Impact of Phase 1 Reopening on Telehealth and In-Person Mental Health Visits in Oregon: Trends and Disparities (2019–2021)

Impact of Phase 1 Reopening on Telehealth and In-Person Mental Health Visits in Oregon: Trends and Disparities (2019–2021) | 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 Impact of Phase 1 Reopening on Telehealth and In-Person Mental Health Visits in Oregon: Trends and Disparities (2019–2021) Sanket Kanekar, Tannista Banerjee This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9131274/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 16 You are reading this latest preprint version Abstract Background The COVID-19 pandemic accelerated telehealth adoption for mental health care. Whether this shift persisted after reopening, and how it varied across diagnoses, insurance types, and geographies, remains unclear. Methods We used administrative claims data from the Oregon All-Payer All-Claims Reporting Program (2019–2021) to construct a weekly county-level panel. We estimated two-way fixed effects models with county, week, and year fixed effects. The treatment variable captured the staggered implementation of Oregon's Phase 1 reopening across 36 counties in May–June 2020. Results Following Phase 1 reopening, telehealth visits for mood and anxiety disorders (ICD-10 F30–F49) increased by 0.699 per 10,000 insured (53 percent, p < 0.01), while in-person visits declined by 0.259 per 10,000 (15 percent, p < 0.01). Relative to Medicare, telehealth use was higher among Medicaid and commercial insurance patients. Urban counties showed significantly greater telehealth adoption (+ 0.535, p < 0.01) and larger in-person declines (− 0.435, p < 0.01). Effects for other mental health conditions (F01–F29, F50–F99) were approximately one-tenth the magnitude. Patient out-of-pocket costs for telehealth increased by 64 percent, while in-person costs declined by 28 percent. Conclusions Mental health care delivery patterns established during the pandemic persisted after reopening. Telehealth remained the dominant modality for mood and anxiety disorders, suggesting a structural shift in service delivery. Policies should support telehealth as a permanent component of mental health care while maintaining in-person pathways. telehealth mental health COVID-19 Phase 1 reopening Oregon mood disorders anxiety disorders health services utilization Figures Figure 1 Figure 2 Background As of August 28, 2025, the Centers for Disease Control and Prevention (CDC) reports approximately 1,234,111 COVID-19-related deaths in the United States [ 1 ]. Globally, there have been 6,881,955 COVID-19 deaths as of March 10, 2023 [ 2 ]. The pandemic coincided with large increases in adverse mental health symptoms among U.S. adults [ 3 , 4 ]. In 2020, screening rates for depression and anxiety rose sharply relative to 2019, with notable increases among males and adults aged 18–29 [ 5 ]. In response to the pandemic, U.S. policymakers implemented federal and state shelter-in-place (SIP) policies to promote social distancing and reduce viral transmission. Research demonstrates that these measures were effective in reducing COVID-19 transmission [ 6 – 8 ]. While SIP orders helped limit viral exposure, they also disrupted daily life, reduced access to routine care, and increased social and economic stress. These effects are especially relevant in the context of mental health, given the close connection between mental and physical well-being [ 9 ]. Other studies suggest that reductions in health care visits would have occurred even without formal restrictions, driven by individual behavioral responses to rising case counts [ 10 ]. Telehealth helped maintain access to mental health services during the pandemic. One study found that 53.9 percent of anxiety disorder visits and 52.6 percent of depression visits in 2020 occurred via telehealth, the highest rates among major clinical conditions [ 11 ]. Despite widespread disruptions, the total number of visits for these diagnoses declined by no more than 11 percent, indicating that telehealth largely offset the reduction in in-person care [ 11 ]. Most existing studies focus on this early surge in telehealth use, often linking it to stay-at-home orders and closure policies [ 10 , 12 ]. However, less is known about how utilization patterns changed after reopening. This study addresses that gap by examining mental health service use during and after Oregon's Phase 1 reopening. In this study, we examine the effect of Oregon's COVID-19 Phase 1 reopening on telehealth and in-person visits for mental health conditions [ 13 ]. We address this question using administrative claims data from the Oregon All Payer All Claims Reporting Program (APAC), covering 2019 to 2021 [ 14 ]. We estimate two-way fixed effects models to identify changes in utilization before and after reopening. We examine three dimensions: (1) mood and anxiety disorders (ICD-10 F30–F49) versus other mental health conditions (F01–F29, F50–F99); (2) differences across Medicaid, Medicare, and commercial insurance; and (3) urban versus rural counties. In addition to visit counts, we measure changes in patient out-of-pocket costs. We account for local pandemic severity and control for county-level socioeconomic conditions [ 15 , 16 ]. Prior research has examined geographic disparities in telehealth mental health use [ 17 ] and the impact of COVID-19 on mental health service expenditures [ 18 ], but to our knowledge, no study has examined the causal effect of reopening policies on telehealth utilization for mental health using staggered county-level variation. Methods Data Source and Population This study analyzed medical claims data from Medicaid, Medicare, and commercial insurance members between 2019 and 2021. The de-identified data were sourced from the Oregon All Payer All Claims Reporting Program (APAC), which covers over 95% of Oregon's insured population [ 14 , 19 ]. The dataset excludes the uninsured, beneficiaries of federal programs such as Tricare and Veterans Affairs, and participants in self-insured plans that do not report to APAC [ 19 ]. We focused on mental health diagnoses classified under ICD-10 codes F01–F99, with emphasis on mood disorders (F30–F39) and anxiety disorders (F40–F49), for which telehealth is widely feasible [ 20 , 21 ]. The dataset includes 10,259,393 patient observations for mental health conditions. We classified visits as telehealth or in-person using place-of-service codes. In-person visits included those occurring in outpatient settings, emergency departments, and physician offices, while telehealth visits were those delivered via telecommunication technologies. Each observation was coded as telehealth (1) or in-person (0). Using patients' ZIP codes, we assigned each claim to a county using the HUD–USPS ZIP Code Crosswalk, retaining only the primary county for each ZIP code based on the highest total address ratio [ 22 ]. We merged this with American Community Survey (ACS) 5-year data for county-level socioeconomic characteristics [ 23 ] and weekly COVID-19 case and death counts from the New York Times database [ 24 ]. We constructed a weekly county-level panel dataset, grouped by gender, urban/rural status, insurance type, diagnosis category, and telehealth status. Descriptive Statistics Our primary outcome variable is the weekly number of patients receiving mental health services, normalized per 10,000 insured individuals at the county level. Table 1 presents summary statistics for mood and anxiety disorders (ICD-10 F30–F49). The sample contains 115,055 telehealth observations and 204,821 in-person observations after collapsing to the analysis level. The average number of telehealth visits is 1.33, and in-person visits is 1.77 per 10,000 insured individuals. In the telehealth sample, 56.6 percent of observations are female, compared with 54.6 percent in the in-person sample. Medicaid enrollees account for the largest share of telehealth visits (50.2 percent), followed by commercial insurance (37.8 percent) and Medicare (12.0 percent). Average age is lower in the telehealth group (36.8 years) than in the in-person group (41.2 years). Table 1 Descriptive Statistics, Oregon APAC 2019–2021 Variables Telehealth Mean In-Person Mean Patients (per 10,000 insured) 1.33 1.77 Phase 1 reopening 0.777 0.514 Age 36.83 41.18 