Impact of the COVID-19 Pandemic on Psychiatric Service Utilization in a Mental Health Referral Unit in Northeast Brazil

Impact of the COVID-19 Pandemic on Psychiatric Service Utilization in a Mental Health Referral Unit in Northeast Brazil | 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 Article Impact of the COVID-19 Pandemic on Psychiatric Service Utilization in a Mental Health Referral Unit in Northeast Brazil Luiz Alves Portela Jr, Brisa Fideles Gandara, Matheus Santos Melo, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7110198/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Purpose : This study evaluated the impact of the COVID-19 pandemic on patterns of psychiatric consultations in a specialized mental health service serving two socioeconomically vulnerable municipalities in Northeast Brazil: Camocim and Granja. Methods : A quasiexperimental design was employed using interrupted time series analysis of routinely collected data from psychiatric consultations between 2017 and 2022. Demographic, clinical, and pharmacological data were extracted from medical records. Spatial and temporal trends were analysed to assess variations in incidence and service demand before and during the pandemic. Results : Psychiatric consultations increased by 60.4% during the pandemic compared to the preceding three years. Anxiety disorders were the most prevalent, particularly among women and young adults. Spatial analyses revealed a concentration of consultations in urban centers, suggesting persistent geographic inequities in access. While an upward trend was evident prior to the pandemic, this pattern plateaued during the pandemic, possibly indicating service saturation. Conclusion : The findings highlight a substantial increase in mental health care demand during the COVID-19 pandemic in structurally underserved settings. They underscore the need to strengthen mental health systems and implement context-specific policies aligned with Sustainable Development Goal 3.4, which promotes mental health and well-being through prevention and early intervention. Health sciences/Diseases Health sciences/Health care Biological sciences/Psychology Social science/Psychology COVID-19 pandemic Psychiatric service utilization Mental health disparities Figures Figure 1 Figure 2 Figure 3 Introduction Global estimates suggest that approximately one in eight individuals will experience a psychiatric disorder in their lifetime, regardless of formal diagnosis. Despite notable progress in identifying risk and protective factors over recent decades, the global burden of mental disorders has continued to rise, reflecting the multifactorial nature of mental illness and enduring gaps in prevention, early detection, and population-level treatment strategies (WHO,2022;WHO,2014). However, much of the epidemiological evidence remains concentrated in high-income countries, even though nearly 80% of individuals affected by mental disorders reside in low- and middle-income countries (WHO,2022). This disproportionate focus limits the development of culturally and structurally appropriate mental health policies in underrepresented regions (WHO,2022;Kola et al,2021). In addition to this paucity of contextualized data, low- and middle-income countries face profound structural challenges in delivering mental health care. These include limited availability and affordability of specialized services, underdiagnosis due to insufficient professional training, and the fragility of mental health care infrastructure. Such systemic barriers are further compounded by individual-level stigma, often reinforced by low educational attainment and restricted access to accurate information. These intersecting factors delay diagnosis, treatment initiation, and long-term engagement with care, thereby perpetuating mental health inequities across and within populations (WHO,2014; Dattani,2023). The COVID-19 pandemic intensified these vulnerabilities, triggering a global mental health crisis characterized by both acute distress and disruption to psychiatric care provision (WHO, 2022). COVID-19 Mental Disorders Collaborators ( 2021 ) reported a 28% increase in depressive disorders and a 26% increase in anxiety disorders during the pandemic, alongside rising rates of suicidal ideation. These effects were particularly severe in contexts where mental health systems were already fragmented, underfunded, and unable to meet baseline demands (Kola et al, 2021 ; Byrne et al, 2021 ). In Brazil, the psychiatric consequences of the pandemic were evident early on, yet few studies have explored regional variations—especially among socioeconomically marginalized populations often rendered invisible in national data (Goulard et al,2021; Lima,2023; Li,2023). Against this backdrop, generating locally grounded evidence is essential to understanding how structural vulnerabilities shape mental health outcomes and service access. Studying psychiatric service utilization in two municipalities in Northeast Brazil provides critical insights into the differential effects of the pandemic in underserved areas. This investigation aligns with Sustainable Development Goal 3.4, which seeks to reduce premature mortality from noncommunicable diseases through prevention, treatment, and mental health promotion (United Nations,2015). Accordingly, this study aims to examine trends in psychiatric consultations and patient profiles before and after the onset of the COVID-19 pandemic, using administrative health data from a specialized service covering the municipalities of Camocim and Granja—both marked by high levels of social vulnerability. Methods Study Design This study utilized a quasiexperimental design incorporating time trend and interrupted time series (ITS) analyses to assess the impact of the COVID-19 pandemic on psychiatric consultations within a public mental health unit located in Camocim, Ceará, Brazil. Population and Setting The study encompassed all psychiatric consultations recorded between 2017 and 2022 at the region’s primary psychiatric referral unit, which serves the municipalities of Camocim and Granja, in addition to nearby localities (Fig. 01 ). Camocim covers 1,120.45 km² and had a population of 62,326 in 2022, while Granja spans 2,663.18 km² with 53,344 residents (IBGE,2025). According to the Brazilian Social Vulnerability Index (IVS), Camocim is classified as having medium vulnerability (0.395) and Granja as having very high vulnerability (0.519) (IPEA,2023). Both municipalities face entrenched socioeconomic disparities that constrain access to essential health services, including mental health care. The psychiatric workforce density is critically low—approximately 4 professionals per 100,000 inhabitants—well below the levels observed in major urban centers such as Brasília or São Paulo, which average 15 per 100,000 (Scheffer et al,2023). Data Sources and Variables Clinical and demographic data were extracted from standardized medical records maintained by the psychiatric unit. Population estimates for 2022 and intercensal projections for 2017–2021 were sourced from the Brazilian Institute of Geography and Statistics (IBGE) via the Unified Health System’s information platform (DATASUS). Geospatial shapefiles were also obtained from IBGE (IBGE,2023). Key variables included municipality of residence, sex, age, urban or rural residence, diagnosis, and prescribed psychotropic medications. Data collection adhered to institutional documentation protocols to ensure internal consistency. Data Analysis Descriptive statistics were calculated using absolute and relative frequencies. Medians for the number of consultations per month were compared across the pre- and post-pandemic periods using the Mann–Whitney U test. Psychiatric consultation incidence rates were calculated per 100,000 inhabitants, focusing exclusively on residents of Camocim and Granja. Spatial distribution patterns were analysed using kernel density estimation based on neighborhood-level georeferencing of residential addresses. Temporal trends in psychiatric consultations were evaluated via Prais–Winsten regression (Bottomley et al,2023), using log-transformed incidence rates as the dependent variable and time (in months) as the independent variable. Monthly percentage change (MPC) was derived from the regression coefficient using the formula MPC = (− 1 + 10^β) × 100, as recommended in the literature (Silva Junior et al,2023). Trends were classified as increasing, decreasing, or stable according to statistical significance. An interrupted time series analysis was conducted, with February 26, 2020—Brazil’s first confirmed COVID-19 case—designated as the intervention point (Lopez Bernal et al,2016). Two additional variables were incorporated: a step variable to capture immediate level changes and a ramp variable to model gradual slope alterations. Level percentage change (LPC) and slope percentage change (SPC) were calculated analogously to MPC. All analyses were conducted using R software (version 3.3.0), with a significance threshold set at p < 0.05. Results A total of 1,797 psychiatric consultations were recorded during the study period, with a marked increase following the onset of the COVID-19 pandemic. In the pre-pandemic period (January 2017 to February 2020), 690 consultations were conducted over 38 months, compared with 1,107 consultations in the 34-month pandemic period (March 2020 to December 2022). Georeferencing was feasible for 1,266 records (70.4%). As depicted in Fig. 2 , both periods revealed a heterogeneous spatial distribution of cases, with a consistent concentration in central urban areas of the two municipalities. Descriptive findings As detailed in Table 1 , the median monthly number of consultations increased from 19.0 (interquartile range [IQR]: 12.5–23.8) prior to the pandemic to 33.5 (IQR: 26.0–33.8) during the pandemic. Camocim accounted for the largest share in both periods, with the monthly median rising from 11.0 to 20.0. Granja and other municipalities also showed increases, though with lower medians (Granja: 4.0 to 7.0; other municipalities: 2.0 to 4.0). Table 1 Monthly psychiatric consultations before and during the COVID-19 pandemic by municipality of residence, sex, age group, and area of residence, Camocim-CE, Brazil (2017–2022). Variables Before the COVID-19 pandemic During the COVID-19 pandemic p-value n % X̅ (IQ - IIIQ) n % X̅ (IQ to IIIQ) Total 690 100.0 19.0 (12.5–23.8) 1.107 100.0 