Availability of mental health care and mental health disorders in Brazil

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De Boni, Jurema Correa da Mota, Julio Castro Alves, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4395839/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose We aimed to describe the prevalence of 12-month reported MHD and evaluate associations with availability mental health (MH) care in Brazil. Methods Data from a nationwide probability survey (n = 16,273) and from the National Registry of Health Services have been analyzed. The main outcomes were 12-month reported diagnosis/treatment for anxiety, depression, and severe MHD. Multivariate logistic regressions were performed to assess the associations of the rates of psychiatrists, outpatient MH services (CAPS) and primary health care services (PHC) with the outcomes. Results The overall prevalence of anxiety, depression, bipolar disorder and schizophrenia were 15.5% (95%CI:14.4–16.6), 7.3% (95%CI:6.6–7.9), 1.0% (95%CI:0.8–1.3), and 0.4% (95%CI 0.3–0.5), respectively, with lower prevalences observed in less developed macroregions. The rate of psychiatrists varied from 1.52 (North) to 12.26 (South)/100,000 inhabitants, the rate of CAPS from 1.52 (North) to 2.72 (Northeast), and the rate of PHC from 26.12 (Southeast) to 52.25 (Northeast). Individuals living in regions with higher rates of psychiatrists and PHCs were more likely to report anxiety and depression, while those living in regions with higher rates of CAPS were more likely report severe MHD. Conclusion The distribution of services mirrors the emphasis on PHC and CAPS to enhance equity within the Brazilian Universal Health System. However, diagnostic and treatment rates remain elevated in regions with larger psychiatrist presence. Addressing information gaps is imperative to optimize MH policies and resources allocation. Anxiety Depression Severe mental health disorders Health Services Accessibility Public Mental Health Figures Figure 1 Figure 2 Introduction Before the COVID-19 pandemic it was estimated that 970 million people (14.6%) lived with a diagnosable mental health disorder (MHD) worldwide, not including substance use disorders and dementia [ 1 ]. Those individuals have been disproportionally affected by premature mortality due to preventable diseases, and severe MHD – such as bipolar disorder (BD) and schizophrenia – may decrease up to 20 years of their life expectancy [ 2 ]. MHD are among the leading causes of years lived with disability (YLDs), accounting for 15·6% of YLDs globally [ 3 ]. Beyond individual-level health outcomes, MHD may play a role in reinforcing social inequalities and poverty. This is of utmost importance in a country with continental dimensions such as Brazil which presents large social inequalities. For instance, the Brazilian GINI index in 2020 was estimated at 48·6%, and the Human Developing Index was 0·754 (decreasing to 0·576 when adjusted for inequalities) [ 4 , 5 ]. It is possible that the less developed regions of the country present inadequate mental health services capacity, impacting both the diagnosis and treatment of MHD and further contributing to increase poverty and inequalities [ 6 ]. There are major gaps to tackle the MHD burden, including information and service gaps. Information gap refers to the lack of data on MHD and insufficient research. In Brazil, the information gap on the MHD prevalence across the general population is huge. Most of the Brazilian data are based on information provided by health services, excluding populations without access to health care. Over the last 20 years, the only household probability sample surveys assessing psychiatric diagnosis (as per DSM or ICD criteria) were restricted to municipalities or regional areas. The most robust was the São Paulo Megacity Mental Health Survey (SPMHS) conducted in 2005/7. In this survey, 5,037 adults were interviewed in 39 cities in the São Paulo Metropolitan Area – which has the largest population density in the country. SPMHS identified a 12-month prevalence of 19·9% (standard error - SE 0·8) for anxiety disorders, 9·4% (SE 0·6) for major depression, and 1·5% (SE 0·2) for bipolar disorder (BD) [ 7 ]. The Brazilian household surveys covering the entire country were not designed to evaluate the diagnosis of MHD. All of them relied on screening instruments or the report of previous MHD diagnosis /treatment. In addition, few MHD were investigated, depression being the most common. For instance, in two small surveys, conducted in 2006 and 2012 (n1 = 3007 and n2 = 4607), the prevalence of positive screening for depression varied from 9·4% (last 7 days) to 28·3% (lifetime), while for anxiety’s it was 17·1% [ 8 , 9 ]. In the latest Brazilian National Health Survey (PNS 2019), the prevalence of a positive screening for depression was 10·2% (95%CI: 9·9–10·6) while the prevalence of other mental disorders was 1·10% (95% CI: 1·00–1·30) [ 10 ]. However, such figures may be overestimated due to the use of screening instruments or underestimated due to evaluation through questions relying on the previous diagnosis (i.e., only individuals with access to care were aware of their MHD diagnoses). Service gap refers to the coverage, range, and quality of the treatments provided. Service gap may reflect either unavailable services, capacity, lack of feasible geographical accessibility (distance or cost) and affordability, or the lack of demand (due to the stigma that stops people from seeking help, for instance). Brazil has one of the largest public health system in the world, the Unified Health System (SUS). The SUS is supposed to provide integral health care, including mental health care, free of charge, to the Brazilian population. Thus, resource allocation is a major challenge in such large system where equity and sustainability should coexist. Despite the existence of governmental data on MH services, such as the number of mental health workers and the number of psychiatric beds, we were not able to find studies regarding the availability of these services to meet the MH demand. Information on the availability of MHD services should be considered a major priority given the consequences of the mental health gap. Additionally, considering the vast Brazilian territory, and socioeconomic and cultural differences between its regions, it is relevant to understand the distribution of diagnosis and services across the regions. Thus, this manuscript aims to: 1) describe the prevalence of 12-month reported MHD across the Brazilian regions; 2) describe the availability MH care across the Brazilian regions; 3) evaluate the association between the availability of care with reported MHD. We hypothesize that the higher the availability of MH care the higher the prevalence of 12-month reported MHD. Methods This is a cross-sectional analysis of data collected in the 3rd Brazilian Household Survey on Substance Use (3rd BHSU), and data obtained in the National Registry of Health Services (CNES). The 3rd BHSU was a nationwide probability sample survey conducted in 2015, detailed elsewhere [ 11 – 15 ], and approved by the institutional review board of the Escola Politécnica Joaquim Venâncio-Fiocruz (CAAE # 35283814.4.0000.5241). The CNES is the Ministry of Health official information system for registering health establishments in the country, regardless of whether they are part of the Unified Health System (SUS) or not [ 16 ]. It includes data on the installed capacity and health care workforce. We followed the STROBE guideline for reporting the present results. Participants The 3rd BHSU interviewed 16,273 individuals, aged 12–65 years old from the entire country. Native individuals living in indigenous villages, inmates, and individuals with physical or mental disabilities precluding to answer the interview were not eligible. In the present analysis, we considered only individuals 20–65 years old. This age bracket was selected to maintain comparability with the latest World Mental Report [ 17 ]. Outcomes We evaluated three outcomes: 12-month diagnosis of anxiety, depression, and severe mental health disorder- SMHD (which includes bipolar disorder (BD), and schizophrenia). The outcomes were assessed by the questions “In the last 12 months, have you been diagnosed by a medical doctor or health professional, or received treatment for… (anxiety, depression, BD, schizophrenia)?”. Possible answers were: No, Yes (received diagnosis), Yes (received treatment), Don’t know /don’t want to answer. Both “yes” options were categorized as a positive diagnosis and all other options were categorized as negative diagnosis (Supplementary Table 1). Main contextual variables of interest The main contextual variables of interest were the indicators of availability of mental health care services: psychiatrists per 100,000 inhabitants, Centers for Psychosocial Attention (CAPS) per 100,000 inhabitants and Primary Health Care services (PHC) per 100,000 inhabitants. The CAPS are open community mental health care services provided by the SUS. They operate at various levels (including outpatient care and rehabilitation), comprise multi-professional teams and may be specialized to address substance use and/or psychiatric emergencies [ 18 ]. These services are not homogeneous, with important diversity in terms of provided services, availability of human resources, size and service capacity. The PHC are the SUS facilities that provide primary care. They are organized by geographic regions and are considered the main entry for MH attention. PHC may include MH personnel, but it is expected that non-specialists are also able to diagnose and treat common mental health disorders – and refer severe MH to specialized treatment (such as CAPS). Those indicators were calculated using CNES database and the Brazilian Institute for Geography and Statistics (IBGE) [ 19 ] estimates of the Brazilian population, both for 2015. Initially, we calculate the indicators aggregated by the five Brazilian macro regions (North, Northeast, Center West, Southeast and South). Afterwards, we calculated them by the 450 Brazilian Health Regions. The Health Regions are formed by contiguous municipalities that share cultural, economic and social identities, communication networks and transport infrastructure. The purpose of these regions is to integrate the organization, planning and execution of health actions and services to guarantee access to public health care. Covariates at the Individual level Individual level covariates were obtained from the 3rd BHSU. Sociodemographic variables were sex assigned at birth (male, female), age (20–24 years, 25–49 years, and 50–65 years), race/ethnicity (which were inquired as per the National Census [ 20 ], and categorized as white, black/mixed, other), schooling (No education or incomplete fundamental; Complete fundamental or incomplete high school; Complete high school or incomplete graduation; Graduation or more), income (up to 1 Brazilian minimum wage- MW; 1–4 MW; and, >4MW) (Brazilian minimum wage corresponded approximately to 2015 US $ 242 dollar/month), occupation (regular job, irregular job, unemployed, and non-economically active), religion (Catholic; Protestant; Other; None), and stable partner (yes, no). Chronic health diseases were assessed by the questions: “In the last 12 months, have you been diagnosed by a medical doctor or health professional, or received treatment for (diabetes, heart disease, hypertension, asthma, HIV/AIDS, cancer, tuberculosis, and renal disease)”. Study size The 3rd BHSU used a stratified four-stage clustered probability sample. The total sample size was calculated to estimate a minimum prevalence of 2% with a relative error of less than 30%, confidence level of 95% and design effect of 1·5. Power allocation (with power = 3/4) was used to distribute the total sample size among the selected strata, using population as the size measure. After the allocation, the sample size was estimated at 16,400 individuals [ 12 , 15 ]. Statistical methods We estimated the prevalence and corresponding 95% confidence intervals (95% CI) of the outcomes, as well as the rates of the main contextual variables, for the five Brazilian macro regions and the 450 Health Regions. Logistic regressions were performed to assess the bivariate association of the independent variables and each one of the outcomes. Multivariate analysis used the backward procedure to reach the most parsimonious model for each outcome. All statistical analyses were performed in R v·4·0·5 software, utilizing the ‘survey’ and ‘srvyr’ libraries and their dependencies, considering calibrated sample weights, design effect and weight calibration [ 21 , 22 ]. Role of the funding source The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of this manuscript. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication. Results The analytical sample included 14,987 individuals which represent 126 million Brazilians aged 20 to 65 years. Most of the sample were female (53·00%), aged 25–49 years (59·.95%), earning 1 to 4 minimum wages/month (66·98%), and reporting a stable partner (71·29%)- as depicted in the Supplementary Table 2. Approximately 18% (95% CI 16·76 − 19·02) of the population received some MHD diagnosis or treatment in the previous 12 months. The overall prevalence of anxiety, depression, BD, and schizophrenia were 15·51% (95%CI 14·44 − 16·59), 7·26% (95%CI 6·64–7·88), 1·03% (95%CI 0·79 − 1·27), and 0·41% (95%CI 0·28 − 0·55). The prevalence of 12-month diagnosis /treatment for MHD by the five Brazilian macro regions are depicted in Table 1 . Prevalences were lower at North and Northeast compared to South and Southeast. The overall rate of psychiatrists in the country was 7.01/100,000 inhabitants, the rate of CAPS was 1·75/100,000 inhabitants, and the rate of PHC was 33·3/100,000 inhabitants. The rate of psychiatrists was higher in the Southeast and the South, while the rates of CAPS and PHC were higher in the Northeast - Table 1 . Table 1 Prevalence of 12-month diagnosis or treatment of selected mental health disorders and availability of MH capacity by the five Brazilian macroregions. 3rd BHSU, CNES and IBGE. Brazil, 2015. North Northeast Center West Southeast South Prevalence of MHD Anxiety 7·75[4·79;10·72] 12·99[11·06;14·92] 14·87[10·77;18·97] 17·58[15·63;19·52] 18·24[16·35;20·12] Depression 3·58[2·02;5·13] 4·82[3·90;5·74] 6·07[4·35;7·79] 7·98[6·97;8·98] 11·85[9·67;14·02] Bipolar disorder 0·25[0·07;0·44] 0·62[0·13;1·11] 1·19[0·48;1·9] 1·31[0·9;1·73] 1·22[0·74;1·7] Schizophrenia 0·01[0;0·04] 0·16[0·02;0·30] 0·31[0·04;0·58] 0·61[0·33;0·89] 0·53[0·24;0·82] Any MHD 9·55[6·26;12·84] 14·77[12·65;16·88] 17·68[13·34;22·03] 19·84[17·87;21·80] 22·04[19·72;24·36] Availability of MH capacity Psychiatrists/100·000 inh 2·31[1·68;2·93] 5·86[5·40;6·32] 5·55[4·46;6·65] 11·47[10·67;12·28] 12·26[11·84;12·68] CAPS/100·000 inh 1·52[1·21;1·82] 2·72[2·42;3·01] 1·68[1·43;1·92] 1·80[1·66;1·94] 2·29[2·17;2·41] PHC/100·000 inh 47·66[41·86;53·47] 52·25[47·71;56·79] 35·16[30·55;39·77] 26·12[23·60;28·65] 40·26[36·33;44·19] 3rd BHSU = 3rd Brazilian Household Survey on Substance Use. CNES = Brazilian National Registry of Health Services. MHD = Mental Health Disorder. MH = Mental Health. Inh = inhabitants. CAPS = Centers for Psychosocial Attention. PHC = Primary Health Care Figure 1 shows the prevalence of any MHD in the 450 Brazilian Health Regions, and Fig. 2 shows the rates of psychiatrists and CAPS in those regions. The highest prevalence of any MHD was found in the Regions located at the South, Southeast, Center West and far north of the country. The highest rate of psychiatrists was found in the Regions located at the South -Southeast, and lowest in the North. The distribution of CAPS is more homogenous across the country, although many health regions from the North and Center West still have less than 2 CAPS/100,000 inhabitants. Figure 1 : Prevalence of 12-month diagnoses/ treatment for any mental health disorder by Brazilian Health Regions. 3rd BHSU. Brazil, 2015. Figure 2 : Availability of mental health capacity by Brazilian Health Regions: A) Rate of psychiatrists per 100,000 inhabitants, and B) rate of CAPS per 100,00 inhabitants. CNES and IBGE, 2015 Table 2 shows the associations between 12-month diagnosis/treatment of MHD and the availability of mental health services. After controlling by contextual and individual covariates, 12-month diagnosis/treatment of anxiety and depression was associated with higher rates of psychiatrists and PHC, but not with CAPS rates. On the other hand, 12-month diagnosis/treatment of SMHD was associated with higher rates of CAPS, but not with psychiatrists or PHC. The association with demographic variables varies across the diagnoses, but all the diagnosis were associated with a concomitant 12-month diagnosis/treatment for some chronic disease. Table 2 Associations between the availability of mental health care and 12-month diagnosis anxiety, depression and severe mental health disorders evaluated by logistic regression models. BHSU-3 and CNES. Brazil, 2015. Mental Health Disorders Anxiety Depression Severe MHD OR [IC95%] aOR [IC95%] OR [IC95%] aOR [IC95%] OR [IC95%] aOR [IC95%] Main Contextual Variables Psychiatrist Rate [0·0,5·0) Ref Ref Ref Ref Ref - [5·0,10·0) 1·32 [ 1·02;1·72] 1·37 [1·05;1·79] 1·62 [1·22;2·15] 1·75 [1·32;2·32] 1·87 [1·07;3·28] - > 10·0 1·50 [1·15;1·97] 1·54 [1·15;2·05] 1·75 [1·32;2·34] 1·87 [1·39;2·51] 1·63 [0·89;33·00] - CAPS Rate [0·0,2·0] Ref - Ref - Ref Ref > 2·0 1·23 [1·03;1·48] - 1·23 [1·00;1·51] - 1·51 [1·00;2·27] 1·80 [1·16;2·82] PHC Rate [0·0,20·0] Ref Ref Ref Ref Ref - > 20·0 1·26 [1·08;1·47] 1·39 [1·17;1·65] 1·31 [1·09;1·57] 1·45 [1·2;1·76] 1·30 [0·89;1·92] - Covariates at individual level Age 20–24 Ref Ref Ref Ref Ref Ref 25–49 1·52[1·23;1·89] 1·34 [1·08;1·67] 3·04 [2·05;4·50] 2·84 [1·89;4·27] 1·97 [0·83;4·67] 2·46 [0·89;6·84] 50–65 2·01[1·60;2·52] 1·24 [0·97;1·58] 4·87 [3·33;7·11] 2·76 [1·85;4·12] 2·01 [0·89;4·54] 1·18 [0·43;3·18] Sex assigned at birth Female Ref Ref Ref Ref Ref Ref Male 0·41[0·36;0·46] 0·44 [0·39;0·5] 0·37[0·31;0·44] 0·44 [0·37;0·54] 0·66[0·41;1·07] 1·05 [0·63;1·74] Race/Ethnicity White Ref Ref Ref Ref Ref - Black/Mixed 0·78[0·69;0·88] 0·82 [0·72;0·92] 0·75[0·64;0·87] 0·76 [0·66;0·88] 1·03[0·71;1·49] - Others 0·67[0·38;1·17] 0·66 [0·36;1·2] 0·79[0·32;1·95] 0·87 [0·32;2·32] 0·24[0·03;1·77] - Religion Catholic Ref Ref Ref - Ref Ref None 0·57[0·46;0·72] 0·74 [0·59;0·94] 0·8[0·58;1·11] - 0·65[0·31;1·36] 0·82 [0·39;1·73] Christian 0·96[0·84;1·09] 0·92 [0·81;1·06] 1·12[0·95;1·31] - 1·14[0·78;1·68] 1·20 [0·78;1·82] Other 1·32[1·04;1·67] 1·20 [0·94;1·54] 1·33[0·98;1·79] - 2·49[1·38;4·5] 3·12 [1·73;5·63] Schooling Complete Fundamental Ref - Ref Ref Ref Ref Incomplete Fundamental 1·13[0·95;1·34] - 1·35[1·12;1·63] 1·17 [0·97;1·41] 2·32[1·4;3·83] 1·91 [1·12;3·27] Complete High School 0·96[0·81;1·14] - 0·77[0·63;0·94] 0·85 [0·69;1·06] 1·81[1·04;3·14] 2·01 [1·16;3·47] Graduated 0·94[0·74;1·19] - 0·64[0·48;0·86] 0·64 [0·47;0·88] 1·42[0·73;2·77] 1·64 [0·80;3·38] Stable Partner Yes Ref - Ref Ref Ref Ref No 0·94[0·84;1·06] - 1·14[0·97;1·33] 1·28 [1·09;1·52] 1·84[1·24;2·74] 2·16 [1·39;3·36] Income + 4MW Ref - Ref - Ref - Up to 1MW 1·08[0·85;1·36] - 1·4[1·06;1·84] - 1·35[0·75;2·44] - Btw 1-4MW 1·14[0·96;1·36] - 1·36[1·1;1·69] - 1·13[0·71;1·80] - Occupation Regular Job Ref Ref Ref Ref Ref Ref Irregular Job 1·22[1·02;1·45] 1·19 [1·00;1·42] 1·21[0·96;1·54] 1·05 [0·83;1·32] 1·16[0·64;2·08] 1·12 [0·60;2·10] Unemployed 1·42[1·17;1·73] 1·43 [1·17;1·74] 1·36[1·03;1·81] 1·36 [1·03;1·81] 2·50[1·37;4·54] 2·57 [1·37;4·85] NEA 1·72[1·49;1·98] 1·14 [0·99;1·33] 2·42[2·02;2·91] 1·45 [1·18;1·78 ] 3·54[2·28;5·49] 3·52 [2·07;6·00] Any Chronic Disease No Ref Ref Ref Ref Ref Ref Yes 3·06[2·69;3·47] 2·92 [2·54;3·36] 3·42[2·92;4] 2·83 [2·38;3·37] 3·17[2·10;4·77] 3·01 [1·97;4·60] * NEA = Non-economically active. Severe MHD includes bipolar disorder and schizophrenia. Discussion This paper shows the huge disparities on the prevalence of 12-month reported MHD, as well as on the availability of mental health care across the Brazilian regions. It also shows that the availability of psychiatrists and PHC was associated with an increased likelihood of 12-month reports of anxiety and depression, while the availability of CAPS was associated with 12-month reports of severe mental health disorders. Anxiety and depression are common mental health disorders and are the most frequent MHD worldwide. Overall, we found that the 12-month prevalence of anxiety in Brazil (15·5%) was similar to the prevalence in Netherlands (15%) [ 23 ] and higher than in the US (12·7%) [ 24 ], while the 12-month prevalence of depression (7·26%) was lower than found in both countries (9·8% and 10·4%, respectively). Notably, there were huge differences on those prevalences within the country, with the less developed regions presenting the lowest prevalence of both diagnoses. Due to these disparities, it is likely that the overall prevalence found in our study is underestimated, making representative surveys covering the entire Brazilian territory of utmost importance for planning MH care. Less developed regions of the country also presented the lowest rates of psychiatrists, and such shortage of specialized professionals may be a reason for the low MHD diagnoses. Attracting specialized personnel to remote regions represents a challenge worldwide due to many reasons, including resource scarcity, lack of infrastructure and low salaries. OECD suggests policy strategies to improve the geographical distribution of health professionals, such as financial incentives and regulating the choice of practice location [ 25 ], and the Brazilian government had a program to improve the distribution of PHC doctors, “Mais Médicos” (More Doctors Program). However, the impact of the program was undermined exactly because medical doctors were not allocated at the more in need