Female 0.566 0.546 Urban 0.402 0.375 Medicare 0.120 0.207 Medicaid 0.502 0.412 Commercial 0.378 0.381 White (%) 84.89 85.69 Black (%) 1.06 1.05 Asian (%) 2.60 2.55 Latino (%) 13.06 13.02 Median household income ( $ ) 64,318 62,476 Unemployment (%) 5.75 5.75 High school diploma (%) 90.5 90.5 N (observations) 115,055 204,821 Source: Oregon APAC, 2019–2021. ICD-10 F30–F49. Unit of observation: gender × urban/rural × insurance type × diagnosis × place of service × week × year × county. Patient variable is per 10,000 insured. County-level socioeconomic variables from ACS. Empirical Strategy This study evaluates the impact of Phase 1 reopening on telehealth utilization in Oregon using a two-way fixed effects model across 36 counties that reopened at different times [ 13 ]. Twenty-nine counties reopened in week 20 of 2020 (May), while the remaining counties reopened by week 25 (June). The identification strategy exploits this variation in timing to assess whether patients continued using telehealth or returned to in-person care following reopening. To assess whether changes in telehealth use were driven by reopening policies or local pandemic conditions, we control for COVID-19 exposure, specifically the number of weeks since each county's first confirmed case and first death [ 10 ]. Our model includes county fixed effects to control for time-invariant county heterogeneity, week fixed effects to control for seasonal patterns, and year fixed effects to control for annual trends. Standard errors are clustered at the county level to account for serial correlation within counties [ 25 ]. Results Figure 1 presents weekly telehealth and in-person visits for mood and anxiety disorders across Figure 1 Weekly telehealth and in-person visits for mood and anxiety disorders (F30–F49), Oregon 2019–2021. Dashed line: stay-home order (week 11, 2020). Dotted line: Phase 1 reopening (week 20, 2020). The shaded area marks the lockdown period. 2019–2021. Panel A shows that telehealth use remained consistently low throughout 2019. In early 2020, utilization remained limited until around week 11, when statewide stay-home orders were issued. Telehealth visits then rose sharply, reaching approximately 30,000 visits per week. After Phase 1 reopening began in week 20, a modest decline was observed, but visit counts did not return to pre-pandemic levels. Telehealth remained elevated through 2021, stabilizing at approximately 25,000 visits per week. Panel B shows the corresponding decline in in-person visits, from approximately 45,000 per week in 2019 to roughly 25,000–30,000 per week in 2020–2021. Figure 2 Telehealth adoption for mood and anxiety disorders (F30–F49). Panel A: weekly visit volume by modality. Panel B: telehealth as a percentage of all visits. Figure 2 illustrates the structural shift in care modality. Panel A shows total visit volume by modality, with telehealth expanding to fill the gap left by declining in-person care. Total visit volume recovered and exceeded pre-pandemic levels by 2021, suggesting that telehealth expanded access rather than merely substituting for in-person care. Panel B shows that the telehealth share of all visits rose from approximately 1 percent in 2019 to over 50 percent during the lockdown period and remained near 48 percent through 2021. Table 2 presents regression estimates of the effect of Phase 1 reopening on mental health service Table 2 Impact of Phase 1 Reopening on Utilization, 2019–2021 Phase 1 reopening F30–F49 Telehealth F30–F49 In-Person Other MH Telehealth Other MH In-Person 0.699*** −0.259*** 0.074*** −0.029 (0.127) (0.085) (0.016) (0.020) Insurance (ref: Medicare) Medicaid 0.742*** −0.353** 0.046* −0.056** (0.164) (0.153) (0.023) (0.025) Commercial 0.412*** −0.590*** 0.091*** −0.062*** (0.087) (0.107) (0.021) (0.015) Location (ref: Rural) Urban 0.535*** −0.435*** 0.042* −0.020 (0.168) (0.147) (0.022) (0.020) Observations 115,055 204,821 116,723 318,607 R-squared 0.437 0.432 0.512 0.465 Notes: DV is patients per 10,000 insured. All models include county, week, and year FE. Controls: gender, age, diagnosis (factor), weeks since first COVID case/death, race, education, income, unemployment. Insurance and urban coefficients from interaction specifications. Clustered SE at county level. *** p < 0.01, ** p < 0.05, * p < 0.1 utilization. The outcome variable measures the number of patients per 10,000 insured individuals. The unit of observation is defined at the level of gender, urban or rural classification, insurance type, diagnosis category, place of service, week, year, and county. The regression includes controls for patient demographics, health characteristics, and county-level socioeconomic indicators. Columns 1 and 2 focus on mood and anxiety disorders (ICD-10 F30–F49). Telehealth visits increased by 0.699 per 10,000 insured after reopening (p < 0.01), a 53 percent increase relative to the mean. In-person visits declined by 0.259 per 10,000 (p < 0.01), a 15 percent decrease. Relative to Medicare, Medicaid patients had 0.742 more telehealth visits (56 percent increase), while commercial insurance patients had 0.412 more (31 percent increase). Urban counties experienced significantly higher telehealth adoption (+ 0.535, p < 0.01) and a larger decline in in-person visits (− 0.435, p < 0.01). Columns 3 and 4 extend the analysis to other mental health conditions (ICD-10 F01–F29 and F50–F99). Reopening led to a significant increase in telehealth visits (0.074 per 10,000, p < 0.01) but no significant change in in-person visits (− 0.029, p = 0.155). The effects were roughly ten times larger for mood and anxiety disorders than for other mental health conditions, suggesting that telehealth is particularly well-suited to conditions amenable to talk-based therapy. Table 3 presents estimates of patient out-of-pocket costs (copayments, coinsurance, and deductibles) for mental health services following reopening, aggregated at the same observational level as Table 2 . For mood and anxiety disorders, patient out-of-pocket spending on telehealth increased by $ 129 (64 percent, p < 0.05), while spending on in-person visits declined by $ 139 (28 percent, p < 0.01). This increase in aggregate telehealth spending reflects higher visit volume rather than higher per-visit costs—at the claim level, the average out-of-pocket cost per telehealth visit ( $ 9.54) is roughly half that of in-person visits ( $ 19.55). Commercial insurance and urban counties showed the largest shifts in both directions. Table 3 Impact of Phase 1 Reopening on Patient Out-of-Pocket Costs, 2019–2021 Phase 1 reopening F30–F49 Telehealth F30–F49 In-Person Other MH Telehealth Other MH In-Person $ 128.5** − $ 138.6*** $ 13.0** − $ 15.0** (48.4) (45.6) (5.6) (5.9) Insurance (ref: Medicare) Medicaid $ 45.0 $ 86.7*** $ 22.4*** $ 16.8* (31.1) (21.5) (6.1) (8.5) Commercial $ 308.9*** − $ 519.2** $ 47.2*** − $ 43.5* (94.7) (219.5) (14.5) (21.9) Location (ref: Rural) Urban $ 258.4*** − $ 466.6** $ 16.3*** − $ 28.5** (83.4) (183.8) (5.4) (12.1) Observations 115,055 204,821 116,723 318,607 R-squared 0.177 0.185 0.150 0.172 Notes: DV is the patient’s out-of-pocket costs (copay + coinsurance + deductible). All models include county, week, and year FE. Controls: gender, age, diagnosis (factor), weeks since first COVID case/death, race, education, income, and unemployment. Insurance and urban coefficients from interaction specifications. Clustered SE at county level. *** p < 0.01, ** p < 0.05, * p < 0.1 Discussion Our findings demonstrate clear differences in mental health service use following Phase 1 reopening. Telehealth visits for mood and anxiety disorders (ICD-10 F30–F49) increased by 53 percent after reopening, while in-person visits declined by 15 percent. These patterns indicate that the surge in telehealth use that began early in the pandemic did not reverse once in-person services became available. This is consistent with prior research showing sustained telehealth use for mental health conditions [ 11 , 26 ]. Cantor et al. (2022) reported a 53 percent increase in telehealth visits across several health services after adjusting for COVID-19 exposure [ 10 ]. The effects for other mental health conditions (ICD-10 F01–F29 and F50–F99) were substantially smaller, with telehealth increasing by 0.074 per 10,000—roughly one-tenth the magnitude observed for