Skip to main content
Download PDF
Download ePub

Determinants of effective treatment coverage for major depressive disorder in the WHO World Mental Health Surveys

International Journal of Mental Health Systems volume 16, Article number: 29 (2022) Cite this article

Abstract

Background

Most individuals with major depressive disorder (MDD) receive either no care or inadequate care. The aims of this study is to investigate potential determinants of effective treatment coverage.

Methods

In order to examine obstacles to providing or receiving care, the type of care received, and the quality and use of that care in a representative sample of individuals with MDD, we analyzed data from 17 WHO World Mental Health Surveys conducted in 15 countries (9 high-income and 6 low/middle-income). Of 35,012 respondents, 3341 had 12-month MDD. We explored the association of socio-economic and demographic characteristics, insurance, and severity with effective treatment coverage and its components, including type of treatment, adequacy of treatment, dose, and adherence.

Results

High level of education (OR = 1.63; 1.19, 2.24), private insurance (OR = 1.62; 1.06, 2.48), and age (30–59yrs; OR = 1.58; 1.21, 2.07) predicted effective treatment coverage for depression in a multivariable logistic regression model. Exploratory bivariate models further indicate that education may follow a dose—response relation; that people with severe depression are more likely to receive any services, but less likely to receive adequate services; and that in low and middle-income countries, private insurance (the only significant predictor) increased the likelihood of receiving effective treatment coverage four times.

Conclusions

In the regression models, specific social determinants predicted effective coverage for major depression. Knowing the factors that determine who does and does not receive treatment contributes to improve our understanding of unmet needs and our ability to develop targeted interventions.

Background

The burden of depression

Mental disorders are the most disabling of all disorder groupings [1, 2], and result in the largest economic impact of all non-communicable disorders [3, 4]. Major depression is the leading cause of disability worldwide with an estimated 4.4 percent (approximately 322 million people) of the world’s population living with the disorder [5]. Depressed individuals are at greater risk for death from suicide, heart disease, stroke, and cancer. [6, 7] The economic costs of depression are enormous as reflected in healthcare utilization, use of social services, loss of productivity in the workplace, and loss of income and benefits for families. [8,9,10,11,12]

Measuring treatment coverage for depression

Despite the availability of effective and cost-effective pharmacological and psychotherapeutic treatments for depression, [13,14,15] under-spending on treatment is common and the majority of individuals in need lack care [16,17,18,19,20,21,21]. A high priority for research is to better understand the bottlenecks or barriers that limit the number of people who receive care. Although many barriers have been well studied (e.g., stigma, mental health literacy, physical access to services), others, such as insurance, have not [22, 23]. The importance of evaluating the extent to which individuals receive effective care is heightened by a global push to achieve universal health coverage under the Sustainable Development Goals. [18,19,20] Several well established methodologies have been proposed across health specialties, but effective treatment coverage indicators in the area of mental health were lacking until recently. [21, 24,25,26]

Based on prior work by our group on minimally adequate treatment for MDD we have recently developed an “effective treatment coverage” indicator by adding adjustments for quality of care and compliance: we factor in severity-specific needs, adequacy of providers, adherence to guidelines (for psychotherapy and psychopharmacology), drug type, and adherence to the indicated dose, based on survey results from 15 countries across four continents [27, 28].

In summary, we have developed an indicator that quantifies utilization, but also includes adjustments for quality of care and user adherence to approximate outcome-based measures and allow for an estimation of potential health gains. Here we investigate how potential determinants statistically predicted the likelihood of receiving effective treatment coverage and its different components to provide a multipronged appraisal of critical obstacles to providing and receiving care.

Methods

Sample

The WHO World Mental Health (WHO-WMH) Surveys Initiative conducted 17 community surveys with 35,012 adult respondents in 15 countries, which include six low- or middle-income countries (LAMICs) and nine high income countries (HICs) (as per the World Bank’s classification). Samples were based on multi-stage clustered area probability household designs; they were nationally representative in 11 surveys, representative of all urbanized areas in two, and of selected regions or Metropolitan areas in the others (Table 1).

Table 1 WMH sample characteristics by World Bank income categories
Full size table

Interviews were face-to-face and conducted in respondents’ homes by trained lay interviewers (training and quality control procedures are described elsewhere) [29]. Respondents were 18 years or older (except in Medellin, Colombia, where they were 19 +). Average response rate weighted by sample size was 70.3% following the American Association for Public Opinion Research RR1w definition [30].

Interviews were divided into two parts to reduce respondent burden. Part I assessed core mental disorders and was administered to all respondents. Part II assessed additional disorders and correlates in all Part I respondents with any disorder, plus a probability subsample of other respondents. Part II data were weighted to adjust for the under-sampling of Part I non-cases, with the resulting Part II prevalence estimates being equivalent to Part I estimates [31]. Of the 71,576 Part I and 35,012 Part II respondents, we focused our analyses on the 3341 Part II respondents with 12-month MDD. Table 2 shows the sociodemographic characteristics of our sample.

Table 2 Sociodemographic distribution of the sample by country-income level, among those with 12-month major depressive disorder
Full size table

Measures and data analysis

The survey instrument was the WHO Composite International Diagnostic Interview (CIDI) Version 3.0 [32], a fully-structured interview generating lifetime and 12-month prevalence of DSM-IV disorders, which includes protocols of translation, back-translation, adaptation, and harmonization across sites [33]. Twelve-month MDD required having a major depressive episode among respondents without a lifetime history of bipolar spectrum disorder [34]. Blinded reappraisal interviews with the Structured Clinical Interview for DSM-IV had good concordance with CIDI diagnoses [35,36,37]. Severity was established using trans-diagnostic criteria defined at the respondent level. Respondents with MDD were considered severe either if they had severe role impairment according to the Sheehan Disability Scale (SDS), met criteria for comorbid substance dependence with a physiological dependence syndrome, or reported a suicide attempt [38]. Respondents not considered severe were considered moderate if they reported moderate role impairment in the SDS or had substance dependence without a physiological dependence syndrome. The remaining cases were considered mild.

