Sociodemographic characteristics related to inequality in depression treatment in Peruvian adults: a concentration index decomposition approach

Sociodemographic characteristics related to inequality in depression treatment in Peruvian adults: a concentration index decomposition approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Sociodemographic characteristics related to inequality in depression treatment in Peruvian adults: a concentration index decomposition approach Claudio Intimayta-Escalante This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4078911/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract Background Depressive symptoms affect a significant proportion of Peruvian population, between 13.8% and 15.1% since 2014 to 2018. However, only the 14.1% did not receive treatment, this gap in treatment is influenced for sociodemographic conditions. The study aim was assessing demographic characteristics related to inequalities in the depression treatment receiving in Peruvian adults. Methods Utilizing data from the 2017–2022 Demographic and Health Survey, we conducted an analytic cross-sectional study. Inequality in treatment receipt was evaluated using concentration curves for estimated Concentration Index (CI), and the Erreygers Concentration Index (ECI), with the wealth index serving as an equity stratified. Decomposition analysis was employed to examine disparities among sociodemographic characteristics, including sex, age, education, residence, health insurance, and ethnicity. Results Of the 35,925 Peruvian adults with depressive symptoms surveyed, only 10.82% received treatment. Our analysis revealed treatment recipients were concentrated in higher wealth quintiles (CI: 22.08, 95% CI: 20.16 to 24.01, p < 0.01). Disparities persisted across various demographic groups, with urban residency (ECI: 0.03, 95% CI: 0.02 to 0.03, p < 0.001), those without education (ECI: 0.05, 95% CI: 0.03 to 0.07, p < 0.001), Afro-Peruvians (ECI: 0.06, 95% CI: 0.04 to 0.08, p < 0.001), and women (ECI: 0.07, 95% CI: 0.06 to 0.08, p < 0.001) experiencing lower received treatment, influenced by wealth quintile. Conclusion Only one in ten Peruvian adults with depressive symptoms received treatment. Sociodemographic conditions such as living in rural areas, outside of the capital region, having low educational level, and identifying as Quechua or Aymara were the main components of inequality in the receipt of treatment for depressive symptoms. Health Inequities Sociodemographic Factors Depression Patient Health Questionnaire Peru. Figures Figure 1 Figure 2 Figure 3 Figure 4 BACKGROUND After the COVID-19 pandemic, it has been an increase of the population with depression and anxiety that represents a public health challenge due to its effects on social development through stigmatization of mental illness and years of life lost due to disability ( 1 ), which disproportionately affects middle and low-income countries ( 2 , 3 ). The high prevalence of depressive symptoms among women, older persons, and people from disadvantaged socioeconomic groups makes it difficult to guarantee equitable access to treatment for this illness ( 4 – 7 ). Furthermore, the receipt of treatment for depression is influenced by various sociodemographic (such as gender, age group, area and place of residence, and ethnicity), economic (educational level, income, and work activity), and health (few resources in mental health or services and centralization of the health system) factors ( 8 – 13 ), limiting the effective implementation of health strategies to address the growing burden of mental illness in the population ( 14 , 15 ). This condition is particularly worrying in countries such as Peru, where mental, neurological, substance-use-related diseases and suicide account for 34% of all years lived with disability ( 16 ). Only between 2014 and 2018, the prevalence of depressive symptoms in Peru was between 13.8% and 15.1% ( 17 ), and the 85.9% did not receive treatment despite the implementation of mental health reforms, which centered on the establishment of community mental health facilities ( 18 , 19 ). This treatment gap is consistent with other middle and low-income countries, where 79–93% of adults with depressive symptoms do not receive treatment for depression ( 20 , 21 ). Although there is not enough evidence on the determinants of inequality in mental health in countries such as Peru, where structural racism is evident and the inequal distribution of resources and health services harms native racial/ethnic groups ( 22 – 24 ), it´s makes crucial to identify the role of this sociodemographic conditions in the receipt of treatment for mental disorders such as depression. This approach could allow the development of public policies that focus on ensuring more equitable and effective access to treatment for this illness. For these reasons, the aim of this study was to evaluate the demographic characteristics related to inequalities in the depression treatment receiving in Peruvian adults. METHODS Study Design Analytical cross-sectional study developed through a secondary analysis of data from the Demographic and Family Health Survey (DHS), a national survey developed by the National Institute of Statistics and Informatics of Peru (INEI) since 2004. In a Latin American country like Peru, with a wide geographic variety and a population of approximately 32 million inhabitants (concentrated mainly in the Lima region, the capital of this country), this survey allows exploring sociodemographic conditions related to health ( 25 ). The DHS estimates are representative annually at the national, urban/rural level and for the 24 regions of Peru along with the constitutional province of Callao (neighboring Lima, also considered part of the capital region). Annual information from the survey between 2017 and 2022 was used to assess sociodemographic and health characteristics of Peruvian adults with depressive symptoms, who are 18 years of age or older and who habitually lived where they were surveyed (Appendix 1). Definition of the Outcome The assessment of depressive symptoms in the DHS of Peru is performed with the Patient Health Questionnaire-9 or PHQ-9 questionnaire, which from 2014 to 2018 evaluated symptoms experienced by participants in the last 12 months (In the last 12 months did you receive treatment from any health professional for depression, sadness, discouragement, lack of interest or irritability?). However, since 2019 it has been changed to a version that evaluates the symptoms experienced by participants in the last two weeks, although the evidence demonstrates the validity and reliability of this instrument in the Peruvian population and its usefulness for the evaluation of depression ( 26 ). This questionnaire, composed of nine questions with four response options, identifies adults without depressive symptoms (0 to 4 points), or with mild (5 to 9 points), moderate (10 to 14 points), moderate to severe (15 to 19 points) or severe (20 or more points) depressive symptoms. In this way it was possible to evaluate which adults with depressive symptoms received treatment for this illness during the last year (In the last 12 months have you received treatment from a health professional for depression, sadness, discouragement, lack of interest or irritability?) Sociodemographic Characteristics Socio-demographic conditions were evaluated, such as sex (female or male), age group (between 18–29, 30–49, 50–64 and 65 or older), level of education (no education, primary, secondary or higher), wealth quintile (first, second, third, fourth and last quintile), area of residence (rural and urban), living in the capital of Peru (yes or no), health insurance affiliation (yes or no), and ethnic group (white or mestizo, Quechua or Aymara, and Afro-Peruvians), fourth and last quintile), area of residence (rural and urban), living in the capital of Peru (yes or no), health insurance affiliation (yes or no) and the ethnic group with which respondents identify (white or mestizo, Quechua or Aymara and Afro-Peruvians). In addition, the wealth quintile was used as an equity stratifier for the inequality analysis, since this variable evaluates the resources used to build their homes, their access to sanitation or water and the assets owned by the respondents ( 27 ). Thus, stratification was carried out according to the wealth quintile to which each participant belonged, with values between 0 (last wealth quintile) and 1 (highest wealth quintile). Statistical Analysis The statistical analysis was developed in STATA v.17.0, including the design of the complex sample of the DHS with the svy package. Thus, categorical variables were described by frequencies and percentages, with their respective 95% confidence interval weighted by the design effect. The numerical variables were described with their averages and their respective confidence intervals. The Rao-Scott test was then used to identify the sociodemographic and health characteristics related to the difference in the receipt of treatment for depression. Poisson regression models weighted by sample design were also used to estimate the association between the sociodemographic variables assessed with the receipt of treatment for depression using the crude Prevalence Ratio (PR) and adjusted (PRa) for the other variables. Inequality Analysis The inequality analysis was performed with concentration curves to plot inequality in the receipt of treatment for depression as a function of socioeconomic status, from the poorest (last wealth quintile) to the richest (first wealth quintile). In this way, the concentration index (CI) was calculated by estimating the area above or below the curve, so that conditions that mediate an agglomeration above the curve are related to inequality more related to poverty; conversely, conditions that mediate an agglomeration below the curve are related to inequality more related to wealth. In addition, the Erreygers Concentration Index (ECI) weighted by complex samples was used ( 28 ). This index ranges between values of -1 and + 1, with values between 0 and + 1 implying that the resource or service evaluated is concentrated among the population with more wealth, while values between − 1 and 0 imply a concentration among the population with less wealth. However, because these indexes are absolute values, a method of decomposing the ECI among socioeconomic conditions that explain inequality related to the receipt of treatment for depression was used with a multivariate Probit regression model, thus estimating the contribution of each variable according to the following formula: $$ECI=4 \left[\sum _{k}{\beta }_{k} . \stackrel{-}{{x}_{k}} .{C}_{k}+{GC}_{\in } \right]$$ Where we have the β k or explanatory coefficient; the ߂ k or the mean of the explanatory variable; the C k or the concentration index of the explanatory variables; and the GC ∈ or the generalized concentration for the error term. Among the explanatory variables, the wealth quintile is not considered because it is used as an equity stratified and its inclusion would only generate values close to zero or an omission of the variables in the model. Additionally, maps were developed to represent the percentage of Peruvian adults with depression who received treatment and the inequality related to this aspect represented with the ECI of the 25 regions of Peru. Ethical Aspects The study was developed by analyzing data from the DHS in Peru, a national survey applied with the informed consent of the participants. Thus, the study was developed respecting the confidentiality and anonymity of the participants, within the framework of the bioethical principles for the development of health research. RESULTS A total of 35944 Peruvian adults with depression were identified, of whom only 35925 had information about receiving treatment for depression and were therefore included in the study. Of the total participants, 64.89% were female and the average age was 44.91 years (95% CI: 44.58 to 45.25). While the majority had at least a high school education (68.68%), were in the bottom three wealth quintiles (64.99%), resided in an urban area (76.43%) and outside the capital region (66.60%), with health insurance (77.94%) and identified as white or mixed race (50.52%). Also, only the 10.82% (95%CI: 10.26–11.41) of adults with depressive symptoms receive treatment (Table 1 ). Sociodemographic characteristics such as sex, education level, wealth index, ethnicity, area, and place of residence mediated a difference in the receipt of treatment for this disease (p < 0.050). In addition, having health insurance and the degree of depressive symptoms mediated a difference in the receipt of treatment for this illness (p < 0.050), although age group did not mediate a significant difference in the severity of depressive symptoms (p < 0.050) (Table 1 ). Likewise, it was possible to identify those characteristics such as sex, age group, level of education, wealth index, area or place of residence, and receipt of treatment were found to differentiate between the degrees of depressive symptoms (p < 0.050) (Table 2 ). In addition, it was identified that adults with secondary education (PRa: 0.86; 95%CI: 0.74 to 0.98; p = 0.027), primary education (PRa: 0.68; 95%CI: 0.55 to 0.83; p < 0.001) or no education (PRa: 0.41; 95%CI: 0.27 to 0.63; p < 0.001), in the last wealth quintile (PRa: 0.41; 95%CI: 0. 30 to 0.58; p < 0.001) or who identified as Quechua or Ayamara (PRa: 0.87; 95%CI: 0.75 to 0.99; p = 0.048) and Afro-Peruvian (PRa: 0.80; 95%CI: 0.65 to 0.97; p = 0.027) had lower receipt of treatment for all Peruvian adults with depressive symptoms. While women (PRa: 1.58; 95%CI: 1.38 to 1.80; p < 0.001), Peruvian adults who lived in the capital (PRa: 1.22; 95%CI: 1.06 to 1.39; p = 0.004) and had health insurance (PRa: 1.39; 95%CI: 1.18 to 1.63; p < 0.001) evidenced a higher receipt of treatment for all Peruvian adults with depressive symptoms. This trend was maintained despite increasing severity of depressive symptoms, although the association of many sociodemographic characteristics was not statistically significant for the scenarios where adults with moderate to severe depressive symptoms were present (Fig. 1 ). In the inequality analysis, it was identified that adults with depressive symptoms who received treatment were concentrated in the highest wealth quintiles (CI: 22.08; 95%CI: 20.16 to 24.01; p < 0.01), while adults with depressive symptoms who did not receive treatment were concentrated in the lowest wealth quintiles (CI: -2.15; 95%CI: -2.35 to -1.95; p < 0.01). In addition, it was specifically identified that as the severity of depressive symptoms increased, from mild (CI: 18.22; 95%CI: 14.79 to 21.66; p < 0.001), moderate (CI: 21.65; 95%CI: 17.62 to 25.67; p < 0. 001), moderate to severe (CI: 24.76; 95%CI: 20.50 to 29.01; p < 0.001) and severe (CI: 26.93; 95%CI: 23.23 to 30.63; p < 0.001), treatment receipt was concentrated in the highest wealth quintiles (Fig. 2 ). In assessing the composition of inequality in the receipt of treatment for adults with depressive symptoms, it was identified that those adults who were male (ECI: 0.12; 95%CI: 0.11 to 0.13; p < 0.001), aged 65 to older (ECI: 0.14; 95%CI: 0.12 to 0. 16; p < 0.001), enrolled in health insurance (ECI: 0.12; 95%CI: 0.11 to 0.13; p < 0.001) and identified as white or mixed race (ECI: 0.11; 95%CI: 0.10 to 0.12; p < 0.001) had higher receipt of treatment for this disease mediated by wealth quintile. In contrast adults with depressive symptoms who lived in the urban area (ECI: 0.03; 95%CI: 0.02 to 0.03; p < 0.001), uneducated (ECI: 0.05; 95%CI: 0.03 to 0.07; p < 0.001), identified as Afro-Peruvian (ECI: 0. 06; 95%CI: 0.04 to 0.08; p < 0.001) or women (ECI: 0.07; 95%CI: 0.06 to 0.08; p < 0.001) had lower receipt of treatment for this disease mediated by wealth quintile (Table 3 ). In the decomposition analysis, the variables included (sex, age group, educational level, area and place of residence, ethnic group, and health insurance affiliation) explained 70.16% of the total variance of inequality in the receipt of treatment for Peruvian adults with depressive symptoms. Thus, the greatest positive contribution to inequality came from residing in rural areas (23.67%) or in the capital region (20.77%), having only primary education (19.97%) and identifying as Quechua or Aymara (10.47%). On the other hand, the negative contribution to inequality came from having health insurance (-8.28%), being in the 50–64 age group (-2.03%) and having only secondary education (-1.77%) (Table 3 ). In addition, the contribution of the different sociodemographic conditions to inequality in the receipt of treatment according to the degree of severity of depressive symptoms remained homogeneous, although they decreased in their contribution to inequality (Fig. 3 ). In the geographic distribution of adults with depressive symptoms, the regions of Ucayali (61.83%), Loreto (55.88%) and San Martin (54.65%) had more adults with mild depressive symptoms. While the regions of Ica (26.25%), Lima (24.82%) and Ayacucho (24.56%) had more adults with moderate depressive symptoms. The regions of Ancash (19.42%), Piura (18.01%) and Amazonas (17.27%) had more adults with moderate to severe depressive symptoms. Similarly, the regions of Puno (18.27%), Amazonas (17.78%), Tumbes (17.39%) and Huánuco (17.15%) had more adults with severe depressive symptoms. Although adults with depressive symptoms receiving treatment were mostly concentrated in regions such as Lima (15.37%), Callao (13.96%) and Ica (13.32%). Although the highest receipt of treatment for this wealth-mediated illness was concentrated in the regions of Piura (ECI: 0.12; 95%CI: 0.07 to 0.18; p < 0. 001), Ancash (ECI: 0.11; 95%CI: 0.06 to 0.16; p < 0.001), La Libertad (ECI: 0.10; 95%CI: 0.06 to 0.15; p < 0.001) and San Martin (ECI: 0.10; 95%CI: 0.06 to 0.15; p < 0.001) (Fig. 4 ). DISCUSSION This study evaluated the demographic characteristics that contribute to inequalities in the receiving of treatment for depression symptoms among Peruvian adults. Only one in ten adults with depressive symptoms receive treatment, which is like other countries such as Argentina (11.6%), Brazil (9.0%), Canada (9.5%), and Guatemala (8.7%) ( 20 ). However, the situation presents a challenge for public health because of the disease's significant prevalence in Peru, which has remained stable over the evaluation period and may increase after the COVID-19 pandemic ( 2 , 17 ). Improved treatment coverage, while not directly reducing prevalence ( 29 ), remains a crucial aspect because of the consequences of untreated depression symptoms, including a decline in quality of life, mental health complications, and an elevated risk of suicide attempts ( 30 – 33 ). The assessed sociodemographic characteristics (sex, education, wealth index, ethnicity, area, and residence) not only influenced the proportion of adults receiving treatment for depressive symptoms but also impacted the severity of the illness ( 34 – 36 ). The multifactorial nature of mental disorders, including depression, explains this phenomenon ( 29 ). Despite women's higher likelihood of developing depressive symptoms, the study revealed that they received more treatment compared to men. Therefore, addressing the gender gap in depression treatment remains crucial in public health strategies ( 4 , 37 , 38 ). Furthermore, there is an association between lower educational levels, lower wealth quintiles, and a decreased likelihood of looking for treatment for depressed symptoms ( 11 , 39 , 40 ). In this situation, sociodemographic characteristics, especially wealth, intricately connected with education ( 41 – 43 ). The research's discovery of a concentration of Peruvian adults with depressive symptoms receiving treatment in the wealthiest quintiles reinforces this observation. The favorable socioeconomic conditions in these quintiles facilitate access to a diverse array of therapies—pharmacological, behavioral, and educational—which effectively tackle the challenges posed by the extended rehabilitation period and help prevent relapses in chronic conditions like depression ( 44 – 46 ). Also, low educational levels significantly impacted a reduced receipt of treatment for depressive symptoms ( 47 , 48 ). Education, within the realm of mental disorders like depression, may act as a protective mechanism by fostering skills in comprehending life experiences and making decisions amid challenging situations that can trigger symptom development ( 49 , 50 ). One of the main factors contributing to inequality in accessing treatment for depressive symptoms is the area and place of residence ( 51 – 53 ). Peruvian adults in rural areas and the capital city encounter heightened difficulties in receiving treatment, emphasizing the concerning disparities ( 54 ). Despite the implementation of mental health reforms during the study period to expand community mental health centers nationwide ( 17 – 19 ), persistent centralization of resources and services in central and southern Peru perpetuates the inequality in treatment receipt, even as symptoms worsen. In Peru those adults with depression who identify themselves as Quechua or Aymara are less likely to receive treatment for their illness ( 24 ). This highlights the unfair treatment that native populations in Peru face when it comes to accessing health services ( 55 ). This is concerning because the lack of treatment not only worsens the disease, but it also affects more Afro-Americans and members of indigenous groups than white people ( 56 – 60 ). The reason behind these disparities could be due to discriminatory practices or a lack of cross-cultural integration, which makes timely treatment difficult ( 61 – 63 ). Additionally, negative attitudes toward mental health services, particularly among non-white ethnic groups, contribute to this problem ( 64 , 65 ). The study showed that as adults age, they receive less treatment for depressed symptoms, even though older age groups are more likely to suffer from depression ( 66 – 68 ). That can be due to a variety of social and biological causes, including isolation, daily living issues, psychological distress, and increased comorbidities ( 69 – 71 ). Prioritizing prevention, early detection, and treatment for older adults with depressive symptoms, especially in vulnerable groups, like those with comorbidities is crucial, because could diminish the effectiveness of current pharmacological and cognitive-behavioral treatments, underscoring the necessity for targeted interventions ( 72 , 73 ). Health insurance affiliation can help to reduce treatment inequality in depression ( 74 , 75 ). Attributed to policies like universal insurance and the establishment of community mental health facilities, diagnosing and treating insured adults becomes more accessible in Peru ( 76 , 77 ). However, there is still a need for cross-cultural care approaches in treating mental disorders like depression, that is difficult when resources and services are more available in capital of Peru ( 19 ), this reinforces the need of decentralization to address gaps in treatment access in rural area and other regions ( 78 – 80 ). The study present limitations because of the design that does not allow evaluated causal relationship between sociodemographic conditions and treatment inequality for depression. Additionally, the evaluation is limited to the absence of treatment in the last 12 months, with unknown reasons for this lack or whether respondents previously received any treatment. The study lacks follow-up to gauge the impact of sociodemographic conditions on exacerbating depressive symptoms and perpetuating treatment inequality. Furthermore, factors like family support, recurrence of mental disorders, challenges in accessing health centers, and reasons limiting treatment access remain unexplored, hindering a comprehensive understanding of the causes of inequalities in treating this condition. CONCLUSIONS In conclusion, only one in ten Peruvian adults with depressive symptoms received treatment, and this did not improve, even if the symptoms worsened. In addition, sociodemographic conditions such as wealth index, educational level, sex, ethnicity, area, and place of residence mediated differences in the proportion of adults with depressive symptoms who received treatment. Furthermore, living in rural areas, in the capital region, having only secondary education, and identifying as Quechua or Aymara were the main components of inequality in the receipt of treatment for depressive symptoms, and this trend was maintained as the severity of these symptoms increased. Abbreviations ECI - Erreygers Concentration Index. CI - Concentration Index. DHS - Demographic and Family Health Survey. INEI - National Institute of Statistics and Informatics of Peru. PHQ-9 – Patient Health Questionnaire-9. PR - Prevalence Ratio. PRa – adjusted Prevalence Ratio. Declarations Ethics approval and consent to participate: The study was developed by analyzing data from the DHS in Peru, a national survey applied with the informed consent of the participants. Consent for publication: All authors approve the publication of this article. Availability of data and materials: The dataset supporting the conclusions of this article is available in the INEI repository: https://proyectos.inei.gob.pe/microdatos/ Competing interests: The authors declare that they have no conflicts of interest Funding: The study was self-funded Authors' contributions: CIE contributed to the development and conceptualization of the research idea, as well as the analysis of the data and interpretation of the results, and the writing of the manuscript. Acknowledgements: None. References Gabet S, Thierry B, Wasfi R, De Groh M, Simonelli G, Hudon C, et al. How is the COVID-19 pandemic impacting our life, mental health, and well-being? Design and preliminary findings of the pan-Canadian longitudinal COHESION Study. medRxiv. 