mood and anxiety disorders. This suggests that telehealth is particularly effective for conditions amenable to talk-based therapy and medication management, whereas conditions requiring direct clinical evaluation may be less suited to remote delivery. This finding extends prior work on geographic disparities in telehealth mental health use [ 17 ] by demonstrating that diagnosis type, not just geography, shapes telehealth adoption. Insurance status was associated with clear differences in telehealth adoption. Compared with Medicare patients, those with Medicaid had 0.742 more telehealth visits per 10,000 insured individuals, and commercial insurance patients had 0.412 more. In-person visits declined more sharply for both groups. These results suggest that Medicaid and commercial populations adopted telehealth more readily after reopening, possibly because telehealth reduces common barriers such as transportation, scheduling difficulties, and out-of-pocket costs. We found significant differences across urban and rural counties. Urban counties experienced both a significant increase in telehealth visits (+ 0.535, p < 0.01) and a larger decline in in-person visits (− 0.435, p < 0.01). This may reflect better broadband infrastructure, larger provider networks offering telehealth, and patient populations more familiar with digital health tools. These findings are consistent with research showing that rural communities face distinct barriers to telehealth adoption even as comfort with the technology increases [ 27 ]. Patient out-of-pocket spending on telehealth increased by 64 percent after reopening, while spending on in-person care declined by 28 percent. Importantly, per-visit out-of-pocket costs are lower for telehealth ( $ 9.54) than for in-person visits ( $ 19.55), indicating that the aggregate cost increase is volume-driven. This finding complements prior work on how COVID-19 affected mental health treatment expenditures [ 18 ] by isolating the reopening effect and distinguishing between modalities. Taken together, the results show that Oregon's Phase 1 reopening did not reverse the shift toward telehealth that began during the pandemic. Telehealth remained an important mode of care across the reopening period, and in-person visits did not return to pre-pandemic levels. The extent of change varied across diagnostic groups, insurance types, and county characteristics, suggesting that both clinical suitability and local conditions shaped telehealth adoption. Limitations This study has several limitations. First, administrative claims data do not capture clinical detail such as symptom severity or treatment outcomes, so we cannot assess whether the quality of care differed between telehealth and in-person visits. Second, the APAC database excludes uninsured individuals, Veterans Affairs beneficiaries, and Indian Health Service enrollees, which may limit generalizability. Third, claims data do not distinguish between audio-only and video telehealth visits; these modalities may differ in clinical appropriateness and patient experience. Fourth, although the two-way fixed effects model controls for time-invariant county characteristics and common time trends, it may not fully account for unobserved county-level shocks that coincided with reopening. Finally, Oregon’s policy environment and demographics may differ from other states, and the results may not generalize to settings with different telehealth regulations, broadband infrastructure, or insurance landscapes. Conclusions Oregon's Phase 1 reopening did not reverse the shift toward telehealth for mental health care. Telehealth visits for mood and anxiety disorders increased by 53 percent after reopening, while in-person visits declined by 15 percent. Policy should treat telehealth as a permanent component of the mental health delivery system. Differences by insurance type and county characteristics highlight the need for policies that expand broadband access, improve digital literacy, and strengthen provider capacity to ensure equitable telehealth access. Systems should maintain clear pathways to in-person evaluation when clinical needs require direct assessment. Abbreviations APAC All Payer All Claims Reporting Program ICD-10 International Classification of Diseases, Tenth Revision SIP Shelter-in-Place ACS American Community Survey CDC Centers for Disease Control and Prevention FE Fixed Effects SE Standard Error DV Dependent Variable Declarations Ethics approval and consent to participate This study used de-identified administrative claims data from the Oregon All Payer All Claims Reporting Program (APAC). The study was approved by the Auburn University Institutional Review Board (protocol 22–283 EX 2206). The Auburn University Institutional Review Board waived the requirement for informed consent because the study used only de-identified administrative data with no direct contact with human participants. This study was conducted in accordance with the principles of the Declaration of Helsinki. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Author Contribution SK and TB have contributed equally to this project. Acknowledgements The authors thank the Oregon All-Payer All-Claims Reporting Program (APAC) for providing the dataset used in this study. Data Availability The data that support the findings of this study are available from the Oregon Health Authority. The data are not publicly available. References COVID Data Tracker. Centers for Disease Control and Prevention. 2024. https://covid.cdc.gov/covid-data-tracker/#datatracker-home . Accessed 28 Aug 2025. Johns Hopkins Coronavirus Resource Center. 2023. https://coronavirus.jhu.edu/map.html . Accessed 10 Mar 2023. Czeisler MÉ, Lane RI, Petrosky E, et al. Mental health, substance use, and suicidal ideation during the COVID-19 pandemic — United States, June 24–30, 2020. MMWR Morb Mortal Wkly Rep. 2020;69(32):1049–57. 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National Center for Chronic Disease Prevention and Health Promotion. 2024. https://www.cdc.gov/mentalhealth/index.htm . Accessed 15 Jan 2025. Cantor J, Sood N, Bravata DM, Pera M, Whaley C. The impact of the COVID-19 pandemic and policy response on health care utilization: evidence from county-level medical claims and cellphone data. J Health Econ. 2022;82:102581. Patel SY, Mehrotra A, Huskamp HA, Uscher-Pines L, Ganguli I, Barnett ML. Variation in telemedicine use and outpatient care during the COVID-19 pandemic in the United States. Health Aff. 2021;40(2):349–58. Ziedan E, Simon KI, Wing C. Effects of state COVID-19 closure policy on non-COVID-19 health care utilization. NBER Working Paper. 2020;(27621). Brown K. Executive Order 20–25: A Safe and Strong Oregon. Office of the Governor, State of Oregon; 2020. https://www.myoregon.gov/2020/05/14/governor-kate-brown-announces-phase-i-counties-reopening/ . Accessed 1 Mar 2025. Oregon Health Authority. APAC Data Requests. 2022. 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Noteboom B, Al-Shdaifat E, Van Baal K, et al. An analysis of telehealth in a post-pandemic rural, Midwestern community: increased comfort and a preference for primary care. BMC Health Serv Res. 2025;25:239. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 12 May, 2026 Reviews received at journal 02 May, 2026 Reviews received at journal 01 May, 2026 Reviews received at journal 28 Apr, 2026 Reviewers agreed at journal 27 Apr, 2026 Reviewers agreed at journal 25 Apr, 2026 Reviewers agreed at journal 24 Apr, 2026 Reviewers agreed at journal 24 Apr, 2026 Reviews received at journal 21 Apr, 2026 Reviewers agreed at journal 16 Apr, 2026 Reviewers agreed at journal 16 Apr, 2026 Reviewers invited by journal 16 Apr, 2026 Editor assigned by journal 13 Apr, 2026 Editor invited by journal 20 Mar, 2026 Submission checks completed at journal 19 Mar, 2026 First submitted to journal 19 Mar, 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. 