ROC curve demonstrated less than ideal performance. Figure 2 shows the ROC curves for each group. 3.5 Sensitivity Analyses We conducted a sensitivity analysis with the initial dataset of 60 patients. First, the analysis identified four outliers that were 3 standard deviations either above or below the average number of rTMS sessions, specifically, 3, 4, 6, and 64. Second, the outliers were removed to examine the strength of a prediction effect without session number in the analysis and ensure there was no potential undue influence on the regression results. This left 56 patients in the sample. Age, insurance status, BMI, number of rTMS sessions, treatment duration and number of psychiatric medications were statistically significant for MADRS, while controlling for all other variables. Age was statistically significant for PHQ-9, controlling for all other variables 4. Discussion 4.1 Effects of rTMS treatment on depression In this retrospective analysis of existing data in the EMR, treatment-resistant depressed psychiatry patients receiving rTMS treatments at a large urban academic outpatient clinic experienced statistically significant improvements in depression severity. Repetitive TMS was shown to be effective in reducing symptoms of depression, based on a well-established inventory for depressive disorder. The improvement demonstrated in this study of rTMS was not only statistically significant but also highly clinically relevant as exhibited by a Cohen’s d value greater than one. In our sample of patients with nonpsychotic severe unipolar depression, recurrent and severe treatment resistance, 63% responded to treatment, of which 40% achieved full remission for MADRS and 53% responded to treatment, of which 28% achieved full remission for PHQ-9. These results correspond to the earlier studies of real-world efficacy, which demonstrated response rates of 58% to 83% and remission rates of 28% to 62% (Sackeim et al., 2020 ). Our data showed patient characteristics were strongly related to rTMS response. A greater treatment response among older patients with higher BMI was observed. Moreover, there were differences in the results between clinician and patients appraisals. Commercial insurance was also associated with response to treatment. Although not directly examined in the present study, variation in treatment response between gender and age could be moderated by other factors such as hormonal fluctuations. For example, Huang et al. ( 2008 ) and Hanlon et al. (2022) showed a relationship between female hormones level, but not age in the therapeutic response. Furthermore, while rTMS does not seem to affect weight, it was found to alter lipid metabolism and thyroid function (Nakazawa et al., 2025 ). Thus, age and weight alone should not be considered a poor prognostic factor of the antidepressant efficacy of rTMS. Type of insurance was statistically predictive of response to treatment. Our results show better response rates in patients with private insurance versus government-funded, primarily Medicare. The treatment outcome of those with commercial insurance versus Medicare suggests a link between this social issue and rTMS response. Since none of the patients treated in our clinic were self-paid, uninsured status likely presented a barrier to this treatment modality. Insurance plans might consider broadening coverage, lessening over-restrictiveness through the prior authorization process and enhancing access for rTMS to less treatment-resistant patients. Equally relevant, many of the participants in the present research resided in modestly affluent neighborhoods. These complex interactions of patient-level characteristics intertwined with community-level health-related social needs, allow for disparities in outcomes to occur (Kreuter et al., 2021 ). Most of the reduction in depression severity occurred between baseline and midpoint, then was sustained until the end of treatment. Even though, our study did not fully support considering rTMS earlier in the treatment algorithm for TRD. Is is evidence for patients with severe, chronic, and refractory depression who already failed multiple psychotropic medication trials to benefit from early intervention (Dalhuisen et al., 2024 ). Notwithstanding this evidence and FDA clearance for use after one failed antidepressant trial, many patients in our clinic were restricted by insurance companies to receive rTMS access through a prior authorization process requiring high disease severity. These often included a minimum of four failed antidepressant trials from different medication classes, failed attempts at augmentation strategies and lack of response to psychotherapy. This disconnect was evident in the present study as we documented a failure to respond to at least four adequate antidepressant trials with different mechanisms of action, and one or more augmentation strategies using an atypical antipsychotic, lithium, an anticonvulsant, or two antidepressants simultaneously. In our study, psychotherapy for the rTMS cohort was not found to have a significant impact on rTMS response. Cuijpers et al. ( 2014 ) found concurrent psychotherapy appeared to be more effective than treatment with antidepressant medication alone suggesting a synergistic effect. The number of medication failures was not linked to patient outcome. This finding supports prior research, demonstrating rTMS is more effective for patients with less treatment resistance (Grammer et al., 2015 ). Baseline anxiety was not shown to significantly predict the reduction in symptoms of depression either. Moreover, use of inpatient and emergency room services over the study duration did not indicate over-utilization of psychiatric resources. 4.2 Health-related social needs on treatment effectiveness Race/ethnicity was not found to be predictive of the patient response. However, the racial and ethnic makeup of our sample was noticeably unrepresentative of the urban core we serve. The surrounding metropolitan area is estimated to be 21.8% African American, 12.9% Hispanic or Latino, and 69.9% White inhabitants (U.S. Census Bureau, 2023 ). In the present study, only 12% of patients belonged to a minoritized racial group in our study. The finding that Blacks were less likely to demonstrate an improvement in depression when compared to White race seems consistent with common findings where patients from marginalized groups are more likely to experience barriers to care, leading to disparities. Smith et al. ( 2025 ) found similar racial and ethnic disparities in administration of rTMS at an urban academic center have been described. Minoritized groups may experience heightened transportation burdens, lack of insurance coverage, lower mental health literacy or poor understanding of the intervention. Possible interventions to remediate these disparities could target increasing awareness of rTMS amongst people of color and improving the racial diversity of TMS referrals and employees in rTMS clinics. 4.3 Strengths and Limitations This study was conducted at a large safety-net single academic institution with a predominantly underserved and minority patient population that promotes strength of the sample. Still, a limitation was the retrospective nature of the study design, which predisposed the study to biases inherent of retrospective cohort studies such as recall bias. The second limitation was the sample size of 60 patients that was determined by convenience and not by statistical power. In particular, the small number of minority participants. Due to the small number of patients comprising a minoritized racial group (n = 6), individual groups were unable to be examined separately. The smaller sample of patients included decreased our statistical power as evidenced by the varying regression results between the MADRS and PHQ-9. This may have contributed to the non-statistical differences in the model and increased the probability of a Type I or II error. Furthermore, the model differences when outliers were removed required care in the interpretation of the findings. The limited dataset that was collected at a single urban center constrains the ability to extrapolate the findings beyond that sufficiently supported by the data. Finally, the generalizability of the results is similarly restricted to comparable patients. Therefore, caution must be taken when drawing conclusions. 5. Conclusions Major depressive disorder (MDD) is a prevalent and debilitating condition that represents a substantial public health challenge impacting quality-of-life, productivity, and overall well-being (World Health Organization, 2021). This real-world study supports the use of rTMS as a treatment for severe TRD in a newly formed rTMS outpatient clinic. Clinical response to rTMS with TRD appears to be guided by individual and clinical factors. The trend was towards better response in our rTMS cohort among those who completed a full treatment course. These findings highlight the importance of revising rTMS coverage policies accordingly to help reduce morbidity and contribute to the growing body of evidence on health disparities that highlight the importance of considering social drivers of health in depression intervention. Furthermore, measures of functional abilities and quality of life may further elaborate on the real-world impact of rTMS treatment for depressed patients. Future research should examine utilization and economic outcomes over an extended time interval after treatment termination. These findings have the potential to provide useful information in the optimization of clinical response while informing service delivery and policy decisions, paving the way for a healthier, more equitable future. Declarations Ethics approval and consent to participate The study was approved by the University of Florida Institutional Review Board (IRB202500308), and the research was conducted in accordance with the Declaration of Helsinki. Informed consent was waived by IRB for retrospective analysis of anonymized data. Disclosure of Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Declaration of competing interests: None Author Contribution P.C. and B.C. conceptualized the study, developed the methodology, and conducted formal analysis, visualization, supervision and investigation.I.T. and B.C. interpreted the data and wrote the manuscript including original draft.K.L. and A.B. data curation, resources