33.5 (26.0–33.8) < 0.001 Municipality Camocim 439 63.6 11.0 (6.3–15.8) 704 63.6 20.0 (18.0–25.0) < 0.001 Granja 167 24.2 4.0 (2.0–5.8) 249 22.5 7.0 (4.3–9.0) < 0.001 Others 84 12.2 2.0 (1.0–3.0) 154 13.9 4.0 (3.0–6.8) < 0.001 Sex Male 252 36.5 6.5 (4.0–8.8) 393 35.5 11.5 (7.0–16.0) < 0.001 Female 438 63.5 12.0 (9.0–15.0) 714 64.5 20.0 (17.0–26.8) < 0.001 Age group (years) Less than 20 86 12.5 2.0 (0.0–3.8) 157 14.2 4.5 (2.0–6.0) < 0.001 20 to 39 277 40.1 7.0 (5.0–10.0) 397 35.9 10.0 (8.0–15.0) < 0.001 40 to 59 186 27.0 6.0 (3.0–7.0) 312 28.2 9.0 (7.5–11.0) < 0.001 60 or older 141 20.4 3.0 (2.0–5.0) 241 21.8 7.0 (5.0–9.3) < 0.001 Zone Urban 555 80.4 15.0 (9.0–20.0) 858 77.5 27.0 (21.0–32.0) < 0.001 Rural 135 19.6 4.0 (2.0–5.0) 223 20.1 6.0 (4.0–9.0) < 0.001 Prescribed medications Antipsychotic 203 29.4 5.0 (3.0–8.0) 296 26.7 9.0 (6.0–11.5) < 0.001 Antidepressant 526 76.2 15.0 (8.0–19.0) 866 78.2 28.0 (21.0–32.0) < 0.001 Anxiolytic 249 36.1 7.0 (4.0–9.0) 436 39.4 13.0 (9.0–17.0) < 0.001 Diagnosis classification Anxiety disorders 249 33.7 7.0 (3.2–9.0) 491 40.0 14.5 (10.25–18.0) < 0.001 Mood disorders 118 16.0 3.0 (1.0–4.0) 153 12.5 4.0 (2.0–6.0) < 0.001 Psychotic disorders 245 33.2 6.0 (3.2–9.0) 439 35.8 14.0 (10.0–15.7) < 0.001 Others 126 17.1 3.0 (2.0–4.0) 126 11.7 3.0 (2.2–5.7) < 0.001 Note: n = absolute frequency; % = relative frequency; 𝑋̅ = median; Q1 = first quartile; Q3 = third quartile; p-value from Mann–Whitney U test. Both sexes demonstrated significant increases, with a more pronounced rise among women (from 12.0 to 20.0), who remained the predominant group in both timeframes. All age categories experienced growth, with the 20–39 age group maintaining the highest consultation volume (median: 7.0 to 10.0). Of note, consultations among adults aged 60 years and older more than doubled (from 3.0 to 7.0). Urban residents represented the majority of patients across both periods, with a median increase from 15.0 to 27.0. Rural residents also saw an increase in consultations (from 4.0 to 6.0). All classes of psychotropic medication showed increased monthly prescription rates. Antidepressants were the most commonly prescribed in both periods (median: 15.0 to 28.0), followed by antipsychotics and anxiolytics. The greatest relative increase was observed in anxiolytic prescriptions (7.0 to 13.0). All diagnostic categories experienced increases in consultation frequency. Anxiety and psychotic disorders remained the most frequent diagnoses (anxiety: 7.0 to 14.5; psychotic: 6.0 to 14.0). The “other” category remained stable in median terms (3.0) but displayed greater variability. All observed increases were statistically significant (p < 0.001), regardless of demographic or clinical stratification. Figure 3 and Table 2 summarize the temporal trends in psychiatric consultations across the study period, including subgroup analyses. Table 2 Temporal trends and interrupted time series analysis of psychiatric consultations before and during the COVID-19 pandemic, Camocim and Granja, Brazil. Variables Before the COVID-19 pandemic During the COVID-19 pandemic LPC (95% CI) p-value SPC (95% CI) p-value MPC (95% CI) p-value Trend MPC (95% CI) p-value Trend Total 4.7 (3.4 to 6.0) < 0.001 Increasing 0.9 (0.7 to 1.2) 0.376 Stable -23.5 (-46.7 to 9.7) 0.142 -3.9 (-5.6 to -2.3) < 0.001 Municipality Camocim 5.4 (3.7 to 7.2) < 0.001 Increasing -0.26 (-1.5 to 1.0) 0.678 Stable -16.9 (-47.0 to 30.3) 0.415 -5.4 (-7.4 to -3.3) < 0.001 Granja 3.8 (2.2 to 5.3) < 0.001 Increasing 2.8 (1.3 to 4.3) < 0.001 Increasing -1.0 (-3.0 to 1.1) 0.341 -46.0 (-64.8 to -17.1) 0.006 Sex Male 6.9 (3.3 to 10.6) < 0.001 Increasing 0.3 (-1.2 to 1.9) 0.678 Stable -40.2 (-73.2 to 33.5) 0.206 -6.1 (-9.7 to -2.3) 0.002 Female 4.9 (3.5 to 6.3) < 0.001 Increasing -1.5 (-4.2 to 1.2) 0.264 Stable 1.6 (-45.6 to 89.8) 0.959 -6.2 (-9.0 to -3.4) < 0.001 Age group Elderly 6.7 (3.1 to 10.5) < 0.001 Increasing -0.7 (-2.9 to 1.6) 0.539 Stable -21.9 (-68.1 to 91.3) 0.583 -7.0 (-10.9 to -2.9) 0.001 Non-elderly 5.5 (3.5 to 7.5) < 0.001 Increasing 0.9 (-3.7 to 2.1) 0.164 Stable -35.1 (-60.3 to 6.2) 0.084 -4.3 (-6.6 to -2.1) < 0.001 Prescribed medications Antipsychotic 7.4 (4.4 to 10.6) < 0.001 Increasing -1.7 (-3.5 to 0.2) 0.728 Stable -27.4 (-65.1 to 51.1) 0.387 -8.6 (-11.8 to -5.3) < 0.001 Antidepressant 4.9 (3.4 to 6.4) < 0.001 Increasing -1.4 (-3.8 to 1.1) 0.255 Stable -3.2 (-43.8 to 89.6) 0.917 -6.3 (-8.9 to -3.5) < 0.001 Anxiolytic 7.5 (4.4 to 10.6) < 0.001 Increasing -1.7 (-3.5 to 0.2) 0.728 Stable -27.4 (-65.1 to 51.1) 0.386 -8.6 (-11.8 to -5.3) < 0.001 Diagnosis classification Anxiety disorders 7.6 (3.8 to 11.5) < 0.001 Increasing 0.8 (-0.8 to 2.5) 0.293 Stable -36.9 (-73.5 to 50.2) 0.293 -6.3 (-10.1 to -2.3) 0.002 Mood disorders 8.0 (3.9 to 12.3) < 0.001 Increasing 0.7 (-0.4 to 1.9) 0.211 Stable -41.5 (-76.7 to 46.9) 0.249 -6.8 (-10.9 to -2.5) 0.002 Psychotic disorders 9.4 (4.0 to 15.1) < 0.001 Increasing -7.0 (-12.9 to -0.8) 0.028 Decreasing -4.9 (-80.0 to 450.9) 0.954 -15.0 (-21.5 to -7.9) < 0.001 MPC: Monthly percentage change; LPC: Level percentage change; SPC: Slope percentage change; CI: 95% confidence interval. Overall, the incidence rate of consultations increased significantly prior to the pandemic (monthly percentage change [MPC] = 4.7%; p < 0.001) and remained statistically stable during the pandemic period (MPC = 0.9%; p = 0.376). Although a significant change in the level of consultations was not observed, changes in the slope of the time series were significant across all variables. Stratified by municipality, Camocim displayed a significant upward trend pre-pandemic (MPC = 5.4%; p < 0.001) followed by stabilization (MPC = -0.26%; p = 0.678), whereas Granja exhibited sustained growth throughout both periods (pre-pandemic: 3.8%; pandemic: 2.8%; p < 0.001 for both). Sex-stratified analyses indicated increasing trends before the pandemic among both males (MPC = 6.9%) and females (MPC = 4.9%), which subsequently stabilized during the pandemic (males: MPC = 0.3%, p = 0.678; females: MPC = -1.5%, p = 0.264). Age-stratified analysis revealed significant upward trends in both older adults (MPC = 6.7%) and younger individuals (MPC = 5.5%) before the pandemic, with both groups experiencing plateaued trends thereafter (older adults: MPC = -0.7%, p = 0.539; younger individuals: MPC = 0.9%, p = 0.164). Prescriptions for antipsychotics, antidepressants, and anxiolytics increased significantly before the pandemic, with subsequent stabilization. In terms of diagnoses, anxiety (MPC = 7.6%), mood (MPC = 8.0%), and psychotic disorders (MPC = 9.4%) showed pre-pandemic increases. During the pandemic, trends for anxiety (MPC = 0.8%, p = 0.293) and mood disorders (MPC = 0.7%, p = 0.211) remained stable, while psychotic disorders declined significantly (MPC = -7.0%; p = 0.028). Discussion To our knowledge, this is the first study to evaluate the impact of the COVID-19 pandemic on psychiatric service utilization in medium-sized, socioeconomically disadvantaged municipalities in Northeast Brazil. By providing empirical evidence from structurally vulnerable settings, this research addresses a critical gap in the literature on the mental health consequences of the pandemic in low-resource environments. Notably, no changes in service infrastructure or workforce occurred during the study period, thus providing a stable context for time series analysis and minimizing the risk of measurement bias. The findings also expose systemic limitations in mental health service capacity, which were further exacerbated during the health crisis. Time trend analyses revealed a significant upward trajectory in psychiatric consultations and psychotropic prescribing prior to the pandemic, followed by a flattening of these trends after the onset of the health emergency. This was particularly evident in the declining slope observed for antipsychotic prescriptions. The prepandemic increase aligns with national and international trends showing rising demand for mental health services over the past decades (Kim et al, 2023 ; Pierce et al,2020; Wu et al(2023); Lopes et al,2022; Lancman,1997; Olfson et al,2019; Kessler et al,2005). In line with this, Brauer et al. demonstrated that psychotropic use has grown more rapidly in low- and middle-income countries (LMICs) compared to high-income nations (Brauer et al,2021). Although psychiatric disorders are widespread globally, their prevalence is strongly influenced by social determinants such as economic insecurity, social adversity, and unemployment—factors that disproportionately affect LMICs (Kola et al,2021; Marquez & Saxena,2016). The present study contributes novel data from a region marked by low GDP per capita, below-average Human Development Index (HDI), sparse population distribution, and limited urban infrastructure. These characteristics render the findings relevant for understanding mental health dynamics in similarly underserved and structurally disadvantaged territories. Women represented the largest proportion of psychiatric consultations, and their increase during the pandemic was more pronounced than that observed among men—reaffirming a well-established pattern in outpatient mental health services (Lopes,2022). Numerous studies have reported higher prevalence of mood and anxiety disorders among women, as well as greater psychotropic medication use relative to men (WHO,2022; COVID-19 Mental Disorders Collaborators,2021; Mazza et al,1995; Kessing et al, 2023 ; Yang et al,2024). Gender-based disparities are compounded by structural and societal factors, including unpaid care work, economic vulnerability, precarious employment, and cultural expectations surrounding appearance and performance. These burdens were intensified during the pandemic, especially among mothers who assumed increased caregiving responsibilities due to school closures. Concurrently, rising rates of domestic violence against women during lockdowns likely contributed to increased psychiatric service demand among this group (Alvarenga & Dias, 2021 ; Piquero et al,2021). Consultations among children and adolescents experienced the highest proportional growth across age categories (82%). Disruptions to schooling, loss of community support, increased family conflict, excessive screen time, and grief were potential triggers for mental health conditions in this population. These cumulative stressors may have reduced the stigma surrounding childhood mental illness, leading to greater