To build our effective treatment coverage indicator, we combined variables related to the provision of services. We classified care providers as: (1) specialist mental health (SMH; psychiatrist, psychologist, other mental health professional in any setting; social worker or counselor in a mental health specialized setting); or (2) general medical (GM; primary care doctor, other medical doctor, any other healthcare professional seen in a GM setting). Respondents provided the number of visits with each in the past 12 months and, for medical providers, clarified whether they received psychotherapy, pharmacotherapy, or both. For each psychotropic used in the past 12 months, the type, dose, and duration were recorded. Further details about the treatment variables are presented elsewhere [39].

Contact coverage involved any 12-month contact with a specialist or general medical provider for a mental health condition. For the pharmacotherapy measures two clinical psychiatrists with expertise in public health (DV, CSW) independently reviewed responses about medications used (which involved selecting from country specific lists including generic and brand names) and classified them. Discrepancies were reconciled by consensus. Adequate medication control required at least four physician visits [39]. Medication adherence required taking the prescribed daily dose on at least 27 out of 30 days (i.e., at least 90% of the time) [40,41,43]. Adequate pharmacotherapy required taking an antidepressant with adequate medication control and adherence (see Additional file 1: Appendix Box S1 for a list of antidepressants). A small fraction of people with MDD may avoid antidepressants due to side effects, failed trials, or other legitimate reasons, so if a non-antidepressant psychotropic was adequately controlled by a psychiatrist with adequate patient adherence, it was also considered adequate.

Any psychotherapy required having two or more visits to any specialty mental health provider among help seekers. Adequate number of sessions required at least eight sessions [39]. Adequate psychotherapy required at least 8 sessions from an adequate provider or still being in treatment after 2 visits. In the case of psychiatrists, for an encounter to be considered as a psychotherapeutic intervention (as opposed of medication control), visits needed to last 30 min or more.

We also defined a severity-specific variable for effective treatment coverage, which for mild and moderate MDD required adequate pharmacotherapy and/or adequate psychotherapy, and for severe MDD both adequate pharmacotherapy and adequate psychotherapy. These summary criteria result from a review of the National Institute for Health and Care Excellence Guidelines (NICE [44]), the Canadian Network for Mood and Anxiety Treatments guidelines (CANMAT [45, 46]), the American Psychiatric Association Practice Guideline for the Treatment of Patients With Major Depressive Disorder (APA [47]), and the WHO mhGAP Intervention Guide [48]. Table 3 shows the components of effective coverage by country and income level.

Table 3 Components of effective coverage among those with 12-month major depressive disorder by country income level
Full size table

The sample for analysis was respondents who met criteria for 12-month MDD. Differences in within-household probabilities of selection and residual discrepancies between sample and population distributions were adjusted for through weights based on census demographic-geographic variables [31]. The Taylor series linearization method [49] implemented in SUDAAN software [50] was used to estimate standard errors to adjust for weighting and geographic clustering of data.

We first ran bivariate logistic regression analyses to explore preliminary significant correlations between a specific set of potential predictors based on previous knowledge (gender, age, marital status, income, education, type of health insurance, private insurance (yes/no), employment status, severity, and survey year) and the outcome of interest, effective treatment coverage for MDD.

We then developed a multivariable logistic regression model to statistically predict effective treatment coverage including all the variables that had shown significance in the bivariate correlations. Significance was established at p < 0.05, and we report the unadjusted p values as well as values adjusted for false discovery rates (FDR) resulting from multiple testing using the Benjamini–Hochberg procedure.

Additionally, for those bivariate models that were significant in predicting “effective treatment coverage”, we conducted exploratory analyses by decomposing this indicator to identify which components may drive coverage for specific subgroups. So, we looked at determinants of contact coverage among those with 12-month MDD, and of the specific components of treatment (i.e. any pharmacotherapy, adequate pharmacotherapy, any psychotherapy, and adequate psychotherapy) among those with 12-month MDD and contact coverage. Finally, we stratified our analyses by country income level, and for people with severe MDD.

Results

Main analysis

Significant predictors of effective treatment coverage for persons with MDD.

In our initial bivariate models, the following variables were associated with effective treatment coverage: age, income, education, type of insurance, private insurance, and severity. After adjusting for the FDR, age, education, type of insurance and private insurance remained significant, while income and severity were not statistically significant (p = 0.055 and 0.073 respectively) (see Table 4).

Table 4 Bivariate predictors of effective coverage and its components among those with 12-Month major depressive disorder, in all countries (n = 3341)
Full size table

Our multivariate model includes all the variables that showed significance in the bivariate logistic regression analyses. In this exploratory analysis, we simplified these variables by creating dummies capturing the values that were significantly associated with increased odds of receiving effective treatment coverage: middle age or not, high income or not, average-high to high education or not, direct private insurance or not. We retained MDD severity as an ordinal variable. Table 5 shows the results: only middle age (OR = 1.6; p =  < 001), high or average high education (OR = 1.6; p = 0.002), and direct private insurance (OR = 1.6; p = 0.025) retain significance, while income and severity lose significance.

Table 5 Multivariate model of effective coverage among those with 12-month major depressive disorder, in all countries (n = 3341)
Full size table

Exploratory analyses

For the variables mentioned above (which were significantly associated with effective coverage in the bivariate analysis), we conducted additional exploratory analyses of the different components of effective coverage. Five findings of potential interest stand out.

First, persons between 30 and 59 years with MDD were more likely than other age groups to get effective treatment coverage for MDD. Among help-seekers, the 18–29 group is significantly less likely to get any pharmacotherapy, followed by the 30–44, the 60 + , and again with the 45–59 being the most likely to receive it. The 60 + help-seekers are the least likely to get any psychotherapy and to get adequate psychotherapy, with other age-groups being two to three times more likely to receive either. The 45–59 group might be the most likely to receive effective treatment coverage because they are more likely to contact services (see Table 4 for details).

Second, with respect to individual-level income, persons with high income are more likely to get any contact coverage. People with the highest individual income are also significantly more likely to get effective treatment coverage than any other subgroup (see Table 4 for details).