areas [ 26 ]. Additionally, the program did not focus on MH and the impact on diagnosing and treating MHD remains to be evaluated. Another action to improve MHD diagnoses and treatment, proposed by the WHO since 2008, is to train non-specialized health professionals for providing MH care [ 27 ]. We observed that the higher the rate of PHC, the higher the rate of 12-month diagnoses/treatment of anxiety and depression. This result suggests some effectiveness of this action to address anxiety and depression, possibly reflecting that individuals with common MHD more frequently use general health services for treatment [ 28 ]. WHO also proposes that MH care should be integrated with the treatment of other chronic diseases in order to improve access to diagnoses and treatment of MHD [ 27 ]. Our data shows that individuals in treatment for chronic diseases had a higher likelihood of being diagnosed/treated for all the MHD evaluated. It is possible that general health practitioners are becoming more aware of MHD, as well as the patients with multiple comorbidities present more severe – i.e., evident- mental health symptoms. In either case, providing integrated treatment may be key step to improve knowledge and decrease the stigma associated with MHD. Nevertheless, individuals in treatment for chronic diseases tend to be older. This characteristic is a major caveat for addressing and preventing MH among youth - a population with increasingly rates of suicide in Brazil whose MH must be considered a priority [ 29 ]. Severe mental health disorders, i.e. bipolar disorder and schizophrenia are risk factors for premature mortality by suicide, cardiovascular disease, and cancer. These are chronic, highly incapacitating diseases, which require long-term treatment. The overall 12-month prevalence of bipolar (1·03%) and schizophrenia (0·46%) found in our study were similar to those estimated worldwide (0·84% and 0·46%, respectively) [ 30 , 31 ], with variations across the country that were like those found for common mental health disorders. Notably, the availability of CAPS increased the diagnoses of SMHD. The CAPS are relatively new in the SUS, and are one of the Ministry of Health’s strategy to replace long-term psychiatric hospitals [ 32 ]. The distribution of these services is regulated by federal laws, and for this reason, their geographic distribution is more homogeneous than the distribution of psychiatrists. It is noteworthy that our work did not evaluate the quality of mental health care neither its effectiveness in terms of public health. The lack of data, both at individual and population level, make it impossible to evaluate which are the best strategies – and the most cost-effective to be adopted by the public health systems. Such answers are pivotal for the sustainability of the SUS, as well as of other public health systems in the world. Ensuring universal health coverage is one of the objectives of sustainable development goals. Ensuring that mental health is covered in those systems is important to tackle the structural disadvantages arising from the relationships of poor MH and poverty that affect patients and their families [ 6 ]. The present analysis is not free of limitations. The prevalence of reported MHD may be underestimated due to the stigma associated with mental health. Although interviewers were carefully trained and confidentiality was ensured, individuals could be ashamed to provide this information. We also did not interview those with mental disabilities precluding to answer the survey. Secondly, we chose to aggregated municipalities accordingly to the SUS health regions due to the distribution of resources across those regions, but the sample 3rd -BHSU was not designed to specifically represent the population within those regions, thus we only presented prevalence and rates for macro regions. Third, as in any cross-sectional design it is not possible to infer on causality when evaluating the associations. Finally, data was collected before the Covid − 19 pandemic which affected both mental health and health services in Brazil. Despite the limitations, our study shows that the distribution of health services is reflecting the efforts made in the last 20 years to increase PHC and CAPS availability in the country. Such effort may be understood as tentative to enhance equity within the Brazilian Unified Health System. However, diagnostic and treatment rates remain elevated in regions with greater psychiatrist presence and new strategies are still needed to improve access to mental health care. A promising strategy is the in course digital transformation of SUS, in which we would hope that mental health is prioritized. Finally, it is imperative that data on the diagnosis of mental health disorders is obtained through well designed nationwide probability surveys. Such studies are the milestones to optimize MH resource allocation and planning, as well as to monitor the efficacy of programs and strategies implemented to tackle mental health disorders. Declarations Acknowledgements This work was partially supported by grants from the Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ - #E-26/010·002428/2019). The 3rd Brazilian Household Survey on Substance Use Study (3rd BHSU) was partially funded by the Secretaria Nacional de Políticas sobre Drogas do Brasil. The funders had no role in the study design, data collection, data analysis, data interpretation, or writing of this manuscript." Authors contributors RBDB conceived the study, interpreted the results and wrote the first draft. JCM, JCA analyzed the data. RCADO consolidated data and interpreted results. PLDNA supervised data analysis and interpreted results. DPB revised the literature and helped on the first draft. FIB coordinated the BHSU-3. FK revised the manuscript for important intellectual content. All authors revised the manuscript and provided intellectual content. All authors have full access to the data and accept responsibility to submit for publication. Competing interests FK has received personal fees as a speaker/consultant from Janssen (Johnson & Johnson), Daiichi Sankyo, Libbs, Abbott, and Teva Pharmaceutical Industries in the last three years. The other authors declare that they have no competing interests. References Global Health Data Exchange. Global Burden of Disease Study 2019 (GBD 2019) Data Resources ; 2019. [Internet] [cited 2022 May 1]. Available from: https://ghdx.healthdata.org/gbd-2019. Accessed 1 May 2022. Chesney E, Goodwin GM, Fazel S. Risks of all-cause and suicide mortality in mental disorders: a meta-review. World Psychiatry. 2014; 13 : 153–60. doi: 10.1002/wps.20128. Vigo D, Thornicroft G, Atun R. Estimating the true global burden of mental illness. Lancet Psychiatry . 2016;3(2):171–178. doi: 10.1016/S2215-0366(15)00505-2 United Nations Development Programme. 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Braz J Psychiatry . 2013; 35 :142–9. doi: 10.1590/1516-4446-2012-0875 Instituto Nacional de Ciência de Tecnologia para Políticas Públicas do Álcool e outras Drogas (INPAD). II Levantamento Nacional de Álcool e Drogas (LENAD ); 2012. [Internet] [cited 2022 May 01]. Available from: https://lenad.uniad.org.br/wp%20content/uploads/2014/03/Lenad-II-Relat%C3%B3rio.pdf Instituto Brasileiro de Geografia e Estatística (IBGE) - Coordenação de Trabalho e Rendimento. Pesquisa Nacional de Saúde 2019: percepção do estado de saúde, estilos de vida, doenças crônicas e saúde bucal. IBGE; 2020. [Internet] [cited 2022 May 01] Available from: https://biblioteca.ibge.gov.br/visualizacao/livros/liv101764.pdf. Fundação Oswaldo Cruz. III Levantamento Nacional sobre o uso de drogas pela população brasileira . Fiocruz; 2017. [Internet] [cited 2022 May 01]. Available from: https://www.arca.fiocruz.br/handle/icict/34614. de Boni R, Vasconcellos M, Silva P, et al. Reproducibility on science: challenges and advances in Brazilian alcohol surveys. Int J Drug Policy . 2019; 74 : 285–91. doi: 10.1016/j.drugpo.2019.07.029 Bertoni N, Szklo A, de Boni R, et al. Electronic cigarettes and narghile users in Brazil: Do they differ from cigarettes smokers? Addict Behav . 2019; 98 :106007. doi: 10.1016/j.addbeh.2019.05.031 Krawczyk N, Silva PL do N, de Boni RB, et al. Non-medical use of opioid analgesics in contemporary Brazil: Findings from the 2015 Brazilian National Household Survey on Substance Use. Glob Public Health. 2020; 15 :299–306. doi: 10.1080/17441692.2019.1629610 Silva PLDN, Vasconcellos MTLD, Bastos FI, et al. First reproducible nationwide survey on substance use in Brazil: survey design and weighting. JSM 2018; : 2507–14. Ministério da Saúde do Brasil. Cadastro Nacional de Estabelecimentos de Saúde (CNES) . [Internet] [cited 2022 May 01]. Available from: http://www.cnes.datasus.gov.br. World Health Organization. World mental health report: Transforming mental health for all. World Health Organization; 2022. [Internet] [cited 2022 May 01]. Available from: https://www.who.int/teams/mental-health-and-substance-use/world-mental-health-report. Ministério da Saúde do Brasil. Centro de Atenção Psicossocial – CAPS . [Internet] [cited 2022 May 01]. Available from: https://www.gov.br/saude/pt-br/acesso-a-informacao/acoes-e-programas/caps. Instituto Brasileiro de Geografia e Estatística (IBGE). Estimates of resident population for Municipalities and Federation Units . IBGE; 2015. [Internet] [cited 2022 May 01]. Available from: https://www.ibge.gov.br/en/statistics/social/population/18448-estimates-of-resident-population-for-municipalities-and-federation-units.html?edicao=18450&t=resultados Instituto Brasileiro de Geografia e Estatística (IBGE). Censo Demográfico 2010. IBGE; 2010. [Internet] [cited 2022 May 01]. Available from: http://www.sidra.ibge.gov.br/bda/orcfam/default.asp. Lumley, T. Package ‘survey’ - Analysis of complex survey samples . 2018. [Internet] [cited 2022 May 01]. Available from: http://r-survey.r-forge.r-project.org/survey/ Ellis, GF, Lumley, T. Srvyr-Like Syntax for summary statistics of survey data . 2018. [Internet] [cited 2022 May 01]. Available from: https://cran.r-project.org/web/packages/srvyr/srvyr.pdf. Ten Have M, Tuithof M, van Dorsselaer S, Schouten F, Luik AI, de Graaf R. Prevalence and trends of common mental disorders from 2007‐2009 to 2019‐2022: results from the Netherlands Mental Health Survey and Incidence Studies (NEMESIS), including comparison of prevalence rates before vs. during the COVID‐19 pandemic. World Psychiatry . 2023; 22 :275–85. doi: 10.1002/wps.21087 Olfson M, Blanco C, Wall MM, Liu SM, Grant BF. Treatment of Common Mental Disorders in the United States: Results from the national epidemiologic survey on alcohol and related conditions-III. J Clin Psychiatry . 2019; 80 : 1–10. doi: 10.4088/JCP.18m12532 OECD. Geographic distribuition of doctors . In: Health at a glance 2023: OECD indicators. 