and project administration. All authors reviewed, edited and approved the final manuscript. References Bondolfi, G., Jermann, F., Rouget, B. W., Gex-Fabry, M., McQuillan, A., Dupont-Willemin, A., Aubry, J. M., & Nguyen, C. (2010). Self- and clinician-rated Montgomery-Asberg Depression Rating Scale: evaluation in clinical practice. Journal of affective disorders , 121 (3), 268–272. https://doi.org/10.1016/j.jad.2009.06.037 Comprehensive mental health action plan 2013–2030. Geneva: World Health Organization; 2021. Licence: CC BY-NC-SA 3.0 IGO. Cuijpers, P., Sijbrandij, M., Koole, S. L., Andersson, G., Beekman, A. T., & Reynolds, C. F., 3rd (2014). Adding psychotherapy to antidepressant medication in depression and anxiety disorders: a meta-analysis. World psychiatry: official journal of the World Psychiatric Association (WPA) , 13 (1), 56–67. https://doi.org/10.1002/wps.20089 Cuijpers, P., Turner, E. H., Koole, S. L., van Dijke, A., & Smit, F. (2014). What is the threshold for a clinically relevant effect? The case of major depressive disorders. Depression and anxiety , 31 (5), 374–378. https://doi.org/10.1002/da.22249 Dalhuisen, I., van Oostrom, I., Spijker, J., Wijnen, B., van Exel, E., van Mierlo, H., de Waardt, D., Arns, M., Tendolkar, I., & van Eijndhoven, P. (2024). rTMS as a Next Step in Antidepressant Nonresponders: A Randomized Comparison With Current Antidepressant Treatment Approaches. The American journal of psychiatry , 181 (9), 806–814. https://doi.org/10.1176/appi.ajp.20230556 Dunner, D. L., Aaronson, S. T., Sackeim, H. A., Janicak, P. G., Carpenter, L. L., Boyadjis, T., Brock, D. G., Bonneh-Barkay, D., Cook, I. A., Lanocha, K., Solvason, H. B., & Demitrack, M. A. (2014). A multisite, naturalistic, observational study of transcranial magnetic stimulation for patients with pharmacoresistant major depressive disorder: durability of benefit over a 1-year follow-up period. The Journal of clinical psychiatry , 75 (12), 1394–1401. https://doi.org/10.4088/JCP.13m08977 Eaton, W. W., Shao, H., Nestadt, G., Lee, H. B., Bienvenu, O. J., & Zandi, P. (2008). Population-based study of first onset and chronicity in major depressive disorder. Archives of general psychiatry , 65 (5), 513–520. https://doi.org/10.1001/archpsyc.65.5.513 Fekadu, A., Donocik, J. G., & Cleare, A. J. (2018). Standardisation framework for the Maudsley staging method for treatment resistance in depression. BMC psychiatry , 18 (1), 100. https://doi.org/10.1186/s12888-018-1679-x Fekadu, A., Wooderson, S., Donaldson, C., Markopoulou, K., Masterson, B., Poon, L., & Cleare, A. J. (2009). A multidimensional tool to quantify treatment resistance in depression: the Maudsley staging method. The Journal of clinical psychiatry , 70 (2), 177–184. https://doi.org/10.4088/jcp.08m04309 Fitzgerald, P. B., Oxley, T. J., Laird, A. R., Kulkarni, J., Egan, G. F., & Daskalakis, Z. J. (2006). An analysis of functional neuroimaging studies of dorsolateral prefrontal cortical activity in depression. Psychiatry research , 148 (1), 33–45. https://doi.org/10.1016/j.pscychresns.2006.04.006 Grammer, G. G., Kuhle, A. R., Clark, C. C., Dretsch, M. N., Williams, K. A., & Cole, J. T. (2015). Severity of Depression Predicts Remission Rates Using Transcranial Magnetic Stimulation. Frontiers in psychiatry , 6 , 114. https://doi.org/10.3389/fpsyt.2015.00114 Hanlon, C. A., & McCalley, D. M. (2022). Sex/Gender as a Factor That Influences Transcranial Magnetic Stimulation Treatment Outcome: Three Potential Biological Explanations. Frontiers in psychiatry , 13 , 869070. https://doi.org/10.3389/fpsyt.2022.869070 Huang, C. C., Wei, I. H., Chou, Y. H., & Su, T. P. (2008). Effect of age, gender, menopausal status, and ovarian hormonal level on rTMS in treatment-resistant depression. Psychoneuroendocrinology , 33 (6), 821–831. https://doi.org/10.1016/j.psyneuen.2008.03.006 IBM SPSS Statistics for Windows, Version 30.0.0 . IBM Corp; 2024. Knighton, A. J., Savitz, L., Belnap, T., Stephenson, B., & VanDerslice, J. (2016). Introduction of an Area Deprivation Index Measuring Patient Socioeconomic Status in an Integrated Health System: Implications for Population Health. EGEMS (Washington, DC) , 4 (3), 1238. https://doi.org/10.13063/2327-9214.1238 Kreuter, M. W., Thompson, T., McQueen, A., & Garg, R. (2021). Addressing Social Needs in Health Care Settings: Evidence, Challenges, and Opportunities for Public Health. Annual review of public health , 42 , 329–344. https://doi.org/10.1146/annurev-publhealth-090419-102204 Kroenke, K., Spitzer, R. L., & Williams, J. B. (2001). The PHQ-9: validity of a brief depression severity measure. Journal of general internal medicine , 16 (9), 606–613. https://doi.org/10.1046/j.1525-1497.2001.016009606.x Löwe, B., Decker, O., Müller, S., Brähler, E., Schellberg, D., Herzog, W., & Herzberg, P. Y. (2008). Validation and standardization of the Generalized Anxiety Disorder Screener (GAD-7) in the general population. Medical care , 46 (3), 266–274. https://doi.org/10.1097/MLR.0b013e318160d093 Marder, K. G., Barbour, T., Ferber, S., Idowu, O., & Itzkoff, A. (2022). Psychiatric Applications of Repetitive Transcranial Magnetic Stimulation. Focus (American Psychiatric Publishing) , 20 (1), 8–18. https://doi.org/10.1176/appi.focus.20210021 Montgomery, S. A., & Asberg, M. (1979). A new depression scale designed to be sensitive to change. The British journal of psychiatry: the journal of mental science , 134 , 382–389. https://doi.org/10.1192/bjp.134.4.382 Nakazawa, A., Matsuda, Y., Yamazaki, R., Taruishi, N., & Kito, S. (2025). Effects of repetitive transcranial magnetic stimulation therapy on weight and lipid metabolism in patients with treatment-resistant depression: A preliminary single-center retrospective cohort study. Neuropsychopharmacology reports , 45 (1), e12494. https://doi.org/10.1002/npr2.12494 R Core Team (2025). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. Rush, A. J., Trivedi, M. H., Wisniewski, S. R., Nierenberg, A. A., Stewart, J. W., Warden, D., Niederehe, G., Thase, M. E., Lavori, P. W., Lebowitz, B. D., McGrath, P. J., Rosenbaum, J. F., Sackeim, H. A., Kupfer, D. J., Luther, J., & Fava, M. (2006). Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: a STAR*D report. The American journal of psychiatry , 163 (11), 1905–1917. https://doi.org/10.1176/ajp.2006.163.11.1905 Sackeim, H. A., Aaronson, S. T., Carpenter, L. L., Hutton, T. M., Mina, M., Pages, K., Verdoliva, S., & West, W. S. (2020). Clinical outcomes in a large registry of patients with major depressive disorder treated with Transcranial Magnetic Stimulation. Journal of affective disorders , 277 , 65–74. https://doi.org/10.1016/j.jad.2020.08.005 Smith, A. C., Abu-Sultanah, M., Holmes, E. G., & Conroy, S. K. (2025). Racial and Ethnic Disparities in Administration of Transcranial Magnetic Stimulation at an Academic Center. Journal of psychiatric practice , 31 (2), 82–84. https://doi.org/10.1097/PRA.0000000000000843 Trapp, N. T., Purgianto, A., Taylor, J. J., Singh, M. K., Oberman, L. M., Mickey, B. J., Youssef, N. A., Solzbacher, D., Zebley, B., Cabrera, L. Y., Conroy, S., Cristancho, M., Richards, J. R., Flood, M. J., Barbour, T., Blumberger, D. M., Taylor, S. F., Feifel, D., Reti, I. M., McClintock, S. M., … National Network of Depression Centers Neuromodulation Task Group (2025). Consensus review and considerations on TMS to treat depression: A comprehensive update endorsed by the National Network of Depression Centers, the Clinical TMS Society, and the International Federation of Clinical Neurophysiology. Clinical neurophysiology: official journal of the International Federation of Clinical Neurophysiology , 170 , 206–233. https://doi.org/10.1016/j.clinph.2024.12.015 U.S. Census Bureau (2023). American Community Survey 1-year estimates. Retrieved from Census Reporter Profile page for Jacksonville, FL Oct. 8, 2025. Weissman, C. R., Bermudes, R. A., Voigt, J., Liston, C., Williams, N., Blumberger, D. M., Fitzgerald, P. B., & Daskalakis, Z. J. (2023). Repetitive Transcranial Magnetic Stimulation for Treatment-Resistant Depression: Mismatch of Evidence and Insurance Coverage Policies in the United States. The Journal of clinical psychiatry , 84 (3), 22com14575. https://doi.org/10.4088/JCP.22com14575 Tables Table 1. MADRS Categorical rTMS response classification. Nonresponse Partial response Full response Chi square P value Variable: n(%) Age 6 (10.0) 1 (1.7) 9 (15.0) Gender Female 15 (25.0) 8 (13.3) 14 (23.3) 0.629 0.730 Male 7 (11.7) 6 (10.0) 10 (16.7) Race Black 2 (3.3) 1 (1.7) 2 (3.3) 1.819 0.769 White 19 (31.7) 13 (21.7) 22 (3.3) Hispanic 1 (1.7) 0 (0.0) 0 (0.0) BMI 12 (20.0) 8 (13.3) 13 (21.7) Insurance Commercial 15 (25.0) 12 (20.0) 15 (25.0) 6.141 0.189 Medicare 5 (8.3) 2 (3.3) 9 (15.0) Medicaid 2 (3.3) 0 (0.0) 0 (0.0) Baseline MADRS 17 (28.3) 11 (18.3) 14 (23.3) Psychotherapy No 17 (28.3) 7 (11.7) 20 (33.3) 5.300 0.071 Yes 5 (8.3) 7 (50.0) 4 (6.7) BMI-Body Mass Index, MADRS-Montgomery-Asberg Depression Rating Scale. Table 2. PHQ-9 Categorical rTMS response classification. Nonresponse Partial response Full response Chi square P value Variable: n(%) Age 7 (11.7) 4 (6.7) 5 (8.3) Gender Female 19 (31.7) 5 (8.3) 13 (21.7) 7.124 0.028 Male 9 (15.0) 10 (16.7) 4 (6.7) Race Black 2 (3.3) 2 (3.3) 1 (1.7) 1.815 0.770 White 25 (41.7) 13 (21.7) 16 (26.7) Hispanic 1 (1.7) 0 (0.0) 0 (0.0) BMI 15 (25.0) 8 (13.3) 10 (16.7) Insurance Commercial 19 (31.7) 11 (18.3) 12 (20.0) 2.406 0.662 Medicare 7 (11.7) 4 (6.7) 5 (8.3) Medicaid 2 (3.3) 0 (0.0) 0 (0.0) Baseline PHQ-9 14 (23.3) 6 (10.0) 9 (15.0) Psychotherapy No 19 (31.7) 11 (18.3) 14 (23.3) 1.137 0.566 Yes 9 (15.0) 4 (6.7) 3 (5.0) BMI-Body Mass Index, PHQ-9-Patient Health Questionnaire. Table 3. MADRS and PHQ-9 General Linear Model results. MADRS PHQ-9 Variable F Sig. F Sig. Age 209.13 <.001 118.31 <.001 Race 0.12 0.732 0.05 0.820 Gender 2.28 0.138 2.18 0.146 BMI 8.03 0.007 2.78 0.102 Insurance 9.62 0.003 3.32 0.075 GAD7 1.01 0.321 1.78 0.189 Maudsley 0.00 0.975 0.15 0.697 Number Sessions 14.15 <.001 3.47 0.068 Duration 5.03 0.030 5.55 0.023 Medications 2.57 0.115 0.59 0.447 Psychotherapy 0.80 0.375 0.13 0.721 ADI 0.04 0.834 0.02 0.882 MADRS-Montgomery-Asberg Depression Rating Scale, PHQ-9-Patient Health Questionnaire, BMI-Body Mass Index, GAD7-General Anxiety Disorder, ADI-Area Deprivation Index. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 18 May, 2026 Reviewers agreed at journal 13 May, 2026 Reviews received at journal 12 May, 2026 Reviewers agreed at journal 08 May, 2026 Reviewers agreed at journal 19 Apr, 2026 Reviews received at journal 02 Mar, 2026 Reviewers agreed at journal 28 Feb, 2026 Reviewers agreed at journal 19 Feb, 2026 Reviewers invited by journal 09 Feb, 2026 Editor invited by journal 03 Feb, 2026 Editor assigned by journal 29 Jan, 2026 Submission checks completed at journal 29 Jan, 2026 First submitted to journal 25 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8695870","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":588784701,"identity":"4d4abf98-1aba-4502-93df-06cbe08981e2","order_by":0,"name":"Peter J Clagnaz","email":"","orcid":"","institution":"University of Florida","correspondingAuthor":false,"prefix":"","firstName":"Peter","middleName":"J","lastName":"Clagnaz","suffix":""},{"id":588784702,"identity":"f647972e-9b53-46cb-86d6-667890a5b514","order_by":1,"name":"Irina Tardif","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIie3RsWrDMBCA4RMGezkyOyR1XkHBQwtp0lex0OAl0DXQQCQM9iLo6tI+jMDQLnmImECnDh5TCKW24qWD7Y6F6l90w33cIACb7Q/mAhHtyERVYTOQUgM4Q4Q2RD7lhji0l7Q1JCLPRgyR0aRIDyc4z64zLpPb6YoJn4OGzYKJDuKOWDZXQOcv+1Ima+QhGLKPuwmS1EegJPdZdlyjE4B/rzVJi14yPgO9q4lMbnCH5gr56ieT+gozBLAILkQMkCkNeY6llArfQhePoKPXOOwiM+W9jz82wTL34lJ8qgf26HFyqLaLqy5yibYvUc3n1kW96z86/X7VZrPZ/k/fI9RQY0aZ6RUAAAAASUVORK5CYII=","orcid":"","institution":"University of Florida","correspondingAuthor":true,"prefix":"","firstName":"Irina","middleName":"","lastName":"Tardif","suffix":""},{"id":588784703,"identity":"56e0f5df-a164-4c2a-bdbd-44bb1d10aac2","order_by":2,"name":"Kitty K Leung","email":"","orcid":"","institution":"University of Florida","correspondingAuthor":false,"prefix":"","firstName":"Kitty","middleName":"K","lastName":"Leung","suffix":""},{"id":588784704,"identity":"4b1de90a-cd0a-4a8a-b4cf-8001048157db","order_by":3,"name":"Amal A Bhullar","email":"","orcid":"","institution":"University of Florida","correspondingAuthor":false,"prefix":"","firstName":"Amal","middleName":"A","lastName":"Bhullar","suffix":""},{"id":588784705,"identity":"d77b63e3-8547-4a95-a19c-7e04ca6c3b7a","order_by":4,"name":"Brian G Celso","email":"","orcid":"","institution":"University of Florida","correspondingAuthor":false,"prefix":"","firstName":"Brian","middleName":"G","lastName":"Celso","suffix":""}],"badges":[],"createdAt":"2026-01-26 02:53:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8695870/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8695870/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102517304,"identity":"276e098e-3289-4082-a0cf-ea875fffc05d","added_by":"auto","created_at":"2026-02-12 13:56:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":22730,"visible":true,"origin":"","legend":"\u003cp\u003eDepression severity over time with repetitive transcranial magnetic stimulation.\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eError bars represent standard deviation.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8695870/v1/6197ad037e650b5340f0fcb8.png"},{"id":102517479,"identity":"74a45f7f-0eec-4aec-b0e4-fe0906143f69","added_by":"auto","created_at":"2026-02-12 13:57:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":25184,"visible":true,"origin":"","legend":"\u003cp\u003eAUC for poor and good responders.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8695870/v1/43cc2c00cf032497dfad79a7.png"},{"id":102517611,"identity":"a69929e9-04d4-40dc-998c-660c0a6ffabc","added_by":"auto","created_at":"2026-02-12 13:57:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":939719,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8695870/v1/d7ab2434-7b3d-4620-9a37-d4326d4499aa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eRepetitive Transcranial Magnetic Stimulation for Treatment Resistant Depression: How patient characteristics and elements of treatment impact post-treatment outcomes\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eOne-third of individuals with Major Depressive Disorder (MDD) fail to respond to first-line treatments (pharmaco- or psychotherapy) often leading to treatment-resistant depression (TRD) (Rush et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Repetitive TMS has become part of the standard of care for TRD, where response rates of 40% to 60% are characteristic (Marder et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Effective treatment, however, requires understanding predictors of treatment response in MDD. Since the effectiveness of rTMS may depend on various demographic, clinical, and treatment-related factors, it is important for providers to be able to identify optimal patient and treatment parameters.\u003c/p\u003e \u003cp\u003eRepetitive transcranial magnetic stimulation (rTMS) is a widely regarded neuromodulation technique used to treat a variety of psychiatric disorders such as major depressive disorder, anxious depression, and obsessive compulsive disorder (Marder et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Other indications for rTMS include migraine with aura and smoking cessation. In attempts to optimize treatment, a variety of different stimulation protocols have been studied, encompassing different brain targets, stimulation parameters, targeting methods, and treatment schedules. This algorithmic uncertainty leaves treating physicians on their own to decide when to consider rTMS as a treatment modality.\u003c/p\u003e \u003cp\u003eRepetitive TMS involves the application of repetitive magnetic pulses through a coil that is positioned above the left dorsolateral prefrontal cortex (Trapp et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This area is a common target for MDD that is responsible for mood regulation, cognitive control, executive functioning, and emotion processing (Fitzgerald et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). However, determining the exact positioning of rTMS within treatment algorithms for TRD remains largely unconfirmed. While Dunner et al. (2012) found approximately two-thirds of responders or remitters continued to show a treatment response over time. On the other hand, prolonged periods of inadequate treatment contributed to a higher risk of chronicity, comorbidity, and suicidality, affirming the need for more effective treatment options for patients with TRD (Eaton et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdditionally, no unified criteria for the definition of TRD has affected the prior authorization process by various insurance carriers (Smith et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Weissman et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) reported that insurance status was likely to create such barriers as access to care. Minoritized groups are often overrepresented in lower socioeconomic categories, which potentially explains their limited ability to undertake such a time- and resource-intensive treatment. As a result, this has led to disparate impact on racial and ethnic minorities who were shown to be underrepresented in treatment (Smith et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Work also remains to be done in overcoming stigma, particularly historical mistrust with the mental health care system in minoritized populations.\u003c/p\u003e \u003cp\u003e This study represented a retrospective, observational analysis to investigate predictors of treatment response-remission specific characteristics of the participating patients, treatment settings, and treatment factors. The objective of our study was to evaluate rTMS at the initiation and after completion of treatment at a newly formed rTMS outpatient clinic. The aim was to examine the effect of rTMS in a sample of patients with nonpsychotic severe unipolar depression recurrent or single episode and severe treatment resistance. We investigated rTMS based on identified patient characteristics, duration of symptoms associated with depression, number of failed medication trials, and the state Area Deprivation Index (ADI). In addition, when used with concurrent psychotherapy, the responsiveness to rTMS was examined. Thus, the knowledge gained can inform the development of more personalized elements of treatments that are based on individual patient and clinical characteristics.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Design\u003c/h2\u003e \u003cp\u003eThis retrospective, observational study consisted of data from patients diagnosed with MDD who received care at the UF Health Jacksonville rTMS clinic between December 2021 and February 2025 and extracted from the EHR. The study was approved by the University of Florida Institutional Review Board (IRB202500308).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Participants\u003c/h2\u003e \u003cp\u003eThe study included encounters of adult patients (\u0026ge;\u0026thinsp;18 years) with treatment-resistant depression. Patient information included age, sex, race/ethnicity, Body Mass Index (BMI), insurance status, and state ADI. Inclusion criteria were patients diagnosed with MDD, severe, single episode or recurrent episode without psychotic symptoms. Major Depressive Disorder was diagnosed based on the DSM-5 symptoms during a structured clinical interview fashioned to match insurance preauthorization. The Maudsley Staging Method (MSM) was also utilized to stage treatment-resistant depression that incorporates three factors: medication failures, severity of illness, and duration of depressive episode (Fekadu et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Participants demonstrated an inadequate response to antidepressant trials, augmentation with a second antidepressant, anticonvulsant, Lithium, or an atypical neuroleptic during this depressive episode. The exclusion criteria included patients with seizure disorders or embedded ferromagnetic material that would be within 30 cm of the treatment coil. Additional exclusion criteria were previous failure of rTMS treatment and pregnancy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Instruments\u003c/h2\u003e \u003cp\u003eThe Montgomery-Asberg Depression Rating Scale (MADRS) consists of 10 items, each rated on a scale from 0 to 6, with higher scores indicating more severe symptoms (Montgomery \u0026amp; Asberg, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1979\u003c/span\u003e; Bondolfi et al., 2008). The total score, ranging from 0 to 60, indicates depression severity (0\u0026ndash;6 normal, 7\u0026ndash;19 mild, 20\u0026ndash;34 moderate, 35\u0026thinsp;+\u0026thinsp;severe).