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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-9131274","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":628481741,"identity":"5ef853a9-96c1-4016-b591-9f46afa49351","order_by":0,"name":"Sanket Kanekar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYDACHhBxgIGBn4G5ASJygFgtkg2MpGoxOECsFvmew88efDhjl298/GDjx59tDHJ8NxLwazE422ZuOONGsuW2M4nN0rxtDMaSBLXwM5hJ83xgNjA7kNggzdjGkLiBkBb5fvZv0n8+1BsY9z9s/gl0WD1BLQxne8ykGW4cNjCQSGyTADoswYCgw86cKZPsOXPcQOLGwzZrnnMShjPPPCDgsJ70bRI/jlUb8PcnH775o8xGnu84IYehAQnSlI+CUTAKRsEowA4Asb5JmLiu1fQAAAAASUVORK5CYII=","orcid":"","institution":"Alabama State University","correspondingAuthor":true,"prefix":"","firstName":"Sanket","middleName":"","lastName":"Kanekar","suffix":""},{"id":628481742,"identity":"b7bc1b9f-fafa-4816-a6c7-5ee440e1c7ae","order_by":1,"name":"Tannista Banerjee","email":"","orcid":"","institution":"Auburn University","correspondingAuthor":false,"prefix":"","firstName":"Tannista","middleName":"","lastName":"Banerjee","suffix":""}],"badges":[],"createdAt":"2026-03-15 22:08:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9131274/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9131274/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107869541,"identity":"69f402e9-2539-4cb3-871d-c6ce8376fae3","added_by":"auto","created_at":"2026-04-27 07:37:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":319768,"visible":true,"origin":"","legend":"\u003cp\u003eWeekly Telehealth and In-Person Mental Health Visits in Oregon, 2019–2021\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9131274/v1/ba713a00500f711a6241e56c.png"},{"id":107832522,"identity":"3f4ed2a5-fc78-4a53-b2c1-56e44138b91f","added_by":"auto","created_at":"2026-04-26 15:33:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":375411,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eTelehealth adoption for mood and anxiety disorders (F30–F49). Panel A: weekly visit volume by modality. Panel B: telehealth as a percentage of all visits.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9131274/v1/c3a58668706eb6ede1704c88.png"},{"id":107871730,"identity":"55479c8c-8eea-4362-82d6-6c0bd1d28179","added_by":"auto","created_at":"2026-04-27 07:53:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":972204,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9131274/v1/f8a85a38-cf76-4901-a20d-7321c970d5fb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of Phase 1 Reopening on Telehealth and In-Person Mental Health Visits in Oregon: Trends and Disparities (2019–2021)","fulltext":[{"header":"Background","content":"\u003cp\u003eAs of August 28, 2025, the Centers for Disease Control and Prevention (CDC) reports approximately 1,234,111 COVID-19-related deaths in the United States [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Globally, there have been 6,881,955 COVID-19 deaths as of March 10, 2023 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The pandemic coincided with large increases in adverse mental health symptoms among U.S. adults [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In 2020, screening rates for depression and anxiety rose sharply relative to 2019, with notable increases among males and adults aged 18\u0026ndash;29 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn response to the pandemic, U.S. policymakers implemented federal and state shelter-in-place (SIP) policies to promote social distancing and reduce viral transmission. Research demonstrates that these measures were effective in reducing COVID-19 transmission [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. While SIP orders helped limit viral exposure, they also disrupted daily life, reduced access to routine care, and increased social and economic stress. These effects are especially relevant in the context of mental health, given the close connection between mental and physical well-being [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Other studies suggest that reductions in health care visits would have occurred even without formal restrictions, driven by individual behavioral responses to rising case counts [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTelehealth helped maintain access to mental health services during the pandemic. One study found that 53.9 percent of anxiety disorder visits and 52.6 percent of depression visits in 2020 occurred via telehealth, the highest rates among major clinical conditions [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Despite widespread disruptions, the total number of visits for these diagnoses declined by no more than 11 percent, indicating that telehealth largely offset the reduction in in-person care [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Most existing studies focus on this early surge in telehealth use, often linking it to stay-at-home orders and closure policies [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, less is known about how utilization patterns changed after reopening. This study addresses that gap by examining mental health service use during and after Oregon's Phase 1 reopening.\u003c/p\u003e \u003cp\u003eIn this study, we examine the effect of Oregon's COVID-19 Phase 1 reopening on telehealth and in-person visits for mental health conditions [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. We address this question using administrative claims data from the Oregon All Payer All Claims Reporting Program (APAC), covering 2019 to 2021 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. We estimate two-way fixed effects models to identify changes in utilization before and after reopening. We examine three dimensions: (1) mood and anxiety disorders (ICD-10 F30\u0026ndash;F49) versus other mental health conditions (F01\u0026ndash;F29, F50\u0026ndash;F99); (2) differences across Medicaid, Medicare, and commercial insurance; and (3) urban versus rural counties. In addition to visit counts, we measure changes in patient out-of-pocket costs. We account for local pandemic severity and control for county-level socioeconomic conditions [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Prior research has examined geographic disparities in telehealth mental health use [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and the impact of COVID-19 on mental health service expenditures [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], but to our knowledge, no study has examined the causal effect of reopening policies on telehealth utilization for mental health using staggered county-level variation.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Source and Population\u003c/h2\u003e \u003cp\u003eThis study analyzed medical claims data from Medicaid, Medicare, and commercial insurance members between 2019 and 2021. The de-identified data were sourced from the Oregon All Payer All Claims Reporting Program (APAC), which covers over 95% of Oregon's insured population [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The dataset excludes the uninsured, beneficiaries of federal programs such as Tricare and Veterans Affairs, and participants in self-insured plans that do not report to APAC [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. We focused on mental health diagnoses classified under ICD-10 codes F01\u0026ndash;F99, with emphasis on mood disorders (F30\u0026ndash;F39) and anxiety disorders (F40\u0026ndash;F49), for which telehealth is widely feasible [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The dataset includes 10,259,393 patient observations for mental health conditions.\u003c/p\u003e \u003cp\u003eWe classified visits as telehealth or in-person using place-of-service codes. In-person visits included those occurring in outpatient settings, emergency departments, and physician offices, while telehealth visits were those delivered via telecommunication technologies. Each observation was coded as telehealth (1) or in-person (0). Using patients' ZIP codes, we assigned each claim to a county using the HUD\u0026ndash;USPS ZIP Code Crosswalk, retaining only the primary county for each ZIP code based on the highest total address ratio [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. We merged this with American Community Survey (ACS) 5-year data for county-level socioeconomic characteristics [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and weekly COVID-19 case and death counts from the New York Times database [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. We constructed a weekly county-level panel dataset, grouped by gender, urban/rural status, insurance type, diagnosis category, and telehealth status.