2022; Available in: https://www.embase.com/search/results?subaction=viewrecord&id=L2018694757&from=export U2 - L2018694757 U4 - 2022-08-17 Santomauro DF, Mantilla Herrera AM, Shadid J, Zheng P, Ashbaugh C, Pigott DM, et al. Global prevalence and burden of depressive and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic. The Lancet. 2021;398(10312):1700-12. Maffly-Kipp J, Eisenbeck N, Carreno DF, Hicks J. Mental health inequalities increase as a function of COVID-19 pandemic severity levels. Soc Sci Med. 2021;285. Available in: https://www.embase.com/search/results?subaction=viewrecord&id=L2013916725&from=export U2 - L2013916725 Peplinski B, McClelland R, Szklo M. Associations between socioeconomic status markers and depressive symptoms by race and gender: results from the Multi-Ethnic Study of Atherosclerosis (MESA). Ann Epidemiol. 2018;28(8):535-542.e1. Rai D, Zitko P, Jones K, Lynch J, Araya R. Country- and individual-level socioeconomic determinants of depression: multilevel cross-national comparison. Br J PSYCHIATRY. 2013;202(3):195-203. Arundell LL, Greenwood H, Baldwin H, Kotas E, Smith S, Trojanowska K, et al. Advancing mental health equality: a mapping review of interventions, economic evaluations and barriers and facilitators. Syst Rev. 2020;9(1):115. Patel V, Burns JK, Dhingra M, Tarver L, Kohrt BA, Lund C. Income inequality and depression: a systematic review and meta-analysis of the association and a scoping review of mechanisms. World Psychiatry. 2018;17(1):76-89. Alegría M, Chatterji P, Wells K, Cao Z, Chen C nan, Takeuchi D, et al. Disparity in Depression Treatment Among Racial and Ethnic Minority Populations in the United States. Psychiatr Serv. noviembre de 2008;59(11):1264-72. Vyas CM, Donneyong M, Mischoulon D, Chang G, Gibson H, Cook NR, et al. Association of Race and Ethnicity With Late-Life Depression Severity, Symptom Burden, and Care. JAMA Netw OPEN. 2020;3(3). Szymkowicz SM, Gerlach AR, Homiack D, Taylor WD. Biological factors influencing depression in later life: role of aging processes and treatment implications. Transl Psychiatry. 2023;13(1):160. Araya R, Zitko P, Markkula N, Rai D, Jones K. Determinants of access to health care for depression in 49 countries: A multilevel analysis. J Affect Disord. 2018;234:80-8. Lopes CS, Hellwig N, de Azevedo e Silva G, Menezes PR. Inequities in access to depression treatment: results of the Brazilian National Health Survey - PNS. Int J EQUITY Health. 2016;15. Hahm HC, Le Cook B, Ault-Brutus A, Alegra M. Intersection of Race-Ethnicity and Gender in Depression Care: Screening, Access, and Minimally Adequate Treatment. Psychiatr Serv. 2015;66(3):258-64. Gilman SE, Fitzmaurice GM, Bruce ML, Ten Have T, Glymour MM, Carliner H, et al. Economic Inequalities in the Effectiveness of a Primary Care Intervention for Depression and Suicidal Ideation. Epidemiology. 2013;24(1):14-22. Tanner LM, Wildman JM, Stoniute A, Still M, Bernard K, Green R, et al. Non-pharmaceutical primary care interventions to improve mental health in deprived populations: a systematic review. Br J Gen Pract. 2023;73(729):e242-8. Pan American Health Organization. The Burden of Mental Disorders in the Region of the Americas, 2018. PAHO; 2018. Available in: https://iris.paho.org/handle/10665.2/49578 Villarreal-Zegarra D, Cabrera-Alva M, Carrillo-Larco RM, Bernabe-Ortiz A. Trends in the prevalence and treatment of depressive symptoms in Peru: a population-based study. BMJ Open. 2020;10(7):e036777. Pan American Health Organization. Progress and challenges of mental health reform in Peru over the last decade. PAHO; 2023. Available in: https://iris.paho.org/handle/10665.2/58312 Castillo-Martell H, Cutipé-Cárdenas Y. Implementation, initial results and sustainability of the reform of mental health services in Peru, 2013-2018. Rev Peru Med Exp Salud Publica. 2019;36(2):326. Smits F, Huijts T. Treatment for depression in 63 countries worldwide: Describing and explaining cross-national differences. Health Place. 2015;31:1-9. Mrejen M, Hone T, Rocha R. Socioeconomic and racial/ethnic inequalities in depression prevalence and the treatment gap in Brazil: A decomposition analysis. SSM Popul Health. 2022;20. Available in: https://www.embase.com/search/results?subaction=viewrecord&id=L2020762521&from=export U2 - L2020762521 Valdivia M. On the ethno-cultural determinants of inequity in maternal and child health in Peru. GRADE. 2005; Available in: https://www.grade.org.pe/publicaciones/1051-sobre-los-determinantes-etnico-culturales-de-la-inequidad-en-salud-materno-infantil-en-el-peru/ Sánchez-Moreno F. La inequidad en salud afecta el desarrollo en el Perú. Rev Peru Med Exp Salud Publica. 2014;30(4). Available in: https://rpmesp.ins.gob.pe/index.php/rpmesp/article/view/251 Sánchez-Villena AR, Ventura-León J. Depression according to ethnic identity in Peruvian population: a national study using network analysis. Acta Colomb Psicol. 2024;27(1):183-96. Technical Direction of Demography and Social Indicators of the National Institute of Statistics and Informatics. PERU: Population Estimates and Projections by Department, Sex and Five-Year Age Groups 1995-2025. INEI; 2009. Available in: http://proyectos.inei.gob.pe/web/biblioineipub/bancopub/Est/Lib0846/libro.pdf Calderón M, Gálvez-Buccollini JA, Cueva G, Ordoñez C, Bromley C, Fiestas F. Validation of the Peruvian version of the PHQ-9 for the diagnosis of depression. Rev Peru Med Exp Salud Publica. 2012;29(4):578-9. Ettman CK, Adam GP, Clark MA, Wilson IB, Vivier PM, Galea S. Wealth and depression: A scoping review. Brain Behav. 2022;12(3). Available in: https://www.embase.com/search/results?subaction=viewrecord&id=L2014979425&from=export U2 - L2014979425 Erreygers G. Correcting the Concentration Index. J Health Econ. 2009;28(2):504-15. Ormel J, Hollon SD, Kessler RC, Cuijpers P, Monroe SM. More treatment but no less depression: The treatment-prevalence paradox. Clin Psychol Rev. 2022;91:102111. Bukh JD, Bock C, Vinberg M, Kessing LV. The effect of prolonged duration of untreated depression on antidepressant treatment outcome. J Affect Disord. 2013;145(1):42-8. Hung CI, Liu CY, Yang CH. Untreated duration predicted the severity of depression at the two-year follow-up point. Hashimoto K, editor. PLOS ONE. 2017;12(9):e0185119. Culpepper L. Impact of Untreated Major Depressive Disorder on Cognition and Daily Function. J Clin Psychiatry. 2015;76(07):e901-e901. Barateau L, Lopez R, Chenini S, Pesenti C, Rassu AL, Jaussent I, et al. Depression and suicidal thoughts in untreated and treated narcolepsy: Systematic analysis. Neurology. 2020;95(20). Available in: https://www.neurology.org/doi/10.1212/WNL.0000000000010737 Lorant V. Socioeconomic Inequalities in Depression: A Meta-Analysis. Am J Epidemiol. 2003;157(2):98-112. Torikka A, Kaltiala-Heino R, Rimpelä A, Marttunen M, Luukkaala T, Rimpelä M. Self-reported depression is increasing among socio-economically disadvantaged adolescents – repeated cross-sectional surveys from Finland from 2000 to 2011. BMC Public Health. 2014;14(1):408. Cook S, Saburova L, Bobrova N, Avdeeva E, Malyutina S, Kudryavtsev AV, et al. Socio-demographic, behavioural and psycho-social factors associated with depression in two Russian cities. J Affect Disord. 2021;290:202-10. Roxo L, Silva M, Perelman J. Gender gap in health service utilisation and outcomes of depression: A cross-country longitudinal analysis of European middle-aged and older adults. Prev Med. 2021;153:106847. Weissman MM. Treatment of Depression: Men and Women Are Different? Am J Psychiatry. 2014;171(4):384-7. Tüzün H, Demirköse H, Özkan S, İlhan MN. Socioeconomic Factors Related to Prevalence, Severity, and Contact Coverage of Depression in Primary Health Care. Psychiatry Clin Psychopharmacol. 2021;31(4):457-67. Prevalence, Severity, and Unmet Need for Treatment of Mental Disorders in the World Health Organization World Mental Health Surveys. JAMA. 2004;291(21):2581. Does education matter? Income inequality and mental health among young adult men. J Mens Health. 2023; Available in: https://www.jomh.org/articles/10.22514/jomh.2023.015 Schlax J, Jünger C, Beutel ME, Münzel T, Pfeiffer N, Wild P, et al. Income and education predict elevated depressive symptoms in the general population: results from the Gutenberg health study. BMC Public Health. 2019;19(1):430. Hinata A, Kabasawa K, Watanabe Y, Kitamura K, Ito Y, Takachi R, et al. Education, household income, and depressive symptoms in middle-aged and older Japanese adults. BMC Public Health. 2021;21(1):2120. Buckman JEJ, Saunders R, Stott J, Cohen ZD, Arundell LL, Eley TC, et al. Socioeconomic Indicators of Treatment Prognosis for Adults With Depression: A Systematic Review and Individual Patient Data Meta-analysis. JAMA Psychiatry. 2022;79(5):406. Mills JA, Suresh V, Chang L, Mayes T, Croarkin PE, Trivedi MH, et al. Socioeconomic Predictors of Treatment Outcomes Among Adults With Major Depressive Disorder. Psychiatr Serv. 2022;73(9):965-9. Folb N, Lund C, Fairall LR, Timmerman V, Levitt NS, Steyn K, et al. Socioeconomic predictors and consequences of depression among primary care attenders with non-communicable diseases in the Western Cape, South Africa: cohort study within a randomised trial. BMC Public Health. 2015;15(1):1194. Patria B. The longitudinal effects of education on depression: Finding from the Indonesian national survey. Front Public Health. 2022;10:1017995. Taple BJ, Chapman R, Schalet BD, Brower R, Griffith JW. The Impact of Education on Depression Assessment: Differential Item Functioning Analysis. Assessment. 2022;29(2):272-84. Bruijniks SJE, Frank U, Tuschen-Caffier B, Werthmann J, Renner F. Skill Improvement Through Learning in Therapy (SKILT): A study protocol for a randomized trial testing the direct effects of cognitive behavioral therapy skill acquisition and role of learning capacity in depression. Clin Psychol Eur. 2023;5(1):e8475. Martinengo L, Stona AC, Tudor Car L, Lee J, Griva K, Car J. Education on Depression in Mental Health Apps: Systematic Assessment of Characteristics and Adherence to Evidence-Based Guidelines. J Med Internet Res. 2022;24(3):e28942. Wong H, Moore K, Angstman KB, Garrison GM. Impact of rural address and distance from clinic on depression outcomes within a primary care medical home practice. BMC Fam Pract. 2019;20(1):123. Jenkins R, Snell-Rood C. Rural Perspectives Challenging Pharmacotherapy. J Behav Health Serv Res. 2021;48(1):112-9. Snell-Rood C, Carpenter-Song E. Depression in a depressed area: Deservingness, mental illness, and treatment in the contemporary rural U.S. Soc Sci Med. 2018;219:78-86. Hernández-Vásquez A, Vargas-Fernández R, Bendezu-Quispe G, Grendas LN. Depression in the Peruvian population and its associated factors: analysis of a national health survey. J Affect Disord. 2020;273:291-7. Intimayta-Escalante C, Rojas-Bolivar D. Ethnics differences in perceptions of inequality in health care access in Peru. 2023;16(4). Porto Chiavegatto Filho AD, Kawachi I, Wang YP, Viana MC, Silveira Guerra Andrade LH. Does income inequality get under the skin? A multilevel analysis of depression, anxiety and mental disorders in Sao Paulo, Brazil. J Epidemiol COMMUNITY Health. 2013;67(11):966-72. Budhwani H, Hearld KR, Chavez-Yenter D. Depression in Racial and Ethnic Minorities: the Impact of Nativity and Discrimination. J Racial Ethn Health Disparities. 2015;2(1):34-42. Lee CH, Duck IM, Sibley CG. Ethnic inequality in diagnosis with depression and anxiety disorders. N Z Med J. 2017;130(1454):10-20. Fortuna LR, Alegria M, Gao S. Retention in depression treatment among ethnic and racial minority groups in the United States. Depress Anxiety. 2010;27(5):485-94. Kim M. Racial/ethnic disparities in depression and its theoretical perspectives. Psychiatr Q. 2014;85(1):1-8. Ward EC. Examining Differential Treatment Effects for Depression in Racial and Ethnic Minority Women: A Qualitative Systematic Review. J Natl Med Assoc. 2007;99(3):265-274. Fuentes D, Aranda MP. Depression Interventions Among Racial and Ethnic Minority Older Adults: A Systematic Review Across 20 Years. Am J Geriatr Psychiatry. 2012;20(11):915-31. Weech‐Maldonado R, Morales LS, Elliott M, Spritzer K, Marshall G, Hays RD. Race/Ethnicity, Language, and Patients’ Assessments of Care in Medicaid Managed Care. Health Serv Res. 2003;38(3):789-808. Conner KO, Copeland VC, Grote NK, Koeske G, Rosen D, Reynolds CF, et al. Mental Health Treatment Seeking Among Older Adults With Depression: The Impact of Stigma and Race. Am J Geriatr Psychiatry. 2010;18(6):531-43. Sleath B, Domino ME, Wiley-Exley E, Martin B, Richards S, Carey T. Antidepressant and Antipsychotic Use and Adherence Among Medicaid Youths: Differences by Race. Community Ment Health J. 2010;46(3):265-72. Alexopoulos GS. Mechanisms and treatment of late-life depression. Transl Psychiatry. 2019;9(1):188. Cohen A, Houck PR, Szanto K, Dew MA, Gilman SE, Reynolds CF. Social Inequalities in Response to Antidepressant Treatment in Older Adults. Arch Gen Psychiatry. 2006;63(1):50. Richardson RA, Keyes KM, Medina JT, Calvo E. Sociodemographic inequalities in depression among older adults: cross-sectional evidence from 18 countries. LANCET PSYCHIATRY. 2020;7(8):673-81. Rosset I, Roriz-Cruz M, Santos JLF, Haas VJ, Fabrício-Wehbe SCC, Rodrigues RAP. Socioeconomic and health differentials between two community-dwelling oldest-old groups. Difer Socioeconômicos E Saúde Entre Duas Comunidades Idosos Longevos. 2011;45(2):391-400. Noel PH. Depression and Comorbid Illness in Elderly Primary Care Patients: Impact on Multiple Domains of Health Status and Well-being. Ann Fam Med. 2004;2(6):555-62. Mindlis I, Wisnivesky JP, Wolf MS, O’Conor R, Federman AD. Comorbidities and depressive symptoms among older adults with asthma. J Asthma. 2022;59(5):910-6. Harpole LH, Williams JW, Olsen MK, Stechuchak KM, Oddone E, Callahan CM, et al. Improving depression outcomes in older adults with comorbid medical illness. Gen Hosp Psychiatry. 2005;27(1):4-12. Bruce ML, Sirey JA. Integrated Care for Depression in Older Primary Care Patients. Can J Psychiatry. 2018;63(7):439-46. Tian D, Qu Z, Wang X, Guo J, Xu F, Zhang X, et al. The role of basic health insurance on depression: an epidemiological cohort study of a randomized community sample in Northwest China. BMC Psychiatry. 2012;12(1):151. Baicker K, Allen HL, Wright BJ, Taubman SL, Finkelstein AN. The Effect of Medicaid on Management of Depression: Evidence From the Oregon Health Insurance Experiment. Milbank Q. 2018;96(1):29-56. De Habich M. Leadership Politics and the Evolution of the Universal Health Insurance Reform in Peru. Health Syst Reform. 2019;5(3):244-9. Jumpa-Armas DV. Universal health insurance in Peru: an approximation 10 years after its implementation. Rev Fac Med Humana. 2019;19(3):75-80. Toyama M, Castillo H, Galea JT, Brandt LR, Mendoza M, Herrera V, et al. Peruvian Mental Health Reform: A Framework for Scaling-up Mental Health Services. Int J Health Policy Manag. 2017;6(9):501-8. Trianni A, Oliveira E Souza R. Transforming mental health for pandemic recovery and social development: recommendations from the PAHO High-Level Commission on Mental Health and COVID-19. Lancet Reg Health - Am. 2023;22:100527. Islam MdR, Rahman MS, Qusar MS. Community‐based decentralized mental health services are essential to prevent the epidemic turn of post‐Covid mental disorders in Bangladesh: A call to action. Health Sci Rep. 2022;5(4):e734. Tables Table 1. Sociodemographic characteristics of Peruvian adults with depressive symptoms Variables Peruvian Adults with Depressive Symptoms (N=18504) Peruvian Adults with Depression who did not receive Treatment (N=32736) Peruvian Adults with Depression who received Treatment (N=3189) P-value** N %* (95%CI) N %* (95%CI) N %* (95%CI) Sex? Male 11362 35.11 (34.28 - 35.92) 10643 92.46 (91.58 - 93.25) 719 7.54 (6.75 - 8.42) <0.001 Female 24449 64.89 (64.08 - 65.72) 21990 87.4 (86.64 - 88.13) 2459 12.6 (11.87 - 13.36) Age Group? 18 to 29 years 8856 25.54 (24.8 - 26.3) 8036 89.24 (88.10 - 90.28) 820 10.76 (9.72 - 11.90) 0.362 30 to 49 years 14497 34.98 (34.24 - 35.73) 13071 88.8 (87.91 - 89.63) 1426 11.2 (10.37 - 12.09) 50 to 64 years 6757 22.8 (22.06 - 23.56) 6202 88.87 (87.43 - 90.16) 555 11.13 (9.84 - 12.57) 65 or more years 5701 16.67 (16.03 - 17.34) 5324 90.29 (88.78 - 91.61) 377 9.71 (8.39 - 11.22) Educational Level? Without Education 2992 6.56 (6.18 - 6.96) 2897 95.69 (94.09 - 96.87) 95 4.31 (3.13 - 5.91) <0.001 Primary 10393 24.73 (24.03 - 25.44) 9747 92.72 (91.82 - 93.52) 646 7.28 (6.48 - 8.18) Secondary 13719 40.17 (39.34 - 41.01) 12473 89.31 (88.34 - 90.21) 1246 10.69 (9.79 - 11.66) Higher Education 8707 28.54 (27.72 - 29.37) 7516 84.42 (83.11 - 85.65) 1191 15.58 (14.35 - 16.89) Wealth Index? Last Quintile 13812 23.09 (22.3 - 23.9) 13095 95.26 (94.67 - 95.79) 717 4.74 (4.21 - 5.33) <0.001 Fourth Quintile 8964 21.7 (20.98 - 22.44) 8223 91.59 (90.62 - 92.48) 741 8.41 (7.52 - 9.38) Third Quintile 5973 20.2 (19.49 - 20.93) 5328 88.93 (87.70 - 90.06) 645 11.07 (9.94 - 12.30) Second Quintile 4274 18.82 (18.05 - 19.62) 3715 86.86 (85.26 - 88.30) 559 13.14 (11.70 - 14.74) First Quintile 2788 16.18 (15.36 - 17.04) 2272 80.25 (78.02 - 82.31) 516 19.75 (17.69 - 21.98) Area of Residence? Rural 14888 23.56 (22.76 - 24.37) 14046 94.87 (94.39 - 95.31) 842 5.13 (4.69 - 5.61) <0.001 Urban 20923 76.44 (75.63 - 77.24) 18587 87.42 (86.67 - 88.14) 2336 12.58 (11.86 - 13.33) Do you live in the Capital? No 31616 66.61 (65.4 - 67.79) 29034 91.39 (90.92 - 91.84) 2582 8.61 (8.16 - 9.08) <0.001 Yes 4195 33.39 (32.21 - 34.6) 3599 84.75 (83.24 - 86.15) 596 15.25 (13.85 - 16.76) Health Insurance? No 6592 22.07 (21.32 - 22.85) 6145 91.39 (90.22 - 92.43) 447 8.61 (7.57 - 9.78) <0.001 Yes 29219 77.93 (77.15 - 78.68) 26488 88.55 (87.87 - 89.20) 2731 11.45 (10.80 - 12.13) Depressive Symptoms Degree? Mild 17307 47.43 (46.56 - 48.29) 16252 92.75 (92.01 - 93.42) 1055 7.25 (6.58 - 7.99) <0.001 Moderate 8108 23.22 (22.49 - 23.96) 7388 89.73 (88.57 - 90.78) 720 10.27 (9.22 - 11.43) Moderate to Severe 5319 15.15 (14.57 - 15.75) 4708 85.21 (83.42 - 86.83) 611 14.79 (13.17 - 16.58) Severe 5077 14.21 (13.64 - 14.8) 4285 80.59 (78.57 - 82.46) 792 19.41 (17.54 - 21.43) Ethnic Group? White or Mestizo 10877 50.54 (49.52 - 51.57) 9658 86.45 (85.34 - 87.48) 1219 13.55 (12.52 - 14.66) <0.001 Quechua or Aymara 14779 39.15 (38.14 - 40.18) 13786 92.12 (91.26 - 92.91) 993 7.88 (7.09 - 8.74) Afro-Peruvian 2387 10.3 (9.73 - 10.91) 2187 92.05 (90.45 - 93.40) 200 7.95 (6.60 - 9.55) *Frequency weighted by complex sample **P-value estimated with Rao-Scott test Table 2. Sociodemographic characteristics of Peruvian adults by degree of depressive symptoms Variables Adults with mild depressive symptoms (n= 17307) Adults with moderate depressive symptoms (n= 8108) Adults with moderate to severe depressive symptoms (n= 5319) Adults with severe depressive symptoms (n= 5077) P-value** %*(95%IC) %*(95%IC) %*(95%IC) %*(95%IC) Sex? Male 53.72 (52.22 - 55.22) 21.8 (20.59 - 23.07) 13.58 (12.64 - 14.59) 10.89 (10.01 - 11.83) <0.001 Female 44.02 (43.00 - 45.04) 23.98 (23.10 - 24.88) 16 (15.26 - 16.76) 16 (15.27 - 16.76) Age Group? 