Introduction
Mental disorders are widespread, inflicting considerable morbidity and impairment (Demyttenaere et al. Reference Demyttenaere, Bruffaerts, Posada-Villa, Gasquet, Kovess, Lepine, Angermeyer, Bernert, de Girolamo, Morosini, Polidori, Kikkawa, Kawakami, Ono, Takeshima, Uda, Karam, Fayyad, Karam, Mneimneh, Medina-Mora, Borges, Lara, de Graaf, Ormel, Gureje, Shen, Huang, Zhang, Alonso, Haro, Vilagut, Bromet, Gluzman, Webb, Kessler, Merikangas, Anthony, Von Korff, Wang, Brugha, Aguilar-Gaxiola, Lee, Heeringa, Pennell, Zaslavsky, Ustun and Chatterji2004; Mathers & Loncar, Reference Mathers and Loncar2006; Kessler et al. Reference Kessler, Aguilar-Gaxiola, Alonso, Chatterji, Lee, Ormel, Ustun and Wang2009), and despite documented effectiveness of treatment (Yatham et al. Reference Yatham, Kennedy, O'Donovan, Parikh, MacQueen, McIntyre, Sharma, Silverstone, Alda, Baruch, Beaulieu, Daigneault, Milev, Young, Ravindran, Schaffer, Connolly and Gorman2005; APA, 2006), a high proportion of people with mental disorders do not receive care (Wang et al. Reference Wang, Aguilar-Gaxiola, Alonso, Angermeyer, Borges, Bromet, Bruffaerts, de Girolamo, de Graaf, Gureje, Haro, Karam, Kessler, Kovess, Lane, Lee, Levinson, Ono, Petukhova, Posada-Villa, Seedat and Wells2007) or drop out of treatment (Edlund et al. Reference Edlund, Wang, Berglund, Katz, Lin and Kessler2002; Olfson et al. Reference Olfson, Mojtabai, Sampson, Hwang, Druss, Wang, Wells, Pincus and Kessler2009). Untreated mental conditions have personal and social consequences and economic loss (Knapp, Reference Knapp2003) and can increase health-care expenditure through a variety of inter-related mechanisms (Prince et al. Reference Prince, Patel, Saxena, Maj, Maselko, Phillips and Rahman2007; Andrade et al. Reference Andrade, Viana, Tofoli and Wang2008). Understanding barriers to treatment constitutes an important endeavor for planning mental health services, setting priorities in allocation of resources and reducing the burden of mental illness (Bebbington, Reference Bebbington1990; Mechanic, Reference Mechanic2002).
Although the importance of identifying barriers to treatment is generally acknowledged, few cross-national data are available and most of these data are from Western developed countries (Wells et al. Reference Wells, Robins, Bushnell, Jarosz and Oakley-Browne1994; Kessler et al. Reference Kessler, Frank, Edlund, Katz, Lin and Leaf1997). Attitudinal barriers to treatment are the ones most commonly reported in these studies (Sareen et al. Reference Sareen, Jagdeo, Cox, Clara, ten Have, Belik, de Graaf and Stein2007; Jagdeo et al. Reference Jagdeo, Cox, Stein and Sareen2009), mainly due to negative health beliefs (Prins et al. Reference Prins, Verhaak, Bensing and van der Meer2008), misinterpretations about consequences of treatment, and stigma. Many people with significant disorders are unaware of treatments that could be helpful (ten Have et al. Reference ten Have, de Graaf, Ormel, Vilagut, Kovess and Alonso2010). Structural barriers, such as inconvenient location or inability to obtain an appointment, are less commonly reported (Alegria et al. Reference Alegria, Bijl, Lin, Walters and Kessler2000), although Sareen et al. (Reference Sareen, Jagdeo, Cox, Clara, ten Have, Belik, de Graaf and Stein2007) found that low-income respondents were significantly more likely to report a financial barrier in the USA than in either Ontario or The Netherlands. Treatment drop-out rates are high, with the most important reasons reported to be lack of satisfaction with service and financial barriers (Edlund et al. Reference Edlund, Wang, Berglund, Katz, Lin and Kessler2002; Olfson et al. Reference Olfson, Mojtabai, Sampson, Hwang, Druss, Wang, Wells, Pincus and Kessler2009).