recognition, help-seeking behavior, and diagnoses during the pandemic (Marques de Miranda,2020; Benton et al,2022; Kaushik et al,2016). Geospatial analyses indicated a persistent concentration of psychiatric consultations in the urban cores of both municipalities. This pattern likely reflects barriers to service access in peripheral and rural areas, particularly in Granja, which is characterized by low population density and fragile infrastructure. Additional structural barriers—such as limited internet access, reduced digital literacy, and poor connectivity—hamper telemedicine implementation and perpetuate misinformation, further delaying care-seeking. Morales et al. emphasized that mental health disparities in rural populations arise from the interplay of socioeconomic, healthcare, and cultural disadvantages, including limited specialized care, lower educational attainment, geographic isolation, and stigma (Kessing et al,2023; Morales et al,2020). The increase in anxiety-related consultations is consistent with patterns observed globally and reflects the cumulative impact of pandemic-related stressors such as fear of infection, social isolation, income loss, and job insecurity (COVID-19 Disorders Colaborators,2021; Kim et al, 2023 ). The widespread dissemination of misinformation—the so-called “infodemic”—further intensified psychological distress, particularly in areas with limited access to reliable information sources (The Lancet Infectious Deseases,2020). Psychotic disorders were the only diagnostic category to exhibit a declining trend during the pandemic. This contrasts with the stabilization observed in mood and anxiety disorders and may reflect a shift in service prioritization, with acute presentations of psychological distress overshadowing chronic cases. Previous studies have highlighted the elevated vulnerability of individuals with severe mental illness during public health emergencies and their increased likelihood of treatment discontinuation (Galea et al, 2007 ; Jones et al,2009). While high-income countries reported temporary declines in service utilization early in the pandemic, often attributed to fear of infection, expanded use of telepsychiatry, and care diversion to primary settings, our findings suggest that stabilization in this context reflects mainly service saturation rather than demand reduction (Kim et al,2023; Patzina et al,2025; Caselli et al,2023). The inability to scale up mental health care delivery in response to growing need underscores pre-existing fragilities in the local system. In Brazil, several strategies were implemented to mitigate the mental health impact of the pandemic, including hybrid service models, home visits, extended medication dispensation, and mobile mental health technologies (Kola et al,2021; Salum et al,2020). Nonetheless, the availability of such interventions was uneven across regions, and many underserved municipalities had limited or no access to specialized psychiatric care, psychotropic medications, or multidisciplinary support services even prior to the pandemic (WHO,2022;Kola,2020). The findings from this study underscore how the pandemic deepened pre-existing disparities and exposed the structural limitations of mental health care systems in vulnerable settings. Limitations and Recommendations This study offers a distinct approach to understanding psychiatric care trends in a region marked by high social vulnerability. However, this study has certain limitations. Although the findings accurately reflect the operational reality of the psychiatric service assessed, saturation in the number of available appointments may have limited our ability to observe the full extent of the demand. If access had expanded, consultation trends could have exhibited different behaviors. Future studies are needed to explore and validate these findings further, particularly by examining longitudinal patterns and incorporating broader regional comparisons. Conclusion This study identifies two pressing public health concerns. First, psychiatric consultations and psychotropic prescriptions increased markedly during the COVID-19 pandemic, with disproportionate effects among women, children, and adolescents. Second, significant barriers to accessing specialized mental health care persist in rural and socioeconomically disadvantaged areas. These findings reinforce how intersecting health and social crises deepen mental health inequities. The psychiatric repercussions of the COVID-19 pandemic were foreseeable—and, to some extent, preventable—highlighting the need for anticipatory, equity-focused public mental health strategies. Future policies must prioritize investments in regional mental health infrastructure, reduce barriers to access, and incorporate local contexts into planning and implementation. Continued monitoring is essential to inform responsive and inclusive mental health systems. Declarations Acknowledgements The authors thank the healthcare and administrative staff of the psychiatric service for their collaboration and support in this study. Ethical statements Ethical Approval All procedures performed in this study were conducted in accordance with the ethical standards of both the national and institutional research committees, in line with the principles outlined in Resolution No. 466/2012 of the Brazilian National Health Council which are consistent with the ethical tenets of the 1964 Declaration of Helsinki and its subsequent amendments or to comparable ethical standards. This research was approved by the Research Ethics Committee of the Federal University of Delta do Parnaíba (Universidade Federal do Delta do Parnaíba – UFDPAR), under approval number 6.134.729, issued on June 21, 2023 (CAAE: 67410122.2.0000.0192). The committee operates at UFDPAR, Campus Ministro Reis Velloso, located at Avenue São Sebastião, 2819, Nossa Senhora de Fátima, Parnaíba – Piauí, Brazil, within the School of Social and Human Sciences (Espaço de Ciências Sociais e Humanas – ECSH), Block 06, Room 46. Contact: [email protected] . Informed Consent As this is a retrospective study utilizing pre-existing medical records, the requirement for informed consent was formally waived, in accordance with Brazilian ethical regulations. Authorization for data access and use was granted through an Institutional authorization letter, which was also included in the assessment conducted by the Ethics Committee for the approval of the research. This document, commonly referred to in Brazil as a “Termo de Fiel Depositário” corresponds to Data Custodianship declaration, issued by the health facility authority, Mr. José Evandro Pontes Ximenes, the technical manager of the institution where the study was conducted. This document formally designates the principal investigator as the legal custodian of the data, ensuring its secure storage and analysis under strict ethical and confidentiality standards. The agreement functions as institutional authorization for the controlled and anonymized use of medical records for scientific purposes and complies with Brazilian ethical guidelines (Resolutions CNS 466/2012 and 510/2016). Author contributions: Study conception: JPAL. Study design: JPAL and GFB. Data collection ACSLA, FMCP, LPIM & JPAL. Data analysis: JPAL and MSM. Supervision: RG and FG, preparation of the manuscript: all authors. Interpretation and revisions of manuscript: all authors. Funding : No funding was received for conducting this study. The authors have no relevant financial or non-financial interests to disclose. Data availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. References World Health Organization (2022) World mental health report: transforming mental health for all. 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Braz J Psychiatry 42:451–452. https://doi.org/10.1590/1516-4446-2020-0010 Kola L (2020) Global mental health and COVID-19. Lancet Psychiatry 7(8):655–657. https://doi.org/10.1016/S2215-0366(20)30235-0 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 15 Dec, 2025 Reviews received at journal 29 Oct, 2025 Reviews received at journal 27 Oct, 2025 Reviewers agreed at journal 08 Oct, 2025 Reviewers agreed at journal 03 Oct, 2025 Reviewers invited by journal 02 Oct, 2025 Editor assigned by journal 27 Sep, 2025 Editor invited by journal 18 Sep, 2025 Submission checks completed at journal 04 Sep, 2025 First submitted to journal 04 Sep, 2025 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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17:14:21","extension":"html","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":129433,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7110198/v1/950a46746a9137dfaccad50d.html"},{"id":93617952,"identity":"6ad661d2-1b4b-4a3a-9d25-6d8e022b6921","added_by":"auto","created_at":"2025-10-15 17:14:21","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":191601,"visible":true,"origin":"","legend":"\u003cp\u003eStudy location: map of South America and Brazil highlighting the state of Ceará and the municipalities of Granja and Camocim.\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7110198/v1/f9af5920d13a755a9acb55a7.jpg"},{"id":93618258,"identity":"3ee1d313-b23b-442d-9329-1f8884192195","added_by":"auto","created_at":"2025-10-15 17:22:21","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":167837,"visible":true,"origin":"","legend":"\u003cp\u003eKernel density map showing the spatial distribution of patients' residential areas before and during the COVID-19 pandemic, Camocim and Granja, Ceará, Brazil (2017–2022).\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7110198/v1/c33fabf520b5ca18b4c3ace2.jpg"},{"id":93619060,"identity":"591f2749-dda0-4621-b520-9aeadd93f660","added_by":"auto","created_at":"2025-10-15 17:30:21","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":47054,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly incidence and trend lines for psychiatric consultations by selected characteristics in Camocim and Granja, Ceará, Brazil (2017–2022).\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7110198/v1/69a58c49a55647e6b0f311a6.jpg"},{"id":93619504,"identity":"d303ea7d-320c-464f-94ec-fe5b7fc225bb","added_by":"auto","created_at":"2025-10-15 17:38:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1420109,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7110198/v1/b0d851f5-0482-4a0b-ac40-1c5250f67d75.