18 to 29 years 52.01 (50.31 - 53.70) 22.13 (20.71 - 23.62) 14.1 (13.00 - 15.29) 11.75 (10.70 - 12.90) <0.001 30 to 49 years 48.7 (47.42 - 49.99) 23.16 (22.07 - 24.29) 14.99 (14.09 - 15.94) 13.15 (12.33 - 14.02) 50 to 64 years 45.57 (43.64 - 47.50) 23.35 (21.74 - 25.04) 15.94 (14.67 - 17.31) 15.14 (13.86 - 16.53) 65 or more years 40.27 (38.25 - 42.33) 24.82 (23.00 - 26.74) 16 (14.51 - 17.60) 18.91 (17.33 - 20.60) Educational Level? Without Education 36.48 (33.83 - 39.21) 25.62 (23.14 - 28.27) 16.28 (14.14 - 18.67) 21.62 (19.42 - 24.00) <0.001 Primary 44.42 (42.92 - 45.93) 24.12 (22.80 - 25.49) 15.9 (14.81 - 17.05) 15.56 (14.50 - 16.69) Secondary 49.58 (48.18 - 50.98) 22.59 (21.43 - 23.79) 14.85 (13.92 - 15.84) 12.97 (12.06 - 13.95) Higher Education 49.51 (47.86 - 51.15) 22.77 (21.39 - 24.21) 14.66 (13.56 - 15.83) 13.07 (12.00 - 14.22) Wealth Index? Last Quintile 46.76 (45.39 - 48.13) 22.94 (21.91 - 24.00) 14.33 (13.50 - 15.20) 15.98 (15.08 - 16.92) 0.090 Fourth Quintile 48.73 (47.17 - 50.29) 22.6 (21.33 - 23.93) 15.33 (14.22 - 16.52) 13.34 (12.34 - 14.40) Third Quintile 46.45 (44.52 - 48.40) 22.7 (21.15 - 24.32) 16.17 (14.81 - 17.62) 14.68 (13.38 - 16.08) Second Quintile 46.95 (44.78 - 49.13) 24.32 (22.39 - 26.36) 15.21 (13.74 - 16.81) 13.52 (12.04 - 15.14) First Quintile 48.4 (45.78 - 51.02) 23.8 (21.66 - 26.09) 14.73 (12.92 - 16.74) 13.07 (11.40 - 14.94) Area of Residence? Rural 47.67 (46.47 - 48.87) 22.34 (21.43 - 23.28) 14.09 (13.36 - 14.86) 15.9 (15.08 - 16.74) <0.001 Urban 47.35 (46.29 - 48.42) 23.49 (22.59 - 24.41) 15.47 (14.75 - 16.23) 13.69 (12.99 - 14.42) Do you live in the Capital? No 48.54 (47.71 - 49.37) 22.44 (21.78 - 23.12) 14.92 (14.36 - 15.50) 14.1 (13.56 - 14.65) 0.012 Yes 45.21 (43.24 - 47.19) 24.76 (23.07 - 26.53) 15.61 (14.29 - 17.02) 14.43 (13.12 - 15.85) Health Insurance? No 48.58 (46.63 - 50.54) 23.66 (22.06 - 25.34) 15.06 (13.81 - 16.40) 12.7 (11.54 - 13.95) 0.070 Yes 47.1 (46.15 - 48.04) 23.09 (22.28 - 23.92) 15.17 (14.52 - 15.86) 14.64 (13.98 - 15.32) Ethnic Group? White or Mestizo 47.96 (46.49 - 49.43) 23.04 (21.79 - 24.34) 15.37 (14.35 - 16.46) 13.63 (12.66 - 14.66) 0.351 Quechua or Aymara 46.92 (45.58 - 48.26) 22.71 (21.63 - 23.82) 15.22 (14.29 - 16.19) 15.16 (14.23 - 16.15) Afro-Peruvian 48.4 (45.45 - 51.37) 23.75 (21.32 - 26.37) 14.85 (12.96 - 16.96) 13 (11.25 - 14.98) Did you receive treatment? No 49.32 (48.40 – 50.24) 23.36 (22.59 – 24.15) 14.48 (13.88 – 15.09) 12.84 (12.27 – 13.44) <0.001 Yes 31.78 (29.25 – 34.43) 22.04 (19.87 – 24.37) 20.70 (18.51 – 23.08) 25.48 (23.07 – 28.05) *Frequency weighted by complex sample **P-value estimated with Rao-Scott test Table 3. Decomposition of socioeconomic inequality related to treatment receipt in Peruvian adults with depressive symptoms Variable ECI (95%IC) P-value* Marginal Effect Elasticity C k Contribution (%) Sex? Male 0.074 (0.064 - 0.084) <0.001 REF Female 0.121 (0.112 - 0.130) <0.001 0.046* -0.06 -0.07 2.86 Age Group? 18 to 29 years 0.141 (0.123 - 0.159) <0.001 REF 30 to 49 years 0.119 (0.103 - 0.135) <0.001 0.048 0.025 -0.088 -2.033 50 to 64 years 0.092 (0.081 - 0.103) <0.001 0.050* 0.02 -0.01 -0.181 65 or more years 0.089 (0.075 - 0.103) <0.001 -0.032 0.008 0.031 0.238 Educational Level? Without Education 0.088 (0.074 - 0.102) <0.001 REF Primary 0.074 (0.062 - 0.086) <0.001 -0.043* -0.054 0.035 -1.774 Secondary 0.075 (0.064 - 0.086) <0.001 0.014 -0.068 -0.315 19.972 Higher Education 0.053 (0.034 - 0.072) 0.005 -0.048* -0.032 -0.132 3.973 Area of Residence? Rural 0.027 (0.022 - 0.032) <0.001 REF Urban 0.090 (0.081 - 0.099) <0.001 0.063* -0.041 -0.618 23.674 Do you live in the Capital? No 0.086 (0.080 - 0.092) <0.001 REF Yes 0.077 (0.060 - 0.094) <0.001 -0.009 0.046 0.481 20.767 Health Insurance? No 0.083 (0.070 - 0.096) <0.001 REF Yes 0.117 (0.109 - 0.125) <0.001 0.035* 0.102 -0.087 -8.282 Ethnic Group? White or Mestizo 0.109 (0.096 - 0.122) <0.001 REF Quechua or Aymara 0.091 (0.080 - 0.102) <0.001 -0.024 -0.033 -0.341 10.473 Afro-Peruvian 0.060 (0.043 - 0.077) <0.001 -0.048* -0.011 -0.046 0.471 Summary of ECI -1.160 70.158 Residual (unexplained) -0.493 29.842 Corrected ECI -1.653 ECI: Erreygers Concentration Index weighted for complex sample by wealth quintile , Elasticity = [β k * x̅ k /μ] , Ck: Concentration Index for the variable evaluated , REF: Category used as reference for estimation in decomposition model. *P-value less than 0.050 for marginal effect. Additional Declarations No competing interests reported. Supplementary Files Appendices.docx Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 13 Mar, 2024 Submission checks completed at journal 12 Mar, 2024 First submitted to journal 11 Mar, 2024 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-4078911","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":279287790,"identity":"64e3b124-0049-4143-b814-0dd10ea4ed95","order_by":0,"name":"Claudio Intimayta-Escalante","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIiWNgGAWjYDACdiDmMQASzBC+HIg48ACfFmaoFh6wlgQGY7CWBIJaoBikJbEBQuMG/M3MzyTeFNjk2bNzJ37m/WGXPj/s8EOgLXZyug3YtUgcZjOTnGOQVszDzLtZmichOXfj7TQDoJZkY7MD2LUYMDOYSfMYHE7sYebdANTCnLtxdgJIy4HEbTi1sH+Dadn8myehPt1wdvoHAlp44LZsA9pyOEFeOge/LRKHeYotgX5J7DnMu81yTtpxww3SOQUHEgxw+4W/vX3jjTd/bBLb+89uvvHGplpefnb65g8fKuzkcGlBAUygyDEAqzQgQjkIMP4AEvINRKoeBaNgFIyCEQMALGRZFoZS+IcAAAAASUVORK5CYII=","orcid":"","institution":"Universidad Nacional Mayor de San Marcos","correspondingAuthor":true,"prefix":"","firstName":"Claudio","middleName":"","lastName":"Intimayta-Escalante","suffix":""}],"badges":[],"createdAt":"2024-03-12 01:25:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4078911/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4078911/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52775933,"identity":"6c73a9cc-c746-4592-a39f-75ecde0b3bbe","added_by":"auto","created_at":"2024-03-15 15:45:53","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":968758,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4078911/v1/63f65db5c2c14e4bc41f58f0.jpg"},{"id":52775672,"identity":"e5f1bf0a-dc9f-4208-a8a2-68bf3216081d","added_by":"auto","created_at":"2024-03-15 15:37:53","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":970600,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4078911/v1/65589df60f5976a2e6d4dc59.jpg"},{"id":52775673,"identity":"9557afde-acc4-449a-a4d1-a342fe9c3aae","added_by":"auto","created_at":"2024-03-15 15:37:53","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":903715,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4078911/v1/d6fddf43b362182bec3ee92b.jpg"},{"id":52775675,"identity":"89da2369-3393-4e23-a884-16d39955d2f2","added_by":"auto","created_at":"2024-03-15 15:37:53","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1454100,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4078911/v1/0c746e438accc1e20b1eecd4.jpg"},{"id":52776270,"identity":"418eacd4-128d-4afc-a331-83ccd49825ed","added_by":"auto","created_at":"2024-03-15 15:53:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1083956,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4078911/v1/ba4f055b-d205-4eea-a24f-45bef5fdbafb.pdf"},{"id":52775676,"identity":"d72acd4f-2345-4534-84d2-3f4bb87900b9","added_by":"auto","created_at":"2024-03-15 15:37:53","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":49815,"visible":true,"origin":"","legend":"","description":"","filename":"Appendices.docx","url":"https://assets-eu.researchsquare.com/files/rs-4078911/v1/53a005a4fed6e7d2278ee2b2.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Sociodemographic characteristics related to inequality in depression treatment in Peruvian adults: a concentration index decomposition approach","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eAfter the COVID-19 pandemic, it has been an increase of the population with depression and anxiety that represents a public health challenge due to its effects on social development through stigmatization of mental illness and years of life lost due to disability (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), which disproportionately affects middle and low-income countries (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The high prevalence of depressive symptoms among women, older persons, and people from disadvantaged socioeconomic groups makes it difficult to guarantee equitable access to treatment for this illness (\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Furthermore, the receipt of treatment for depression is influenced by various sociodemographic (such as gender, age group, area and place of residence, and ethnicity), economic (educational level, income, and work activity), and health (few resources in mental health or services and centralization of the health system) factors (\u003cspan additionalcitationids=\"CR9 CR10 CR11 CR12\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), limiting the effective implementation of health strategies to address the growing burden of mental illness in the population (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis condition is particularly worrying in countries such as Peru, where mental, neurological, substance-use-related diseases and suicide account for 34% of all years lived with disability (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Only between 2014 and 2018, the prevalence of depressive symptoms in Peru was between 13.8% and 15.1% (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), and the 85.9% did not receive treatment despite the implementation of mental health reforms, which centered on the establishment of community mental health facilities (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). This treatment gap is consistent with other middle and low-income countries, where 79\u0026ndash;93% of adults with depressive symptoms do not receive treatment for depression (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough there is not enough evidence on the determinants of inequality in mental health in countries such as Peru, where structural racism is evident and the inequal distribution of resources and health services harms native racial/ethnic groups (\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), it\u0026acute;s makes crucial to identify the role of this sociodemographic conditions in the receipt of treatment for mental disorders such as depression. This approach could allow the development of public policies that focus on ensuring more equitable and effective access to treatment for this illness. For these reasons, the aim of this study was to evaluate the demographic characteristics related to inequalities in the depression treatment receiving in Peruvian adults.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eAnalytical cross-sectional study developed through a secondary analysis of data from the Demographic and Family Health Survey (DHS), a national survey developed by the National Institute of Statistics and Informatics of Peru (INEI) since 2004. In a Latin American country like Peru, with a wide geographic variety and a population of approximately 32\u0026nbsp;million inhabitants (concentrated mainly in the Lima region, the capital of this country), this survey allows exploring sociodemographic conditions related to health (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). The DHS estimates are representative annually at the national, urban/rural level and for the 24 regions of Peru along with the constitutional province of Callao (neighboring Lima, also considered part of the capital region). Annual information from the survey between 2017 and 2022 was used to assess sociodemographic and health characteristics of Peruvian adults with depressive symptoms, who are 18 years of age or older and who habitually lived where they were surveyed (Appendix 1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDefinition of the Outcome\u003c/h2\u003e \u003cp\u003eThe assessment of depressive symptoms in the DHS of Peru is performed with the Patient Health Questionnaire-9 or PHQ-9 questionnaire, which from 2014 to 2018 evaluated symptoms experienced by participants in the last 12 months (In the last 12 months did you receive treatment from any health professional for depression, sadness, discouragement, lack of interest or irritability?). However, since 2019 it has been changed to a version that evaluates the symptoms experienced by participants in the last two weeks, although the evidence demonstrates the validity and reliability of this instrument in the Peruvian population and its usefulness for the evaluation of depression (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). This questionnaire, composed of nine questions with four response options, identifies adults without depressive symptoms (0 to 4 points), or with mild (5 to 9 points), moderate (10 to 14 points), moderate to severe (15 to 19 points) or severe (20 or more points) depressive symptoms. In this way it was possible to evaluate which adults with depressive symptoms received treatment for this illness during the last year (In the last 12 months have you received treatment from a health professional for depression, sadness, discouragement, lack of interest or irritability?)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSociodemographic Characteristics\u003c/h2\u003e \u003cp\u003eSocio-demographic conditions were evaluated, such as sex (female or male), age group (between 18\u0026ndash;29, 30\u0026ndash;49, 50\u0026ndash;64 and 65 or older), level of education (no education, primary, secondary or higher), wealth quintile (first, second, third, fourth and last quintile), area of residence (rural and urban), living in the capital of Peru (yes or no), health insurance affiliation (yes or no), and ethnic group (white or mestizo, Quechua or Aymara, and Afro-Peruvians), fourth and last quintile), area of residence (rural and urban), living in the capital of Peru (yes or no), health insurance affiliation (yes or no) and the ethnic group with which respondents identify (white or mestizo, Quechua or Aymara and Afro-Peruvians). In addition, the wealth quintile was used as an equity stratifier for the inequality analysis, since this variable evaluates the resources used to build their homes, their access to sanitation or water and the assets owned by the respondents (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Thus, stratification was carried out according to the wealth quintile to which each participant belonged, with values between 0 (last wealth quintile) and 1 (highest wealth quintile).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eThe statistical analysis was developed in STATA v.17.0, including the design of the complex sample of the DHS with the svy package. Thus, categorical variables were described by frequencies and percentages, with their respective 95% confidence interval weighted by the design effect. The numerical variables were described with their averages and their respective confidence intervals. The Rao-Scott test was then used to identify the sociodemographic and health characteristics related to the difference in the receipt of treatment for depression. Poisson regression models weighted by sample design were also used to estimate the association between the sociodemographic variables assessed with the receipt of treatment for depression using the crude Prevalence Ratio (PR) and adjusted (PRa) for the other variables.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eInequality Analysis\u003c/h2\u003e \u003cp\u003eThe inequality analysis was performed with concentration curves to plot inequality in the receipt of treatment for depression as a function of socioeconomic status, from the poorest (last wealth quintile) to the richest (first wealth quintile). In this way, the concentration index (CI) was calculated by estimating the area above or below the curve, so that conditions that mediate an agglomeration above the curve are related to inequality more related to poverty; conversely, conditions that mediate an agglomeration below the curve are related to inequality more related to wealth.\u003c/p\u003e \u003cp\u003eIn addition, the Erreygers Concentration Index (ECI) weighted by complex samples was used (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). This index ranges between values of -1 and +\u0026thinsp;1, with values between 0 and +\u0026thinsp;1 implying that the resource or service evaluated is concentrated among the population with more wealth, while values between \u0026minus;\u0026thinsp;1 and 0 imply a concentration among the population with less wealth. However, because these indexes are absolute values, a method of decomposing the ECI among socioeconomic conditions that explain inequality related to the receipt of treatment for depression was used with a multivariate Probit regression model, thus estimating the contribution of each variable according to the following formula:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$ECI=4 \\left[\\sum _{k}{\\beta }_{k} . \\stackrel{-}{{x}_{k}} .{C}_{k}+{GC}_{\\in } \\right]$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere we have the \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003ek\u003c/em\u003e\u003c/sub\u003e or explanatory coefficient; the \u003cem\u003e߂\u003c/em\u003e\u003csub\u003e\u003cem\u003ek\u003c/em\u003e\u003c/sub\u003e or the mean of the explanatory variable; the \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ek\u003c/em\u003e\u003c/sub\u003e or the concentration index of the explanatory variables; and the \u003cem\u003eGC\u003c/em\u003e\u003csub\u003e\u003cem\u003e\u0026isin;\u003c/em\u003e\u003c/sub\u003e or the generalized concentration for the error term. Among the explanatory variables, the wealth quintile is not considered because it is used as an equity stratified and its inclusion would only generate values close to zero or an omission of the variables in the model.\u003c/p\u003e \u003cp\u003eAdditionally, maps were developed to represent the percentage of Peruvian adults with depression who received treatment and the inequality related to this aspect represented with the ECI of the 25 regions of Peru.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEthical Aspects\u003c/h2\u003e \u003cp\u003eThe study was developed by analyzing data from the DHS in Peru, a national survey applied with the informed consent of the participants. Thus, the study was developed respecting the confidentiality and anonymity of the participants, within the framework of the bioethical principles for the development of health research.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eA total of 35944 Peruvian adults with depression were identified, of whom only 35925 had information about receiving treatment for depression and were therefore included in the study. Of the total participants, 64.89% were female and the average age was 44.91 years (95% CI: 44.58 to 45.25). While the majority had at least a high school education (68.68%), were in the bottom three wealth quintiles (64.99%), resided in an urban area (76.43%) and outside the capital region (66.60%), with health insurance (77.94%) and identified as white or mixed race (50.52%). Also, only the 10.82% (95%CI: 10.26\u0026ndash;11.41) of adults with depressive symptoms receive treatment (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSociodemographic characteristics such as sex, education level, wealth index, ethnicity, area, and place of residence mediated a difference in the receipt of treatment for this disease (p\u0026thinsp;\u0026lt;\u0026thinsp;0.050). In addition, having health insurance and the degree of depressive symptoms mediated a difference in the receipt of treatment for this illness (p\u0026thinsp;\u0026lt;\u0026thinsp;0.050), although age group did not mediate a significant difference in the severity of depressive symptoms (p\u0026thinsp;\u0026lt;\u0026thinsp;0.050) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Likewise, it was possible to identify those characteristics such as sex, age group, level of education, wealth index, area or place of residence, and receipt of treatment were found to differentiate between the degrees of depressive symptoms (p\u0026thinsp;\u0026lt;\u0026thinsp;0.050) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition, it was identified that adults with secondary education (PRa: 0.86; 95%CI: 0.74 to 0.98; p\u0026thinsp;=\u0026thinsp;0.027), primary education (PRa: 0.68; 95%CI: 0.55 to 0.83; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) or no education (PRa: 0.41; 95%CI: 0.27 to 0.63; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), in the last wealth quintile (PRa: 0.41; 95%CI: 0. 30 to 0.58; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) or who identified as Quechua or Ayamara (PRa: 0.87; 95%CI: 0.75 to 0.99; p\u0026thinsp;=\u0026thinsp;0.048) and Afro-Peruvian (PRa: 0.80; 95%CI: 0.65 to 0.97; p\u0026thinsp;=\u0026thinsp;0.027) had lower receipt of treatment for all Peruvian adults with depressive symptoms. While women (PRa: 1.58; 95%CI: 1.38 to 1.80; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Peruvian adults who lived in the capital (PRa: 1.22; 95%CI: 1.06 to 1.39; p\u0026thinsp;=\u0026thinsp;0.004) and had health insurance (PRa: 1.39; 95%CI: 1.18 to 1.63; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) evidenced a higher receipt of treatment for all Peruvian adults with depressive symptoms. This trend was maintained despite increasing severity of depressive symptoms, although the association of many sociodemographic characteristics was not statistically significant for the scenarios where adults with moderate to severe depressive symptoms were present (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the inequality analysis, it was identified that adults with depressive symptoms who received treatment were concentrated in the highest wealth quintiles (CI: 22.08; 95%CI: 20.16 to 24.01; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while adults with depressive symptoms who did not receive treatment were concentrated in the lowest wealth quintiles (CI: -2.15; 95%CI: -2.35 to -1.95; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). In addition, it was specifically identified that as the severity of depressive symptoms increased, from mild (CI: 18.22; 95%CI: 14.79 to 21.66; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), moderate (CI: 21.65; 95%CI: 17.62 to 25.67; p\u0026thinsp;\u0026lt;\u0026thinsp;0. 001), moderate to severe (CI: 24.76; 95%CI: 20.50 to 29.01; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and severe (CI: 26.93; 95%CI: 23.23 to 30.63; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), treatment receipt was concentrated in the highest wealth quintiles (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn assessing the composition of inequality in the receipt of treatment for adults with depressive symptoms, it was identified that those adults who were male (ECI: 0.12; 95%CI: 0.11 to 0.13; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), aged 65 to older (ECI: 0.14; 95%CI: 0.12 to 0. 16; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), enrolled in health insurance (ECI: 0.12; 95%CI: 0.11 to 0.13; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and identified as white or mixed race (ECI: 0.11; 95%CI: 0.10 to 0.12; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) had higher receipt of treatment for this disease mediated by wealth quintile. In contrast adults with depressive symptoms who lived in the urban area (ECI: 0.03; 95%CI: 0.02 to 0.03; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), uneducated (ECI: 0.05; 95%CI: 0.03 to 0.07; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), identified as Afro-Peruvian (ECI: 0. 06; 95%CI: 0.04 to 0.08; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) or women (ECI: 0.07; 95%CI: 0.06 to 0.08; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) had lower receipt of treatment for this disease mediated by wealth quintile (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the decomposition analysis, the variables included (sex, age group, educational level, area and place of residence, ethnic group, and health insurance affiliation) explained 70.16% of the total variance of inequality in the receipt of treatment for Peruvian adults with depressive symptoms. Thus, the greatest positive contribution to inequality came from residing in rural areas (23.67%) or in the capital region (20.77%), having only primary education (19.97%) and identifying as Quechua or Aymara (10.47%). On the other hand, the negative contribution to inequality came from having health insurance (-8.28%), being in the 50\u0026ndash;64 age group (-2.03%) and having only secondary education (-1.77%) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In addition, the contribution of the different sociodemographic conditions to inequality in the receipt of treatment according to the degree of severity of depressive symptoms remained homogeneous, although they decreased in their contribution to inequality (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the geographic distribution of adults with depressive symptoms, the regions of Ucayali (61.83%), Loreto (55.88%) and San Martin (54.65%) had more adults with mild depressive symptoms. While the regions of Ica (26.25%), Lima (24.82%) and Ayacucho (24.56%) had more adults with moderate depressive symptoms. The regions of Ancash (19.42%), Piura (18.01%) and Amazonas (17.27%) had more adults with moderate to severe depressive symptoms. Similarly, the regions of Puno (18.27%), Amazonas (17.78%), Tumbes (17.39%) and Hu\u0026aacute;nuco (17.15%) had more adults with severe depressive symptoms. Although adults with depressive symptoms receiving treatment were mostly concentrated in regions such as Lima (15.37%), Callao (13.96%) and Ica (13.32%). Although the highest receipt of treatment for this wealth-mediated illness was concentrated in the regions of Piura (ECI: 0.12; 95%CI: 0.07 to 0.18; p\u0026thinsp;\u0026lt;\u0026thinsp;0. 001), Ancash (ECI: 0.11; 95%CI: 0.06 to 0.16; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), La Libertad (ECI: 0.10; 95%CI: 0.06 to 0.15; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and San Martin (ECI: 0.10; 95%CI: 0.06 to 0.15; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study evaluated the demographic characteristics that contribute to inequalities in the receiving of treatment for depression symptoms among Peruvian adults. Only one in ten adults with depressive symptoms receive treatment, which is like other countries such as Argentina (11.6%), Brazil (9.0%), Canada (9.5%), and Guatemala (8.7%) (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). However, the situation presents a challenge for public health because of the disease's significant prevalence in Peru, which has remained stable over the evaluation period and may increase after the COVID-19 pandemic (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Improved treatment coverage, while not directly reducing prevalence (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e), remains a crucial aspect because of the consequences of untreated depression symptoms, including a decline in quality of life, mental health complications, and an elevated risk of suicide attempts (\u003cspan additionalcitationids=\"CR31 CR32\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe assessed sociodemographic characteristics (sex, education, wealth index, ethnicity, area, and residence) not only influenced the proportion of adults receiving treatment for depressive symptoms but also impacted the severity of the illness (\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). The multifactorial nature of mental disorders, including depression, explains this phenomenon (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Despite women's higher likelihood of developing depressive symptoms, the study revealed that they received more treatment compared to men. Therefore, addressing the gender gap in depression treatment remains crucial in public health strategies (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, there is an association between lower educational levels, lower wealth quintiles, and a decreased likelihood of looking for treatment for depressed symptoms (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). In this situation, sociodemographic characteristics, especially wealth, intricately connected with education (\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). The research's discovery of a concentration of Peruvian adults with depressive symptoms receiving treatment in the wealthiest quintiles reinforces this observation. The favorable socioeconomic conditions in these quintiles facilitate access to a diverse array of therapies\u0026mdash;pharmacological, behavioral, and educational\u0026mdash;which effectively tackle the challenges posed by the extended rehabilitation period and help prevent relapses in chronic conditions like depression (\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Also, low educational levels significantly impacted a reduced receipt of treatment for depressive symptoms (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). Education, within the realm of mental disorders like depression, may act as a protective mechanism by fostering skills in comprehending life experiences and making decisions amid challenging situations that can trigger symptom development (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOne of the main factors contributing to inequality in accessing treatment for depressive symptoms is the area and place of residence (\u003cspan additionalcitationids=\"CR52\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). Peruvian adults in rural areas and the capital city encounter heightened difficulties in receiving treatment, emphasizing the concerning disparities (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). Despite the implementation of mental health reforms during the study period to expand community mental health centers nationwide (\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), persistent centralization of resources and services in central and southern Peru perpetuates the inequality in treatment receipt, even as symptoms worsen.\u003c/p\u003e \u003cp\u003eIn Peru those adults with depression who identify themselves as Quechua or Aymara are less likely to receive treatment for their illness (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). This highlights the unfair treatment that native populations in Peru face when it comes to accessing health services (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). This is concerning because the lack of treatment not only worsens the disease, but it also affects more Afro-Americans and members of indigenous groups than white people (\u003cspan additionalcitationids=\"CR57 CR58 CR59\" citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e). The reason behind these disparities could be due to discriminatory practices or a lack of cross-cultural integration, which makes timely treatment difficult (\u003cspan additionalcitationids=\"CR62\" citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e). Additionally, negative attitudes toward mental health services, particularly among non-white ethnic groups, contribute to this problem (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe study showed that as adults age, they receive less treatment for depressed symptoms, even though older age groups are more likely to suffer from depression (\u003cspan additionalcitationids=\"CR67\" citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e). That can be due to a variety of social and biological causes, including isolation, daily living issues, psychological distress, and increased comorbidities (\u003cspan additionalcitationids=\"CR70\" citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e). Prioritizing prevention, early detection, and treatment for older adults with depressive symptoms, especially in vulnerable groups, like those with comorbidities is crucial, because could diminish the effectiveness of current pharmacological and cognitive-behavioral treatments, underscoring the necessity for targeted interventions (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHealth insurance affiliation can help to reduce treatment inequality in depression (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e). Attributed to policies like universal insurance and the establishment of community mental health facilities, diagnosing and treating insured adults becomes more accessible in Peru (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e). However, there is still a need for cross-cultural care approaches in treating mental disorders like depression, that is difficult when resources and services are more available in capital of Peru (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), this reinforces the need of decentralization to address gaps in treatment access in rural area and other regions (\u003cspan additionalcitationids=\"CR79\" citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe study present limitations because of the design that does not allow evaluated causal relationship between sociodemographic conditions and treatment inequality for depression. Additionally, the evaluation is limited to the absence of treatment in the last 12 months, with unknown reasons for this lack or whether respondents previously received any treatment. The study lacks follow-up to gauge the impact of sociodemographic conditions on exacerbating depressive symptoms and perpetuating treatment inequality. Furthermore, factors like family support, recurrence of mental disorders, challenges in accessing health centers, and reasons limiting treatment access remain unexplored, hindering a comprehensive understanding of the causes of inequalities in treating this condition.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eIn conclusion, only one in ten Peruvian adults with depressive symptoms received treatment, and this did not improve, even if the symptoms worsened. In addition, sociodemographic conditions such as wealth index, educational level, sex, ethnicity, area, and place of residence mediated differences in the proportion of adults with depressive symptoms who received treatment. Furthermore, living in rural areas, in the capital region, having only secondary education, and identifying as Quechua or Aymara were the main components of inequality in the receipt of treatment for depressive symptoms, and this trend was maintained as the severity of these symptoms increased.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eECI - Erreygers Concentration Index.\u003c/p\u003e\n\u003cp\u003eCI - Concentration Index.\u003c/p\u003e\n\u003cp\u003eDHS - Demographic and Family Health Survey.\u003c/p\u003e\n\u003cp\u003eINEI - National Institute of Statistics and Informatics of Peru.\u003c/p\u003e\n\u003cp\u003ePHQ-9 \u0026ndash; Patient Health Questionnaire-9.\u003c/p\u003e\n\u003cp\u003ePR - Prevalence Ratio.\u003c/p\u003e\n\u003cp\u003ePRa \u0026ndash; adjusted Prevalence Ratio.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e The study was developed by analyzing data from the DHS in Peru, a national survey applied with the informed consent of the participants.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e All authors approve the publication of this article.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e The dataset supporting the conclusions of this article is available in the INEI repository: https://proyectos.inei.gob.pe/microdatos/\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors declare that they have no conflicts of interest\u003c/li\u003e\n \u003cli\u003eFunding: The study was self-funded\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u003c/strong\u003e CIE contributed to the development and conceptualization of the research idea, as well as the analysis of the data and interpretation of the results, and the writing of the manuscript.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e None.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGabet S, Thierry B, Wasfi R, De Groh M, Simonelli G, Hudon C, et al. How is the COVID-19 pandemic impacting our life, mental health, and well-being? Design and preliminary findings of the pan-Canadian longitudinal COHESION Study. medRxiv. 2022; Available in: https://www.embase.com/search/results?subaction=viewrecord\u0026amp;id=L2018694757\u0026amp;from=export U2 - L2018694757 U4 - 2022-08-17\u003c/li\u003e\n\u003cli\u003eSantomauro DF, Mantilla Herrera AM, Shadid J, Zheng P, Ashbaugh C, Pigott DM, et al. Global prevalence and burden of depressive and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic. The Lancet. 2021;398(10312):1700-12. \u003c/li\u003e\n\u003cli\u003eMaffly-Kipp J, Eisenbeck N, Carreno DF, Hicks J. Mental health inequalities increase as a function of COVID-19 pandemic severity levels. Soc Sci Med. 2021;285. Available in: https://www.embase.com/search/results?subaction=viewrecord\u0026amp;id=L2013916725\u0026amp;from=export U2 - L2013916725\u003c/li\u003e\n\u003cli\u003ePeplinski B, McClelland R, Szklo M. Associations between socioeconomic status markers and depressive symptoms by race and gender: results from the Multi-Ethnic Study of Atherosclerosis (MESA). Ann Epidemiol. 2018;28(8):535-542.e1. \u003c/li\u003e\n\u003cli\u003eRai D, Zitko P, Jones K, Lynch J, Araya R. Country- and individual-level socioeconomic determinants of depression: multilevel cross-national comparison. Br J PSYCHIATRY. 2013;202(3):195-203. \u003c/li\u003e\n\u003cli\u003eArundell LL, Greenwood H, Baldwin H, Kotas E, Smith S, Trojanowska K, et al. Advancing mental health equality: a mapping review of interventions, economic evaluations and barriers and facilitators. Syst Rev. 2020;9(1):115. \u003c/li\u003e\n\u003cli\u003ePatel V, Burns JK, Dhingra M, Tarver L, Kohrt BA, Lund C. Income inequality and depression: a systematic review and meta-analysis of the association and a scoping review of mechanisms. World Psychiatry. 2018;17(1):76-89. \u003c/li\u003e\n\u003cli\u003eAlegr\u0026iacute;a M, Chatterji P, Wells K, Cao Z, Chen C nan, Takeuchi D, et al. Disparity in Depression Treatment Among Racial and Ethnic Minority Populations in the United States. Psychiatr Serv. noviembre de 2008;59(11):1264-72. \u003c/li\u003e\n\u003cli\u003eVyas CM, Donneyong M, Mischoulon D, Chang G, Gibson H, Cook NR, et al. Association of Race and Ethnicity With Late-Life Depression Severity, Symptom Burden, and Care. JAMA Netw OPEN. 2020;3(3). \u003c/li\u003e\n\u003cli\u003eSzymkowicz SM, Gerlach AR, Homiack D, Taylor WD. Biological factors influencing depression in later life: role of aging processes and treatment implications. Transl Psychiatry. 2023;13(1):160. \u003c/li\u003e\n\u003cli\u003eAraya R, Zitko P, Markkula N, Rai D, Jones K. Determinants of access to health care for depression in 49 countries: A multilevel analysis. J Affect Disord. 2018;234:80-8. \u003c/li\u003e\n\u003cli\u003eLopes CS, Hellwig N, de Azevedo e Silva G, Menezes PR. Inequities in access to depression treatment: results of the Brazilian National Health Survey - PNS. Int J EQUITY Health. 2016;15. \u003c/li\u003e\n\u003cli\u003eHahm HC, Le Cook B, Ault-Brutus A, Alegra M. Intersection of Race-Ethnicity and Gender in Depression Care: Screening, Access, and Minimally Adequate Treatment. Psychiatr Serv. 2015;66(3):258-64. \u003c/li\u003e\n\u003cli\u003eGilman SE, Fitzmaurice GM, Bruce ML, Ten Have T, Glymour MM, Carliner H, et al. Economic Inequalities in the Effectiveness of a Primary Care Intervention for Depression and Suicidal Ideation. Epidemiology. 2013;24(1):14-22. \u003c/li\u003e\n\u003cli\u003eTanner LM, Wildman JM, Stoniute A, Still M, Bernard K, Green R, et al. Non-pharmaceutical primary care interventions to improve mental health in deprived populations: a systematic review. Br J Gen Pract. 2023;73(729):e242-8. \u003c/li\u003e\n\u003cli\u003ePan American Health Organization. The Burden of Mental Disorders in the Region of the Americas, 2018. PAHO; 2018. Available in: https://iris.paho.org/handle/10665.2/49578\u003c/li\u003e\n\u003cli\u003eVillarreal-Zegarra D, Cabrera-Alva M, Carrillo-Larco RM, Bernabe-Ortiz A. Trends in the prevalence and treatment of depressive symptoms in Peru: a population-based study. BMJ Open. 2020;10(7):e036777. \u003c/li\u003e\n\u003cli\u003ePan American Health Organization. Progress and challenges of mental health reform in Peru over the last decade. PAHO; 2023. Available in: https://iris.paho.org/handle/10665.2/58312\u003c/li\u003e\n\u003cli\u003eCastillo-Martell H, Cutip\u0026eacute;-C\u0026aacute;rdenas Y. Implementation, initial results and sustainability of the reform of mental health services in Peru, 2013-2018. Rev Peru Med Exp Salud Publica. 2019;36(2):326. \u003c/li\u003e\n\u003cli\u003eSmits F, Huijts T. Treatment for depression in 63 countries worldwide: Describing and explaining cross-national differences. Health Place. 2015;31:1-9. \u003c/li\u003e\n\u003cli\u003eMrejen M, Hone T, Rocha R. Socioeconomic and racial/ethnic inequalities in depression prevalence and the treatment gap in Brazil: A decomposition analysis. SSM Popul Health. 2022;20. Available in: https://www.embase.com/search/results?subaction=viewrecord\u0026amp;id=L2020762521\u0026amp;from=export U2 - L2020762521\u003c/li\u003e\n\u003cli\u003eValdivia M. On the ethno-cultural determinants of inequity in maternal and child health in Peru. GRADE. 2005; Available in: https://www.grade.org.pe/publicaciones/1051-sobre-los-determinantes-etnico-culturales-de-la-inequidad-en-salud-materno-infantil-en-el-peru/\u003c/li\u003e\n\u003cli\u003eS\u0026aacute;nchez-Moreno F. La inequidad en salud afecta el desarrollo en el Per\u0026uacute;. Rev Peru Med Exp Salud Publica. 2014;30(4). Available in: https://rpmesp.ins.gob.pe/index.php/rpmesp/article/view/251\u003c/li\u003e\n\u003cli\u003eS\u0026aacute;nchez-Villena AR, Ventura-Le\u0026oacute;n J. Depression according to ethnic identity in Peruvian population: a national study using network analysis. Acta Colomb Psicol. 2024;27(1):183-96. \u003c/li\u003e\n\u003cli\u003eTechnical Direction of Demography and Social Indicators of the National Institute of Statistics and Informatics. PERU: Population Estimates and Projections by Department, Sex and Five-Year Age Groups 1995-2025. INEI; 2009. Available in: http://proyectos.inei.gob.pe/web/biblioineipub/bancopub/Est/Lib0846/libro.pdf\u003c/li\u003e\n\u003cli\u003eCalder\u0026oacute;n M, G\u0026aacute;lvez-Buccollini JA, Cueva G, Ordo\u0026ntilde;ez C, Bromley C, Fiestas F. Validation of the Peruvian version of the PHQ-9 for the diagnosis of depression. Rev Peru Med Exp Salud Publica. 2012;29(4):578-9. \u003c/li\u003e\n\u003cli\u003eEttman CK, Adam GP, Clark MA, Wilson IB, Vivier PM, Galea S. Wealth and depression: A scoping review. Brain Behav. 2022;12(3). Available in: https://www.embase.com/search/results?subaction=viewrecord\u0026amp;id=L2014979425\u0026amp;from=export U2 - L2014979425\u003c/li\u003e\n\u003cli\u003eErreygers G. Correcting the Concentration Index. J Health Econ. 2009;28(2):504-15. \u003c/li\u003e\n\u003cli\u003eOrmel J, Hollon SD, Kessler RC, Cuijpers P, Monroe SM. More treatment but no less depression: The treatment-prevalence paradox. Clin Psychol Rev. 2022;91:102111. \u003c/li\u003e\n\u003cli\u003eBukh JD, Bock C, Vinberg M, Kessing LV. The effect of prolonged duration of untreated depression on antidepressant treatment outcome. J Affect Disord. 2013;145(1):42-8. \u003c/li\u003e\n\u003cli\u003eHung CI, Liu CY, Yang CH. Untreated duration predicted the severity of depression at the two-year follow-up point. Hashimoto K, editor. PLOS ONE. 2017;12(9):e0185119. \u003c/li\u003e\n\u003cli\u003eCulpepper L. Impact of Untreated Major Depressive Disorder on Cognition and Daily Function. J Clin Psychiatry. 2015;76(07):e901-e901. \u003c/li\u003e\n\u003cli\u003eBarateau L, Lopez R, Chenini S, Pesenti C, Rassu AL, Jaussent I, et al. Depression and suicidal thoughts in untreated and treated narcolepsy: Systematic analysis. Neurology. 2020;95(20). Available in: https://www.neurology.org/doi/10.1212/WNL.0000000000010737\u003c/li\u003e\n\u003cli\u003eLorant V. Socioeconomic Inequalities in Depression: A Meta-Analysis. Am J Epidemiol. 2003;157(2):98-112. \u003c/li\u003e\n\u003cli\u003eTorikka A, Kaltiala-Heino R, Rimpel\u0026auml; A, Marttunen M, Luukkaala T, Rimpel\u0026auml; M. Self-reported depression is increasing among socio-economically disadvantaged adolescents \u0026ndash; repeated cross-sectional surveys from Finland from 2000 to 2011. BMC Public Health. 2014;14(1):408. \u003c/li\u003e\n\u003cli\u003eCook S, Saburova L, Bobrova N, Avdeeva E, Malyutina S, Kudryavtsev AV, et al. Socio-demographic, behavioural and psycho-social factors associated with depression in two Russian cities. J Affect Disord. 2021;290:202-10. \u003c/li\u003e\n\u003cli\u003eRoxo L, Silva M, Perelman J. Gender gap in health service utilisation and outcomes of depression: A cross-country longitudinal analysis of European middle-aged and older adults. Prev Med. 2021;153:106847. \u003c/li\u003e\n\u003cli\u003eWeissman MM. Treatment of Depression: Men and Women Are Different? Am J Psychiatry. 2014;171(4):384-7. \u003c/li\u003e\n\u003cli\u003eT\u0026uuml;z\u0026uuml;n H, Demirk\u0026ouml;se H, \u0026Ouml;zkan S, İlhan MN. Socioeconomic Factors Related to Prevalence, Severity, and Contact Coverage of Depression in Primary Health Care. Psychiatry Clin Psychopharmacol. 2021;31(4):457-67. \u003c/li\u003e\n\u003cli\u003ePrevalence, Severity, and Unmet Need for Treatment of Mental Disorders in the World Health Organization World Mental Health Surveys. JAMA. 2004;291(21):2581. \u003c/li\u003e\n\u003cli\u003eDoes education matter? Income inequality and mental health among young adult men. J Mens Health. 