2024. [Internet] [cited 2022 Apr 1]. Available from: https://www.oecd-ilibrary.org/sites/23104a05-en/index.html?itemId=/content/component/23104a05-en. Hone T, Powell-Jackson T, Santos LMP, et al. Impact of the Programa Mais médicos (More Doctors Programme) on primary care doctor supply and amenable mortality: quasi-experimental study of 5565 Brazilian municipalities. BMC Health Serv Res . 2020; 20 : 873. doi: 10.1186/s12913-020-05716-2 World Health Organization. mhGAP Intervention Guide for Mental, Neurological and Substance Use Disorders in Non-Specialized Health Settings: Mental Health Gap Action Programme (mhGAP), version 2.0. World Health Organization. 2016. [Internet] [cited 2022 Apr 15]. Available from: https://iris.who.int/handle/10665/250239 Moitra M, Santomauro D, Collins PY, et al. The global gap in treatment coverage for major depressive disorder in 84 countries from 2000–2019: A systematic review and Bayesian meta-regression analysis. PLoS Med . 2022; 19 :e1003901. doi: 10.1371/journal.pmed.1003901 Oliveira Alves FJ, Fialho E, Paiva de Araújo JA, et al. The rising trends of self-harm in Brazil: an ecological analysis of notifications, hospitalisations, and mortality between 2011 and 2022. Lancet Reg Health Am . 2024; 31 :100691. doi: 10.1016/j.lana.2024.100691 Clemente AS, Diniz BS, Nicolato R, et al. Bipolar disorder prevalence: a systematic review and meta-analysis of the literature. Braz J Psychiatry . 2015; 37 (2):155–61. doi: 10.1590/1516-4446-2012-1693 Saha S, Chant D, Welham J, McGrath J. A Systematic Review of the Prevalence of Schizophrenia. PLoS Med . 2005; 2 :e141. doi: 10.1371/journal.pmed.0020141 Rocha HA da, Reis IA, Santos MA da C, Melo APS, Cherchiglia ML. Psychiatric hospitalizations by the Unified Health System in Brazil between 2000 and 2014. Rev Saude Publica . 2021; 55 :14. doi: 10.11606/s1518-8787.2021055002155 Additional Declarations No competing interests reported. Supplementary Files DeBoniMHLNUD090524SocialSupp.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4395839","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":304521665,"identity":"76bd180f-c3b4-427b-bbbe-1bada02746f6","order_by":0,"name":"Raquel B. 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Brazil, 2015.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4395839/v1/5c9f687098a3926f610eca0c.png"},{"id":57297312,"identity":"4f00ffe3-3b0f-45b6-95ed-2da530c225cc","added_by":"auto","created_at":"2024-05-28 20:10:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3560888,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAvailability of mental health capacity by Brazilian Health Regions: A) Rate of psychiatrists per 100,000 inhabitants, and B) rate of CAPS per 100,00 inhabitants. CNES and IBGE, 2015\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig22.png","url":"https://assets-eu.researchsquare.com/files/rs-4395839/v1/64f82c16a7770fe209571f04.png"},{"id":59722130,"identity":"8a964030-dc09-4c24-aaa0-22929b2d7cb8","added_by":"auto","created_at":"2024-07-05 09:44:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5577282,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4395839/v1/db0b52fd-a02e-4fa3-a7a7-126b01e336eb.pdf"},{"id":57297310,"identity":"93e8990c-7f13-4e85-bea6-6d11ed6d19ba","added_by":"auto","created_at":"2024-05-28 20:10:54","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":30082,"visible":true,"origin":"","legend":"","description":"","filename":"DeBoniMHLNUD090524SocialSupp.docx","url":"https://assets-eu.researchsquare.com/files/rs-4395839/v1/dc9be6d9bdaa757dd6bfba90.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Availability of mental health care and mental health disorders in Brazil","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBefore the COVID-19 pandemic it was estimated that 970\u0026nbsp;million people (14.6%) lived with a diagnosable mental health disorder (MHD) worldwide, not including substance use disorders and dementia [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Those individuals have been disproportionally affected by premature mortality due to preventable diseases, and severe MHD \u0026ndash; such as bipolar disorder (BD) and schizophrenia \u0026ndash; may decrease up to 20 years of their life expectancy [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. MHD are among the leading causes of years lived with disability (YLDs), accounting for 15\u0026middot;6% of YLDs globally [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Beyond individual-level health outcomes, MHD may play a role in reinforcing social inequalities and poverty. This is of utmost importance in a country with continental dimensions such as Brazil which presents large social inequalities. For instance, the Brazilian GINI index in 2020 was estimated at 48\u0026middot;6%, and the Human Developing Index was 0\u0026middot;754 (decreasing to 0\u0026middot;576 when adjusted for inequalities) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. It is possible that the less developed regions of the country present inadequate mental health services capacity, impacting both the diagnosis and treatment of MHD and further contributing to increase poverty and inequalities [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere are major gaps to tackle the MHD burden, including information and service gaps. Information gap refers to the lack of data on MHD and insufficient research. In Brazil, the information gap on the MHD prevalence across the general population is huge. Most of the Brazilian data are based on information provided by health services, excluding populations without access to health care. Over the last 20 years, the only household probability sample surveys assessing psychiatric diagnosis (as per DSM or ICD criteria) were restricted to municipalities or regional areas. The most robust was the \u003cem\u003eS\u0026atilde;o Paulo Megacity Mental Health Survey (SPMHS)\u003c/em\u003e conducted in 2005/7. In this survey, 5,037 adults were interviewed in 39 cities in the S\u0026atilde;o Paulo Metropolitan Area \u0026ndash; which has the largest population density in the country. SPMHS identified a 12-month prevalence of 19\u0026middot;9% (standard error - SE 0\u0026middot;8) for anxiety disorders, 9\u0026middot;4% (SE 0\u0026middot;6) for major depression, and 1\u0026middot;5% (SE 0\u0026middot;2) for bipolar disorder (BD) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe Brazilian household surveys covering the entire country were not designed to evaluate the diagnosis of MHD. All of them relied on screening instruments or the report of previous MHD diagnosis /treatment. In addition, few MHD were investigated, depression being the most common. For instance, in two small surveys, conducted in 2006 and 2012 (n1\u0026thinsp;=\u0026thinsp;3007 and n2\u0026thinsp;=\u0026thinsp;4607), the prevalence of positive screening for depression varied from 9\u0026middot;4% (last 7 days) to 28\u0026middot;3% (lifetime), while for anxiety\u0026rsquo;s it was 17\u0026middot;1% [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In the latest Brazilian National Health Survey (PNS 2019), the prevalence of a positive screening for depression was 10\u0026middot;2% (95%CI: 9\u0026middot;9\u0026ndash;10\u0026middot;6) while the prevalence of other mental disorders was 1\u0026middot;10% (95% CI: 1\u0026middot;00\u0026ndash;1\u0026middot;30) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, such figures may be overestimated due to the use of screening instruments or underestimated due to evaluation through questions relying on the previous diagnosis (i.e., only individuals with access to care were aware of their MHD diagnoses).\u003c/p\u003e \u003cp\u003eService gap refers to the coverage, range, and quality of the treatments provided. Service gap may reflect either unavailable services, capacity, lack of feasible geographical accessibility (distance or cost) and affordability, or the lack of demand (due to the stigma that stops people from seeking help, for instance). Brazil has one of the largest public health system in the world, the Unified Health System (SUS). The SUS is supposed to provide integral health care, including mental health care, free of charge, to the Brazilian population. Thus, resource allocation is a major challenge in such large system where equity and sustainability should coexist.\u003c/p\u003e \u003cp\u003eDespite the existence of governmental data on MH services, such as the number of mental health workers and the number of psychiatric beds, we were not able to find studies regarding the availability of these services to meet the MH demand. Information on the availability of MHD services should be considered a major priority given the consequences of the mental health gap. Additionally, considering the vast Brazilian territory, and socioeconomic and cultural differences between its regions, it is relevant to understand the distribution of diagnosis and services across the regions. Thus, this manuscript aims to: 1) describe the prevalence of 12-month reported MHD across the Brazilian regions; 2) describe the availability MH care across the Brazilian regions; 3) evaluate the association between the availability of care with reported MHD. We hypothesize that the higher the availability of MH care the higher the prevalence of 12-month reported MHD.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis is a cross-sectional analysis of data collected in the 3rd Brazilian Household Survey on Substance Use (3rd BHSU), and data obtained in the National Registry of Health Services (CNES). The 3rd BHSU was a nationwide probability sample survey conducted in 2015, detailed elsewhere [\u003cspan additionalcitationids=\"CR12 CR13 CR14\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], and approved by the institutional review board of the Escola Polit\u0026eacute;cnica Joaquim Ven\u0026acirc;ncio-Fiocruz (CAAE # 35283814.4.0000.5241). The CNES is the Ministry of Health official information system for registering health establishments in the country, regardless of whether they are part of the Unified Health System (SUS) or not [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. It includes data on the installed capacity and health care workforce.\u003c/p\u003e \u003cp\u003e We followed the STROBE guideline for reporting the present results.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eThe 3rd BHSU interviewed 16,273 individuals, aged 12\u0026ndash;65 years old from the entire country. Native individuals living in indigenous villages, inmates, and individuals with physical or mental disabilities precluding to answer the interview were not eligible. In the present analysis, we considered only individuals 20\u0026ndash;65 years old. This age bracket was selected to maintain comparability with the latest World Mental Report [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eOutcomes\u003c/h2\u003e \u003cp\u003eWe evaluated three outcomes: 12-month diagnosis of anxiety, depression, and severe mental health disorder- SMHD (which includes bipolar disorder (BD), and schizophrenia). The outcomes were assessed by the questions \u0026ldquo;In the last 12 months, have you been diagnosed by a medical doctor or health professional, or received treatment for\u0026hellip; (anxiety, depression, BD, schizophrenia)?