Third, persons with highest levels of education are most likely to get effective treatment coverage, with a dose–response relationship. Interestingly, with respect to contact coverage, people with the lowest level of education do not significantly differ from those with higher education, and the inequality seems to stem from the inadequacy of the pharmacotherapy and psychotherapy, which results in the fact that those with low level of education are less than half as likely to get effective treatment coverage (see Table 4 for details).

Fourth, persons with direct private insurance are more than twice as likely to get effective treatment coverage as those with no insurance (i.e., who would need to pay out of pocket). This inequality seems to be driven by the increased likelihood of getting contact coverage, of getting any psychotherapy and adequate psychotherapy for those with private insurance (see Table 4 for details).

Fifth, persons with moderate disorders are the most likely to receive effective treatment coverage. The reason is that even though people with severe depression are more likely to have any contact coverage, any/adequate psychotherapy, or any/adequate pharmacotherapy, they are less likely to receive the adequate combination of pharmacotherapy and psychotherapy that they need. Whereas people with moderate depression are less likely to get any services, but more likely for these services to meet the more basic package they require. Persons with mild disorders receive significantly less of any and all service components (see Table 4 for details).

Country-income level and severity

In HICs, both age and education were significant determinants of effective treatment coverage. (see Additional file 1: Appendix Table S1 for details). Thirty to 59 year-old and higher educated people with MDD receive are more likely to receive effective treatment coverage. In LAMICs, the only significant predictor of effective treatment coverage was having direct private insurance: patients with direct private insurance were four times more likely to receive effective treatment coverage than all others (p = 0.008; see Additional file 1: Appendix Table S2 for details).

To better understand the exact reasons why severely affected persons with MDD did not obtain treatment that meets the criteria for effective treatment coverage, we additionally studied severe MDD cases in all countries and in HICs and LAMICs separately. For severely affected people across countries, a high personal income doubled the likelihood of receiving any contact coverage (p < 0.01). For people receiving any services, having direct private insurance doubled the likelihood of receiving adequate psychotherapy (p = 0.02), and being female doubled the likelihood of receiving any psychopharmacology (p = 0.009). Finally, people aged 45 to 59 were the most likely to receive contact coverage (p = 0.018). See Additional file 1: Appendix Table S3 for details.

In HICs, severely affected people aged 45 to 59 were also more likely to have contact coverage (p < 0.01) and receive any pharmacotherapy (p = 0.017). People with private insurance were 5.6 times more likely to receive any pharmacotherapy compared to people without insurance (p = 0.042). See Additional file 1: Appendix Table S4 for details.

Focusing on the coverage for severely affected people in LAMICs our findings indicate that men (OR = 1.7; p = 0.018), people with high income (reference group, more than twice as likely than all other groups; p = 0.02), high education (reference group; p = 0.019), and direct private insurance were more likely to have contact coverage (OR = 3.6; p = 0.003). Further, people with direct private insurance were nearly four times more likely to get any and adequate psychotherapy (p = 0.045 and 0.034 respectively). Finally, married people were significantly less likely to receive any psychotherapy (p = 0.023), adequate psychotherapy (p = 0.048), and adequate pharmacotherapy (p = 0.032). See Additional file 1: Appendix Table S5 for details.

Discussion

Though our initial bivariate models indicate that age, income, education, insurance, and severity may be associated with effective treatment coverage for depression, after inclusion in a multivariable model and adjustment for multiple testing, only some of these variables retain significance: being 30 to 59 years old, having higher education levels, and having direct private insurance significantly contribute to increased likelihood of receiving effective treatment coverage.

Our exploratory analyses suggest that in LAMICs the only significant association with effective treatment coverage may be having private insurance. Also, for the most severely affected people in LAMICs, being a man, having high income, high education, and direct private insurance are all significantly associated with receiving contact coverage, a precondition of effective treatment coverage.

Our study adds critical information by integrating subject and demographic variables, severity of depression, type of insurance, and adequacy of care, all leading to an increased understanding of effective treatment coverage and its determinants. Our findings also raise relevant policy questions. First, the fact that education level is a determinant of effective treatment coverage offers potentially interesting areas of intervention. A known barrier to care for mental services is mental health literacy [51]. This refers to knowledge about dysfunction, resources, and the means through which they are accessed. Also, lower levels of education make it more difficult to identify sadness, diminished pleasure, loss of energy, feelings of worthlessness or guilt as medical conditions that may need treatment. Or, even if identified as such, the ability to activate the organizational levers required to receive such care may depend on a nuanced understanding of how the health care system works, and of users’ rights to healthcare in different settings. Each of these facets relate to mental health literacy, providing a parsimonious interpretation of the effect. Also, the findings convey the increased need to more assertively and responsively provide services for those at lower educational levels and limited mental health literacy. Promising work is well underway toward this end, as for example, with the use of digital mental health services [52, 53].

Second, it is not clear why private insurance was the only form of financial protection significantly associated with effective treatment coverage. “Any other type of insurance”, which included social security and publicly funded healthcare, was not significantly different from “no insurance” in our bivariate analyses. In HICs, we found that both “direct private” and “any other insurance” were significantly different from “no insurance’ when it comes to the provision of contact coverage, but that difference is lost as we adjust for the quality of those services. In LAMICs, the odds of receiving effective treatment coverage were 3.8 with “direct private insurance” vs all other. One hypothesis would be that other forms of insurance in most of these LAMICs are insufficiently developed to significantly increase even contact coverage, let alone effective treatment coverage. Another hypothesis would be that the quality of mental health care was only meaningfully better with private insurance, particularly in LAMICs. In addition, it is possible that private insurance covaries with education and income and our study showed these factors very much relate to effective treatment coverage. Ultimately, in the multivariable model pooling all countries direct private insurance significantly increased the odds of effective treatment coverage.

There are important limitations to note. First, service utilization and adherence data relied on self-reports that may be biased. We focused on 12-month treatment rather than longer recall periods to minimize recall bias. More stringent methods (e.g., blood samples, pill counts) are impractical for population-level investigations, making surveys acceptable to assess adherence. 80% and 90% have previously been used as compliance thresholds [40,41,42], so we used the most stringent one (taking the indicated dose at least 90% of the time) to compensate for potential bias. Additionally, given that our surveys span 15 years (2001 to 2015) and all country income levels, we have not included computer-, peer-, or community provider-delivered interventions due to their inconsistency across time and countries.