2023; Available in: https://www.jomh.org/articles/10.22514/jomh.2023.015\u003c/li\u003e\n\u003cli\u003eSchlax J, J\u0026uuml;nger C, Beutel ME, M\u0026uuml;nzel T, Pfeiffer N, Wild P, et al. Income and education predict elevated depressive symptoms in the general population: results from the Gutenberg health study. BMC Public Health. 2019;19(1):430. \u003c/li\u003e\n\u003cli\u003eHinata A, Kabasawa K, Watanabe Y, Kitamura K, Ito Y, Takachi R, et al. Education, household income, and depressive symptoms in middle-aged and older Japanese adults. BMC Public Health. 2021;21(1):2120. \u003c/li\u003e\n\u003cli\u003eBuckman JEJ, Saunders R, Stott J, Cohen ZD, Arundell LL, Eley TC, et al. Socioeconomic Indicators of Treatment Prognosis for Adults With Depression: A Systematic Review and Individual Patient Data Meta-analysis. JAMA Psychiatry. 2022;79(5):406. \u003c/li\u003e\n\u003cli\u003eMills JA, Suresh V, Chang L, Mayes T, Croarkin PE, Trivedi MH, et al. Socioeconomic Predictors of Treatment Outcomes Among Adults With Major Depressive Disorder. Psychiatr Serv. 2022;73(9):965-9. \u003c/li\u003e\n\u003cli\u003eFolb N, Lund C, Fairall LR, Timmerman V, Levitt NS, Steyn K, et al. Socioeconomic predictors and consequences of depression among primary care attenders with non-communicable diseases in the Western Cape, South Africa: cohort study within a randomised trial. BMC Public Health. 2015;15(1):1194. \u003c/li\u003e\n\u003cli\u003ePatria B. The longitudinal effects of education on depression: Finding from the Indonesian national survey. Front Public Health. 2022;10:1017995. \u003c/li\u003e\n\u003cli\u003eTaple BJ, Chapman R, Schalet BD, Brower R, Griffith JW. The Impact of Education on Depression Assessment: Differential Item Functioning Analysis. Assessment. 2022;29(2):272-84. \u003c/li\u003e\n\u003cli\u003eBruijniks SJE, Frank U, Tuschen-Caffier B, Werthmann J, Renner F. Skill Improvement Through Learning in Therapy (SKILT): A study protocol for a randomized trial testing the direct effects of cognitive behavioral therapy skill acquisition and role of learning capacity in depression. Clin Psychol Eur. 2023;5(1):e8475. \u003c/li\u003e\n\u003cli\u003eMartinengo L, Stona AC, Tudor Car L, Lee J, Griva K, Car J. Education on Depression in Mental Health Apps: Systematic Assessment of Characteristics and Adherence to Evidence-Based Guidelines. J Med Internet Res. 2022;24(3):e28942. \u003c/li\u003e\n\u003cli\u003eWong H, Moore K, Angstman KB, Garrison GM. Impact of rural address and distance from clinic on depression outcomes within a primary care medical home practice. BMC Fam Pract. 2019;20(1):123. \u003c/li\u003e\n\u003cli\u003eJenkins R, Snell-Rood C. Rural Perspectives Challenging Pharmacotherapy. J Behav Health Serv Res. 2021;48(1):112-9. \u003c/li\u003e\n\u003cli\u003eSnell-Rood C, Carpenter-Song E. Depression in a depressed area: Deservingness, mental illness, and treatment in the contemporary rural U.S. Soc Sci Med. 2018;219:78-86. \u003c/li\u003e\n\u003cli\u003eHern\u0026aacute;ndez-V\u0026aacute;squez A, Vargas-Fern\u0026aacute;ndez R, Bendezu-Quispe G, Grendas LN. Depression in the Peruvian population and its associated factors: analysis of a national health survey. J Affect Disord. 2020;273:291-7. \u003c/li\u003e\n\u003cli\u003eIntimayta-Escalante C, Rojas-Bolivar D. Ethnics differences in perceptions of inequality in health care access in Peru. 2023;16(4). \u003c/li\u003e\n\u003cli\u003ePorto Chiavegatto Filho AD, Kawachi I, Wang YP, Viana MC, Silveira Guerra Andrade LH. Does income inequality get under the skin? A multilevel analysis of depression, anxiety and mental disorders in Sao Paulo, Brazil. J Epidemiol COMMUNITY Health. 2013;67(11):966-72. \u003c/li\u003e\n\u003cli\u003eBudhwani H, Hearld KR, Chavez-Yenter D. Depression in Racial and Ethnic Minorities: the Impact of Nativity and Discrimination. J Racial Ethn Health Disparities. 2015;2(1):34-42. \u003c/li\u003e\n\u003cli\u003eLee CH, Duck IM, Sibley CG. Ethnic inequality in diagnosis with depression and anxiety disorders. N Z Med J. 2017;130(1454):10-20. \u003c/li\u003e\n\u003cli\u003eFortuna LR, Alegria M, Gao S. Retention in depression treatment among ethnic and racial minority groups in the United States. Depress Anxiety. 2010;27(5):485-94. \u003c/li\u003e\n\u003cli\u003eKim M. Racial/ethnic disparities in depression and its theoretical perspectives. Psychiatr Q. 2014;85(1):1-8. \u003c/li\u003e\n\u003cli\u003eWard EC. Examining Differential Treatment Effects for Depression in Racial and Ethnic Minority Women: A Qualitative Systematic Review. J Natl Med Assoc. 2007;99(3):265-274.\u003c/li\u003e\n\u003cli\u003eFuentes D, Aranda MP. Depression Interventions Among Racial and Ethnic Minority Older Adults: A Systematic Review Across 20 Years. Am J Geriatr Psychiatry. 2012;20(11):915-31. \u003c/li\u003e\n\u003cli\u003eWeech‐Maldonado R, Morales LS, Elliott M, Spritzer K, Marshall G, Hays RD. Race/Ethnicity, Language, and Patients\u0026rsquo; Assessments of Care in Medicaid Managed Care. Health Serv Res. 2003;38(3):789-808. \u003c/li\u003e\n\u003cli\u003eConner KO, Copeland VC, Grote NK, Koeske G, Rosen D, Reynolds CF, et al. Mental Health Treatment Seeking Among Older Adults With Depression: The Impact of Stigma and Race. Am J Geriatr Psychiatry. 2010;18(6):531-43. \u003c/li\u003e\n\u003cli\u003eSleath B, Domino ME, Wiley-Exley E, Martin B, Richards S, Carey T. Antidepressant and Antipsychotic Use and Adherence Among Medicaid Youths: Differences by Race. Community Ment Health J. 2010;46(3):265-72. \u003c/li\u003e\n\u003cli\u003eAlexopoulos GS. Mechanisms and treatment of late-life depression. Transl Psychiatry. 2019;9(1):188. \u003c/li\u003e\n\u003cli\u003eCohen A, Houck PR, Szanto K, Dew MA, Gilman SE, Reynolds CF. Social Inequalities in Response to Antidepressant Treatment in Older Adults. Arch Gen Psychiatry. 2006;63(1):50. \u003c/li\u003e\n\u003cli\u003eRichardson RA, Keyes KM, Medina JT, Calvo E. Sociodemographic inequalities in depression among older adults: cross-sectional evidence from 18 countries. LANCET PSYCHIATRY. 2020;7(8):673-81. \u003c/li\u003e\n\u003cli\u003eRosset I, Roriz-Cruz M, Santos JLF, Haas VJ, Fabr\u0026iacute;cio-Wehbe SCC, Rodrigues RAP. Socioeconomic and health differentials between two community-dwelling oldest-old groups. Difer Socioecon\u0026ocirc;micos E Sa\u0026uacute;de Entre Duas Comunidades Idosos Longevos. 2011;45(2):391-400. \u003c/li\u003e\n\u003cli\u003eNoel PH. Depression and Comorbid Illness in Elderly Primary Care Patients: Impact on Multiple Domains of Health Status and Well-being. Ann Fam Med. 2004;2(6):555-62. \u003c/li\u003e\n\u003cli\u003eMindlis I, Wisnivesky JP, Wolf MS, O\u0026rsquo;Conor R, Federman AD. Comorbidities and depressive symptoms among older adults with asthma. J Asthma. 2022;59(5):910-6. \u003c/li\u003e\n\u003cli\u003eHarpole LH, Williams JW, Olsen MK, Stechuchak KM, Oddone E, Callahan CM, et al. Improving depression outcomes in older adults with comorbid medical illness. Gen Hosp Psychiatry. 2005;27(1):4-12. \u003c/li\u003e\n\u003cli\u003eBruce ML, Sirey JA. Integrated Care for Depression in Older Primary Care Patients. Can J Psychiatry. 2018;63(7):439-46. \u003c/li\u003e\n\u003cli\u003eTian D, Qu Z, Wang X, Guo J, Xu F, Zhang X, et al. The role of basic health insurance on depression: an epidemiological cohort study of a randomized community sample in Northwest China. BMC Psychiatry. 2012;12(1):151. \u003c/li\u003e\n\u003cli\u003eBaicker K, Allen HL, Wright BJ, Taubman SL, Finkelstein AN. The Effect of Medicaid on Management of Depression: Evidence From the Oregon Health Insurance Experiment. Milbank Q. 2018;96(1):29-56. \u003c/li\u003e\n\u003cli\u003eDe Habich M. Leadership Politics and the Evolution of the Universal Health Insurance Reform in Peru. Health Syst Reform. 2019;5(3):244-9. \u003c/li\u003e\n\u003cli\u003eJumpa-Armas DV. Universal health insurance in Peru: an approximation 10 years after its implementation. Rev Fac Med Humana. 2019;19(3):75-80. \u003c/li\u003e\n\u003cli\u003eToyama M, Castillo H, Galea JT, Brandt LR, Mendoza M, Herrera V, et al. Peruvian Mental Health Reform: A Framework for Scaling-up Mental Health Services. Int J Health Policy Manag. 2017;6(9):501-8. \u003c/li\u003e\n\u003cli\u003eTrianni A, Oliveira E Souza R. Transforming mental health for pandemic recovery and social development: recommendations from the PAHO High-Level Commission on Mental Health and COVID-19. Lancet Reg Health - Am. 2023;22:100527. \u003c/li\u003e\n\u003cli\u003eIslam MdR, Rahman MS, Qusar MS. Community‐based decentralized mental health services are essential to prevent the epidemic turn of post‐Covid mental disorders in Bangladesh: A call to action. Health Sci Rep. 2022;5(4):e734.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"759\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"8\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Sociodemographic characteristics of Peruvian adults with depressive symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.86842105263158%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.894736842105264%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ePeruvian Adults with Depressive Symptoms (N=18504)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.63157894736842%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ePeruvian Adults with Depression who did not receive Treatment (N=32736)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.63157894736842%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ePeruvian Adults with Depression who received Treatment (N=3189)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.973684210526316%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.317214700193423%\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.338491295938105%\"\u003e\n \u003cp\u003e\u003cstrong\u003e%* (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.317214700193423%\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.758220502901352%\"\u003e\n \u003cp\u003e\u003cstrong\u003e%* (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.317214700193423%\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.95164410058027%\"\u003e\n \u003cp\u003e\u003cstrong\u003e%* (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.90118577075099%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.665349143610013%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.25955204216074%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.665349143610013%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.86429512516469%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.665349143610013%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.99604743083004%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.982872200263505%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.90118577075099%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.665349143610013%\" valign=\"top\"\u003e\n \u003cp\u003e11362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.25955204216074%\" valign=\"top\"\u003e\n \u003cp\u003e35.11 (34.28 - 35.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.665349143610013%\" valign=\"top\"\u003e\n \u003cp\u003e10643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.86429512516469%\" valign=\"top\"\u003e\n \u003cp\u003e92.46 (91.58 - 93.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.665349143610013%\" valign=\"top\"\u003e\n \u003cp\u003e719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.99604743083004%\" valign=\"top\"\u003e\n \u003cp\u003e7.54 (6.75 - 8.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.982872200263505%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.770538243626063%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e24449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.555240793201133%\" valign=\"top\"\u003e\n \u003cp\u003e64.89 (64.08 - 65.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e21990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.13031161473088%\" valign=\"top\"\u003e\n \u003cp\u003e87.4 (86.64 - 88.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e2459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.271954674220964%\" valign=\"top\"\u003e\n \u003cp\u003e12.6 (11.87 - 13.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.566534914361%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge Group?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.665349143610013%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.86429512516469%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.665349143610013%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.99604743083004%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.99604743083004%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.99604743083004%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.90118577075099%\"\u003e\n \u003cp\u003e18 to 29 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.665349143610013%\" valign=\"top\"\u003e\n \u003cp\u003e8856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.25955204216074%\" valign=\"top\"\u003e\n \u003cp\u003e25.54 (24.8 - 26.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.665349143610013%\" valign=\"top\"\u003e\n \u003cp\u003e8036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.86429512516469%\" valign=\"top\"\u003e\n \u003cp\u003e89.24 (88.10 - 90.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.665349143610013%\" valign=\"top\"\u003e\n \u003cp\u003e820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.99604743083004%\" valign=\"top\"\u003e\n \u003cp\u003e10.76 (9.72 - 11.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.982872200263505%\" rowspan=\"4\"\u003e\n \u003cp\u003e0.362\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.770538243626063%\"\u003e\n \u003cp\u003e30 to 49 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e14497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.555240793201133%\" valign=\"top\"\u003e\n \u003cp\u003e34.98 (34.24 - 35.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e13071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.13031161473088%\" valign=\"top\"\u003e\n \u003cp\u003e88.8 (87.91 - 89.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e1426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.271954674220964%\" valign=\"top\"\u003e\n \u003cp\u003e11.2 (10.37 - 12.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.770538243626063%\"\u003e\n \u003cp\u003e50 to 64 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e6757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.555240793201133%\" valign=\"top\"\u003e\n \u003cp\u003e22.8 (22.06 - 23.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e6202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.13031161473088%\" valign=\"top\"\u003e\n \u003cp\u003e88.87 (87.43 - 90.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.271954674220964%\" valign=\"top\"\u003e\n \u003cp\u003e11.13 (9.84 - 12.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.770538243626063%\"\u003e\n \u003cp\u003e65 or more years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e5701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.555240793201133%\" valign=\"top\"\u003e\n \u003cp\u003e16.67 (16.03 - 17.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e5324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.13031161473088%\" valign=\"top\"\u003e\n \u003cp\u003e90.29 (88.78 - 91.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.271954674220964%\" valign=\"top\"\u003e\n \u003cp\u003e9.71 (8.39 - 11.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.566534914361%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducational Level?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.665349143610013%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.86429512516469%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.665349143610013%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.99604743083004%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.99604743083004%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.99604743083004%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.90118577075099%\" valign=\"top\"\u003e\n \u003cp\u003eWithout Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.665349143610013%\" valign=\"top\"\u003e\n \u003cp\u003e2992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.25955204216074%\" valign=\"top\"\u003e\n \u003cp\u003e6.56 (6.18 - 6.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.665349143610013%\" valign=\"top\"\u003e\n \u003cp\u003e2897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.86429512516469%\" valign=\"top\"\u003e\n \u003cp\u003e95.69 (94.09 - 96.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.665349143610013%\" valign=\"top\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.99604743083004%\" valign=\"top\"\u003e\n \u003cp\u003e4.31 (3.13 - 5.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.982872200263505%\" rowspan=\"4\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.770538243626063%\" valign=\"top\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e10393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.555240793201133%\" valign=\"top\"\u003e\n \u003cp\u003e24.73 (24.03 - 25.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e9747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.13031161473088%\" valign=\"top\"\u003e\n \u003cp\u003e92.72 (91.82 - 93.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.271954674220964%\" valign=\"top\"\u003e\n \u003cp\u003e7.28 (6.48 - 8.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.770538243626063%\" valign=\"top\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e13719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.555240793201133%\" valign=\"top\"\u003e\n \u003cp\u003e40.17 (39.34 - 41.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e12473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.13031161473088%\" valign=\"top\"\u003e\n \u003cp\u003e89.31 (88.34 - 90.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e1246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.271954674220964%\" valign=\"top\"\u003e\n \u003cp\u003e10.69 (9.79 - 11.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.770538243626063%\" valign=\"top\"\u003e\n \u003cp\u003eHigher Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e8707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.555240793201133%\" valign=\"top\"\u003e\n \u003cp\u003e28.54 (27.72 - 29.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e7516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.13031161473088%\" valign=\"top\"\u003e\n \u003cp\u003e84.42 (83.11 - 85.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e1191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.271954674220964%\" valign=\"top\"\u003e\n \u003cp\u003e15.58 (14.35 - 16.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.566534914361%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eWealth Index?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.665349143610013%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.86429512516469%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.665349143610013%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.99604743083004%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.99604743083004%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.99604743083004%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.90118577075099%\" valign=\"top\"\u003e\n \u003cp\u003eLast Quintile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.665349143610013%\" valign=\"top\"\u003e\n \u003cp\u003e13812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.25955204216074%\" valign=\"top\"\u003e\n \u003cp\u003e23.09 (22.3 - 23.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.665349143610013%\" valign=\"top\"\u003e\n \u003cp\u003e13095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.86429512516469%\" valign=\"top\"\u003e\n \u003cp\u003e95.26 (94.67 - 95.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.665349143610013%\" valign=\"top\"\u003e\n \u003cp\u003e717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.99604743083004%\" valign=\"top\"\u003e\n \u003cp\u003e4.74 (4.21 - 5.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.982872200263505%\" rowspan=\"5\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.770538243626063%\" valign=\"top\"\u003e\n \u003cp\u003eFourth Quintile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e8964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.555240793201133%\" valign=\"top\"\u003e\n \u003cp\u003e21.7 (20.98 - 22.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e8223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.13031161473088%\" valign=\"top\"\u003e\n \u003cp\u003e91.59 (90.62 - 92.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.271954674220964%\" valign=\"top\"\u003e\n \u003cp\u003e8.41 (7.52 - 9.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.770538243626063%\" valign=\"top\"\u003e\n \u003cp\u003eThird Quintile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e5973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.555240793201133%\" valign=\"top\"\u003e\n \u003cp\u003e20.2 (19.49 - 20.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e5328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.13031161473088%\" valign=\"top\"\u003e\n \u003cp\u003e88.93 (87.70 - 90.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.271954674220964%\" valign=\"top\"\u003e\n \u003cp\u003e11.07 (9.94 - 12.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.770538243626063%\" valign=\"top\"\u003e\n \u003cp\u003eSecond Quintile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e4274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.555240793201133%\" valign=\"top\"\u003e\n \u003cp\u003e18.82 (18.05 - 19.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e3715\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.13031161473088%\" valign=\"top\"\u003e\n \u003cp\u003e86.86 (85.26 - 88.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.271954674220964%\" valign=\"top\"\u003e\n \u003cp\u003e13.14 (11.70 - 14.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.770538243626063%\" valign=\"top\"\u003e\n \u003cp\u003eFirst Quintile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e2788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.555240793201133%\" valign=\"top\"\u003e\n \u003cp\u003e16.18 (15.36 - 17.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e2272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.13031161473088%\" valign=\"top\"\u003e\n \u003cp\u003e80.25 (78.02 - 82.