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of the COVID-19 Pandemic on Psychiatric Service Utilization in a Mental Health Referral Unit in Northeast Brazil","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlobal estimates suggest that approximately one in eight individuals will experience a psychiatric disorder in their lifetime, regardless of formal diagnosis. Despite notable progress in identifying risk and protective factors over recent decades, the global burden of mental disorders has continued to rise, reflecting the multifactorial nature of mental illness and enduring gaps in prevention, early detection, and population-level treatment strategies (WHO,2022;WHO,2014).\u003c/p\u003e\u003cp\u003eHowever, much of the epidemiological evidence remains concentrated in high-income countries, even though nearly 80% of individuals affected by mental disorders reside in low- and middle-income countries (WHO,2022). This disproportionate focus limits the development of culturally and structurally appropriate mental health policies in underrepresented regions (WHO,2022;Kola et al,2021).\u003c/p\u003e\u003cp\u003eIn addition to this paucity of contextualized data, low- and middle-income countries face profound structural challenges in delivering mental health care. These include limited availability and affordability of specialized services, underdiagnosis due to insufficient professional training, and the fragility of mental health care infrastructure. Such systemic barriers are further compounded by individual-level stigma, often reinforced by low educational attainment and restricted access to accurate information. These intersecting factors delay diagnosis, treatment initiation, and long-term engagement with care, thereby perpetuating mental health inequities across and within populations (WHO,2014; Dattani,2023).\u003c/p\u003e\u003cp\u003eThe COVID-19 pandemic intensified these vulnerabilities, triggering a global mental health crisis characterized by both acute distress and disruption to psychiatric care provision (WHO, 2022). COVID-19 Mental Disorders Collaborators (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) reported a 28% increase in depressive disorders and a 26% increase in anxiety disorders during the pandemic, alongside rising rates of suicidal ideation. These effects were particularly severe in contexts where mental health systems were already fragmented, underfunded, and unable to meet baseline demands (Kola et al, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Byrne et al, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn Brazil, the psychiatric consequences of the pandemic were evident early on, yet few studies have explored regional variations\u0026mdash;especially among socioeconomically marginalized populations often rendered invisible in national data (Goulard et al,2021; Lima,2023; Li,2023).\u003c/p\u003e\u003cp\u003eAgainst this backdrop, generating locally grounded evidence is essential to understanding how structural vulnerabilities shape mental health outcomes and service access. Studying psychiatric service utilization in two municipalities in Northeast Brazil provides critical insights into the differential effects of the pandemic in underserved areas. This investigation aligns with Sustainable Development Goal 3.4, which seeks to reduce premature mortality from noncommunicable diseases through prevention, treatment, and mental health promotion (United Nations,2015).\u003c/p\u003e\u003cp\u003eAccordingly, this study aims to examine trends in psychiatric consultations and patient profiles before and after the onset of the COVID-19 pandemic, using administrative health data from a specialized service covering the municipalities of Camocim and Granja\u0026mdash;both marked by high levels of social vulnerability.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design\u003c/h2\u003e\u003cp\u003eThis study utilized a quasiexperimental design incorporating time trend and interrupted time series (ITS) analyses to assess the impact of the COVID-19 pandemic on psychiatric consultations within a public mental health unit located in Camocim, Cear\u0026aacute;, Brazil.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePopulation and Setting\u003c/h3\u003e\n\u003cp\u003eThe study encompassed all psychiatric consultations recorded between 2017 and 2022 at the region\u0026rsquo;s primary psychiatric referral unit, which serves the municipalities of Camocim and Granja, in addition to nearby localities (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e01\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCamocim covers 1,120.45 km\u0026sup2; and had a population of 62,326 in 2022, while Granja spans 2,663.18 km\u0026sup2; with 53,344 residents (IBGE,2025). According to the Brazilian Social Vulnerability Index (IVS), Camocim is classified as having medium vulnerability (0.395) and Granja as having very high vulnerability (0.519) (IPEA,2023). Both municipalities face entrenched socioeconomic disparities that constrain access to essential health services, including mental health care. The psychiatric workforce density is critically low\u0026mdash;approximately 4 professionals per 100,000 inhabitants\u0026mdash;well below the levels observed in major urban centers such as Bras\u0026iacute;lia or S\u0026atilde;o Paulo, which average 15 per 100,000 (Scheffer et al,2023).\u003c/p\u003e\n\u003ch3\u003eData Sources and Variables\u003c/h3\u003e\n\u003cp\u003eClinical and demographic data were extracted from standardized medical records maintained by the psychiatric unit. Population estimates for 2022 and intercensal projections for 2017\u0026ndash;2021 were sourced from the Brazilian Institute of Geography and Statistics (IBGE) via the Unified Health System\u0026rsquo;s information platform (DATASUS). Geospatial shapefiles were also obtained from IBGE (IBGE,2023).\u003c/p\u003e\u003cp\u003eKey variables included municipality of residence, sex, age, urban or rural residence, diagnosis, and prescribed psychotropic medications. Data collection adhered to institutional documentation protocols to ensure internal consistency.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eData Analysis\u003c/h2\u003e\u003cp\u003eDescriptive statistics were calculated using absolute and relative frequencies. Medians for the number of consultations per month were compared across the pre- and post-pandemic periods using the Mann\u0026ndash;Whitney U test. Psychiatric consultation incidence rates were calculated per 100,000 inhabitants, focusing exclusively on residents of Camocim and Granja.\u003c/p\u003e\u003cp\u003eSpatial distribution patterns were analysed using kernel density estimation based on neighborhood-level georeferencing of residential addresses.\u003c/p\u003e\u003cp\u003eTemporal trends in psychiatric consultations were evaluated via Prais\u0026ndash;Winsten regression (Bottomley et al,2023), using log-transformed incidence rates as the dependent variable and time (in months) as the independent variable. Monthly percentage change (MPC) was derived from the regression coefficient using the formula MPC = (\u0026minus;\u0026thinsp;1\u0026thinsp;+\u0026thinsp;10^β) \u0026times; 100, as recommended in the literature (Silva Junior et al,2023). Trends were classified as increasing, decreasing, or stable according to statistical significance.\u003c/p\u003e\u003cp\u003eAn interrupted time series analysis was conducted, with February 26, 2020\u0026mdash;Brazil\u0026rsquo;s first confirmed COVID-19 case\u0026mdash;designated as the intervention point (Lopez Bernal et al,2016). Two additional variables were incorporated: a step variable to capture immediate level changes and a ramp variable to model gradual slope alterations. Level percentage change (LPC) and slope percentage change (SPC) were calculated analogously to MPC.\u003c/p\u003e\u003cp\u003eAll analyses were conducted using R software (version 3.3.0), with a significance threshold set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 1,797 psychiatric consultations were recorded during the study period, with a marked increase following the onset of the COVID-19 pandemic. In the pre-pandemic period (January 2017 to February 2020), 690 consultations were conducted over 38 months, compared with 1,107 consultations in the 34-month pandemic period (March 2020 to December 2022).\u003c/p\u003e\u003cp\u003eGeoreferencing was feasible for 1,266 records (70.4%). As depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, both periods revealed a heterogeneous spatial distribution of cases, with a consistent concentration in central urban areas of the two municipalities.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eDescriptive findings\u003c/h2\u003e\u003cp\u003eAs detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the median monthly number of consultations increased from 19.0 (interquartile range [IQR]: 12.5\u0026ndash;23.8) prior to the pandemic to 33.5 (IQR: 26.0\u0026ndash;33.8) during the pandemic. Camocim accounted for the largest share in both periods, with the monthly median rising from 11.0 to 20.0. Granja and other municipalities also showed increases, though with lower medians (Granja: 4.0 to 7.0; other municipalities: 2.0 to 4.0).\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\u003eMonthly psychiatric consultations before and during the COVID-19 pandemic by municipality of residence, sex, age group, and area of residence, Camocim-CE, Brazil (2017\u0026ndash;2022).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\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\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eBefore the COVID-19 pandemic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003eDuring the COVID-19 pandemic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003en\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e%\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eX̅ (IQ - IIIQ)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003en\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e%\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eX̅ (IQ to IIIQ)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e690\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19.0 (12.5\u0026ndash;23.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e33.5 (26.0\u0026ndash;33.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMunicipality\u003c/b\u003e\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\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCamocim\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e439\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e63.