Differences among population groups in their willingness to report mental disorders and obtain help have been reported (Bhui et al. Reference Bhui, Warfa, Edonya, McKenzie and Bhugra2007; Saxena et al. Reference Saxena, Thornicroft, Knapp and Whiteford2007; Hernandez et al. Reference Hernandez, Nesman, Mowery, Acevedo-Polakovich and Callejas2009) and are due to embarrassment about reporting symptoms, misinformation about mental illness, stigma and poor competence of health professionals in detecting problems in culturally diverse societies. Obtaining cross-national information in countries with different levels of development is essential for the identification of unmet needs and is an important step in the action to reduce this gap. The World Health Organization (WHO) World Mental Health (WMH) surveys represent a unique opportunity to do this across countries with different levels of development, health policy and delivery systems. The current report, based on data from the WMH surveys, represents the first cross-national study to include standardized clinical severity measures of specific disorders and examine effects of perceived need, structural barriers and attitudinal barriers to initiation and continuation of treatment for mental disorders.
Method
Survey respondents
Twenty-five WHO WMH surveys were carried out in 24 countries [two surveys in the People's Republic of China (PRC)]: six low- and lower-middle-income countries (Colombia, India, Iraq, Nigeria, PRC and Ukraine), six upper-middle-income countries (Brazil, Bulgaria, Lebanon, Mexico, Romania and South Africa) and 12 high-income countries (Belgium, France, Germany, Italy, The Netherlands, Spain, Japan, New Zealand, Israel, Northern Ireland, Portugal and the USA) (Table 1). Seventeen surveys were based on nationally representative household samples, two (Colombia and Mexico) on samples representative of urban areas, one of selected states (Nigeria) and the remaining four of selected metropolitan areas (Brazil, India, Japan and PRC). In the latter cases, the surveys represented either only one area (São Paulo in Brazil, Pondicherry in India), three areas (Beijing, Shanghai and Shenzhen in PRC) or 11 different areas (Japan). We refer to these areas as São Paulo, Pondicherry, PRC – Beijing/Shanghai, PRC – Shenzhen and Metropolitan Japan to distinguish them from the more broadly representative nation samples in other countries. Trained lay interviewers conducted face-to-face interviews with respondents aged ⩾18 years in all surveys. Respondents were selected using multistage household probability samples. The total sample size was 121 899. The weighted average response rate across all countries was 72.0%. All surveys were approved by the local human subjects committee.
Table 1. WMH sample characteristics by World Bank income categories a
WMH, World Mental Health; PRC, People's Republic of China; NSMH, The Colombian National Study of Mental Health; WMHI, World Mental Health India; IMHS, Iraq Mental Health Survey; NSMHW, The Nigerian Survey of Mental Health and Wellbeing; B-WMH, The Beijing World Mental Health Survey; S-WMH, The Shanghai World Mental Health Survey; CMDPSD, Comorbid Mental Disorders during Periods of Social Disruption; NSHS, Bulgaria National Survey of Health and Stress; LEBANON, Lebanese Evaluation of the Burden of Ailments and Needs of the Nation; M-NCS, The Mexico National Comorbidity Survey; RMHS, Romania Mental Health Survey; SASH, South Africa Health Survey; ESEMeD, The European Study of the Epidemiology of Mental Disorders; NHS, Israel National Health Survey; WMHJ2002–2006, World Mental Health Japan Survey; NZMHS, New Zealand Mental Health Survey; NISHS, Northern Ireland Study of Health and Stress; NMHS, Portugal National Mental Health Survey; NCS-R, The US National Comorbidity Survey Replication.
a The World Bank (2008).
b Most WMH surveys are based on stratified multistage clustered area probability household samples in which samples of areas equivalent to counties or municipalities in the USA were selected in the first stage followed by one or more subsequent stages of geographic sampling (e.g. towns within counties, blocks within towns, households within blocks) to arrive at a sample of households, in each of which a listing of household members was created and one or two people were selected from this listing to be interviewed. No substitution was allowed when the originally sampled household resident could not be interviewed. These household samples were selected from Census area data in all countries other than France (where telephone directories were used to select households) and The Netherlands (where postal registries were used to select households). Several WMH surveys (Belgium, Germany, Italy) used municipal resident registries to select respondents without listing households. The Japanese sample is the only totally unclustered sample, with households randomly selected in each of the 11 metropolitan areas and one random respondent selected in each sample household. Seventeen of the 25 surveys are based on nationally representative household samples.
c The response rate was calculated as the ratio of the number of households in which an interview was completed to the number of households originally sampled, excluding from the denominator households known not to be eligible either because of being vacant at the time of initial contact or because the residents were unable to speak the designated languages of the survey. The weighted average response rate is 72.0%.