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.271954674220964%\" valign=\"top\"\u003e\n \u003cp\u003e19.75 (17.69 - 21.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.566534914361%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eArea of Residence?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.665349143610013%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.86429512516469%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.665349143610013%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.99604743083004%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.99604743083004%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.99604743083004%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.90118577075099%\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.665349143610013%\" valign=\"top\"\u003e\n \u003cp\u003e14888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.25955204216074%\" valign=\"top\"\u003e\n \u003cp\u003e23.56 (22.76 - 24.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.665349143610013%\" valign=\"top\"\u003e\n \u003cp\u003e14046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.86429512516469%\" valign=\"top\"\u003e\n \u003cp\u003e94.87 (94.39 - 95.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.665349143610013%\" valign=\"top\"\u003e\n \u003cp\u003e842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.99604743083004%\" valign=\"top\"\u003e\n \u003cp\u003e5.13 (4.69 - 5.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.982872200263505%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.770538243626063%\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e20923\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.555240793201133%\" valign=\"top\"\u003e\n \u003cp\u003e76.44 (75.63 - 77.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e18587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.13031161473088%\" valign=\"top\"\u003e\n \u003cp\u003e87.42 (86.67 - 88.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e2336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.271954674220964%\" valign=\"top\"\u003e\n \u003cp\u003e12.58 (11.86 - 13.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.566534914361%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDo you live in the Capital?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.665349143610013%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.86429512516469%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.665349143610013%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.99604743083004%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.99604743083004%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.99604743083004%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.90118577075099%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.665349143610013%\" valign=\"top\"\u003e\n \u003cp\u003e31616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.25955204216074%\" valign=\"top\"\u003e\n \u003cp\u003e66.61 (65.4 - 67.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.665349143610013%\" valign=\"top\"\u003e\n \u003cp\u003e29034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.86429512516469%\" valign=\"top\"\u003e\n \u003cp\u003e91.39 (90.92 - 91.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.665349143610013%\" valign=\"top\"\u003e\n \u003cp\u003e2582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.99604743083004%\" valign=\"top\"\u003e\n \u003cp\u003e8.61 (8.16 - 9.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.982872200263505%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.770538243626063%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e4195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.555240793201133%\" valign=\"top\"\u003e\n \u003cp\u003e33.39 (32.21 - 34.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e3599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.13031161473088%\" valign=\"top\"\u003e\n \u003cp\u003e84.75 (83.24 - 86.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.271954674220964%\" valign=\"top\"\u003e\n \u003cp\u003e15.25 (13.85 - 16.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.90118577075099%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealth Insurance?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.665349143610013%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.86429512516469%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.665349143610013%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.99604743083004%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.99604743083004%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.99604743083004%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.99604743083004%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.90118577075099%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.665349143610013%\" valign=\"top\"\u003e\n \u003cp\u003e6592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.25955204216074%\" valign=\"top\"\u003e\n \u003cp\u003e22.07 (21.32 - 22.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.665349143610013%\" valign=\"top\"\u003e\n \u003cp\u003e6145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.86429512516469%\" valign=\"top\"\u003e\n \u003cp\u003e91.39 (90.22 - 92.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.665349143610013%\" valign=\"top\"\u003e\n \u003cp\u003e447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.99604743083004%\" valign=\"top\"\u003e\n \u003cp\u003e8.61 (7.57 - 9.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.982872200263505%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.770538243626063%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e29219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.555240793201133%\" valign=\"top\"\u003e\n \u003cp\u003e77.93 (77.15 - 78.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e26488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.13031161473088%\" valign=\"top\"\u003e\n \u003cp\u003e88.55 (87.87 - 89.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e2731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.271954674220964%\" valign=\"top\"\u003e\n \u003cp\u003e11.45 (10.80 - 12.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.90118577075099%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDepressive Symptoms Degree?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.665349143610013%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.25955204216074%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.665349143610013%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.86429512516469%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.665349143610013%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.99604743083004%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.982872200263505%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.90118577075099%\" valign=\"top\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.665349143610013%\" valign=\"top\"\u003e\n \u003cp\u003e17307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.25955204216074%\" valign=\"top\"\u003e\n \u003cp\u003e47.43 (46.56 - 48.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.665349143610013%\" valign=\"top\"\u003e\n \u003cp\u003e16252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.86429512516469%\" valign=\"top\"\u003e\n \u003cp\u003e92.75 (92.01 - 93.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.665349143610013%\" valign=\"top\"\u003e\n \u003cp\u003e1055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.99604743083004%\" valign=\"top\"\u003e\n \u003cp\u003e7.25 (6.58 - 7.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.982872200263505%\" rowspan=\"4\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.770538243626063%\" valign=\"top\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e8108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.555240793201133%\" valign=\"top\"\u003e\n \u003cp\u003e23.22 (22.49 - 23.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e7388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.13031161473088%\" valign=\"top\"\u003e\n \u003cp\u003e89.73 (88.57 - 90.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.271954674220964%\" valign=\"top\"\u003e\n \u003cp\u003e10.27 (9.22 - 11.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.770538243626063%\" valign=\"top\"\u003e\n \u003cp\u003eModerate to Severe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e5319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.555240793201133%\" valign=\"top\"\u003e\n \u003cp\u003e15.15 (14.57 - 15.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e4708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.13031161473088%\" valign=\"top\"\u003e\n \u003cp\u003e85.21 (83.42 - 86.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e611\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.271954674220964%\" valign=\"top\"\u003e\n \u003cp\u003e14.79 (13.17 - 16.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.770538243626063%\" valign=\"top\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e5077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.555240793201133%\" valign=\"top\"\u003e\n \u003cp\u003e14.21 (13.64 - 14.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e4285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.13031161473088%\" valign=\"top\"\u003e\n \u003cp\u003e80.59 (78.57 - 82.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.271954674220964%\" valign=\"top\"\u003e\n \u003cp\u003e19.41 (17.54 - 21.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.90118577075099%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEthnic Group?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.665349143610013%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.86429512516469%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.665349143610013%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.99604743083004%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.99604743083004%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.99604743083004%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.99604743083004%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.90118577075099%\" valign=\"top\"\u003e\n \u003cp\u003eWhite or Mestizo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.665349143610013%\" valign=\"top\"\u003e\n \u003cp\u003e10877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.25955204216074%\" valign=\"top\"\u003e\n \u003cp\u003e50.54 (49.52 - 51.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.665349143610013%\" valign=\"top\"\u003e\n \u003cp\u003e9658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.86429512516469%\" valign=\"top\"\u003e\n \u003cp\u003e86.45 (85.34 - 87.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.665349143610013%\" valign=\"top\"\u003e\n \u003cp\u003e1219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.99604743083004%\" valign=\"top\"\u003e\n \u003cp\u003e13.55 (12.52 - 14.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.982872200263505%\" rowspan=\"3\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.770538243626063%\" valign=\"top\"\u003e\n \u003cp\u003eQuechua or Aymara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e14779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.555240793201133%\" valign=\"top\"\u003e\n \u003cp\u003e39.15 (38.14 - 40.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e13786\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.13031161473088%\" valign=\"top\"\u003e\n \u003cp\u003e92.12 (91.26 - 92.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.271954674220964%\" valign=\"top\"\u003e\n \u003cp\u003e7.88 (7.09 - 8.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.770538243626063%\" valign=\"top\"\u003e\n \u003cp\u003eAfro-Peruvian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e2387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.555240793201133%\" valign=\"top\"\u003e\n \u003cp\u003e10.3 (9.73 - 10.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e2187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.13031161473088%\" valign=\"top\"\u003e\n \u003cp\u003e92.05 (90.45 - 93.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.090651558073654%\" valign=\"top\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.271954674220964%\" valign=\"top\"\u003e\n \u003cp\u003e7.95 (6.60 - 9.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"8\" valign=\"bottom\"\u003e\n \u003cp\u003e*Frequency weighted by complex sample\u003c/p\u003e\n \u003cp\u003e**P-value estimated with Rao-Scott test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"969\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eSociodemographic characteristics of Peruvian adults by degree of depressive symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.56198347107438%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.56198347107438%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdults with mild depressive symptoms (n= 17307)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.454545454545453%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdults with moderate\u0026nbsp;\u003cbr\u003e\u0026nbsp;depressive symptoms (n= 8108)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.454545454545453%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdults with moderate to severe depressive symptoms (n= 5319)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49173553719008%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdults with severe depressive symptoms (n= 5077)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.475206611570248%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.818791946308725%\"\u003e\n \u003cp\u003e\u003cstrong\u003e%*(95%IC)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.57718120805369%\"\u003e\n \u003cp\u003e\u003cstrong\u003e%*(95%IC)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.57718120805369%\"\u003e\n \u003cp\u003e\u003cstrong\u003e%*(95%IC)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.026845637583893%\"\u003e\n \u003cp\u003e\u003cstrong\u003e%*(95%IC)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.56198347107438%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.56198347107438%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.454545454545453%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.454545454545453%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.49173553719008%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.475206611570248%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.56198347107438%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.56198347107438%\" valign=\"top\"\u003e\n \u003cp\u003e53.72 (52.22 - 55.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.454545454545453%\" valign=\"top\"\u003e\n \u003cp\u003e21.8 (20.59 - 23.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.454545454545453%\" valign=\"top\"\u003e\n \u003cp\u003e13.58 (12.64 - 14.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49173553719008%\" valign=\"top\"\u003e\n \u003cp\u003e10.89 (10.01 - 11.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.475206611570248%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.579234972677597%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.579234972677597%\" valign=\"top\"\u003e\n \u003cp\u003e44.02 (43.00 - 45.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.639344262295083%\" valign=\"top\"\u003e\n \u003cp\u003e23.98 (23.10 - 24.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.639344262295083%\" valign=\"top\"\u003e\n \u003cp\u003e16 (15.26 - 16.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.562841530054644%\" valign=\"top\"\u003e\n \u003cp\u003e16 (15.27 - 16.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.56198347107438%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge Group?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.56198347107438%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.454545454545453%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.454545454545453%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.49173553719008%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.475206611570248%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.56198347107438%\"\u003e\n \u003cp\u003e18 to 29 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.56198347107438%\" valign=\"top\"\u003e\n \u003cp\u003e52.01 (50.31 - 53.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.454545454545453%\" valign=\"top\"\u003e\n \u003cp\u003e22.13 (20.71 - 23.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.454545454545453%\" valign=\"top\"\u003e\n \u003cp\u003e14.1 (13.00 - 15.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49173553719008%\" valign=\"top\"\u003e\n \u003cp\u003e11.75 (10.70 - 12.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.475206611570248%\" rowspan=\"4\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.579234972677597%\"\u003e\n \u003cp\u003e30 to 49 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.579234972677597%\" valign=\"top\"\u003e\n \u003cp\u003e48.7 (47.42 - 49.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.639344262295083%\" valign=\"top\"\u003e\n \u003cp\u003e23.16 (22.07 - 24.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.639344262295083%\" valign=\"top\"\u003e\n \u003cp\u003e14.99 (14.09 - 15.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.562841530054644%\" valign=\"top\"\u003e\n \u003cp\u003e13.15 (12.33 - 14.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.579234972677597%\"\u003e\n \u003cp\u003e50 to 64 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.579234972677597%\" valign=\"top\"\u003e\n \u003cp\u003e45.57 (43.64 - 47.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.639344262295083%\" valign=\"top\"\u003e\n \u003cp\u003e23.35 (21.74 - 25.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.639344262295083%\" valign=\"top\"\u003e\n \u003cp\u003e15.94 (14.67 - 17.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.562841530054644%\" valign=\"top\"\u003e\n \u003cp\u003e15.14 (13.86 - 16.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.579234972677597%\"\u003e\n \u003cp\u003e65 or more years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.579234972677597%\" valign=\"top\"\u003e\n \u003cp\u003e40.27 (38.25 - 42.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.639344262295083%\" valign=\"top\"\u003e\n \u003cp\u003e24.82 (23.00 - 26.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.639344262295083%\" valign=\"top\"\u003e\n \u003cp\u003e16 (14.51 - 17.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.562841530054644%\" valign=\"top\"\u003e\n \u003cp\u003e18.91 (17.33 - 20.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.56198347107438%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducational Level?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.56198347107438%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.454545454545453%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.454545454545453%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.49173553719008%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.475206611570248%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.56198347107438%\" valign=\"top\"\u003e\n \u003cp\u003eWithout Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.56198347107438%\" valign=\"top\"\u003e\n \u003cp\u003e36.48 (33.83 - 39.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.454545454545453%\" valign=\"top\"\u003e\n \u003cp\u003e25.62 (23.14 - 28.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.454545454545453%\" valign=\"top\"\u003e\n \u003cp\u003e16.28 (14.14 - 18.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49173553719008%\" valign=\"top\"\u003e\n \u003cp\u003e21.62 (19.42 - 24.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.475206611570248%\" rowspan=\"4\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.579234972677597%\" valign=\"top\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.579234972677597%\" valign=\"top\"\u003e\n \u003cp\u003e44.42 (42.92 - 45.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.639344262295083%\" valign=\"top\"\u003e\n \u003cp\u003e24.12 (22.80 - 25.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.639344262295083%\" valign=\"top\"\u003e\n \u003cp\u003e15.9 (14.81 - 17.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.562841530054644%\" valign=\"top\"\u003e\n \u003cp\u003e15.56 (14.50 - 16.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.579234972677597%\" valign=\"top\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.579234972677597%\" valign=\"top\"\u003e\n \u003cp\u003e49.58 (48.18 - 50.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.639344262295083%\" valign=\"top\"\u003e\n \u003cp\u003e22.59 (21.43 - 23.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.639344262295083%\" valign=\"top\"\u003e\n \u003cp\u003e14.85 (13.92 - 15.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.562841530054644%\" valign=\"top\"\u003e\n \u003cp\u003e12.97 (12.06 - 13.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.579234972677597%\" valign=\"top\"\u003e\n \u003cp\u003eHigher Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.579234972677597%\" valign=\"top\"\u003e\n \u003cp\u003e49.51 (47.86 - 51.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.639344262295083%\" valign=\"top\"\u003e\n \u003cp\u003e22.77 (21.39 - 24.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.639344262295083%\" valign=\"top\"\u003e\n \u003cp\u003e14.66 (13.56 - 15.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.562841530054644%\" valign=\"top\"\u003e\n \u003cp\u003e13.07 (12.00 - 14.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.56198347107438%\"\u003e\n \u003cp\u003e\u003cstrong\u003eWealth Index?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.56198347107438%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.454545454545453%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.454545454545453%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49173553719008%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.475206611570248%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.56198347107438%\" valign=\"top\"\u003e\n \u003cp\u003eLast Quintile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.56198347107438%\" valign=\"top\"\u003e\n \u003cp\u003e46.76 (45.39 - 48.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.454545454545453%\" valign=\"top\"\u003e\n \u003cp\u003e22.94 (21.91 - 24.