\u003c/p\u003e \u003cp\u003eThe Generalized Anxiety Disorder-7 (GAD-7) is a 7-item questionnaire used to screen for and assess the severity of anxiety symptoms with well established validity and reliability (L\u0026ouml;we et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Scores are categorized as: 0\u0026ndash;4 (minimal), 5\u0026ndash;9 (mild), 10\u0026ndash;14 (moderate), and 15\u0026ndash;21 (severe). The GAD-7 was used to screen for the presence of anxiety symptoms.\u003c/p\u003e \u003cp\u003eThe Patient Health Questionnaire (PHQ-9) is a widely recognized and well validated test of depression that has been used across many conditions (Kroenke et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). The test consists of 9 items. The scores range from 0 to 27 with cutpoints of 5, 10, 15 and 20 that represent mild, moderate, moderately severe and severe levels of depressive symptoms, respectively.\u003c/p\u003e \u003cp\u003eThe Maudsley Staging Method (MSM) was developed to define and stage treatment-resistant depression. The MSM is calculated based on duration and severity of illness, medication failures, augentation, and ECT usage (Fekadu et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The overall maximum score for MSM is 15. Fekadu et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) standardized the MSM by adding severity score categories of Mild (3\u0026ndash;6), Moderate (7\u0026ndash;10), and Severe (11\u0026ndash;15).\u003c/p\u003e \u003cp\u003eThe Area Deprivation Index (ADI) measures neighborhood disadvantage using a combination of 17 variables such as income, education, employment, and housing quality, and used to inform health delivery and policy, especially for the most disadvantaged neighborhood groups (Knighton et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Neighborhoods are ranked relative to others within a specific state then grouped into deciles (1 to 10), where higher numbers indicate greater disadvantage. The Centers for Medicare and Medicaid Services have used this validated measure to measure neighborhood disadvantage.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Procedure\u003c/h2\u003e \u003cp\u003e Participants completed the MADRS, PHQ-9, and GAD-7 at the initiation of treatment, at the treatment midpoint, and at the completion of treatment. Psychiatrists supervising rTMS administered the clinician-rated MADRS. The PHQ-9 scores were provided by self-report. The GAD-7 appraised anxiety level during the clinical interview. Information also collected included duration of depression measured in years, antidepressant medication trials, type of insurance (commercial, Medicare, Medicaid), additional psychiatric diagnoses, number of Emergency Department (ED) visits, readmissions to a psychiatric hospital, and concomitant psychotherapy. The primary outcomes were change in depression severity based on the MADRS and PHQ-9 depression inventories. Secondary outcomes included the responses to rTMS according to age, sex, race, insurance status, MSM, GAD-7, number of medications, psychotherapy, and state ADI. To manage missing data, analyses were performed with data from the intention-to-treat sample.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Interventions\u003c/h2\u003e \u003cp\u003eThe study participants completed the UF Health psychiatry rTMS protocol (see \u003cb\u003eTable S1\u003c/b\u003e for the delivery protocol). The rTMS device, the MagStim, uses high frequency (10 Hz) transcranial magnetic stimulation over the left dorsolateral prefrontal cortex (DLPFC), which has been shown to be clinically effective for depression. The trial of rTMS consisted of a course of 36 rTMS sessions over 9 weeks of treatment. Study participants were assessed at the end of treatment as either full remission, partial response, or no response. Pharmacological therapy for MDD consisted of continuing the current antidepressant medication prescribed that included SSRI, SNRI, DNRI, and TCA. A participant\u0026rsquo;s treatment may have also been augmented with lithium, an anticonvulsant or with a second-generation antipsychotic. Those with additional comorbid psychiatric diagnoses such as ADD, GAD, or PTSD, were to continue their current medication regime according to standard of care. Participants who received individual psychotherapy which might have consisted of cognitive-behavioral therapy, behavioral activation therapy, or supportive therapy, followed up as during usual care.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical Analysis\u003c/h2\u003e \u003cp\u003eFor continuous variables, the data was summarized with means and standard deviations. For categorical variables, data was summarized using the sample size (n) and percentage (%). To evaluate concordance between the MADRS clinician ratings and the self-reported PHQ-9, the correlation between these scores was computed using Pearson \u003cem\u003er\u003c/em\u003e statistic. The chi square test was used to determine differences among the classification percentages. Independent and paired \u003cem\u003et-\u003c/em\u003etests were performed to assess the change in MADRS and PHQ-9 scores, for ages less than or 65 and greater and baseline scores. A paired-sample \u003cem\u003et-\u003c/em\u003etest was utilized to assess the effectiveness of rTMS treatments in reducing depressive symptoms by comparing the initial treatment MADRS and PHQ-9 with the endpoint MADRS and PHQ-9 scores at treatment termination. Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e were calculated to determine the effect size of the difference. A clinically relevant effect for treatment of depression was defined by the minimal important difference from a patient perspective, which corresponds to a Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e of 0.24 (Cuijpers et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUsing a data-driven approach, optimal cutpoints were identified to classify patients into three distinct categories. The definition of treatment non-response was a reduction of less than 50% in the endpoint compared to baseline total score on the MADRS. A partial response rate was defined as a minimum of 50% or more reduction in post-treatment scores on the MADRS. Full remission was defined as the cut-off value on the MADRS of \u0026lt;\u0026thinsp;10. Nonresponders on the PHQ-9 was a reduction of less than 50% in the endpoint compared to baseline total score on the PHQ-9. Partial response was defined as \u0026ge;\u0026thinsp;50% reduction in PHQ-9 scores at final assessment relative to pre-TMS baseline. Full remission was defined as a final PHQ-9 score of less than 5.\u003c/p\u003e \u003cp\u003eA multivariable general linear model (GLM) fitted to compare change in depression severity and adjusted for all covariates, was used to determine the contribution of the predictor variables with the change in depressive symptom severity over the course of treatment. This analysis was performed with scores on the MADRS and PHQ-9 as the dependent variables to determine the between-subjects main effects. Age, sex, race/ethnicity, BMI, insurance status, GAD-7, MSM, number of medications, number of sessions, duration of treatment, psychotherapy, and state ADI were added to the regression model as covariates to control for confounding. To assess the predictive performance of our rTMS response model, discrimination was evaluated using area under the receiver operating characteristic curve (AUC). Statistical analyses were performed with SPSS, version 30.0, and R 4.5.1. Tests were two-tailed with an alpha level of 0.05 for the primary analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Demographic Characteristics\u003c/h2\u003e \u003cp\u003eA total of 60 participants were included. Forty-five (75%) of the participants completed the full 36 sessions of rTMS treatment per protocol. Thirteen patients received less than 36 treatments that ranged from 3 to 35 rTMS sessions. Three participants received greater than 36 treatments, specifically, 48, 51, and 64 sessions, respectively. The average age was 51.9 (SD\u0026thinsp;=\u0026thinsp;17.7) years. Of the included participants, 37 (61.7%) were female. Regarding race/ethnicity, 54 participants (90.0%) were non-Hispanic White, 5 participants (8.3%) were Non-Hispanic Black, and one was Hispanic (1.7%). The average BMI was 31.0 (SD\u0026thinsp;=\u0026thinsp;7.7). The insurance status was 42 (70.0%) had commercial insurance, 16 (26.7%) had Medicare, and 2 (3.3%) with Medicaid. The average GAD-7 anxiety score was 14.0 (SD\u0026thinsp;=\u0026thinsp;5.5). The average MSM was 11.20 (1.3). The median number of psychiatric medications was three. There were 16 participants who received psychotherapy (26.7%). The average state ADI for the sample was 4.8 (SD\u0026thinsp;=\u0026thinsp;2.7). The average baseline MADRS and PHQ-9 scores were (35.0, SD\u0026thinsp;=\u0026thinsp;6.4; 20.1, SD\u0026thinsp;=\u0026thinsp;3.9), respectively. Eight patients had at least one inpatient psychiatric hospitalization and six visited an Emergency Department during the study period. The percentages and chi square values of the patients based on the categorical rTMS response classification (nonresponse, partial response, full response) for the MADRS and PHQ-9 are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, respectively. \u003cb\u003eTable S2\u003c/b\u003e lists the prescribed psychiatric medication for each patient.