Also, many other critical variables might well influence the variables we investigated. It was not possible through our data to establish the relative importance of the many other health system, socioeconomic, and environmental variables that may determine utilization patterns.

Finally, there are limitations to both theory-driven multivariable models and those that result from retaining significant bivariate correlations. We chose a model building strategy that combines both approaches in a purposeful manner, and clearly describe each step. The rationale for conducting preliminary bivariate analyses on a set of potentially relevant variables was twofold. First, in our experience, some of these variables are highly correlated, and including them all in a purely theory driven multivariable model may show spurious significant correlations. Second, these bivariate associations are also relevant to then explore the associations with the components of our composite variable of interest. Further, given that we explore a limited number of variables, the likelihood of random significance is minimal.

Conclusions

In summary, our findings suggest that improving financial protection may expand effective treatment coverage going beyond the direct impact of individual income. However, the findings also show that state funded health care and social security in the real world seem to expand contact coverage but are not significantly different from no insurance when an adjustment is made for the quality of services rendered. In addition, the significant impact of having a higher education calls into question the accessibility of services and may justify population-level interventions to counter stigma, decrease barriers, and increase acceptability of services. Finally, addressing entrenched sources of inequality, such as gender, income and education may be of particular importance for severely affected patients in LAMICs.

Availability of data and materials

Access to the cross-national World Mental Health (WMH) data is governed by the organizations funding and responsible for survey data collection in each country. These organizations made data available to the WMH consortium through restricted data sharing agreements that do not allow us to release the data to third parties. The exception is that the U.S. data are available for secondary analysis via the Inter-University Consortium for Political and Social Research (ICPSR), http://www.icpsr.umich.edu/icpsrweb/ICPSR/series/00527.

Abbreviations

MDD:

Major depressive disorder

WHO-WMH:

World Health Organization World Mental Health

OR:

Odds ratio

LAMICs:

Low and middle income countries

HICs:

High income countries

RR1w:

Response rate 1, weighted

DSM-IV:

Diagnostic and Statistical Manual of Mental Disorders, fourth edition

CIDI:

Composite international diagnostic interview

SDS:

Sheehan disability scale

SMH:

Specialist mental health services

GM:

General medical services

NICE:

National Institute for Health and Care Excellence Guidelines

CANMAT:

Canadian Network for Mood and Anxiety Treatments guidelines

APA:

American Psychiatric Association

mhGAP:

Mental health gap guide

FDR:

False discovery rate

References

  1. Vigo DV, Thornicroft G, Atun R. Estimating the true global burden of mental illness. Lancet Psychiatry. 2016;3:171–8.

    Article  PubMed  Google Scholar 

  2. Salomon JA, Haagsma JA, Davis A, et al. Disability weights for the global burden of disease 2013 study. Lancet Glob Health. 2015;3(11):e712–23.

    Article  PubMed  Google Scholar 

  3. Bloom DE, Cafiero ET, Jané-Llopis E, et al. The global economic burden of noncommunicable diseases. Geneva: World Health Organization; 2011.

    Google Scholar 

  4. Goetzel RZ, Long SR, Ozminkowski RJ, Hawkins K, Wang S, Lynch W. Health, absence, disability, and presenteeism cost estimates of certain physical and mental health conditions affecting U.S. employers. Occ Envirn Med. 2004;46(4):398–412.

    Article  Google Scholar 

  5. World Health Organization. Depression and other common mental disorders: global health estimates. Geneva: World Health Organization; 2017.

    Google Scholar 

  6. Lépine J-P, Briley M. The increasing burden of depression. Neuropsychaitr Dis Treat. 2011;7(Suppl 1):3.

    Google Scholar 

  7. Wang Y-H, Li J-Q, Shi J-F, et al. Depression and anxiety in relation to cancer incidence and mortality: a systematic review and meta-analysis of cohort studies. Mol Psychiatry. 2020;25(7):1487–99.

    Article  PubMed  Google Scholar 

  8. Chiu M, Lebenbaum M, Cheng J, de Oliveira C, Kurdyak P. The direct healthcare costs associated with psychological distress and major depression: a population-based cohort study in Ontario, Canada. PLoS ONE. 2017;12(9): e0184268.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Kessler RC. The costs of depression. Psychiatr Clin North Am. 2012;35(1):1–14.

    Article  PubMed  Google Scholar 

  10. Tanner J-A, Hensel J, Davies PE, Brown LC, Dechairo BM, Mulsant BH. Economic burden of depression and associated resource use in Manitoba, Canada. Can J Psychiatry. 2020;65(5):338–46.

    Article  PubMed  Google Scholar 

  11. Yamabe K, Liebert R, Flores N, Pashos CL. Health-related quality of life outcomes, economic burden, and associated costs among diagnosed and undiagnosed depression patients in Japan. Clinicoecon Outcomes Res. 2019;11:233–43.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Hockenberry JM, Joski P, Yarbrough C, Druss BG. Trends in treatment and spending for patients receiving outpatient treatment of depression in the United States, 1998–2015. JAMA. 2019;76(8):810–7.

    Google Scholar 

  13. Cuijpers P, Van Straten A, Smit F. Preventing the incidence of new cases of mental disorders: a meta-analytic review. J Nerv Ment Dis. 2005;193(2):119–25.

    Article  PubMed  Google Scholar 

  14. Prukkanone B, Vos T, Bertram M, Lim S. Cost-effectiveness analysis for antidepressants and cognitive behavioral therapy for major depression in Thailand. Value Health. 2012;15(1):S3–8.

    Article  PubMed  Google Scholar 

  15. Wiles NJ, Thomas L, Turner N, et al. Long-term effectiveness and cost-effectiveness of cognitive behavioural therapy as an adjunct to pharmacotherapy for treatment-resistant depression in primary care: follow-up of the CoBalT randomised controlled trial. Lancet Psychiatry. 2016;3(2):137–44.