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.454545454545453%\" valign=\"top\"\u003e\n \u003cp\u003e14.33 (13.50 - 15.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49173553719008%\" valign=\"top\"\u003e\n \u003cp\u003e15.98 (15.08 - 16.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.475206611570248%\" rowspan=\"5\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.579234972677597%\" valign=\"top\"\u003e\n \u003cp\u003eFourth Quintile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.579234972677597%\" valign=\"top\"\u003e\n \u003cp\u003e48.73 (47.17 - 50.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.639344262295083%\" valign=\"top\"\u003e\n \u003cp\u003e22.6 (21.33 - 23.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.639344262295083%\" valign=\"top\"\u003e\n \u003cp\u003e15.33 (14.22 - 16.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.562841530054644%\" valign=\"top\"\u003e\n \u003cp\u003e13.34 (12.34 - 14.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.579234972677597%\" valign=\"top\"\u003e\n \u003cp\u003eThird Quintile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.579234972677597%\" valign=\"top\"\u003e\n \u003cp\u003e46.45 (44.52 - 48.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.639344262295083%\" valign=\"top\"\u003e\n \u003cp\u003e22.7 (21.15 - 24.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.639344262295083%\" valign=\"top\"\u003e\n \u003cp\u003e16.17 (14.81 - 17.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.562841530054644%\" valign=\"top\"\u003e\n \u003cp\u003e14.68 (13.38 - 16.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.579234972677597%\" valign=\"top\"\u003e\n \u003cp\u003eSecond Quintile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.579234972677597%\" valign=\"top\"\u003e\n \u003cp\u003e46.95 (44.78 - 49.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.639344262295083%\" valign=\"top\"\u003e\n \u003cp\u003e24.32 (22.39 - 26.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.639344262295083%\" valign=\"top\"\u003e\n \u003cp\u003e15.21 (13.74 - 16.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.562841530054644%\" valign=\"top\"\u003e\n \u003cp\u003e13.52 (12.04 - 15.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.579234972677597%\" valign=\"top\"\u003e\n \u003cp\u003eFirst Quintile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.579234972677597%\" valign=\"top\"\u003e\n \u003cp\u003e48.4 (45.78 - 51.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.639344262295083%\" valign=\"top\"\u003e\n \u003cp\u003e23.8 (21.66 - 26.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.639344262295083%\" valign=\"top\"\u003e\n \u003cp\u003e14.73 (12.92 - 16.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.562841530054644%\" valign=\"top\"\u003e\n \u003cp\u003e13.07 (11.40 - 14.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.56198347107438%\"\u003e\n \u003cp\u003e\u003cstrong\u003eArea of Residence?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.56198347107438%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.454545454545453%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.454545454545453%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.49173553719008%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.475206611570248%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.56198347107438%\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.56198347107438%\" valign=\"top\"\u003e\n \u003cp\u003e47.67 (46.47 - 48.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.454545454545453%\" valign=\"top\"\u003e\n \u003cp\u003e22.34 (21.43 - 23.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.454545454545453%\" valign=\"top\"\u003e\n \u003cp\u003e14.09 (13.36 - 14.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49173553719008%\" valign=\"top\"\u003e\n \u003cp\u003e15.9 (15.08 - 16.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.475206611570248%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.579234972677597%\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.579234972677597%\" valign=\"top\"\u003e\n \u003cp\u003e47.35 (46.29 - 48.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.639344262295083%\" valign=\"top\"\u003e\n \u003cp\u003e23.49 (22.59 - 24.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.639344262295083%\" valign=\"top\"\u003e\n \u003cp\u003e15.47 (14.75 - 16.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.562841530054644%\" valign=\"top\"\u003e\n \u003cp\u003e13.69 (12.99 - 14.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.56198347107438%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDo you live in the Capital?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.56198347107438%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.454545454545453%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.454545454545453%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.49173553719008%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.475206611570248%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.56198347107438%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.56198347107438%\" valign=\"top\"\u003e\n \u003cp\u003e48.54 (47.71 - 49.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.454545454545453%\" valign=\"top\"\u003e\n \u003cp\u003e22.44 (21.78 - 23.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.454545454545453%\" valign=\"top\"\u003e\n \u003cp\u003e14.92 (14.36 - 15.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49173553719008%\" valign=\"top\"\u003e\n \u003cp\u003e14.1 (13.56 - 14.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.475206611570248%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.579234972677597%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.579234972677597%\" valign=\"top\"\u003e\n \u003cp\u003e45.21 (43.24 - 47.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.639344262295083%\" valign=\"top\"\u003e\n \u003cp\u003e24.76 (23.07 - 26.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.639344262295083%\" valign=\"top\"\u003e\n \u003cp\u003e15.61 (14.29 - 17.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.562841530054644%\" valign=\"top\"\u003e\n \u003cp\u003e14.43 (13.12 - 15.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.56198347107438%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealth Insurance?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.56198347107438%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.454545454545453%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.454545454545453%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.49173553719008%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.475206611570248%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.56198347107438%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.56198347107438%\" valign=\"top\"\u003e\n \u003cp\u003e48.58 (46.63 - 50.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.454545454545453%\" valign=\"top\"\u003e\n \u003cp\u003e23.66 (22.06 - 25.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.454545454545453%\" valign=\"top\"\u003e\n \u003cp\u003e15.06 (13.81 - 16.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49173553719008%\" valign=\"top\"\u003e\n \u003cp\u003e12.7 (11.54 - 13.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.475206611570248%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.579234972677597%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.579234972677597%\" valign=\"top\"\u003e\n \u003cp\u003e47.1 (46.15 - 48.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.639344262295083%\" valign=\"top\"\u003e\n \u003cp\u003e23.09 (22.28 - 23.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.639344262295083%\" valign=\"top\"\u003e\n \u003cp\u003e15.17 (14.52 - 15.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.562841530054644%\" valign=\"top\"\u003e\n \u003cp\u003e14.64 (13.98 - 15.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.56198347107438%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEthnic Group?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.56198347107438%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.454545454545453%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.454545454545453%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.49173553719008%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.475206611570248%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.56198347107438%\" valign=\"top\"\u003e\n \u003cp\u003eWhite or Mestizo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.56198347107438%\" valign=\"top\"\u003e\n \u003cp\u003e47.96 (46.49 - 49.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.454545454545453%\" valign=\"top\"\u003e\n \u003cp\u003e23.04 (21.79 - 24.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.454545454545453%\" valign=\"top\"\u003e\n \u003cp\u003e15.37 (14.35 - 16.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49173553719008%\" valign=\"top\"\u003e\n \u003cp\u003e13.63 (12.66 - 14.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.475206611570248%\" rowspan=\"3\"\u003e\n \u003cp\u003e0.351\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.579234972677597%\" valign=\"top\"\u003e\n \u003cp\u003eQuechua or Aymara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.579234972677597%\" valign=\"top\"\u003e\n \u003cp\u003e46.92 (45.58 - 48.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.639344262295083%\" valign=\"top\"\u003e\n \u003cp\u003e22.71 (21.63 - 23.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.639344262295083%\" valign=\"top\"\u003e\n \u003cp\u003e15.22 (14.29 - 16.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.562841530054644%\" valign=\"top\"\u003e\n \u003cp\u003e15.16 (14.23 - 16.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.579234972677597%\" valign=\"top\"\u003e\n \u003cp\u003eAfro-Peruvian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.579234972677597%\" valign=\"top\"\u003e\n \u003cp\u003e48.4 (45.45 - 51.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.639344262295083%\" valign=\"top\"\u003e\n \u003cp\u003e23.75 (21.32 - 26.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.639344262295083%\" valign=\"top\"\u003e\n \u003cp\u003e14.85 (12.96 - 16.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.562841530054644%\" valign=\"top\"\u003e\n \u003cp\u003e13 (11.25 - 14.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.56198347107438%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDid you receive treatment?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.56198347107438%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.454545454545453%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.454545454545453%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49173553719008%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.475206611570248%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.56198347107438%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.56198347107438%\" valign=\"top\"\u003e\n \u003cp\u003e49.32 (48.40 \u0026ndash; 50.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.454545454545453%\" valign=\"top\"\u003e\n \u003cp\u003e23.36 (22.59 \u0026ndash; 24.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.454545454545453%\" valign=\"top\"\u003e\n \u003cp\u003e14.48 (13.88 \u0026ndash; 15.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49173553719008%\" valign=\"top\"\u003e\n \u003cp\u003e12.84 (12.27 \u0026ndash; 13.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.475206611570248%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.579234972677597%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.579234972677597%\" valign=\"top\"\u003e\n \u003cp\u003e31.78 (29.25 \u0026ndash; 34.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.639344262295083%\" valign=\"top\"\u003e\n \u003cp\u003e22.04 (19.87 \u0026ndash; 24.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.639344262295083%\" valign=\"top\"\u003e\n \u003cp\u003e20.70 (18.51 \u0026ndash; 23.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.562841530054644%\" valign=\"top\"\u003e\n \u003cp\u003e25.48 (23.07 \u0026ndash; 28.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\"\u003e\n \u003cp\u003e*Frequency weighted by complex sample\u003c/p\u003e\n \u003cp\u003e**P-value estimated with Rao-Scott test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"615\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"8\" style=\"width: 52.1578%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Decomposition of socioeconomic inequality related to treatment receipt in Peruvian adults with depressive symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.592833876221498%\" style=\"width: 13.5762%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.661237785016286%\" style=\"width: 8.7196%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eECI (95%IC)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.49185667752443%\" style=\"width: 5.944%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912052117263844%\" style=\"width: 9.8234%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarginal Effect\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.237785016286646%\" style=\"width: 9.2715%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eElasticity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.328990228013029%\" style=\"width: 6.181%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eC\u003csub\u003ek\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.775244299674267%\" style=\"width: 12.9139%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eContribution (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" style=\"width: 0.883%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.592833876221498%\" style=\"width: 13.5762%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.661237785016286%\" valign=\"top\" style=\"width: 8.7196%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.49185667752443%\" valign=\"bottom\" style=\"width: 5.944%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912052117263844%\" valign=\"bottom\" style=\"width: 9.8234%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.237785016286646%\" valign=\"bottom\" style=\"width: 9.2715%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.328990228013029%\" valign=\"bottom\" style=\"width: 6.181%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.775244299674267%\" valign=\"top\" style=\"width: 12.9139%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" style=\"width: 0.883%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.592833876221498%\" style=\"width: 13.5762%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.661237785016286%\" valign=\"top\" style=\"width: 8.7196%;\"\u003e\n \u003cp\u003e0.074 (0.064 - 0.084)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.49185667752443%\" style=\"width: 5.944%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912052117263844%\" valign=\"bottom\" style=\"width: 9.8234%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eREF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.237785016286646%\" style=\"width: 9.2715%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.328990228013029%\" valign=\"bottom\" style=\"width: 6.181%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.775244299674267%\" valign=\"bottom\" style=\"width: 12.9139%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" style=\"width: 0.883%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.592833876221498%\" style=\"width: 13.5762%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.661237785016286%\" valign=\"top\" style=\"width: 8.7196%;\"\u003e\n \u003cp\u003e0.121 (0.112 - 0.130)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.49185667752443%\" style=\"width: 5.944%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912052117263844%\" valign=\"bottom\" style=\"width: 9.8234%;\"\u003e\n \u003cp\u003e0.046*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.237785016286646%\" valign=\"bottom\" style=\"width: 9.2715%;\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.328990228013029%\" valign=\"bottom\" style=\"width: 6.181%;\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.775244299674267%\" valign=\"bottom\" style=\"width: 12.9139%;\"\u003e\n \u003cp\u003e2.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" style=\"width: 0.883%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.34146341463415%\" style=\"width: 13.5762%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge Group?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.34146341463415%\" style=\"width: 8.7196%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.479674796747967%\" valign=\"top\" style=\"width: 4.0839%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.894308943089431%\" valign=\"top\" style=\"width: 9.8234%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.21951219512195%\" valign=\"top\" style=\"width: 9.2715%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.317073170731708%\" valign=\"top\" style=\"width: 6.181%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.747967479674795%\" valign=\"top\" style=\"width: 12.9139%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.747967479674795%\" valign=\"top\" style=\"width: 0.883%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.592833876221498%\" style=\"width: 13.5762%;\"\u003e\n \u003cp\u003e18 to 29 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.661237785016286%\" valign=\"top\" style=\"width: 8.7196%;\"\u003e\n \u003cp\u003e0.141 (0.123 - 0.159)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.49185667752443%\" style=\"width: 6.5121%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912052117263844%\" valign=\"bottom\" style=\"width: 9.8234%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eREF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.237785016286646%\" style=\"width: 9.2715%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.328990228013029%\" valign=\"bottom\" style=\"width: 6.181%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.775244299674267%\" style=\"width: 12.9139%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" style=\"width: 0.883%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.592833876221498%\" style=\"width: 13.5762%;\"\u003e\n \u003cp\u003e30 to 49 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.661237785016286%\" valign=\"top\" style=\"width: 8.7196%;\"\u003e\n \u003cp\u003e0.119 (0.103 - 0.135)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.49185667752443%\" style=\"width: 6.5121%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912052117263844%\" valign=\"bottom\" style=\"width: 9.8234%;\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.237785016286646%\" valign=\"top\" style=\"width: 9.2715%;\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.328990228013029%\" valign=\"top\" style=\"width: 6.181%;\"\u003e\n \u003cp\u003e-0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.775244299674267%\" valign=\"top\" style=\"width: 12.9139%;\"\u003e\n \u003cp\u003e-2.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" style=\"width: 0.883%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.592833876221498%\" style=\"width: 13.5762%;\"\u003e\n \u003cp\u003e50 to 64 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.661237785016286%\" valign=\"top\" style=\"width: 8.7196%;\"\u003e\n \u003cp\u003e0.092 (0.081 - 0.103)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.49185667752443%\" style=\"width: 6.5121%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912052117263844%\" valign=\"bottom\" style=\"width: 9.8234%;\"\u003e\n \u003cp\u003e0.050*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.237785016286646%\" valign=\"top\" style=\"width: 9.2715%;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.328990228013029%\" valign=\"top\" style=\"width: 6.181%;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.775244299674267%\" valign=\"top\" style=\"width: 12.9139%;\"\u003e\n \u003cp\u003e-0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" style=\"width: 0.883%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.592833876221498%\" style=\"width: 13.5762%;\"\u003e\n \u003cp\u003e65 or more years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.661237785016286%\" valign=\"top\" style=\"width: 8.7196%;\"\u003e\n \u003cp\u003e0.089 (0.075 - 0.103)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.49185667752443%\" style=\"width: 6.5121%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912052117263844%\" valign=\"bottom\" style=\"width: 9.8234%;\"\u003e\n \u003cp\u003e-0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.237785016286646%\" valign=\"top\" style=\"width: 9.2715%;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.328990228013029%\" valign=\"top\" style=\"width: 6.181%;\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.775244299674267%\" valign=\"top\" style=\"width: 12.9139%;\"\u003e\n \u003cp\u003e0.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" style=\"width: 0.883%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.34146341463415%\" colspan=\"2\" style=\"width: 24.8344%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducational Level?