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 PHQ-9 and MADRS concordance\u003c/h2\u003e \u003cp\u003eThere was strong agreement between the PHQ-9 and MADRS ratings, with a correlation coefficient across all paired ratings of (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.562, \u003cb\u003eP\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e, (95% CI: 0.359\u0026ndash;0.714) at baseline and (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.924, \u003cb\u003eP\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e, (95% CI: 0.876\u0026ndash;0.954) at the end of treatment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Change in Depression Severity\u003c/h2\u003e \u003cp\u003eIndependent \u003cem\u003et\u003c/em\u003e-tests for age and baseline MADRS and PHQ-9 were not statistically significant (\u003cem\u003et\u003c/em\u003e=-0.21, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.84; \u003cem\u003et\u003c/em\u003e=-0.26, \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.80, respectively). Paired-sample \u003cem\u003et\u003c/em\u003e-tests were utilized to assess the effectiveness of TMS treatments in reducing depressive symptoms. The end-of-treatment MADRS scores were statistically significantly different from the pre-treatment scores (\u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;13.13, \u003cb\u003eP\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e). Furthermore, the effect size of the difference was (\u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.70, 95% CI: 1.30\u0026ndash;2.09). The end-of-treatment PHQ-9 scores were statistically significantly different from the pre-treatment scores (\u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10.64, \u003cb\u003eP\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e). Furthermore, the effect size of the difference was (\u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.37, 95% CI: 1.02\u0026ndash;1.73). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the change in depression severity over time with rTMS at baseline, midpoint of treatment, and the endpoint.\u003c/p\u003e \u003cp\u003eA multivariate GLM was fitted to compare change in depression severity with the outcome variables MADRS and PHQ-9. Predictor variables, age, sex, race/ethnicity, BMI, insurance status, GAD-7, MSM, number of medications, number of sessions, duration of treatment, psychotherapy, and state ADI were entered to control for confounding. The overall model fit for MADRS and PHQ-9 were statistically significant (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;21.06, \u003cb\u003eP\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.001;\u003c/b\u003e \u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;11.53, \u003cb\u003eP\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e), respecively. Age, insurance status, BMI, number of rTMS sessions, and treatment duration were statistically significant for MADRS, controlling for all other variables. Age and treatment duration were statistically significant for PHQ-9, controlling for all other variables (See Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Insurance status, BMI, and number of sessions were not statistically significant. Baseline anxiety was not statistically significantly associated with the reduction of depressive symptoms (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.32 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.19), respectively. The state ADI was not statistically significant either (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.83 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.88), respectively. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e displays the regression model results for both the MADRS and PHQ-9.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Model Performance Comparison\u003c/h2\u003e \u003cp\u003eReceiver Operator Characteristics curves for the rTMS response model compared the ability to discriminate poor from good responders and evaluate their predictive performance. The cut point was above or below the 50% reduction in MADRS and PHQ-9 scores. The ROC curve for MADRS demonstrated better discrimination for poor responders (AUC\u0026thinsp;=\u0026thinsp;0.854), while the AUC for good responders was (AUC\u0026thinsp;=\u0026thinsp;0.664). Area difference between the two curves was not statistically different (\u003cem\u003ez\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.18, 95% CI: -0.13-0.51, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.24). Overall model quality for the poor TMS responders substantially improved prediction (0.69), whereas for good responders (0.39), the ROC curve demonstrated less than ideal performance. The ROC curve for PHQ-9 demonstrated slightly better discrimination for good responders (AUC\u0026thinsp;=\u0026thinsp;0.505), while the AUC for poor responders was (AUC\u0026thinsp;=\u0026thinsp;0.496). Area difference between the two curves was not statistically different (\u003cem\u003ez\u003c/em\u003e=-0.045, 95% CI: -0.41-0.40, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.96). Overall model quality for the poor TMS responders showed improved prediction (0.24), whereas for good responders (0.19), the ROC curve demonstrated less than ideal performance. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the ROC curves for each group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Sensitivity Analyses\u003c/h2\u003e \u003cp\u003eWe conducted a sensitivity analysis with the initial dataset of 60 patients. First, the analysis identified four outliers that were 3 standard deviations either above or below the average number of rTMS sessions, specifically, 3, 4, 6, and 64. Second, the outliers were removed to examine the strength of a prediction effect without session number in the analysis and ensure there was no potential undue influence on the regression results. This left 56 patients in the sample. Age, insurance status, BMI, number of rTMS sessions, treatment duration and number of psychiatric medications were statistically significant for MADRS, while controlling for all other variables. Age was statistically significant for PHQ-9, controlling for all other variables\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Effects of rTMS treatment on depression\u003c/h2\u003e \u003cp\u003eIn this retrospective analysis of existing data in the EMR, treatment-resistant depressed psychiatry patients receiving rTMS treatments at a large urban academic outpatient clinic experienced statistically significant improvements in depression severity. Repetitive TMS was shown to be effective in reducing symptoms of depression, based on a well-established inventory for depressive disorder. The improvement demonstrated in this study of rTMS was not only statistically significant but also highly clinically relevant as exhibited by a Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e value greater than one. In our sample of patients with nonpsychotic severe unipolar depression, recurrent and severe treatment resistance, 63% responded to treatment, of which 40% achieved full remission for MADRS and 53% responded to treatment, of which 28% achieved full remission for PHQ-9. These results correspond to the earlier studies of real-world efficacy, which demonstrated response rates of 58% to 83% and remission rates of 28% to 62% (Sackeim et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur data showed patient characteristics were strongly related to rTMS response. A greater treatment response among older patients with higher BMI was observed. Moreover, there were differences in the results between clinician and patients appraisals. Commercial insurance was also associated with response to treatment. Although not directly examined in the present study, variation in treatment response between gender and age could be moderated by other factors such as hormonal fluctuations. For example, Huang et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) and Hanlon et al. (2022) showed a relationship between female hormones level, but not age in the therapeutic response. Furthermore, while rTMS does not seem to affect weight, it was found to alter lipid metabolism and thyroid function (Nakazawa et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Thus, age and weight alone should not be considered a poor prognostic factor of the antidepressant efficacy of rTMS.\u003c/p\u003e \u003cp\u003eType of insurance was statistically predictive of response to treatment. Our results show better response rates in patients with private insurance versus government-funded, primarily Medicare. The treatment outcome of those with commercial insurance versus Medicare suggests a link between this social issue and rTMS response. Since none of the patients treated in our clinic were self-paid, uninsured status likely presented a barrier to this treatment modality. Insurance plans might consider broadening coverage, lessening over-restrictiveness through the prior authorization process and enhancing access for rTMS to less treatment-resistant patients. Equally relevant, many of the participants in the present research resided in modestly affluent neighborhoods. These complex interactions of patient-level characteristics intertwined with community-level health-related social needs, allow for disparities in outcomes to occur (Kreuter et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMost of the reduction in depression severity occurred between baseline and midpoint, then was sustained until the end of treatment. Even though, our study did not fully support considering rTMS earlier in the treatment algorithm for TRD. Is is evidence for patients with severe, chronic, and refractory depression who already failed multiple psychotropic medication trials to benefit from early intervention (Dalhuisen et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Notwithstanding this evidence and FDA clearance for use after one failed antidepressant trial, many patients in our clinic were restricted by insurance companies to receive rTMS access through a prior authorization process requiring high disease severity. These often included a minimum of four failed antidepressant trials from different medication classes, failed attempts at augmentation strategies and lack of response to psychotherapy. This disconnect was evident in the present study as we documented a failure to respond to at least four adequate antidepressant trials with different mechanisms of action, and one or more augmentation strategies using an atypical antipsychotic, lithium, an anticonvulsant, or two antidepressants simultaneously.\u003c/p\u003e \u003cp\u003eIn our study, psychotherapy for the rTMS cohort was not found to have a significant impact on rTMS response. Cuijpers et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) found concurrent psychotherapy appeared to be more effective than treatment with antidepressant medication alone suggesting a synergistic effect. The number of medication failures was not linked to patient outcome. This finding supports prior research, demonstrating rTMS is more effective for patients with less treatment resistance (Grammer et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Baseline anxiety was not shown to significantly predict the reduction in symptoms of depression either. Moreover, use of inpatient and emergency room services over the study duration did not indicate over-utilization of psychiatric resources.