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.0 (6.3\u0026ndash;15.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e704\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e63.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e20.0 (18.0\u0026ndash;25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGranja\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.0 (2.0\u0026ndash;5.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e249\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e22.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e7.0 (4.3\u0026ndash;9.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.0 (1.0\u0026ndash;3.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e154\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e13.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4.0 (3.0\u0026ndash;6.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\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\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e252\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e36.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.5 (4.0\u0026ndash;8.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e393\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e35.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e11.5 (7.0\u0026ndash;16.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e438\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e63.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12.0 (9.0\u0026ndash;15.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e714\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e64.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e20.0 (17.0\u0026ndash;26.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge group (years)\u003c/b\u003e\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\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLess than 20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.0 (0.0\u0026ndash;3.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e14.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4.5 (2.0\u0026ndash;6.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20 to 39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e277\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e40.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.0 (5.0\u0026ndash;10.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e397\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e35.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e10.0 (8.0\u0026ndash;15.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e40 to 59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e186\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.0 (3.0\u0026ndash;7.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e312\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e28.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e9.0 (7.5\u0026ndash;11.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e60 or older\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.0 (2.0\u0026ndash;5.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e241\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e21.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e7.0 (5.0\u0026ndash;9.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eZone\u003c/b\u003e\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\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e555\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e80.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.0 (9.0\u0026ndash;20.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e858\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e77.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e27.0 (21.0\u0026ndash;32.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.0 (2.0\u0026ndash;5.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e223\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e20.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e6.0 (4.0\u0026ndash;9.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePrescribed medications\u003c/b\u003e\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\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAntipsychotic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.0 (3.0\u0026ndash;8.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e296\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e26.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e9.0 (6.0\u0026ndash;11.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAntidepressant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e526\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e76.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.0 (8.0\u0026ndash;19.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e866\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e78.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e28.0 (21.0\u0026ndash;32.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnxiolytic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e249\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e36.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.0 (4.0\u0026ndash;9.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e436\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e39.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e13.0 (9.0\u0026ndash;17.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiagnosis classification\u003c/b\u003e\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\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnxiety disorders\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e249\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e33.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.0 (3.2\u0026ndash;9.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e491\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e40.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e14.5 (10.25\u0026ndash;18.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMood disorders\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.0 (1.0\u0026ndash;4.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e12.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4.0 (2.0\u0026ndash;6.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePsychotic disorders\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e245\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e33.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.0 (3.2\u0026ndash;9.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e439\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e35.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e14.0 (10.0\u0026ndash;15.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.0 (2.0\u0026ndash;4.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e11.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3.0 (2.2\u0026ndash;5.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cem\u003eNote: n\u0026thinsp;=\u0026thinsp;absolute frequency; % = relative frequency; \u0026#119883;̅ = median; Q1\u0026thinsp;=\u0026thinsp;first quartile; Q3\u0026thinsp;=\u0026thinsp;third quartile; p-value from Mann\u0026ndash;Whitney U test.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eBoth sexes demonstrated significant increases, with a more pronounced rise among women (from 12.0 to 20.0), who remained the predominant group in both timeframes. All age categories experienced growth, with the 20\u0026ndash;39 age group maintaining the highest consultation volume (median: 7.0 to 10.0). Of note, consultations among adults aged 60 years and older more than doubled (from 3.0 to 7.0).\u003c/p\u003e\u003cp\u003eUrban residents represented the majority of patients across both periods, with a median increase from 15.0 to 27.0. Rural residents also saw an increase in consultations (from 4.0 to 6.0).\u003c/p\u003e\u003cp\u003eAll classes of psychotropic medication showed increased monthly prescription rates. Antidepressants were the most commonly prescribed in both periods (median: 15.0 to 28.0), followed by antipsychotics and anxiolytics. The greatest relative increase was observed in anxiolytic prescriptions (7.0 to 13.0).\u003c/p\u003e\u003cp\u003eAll diagnostic categories experienced increases in consultation frequency. Anxiety and psychotic disorders remained the most frequent diagnoses (anxiety: 7.0 to 14.5; psychotic: 6.0 to 14.0). The \u0026ldquo;other\u0026rdquo; category remained stable in median terms (3.0) but displayed greater variability.\u003c/p\u003e\u003cp\u003eAll observed increases were statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), regardless of demographic or clinical stratification.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarize the temporal trends in psychiatric consultations across the study period, including subgroup analyses.\u003c/p\u003e\u003cp\u003e\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\u003eTemporal trends and interrupted time series analysis of psychiatric consultations before and during the COVID-19 pandemic, Camocim and Granja, Brazil.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"12\"\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=\"char\" char=\".\" 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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\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\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eBefore the COVID-19 pandemic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003eDuring the COVID-19 pandemic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eLPC (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eSPC (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMPC (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTrend\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMPC (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTrend\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.7 (3.4 to 6.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIncreasing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.9 (0.7 to 1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.376\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eStable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-23.5 (-46.7 to 9.