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.479674796747967%\" valign=\"top\" style=\"width: 4.0839%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.894308943089431%\" valign=\"top\" style=\"width: 9.8234%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.21951219512195%\" valign=\"top\" style=\"width: 9.2715%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.317073170731708%\" valign=\"top\" style=\"width: 6.181%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.747967479674795%\" colspan=\"2\" valign=\"top\" style=\"width: 13.7969%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.592833876221498%\" valign=\"top\" style=\"width: 13.5762%;\"\u003e\n \u003cp\u003eWithout Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.661237785016286%\" valign=\"top\" style=\"width: 8.7196%;\"\u003e\n \u003cp\u003e0.088 (0.074 - 0.102)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.49185667752443%\" style=\"width: 6.5121%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912052117263844%\" valign=\"bottom\" style=\"width: 9.8234%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eREF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.237785016286646%\" style=\"width: 9.2715%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.328990228013029%\" valign=\"bottom\" style=\"width: 6.181%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.775244299674267%\" style=\"width: 12.9139%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" style=\"width: 0.883%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.592833876221498%\" valign=\"top\" style=\"width: 13.5762%;\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.661237785016286%\" valign=\"top\" style=\"width: 8.7196%;\"\u003e\n \u003cp\u003e0.074 (0.062 - 0.086)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.49185667752443%\" style=\"width: 6.5121%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912052117263844%\" valign=\"bottom\" style=\"width: 9.8234%;\"\u003e\n \u003cp\u003e-0.043*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.237785016286646%\" valign=\"top\" style=\"width: 9.2715%;\"\u003e\n \u003cp\u003e-0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.328990228013029%\" valign=\"top\" style=\"width: 6.181%;\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.775244299674267%\" valign=\"top\" style=\"width: 12.9139%;\"\u003e\n \u003cp\u003e-1.774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" style=\"width: 0.883%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.592833876221498%\" valign=\"top\" style=\"width: 13.5762%;\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.661237785016286%\" valign=\"top\" style=\"width: 8.7196%;\"\u003e\n \u003cp\u003e0.075 (0.064 - 0.086)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.49185667752443%\" style=\"width: 6.5121%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912052117263844%\" valign=\"bottom\" style=\"width: 9.8234%;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.237785016286646%\" valign=\"top\" style=\"width: 9.2715%;\"\u003e\n \u003cp\u003e-0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.328990228013029%\" valign=\"top\" style=\"width: 6.181%;\"\u003e\n \u003cp\u003e-0.315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.775244299674267%\" valign=\"top\" style=\"width: 12.9139%;\"\u003e\n \u003cp\u003e19.972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" style=\"width: 0.883%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.592833876221498%\" valign=\"top\" style=\"width: 13.5762%;\"\u003e\n \u003cp\u003eHigher Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.661237785016286%\" valign=\"top\" style=\"width: 8.7196%;\"\u003e\n \u003cp\u003e0.053 (0.034 - 0.072)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.49185667752443%\" style=\"width: 6.5121%;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912052117263844%\" valign=\"bottom\" style=\"width: 9.8234%;\"\u003e\n \u003cp\u003e-0.048*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.237785016286646%\" valign=\"top\" style=\"width: 9.2715%;\"\u003e\n \u003cp\u003e-0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.328990228013029%\" valign=\"top\" style=\"width: 6.181%;\"\u003e\n \u003cp\u003e-0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.775244299674267%\" valign=\"top\" style=\"width: 12.9139%;\"\u003e\n \u003cp\u003e3.973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" style=\"width: 0.883%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.34146341463415%\" colspan=\"2\" style=\"width: 24.8344%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eArea of Residence?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.479674796747967%\" valign=\"top\" style=\"width: 4.0839%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.894308943089431%\" valign=\"top\" style=\"width: 9.8234%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.21951219512195%\" valign=\"top\" style=\"width: 9.2715%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.317073170731708%\" valign=\"top\" style=\"width: 6.181%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.747967479674795%\" colspan=\"2\" valign=\"top\" style=\"width: 13.7969%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.592833876221498%\" style=\"width: 13.5762%;\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.661237785016286%\" valign=\"top\" style=\"width: 8.7196%;\"\u003e\n \u003cp\u003e0.027 (0.022 - 0.032)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.49185667752443%\" style=\"width: 6.5121%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912052117263844%\" valign=\"bottom\" style=\"width: 9.8234%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eREF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.237785016286646%\" style=\"width: 9.2715%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.328990228013029%\" valign=\"bottom\" style=\"width: 6.181%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.775244299674267%\" valign=\"bottom\" style=\"width: 12.9139%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" style=\"width: 0.883%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.592833876221498%\" style=\"width: 13.5762%;\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.661237785016286%\" valign=\"top\" style=\"width: 8.7196%;\"\u003e\n \u003cp\u003e0.090 (0.081 - 0.099)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.49185667752443%\" style=\"width: 6.5121%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912052117263844%\" valign=\"bottom\" style=\"width: 9.8234%;\"\u003e\n \u003cp\u003e0.063*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.237785016286646%\" valign=\"top\" style=\"width: 9.2715%;\"\u003e\n \u003cp\u003e-0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.328990228013029%\" valign=\"top\" style=\"width: 6.181%;\"\u003e\n \u003cp\u003e-0.618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.775244299674267%\" valign=\"top\" style=\"width: 12.9139%;\"\u003e\n \u003cp\u003e23.674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" style=\"width: 0.883%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.34146341463415%\" colspan=\"2\" style=\"width: 24.8344%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDo you live in the Capital?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.479674796747967%\" valign=\"top\" style=\"width: 4.0839%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.894308943089431%\" valign=\"top\" style=\"width: 9.8234%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.21951219512195%\" valign=\"top\" style=\"width: 9.2715%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.317073170731708%\" valign=\"top\" style=\"width: 6.181%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.747967479674795%\" colspan=\"2\" valign=\"top\" style=\"width: 13.7969%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.592833876221498%\" style=\"width: 13.5762%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.661237785016286%\" valign=\"top\" style=\"width: 8.7196%;\"\u003e\n \u003cp\u003e0.086 (0.080 - 0.092)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.49185667752443%\" style=\"width: 6.5121%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912052117263844%\" valign=\"bottom\" style=\"width: 9.8234%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eREF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.237785016286646%\" style=\"width: 9.2715%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.328990228013029%\" valign=\"bottom\" style=\"width: 6.181%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.775244299674267%\" valign=\"bottom\" style=\"width: 12.9139%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" style=\"width: 0.883%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.592833876221498%\" style=\"width: 13.5762%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.661237785016286%\" valign=\"top\" style=\"width: 8.7196%;\"\u003e\n \u003cp\u003e0.077 (0.060 - 0.094)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.49185667752443%\" style=\"width: 6.5121%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912052117263844%\" valign=\"bottom\" style=\"width: 9.8234%;\"\u003e\n \u003cp\u003e-0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.237785016286646%\" valign=\"top\" style=\"width: 9.2715%;\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.328990228013029%\" valign=\"top\" style=\"width: 6.181%;\"\u003e\n \u003cp\u003e0.481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.775244299674267%\" valign=\"top\" style=\"width: 12.9139%;\"\u003e\n \u003cp\u003e20.767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" style=\"width: 0.883%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.592833876221498%\" style=\"width: 13.5762%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealth Insurance?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.661237785016286%\" valign=\"top\" style=\"width: 8.7196%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.49185667752443%\" style=\"width: 6.5121%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912052117263844%\" valign=\"top\" style=\"width: 9.8234%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.237785016286646%\" valign=\"top\" style=\"width: 9.2715%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.328990228013029%\" valign=\"top\" style=\"width: 6.181%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.775244299674267%\" valign=\"top\" style=\"width: 12.9139%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" style=\"width: 0.883%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.592833876221498%\" style=\"width: 13.5762%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.661237785016286%\" valign=\"top\" style=\"width: 8.7196%;\"\u003e\n \u003cp\u003e0.083 (0.070 - 0.096)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.49185667752443%\" style=\"width: 6.5121%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912052117263844%\" valign=\"bottom\" style=\"width: 9.8234%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eREF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.237785016286646%\" valign=\"bottom\" style=\"width: 9.2715%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.328990228013029%\" valign=\"bottom\" style=\"width: 6.181%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.775244299674267%\" valign=\"bottom\" style=\"width: 12.9139%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" style=\"width: 0.883%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.592833876221498%\" style=\"width: 13.5762%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.661237785016286%\" valign=\"top\" style=\"width: 8.7196%;\"\u003e\n \u003cp\u003e0.117 (0.109 - 0.125)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.49185667752443%\" style=\"width: 6.5121%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912052117263844%\" valign=\"bottom\" style=\"width: 9.8234%;\"\u003e\n \u003cp\u003e0.035*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.237785016286646%\" valign=\"top\" style=\"width: 9.2715%;\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.328990228013029%\" valign=\"top\" style=\"width: 6.181%;\"\u003e\n \u003cp\u003e-0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.775244299674267%\" valign=\"top\" style=\"width: 12.9139%;\"\u003e\n \u003cp\u003e-8.282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" style=\"width: 0.883%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.592833876221498%\" style=\"width: 13.5762%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEthnic Group?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.661237785016286%\" valign=\"top\" style=\"width: 8.7196%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.49185667752443%\" style=\"width: 6.5121%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912052117263844%\" valign=\"top\" style=\"width: 9.8234%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.237785016286646%\" valign=\"top\" style=\"width: 9.2715%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.328990228013029%\" valign=\"top\" style=\"width: 6.181%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.775244299674267%\" valign=\"top\" style=\"width: 12.9139%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" style=\"width: 0.883%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.592833876221498%\" valign=\"top\" style=\"width: 13.5762%;\"\u003e\n \u003cp\u003eWhite or Mestizo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.661237785016286%\" valign=\"top\" style=\"width: 8.7196%;\"\u003e\n \u003cp\u003e0.109 (0.096 - 0.122)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.49185667752443%\" style=\"width: 6.5121%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912052117263844%\" valign=\"bottom\" style=\"width: 9.8234%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eREF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.237785016286646%\" style=\"width: 9.2715%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.328990228013029%\" valign=\"bottom\" style=\"width: 6.181%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.775244299674267%\" valign=\"bottom\" style=\"width: 12.9139%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" style=\"width: 0.883%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.592833876221498%\" valign=\"top\" style=\"width: 13.5762%;\"\u003e\n \u003cp\u003eQuechua or Aymara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.661237785016286%\" valign=\"top\" style=\"width: 8.7196%;\"\u003e\n \u003cp\u003e0.091 (0.080 - 0.102)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.49185667752443%\" style=\"width: 6.5121%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912052117263844%\" valign=\"bottom\" style=\"width: 9.8234%;\"\u003e\n \u003cp\u003e-0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.237785016286646%\" valign=\"top\" style=\"width: 9.2715%;\"\u003e\n \u003cp\u003e-0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.328990228013029%\" valign=\"top\" style=\"width: 6.181%;\"\u003e\n \u003cp\u003e-0.341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.775244299674267%\" valign=\"top\" style=\"width: 12.9139%;\"\u003e\n \u003cp\u003e10.473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" style=\"width: 0.883%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.592833876221498%\" valign=\"top\" style=\"width: 13.5762%;\"\u003e\n \u003cp\u003eAfro-Peruvian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.661237785016286%\" valign=\"top\" style=\"width: 8.7196%;\"\u003e\n \u003cp\u003e0.060 (0.043 - 0.077)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.49185667752443%\" style=\"width: 6.5121%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912052117263844%\" valign=\"bottom\" style=\"width: 9.8234%;\"\u003e\n \u003cp\u003e-0.048*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.237785016286646%\" valign=\"top\" style=\"width: 9.2715%;\"\u003e\n \u003cp\u003e-0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.328990228013029%\" valign=\"top\" style=\"width: 6.181%;\"\u003e\n \u003cp\u003e-0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.775244299674267%\" valign=\"top\" style=\"width: 12.9139%;\"\u003e\n \u003cp\u003e0.471\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" style=\"width: 0.883%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.592833876221498%\" valign=\"top\" style=\"width: 13.5762%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSummary of ECI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.661237785016286%\" valign=\"top\" style=\"width: 8.7196%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.49185667752443%\" style=\"width: 6.5121%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912052117263844%\" valign=\"bottom\" style=\"width: 9.8234%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.237785016286646%\" valign=\"bottom\" style=\"width: 9.2715%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.328990228013029%\" valign=\"bottom\" style=\"width: 6.181%;\"\u003e\n \u003cp\u003e-1.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.775244299674267%\" valign=\"bottom\" style=\"width: 12.9139%;\"\u003e\n \u003cp\u003e70.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" style=\"width: 0.883%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.592833876221498%\" valign=\"top\" style=\"width: 13.5762%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidual (unexplained)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.661237785016286%\" valign=\"top\" style=\"width: 8.7196%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.49185667752443%\" style=\"width: 6.5121%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912052117263844%\" valign=\"bottom\" style=\"width: 9.8234%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.237785016286646%\" valign=\"bottom\" style=\"width: 9.2715%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.328990228013029%\" valign=\"bottom\" style=\"width: 6.181%;\"\u003e\n \u003cp\u003e-0.493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.775244299674267%\" valign=\"bottom\" style=\"width: 12.9139%;\"\u003e\n \u003cp\u003e29.842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" style=\"width: 0.883%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.592833876221498%\" valign=\"top\" style=\"width: 13.5762%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCorrected ECI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.661237785016286%\" valign=\"top\" style=\"width: 8.7196%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.49185667752443%\" style=\"width: 6.5121%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912052117263844%\" valign=\"bottom\" style=\"width: 9.8234%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.237785016286646%\" valign=\"bottom\" style=\"width: 9.2715%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.328990228013029%\" valign=\"bottom\" style=\"width: 6.181%;\"\u003e\n \u003cp\u003e-1.653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.775244299674267%\" valign=\"bottom\" style=\"width: 12.9139%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" style=\"width: 0.883%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"8\" style=\"width: 67.7704%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eECI:\u003c/strong\u003e Erreygers Concentration Index weighted for complex sample by wealth quintile\u003cstrong\u003e, Elasticity =\u0026nbsp;\u003c/strong\u003e[\u0026beta;\u003csub\u003ek\u003c/sub\u003e * x̅\u003csub\u003ek\u003c/sub\u003e /\u0026mu;]\u003cstrong\u003e, Ck:\u0026nbsp;\u003c/strong\u003eConcentration Index for the variable evaluated\u003cstrong\u003e, REF:\u0026nbsp;\u003c/strong\u003eCategory used as reference for estimation in decomposition model.\u003c/p\u003e\n \u003cp\u003e*P-value less than 0.050 for marginal effect.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-for-equity-in-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijeh","sideBox":"Learn more about [International Journal for Equity in Health](http://equityhealthj.biomedcentral.com)","snPcode":"12939","submissionUrl":"https://submission.nature.com/new-submission/12939/3","title":"International Journal for Equity in Health","twitterHandle":"@equityhealthj","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Health Inequities, Sociodemographic Factors, Depression, Patient Health Questionnaire, Peru.","lastPublishedDoi":"10.21203/rs.3.rs-4078911/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4078911/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eDepressive symptoms affect a significant proportion of Peruvian population, between 13.8% and 15.1% since 2014 to 2018. However, only the 14.1% did not receive treatment, this gap in treatment is influenced for sociodemographic conditions. The study aim was assessing demographic characteristics related to inequalities in the depression treatment receiving in Peruvian adults.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eUtilizing data from the 2017\u0026ndash;2022 Demographic and Health Survey, we conducted an analytic cross-sectional study. Inequality in treatment receipt was evaluated using concentration curves for estimated Concentration Index (CI), and the Erreygers Concentration Index (ECI), with the wealth index serving as an equity stratified. Decomposition analysis was employed to examine disparities among sociodemographic characteristics, including sex, age, education, residence, health insurance, and ethnicity.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eOf the 35,925 Peruvian adults with depressive symptoms surveyed, only 10.82% received treatment. Our analysis revealed treatment recipients were concentrated in higher wealth quintiles (CI: 22.08, 95% CI: 20.16 to 24.01, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Disparities persisted across various demographic groups, with urban residency (ECI: 0.03, 95% CI: 0.02 to 0.03, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), those without education (ECI: 0.05, 95% CI: 0.03 to 0.07, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Afro-Peruvians (ECI: 0.06, 95% CI: 0.04 to 0.08, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and women (ECI: 0.07, 95% CI: 0.06 to 0.08, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) experiencing lower received treatment, influenced by wealth quintile.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e \u003cp\u003eOnly one in ten Peruvian adults with depressive symptoms received treatment. Sociodemographic conditions such as living in rural areas, outside of the capital region, having low educational level, and identifying as Quechua or Aymara were the main components of inequality in the receipt of treatment for depressive symptoms.\u003c/p\u003e","manuscriptTitle":"Sociodemographic characteristics related to inequality in depression treatment in Peruvian adults: a concentration index decomposition approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-15 15:37:48","doi":"10.21203/rs.3.rs-4078911/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorAssigned","content":"","date":"2024-03-13T15:55:19+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-13T01:36:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal for Equity in Health","date":"2024-03-12T00:48:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-for-equity-in-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijeh","sideBox":"Learn more about [International Journal for Equity in Health](http://equityhealthj.biomedcentral.com)","snPcode":"12939","submissionUrl":"https://submission.nature.com/new-submission/12939/3","title":"International Journal for Equity in Health","twitterHandle":"@equityhealthj","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a85b85d9-dc1d-45c7-8012-63882fd5b15f","owner":[],"postedDate":"March 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-03-15T15:37:48+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-15 15:37:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4078911","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4078911","identity":"rs-4078911","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}