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Health-related social needs on treatment effectiveness\u003c/h2\u003e \u003cp\u003eRace/ethnicity was not found to be predictive of the patient response. However, the racial and ethnic makeup of our sample was noticeably unrepresentative of the urban core we serve. The surrounding metropolitan area is estimated to be 21.8% African American, 12.9% Hispanic or Latino, and 69.9% White inhabitants (U.S. Census Bureau, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In the present study, only 12% of patients belonged to a minoritized racial group in our study. The finding that Blacks were less likely to demonstrate an improvement in depression when compared to White race seems consistent with common findings where patients from marginalized groups are more likely to experience barriers to care, leading to disparities. Smith et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) found similar racial and ethnic disparities in administration of rTMS at an urban academic center have been described. Minoritized groups may experience heightened transportation burdens, lack of insurance coverage, lower mental health literacy or poor understanding of the intervention. Possible interventions to remediate these disparities could target increasing awareness of rTMS amongst people of color and improving the racial diversity of TMS referrals and employees in rTMS clinics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Strengths and Limitations\u003c/h2\u003e \u003cp\u003eThis study was conducted at a large safety-net single academic institution with a predominantly underserved and minority patient population that promotes strength of the sample. Still, a limitation was the retrospective nature of the study design, which predisposed the study to biases inherent of retrospective cohort studies such as recall bias. The second limitation was the sample size of 60 patients that was determined by convenience and not by statistical power. In particular, the small number of minority participants. Due to the small number of patients comprising a minoritized racial group (n\u0026thinsp;=\u0026thinsp;6), individual groups were unable to be examined separately. The smaller sample of patients included decreased our statistical power as evidenced by the varying regression results between the MADRS and PHQ-9. This may have contributed to the non-statistical differences in the model and increased the probability of a Type I or II error. Furthermore, the model differences when outliers were removed required care in the interpretation of the findings. The limited dataset that was collected at a single urban center constrains the ability to extrapolate the findings beyond that sufficiently supported by the data. Finally, the generalizability of the results is similarly restricted to comparable patients. Therefore, caution must be taken when drawing conclusions.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eMajor depressive disorder (MDD) is a prevalent and debilitating condition that represents a substantial public health challenge impacting quality-of-life, productivity, and overall well-being (World Health Organization, 2021). This real-world study supports the use of rTMS as a treatment for severe TRD in a newly formed rTMS outpatient clinic. Clinical response to rTMS with TRD appears to be guided by individual and clinical factors. The trend was towards better response in our rTMS cohort among those who completed a full treatment course. These findings highlight the importance of revising rTMS coverage policies accordingly to help reduce morbidity and contribute to the growing body of evidence on health disparities that highlight the importance of considering social drivers of health in depression intervention. Furthermore, measures of functional abilities and quality of life may further elaborate on the real-world impact of rTMS treatment for depressed patients. Future research should examine utilization and economic outcomes over an extended time interval after treatment termination. These findings have the potential to provide useful information in the optimization of clinical response while informing service delivery and policy decisions, paving the way for a healthier, more equitable future.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eThe study was approved by the University of Florida Institutional Review Board (IRB202500308), and the research was conducted in accordance with the Declaration of Helsinki. Informed consent was waived by IRB for retrospective analysis of anonymized data.\u003c/p\u003e\n\u003ch2\u003eDisclosure\u0026nbsp;of Funding:\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003ch2\u003eDeclaration of competing interests:\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eP.C. and B.C. conceptualized the study, developed the methodology, and conducted formal analysis, visualization, supervision and investigation.I.T. and B.C. interpreted the data and wrote the manuscript including original draft.K.L. and A.B. data curation, resources and project administration. All authors reviewed, edited and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBondolfi, G., Jermann, F., Rouget, B. W., Gex-Fabry, M., McQuillan, A., Dupont-Willemin, A., Aubry, J. M., \u0026amp; Nguyen, C. (2010). Self- and clinician-rated Montgomery-Asberg Depression Rating Scale: evaluation in clinical practice. \u003cem\u003eJournal of affective disorders\u003c/em\u003e, \u003cem\u003e121\u003c/em\u003e(3), 268\u0026ndash;272. https://doi.org/10.1016/j.jad.2009.06.037\u003c/li\u003e\n\u003cli\u003eComprehensive mental health action plan 2013\u0026ndash;2030. Geneva: World Health Organization; 2021. Licence: CC BY-NC-SA 3.0 IGO.\u003c/li\u003e\n\u003cli\u003eCuijpers, P., Sijbrandij, M., Koole, S. L., Andersson, G., Beekman, A. T., \u0026amp; Reynolds, C. F., 3rd (2014). Adding psychotherapy to antidepressant medication in depression and anxiety disorders: a meta-analysis. \u003cem\u003eWorld psychiatry: official journal of the World Psychiatric Association (WPA)\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(1), 56\u0026ndash;67. https://doi.org/10.1002/wps.20089\u003c/li\u003e\n\u003cli\u003eCuijpers, P., Turner, E. H., Koole, S. L., van Dijke, A., \u0026amp; Smit, F. (2014). What is the threshold for a clinically relevant effect? The case of major depressive disorders. \u003cem\u003eDepression and anxiety\u003c/em\u003e, \u003cem\u003e31\u003c/em\u003e(5), 374\u0026ndash;378. https://doi.org/10.1002/da.22249\u003c/li\u003e\n\u003cli\u003eDalhuisen, I., van Oostrom, I., Spijker, J., Wijnen, B., van Exel, E., van Mierlo, H., de Waardt, D., Arns, M., Tendolkar, I., \u0026amp; van Eijndhoven, P. (2024). rTMS as a Next Step in Antidepressant Nonresponders: A Randomized Comparison With Current Antidepressant Treatment Approaches. \u003cem\u003eThe American journal of psychiatry\u003c/em\u003e, \u003cem\u003e181\u003c/em\u003e(9), 806\u0026ndash;814. https://doi.org/10.1176/appi.ajp.20230556\u003c/li\u003e\n\u003cli\u003eDunner, D. L., Aaronson, S. T., Sackeim, H. A., Janicak, P. G., Carpenter, L. L., Boyadjis, T., Brock, D. G., Bonneh-Barkay, D., Cook, I. A., Lanocha, K., Solvason, H. B., \u0026amp; Demitrack, M. A. (2014). A multisite, naturalistic, observational study of transcranial magnetic stimulation for patients with pharmacoresistant major depressive disorder: durability of benefit over a 1-year follow-up period. \u003cem\u003eThe Journal of clinical psychiatry\u003c/em\u003e, \u003cem\u003e75\u003c/em\u003e(12), 1394\u0026ndash;1401. https://doi.org/10.4088/JCP.13m08977\u003c/li\u003e\n\u003cli\u003eEaton, W. W., Shao, H., Nestadt, G., Lee, H. B., Bienvenu, O. J., \u0026amp; Zandi, P. (2008). Population-based study of first onset and chronicity in major depressive disorder. \u003cem\u003eArchives of general psychiatry\u003c/em\u003e, \u003cem\u003e65\u003c/em\u003e(5), 513\u0026ndash;520. https://doi.org/10.1001/archpsyc.65.5.513\u003c/li\u003e\n\u003cli\u003eFekadu, A., Donocik, J. G., \u0026amp; Cleare, A. J. (2018). Standardisation framework for the Maudsley staging method for treatment resistance in depression. \u003cem\u003eBMC psychiatry\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(1), 100. https://doi.org/10.1186/s12888-018-1679-x\u003c/li\u003e\n\u003cli\u003eFekadu, A., Wooderson, S., Donaldson, C., Markopoulou, K., Masterson, B., Poon, L., \u0026amp; Cleare, A. J. (2009). A multidimensional tool to quantify treatment resistance in depression: the Maudsley staging method. \u003cem\u003eThe Journal of clinical psychiatry\u003c/em\u003e, \u003cem\u003e70\u003c/em\u003e(2), 177\u0026ndash;184. https://doi.org/10.4088/jcp.08m04309\u003c/li\u003e\n\u003cli\u003eFitzgerald, P. B., Oxley, T. J., Laird, A. R., Kulkarni, J., Egan, G. F., \u0026amp; Daskalakis, Z. J. (2006). An analysis of functional neuroimaging studies of dorsolateral prefrontal cortical activity in depression. \u003cem\u003ePsychiatry research\u003c/em\u003e, \u003cem\u003e148\u003c/em\u003e(1), 33\u0026ndash;45. https://doi.org/10.1016/j.pscychresns.2006.04.006\u003c/li\u003e\n\u003cli\u003eGrammer, G. G., Kuhle, A. R., Clark, C. C., Dretsch, M. N., Williams, K. A., \u0026amp; Cole, J. T. (2015). Severity of Depression Predicts Remission Rates Using Transcranial Magnetic Stimulation. \u003cem\u003eFrontiers in psychiatry\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e, 114. https://doi.org/10.3389/fpsyt.2015.00114\u003c/li\u003e\n\u003cli\u003eHanlon, C. A., \u0026amp; McCalley, D. M. (2022). Sex/Gender as a Factor That Influences Transcranial Magnetic Stimulation Treatment Outcome: Three Potential Biological Explanations. \u003cem\u003eFrontiers in psychiatry\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e, 869070. https://doi.org/10.3389/fpsyt.2022.869070\u003c/li\u003e\n\u003cli\u003eHuang, C. C., Wei, I. H., Chou, Y. H., \u0026amp; Su, T. P. (2008). Effect of age, gender, menopausal status, and ovarian hormonal level on rTMS in treatment-resistant depression. \u003cem\u003ePsychoneuroendocrinology\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e(6), 821\u0026ndash;831. https://doi.org/10.1016/j.psyneuen.2008.03.006\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eIBM SPSS Statistics for Windows, Version 30.0.0\u003c/em\u003e. IBM Corp; 2024.