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDescriptive Statistics\u003c/h3\u003e\n\u003cp\u003eOur primary outcome variable is the weekly number of patients receiving mental health services, normalized per 10,000 insured individuals at the county level. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents summary statistics for mood and anxiety disorders (ICD-10 F30\u0026ndash;F49). The sample contains 115,055 telehealth observations and 204,821 in-person observations after collapsing to the analysis level. The average number of telehealth visits is 1.33, and in-person visits is 1.77 per 10,000 insured individuals. In the telehealth sample, 56.6 percent of observations are female, compared with 54.6 percent in the in-person sample. Medicaid enrollees account for the largest share of telehealth visits (50.2 percent), followed by commercial insurance (37.8 percent) and Medicare (12.0 percent). Average age is lower in the telehealth group (36.8 years) than in the in-person group (41.2 years).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistics, Oregon APAC 2019\u0026ndash;2021\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTelehealth Mean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIn-Person Mean\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatients (per 10,000 insured)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhase 1 reopening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.514\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.546\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.375\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicare\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.207\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicaid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.412\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommercial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.381\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLatino (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian household income (\u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64,318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62,476\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployment (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school diploma (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN (observations)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e115,055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e204,821\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eSource: Oregon APAC, 2019\u0026ndash;2021. ICD-10 F30\u0026ndash;F49. Unit of observation: gender \u0026times; urban/rural \u0026times; insurance type \u0026times; diagnosis \u0026times; place of service \u0026times; week \u0026times; year \u0026times; county. Patient variable is per 10,000 insured. County-level socioeconomic variables from ACS.\u003c/em\u003e \u003c/p\u003e\n\u003ch3\u003eEmpirical Strategy\u003c/h3\u003e\n\u003cp\u003eThis study evaluates the impact of Phase 1 reopening on telehealth utilization in Oregon using a two-way fixed effects model across 36 counties that reopened at different times [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Twenty-nine counties reopened in week 20 of 2020 (May), while the remaining counties reopened by week 25 (June). The identification strategy exploits this variation in timing to assess whether patients continued using telehealth or returned to in-person care following reopening. To assess whether changes in telehealth use were driven by reopening policies or local pandemic conditions, we control for COVID-19 exposure, specifically the number of weeks since each county's first confirmed case and first death [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Our model includes county fixed effects to control for time-invariant county heterogeneity, week fixed effects to control for seasonal patterns, and year fixed effects to control for annual trends. Standard errors are clustered at the county level to account for serial correlation within counties [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents weekly telehealth and in-person visits for mood and anxiety disorders across\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cem\u003eWeekly telehealth and in-person visits for mood and anxiety disorders (F30\u0026ndash;F49), Oregon 2019\u0026ndash;2021. Dashed line: stay-home order (week 11, 2020). Dotted line: Phase 1 reopening (week 20, 2020). The shaded area marks the lockdown period.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e2019\u0026ndash;2021. Panel A shows that telehealth use remained consistently low throughout 2019. In early 2020, utilization remained limited until around week 11, when statewide stay-home orders were issued. Telehealth visits then rose sharply, reaching approximately 30,000 visits per week. After Phase 1 reopening began in week 20, a modest decline was observed, but visit counts did not return to pre-pandemic levels. Telehealth remained elevated through 2021, stabilizing at approximately 25,000 visits per week. Panel B shows the corresponding decline in in-person visits, from approximately 45,000 per week in 2019 to roughly 25,000\u0026ndash;30,000 per week in 2020\u0026ndash;2021.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cem\u003eTelehealth adoption for mood and anxiety disorders (F30\u0026ndash;F49). Panel A: weekly visit volume by modality. Panel B: telehealth as a percentage of all visits.\u003c/em\u003e\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the structural shift in care modality. Panel A shows total visit volume by modality, with telehealth expanding to fill the gap left by declining in-person care. Total visit volume recovered and exceeded pre-pandemic levels by 2021, suggesting that telehealth expanded access rather than merely substituting for in-person care. Panel B shows that the telehealth share of all visits rose from approximately 1 percent in 2019 to over 50 percent during the lockdown period and remained near 48 percent through 2021.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents regression estimates of the effect of Phase 1 reopening on mental health service\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eImpact of Phase 1 Reopening on Utilization, 2019\u0026ndash;2021\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePhase 1 reopening\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF30\u0026ndash;F49\u003c/p\u003e \u003cp\u003eTelehealth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF30\u0026ndash;F49\u003c/p\u003e \u003cp\u003eIn-Person\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOther MH\u003c/p\u003e \u003cp\u003eTelehealth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOther MH\u003c/p\u003e \u003cp\u003eIn-Person\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.699***\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.259***\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.074***\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.029\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.127)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.085)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.020)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsurance (ref: Medicare)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicaid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.742***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.353**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.046*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.056**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.164)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.153)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.025)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommercial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.412***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.590***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.091***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.062***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.087)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.107)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.015)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation (ref: Rural)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.535***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.435***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.042*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.168)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.147)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.020)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e115,055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e204,821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e116,723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e318,607\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.465\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNotes: DV is patients per 10,000 insured. All models include county, week, and year FE. Controls: gender, age, diagnosis (factor), weeks since first COVID case/death, race, education, income, unemployment. Insurance and urban coefficients from interaction specifications. Clustered SE at county level. *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eutilization. The outcome variable measures the number of patients per 10,000 insured individuals. The unit of observation is defined at the level of gender, urban or rural classification, insurance type, diagnosis category, place of service, week, year, and county. The regression includes controls for patient demographics, health characteristics, and county-level socioeconomic indicators.\u003c/p\u003e \u003cp\u003eColumns 1 and 2 focus on mood and anxiety disorders (ICD-10 F30\u0026ndash;F49). Telehealth visits increased by 0.699 per 10,000 insured after reopening (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), a 53 percent increase relative to the mean. In-person visits declined by 0.259 per 10,000 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), a 15 percent decrease. Relative to Medicare, Medicaid patients had 0.742 more telehealth visits (56 percent increase), while commercial insurance patients had 0.412 more (31 percent increase). Urban counties experienced significantly higher telehealth adoption (+\u0026thinsp;0.535, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and a larger decline in in-person visits (\u0026minus;\u0026thinsp;0.435, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003eColumns 3 and 4 extend the analysis to other mental health conditions (ICD-10 F01\u0026ndash;F29 and F50\u0026ndash;F99). Reopening led to a significant increase in telehealth visits (0.074 per 10,000, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) but no significant change in in-person visits (\u0026minus;\u0026thinsp;0.029, p\u0026thinsp;=\u0026thinsp;0.155). The effects were roughly ten times larger for mood and anxiety disorders than for other mental health conditions, suggesting that telehealth is particularly well-suited to conditions amenable to talk-based therapy.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents estimates of patient out-of-pocket costs (copayments, coinsurance, and deductibles) for mental health services following reopening, aggregated at the same observational level as Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. For mood and anxiety disorders, patient out-of-pocket spending on telehealth increased by \u003cspan\u003e$\u003c/span\u003e129 (64 percent, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while spending on in-person visits declined by \u003cspan\u003e$\u003c/span\u003e139 (28 percent, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This increase in aggregate telehealth spending reflects higher visit volume rather than higher per-visit costs\u0026mdash;at the claim level, the average out-of-pocket cost per telehealth visit (\u003cspan\u003e$\u003c/span\u003e9.54) is roughly half that of in-person visits (\u003cspan\u003e$\u003c/span\u003e19.55). Commercial insurance and urban counties showed the largest shifts in both directions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eImpact of Phase 1 Reopening on Patient Out-of-Pocket Costs, 2019\u0026ndash;2021\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePhase 1 reopening\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF30\u0026ndash;F49\u003c/p\u003e \u003cp\u003eTelehealth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF30\u0026ndash;F49\u003c/p\u003e \u003cp\u003eIn-Person\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOther MH\u003c/p\u003e \u003cp\u003eTelehealth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOther MH\u003c/p\u003e \u003cp\u003eIn-Person\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e128.5**\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u003cspan\u003e$\u003c/span\u003e138.6***\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e13.0**\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u003cspan\u003e$\u003c/span\u003e15.0**\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(48.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(45.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(5.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsurance (ref: Medicare)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicaid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e45.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e86.7***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e22.4***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e16.8*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(31.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(8.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommercial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e308.9***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u003cspan\u003e$\u003c/span\u003e519.2**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e47.2***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u003cspan\u003e$\u003c/span\u003e43.5*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(94.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(219.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(14.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(21.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation (ref: Rural)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e258.4***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u003cspan\u003e$\u003c/span\u003e466.6**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e16.3***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u003cspan\u003e$\u003c/span\u003e28.5**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(83.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(183.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(12.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e115,055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e204,821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e116,723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e318,607\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNotes: DV is the patient\u0026rsquo;s out-of-pocket costs (copay\u0026thinsp;+\u0026thinsp;coinsurance\u0026thinsp;+\u0026thinsp;deductible). All models include county, week, and year FE. Controls: gender, age, diagnosis (factor), weeks since first COVID case/death, race, education, income, and unemployment. Insurance and urban coefficients from interaction specifications. Clustered SE at county level. *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur findings demonstrate clear differences in mental health service use following Phase 1 reopening. Telehealth visits for mood and anxiety disorders (ICD-10 F30\u0026ndash;F49) increased by 53 percent after reopening, while in-person visits declined by 15 percent. These patterns indicate that the surge in telehealth use that began early in the pandemic did not reverse once in-person services became available. This is consistent with prior research showing sustained telehealth use for mental health conditions [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Cantor et al. (2022) reported a 53 percent increase in telehealth visits across several health services after adjusting for COVID-19 exposure [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe effects for other mental health conditions (ICD-10 F01\u0026ndash;F29 and F50\u0026ndash;F99) were substantially smaller, with telehealth increasing by 0.074 per 10,000\u0026mdash;roughly one-tenth the magnitude observed for mood and anxiety disorders. This suggests that telehealth is particularly effective for conditions amenable to talk-based therapy and medication management, whereas conditions requiring direct clinical evaluation may be less suited to remote delivery. This finding extends prior work on geographic disparities in telehealth mental health use [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] by demonstrating that diagnosis type, not just geography, shapes telehealth adoption.