\u0026rdquo;. Possible answers were: No, Yes (received diagnosis), Yes (received treatment), Don\u0026rsquo;t know /don\u0026rsquo;t want to answer. Both \u0026ldquo;yes\u0026rdquo; options were categorized as a positive diagnosis and all other options were categorized as negative diagnosis (Supplementary Table\u0026nbsp;1).\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eMain contextual variables of interest\u003c/h2\u003e \u003cp\u003eThe main contextual variables of interest were the indicators of availability of mental health care services: psychiatrists per 100,000 inhabitants, Centers for Psychosocial Attention (CAPS) per 100,000 inhabitants and Primary Health Care services (PHC) per 100,000 inhabitants. The CAPS are open community mental health care services provided by the SUS. They operate at various levels (including outpatient care and rehabilitation), comprise multi-professional teams and may be specialized to address substance use and/or psychiatric emergencies [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. These services are not homogeneous, with important diversity in terms of provided services, availability of human resources, size and service capacity. The PHC are the SUS facilities that provide primary care. They are organized by geographic regions and are considered the main entry for MH attention. PHC may include MH personnel, but it is expected that non-specialists are also able to diagnose and treat common mental health disorders \u0026ndash; and refer severe MH to specialized treatment (such as CAPS).\u003c/p\u003e \u003cp\u003eThose indicators were calculated using CNES database and the Brazilian Institute for Geography and Statistics (IBGE) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] estimates of the Brazilian population, both for 2015. Initially, we calculate the indicators aggregated by the five Brazilian macro regions (North, Northeast, Center West, Southeast and South). Afterwards, we calculated them by the 450 Brazilian Health Regions. The Health Regions are formed by contiguous municipalities that share cultural, economic and social identities, communication networks and transport infrastructure. The purpose of these regions is to integrate the organization, planning and execution of health actions and services to guarantee access to public health care.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eCovariates at the Individual level\u003c/h2\u003e \u003cp\u003eIndividual level covariates were obtained from the 3rd BHSU. Sociodemographic variables were sex assigned at birth (male, female), age (20\u0026ndash;24 years, 25\u0026ndash;49 years, and 50\u0026ndash;65 years), race/ethnicity (which were inquired as per the National Census [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and categorized as white, black/mixed, other), schooling (No education or incomplete fundamental; Complete fundamental or incomplete high school; Complete high school or incomplete graduation; Graduation or more), income (up to 1 Brazilian minimum wage- MW; 1\u0026ndash;4 MW; and, \u0026gt;4MW) (Brazilian minimum wage corresponded approximately to 2015 US\u003cspan\u003e$\u003c/span\u003e 242 dollar/month), occupation (regular job, irregular job, unemployed, and non-economically active), religion (Catholic; Protestant; Other; None), and stable partner (yes, no). Chronic health diseases were assessed by the questions: \u0026ldquo;In the last 12 months, have you been diagnosed by a medical doctor or health professional, or received treatment for (diabetes, heart disease, hypertension, asthma, HIV/AIDS, cancer, tuberculosis, and renal disease)\u0026rdquo;.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStudy size\u003c/h2\u003e \u003cp\u003eThe 3rd BHSU used a stratified four-stage clustered probability sample. The total sample size was calculated to estimate a minimum prevalence of 2% with a relative error of less than 30%, confidence level of 95% and design effect of 1\u0026middot;5. Power allocation (with power\u0026thinsp;=\u0026thinsp;3/4) was used to distribute the total sample size among the selected strata, using population as the size measure. After the allocation, the sample size was estimated at 16,400 individuals [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical methods\u003c/h2\u003e \u003cp\u003eWe estimated the prevalence and corresponding 95% confidence intervals (95% CI) of the outcomes, as well as the rates of the main contextual variables, for the five Brazilian macro regions and the 450 Health Regions.\u003c/p\u003e \u003cp\u003eLogistic regressions were performed to assess the bivariate association of the independent variables and each one of the outcomes. Multivariate analysis used the backward procedure to reach the most parsimonious model for each outcome. All statistical analyses were performed in R v\u0026middot;4\u0026middot;0\u0026middot;5 software, utilizing the \u0026lsquo;survey\u0026rsquo; and \u0026lsquo;srvyr\u0026rsquo; libraries and their dependencies, considering calibrated sample weights, design effect and weight calibration [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eRole of the funding source\u003c/h2\u003e \u003cp\u003eThe funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of this manuscript. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe analytical sample included 14,987 individuals which represent 126\u0026nbsp;million Brazilians aged 20 to 65 years. Most of the sample were female (53\u0026middot;00%), aged 25\u0026ndash;49 years (59\u0026middot;.95%), earning 1 to 4 minimum wages/month (66\u0026middot;98%), and reporting a stable partner (71\u0026middot;29%)- as depicted in the Supplementary Table\u0026nbsp;2.\u003c/p\u003e \u003cp\u003eApproximately 18% (95% CI 16\u0026middot;76\u0026thinsp;\u0026minus;\u0026thinsp;19\u0026middot;02) of the population received some MHD diagnosis or treatment in the previous 12 months. The overall prevalence of anxiety, depression, BD, and schizophrenia were 15\u0026middot;51% (95%CI 14\u0026middot;44\u0026thinsp;\u0026minus;\u0026thinsp;16\u0026middot;59), 7\u0026middot;26% (95%CI 6\u0026middot;64\u0026ndash;7\u0026middot;88), 1\u0026middot;03% (95%CI 0\u0026middot;79\u0026thinsp;\u0026minus;\u0026thinsp;1\u0026middot;27), and 0\u0026middot;41% (95%CI 0\u0026middot;28\u0026thinsp;\u0026minus;\u0026thinsp;0\u0026middot;55). The prevalence of 12-month diagnosis /treatment for MHD by the five Brazilian macro regions are depicted in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Prevalences were lower at North and Northeast compared to South and Southeast.\u003c/p\u003e \u003cp\u003eThe overall rate of psychiatrists in the country was 7.01/100,000 inhabitants, the rate of CAPS was 1\u0026middot;75/100,000 inhabitants, and the rate of PHC was 33\u0026middot;3/100,000 inhabitants. The rate of psychiatrists was higher in the Southeast and the South, while the rates of CAPS and PHC were higher in the Northeast - Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003ePrevalence of 12-month diagnosis or treatment of selected mental health disorders and availability of MH capacity by the five Brazilian macroregions. 3rd BHSU, CNES and IBGE. Brazil, 2015.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNorth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNortheast\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCenter West\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSoutheast\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSouth\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevalence of MHD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u0026middot;75[4\u0026middot;79;10\u0026middot;72]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u0026middot;99[11\u0026middot;06;14\u0026middot;92]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u0026middot;87[10\u0026middot;77;18\u0026middot;97]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17\u0026middot;58[15\u0026middot;63;19\u0026middot;52]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18\u0026middot;24[16\u0026middot;35;20\u0026middot;12]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u0026middot;58[2\u0026middot;02;5\u0026middot;13]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u0026middot;82[3\u0026middot;90;5\u0026middot;74]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u0026middot;07[4\u0026middot;35;7\u0026middot;79]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u0026middot;98[6\u0026middot;97;8\u0026middot;98]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11\u0026middot;85[9\u0026middot;67;14\u0026middot;02]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBipolar disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026middot;25[0\u0026middot;07;0\u0026middot;44]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026middot;62[0\u0026middot;13;1\u0026middot;11]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026middot;19[0\u0026middot;48;1\u0026middot;9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u0026middot;31[0\u0026middot;9;1\u0026middot;73]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u0026middot;22[0\u0026middot;74;1\u0026middot;7]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchizophrenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026middot;01[0;0\u0026middot;04]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026middot;16[0\u0026middot;02;0\u0026middot;30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026middot;31[0\u0026middot;04;0\u0026middot;58]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026middot;61[0\u0026middot;33;0\u0026middot;89]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026middot;53[0\u0026middot;24;0\u0026middot;82]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny MHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u0026middot;55[6\u0026middot;26;12\u0026middot;84]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u0026middot;77[12\u0026middot;65;16\u0026middot;88]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17\u0026middot;68[13\u0026middot;34;22\u0026middot;03]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19\u0026middot;84[17\u0026middot;87;21\u0026middot;80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22\u0026middot;04[19\u0026middot;72;24\u0026middot;36]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAvailability of MH capacity\u003c/b\u003e\u003c/p\u003e \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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePsychiatrists/100\u0026middot;000 