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-3.9 (-5.6 to -2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMunicipality\u003c/b\u003e\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\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCamocim\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.4 (3.7 to 7.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIncreasing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.26 (-1.5 to 1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.678\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eStable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-16.9 (-47.0 to 30.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.415\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-5.4 (-7.4 to -3.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGranja\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.8 (2.2 to 5.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIncreasing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.8 (1.3 to 4.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eIncreasing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-1.0 (-3.0 to 1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.341\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-46.0 (-64.8 to -17.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\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\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.9 (3.3 to 10.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIncreasing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.3 (-1.2 to 1.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.678\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eStable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-40.2 (-73.2 to 33.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.206\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-6.1 (-9.7 to -2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.002\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\u003e4.9 (3.5 to 6.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIncreasing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.5 (-4.2 to 1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eStable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.6 (-45.6 to 89.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.959\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-6.2 (-9.0 to -3.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge group\u003c/b\u003e\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\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eElderly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.7 (3.1 to 10.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIncreasing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.7 (-2.9 to 1.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.539\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eStable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-21.9 (-68.1 to 91.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.583\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-7.0 (-10.9 to -2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-elderly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.5 (3.5 to 7.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIncreasing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.9 (-3.7 to 2.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.164\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eStable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-35.1 (-60.3 to 6.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-4.3 (-6.6 to -2.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePrescribed medications\u003c/b\u003e\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\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAntipsychotic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.4 (4.4 to 10.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIncreasing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.7 (-3.5 to 0.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.728\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eStable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-27.4 (-65.1 to 51.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.387\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-8.6 (-11.8 to -5.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAntidepressant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.9 (3.4 to 6.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIncreasing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.4 (-3.8 to 1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.255\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eStable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-3.2 (-43.8 to 89.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.917\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-6.3 (-8.9 to -3.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnxiolytic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.5 (4.4 to 10.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIncreasing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.7 (-3.5 to 0.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.728\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eStable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-27.4 (-65.1 to 51.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.386\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-8.6 (-11.8 to -5.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiagnosis classification\u003c/b\u003e\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\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnxiety disorders\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.6 (3.8 to 11.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIncreasing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.8 (-0.8 to 2.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.293\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eStable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-36.9 (-73.5 to 50.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.293\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-6.3 (-10.1 to -2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMood disorders\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.0 (3.9 to 12.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIncreasing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.7 (-0.4 to 1.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.211\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eStable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-41.5 (-76.7 to 46.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.249\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-6.8 (-10.9 to -2.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePsychotic disorders\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.4 (4.0 to 15.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIncreasing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-7.0 (-12.9 to -0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eDecreasing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-4.9 (-80.0 to 450.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.954\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-15.0 (-21.5 to -7.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"12\"\u003e\u003cem\u003eMPC: Monthly percentage change; LPC: Level percentage change; SPC: Slope percentage change; CI: 95% confidence interval.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eOverall, the incidence rate of consultations increased significantly prior to the pandemic (monthly percentage change [MPC]\u0026thinsp;=\u0026thinsp;4.7%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and remained statistically stable during the pandemic period (MPC\u0026thinsp;=\u0026thinsp;0.9%; p\u0026thinsp;=\u0026thinsp;0.376). Although a significant change in the level of consultations was not observed, changes in the slope of the time series were significant across all variables.\u003c/p\u003e\u003cp\u003eStratified by municipality, Camocim displayed a significant upward trend pre-pandemic (MPC\u0026thinsp;=\u0026thinsp;5.4%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) followed by stabilization (MPC = -0.26%; p\u0026thinsp;=\u0026thinsp;0.678), whereas Granja exhibited sustained growth throughout both periods (pre-pandemic: 3.8%; pandemic: 2.8%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for both).\u003c/p\u003e\u003cp\u003eSex-stratified analyses indicated increasing trends before the pandemic among both males (MPC\u0026thinsp;=\u0026thinsp;6.9%) and females (MPC\u0026thinsp;=\u0026thinsp;4.9%), which subsequently stabilized during the pandemic (males: MPC\u0026thinsp;=\u0026thinsp;0.3%, p\u0026thinsp;=\u0026thinsp;0.678; females: MPC = -1.5%, p\u0026thinsp;=\u0026thinsp;0.264).\u003c/p\u003e\u003cp\u003eAge-stratified analysis revealed significant upward trends in both older adults (MPC\u0026thinsp;=\u0026thinsp;6.7%) and younger individuals (MPC\u0026thinsp;=\u0026thinsp;5.5%) before the pandemic, with both groups experiencing plateaued trends thereafter (older adults: MPC = -0.7%, p\u0026thinsp;=\u0026thinsp;0.539; younger individuals: MPC\u0026thinsp;=\u0026thinsp;0.9%, p\u0026thinsp;=\u0026thinsp;0.164).\u003c/p\u003e\u003cp\u003ePrescriptions for antipsychotics, antidepressants, and anxiolytics increased significantly before the pandemic, with subsequent stabilization.\u003c/p\u003e\u003cp\u003eIn terms of diagnoses, anxiety (MPC\u0026thinsp;=\u0026thinsp;7.6%), mood (MPC\u0026thinsp;=\u0026thinsp;8.0%), and psychotic disorders (MPC\u0026thinsp;=\u0026thinsp;9.4%) showed pre-pandemic increases. During the pandemic, trends for anxiety (MPC\u0026thinsp;=\u0026thinsp;0.8%, p\u0026thinsp;=\u0026thinsp;0.293) and mood disorders (MPC\u0026thinsp;=\u0026thinsp;0.7%, p\u0026thinsp;=\u0026thinsp;0.211) remained stable, while psychotic disorders declined significantly (MPC = -7.0%; p\u0026thinsp;=\u0026thinsp;0.028).