    Article  PubMed  Google Scholar 

  16. Kessler RC, Demyttenaere K, Bruffaerts R, et al. Prevalence, severity, and unmet need for treatment of mental disorders in the World Health Organization world mental health surveys. JAMA. 2004;291(21):2581–90.

    Article  CAS  PubMed  Google Scholar 

  17. Vigo DV, Kestel D, Pendakur K, Thornicroft G, Atun R. Disease burden and government spending on mental, neurological, and substance use disorders, and self-harm: cross-sectional, ecological study of health system response in the Americas. Lancet Public Health. 2019;4(2):e89–96.

    Article  PubMed  Google Scholar 

  18. Vigo D, Patel V, Becker A, Bloom D, Yip W, Raviola G, et al. A partnership for transforming mental health globally. Lancet Psychiatry. 2019;6(4):350–6.

    Article  PubMed  Google Scholar 

  19. World Health Organization. Thirteenth general programme of work, 2019–2023: promote health, keep the world safe, serve the vulnerable. Geneva: World Health Organization; 2019.

    Google Scholar 

  20. GBD Universal Health Coverage Collaborators. Measuring universal health coverage based on an index of effective coverage of health services in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2019;396(10258):1250–84.

    Google Scholar 

  21. Tanahashi T. Health service coverage and its evaluation. Bull World Health Organ. 1978;56(2):295–303.

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Andrade LH, Alonso J, Mneimneh Z, Wells JE, Al-Hamzawi A, Borges G, Kessler RC, et al. Barriers to mental health treatment: results from the WHO World Mental Health Surveys. Psychol Med. 2014;44(06):1303–17.

    Article  CAS  PubMed  Google Scholar 

  23. Sarikhani Y, Bastani P, Rafiee M, Kavosi Z, Ravangard R. Key barriers to the provision and utilization of mental health services in low-and middle-income countries: a scope study. Commun Ment Health J. 2021;57(5):836–52.

    Article  Google Scholar 

  24. Levy-Bruhl D, Soucat A, Osseni R, et al. The Bamako initiative in Benin and Guinea: improving the effectiveness of primary health care. Int J Health Plan Manage. 1997;12(S1):S49–79.

    Article  Google Scholar 

  25. Shengelia B, Tandon A, Adams OB, Murray CJL. Access, utilization, quality, and effective coverage: an integrated conceptual framework and measurement strategy. Soc Sci Med. 2005;61:97–109.

    Article  PubMed  Google Scholar 

  26. Amouzou A, Leslie HH, Ram M, et al. Advances in the measurement of coverage for RMNCH and nutrition: from contact to effective coverage. BMJ Glob Health. 2019;4(Suppl 4): e001297.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Thornicroft G, Chatterji S, Evans-Lacko S, et al. Undertreatment of people with major depressive disorder in 21 countries. Br J Psychiatry. 2017;210(2):119–24.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Vigo D, Haro JP, Hwang I, et al. Toward measuring effective treatment coverage: critical bottlenecks in quality- and useradjusted coverage for major depressive disorder. Psychol Med. 2020. https://doi.org/10.1017/S0033291720003797.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Pennell B, Mneimneh Z, Bowers A, et al. Implementation of the World Mental Health Surveys. The WHO World Mental Health Surveys: global perspectives on the epidemiology of mental disorders. New York: Cambridge University Press; 2008. p. 33–57.

    Google Scholar 

  30. American Association for Public Opinion Research. Standard definitions: final dispositions of case codes and outcome rates for surveys. 9th ed. Oakbroook Terrace: AAPOR; 2016.

    Google Scholar 

  31. Heeringa S, Wells J, Hubbard F, et al. Sample designs and sampling procedures. In: Kessler RC, Üstün TB, editors., et al., The WHO World Mental Health Surveys: global perspectives on the epidemiology of mental disorders. New York: Cambridge University Press; 2008. p. 14–32.

    Google Scholar 

  32. Kessler RC, Üstün TB. The World Mental Health (WMH) survey initiative version of the world health organization (WHO) composite international diagnostic interview (CIDI). Int J Methods in Psychiatr Res. 2004;13(2):93–121.

    Article  Google Scholar 

  33. Harkness J, Pennell B, Villar A, Gebler N, Aguilar-Gaxiola S, Bilgen I. Translation procedures and translation assessment in the World Mental Health Survey Initiative. In: Kessler RC, Üstün TB, editors. The WHO World Mental Health Surveys: global perspectives on the epidemiology of mental disorders. New York: Cambridge University Press; 2008. p. 91–113.

    Google Scholar 

  34. Merikangas KR, Jin R, He J-P, et al. Prevalence and correlates of bipolar spectrum disorder in the world mental health survey initiative. Arch Gen Psychiatry. 2011;68(3):241–51.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Spitzer M, Robert L, Gibbon M, Williams J. Structured clinical interview for DSM-IV-TR axis I disorders, research version, non-patient edition (SCID-I/NP). New York: Biometrics Research, New York State Psychiatric Institute; 2002.

    Google Scholar 

  36. Haro JM, Arbabzadeh-Bouchez S, Brugha TS, et al. Concordance of the Composite International Diagnostic Interview Version 3.0 (CIDI 3.0) with standardized clinical assessments in the WHO World Mental Health surveys. Int J Methods in Psychiatr Res. 2006;15(4):167–80.

    Article  Google Scholar 

  37. Demyttenaere KBR, Posada-Villa J, et al. Prevalence, severity, and unmet need for treatment of mental disorders in the World Health Organization world mental health surveys. JAMA. 2004;291(21):2581.

    Article  PubMed  Google Scholar 

  38. Sheehan DV, Harnett-Sheehan K, Raj BA. The measurement of disability. Int Clin Psychopharmacol. 1996;11(Supplement 3):89–95.

    Article  PubMed  Google Scholar 

  39. Wang PS, Aguilar-Gaxiola S, Alonso J, et al. Use of mental health services for anxiety, mood, and substance disorders in 17 countries in the WHO world mental health surveys. Lancet. 2007;370(9590):841–50.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Osterberg L, Blaschke T. Adherence to medication. N Engl J Med. 2005;353(5):487–97.