inh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u0026middot;31[1\u0026middot;68;2\u0026middot;93]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u0026middot;86[5\u0026middot;40;6\u0026middot;32]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u0026middot;55[4\u0026middot;46;6\u0026middot;65]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11\u0026middot;47[10\u0026middot;67;12\u0026middot;28]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12\u0026middot;26[11\u0026middot;84;12\u0026middot;68]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAPS/100\u0026middot;000 inh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026middot;52[1\u0026middot;21;1\u0026middot;82]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u0026middot;72[2\u0026middot;42;3\u0026middot;01]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026middot;68[1\u0026middot;43;1\u0026middot;92]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u0026middot;80[1\u0026middot;66;1\u0026middot;94]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u0026middot;29[2\u0026middot;17;2\u0026middot;41]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHC/100\u0026middot;000 inh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47\u0026middot;66[41\u0026middot;86;53\u0026middot;47]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52\u0026middot;25[47\u0026middot;71;56\u0026middot;79]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35\u0026middot;16[30\u0026middot;55;39\u0026middot;77]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26\u0026middot;12[23\u0026middot;60;28\u0026middot;65]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40\u0026middot;26[36\u0026middot;33;44\u0026middot;19]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e3rd BHSU\u0026thinsp;=\u0026thinsp;3rd Brazilian Household Survey on Substance Use. CNES\u0026thinsp;=\u0026thinsp;Brazilian National Registry of Health Services. MHD\u0026thinsp;=\u0026thinsp;Mental Health Disorder. MH\u0026thinsp;=\u0026thinsp;Mental Health. Inh\u0026thinsp;=\u0026thinsp;inhabitants. CAPS\u0026thinsp;=\u0026thinsp;Centers for Psychosocial Attention. PHC\u0026thinsp;=\u0026thinsp;Primary Health Care\u003c/p\u003e \u003cp\u003e \u003cem\u003eFigure 1\u003c/em\u003e shows the prevalence of any MHD in the 450 Brazilian Health Regions, and \u003cem\u003eFig.\u0026nbsp;2\u003c/em\u003e shows the rates of psychiatrists and CAPS in those regions. The highest prevalence of any MHD was found in the Regions located at the South, Southeast, Center West and far north of the country. The highest rate of psychiatrists was found in the Regions located at the South -Southeast, and lowest in the North. The distribution of CAPS is more homogenous across the country, although many health regions from the North and Center West still have less than 2 CAPS/100,000 inhabitants.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 1\u003c/b\u003e: \u003cb\u003ePrevalence of 12-month diagnoses/ treatment for any mental health disorder by Brazilian Health Regions. 3rd BHSU. Brazil, 2015.\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 2\u003c/b\u003e: \u003cb\u003eAvailability of mental health capacity by Brazilian Health Regions: A) Rate of psychiatrists per 100,000 inhabitants, and B) rate of CAPS per 100,00 inhabitants. CNES and IBGE, 2015\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the associations between 12-month diagnosis/treatment of MHD and the availability of mental health services. After controlling by contextual and individual covariates, 12-month diagnosis/treatment of anxiety and depression was associated with higher rates of psychiatrists and PHC, but not with CAPS rates.\u003c/p\u003e \u003cp\u003eOn the other hand, 12-month diagnosis/treatment of SMHD was associated with higher rates of CAPS, but not with psychiatrists or PHC.\u003c/p\u003e \u003cp\u003eThe association with demographic variables varies across the diagnoses, but all the diagnosis were associated with a concomitant 12-month diagnosis/treatment for some chronic disease.\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\u003e\u003cb\u003eAssociations between the availability of mental health care and 12-month diagnosis anxiety, depression and severe mental health disorders evaluated by logistic regression models. BHSU-3 and CNES. Brazil, 2015.\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eMental Health Disorders\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003eSevere MHD\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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR [IC95%]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eaOR [IC95%]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eOR [IC95%]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eaOR [IC95%]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eOR [IC95%]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eaOR [IC95%]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMain Contextual Variables\u003c/b\u003e\u003c/p\u003e \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\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePsychiatrist Rate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[0\u0026middot;0,5\u0026middot;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[5\u0026middot;0,10\u0026middot;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026middot;32 [ 1\u0026middot;02;1\u0026middot;72]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1\u0026middot;37 [1\u0026middot;05;1\u0026middot;79]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1\u0026middot;62 [1\u0026middot;22;2\u0026middot;15]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1\u0026middot;75 [1\u0026middot;32;2\u0026middot;32]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1\u0026middot;87 [1\u0026middot;07;3\u0026middot;28]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10\u0026middot;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026middot;50 [1\u0026middot;15;1\u0026middot;97]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1\u0026middot;54 [1\u0026middot;15;2\u0026middot;05]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1\u0026middot;75 [1\u0026middot;32;2\u0026middot;34]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1\u0026middot;87 [1\u0026middot;39;2\u0026middot;51]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1\u0026middot;63 [0\u0026middot;89;33\u0026middot;00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCAPS Rate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[0\u0026middot;0,2\u0026middot;0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;2\u0026middot;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026middot;23 [1\u0026middot;03;1\u0026middot;48]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1\u0026middot;23 [1\u0026middot;00;1\u0026middot;51]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1\u0026middot;51 [1\u0026middot;00;2\u0026middot;27]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e1\u0026middot;80 [1\u0026middot;16;2\u0026middot;82]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePHC Rate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[0\u0026middot;0,20\u0026middot;0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;20\u0026middot;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026middot;26 [1\u0026middot;08;1\u0026middot;47]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1\u0026middot;39 [1\u0026middot;17;1\u0026middot;65]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1\u0026middot;31 [1\u0026middot;09;1\u0026middot;57]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1\u0026middot;45 [1\u0026middot;2;1\u0026middot;76]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1\u0026middot;30 [0\u0026middot;89;1\u0026middot;92]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCovariates at individual level\u003c/b\u003e\u003c/p\u003e \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\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026middot;52[1\u0026middot;23;1\u0026middot;89]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1\u0026middot;34 [1\u0026middot;08;1\u0026middot;67]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e3\u0026middot;04 [2\u0026middot;05;4\u0026middot;50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e2\u0026middot;84 [1\u0026middot;89;4\u0026middot;27]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1\u0026middot;97 [0\u0026middot;83;4\u0026middot;67]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u0026middot;46 [0\u0026middot;89;6\u0026middot;84]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u0026ndash;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u0026middot;01[1\u0026middot;60;2\u0026middot;52]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026middot;24 [0\u0026middot;97;1\u0026middot;58]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e4\u0026middot;87 [3\u0026middot;33;7\u0026middot;11]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e2\u0026middot;76 [1\u0026middot;85;4\u0026middot;12]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e2\u0026middot;01 [0\u0026middot;89;4\u0026middot;54]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u0026middot;18 [0\u0026middot;43;3\u0026middot;18]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex assigned at birth\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026middot;41[0\u0026middot;36;0\u0026middot;46]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0\u0026middot;44 [0\u0026middot;39;0\u0026middot;5]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0\u0026middot;37[0\u0026middot;31;0\u0026middot;44]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u0026middot;44 [0\u0026middot;37;0\u0026middot;54]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0\u0026middot;66[0\u0026middot;41;1\u0026middot;07]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u0026middot;05 [0\u0026middot;63;1\u0026middot;74]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace/Ethnicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlack/Mixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026middot;78[0\u0026middot;69;0\u0026middot;88]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0\u0026middot;82 [0\u0026middot;72;0\u0026middot;92]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0\u0026middot;75[0\u0026middot;64;0\u0026middot;87]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0\u0026middot;76 [0\u0026middot;66;0\u0026middot;88]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1\u0026middot;03[0\u0026middot;71;1\u0026middot;49]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026middot;67[0\u0026middot;38;1\u0026middot;17]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026middot;66 [0\u0026middot;36;1\u0026middot;2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0\u0026middot;79[0\u0026middot;32;1\u0026middot;95]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u0026middot;87 [0\u0026middot;32;2\u0026middot;32]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0\u0026middot;24[0\u0026middot;03;1\u0026middot;77]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReligion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCatholic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026middot;57[0\u0026middot;46;0\u0026middot;72]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0\u0026middot;74 [0\u0026middot;59;0\u0026middot;94]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0\u0026middot;8[0\u0026middot;58;1\u0026middot;11]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0\u0026middot;65[0\u0026middot;31;1\u0026middot;36]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u0026middot;82 [0\u0026middot;39;1\u0026middot;73]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChristian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026middot;96[0\u0026middot;84;1\u0026middot;09]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026middot;92 [0\u0026middot;81;1\u0026middot;06]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1\u0026middot;12[0\u0026middot;95;1\u0026middot;31]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1\u0026middot;14[0\u0026middot;78;1\u0026middot;68]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u0026middot;20 [0\u0026middot;78;1\u0026middot;82]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026middot;32[1\u0026middot;04;1\u0026middot;67]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026middot;20 [0\u0026middot;94;1\u0026middot;54]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1\u0026middot;33[0\u0026middot;98;1\u0026middot;79]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e2\u0026middot;49[1\u0026middot;38;4\u0026middot;5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e3\u0026middot;12 [1\u0026middot;73;5\u0026middot;63]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSchooling\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComplete Fundamental\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncomplete Fundamental\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026middot;13[0\u0026middot;95;1\u0026middot;34]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1\u0026middot;35[1\u0026middot;12;1\u0026middot;63]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u0026middot;17 [0\u0026middot;97;1\u0026middot;41]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e2\u0026middot;32[1\u0026middot;4;3\u0026middot;83]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e1\u0026middot;91 [1\u0026middot;12;3\u0026middot;27]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComplete High School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026middot;96[0\u0026middot;81;1\u0026middot;14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0\u0026middot;77[0\u0026middot;63;0\u0026middot;94]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u0026middot;85 [0\u0026middot;69;1\u0026middot;06]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1\u0026middot;81[1\u0026middot;04;3\u0026middot;14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e2\u0026middot;01 [1\u0026middot;16;3\u0026middot;47]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGraduated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026middot;94[0\u0026middot;74;1\u0026middot;19]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0\u0026middot;64[0\u0026middot;48;0\u0026middot;86]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0\u0026middot;64 [0\u0026middot;47;0\u0026middot;88]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1\u0026middot;42[0\u0026middot;73;2\u0026middot;77]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u0026middot;64 [0\u0026middot;80;3\u0026middot;38]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStable Partner\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026middot;94[0\u0026middot;84;1\u0026middot;06]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1\u0026middot;14[0\u0026middot;97;1\u0026middot;33]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1\u0026middot;28 [1\u0026middot;09;1\u0026middot;52]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1\u0026middot;84[1\u0026middot;24;2\u0026middot;74]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e2\u0026middot;16 [1\u0026middot;39;3\u0026middot;36]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIncome\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;4MW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUp to 1MW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026middot;08[0\u0026middot;85;1\u0026middot;36]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1\u0026middot;4[1\u0026middot;06;1\u0026middot;84]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1\u0026middot;35[0\u0026middot;75;2\u0026middot;44]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBtw 1-4MW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026middot;14[0\u0026middot;96;1\u0026middot;36]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1\u0026middot;36[1\u0026middot;1;1\u0026middot;69]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1\u0026middot;13[0\u0026middot;71;1\u0026middot;80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOccupation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegular Job\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIrregular Job\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026middot;22[1\u0026middot;02;1\u0026middot;45]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1\u0026middot;19 [1\u0026middot;00;1\u0026middot;42]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1\u0026middot;21[0\u0026middot;96;1\u0026middot;54]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u0026middot;05 [0\u0026middot;83;1\u0026middot;32]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1\u0026middot;16[0\u0026middot;64;2\u0026middot;08]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u0026middot;12 [0\u0026middot;60;2\u0026middot;10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026middot;42[1\u0026middot;17;1\u0026middot;73]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1\u0026middot;43 [1\u0026middot;17;1\u0026middot;74]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1\u0026middot;36[1\u0026middot;03;1\u0026middot;81]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1\u0026middot;36 [1\u0026middot;03;1\u0026middot;81]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e2\u0026middot;50[1\u0026middot;37;4\u0026middot;54]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e2\u0026middot;57 [1\u0026middot;37;4\u0026middot;85]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026middot;72[1\u0026middot;49;1\u0026middot;98]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026middot;14 [0\u0026middot;99;1\u0026middot;33]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e2\u0026middot;42[2\u0026middot;02;2\u0026middot;91]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1\u0026middot;45 [1\u0026middot;18;1\u0026middot;78\u003c/b\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e3\u0026middot;54[2\u0026middot;28;5\u0026middot;49]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e3\u0026middot;52 [2\u0026middot;07;6\u0026middot;00]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eAny Chronic Disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u0026middot;06[2\u0026middot;69;3\u0026middot;47]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2\u0026middot;92 [2\u0026middot;54;3\u0026middot;36]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e3\u0026middot;42[2\u0026middot;92;4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e2\u0026middot;83 [2\u0026middot;38;3\u0026middot;37]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e3\u0026middot;17[2\u0026middot;10;4\u0026middot;77]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e3\u0026middot;01 [1\u0026middot;97;4\u0026middot;60]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e* NEA\u0026thinsp;=\u0026thinsp;Non-economically active. Severe MHD includes bipolar disorder and schizophrenia.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis paper shows the huge disparities on the prevalence of 12-month reported MHD, as well as on the availability of mental health care across the Brazilian regions. It also shows that the availability of psychiatrists and PHC was associated with an increased likelihood of 12-month reports of anxiety and depression, while the availability of CAPS was associated with 12-month reports of severe mental health disorders.\u003c/p\u003e \u003cp\u003eAnxiety and depression are common mental health disorders and are the most frequent MHD worldwide. Overall, we found that the 12-month prevalence of anxiety in Brazil (15\u0026middot;5%) was similar to the prevalence in Netherlands (15%) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and higher than in the US (12\u0026middot;7%) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], while the 12-month prevalence of depression (7\u0026middot;26%) was lower than found in both countries (9\u0026middot;8% and 10\u0026middot;4%, respectively). Notably, there were huge differences on those prevalences within the country, with the less developed regions presenting the lowest prevalence of both diagnoses. Due to these disparities, it is likely that the overall prevalence found in our study is underestimated, making representative surveys covering the entire Brazilian territory of utmost importance for planning MH care.\u003c/p\u003e \u003cp\u003eLess developed regions of the country also presented the lowest rates of psychiatrists, and such shortage of specialized professionals may be a reason for the low MHD diagnoses. Attracting specialized personnel to remote regions represents a challenge worldwide due to many reasons, including resource scarcity, lack of infrastructure and low salaries. OECD suggests policy strategies to improve the geographical distribution of health professionals, such as financial incentives and regulating the choice of practice location [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], and the Brazilian government had a program to improve the distribution of PHC doctors, \u0026ldquo;Mais M\u0026eacute;dicos\u0026rdquo; (More Doctors Program). However, the impact of the program was undermined exactly because medical doctors were not allocated at the more in need areas [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Additionally, the program did not focus on MH and the impact on diagnosing and treating MHD remains to be evaluated.