\u003c/li\u003e\n\u003cli\u003eKnighton, A. J., Savitz, L., Belnap, T., Stephenson, B., \u0026amp; VanDerslice, J. (2016). Introduction of an Area Deprivation Index Measuring Patient Socioeconomic Status in an Integrated Health System: Implications for Population Health. \u003cem\u003eEGEMS (Washington, DC)\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(3), 1238. https://doi.org/10.13063/2327-9214.1238\u003c/li\u003e\n\u003cli\u003eKreuter, M. W., Thompson, T., McQueen, A., \u0026amp; Garg, R. (2021). Addressing Social Needs in Health Care Settings: Evidence, Challenges, and Opportunities for Public Health. \u003cem\u003eAnnual review of public health\u003c/em\u003e, \u003cem\u003e42\u003c/em\u003e, 329\u0026ndash;344. https://doi.org/10.1146/annurev-publhealth-090419-102204\u003c/li\u003e\n\u003cli\u003eKroenke, K., Spitzer, R. L., \u0026amp; Williams, J. B. (2001). The PHQ-9: validity of a brief depression severity measure. \u003cem\u003eJournal of general internal medicine\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(9), 606\u0026ndash;613. https://doi.org/10.1046/j.1525-1497.2001.016009606.x\u003c/li\u003e\n\u003cli\u003eL\u0026ouml;we, B., Decker, O., M\u0026uuml;ller, S., Br\u0026auml;hler, E., Schellberg, D., Herzog, W., \u0026amp; Herzberg, P. Y. (2008). Validation and standardization of the Generalized Anxiety Disorder Screener (GAD-7) in the general population. \u003cem\u003eMedical care\u003c/em\u003e, \u003cem\u003e46\u003c/em\u003e(3), 266\u0026ndash;274. https://doi.org/10.1097/MLR.0b013e318160d093\u003c/li\u003e\n\u003cli\u003eMarder, K. G., Barbour, T., Ferber, S., Idowu, O., \u0026amp; Itzkoff, A. (2022). Psychiatric Applications of Repetitive Transcranial Magnetic Stimulation. \u003cem\u003eFocus (American Psychiatric Publishing)\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(1), 8\u0026ndash;18. https://doi.org/10.1176/appi.focus.20210021\u003c/li\u003e\n\u003cli\u003eMontgomery, S. A., \u0026amp; Asberg, M. (1979). A new depression scale designed to be sensitive to change. \u003cem\u003eThe British journal of psychiatry: the journal of mental science\u003c/em\u003e, \u003cem\u003e134\u003c/em\u003e, 382\u0026ndash;389. https://doi.org/10.1192/bjp.134.4.382\u003c/li\u003e\n\u003cli\u003eNakazawa, A., Matsuda, Y., Yamazaki, R., Taruishi, N., \u0026amp; Kito, S. (2025). Effects of repetitive transcranial magnetic stimulation therapy on weight and lipid metabolism in patients with treatment-resistant depression: A preliminary single-center retrospective cohort study. \u003cem\u003eNeuropsychopharmacology reports\u003c/em\u003e, \u003cem\u003e45\u003c/em\u003e(1), e12494. https://doi.org/10.1002/npr2.12494\u003c/li\u003e\n\u003cli\u003eR Core Team (2025). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.\u003c/li\u003e\n\u003cli\u003eRush, A. J., Trivedi, M. H., Wisniewski, S. R., Nierenberg, A. A., Stewart, J. W., Warden, D., Niederehe, G., Thase, M. E., Lavori, P. W., Lebowitz, B. D., McGrath, P. J., Rosenbaum, J. F., Sackeim, H. A., Kupfer, D. J., Luther, J., \u0026amp; Fava, M. (2006). Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: a STAR*D report. \u003cem\u003eThe American journal of psychiatry\u003c/em\u003e, \u003cem\u003e163\u003c/em\u003e(11), 1905\u0026ndash;1917. https://doi.org/10.1176/ajp.2006.163.11.1905\u003c/li\u003e\n\u003cli\u003eSackeim, H. A., Aaronson, S. T., Carpenter, L. L., Hutton, T. M., Mina, M., Pages, K., Verdoliva, S., \u0026amp; West, W. S. (2020). Clinical outcomes in a large registry of patients with major depressive disorder treated with Transcranial Magnetic Stimulation. \u003cem\u003eJournal of affective disorders\u003c/em\u003e, \u003cem\u003e277\u003c/em\u003e, 65\u0026ndash;74. https://doi.org/10.1016/j.jad.2020.08.005\u003c/li\u003e\n\u003cli\u003eSmith, A. C., Abu-Sultanah, M., Holmes, E. G., \u0026amp; Conroy, S. K. (2025). Racial and Ethnic Disparities in Administration of Transcranial Magnetic Stimulation at an Academic Center. \u003cem\u003eJournal of psychiatric practice\u003c/em\u003e, \u003cem\u003e31\u003c/em\u003e(2), 82\u0026ndash;84. https://doi.org/10.1097/PRA.0000000000000843\u003c/li\u003e\n\u003cli\u003eTrapp, N. T., Purgianto, A., Taylor, J. J., Singh, M. K., Oberman, L. M., Mickey, B. J., Youssef, N. A., Solzbacher, D., Zebley, B., Cabrera, L. Y., Conroy, S., Cristancho, M., Richards, J. R., Flood, M. J., Barbour, T., Blumberger, D. M., Taylor, S. F., Feifel, D., Reti, I. M., McClintock, S. M., \u0026hellip; National Network of Depression Centers Neuromodulation Task Group (2025). Consensus review and considerations on TMS to treat depression: A comprehensive update endorsed by the National Network of Depression Centers, the Clinical TMS Society, and the International Federation of Clinical Neurophysiology. \u003cem\u003eClinical neurophysiology: official journal of the International Federation of Clinical Neurophysiology\u003c/em\u003e, \u003cem\u003e170\u003c/em\u003e, 206\u0026ndash;233. https://doi.org/10.1016/j.clinph.2024.12.015\u003c/li\u003e\n\u003cli\u003eU.S. Census Bureau (2023). American Community Survey 1-year estimates. Retrieved from Census Reporter Profile page for Jacksonville, FL Oct. 8, 2025. \u0026lt;http://censusreporter.org/profiles/16000US1235000-jacksonville-fl/\u0026gt;\u003c/li\u003e\n\u003cli\u003eWeissman, C. R., Bermudes, R. A., Voigt, J., Liston, C., Williams, N., Blumberger, D. M., Fitzgerald, P. B., \u0026amp; Daskalakis, Z. J. (2023). Repetitive Transcranial Magnetic Stimulation for Treatment-Resistant Depression: Mismatch of Evidence and Insurance Coverage Policies in the United States. \u003cem\u003eThe Journal of clinical psychiatry\u003c/em\u003e, \u003cem\u003e84\u003c/em\u003e(3), 22com14575. https://doi.org/10.4088/JCP.22com14575\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. MADRS Categorical rTMS response classification.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eNonresponse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003ePartial response\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eFull response\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; Chi square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eVariable: n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026lt; 65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e16 (26.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e13 (21.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e15 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;65 and \u0026gt;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e6 (10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e9 (15.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e15 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e8 (13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e14 (23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.730\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e7 (11.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e6 (10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e10 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.769\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e19 (31.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e13 (21.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e22 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026lt; 30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e10 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e6 (10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e11 (18.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.983\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;30 and \u0026gt;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e12 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e8 (13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e13 (21.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eInsurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Commercial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e15 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e12 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e15 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e6.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.189\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Medicare\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5 (8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e9 (15.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Medicaid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eBaseline MADRS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026lt; 35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5 (8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3 (5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e10 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.273\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;35 and \u0026gt;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e17 (28.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e11 (18.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e14 (23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePsychotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e17 (28.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e7 (11.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e20 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5.300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5 (8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e7 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eBMI-Body Mass Index, MADRS-Montgomery-Asberg Depression Rating Scale.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2. PHQ-9 Categorical rTMS response classification.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eNonresponse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003ePartial response\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eFull response\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; Chi square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eVariable: n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026lt; 65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e21 (35.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e11 (18.