    Article  CAS  PubMed  Google Scholar 

  41. Jeon-Slaughter H. Economic factors in of patients’ nonadherence to antidepressant treatment. Soc Psychiatry Psychiatr Epidemiol. 2012;47(12):1985–98.

    Article  PubMed  Google Scholar 

  42. Cramer JA, Rosenheck R. Compliance with medication regimens for mental and physical disorders. Psychiatr Serv. 1998;49(2):196–201.

    Article  CAS  PubMed  Google Scholar 

  43. Ten Have M, Nuyen J, Beekman A, De Graaf R. Common mental disorder severity and its association with treatment contact and treatment intensity for mental health problems. Psychol Med. 2013;43(10):2203–13.

    Article  PubMed  Google Scholar 

  44. National Institute for Health and Care Excellence. Depression in adults: Recognition and management. Clinical guideline [CG90]. London, England; 2009.

  45. Kennedy SH, Lam RW, McIntyre RS, et al. Canadian network for mood and anxiety treatments (CANMAT) 2016 clinical guidelines for the management of adults with major depressive disorder. Can J Psychiatry. 2016;61(9):540–60.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Parikh SV, Quilty LC, Ravitz P, et al. Canadian network for mood and anxiety treatments (CANMAT) 2016 clinical guidelines for the management of adults with major depressive disorder: Section 2. Psychological treatments. Can J Psychiatry. 2016;61(9):524–39.

    Article  PubMed  PubMed Central  Google Scholar 

  47. American Psychiatric Association. Practice guidelines for the treatment of patients With major depressive disorder. Washington: American Psychiatric Publishing; 2010.

    Google Scholar 

  48. World Health Organization. mhGAP intervention guide for mental, neurological and substance use disorders in non-specialized health settings: mental health Gap Action Programme (mhGAP), version 2.0. World Health Organization. https://www.who.int/publications/i/item/mhgap-intervention-guide---version-2.0. Accessed 15 Sept 2020.

  49. Wolter K. Introduction to Variance Estimation. New York: Springer-Verlag; 1985.

    Google Scholar 

  50. Research Triangle Institute. SUDAAN 8.0. Research Triangle Park, NC; 2002.

  51. Jorm AF. Mental health literacy: empowering the community to take action for better mental health. Am Psychol. 2012;67(3):231.

    Article  PubMed  Google Scholar 

  52. Schueller SM, Hunter JF, Figueroa C, Aguilera A. Use of digital mental health for marginalized and underserved populations. Curr Treat Options Psychiatry. 2019;6(3):243–55.

    Article  Google Scholar 

  53. Li J, Brar A. The use and impact of digital technologies for and on the mental health and wellbeing of Indigenous people: a systematic review of empirical studies. Comput Hum Behav. 2022;126: 106988.

    Article  Google Scholar 

Download references

Acknowledgements

The WHO World Mental Health Survey collaborators are Sergio Aguilar-Gaxiola, MD, PhD; Ali Al-Hamzawi, MD; Mohammed Salih Al-Kaisy, MD; Jordi Alonso, MD, PhD; Yasmin Altwaijri, PhD; Laura Helena Andrade, MD, PhD; Lukoye Atwoli, MD, PhD; Corina Benjet, PhD; Guilherme Borges, ScD; Evelyn J. Bromet, PhD; Ronny Bruffaerts, PhD; Brendan Bunting, PhD; Jose Miguel Caldas-de-Almeida, MD, PhD; Graça Cardoso, MD, PhD; Somnath Chatterji, MD; Alfredo H. Cia, MD; Louisa Degenhardt, PhD; Koen Demyttenaere, MD, PhD; Silvia Florescu, MD, PhD; Giovanni de Girolamo, MD; Oye Gureje, MD, DSc, FRCPsych; Josep Maria Haro, MD, PhD; Meredith Harris, PhD; Hristo Hinkov, MD, PhD; Chi-yi Hu, MD, PhD; Peter de Jonge, PhD; Aimee Nasser Karam, PhD; Elie G. Karam, MD; Norito Kawakami, MD, DMSc; Ronald C. Kessler, PhD; Andrzej Kiejna, MD, PhD; Viviane Kovess-Masfety, MD, PhD; Sing Lee, MBBS; Jean-Pierre Lepine, MD; John McGrath, MD, PhD; Maria Elena Medina-Mora, PhD; Zeina Mneimneh, PhD; Jacek Moskalewicz, PhD; Fernando Navarro-Mateu, MD, PhD; Marina Piazza, MPH, ScD; Jose Posada-Villa, MD; Kate M. Scott, PhD; Tim Slade, PhD; Juan Carlos Stagnaro, MD, PhD; Dan J. Stein, FRCPC, PhD; Margreet ten Have, PhD; Yolanda Torres, MPH, Dra.HC; Maria Carmen Viana, MD, PhD; Daniel V. Vigo, MD, DrPH; Harvey Whiteford, MBBS, PhD; David R. Williams, MPH, PhD; Bogdan Wojtyniak, ScD.

Funding

The World Health Organization World Mental Health (WMH) Survey Initiative is supported by the United States National Institute of Mental Health (NIMH; R01 MH070884), the John D. and Catherine T. MacArthur Foundation, the Pfizer Foundation, the United States Public Health Service (R13-MH066849, R01-MH069864, and R01 DA016558), the Fogarty International Center (FIRCA R03-TW006481), the Pan American Health Organization, Eli Lilly and Company, Ortho-McNeil Pharmaceutical Inc., GlaxoSmithKline, and Bristol-Myers Squibb. We thank the staff of the WMH Data Collection and Data Analysis Coordination Centres for assistance with instrumentation, fieldwork, and consultation on data analysis.