\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo our knowledge, this is the first study to evaluate the impact of the COVID-19 pandemic on psychiatric service utilization in medium-sized, socioeconomically disadvantaged municipalities in Northeast Brazil. By providing empirical evidence from structurally vulnerable settings, this research addresses a critical gap in the literature on the mental health consequences of the pandemic in low-resource environments. Notably, no changes in service infrastructure or workforce occurred during the study period, thus providing a stable context for time series analysis and minimizing the risk of measurement bias. The findings also expose systemic limitations in mental health service capacity, which were further exacerbated during the health crisis.\u003c/p\u003e\u003cp\u003eTime trend analyses revealed a significant upward trajectory in psychiatric consultations and psychotropic prescribing prior to the pandemic, followed by a flattening of these trends after the onset of the health emergency. This was particularly evident in the declining slope observed for antipsychotic prescriptions. The prepandemic increase aligns with national and international trends showing rising demand for mental health services over the past decades (Kim et al, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Pierce et al,2020; Wu et al(2023); Lopes et al,2022; Lancman,1997; Olfson et al,2019; Kessler et al,2005). In line with this, Brauer et al. demonstrated that psychotropic use has grown more rapidly in low- and middle-income countries (LMICs) compared to high-income nations (Brauer et al,2021). Although psychiatric disorders are widespread globally, their prevalence is strongly influenced by social determinants such as economic insecurity, social adversity, and unemployment\u0026mdash;factors that disproportionately affect LMICs (Kola et al,2021; Marquez \u0026amp; Saxena,2016).\u003c/p\u003e\u003cp\u003eThe present study contributes novel data from a region marked by low GDP per capita, below-average Human Development Index (HDI), sparse population distribution, and limited urban infrastructure. These characteristics render the findings relevant for understanding mental health dynamics in similarly underserved and structurally disadvantaged territories.\u003c/p\u003e\u003cp\u003eWomen represented the largest proportion of psychiatric consultations, and their increase during the pandemic was more pronounced than that observed among men\u0026mdash;reaffirming a well-established pattern in outpatient mental health services (Lopes,2022). Numerous studies have reported higher prevalence of mood and anxiety disorders among women, as well as greater psychotropic medication use relative to men (WHO,2022; COVID-19 Mental Disorders Collaborators,2021; Mazza et al,1995; Kessing et al, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yang et al,2024). Gender-based disparities are compounded by structural and societal factors, including unpaid care work, economic vulnerability, precarious employment, and cultural expectations surrounding appearance and performance. These burdens were intensified during the pandemic, especially among mothers who assumed increased caregiving responsibilities due to school closures. Concurrently, rising rates of domestic violence against women during lockdowns likely contributed to increased psychiatric service demand among this group (Alvarenga \u0026amp; Dias, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Piquero et al,2021).\u003c/p\u003e\u003cp\u003eConsultations among children and adolescents experienced the highest proportional growth across age categories (82%). Disruptions to schooling, loss of community support, increased family conflict, excessive screen time, and grief were potential triggers for mental health conditions in this population. These cumulative stressors may have reduced the stigma surrounding childhood mental illness, leading to greater recognition, help-seeking behavior, and diagnoses during the pandemic (Marques de Miranda,2020; Benton et al,2022; Kaushik et al,2016).\u003c/p\u003e\u003cp\u003eGeospatial analyses indicated a persistent concentration of psychiatric consultations in the urban cores of both municipalities. This pattern likely reflects barriers to service access in peripheral and rural areas, particularly in Granja, which is characterized by low population density and fragile infrastructure. Additional structural barriers\u0026mdash;such as limited internet access, reduced digital literacy, and poor connectivity\u0026mdash;hamper telemedicine implementation and perpetuate misinformation, further delaying care-seeking. Morales et al. emphasized that mental health disparities in rural populations arise from the interplay of socioeconomic, healthcare, and cultural disadvantages, including limited specialized care, lower educational attainment, geographic isolation, and stigma (Kessing et al,2023; Morales et al,2020).\u003c/p\u003e\u003cp\u003eThe increase in anxiety-related consultations is consistent with patterns observed globally and reflects the cumulative impact of pandemic-related stressors such as fear of infection, social isolation, income loss, and job insecurity (COVID-19 Disorders Colaborators,2021; Kim et al, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The widespread dissemination of misinformation\u0026mdash;the so-called \u0026ldquo;infodemic\u0026rdquo;\u0026mdash;further intensified psychological distress, particularly in areas with limited access to reliable information sources (The Lancet Infectious Deseases,2020).\u003c/p\u003e\u003cp\u003ePsychotic disorders were the only diagnostic category to exhibit a declining trend during the pandemic. This contrasts with the stabilization observed in mood and anxiety disorders and may reflect a shift in service prioritization, with acute presentations of psychological distress overshadowing chronic cases. Previous studies have highlighted the elevated vulnerability of individuals with severe mental illness during public health emergencies and their increased likelihood of treatment discontinuation (Galea et al, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Jones et al,2009).\u003c/p\u003e\u003cp\u003eWhile high-income countries reported temporary declines in service utilization early in the pandemic, often attributed to fear of infection, expanded use of telepsychiatry, and care diversion to primary settings, our findings suggest that stabilization in this context reflects mainly service saturation rather than demand reduction (Kim et al,2023; Patzina et al,2025; Caselli et al,2023). The inability to scale up mental health care delivery in response to growing need underscores pre-existing fragilities in the local system.\u003c/p\u003e\u003cp\u003eIn Brazil, several strategies were implemented to mitigate the mental health impact of the pandemic, including hybrid service models, home visits, extended medication dispensation, and mobile mental health technologies (Kola et al,2021; Salum et al,2020). Nonetheless, the availability of such interventions was uneven across regions, and many underserved municipalities had limited or no access to specialized psychiatric care, psychotropic medications, or multidisciplinary support services even prior to the pandemic (WHO,2022;Kola,2020). The findings from this study underscore how the pandemic deepened pre-existing disparities and exposed the structural limitations of mental health care systems in vulnerable settings.\u003c/p\u003e\n\u003ch3\u003eLimitations and Recommendations\u003c/h3\u003e\n\u003cp\u003eThis study offers a distinct approach to understanding psychiatric care trends in a region marked by high social vulnerability. However, this study has certain limitations. Although the findings accurately reflect the operational reality of the psychiatric service assessed, saturation in the number of available appointments may have limited our ability to observe the full extent of the demand. If access had expanded, consultation trends could have exhibited different behaviors. Future studies are needed to explore and validate these findings further, particularly by examining longitudinal patterns and incorporating broader regional comparisons.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study identifies two pressing public health concerns. First, psychiatric consultations and psychotropic prescriptions increased markedly during the COVID-19 pandemic, with disproportionate effects among women, children, and adolescents. Second, significant barriers to accessing specialized mental health care persist in rural and socioeconomically disadvantaged areas.\u003c/p\u003e\u003cp\u003eThese findings reinforce how intersecting health and social crises deepen mental health inequities. The psychiatric repercussions of the COVID-19 pandemic were foreseeable\u0026mdash;and, to some extent, preventable\u0026mdash;highlighting the need for anticipatory, equity-focused public mental health strategies. Future policies must prioritize investments in regional mental health infrastructure, reduce barriers to access, and incorporate local contexts into planning and implementation. Continued monitoring is essential to inform responsive and inclusive mental health systems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the healthcare and administrative staff of the psychiatric service for their collaboration and support in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical statements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;All procedures performed in this study were conducted in accordance with the ethical standards of both the national and institutional research committees, in line with the principles outlined in Resolution No. 466/2012 of the Brazilian National Health Council which are consistent with the ethical tenets of the 1964 Declaration of Helsinki and its subsequent amendments or to comparable ethical standards.\u003c/p\u003e\n\u003cp\u003eThis research was approved by the Research Ethics Committee of the Federal University of Delta do Parna\u0026iacute;ba (Universidade Federal do Delta do Parna\u0026iacute;ba \u0026ndash; UFDPAR), under approval number 6.134.729, issued on June 21, 2023 (CAAE: 67410122.2.0000.0192). The committee operates at UFDPAR, Campus Ministro Reis Velloso, located at Avenue S\u0026atilde;o Sebasti\u0026atilde;o, 2819, Nossa Senhora de F\u0026aacute;tima, Parna\u0026iacute;ba \u0026ndash; Piau\u0026iacute;, Brazil, within the School of Social and Human Sciences (Espa\u0026ccedil;o de Ci\u0026ecirc;ncias Sociais e Humanas \u0026ndash; ECSH), Block 06, Room 46. Contact: [email protected].