key: cord-0942552-s1xlw4sc authors: Szmulewicz, Alejandro G.; Benson, Nicole M.; Hsu, John; Hernán, Miguel A.; Öngür, Dost title: Effects of COVID‐19 pandemic on mental health outcomes in a cohort of early psychosis patients date: 2021-01-11 journal: Early Interv Psychiatry DOI: 10.1111/eip.13113 sha: 6036da2629d8e6184ab791c90b6ca05b53262523 doc_id: 942552 cord_uid: s1xlw4sc AIM: To evaluate the impact of the COVID‐19 pandemic on clinical outcomes, we used data from Electronic Health Records from 128 patients receiving care at a First Episode Psychosis clinic. METHODS: Rates of admission or emergency room (ER) visits from January 2020 to July 2020 were analysed using difference‐in‐difference regression. We used the same weeks in 2019 to control for seasonality. RESULTS: We found 17 hospitalizations or ER visits between 1 January 2020 and 13 March 2020 (incidence rate: 71.4 events/1000 person‐weeks) and 6 between 14 March 2020 and 20 June 2020 (incidence rate: 18.5 events/1000 person‐weeks) for an incidence rate ratio of 0.26. The severity of presentation worsened after transition to telemedicine. No signs of significant interruptions of care were found. CONCLUSIONS: We report that patients have avoided accessing higher levels of care, except in extreme cases. We argue that this is not a sustainable trajectory and that public health actions are required. medical record data to compare post-pandemic clinical outcomes to control periods before the pandemic. We used data from the Electronic Health Records (EHR) to identify all active patients receiving care at McLean OnTrack, McLean Hospital's subspecialty FEP clinic (Shinn et al., 2017) . We extracted information available at each clinical encounter from 1 January 2019 to 20 June 2019 and 1 January 2020 to 20 June 2020. Clinical encounters occur on a weekly, fortnightly, or monthly basis depending on clinical status. Patients could enter the cohort (i.e., enrol to our program) at any time within that time window. Patients who were lost-to-follow-up (i.e., did not reach 20 June 2020 enrolled in our cohort) contributed with information only during the weeks that they remained enrolled. The study was approved by the MassGeneral Brigham institutional review board. Demographic and past psychiatric information was extracted from the admission interview. At each clinical encounter, we extracted information on symptomatic status (by means of MIRECC GAF scores, which is measured from 0 to 100 and higher scores imply better symptomatic profile) (Niv et al., 2007) , medication type, medication dosage (using chlorpromazine equivalents [CEQ]) (Leucht et al., 2016) , adherence (using the Brief Adherence Rating Scale [BARS] , which uses a visual analogue to assess the proportion of doses taken by the patient since the last visit, between 0% and 100%) (Byerly et al., 2008) , substance abuse (measured as times per week of each substance reported), and employment and education status. All hospital admissions, as well as emergency room (ER) visits, were recorded. The severity of the presentation at hospital admission was assessed using a composite score including involuntary commitment, police involvement, behavioural component (bizarre, aggression, or assault), and degree of suicidality (ideation, plan, or attempt). We defined interruption of care in the context of COVID-19 as no telemedicine visits in a patient who was active as of 1 March 2020. The incidence rate ratio of hospitalizations and ER visits was calculated by comparing the incidence rates for the pre-period (1 January-13 March) and post-period (14 March-20 June) in 2020. The series of weekly counts were assumed to follow a zero-inflated Poisson distribution allowing for overdispersion to plot their trajectory. Observed trajectories of symptoms scores, proportion of employment, and substance abuse were also plotted. To test whether changes were due to seasonal trends, rates for the same weekly periods between 1 January and 20 June in 2019 and 2020 were recorded. We adopted a difference-in-difference model where the effect of the transition to telemedicine was assessed by an interaction term between an indicator of week after 13 March and year (Table S1 , Supporting Information). To assess the gradual effects of the transition over time, we included an interaction term between week and the indicator of posttransition when appropriate. Two sensitivity analyses were conducted. First, since MIRECC GAF score assignment based on clinic encounter notes can be imprecise and subject to bias, we explored changes in the trends of medication dosage. Finally, to properly deal with losses-to-follow-up, we used inverse probability of censoring weights [IPCW]) (Robins & Finkelstein, 2000) using all variables in Table 1 . Clinical and demographic characteristics of the sample are shown in Table 1 In this clinical cohort of FEP patients, we used EHR data to demonstrate significant reductions in hospitalizations and ER visits with an accompanying increase in severity of presentations after institution of 'stay-at-home' orders due to the COVID-19 pandemic. These observations are in keeping with recent reports in cardiovascular and other clinical areas (Solomon et al., 2020) . On the other hand, we found no significant interruptions in clinical care; there was a low proportion of patients not having telemedicine visits and interval between two consecutive visits was significantly shortened after the transition. Despite these encouraging findings, there were also signs of underlying clinical deterioration of this cohort, eroding medication adherence, and abrupt socio-economic consequences in this population. F I G U R E 1 Trajectories of symptomatic scores (a), employment status (b), marijuana use (c), and hospitalization and ER visits (d) before and during the COVID-19 pandemic in 2020 and during the same period in 2019 Some limitations need to be considered when appraising these results, namely that the study sample comes from a single centre, and that symptomatic, employment, and education status information was extracted from EHR and is usually based on self-report from the patient to the clinician, and could reflect reporting errors. Future studies should validate our findings using different data sources. Finally, we were unable to evaluate challenges associated with the transition to telemedicine visits that could help explain symptomatic deterioration (e.g., length of the visit, engagement of the client, aversion of clients to full disclosure of symptoms in this context). Taken together, our results indicate that this cohort of FEP patients have by and large remained in ongoing routine care during the COVID-19 pandemic but they have avoided accessing higher levels of care except in extreme cases despite concerning clinical and socio-economic developments. This is not a sustainable trajectory for the FEP population, and clinicians caring for these patients need to be prepared for the accumulation of adverse developments to translate to abrupt clinical events. In addition, new and innovative approaches for FEP care may be needed to improve outcomes, since we observe deterioration despite normal or supra-normal numbers of routine clinic visits. 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The data that support the findings of this study are available from the corresponding author upon reasonable request.