\u003c/p\u003e \u003cp\u003eAnother action to improve MHD diagnoses and treatment, proposed by the WHO since 2008, is to train non-specialized health professionals for providing MH care [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. We observed that the higher the rate of PHC, the higher the rate of 12-month diagnoses/treatment of anxiety and depression. This result suggests some effectiveness of this action to address anxiety and depression, possibly reflecting that individuals with common MHD more frequently use general health services for treatment [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWHO also proposes that MH care should be integrated with the treatment of other chronic diseases in order to improve access to diagnoses and treatment of MHD [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Our data shows that individuals in treatment for chronic diseases had a higher likelihood of being diagnosed/treated for all the MHD evaluated. It is possible that general health practitioners are becoming more aware of MHD, as well as the patients with multiple comorbidities present more severe \u0026ndash; i.e., evident- mental health symptoms. In either case, providing integrated treatment may be key step to improve knowledge and decrease the stigma associated with MHD. Nevertheless, individuals in treatment for chronic diseases tend to be older. This characteristic is a major caveat for addressing and preventing MH among youth - a population with increasingly rates of suicide in Brazil whose MH must be considered a priority [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSevere mental health disorders, i.e. bipolar disorder and schizophrenia are risk factors for premature mortality by suicide, cardiovascular disease, and cancer. These are chronic, highly incapacitating diseases, which require long-term treatment. The overall 12-month prevalence of bipolar (1\u0026middot;03%) and schizophrenia (0\u0026middot;46%) found in our study were similar to those estimated worldwide (0\u0026middot;84% and 0\u0026middot;46%, respectively) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], with variations across the country that were like those found for common mental health disorders. Notably, the availability of CAPS increased the diagnoses of SMHD. The CAPS are relatively new in the SUS, and are one of the Ministry of Health\u0026rsquo;s strategy to replace long-term psychiatric hospitals [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The distribution of these services is regulated by federal laws, and for this reason, their geographic distribution is more homogeneous than the distribution of psychiatrists.\u003c/p\u003e \u003cp\u003eIt is noteworthy that our work did not evaluate the quality of mental health care neither its effectiveness in terms of public health. The lack of data, both at individual and population level, make it impossible to evaluate which are the best strategies \u0026ndash; and the most cost-effective to be adopted by the public health systems. Such answers are pivotal for the sustainability of the SUS, as well as of other public health systems in the world. Ensuring universal health coverage is one of the objectives of sustainable development goals. Ensuring that mental health is covered in those systems is important to tackle the structural disadvantages arising from the relationships of poor\u003c/p\u003e \u003cp\u003eMH and poverty that affect patients and their families [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe present analysis is not free of limitations. The prevalence of reported MHD may be underestimated due to the stigma associated with mental health. Although interviewers were carefully trained and confidentiality was ensured, individuals could be ashamed to provide this information. We also did not interview those with mental disabilities precluding to answer the survey. Secondly, we chose to aggregated municipalities accordingly to the SUS health regions due to the distribution of resources across those regions, but the sample 3rd -BHSU was not designed to specifically represent the population within those regions, thus we only presented prevalence and rates for macro regions. Third, as in any cross-sectional design it is not possible to infer on causality when evaluating the associations. Finally, data was collected before the Covid \u0026minus;\u0026thinsp;19 pandemic which affected both mental health and health services in Brazil.\u003c/p\u003e \u003cp\u003eDespite the limitations, our study shows that the distribution of health services is reflecting the efforts made in the last 20 years to increase PHC and CAPS availability in the country. Such effort may be understood as tentative to enhance equity within the Brazilian Unified Health System. However, diagnostic and treatment rates remain elevated in regions with greater psychiatrist presence and new strategies are still needed to improve access to mental health care. A promising strategy is the in course digital transformation of SUS, in which we would hope that mental health is prioritized. Finally, it is imperative that data on the diagnosis of mental health disorders is obtained through well designed nationwide probability surveys. Such studies are the milestones to optimize MH resource allocation and planning, as well as to monitor the efficacy of programs and strategies implemented to tackle mental health disorders.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was partially supported by grants from the Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ - #E-26/010·002428/2019). The 3rd Brazilian Household Survey on Substance Use Study (3rd BHSU) was partially funded by the Secretaria Nacional de Políticas sobre Drogas do Brasil. The funders had no role in the study design, data collection, data analysis, data interpretation, or writing of this manuscript.\"\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contributors\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRBDB conceived the study, interpreted the results and wrote the first draft. JCM, JCA analyzed the data. RCADO consolidated data and interpreted results. PLDNA supervised data analysis and interpreted results. DPB revised the literature and helped on the first draft. FIB coordinated the BHSU-3. FK revised the manuscript for important intellectual content. All authors revised the manuscript and provided intellectual content. All authors have full access to the data and accept responsibility to submit for publication.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFK has received personal fees as a speaker/consultant from Janssen (Johnson \u0026amp; Johnson), Daiichi Sankyo, Libbs, Abbott, and Teva Pharmaceutical Industries in the last three years. The other authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGlobal Health Data Exchange. \u003cem\u003eGlobal Burden of Disease Study 2019 (GBD 2019) Data Resources\u003c/em\u003e; 2019. [Internet] [cited 2022 May 1]. Available from: https://ghdx.healthdata.org/gbd-2019. Accessed 1 May 2022. \u003c/li\u003e\n\u003cli\u003eChesney E, Goodwin GM, Fazel S. 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Impact of the Programa Mais m\u0026eacute;dicos (More Doctors Programme) on primary care doctor supply and amenable mortality: quasi-experimental study of 5565 Brazilian municipalities. \u003cem\u003eBMC Health Serv Res\u003c/em\u003e. 2020; \u003cstrong\u003e20\u003c/strong\u003e: 873. doi: 10.1186/s12913-020-05716-2\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. mhGAP Intervention Guide for Mental, Neurological and Substance Use Disorders in Non-Specialized Health Settings: Mental Health Gap Action Programme (mhGAP), version 2.0. World Health Organization. 2016. [Internet] [cited 2022 Apr 15]. Available from: https://iris.who.int/handle/10665/250239\u003c/li\u003e\n\u003cli\u003eMoitra M, Santomauro D, Collins PY, et al. 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Bipolar disorder prevalence: a systematic review and meta-analysis of the literature. \u003cem\u003eBraz J Psychiatry\u003c/em\u003e. 2015; \u003cstrong\u003e37\u003c/strong\u003e(2):155\u0026ndash;61. doi: 10.1590/1516-4446-2012-1693\u003c/li\u003e\n\u003cli\u003eSaha S, Chant D, Welham J, McGrath J. A Systematic Review of the Prevalence of Schizophrenia. \u003cem\u003ePLoS Med\u003c/em\u003e. 2005; \u003cstrong\u003e2\u003c/strong\u003e:e141. doi: 10.1371/journal.pmed.0020141\u003c/li\u003e\n\u003cli\u003eRocha HA da, Reis IA, Santos MA da C, Melo APS, Cherchiglia ML. Psychiatric hospitalizations by the Unified Health System in Brazil between 2000 and 2014. \u003cem\u003eRev Saude Publica\u003c/em\u003e. 2021; \u003cstrong\u003e55\u003c/strong\u003e:14. doi: 10.11606/s1518-8787.2021055002155\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Anxiety, Depression, Severe mental health disorders, Health Services Accessibility, Public Mental Health","lastPublishedDoi":"10.21203/rs.3.rs-4395839/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4395839/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003ePurpose\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe aimed to describe the prevalence of 12-month reported MHD and evaluate associations with availability mental health (MH) care in Brazil.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eData from a nationwide probability survey (n\u0026thinsp;=\u0026thinsp;16,273) and from the National Registry of Health Services have been analyzed. The main outcomes were 12-month reported diagnosis/treatment for anxiety, depression, and severe MHD. Multivariate logistic regressions were performed to assess the associations of the rates of psychiatrists, outpatient MH services (CAPS) and primary health care services (PHC) with the outcomes.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe overall prevalence of anxiety, depression, bipolar disorder and schizophrenia were 15.5% (95%CI:14.4\u0026ndash;16.6), 7.3% (95%CI:6.6\u0026ndash;7.9), 1.0% (95%CI:0.8\u0026ndash;1.3), and 0.4% (95%CI 0.3\u0026ndash;0.5), respectively, with lower prevalences observed in less developed macroregions. The rate of psychiatrists varied from 1.52 (North) to 12.26 (South)/100,000 inhabitants, the rate of CAPS from 1.52 (North) to 2.72 (Northeast), and the rate of PHC from 26.12 (Southeast) to 52.25 (Northeast). Individuals living in regions with higher rates of psychiatrists and PHCs were more likely to report anxiety and depression, while those living in regions with higher rates of CAPS were more likely report severe MHD.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe distribution of services mirrors the emphasis on PHC and CAPS to enhance equity within the Brazilian Universal Health System. However, diagnostic and treatment rates remain elevated in regions with larger psychiatrist presence. Addressing information gaps is imperative to optimize MH policies and resources allocation.\u003c/p\u003e","manuscriptTitle":"Availability of mental health care and mental health disorders in Brazil","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-28 20:10:50","doi":"10.21203/rs.3.rs-4395839/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ff207de9-d180-47a5-9b30-72a14c97a3e8","owner":[],"postedDate":"May 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-05T09:36:28+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-28 20:10:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4395839","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4395839","identity":"rs-4395839","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}