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs this is a retrospective study utilizing pre-existing medical records, the requirement for informed consent was formally waived, in accordance with Brazilian ethical regulations. Authorization for data access and use was granted through an Institutional authorization letter, which was also included in the assessment conducted by the Ethics Committee for the approval of the research. This document, commonly referred to in Brazil as a \u0026ldquo;Termo de Fiel Deposit\u0026aacute;rio\u0026rdquo; corresponds to Data Custodianship declaration, issued by the health facility authority, Mr. Jos\u0026eacute; Evandro Pontes Ximenes, the technical manager of the institution where the study was conducted. This document formally designates the principal investigator as the legal custodian of the data, ensuring its secure storage and analysis under strict ethical and confidentiality standards. The agreement functions as institutional authorization for the controlled and anonymized use of medical records for scientific purposes and complies with Brazilian ethical guidelines (Resolutions CNS 466/2012 and 510/2016).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u0026nbsp;\u003c/strong\u003eStudy conception: JPAL. Study design: JPAL and GFB. Data collection ACSLA, FMCP, LPIM \u0026amp; JPAL. Data analysis: JPAL and MSM. Supervision: RG and FG, preparation of the manuscript: all authors. Interpretation and revisions of manuscript: all authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: No funding was received for conducting this study.\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organization (2022) World mental health report: transforming mental health for all. Geneva: World Health Organization. https://www.who.int/publications/i/item/9789240049338\u003c/li\u003e\n\u003cli\u003eWorld Health Organization, Calouste Gulbenkian Foundation (2014) Social determinants of mental health. Geneva: World Health Organization. https://iris.who.int/bitstream/handle/10665/112828/9789241506809_eng.pdf\u003c/li\u003e\n\u003cli\u003eKola L, Kohrt BA, Hanlon C, Naslund JA, Sikander S, Balaji M, et al. (2021) COVID-19 mental health impact and responses in low-income and middle-income countries: reimagining global mental health. The Lancet Psychiatry 8(6):535\u0026ndash;550. https://doi.org/10.1016/S2215-0366(21)00025-0\u003c/li\u003e\n\u003cli\u003eDattani S (2023) How do researchers study the prevalence of mental illnesses? Our World in Data. https://ourworldindata.org/how-do-researchers-study-the-prevalence-of-mental-illnesses\u003c/li\u003e\n\u003cli\u003eCOVID-19 Mental Disorders Collaborators (2021) Global prevalence and burden of depressive and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic. Lancet 398(10312):1700\u0026ndash;1712. https://doi.org/10.1016/S0140-6736(21)02143-7\u003c/li\u003e\n\u003cli\u003eByrne A, Barber R, Lim CH (2021) Impact of the COVID-19 pandemic \u0026ndash; a mental health service perspective. Prog Neurol Psychiatry 25(2):27\u0026ndash;33b.\u003c/li\u003e\n\u003cli\u003eGoularte JF, Serafim SD, Colombo R, Hogg B, Caldieraro MA, Rosa AR (2021) COVID-19 and mental health in Brazil: psychiatric symptoms in the general population. 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JAMA Psychiatry 80(10):1000\u0026ndash;1008. https://doi.org/10.1001/jamapsychiatry.2023.2261\u003c/li\u003e\n\u003cli\u003eYang Y, Fang F, Arnberg FK, Kuja-Halkola R, D\u0026rsquo;Onofrio BM, Larsson H, et al. (2024) Sex differences in clinically diagnosed psychiatric disorders over the lifespan: a nationwide register-based study in Sweden. Lancet Reg Health Eur 47:101105. https://doi.org/10.1016/j.lanepe.2023.101105\u003c/li\u003e\n\u003cli\u003eAlvarenga R, Dias MK (2021) Epidemia de drogas psiqui\u0026aacute;tricas: tipologias de uso na sociedade do cansa\u0026ccedil;o. Psicol Soc 33:e235950. https://doi.org/10.1590/1807-0310/2021v33235950\u003c/li\u003e\n\u003cli\u003ePiquero AR, Jennings WG, Jemison E, Kaukinen C, Knaul FM (2021) Domestic violence during the COVID-19 pandemic \u0026ndash; evidence from a systematic review and meta-analysis. J Crim Justice 74:101806. https://doi.org/10.1016/j.jcrimjus.2021.101806\u003c/li\u003e\n\u003cli\u003eMarques de Miranda D, da Silva Athanasio B, Sena Oliveira AC, Simoes-e-Silva AC (2020) How is COVID-19 pandemic impacting mental health of children and adolescents? Int J Disaster Risk Reduct 51:101845. https://doi.org/10.1016/j.ijdrr.2020.101845\u003c/li\u003e\n\u003cli\u003eBenton T, Njoroge WFM, Ng WYK (2022) Sounding the alarm for children\u0026rsquo;s mental health during the COVID-19 pandemic. JAMA Pediatr 176(4):e216295. https://doi.org/10.1001/jamapediatrics.2021.6295\u003c/li\u003e\n\u003cli\u003eKaushik A, Kostaki E, Kyriakopoulos M (2016) The stigma of mental illness in children and adolescents: a systematic review. Psychiatry Res 243:469\u0026ndash;494. https://doi.org/10.1016/j.psychres.2016.04.040\u003c/li\u003e\n\u003cli\u003eMorales DA, Barksdale CL, Beckel-Mitchener AC (2020) A call to action to address rural mental health disparities. J Clin Transl Sci 4(5):463\u0026ndash;467. https://doi.org/10.1017/cts.2020.42\u003c/li\u003e\n\u003cli\u003eThe Lancet Infectious Diseases (2020) The COVID-19 infodemic. Lancet Infect Dis 20(8):875. https://doi.org/10.1016/S1473-3099(20)30565-X\u003c/li\u003e\n\u003cli\u003eGalea S, Brewin CR, Gruber M, Jones RT, King DW, King LA, et al. (2007) Exposure to hurricane-related stressors and mental illness after Hurricane Katrina. Arch Gen Psychiatry 64(12):1427\u0026ndash;1434. https://doi.org/10.1001/archpsyc.64.12.1427\u003c/li\u003e\n\u003cli\u003eJones L, Asare JB, Masri ME, Mohanraj A, Sherief H, van Ommeren M (2009) Severe mental disorders in complex emergencies. Lancet 374(9690):654\u0026ndash;661. https://doi.org/10.1016/S0140-6736(09)61427-1\u003c/li\u003e\n\u003cli\u003ePatzina A, Collischon M, Hoffmann R, Obrizan M (2025) Mental health in Germany before, during and after the COVID-19 pandemic. PLoS One 20(1):e0313689. https://doi.org/10.1371/journal.pone.0313689\u003c/li\u003e\n\u003cli\u003eCaselli I, Ielmini M, Bellini A, Marchetti S, Lucca G, Vitiello E, et al. (2023) The impact of COVID-19 pandemic on mental health services: a comparison between first psychiatric consultations before and after the pandemic. Clin Neuropsychiatry 20:233\u0026ndash;239.\u003c/li\u003e\n\u003cli\u003eSalum GA, Rehmenklau JF, Csordas MC, Pereira FP, Castan JU, Ferreira AB, et al. (2020) Supporting people with severe mental health conditions during the COVID-19 pandemic: considerations for low- and middle-income countries using telehealth case management. Braz J Psychiatry 42:451\u0026ndash;452. https://doi.org/10.1590/1516-4446-2020-0010\u003c/li\u003e\n\u003cli\u003eKola L (2020) Global mental health and COVID-19. Lancet Psychiatry 7(8):655\u0026ndash;657. https://doi.org/10.1016/S2215-0366(20)30235-0\u003c/li\u003e\n\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":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"COVID-19 pandemic, Psychiatric service utilization, Mental health disparities","lastPublishedDoi":"10.21203/rs.3.rs-7110198/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7110198/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose\u003c/strong\u003e: This study evaluated the impact of the COVID-19 pandemic on patterns of psychiatric consultations in a specialized mental health service serving two socioeconomically vulnerable municipalities in Northeast Brazil: Camocim and Granja.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: A quasiexperimental design was employed using interrupted time series analysis of routinely collected data from psychiatric consultations between 2017 and 2022. Demographic, clinical, and pharmacological data were extracted from medical records. Spatial and temporal trends were analysed to assess variations in incidence and service demand before and during the pandemic.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Psychiatric consultations increased by 60.4% during the pandemic compared to the preceding three years. Anxiety disorders were the most prevalent, particularly among women and young adults. Spatial analyses revealed a concentration of consultations in urban centers, suggesting persistent geographic inequities in access. While an upward trend was evident prior to the pandemic, this pattern plateaued during the pandemic, possibly indicating service saturation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: The findings highlight a substantial increase in mental health care demand during the COVID-19 pandemic in structurally underserved settings. They underscore the need to strengthen mental health systems and implement context-specific policies aligned with Sustainable Development Goal 3.4, which promotes mental health and well-being through prevention and early intervention.\u003c/p\u003e","manuscriptTitle":"Impact of the COVID-19 Pandemic on Psychiatric Service Utilization in a Mental Health Referral Unit in Northeast Brazil","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-15 17:14:16","doi":"10.21203/rs.3.rs-7110198/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-15T11:18:42+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-29T14:16:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-27T16:09:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"254989512707910584555940130183071728959","date":"2025-10-08T10:38:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"283917603442248703343155387027241564854","date":"2025-10-03T09:04:35+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-02T20:08:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-27T12:42:39+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-18T07:45:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-04T20:59:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2025-09-04T20:57:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"bc5f091b-aafb-46e0-86c6-c98dc11f3fb0","owner":[],"postedDate":"October 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":56293969,"name":"Health sciences/Diseases"},{"id":56293970,"name":"Health sciences/Health care"},{"id":56293971,"name":"Biological sciences/Psychology"},{"id":56293972,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-03-22T17:23:43+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-15 17:14:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7110198","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7110198","identity":"rs-7110198","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}