The Argentina survey – Estudio Argentino de Epidemiología en Salud Mental (EASM) – was supported by a grant from the Argentinian Ministry of Health (Ministerio de Salud de la Nación)—(Grant Number 2002–17,270/13 − 5). The São Paulo Megacity Mental Health Survey is supported by the State of São Paulo Research Foundation (FAPESP) Thematic Project Grant 03/00204–3. The Colombian National Study of Mental Health (NSMH) is supported by the Ministry of Social Protection. The Mental Health Study Medellín – Colombia was carried out and supported jointly by the Center for Excellence on Research in Mental Health (CES University) and the Secretary of Health of Medellín. The ESEMeD project is funded by the European Commission (Contracts QLG5-1999–01,042; SANCO 2,004,123, and EAHC 20,081,308), (the Piedmont Region (Italy)), Fondo de Investigación Sanitaria, Instituto de Salud Carlos III, Spain (FIS 00/0028), Ministerio de Ciencia y Tecnología, Spain (SAF 2000–158-CE), Generalitat de Catalunya (2017 SGR 452; 2014 SGR 748), Instituto de Salud Carlos III (CIBER CB06/02/0046, RETICS RD06/0011 REM-TAP), and other local agencies and by an unrestricted educational grant from GlaxoSmithKline. The Lebanese Evaluation of the Burden of Ailments and Needs Of the Nation (L.E.B.A.N.O.N.) is supported by the Lebanese Ministry of Public Health, the WHO (Lebanon), National Institute of Health/Fogarty International Center (R03 TW006481-01), anonymous private donations to IDRAAC, Lebanon, and unrestricted grants from, Algorithm, AstraZeneca, Benta, Bella Pharma, Eli Lilly, Glaxo Smith Kline, Lundbeck, Novartis, OmniPharma, Pfizer, Phenicia, Servier, UPO. The Mexican National Comorbidity Survey (MNCS) is supported by The National Institute of Psychiatry Ramon de la Fuente (INPRFMDIES 4280) and by the National Council on Science and Technology (CONACyT-G30544- H), with supplemental support from the Pan American Health Organization (PAHO). The Nigerian Survey of Mental Health and Wellbeing (NSMHW) is supported by the WHO (Geneva), the WHO (Nigeria), and the Federal Ministry of Health, Abuja, Nigeria. The Portuguese Mental Health Study was carried out by the Department of Mental Health, Faculty of Medical Sciences, NOVA University of Lisbon, with collaboration of the Portuguese Catholic University, and was funded by Champalimaud Foundation, Gulbenkian Foundation, Foundation for Science and Technology (FCT) and Ministry of Health. The Romania WMH study projects “Policies in Mental Health Area” and “National Study regarding Mental Health and Services Use” were carried out by National School of Public Health & Health Services Management (former National Institute for Research & Development in Health), with technical support of Metro Media Transilvania, the National Institute of Statistics-National Centre for Training in Statistics, SC Cheyenne Services SRL, Statistics Netherlands and were funded by Ministry of Public Health (former Ministry of Health) with supplemental support of Eli Lilly Romania SRL. The Psychiatric Enquiry to General Population in Southeast Spain – Murcia (PEGASUS-Murcia) Project has been financed by the Regional Health Authorities of Murcia (Servicio Murciano de Salud and Consejería de Sanidad y Política Social) and Fundación para la Formación e Investigación Sanitarias (FFIS) of Murcia. The US National Comorbidity Survey Replication (NCS-R) is supported by the National Institute of Mental Health (NIMH; U01-MH60220) with supplemental support from the National Institute of Drug Abuse (NIDA), the Substance Abuse and Mental Health Services Administration (SAMHSA), the Robert Wood Johnson Foundation (RWJF; Grant 044708), and the John W. Alden Trust.

Dr. Laura Helena Andrade is supported by the Brazilian Council for Scientific and Technological Development (CNPq Grant # 307933/2019-9).

A complete list of all within-country and cross-national WMH publications can be found at http://www.hcp.med.harvard.edu/wmh/. Role of the funding source: the funders had no role in study design; in the collection, analysis, and interpretation of the data; in the writing of the report; and in the decision to submit the manuscript for publication. Dr. Vigo had full access to all the data in the study and had final responsibility for the decision to submit for publication.

Author information

Authors and Affiliations

  1. Department of Psychiatry, University of British Columbia, UBC Hospital – Detwiller Pavilion, Room 2813, 2255 Wesbrook Mall, UBC Vancouver Campus, Vancouver, BC, V6T 2A1, Canada

    Daniel V. Vigo

  2. Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, USA

    Daniel V. Vigo

  3. Department of Psychology, Yale University, 2 Hillhouse Avenue- 208205, New Haven, CT, 06520, USA

    Alan E. Kazdin

  4. Department of Health Care Policy, Harvard Medical School, 180 Longwood Avenue, Boston, MA, 02115, USA

    Nancy A. Sampson, Irving Hwang & Ronald C. Kessler

  5. IMIM-Hospital del Mar Medical Research Institute, PRBB Building, Doctor Aiguader, 88, 08003, Barcelona, Spain

    Jordi Alonso

  6. University of São Paulo Medical School, Núcleo de Epidemiologia Psiquiátrica - LIM 23, Rua Dr. Ovidio Pires de Campos, 785, São Paulo, CEP, 05403-010, Brazil

    Laura Helena Andrade

  7. Department of Psychiatry, University College Hospital, University of Ibadan, Ibadan, 5116, PMB, Nigeria

    Olatunde Ayinde & Oye Gureje

  8. National Institute of Psychiatry Ramón de La Fuente Muñiz, Calzada México-Xochimilco, 101, Colonia San Lorenzo Huipulco, 14370, Mexico City, DF, Mexico

    Guilherme Borges

  9. University Psychiatric Center, University of Louvain – KU Leuven, Herestraat 49, B-3000, Leuven, Belgium

    Ronny Bruffaerts

  10. School of Psychology, Ulster University, College Avenue, Londonderry, BT48 7JL, UK

    Brendan Bunting

  11. Istituti di Ricovero e Cura a Carattere Scientifico (IRCCS)-St. John of God Clinical Research Centre, Via Pilastroni 4, Brescia, Italy