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e12 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.949\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;65 and \u0026gt;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e7 (11.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5 (8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e19 (31.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5 (8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e13 (21.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e7.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.028\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e9 (15.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e10 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.770\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e25 (41.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e13 (21.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e16 (26.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026lt; 30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e13 (21.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e7 (11.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e7 (11.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.932\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;30 and \u0026gt;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e15 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e8 (13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e10 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eInsurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Commercial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e19 (31.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e11 (18.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e12 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.406\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.662\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Medicare\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e7 (11.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5 (8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Medicaid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eBaseline PHQ-9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026lt; 20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e14 (23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e9 (15.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e8 (13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.743\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;20 and \u0026gt;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e14 (23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e6 (10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e9 (15.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePsychotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e19 (31.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e11 (18.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e14 (23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.566\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e9 (15.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3 (5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eBMI-Body Mass Index, PHQ-9-Patient Health Questionnaire.\u003c/p\u003e\n\u003cp\u003eTable 3. MADRS and PHQ-9 General Linear Model results.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"655\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003eMADRS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003ePHQ-9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003eSig.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e209.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e118.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.820\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e2.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e2.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e8.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e2.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003eInsurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e9.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e3.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003eGAD7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.189\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003eMaudsley\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.697\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003eNumber Sessions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e14.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e3.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003eDuration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e5.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.030\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e5.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.023\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003eMedications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e2.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.447\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003ePsychotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.721\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003eADI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.882\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eMADRS-Montgomery-Asberg Depression Rating Scale, PHQ-9-Patient Health Questionnaire, BMI-Body Mass Index, GAD7-General Anxiety Disorder, ADI-Area Deprivation Index.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Transcranial magnetic stimulation, Treatment-resistant depression","lastPublishedDoi":"10.21203/rs.3.rs-8695870/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8695870/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction:\u003c/h2\u003e \u003cp\u003eThe present study assessed the effectiveness of repetitive Transcranial Magnetic Stimulation (rTMS) in reducing depressive symptoms in patients with treatment-resistant depression.\u003c/p\u003e\u003ch2\u003eMethods and materials:\u003c/h2\u003e \u003cp\u003eA retrospective study conducted at an academic rTMS outpatient clinic. Data were collected from adult patients (\u0026ge;\u0026thinsp;18 years) with treatment-resistant, nonpsychotic unipolar depression. The Montgomery-Asberg Depression Rating Scale (MADRS) and Patient Health Questionnaire (PHQ-9) were administered at the initiation of treatment, at midpoint, and at completion. The Area Deprivation Index (ADI) was calculated to measure neighborhood disadvantage. Independent and paired \u003cem\u003et-\u003c/em\u003etests were performed to assess the change in MADRS and PHQ-9 scores, for ages of less than or 65 and greater for baseline scores. Participants were assessed at the end of treatment as either full remission, partial response, or lack of response. The effectiveness of rTMS was analyzed with a multivariable general linear model (GLM) fitted to compare change in depression severity, while controlling for confounders.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eSixty patients were included. Forty-five (75%) of the participants completed the 36 or more sessions of rTMS treatment. The end-of-treatment MADRS and PHQ-9 scores were statistically significantly different from the pre-treament scores (\u003cb\u003eP\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e). The effect sizes were (\u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.70 and 1.37), respectively. Regression modeling showed age, insurance status, Body Mass Index (BMI), number of sessions and duration of treatment were statistically significant. The MADRS demonstrated better model quality to discriminate good from poor responders than the PHQ-9.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe rTMS treatment was shown to be an effective modality for treatment-resistant depression. The analysis found that patient characteristics and treatment factors were predictive of the patient response. Future studies should investigate the link between overall functioning, health-related social needs and rTMS response.\u003c/p\u003e","manuscriptTitle":"Repetitive Transcranial Magnetic Stimulation for Treatment Resistant Depression: How patient characteristics and elements of treatment impact post-treatment outcomes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-12 13:53:51","doi":"10.21203/rs.3.rs-8695870/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-18T13:06:06+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"320439175844869890430032449149374740806","date":"2026-05-13T14:48:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-12T11:50:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"215862404438394090805970562482148006111","date":"2026-05-08T16:37:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"102317705198111651120741678954497040818","date":"2026-04-19T04:34:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-03T02:51:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"149749627315874236611793633234424711581","date":"2026-03-01T01:47:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"82847596657964454036576120806898516660","date":"2026-02-19T16:18:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-09T13:59:44+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-03T05:48:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-29T05:03:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-29T05:02:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychiatry","date":"2026-01-26T02:39:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"acd19a70-40c8-4fff-8ef6-002a8163d111","owner":[],"postedDate":"February 12th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-18T13:06:06+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"320439175844869890430032449149374740806","date":"2026-05-13T14:48:50+00:00","index":80,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-12T11:50:11+00:00","index":79,"fulltext":""},{"type":"reviewerAgreed","content":"215862404438394090805970562482148006111","date":"2026-05-08T16:37:16+00:00","index":76,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T13:10:07+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-12 13:53:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8695870","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8695870","identity":"rs-8695870","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}