\u003c/p\u003e \u003cp\u003eInsurance status was associated with clear differences in telehealth adoption. Compared with Medicare patients, those with Medicaid had 0.742 more telehealth visits per 10,000 insured individuals, and commercial insurance patients had 0.412 more. In-person visits declined more sharply for both groups. These results suggest that Medicaid and commercial populations adopted telehealth more readily after reopening, possibly because telehealth reduces common barriers such as transportation, scheduling difficulties, and out-of-pocket costs.\u003c/p\u003e \u003cp\u003eWe found significant differences across urban and rural counties. Urban counties experienced both a significant increase in telehealth visits (+\u0026thinsp;0.535, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and a larger decline in in-person visits (\u0026minus;\u0026thinsp;0.435, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This may reflect better broadband infrastructure, larger provider networks offering telehealth, and patient populations more familiar with digital health tools. These findings are consistent with research showing that rural communities face distinct barriers to telehealth adoption even as comfort with the technology increases [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePatient out-of-pocket spending on telehealth increased by 64 percent after reopening, while spending on in-person care declined by 28 percent. Importantly, per-visit out-of-pocket costs are lower for telehealth (\u003cspan\u003e$\u003c/span\u003e9.54) than for in-person visits (\u003cspan\u003e$\u003c/span\u003e19.55), indicating that the aggregate cost increase is volume-driven. This finding complements prior work on how COVID-19 affected mental health treatment expenditures [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] by isolating the reopening effect and distinguishing between modalities.\u003c/p\u003e \u003cp\u003eTaken together, the results show that Oregon's Phase 1 reopening did not reverse the shift toward telehealth that began during the pandemic. Telehealth remained an important mode of care across the reopening period, and in-person visits did not return to pre-pandemic levels. The extent of change varied across diagnostic groups, insurance types, and county characteristics, suggesting that both clinical suitability and local conditions shaped telehealth adoption.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study has several limitations. First, administrative claims data do not capture clinical detail such as symptom severity or treatment outcomes, so we cannot assess whether the quality of care differed between telehealth and in-person visits. Second, the APAC database excludes uninsured individuals, Veterans Affairs beneficiaries, and Indian Health Service enrollees, which may limit generalizability. Third, claims data do not distinguish between audio-only and video telehealth visits; these modalities may differ in clinical appropriateness and patient experience. Fourth, although the two-way fixed effects model controls for time-invariant county characteristics and common time trends, it may not fully account for unobserved county-level shocks that coincided with reopening. Finally, Oregon\u0026rsquo;s policy environment and demographics may differ from other states, and the results may not generalize to settings with different telehealth regulations, broadband infrastructure, or insurance landscapes.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOregon's Phase 1 reopening did not reverse the shift toward telehealth for mental health care. Telehealth visits for mood and anxiety disorders increased by 53 percent after reopening, while in-person visits declined by 15 percent. Policy should treat telehealth as a permanent component of the mental health delivery system. Differences by insurance type and county characteristics highlight the need for policies that expand broadband access, improve digital literacy, and strengthen provider capacity to ensure equitable telehealth access. Systems should maintain clear pathways to in-person evaluation when clinical needs require direct assessment.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAPAC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAll Payer All Claims Reporting Program\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICD-10\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternational Classification of Diseases, Tenth Revision\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSIP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eShelter-in-Place\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eACS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAmerican Community Survey\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCDC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCenters for Disease Control and Prevention\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFixed Effects\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard Error\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDependent Variable\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eThis study used de-identified administrative claims data from the Oregon All Payer All Claims Reporting Program (APAC). The study was approved by the Auburn University Institutional Review Board (protocol 22\u0026ndash;283 EX 2206). The Auburn University Institutional Review Board waived the requirement for informed consent because the study used only de-identified administrative data with no direct contact with human participants. This study was conducted in accordance with the principles of the Declaration of Helsinki.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSK and TB have contributed equally to this project.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe authors thank the Oregon All-Payer All-Claims Reporting Program (APAC) for providing the dataset used in this study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are available from the Oregon Health Authority. The data are not publicly available.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCOVID Data Tracker. Centers for Disease Control and Prevention. 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://covid.cdc.gov/covid-data-tracker/#datatracker-home\u003c/span\u003e\u003cspan address=\"https://covid.cdc.gov/covid-data-tracker/#datatracker-home\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 28 Aug 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohns Hopkins Coronavirus Resource Center. 2023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://coronavirus.jhu.edu/map.html\u003c/span\u003e\u003cspan address=\"https://coronavirus.jhu.edu/map.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 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National Center for Chronic Disease Prevention and Health Promotion. 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/mentalhealth/index.htm\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/mentalhealth/index.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 15 Jan 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCantor J, Sood N, Bravata DM, Pera M, Whaley C. The impact of the COVID-19 pandemic and policy response on health care utilization: evidence from county-level medical claims and cellphone data. J Health Econ. 2022;82:102581.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatel SY, Mehrotra A, Huskamp HA, Uscher-Pines L, Ganguli I, Barnett ML. Variation in telemedicine use and outpatient care during the COVID-19 pandemic in the United States. Health Aff. 2021;40(2):349\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZiedan E, Simon KI, Wing C. Effects of state COVID-19 closure policy on non-COVID-19 health care utilization. NBER Working Paper. 2020;(27621).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrown K. Executive Order 20\u0026ndash;25: A Safe and Strong Oregon. Office of the Governor, State of Oregon; 2020. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.myoregon.gov/2020/05/14/governor-kate-brown-announces-phase-i-counties-reopening/\u003c/span\u003e\u003cspan address=\"https://www.myoregon.gov/2020/05/14/governor-kate-brown-announces-phase-i-counties-reopening/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 1 Mar 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOregon Health Authority. APAC Data Requests. 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKattih N, Mansour F. The impact of the COVID pandemic on health, healthcare utilization, and healthcare spending. Res Econ. 2024;78(2):100951.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCantor JH, McBain RK, Pera MF, Bravata DM, Whaley CM. Who is (and is not) receiving telemedicine care during the COVID-19 pandemic. Am J Prev Med. 2021;61(3):434\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRamsetty A, Zhang R, Gatchel RJ, Winkley E. Geographic disparities in telemedicine mental health use by applying three way ANOVA on Medicaid claims population data. BMC Health Serv Res. 2024;24:498.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaRocca SA, Bhatt AS, Engel LS, et al. Impact of the COVID-19 pandemic on treatment for mental health needs: a perspective on service use patterns and expenditures from commercial medical claims data. BMC Health Serv Res. 2023;23:152.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOregon Health Authority. APAC Data User Guide. 2022. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.oregon.gov/oha/HPA/ANALYTICS/APAC%20Page%20Docs/APAC-Data-User-Guide.pdf\u003c/span\u003e\u003cspan address=\"https://www.oregon.gov/oha/HPA/ANALYTICS/APAC%20Page%20Docs/APAC-Data-User-Guide.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 1 Mar 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHolingue C, Badillo-Goicoechea E, Riehm KE, et al. Mental distress during the COVID-19 pandemic among US adults without a pre-existing mental health condition. Prev Med. 2020;139:106231.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaquet M, Luciano S, Geddes JR, Harrison PJ. Bidirectional associations between COVID-19 and psychiatric disorder: retrospective cohort studies of 62,354 COVID-19 cases in the USA. Lancet Psychiatry. 2021;8(2):130\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eU.S. Department of Housing and Urban Development. HUD-USPS ZIP Code Crosswalk Files. 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eU.S. Census Bureau. American Community Survey (ACS). 2025. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.census.gov/programs-surveys/acs\u003c/span\u003e\u003cspan address=\"https://www.census.gov/programs-surveys/acs\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 1 Mar 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThe New York Times. Coronavirus (COVID-19) Data in the United States. 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBertrand M, Duflo E, Mullainathan S. How much should we trust differences-in-differences estimates? Q J Econ. 2004;119(1):249\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePredmore ZS, Roth E, Breslau J, Fischer SH, Uscher-Pines L. Assessment of patient preferences for telehealth in post-COVID-19 pandemic health care. JAMA Netw Open. 2021;4(12):e2136405.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNoteboom B, Al-Shdaifat E, Van Baal K, et al. An analysis of telehealth in a post-pandemic rural, Midwestern community: increased comfort and a preference for primary care. BMC Health Serv Res. 2025;25:239.\u003c/span\u003e\u003c/li\u003e\u003c/ol\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-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"telehealth, mental health, COVID-19, Phase 1 reopening, Oregon, mood disorders, anxiety disorders, health services utilization","lastPublishedDoi":"10.21203/rs.3.rs-9131274/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9131274/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe COVID-19 pandemic accelerated telehealth adoption for mental health care. Whether this shift persisted after reopening, and how it varied across diagnoses, insurance types, and geographies, remains unclear.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe used administrative claims data from the Oregon All-Payer All-Claims Reporting Program (2019\u0026ndash;2021) to construct a weekly county-level panel. We estimated two-way fixed effects models with county, week, and year fixed effects. The treatment variable captured the staggered implementation of Oregon's Phase 1 reopening across 36 counties in May\u0026ndash;June 2020.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFollowing Phase 1 reopening, telehealth visits for mood and anxiety disorders (ICD-10 F30\u0026ndash;F49) increased by 0.699 per 10,000 insured (53 percent, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while in-person visits declined by 0.259 per 10,000 (15 percent, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Relative to Medicare, telehealth use was higher among Medicaid and commercial insurance patients. Urban counties showed significantly greater telehealth adoption (+\u0026thinsp;0.535, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and larger in-person declines (\u0026minus;\u0026thinsp;0.435, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Effects for other mental health conditions (F01\u0026ndash;F29, F50\u0026ndash;F99) were approximately one-tenth the magnitude. Patient out-of-pocket costs for telehealth increased by 64 percent, while in-person costs declined by 28 percent.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eMental health care delivery patterns established during the pandemic persisted after reopening. Telehealth remained the dominant modality for mood and anxiety disorders, suggesting a structural shift in service delivery. Policies should support telehealth as a permanent component of mental health care while maintaining in-person pathways.\u003c/p\u003e","manuscriptTitle":"Impact of Phase 1 Reopening on Telehealth and In-Person Mental Health Visits in Oregon: Trends and Disparities (2019–2021)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-26 15:33:26","doi":"10.21203/rs.3.rs-9131274/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-12T14:38:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-02T21:13:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-01T22:05:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-28T17:54:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"132999448661332530073357345073967810037","date":"2026-04-27T13:33:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"168149088711681191465611308303632542369","date":"2026-04-25T17:49:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"118089565558585840468119318586128148374","date":"2026-04-24T13:57:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"279857492081432043075025795756080151433","date":"2026-04-24T13:12:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-21T19:09:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"334670241997082768599379981750507615551","date":"2026-04-16T18:40:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"238232018183218744822847820728629628063","date":"2026-04-16T13:47:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-16T09:58:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-13T08:18:56+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-20T08:27:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-19T20:40:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2026-03-19T20:36:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9be68a8c-7319-41d1-b486-231a3508dc67","owner":[],"postedDate":"April 26th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-12T14:38:05+00:00","index":84,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-02T21:13:41+00:00","index":83,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-01T22:05:52+00:00","index":82,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-26T15:33:26+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-26 15:33:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9131274","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9131274","identity":"rs-9131274","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}