    Giovanni de Girolamo

  12. National School of Public Health, Management and Development, 31 Vaselor Str., 21253, Bucharest, Romania

    Silvia Florescu & Josep Maria Haro

  13. Parc Sanitari Sant Joan de Déu, Dr. Antoni Pujades, 42, 08830, Sant Boi de Llobregat, Barcelona, Spain

    Josep Maria Haro

  14. School of Public Health, The University of Queensland, c/o QCMHR, Locked Bag 500, Archerfield, QLD, 4108, Australia

    Meredith G. Harris

  15. Queensland Centre for Mental Health Research, The Park Centre for Mental Health, Wolston Park Rd,, Wacol, QLD, 4076, Australia

    Meredith G. Harris

  16. Department of Psychiatry & Clinical Psychology, St. George Hospital University Medical Center, Beirut, 166378, Ashrafieh, Lebanon

    Elie G. Karam & Georges Karam

  17. Department of Psychiatry and Clinical Psychology, Faculty of Medicine, Balamand University, Rond Point Saloumeh, Sin el Fil, Beirut, Lebanon

    Elie G. Karam & Georges Karam

  18. EHESP-School of Public Health, Paris Descartes University, 20 Avenue George Sand La Plaine, 93210, Saint Denis, France

    Viviane Kovess-Masfety

  19. Department of Psychiatry, Chinese University of Hong Kong, Tai Po, Hong Kong, China

    Sing Lee

  20. Murcia Health Service, C/ Lorca, nº 58. -El Palmar, 30120, Murcia, Spain

    Fernando Navarro-Mateu

  21. Colegio Mayor de Cundinamarca University, Calle 28 No. 6-02, 11001000, Bogotá, Colombia

    José Posada-Villa

  22. Department of Psychological Medicine, University of Otago, PO Box 56, Dunedin, 9054, New Zealand

    Kate Scott

  23. Faculty of Medicine, University of Buenos Aires, Paraguay 2155, C1121ABG CABA, Buenos Aires, Argentina

    Juan Carlos Stagnaro

  24. Trimbos-Instituut, Netherlands Institute of Mental Health and Addiction, Da Costakade 45Postbus 725, 3500, 3521 VS, Utrecht, The Netherlands

    Margreet ten Have

  25. Department of Psychiatry, National Taiwan University Hospital & College of Medicine, National Taiwan University, No. 7, Chung-Shan South Road, Taipei, 10002, Taiwan

    Chi-Shin Wu

  26. Lisbon Institute of Global Mental Health and Chronic Diseases Research Center (CEDOC), Nova Medical School, Universidade Nova de Lisboa, Campo Mártires da Pátria, 130, 1169-056, Lisbon, Portugal

    Miguel Xavier

  27. Institute for Development Research Advocacy and Applied Care (IDRAAC), Achrafieh, St. George Hospital Street, Beirut, Lebanon

    Elie G. Karam & Georges Karam

  28. CIBER en Epidemiología Y Salud Pública (CIBERESP), Av. Monforte de Lemos, 3-5. Pabellón 11. Planta 0, 28029, Madrid, Spain

    Jordi Alonso

  29. Pompeu Fabra University (UPF), Plaça de la Mercè, 10-12, 08002, Barcelona, Spain

    Jordi Alonso

  30. G/F Multicentre, Tai Po Hospital, Tai Po, Hong Kong, China

    Sing Lee

Authors
  1. Daniel V. Vigo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alan E. Kazdin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nancy A. Sampson
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Irving Hwang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jordi Alonso
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Laura Helena Andrade
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Olatunde Ayinde
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Guilherme Borges
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Ronny Bruffaerts
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Brendan Bunting
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Giovanni de Girolamo
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Silvia Florescu
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Oye Gureje
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Josep Maria Haro
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Meredith G. Harris
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Elie G. Karam
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Georges Karam
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Viviane Kovess-Masfety
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Sing Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Fernando Navarro-Mateu
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. José Posada-Villa
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Kate Scott
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Juan Carlos Stagnaro
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Margreet ten Have
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Chi-Shin Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Miguel Xavier
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. Ronald C. Kessler
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

DVV and RCK conceived the study, provided overall guidance and prepared the first draft. NS supervised data analyses, reviewed results and reviewed and contributed to the report. IH conducted data analyses. All other authors provided data, reviewed results and/or reviewed and contributed to the report. All the authors read and approved the final manuscript.

Corresponding author

Correspondence to Daniel V. Vigo.

Ethics declarations

Ethics approval and consent to participate

At all survey sites, the local ethics or institutional review committee reviewed and approved the protocol to ensure protection of human subjects, in line with appropriate international and local guidelines.

Consent for publication

Not applicable.

Competing interests

Dr. Alonso reports grants from INSTITUTO DE SALUD CARLOS iii—FIS, Spain, AGAUR, Catalonia, Spain, and FPU, Spain, during the conduct of the study. Dr. Navarro-Mateu reports non-financial support from Otsuka outside the submitted work. In the past 3 years, Dr. Kessler was a consultant for Datastat, Inc., Holmusk, RallyPoint Networks, Inc., and Sage Pharmaceuticals. He has stock options in Mirah, PYM, and Roga Sciences. All other authors report no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: Table S1.

Bivariate predictors of effective coverage and its components among those with 12-month major depressive disorder, in high income countries (n=1991)1. Table S2. Bivariate predictors of effective coverage and its components among those with 12-month major depressive disorder, in low/middle-income countries (n=1350)1. Table S3. Bivariate predictors of effective coverage and its components among those with 12-month major depressive disorder, in all countries, among severe cases (n=1244)1. Table S4. Bivariate predictors of effective coverage and its components among those with 12-month major depressive disorder, in high income countries, among severe cases (n=730)1. Table S5. Bivariate predictors of effective coverage and its components among those with 12-month major depressive disorder, in low and middle countries, among severe cases (n=514)1. Box S1. Antidepressants and classes

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vigo, D.V., Kazdin, A.E., Sampson, N.A. et al. Determinants of effective treatment coverage for major depressive disorder in the WHO World Mental Health Surveys. Int J Ment Health Syst 16, 29 (2022). https://doi.org/10.1186/s13033-022-00539-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s13033-022-00539-6

Keywords

International Journal of Mental Health Systems

ISSN: 1752-4458