Subsampling was used in most surveys to reduce respondent burden by dividing the interview into two parts. Part 1 included core diagnostic assessment. Part 2 included information about correlates and disorders of secondary interest. All respondents completed Part 1. All Part 1 respondents who met criteria for any disorder and a subsample of approximately 25% of others were administered Part 2. Part 2 respondents were weighted by the inverse of their probability of selection to adjust for differential sampling. Four surveys administered the Part 2 survey to 100% of respondents (Romania, Israel, Iraq, South Africa). The Part 2 sample included 63 678 respondents, of whom 32 387 were from high-, 15 240 from upper-middle- and 16 051 from low- and lower-middle-income countries. Because questions regarding reasons for not using services and drop-out were usually asked in Part 2, the present analyses are limited to this subsample. Part 2 data were weighted to adjust not only for undersampling of non-cases from Part 1 but also for differential within-household probability of selection and for residual aggregate discrepancies between samples and populations on a wide range of sociodemographic and geographic variables (Heeringa et al. Reference Heeringa, Wells, Hubbard, Mneimneh, Chiu, Sampson, Berglund, Kessler and Üstün2008).
Diagnostic assessment
DSM-IV diagnoses were based on the Composite International Diagnostic Interview (CIDI; Kessler & Üstün, Reference Kessler and Üstün2004), a fully structured lay interview. Analyses reported here were restricted to respondents with at least one DSM-IV disorder in the previous 12 months. Disorders included anxiety disorders (panic disorder, generalized anxiety disorder, agoraphobia without panic disorder, specific phobia, social phobia, post-traumatic stress disorder, obsessive–compulsive disorder, separation anxiety disorder), mood disorders (major depressive disorder, dysthymia, bipolar disorder I, II or subthreshold), disruptive behavior disorders [oppositional defiant disorder, conduct disorder, attention-deficit/hyperactivity disorder (ADHD), intermittent explosive disorder] and substance use disorders (alcohol and drug abuse with or without dependence). Blind clinical reinterviews using the Structured Clinical Interview for DSM-IV (SCID; First et al. Reference First, Spitzer, Gibbon and Williams2002) with a probability subsample of WMH respondents and found generally good concordance between diagnoses based on the CIDI and SCID (Haro et al. Reference Haro, Arbabzadeh-Bouchez, Brugha, de Girolamo, Guyer, Jin, Lepine, Mazzi, Reneses, Vilagut Saiz, Sampson and Kessler2006). CIDI–SCID concordance for 12-month disorders assessed by area under the receiver operating characteristic curve (AUC) was 0.73 for any anxiety disorder, 0.93 for any mood disorder, 0.86 for substance abuse with or without dependence, 0.86 for ADHD (the only disruptive behavior disorder assessed in the SCID) and 0.76 for any disorder.
Levels of severity
Serious 12-month disorders were defined as: bipolar I disorder or substance dependence with a physiological dependence syndrome; making a suicide attempt in conjunction with any other disorder; reporting severe role impairment due to a mental disorder in at least two areas of functioning measured by the disorder-specific Sheehan Disability Scales (SDS; Leon et al. Reference Leon, Olfson, Portera, Farber and Sheehan1997); or having overall functional impairment from any disorder consistent with a Global Assessment of Functioning (GAF; Endicott et al. Reference Endicott, Spitzer, Fleiss and Cohen1976) score of ⩽50. Disorders not classified as serious were classified as moderate if the respondent had substance dependence without a physiological dependence syndrome or at least moderate interference in any SDS domain. All other disorders were classified as mild.
Use of services
Twelve-month treatment was assessed by asking respondents if they saw any of a long list of professionals either as an out-patient or an in-patient for problems with emotions, nerves, mental health or use of alcohol or drugs. Included were mental health professionals (e.g. psychiatrist, psychologist), general medical professionals (e.g. general practitioner, occupational therapist), religious counselors (e.g. minister, rabbi) and traditional healers (e.g. herbalist, spiritualist). The list varied across countries depending on the local services provided.
Barriers for not using services and reasons for not continuing to use them
Respondents who reported no use of mental health services were asked whether there was a time in the past 12 months when they felt they might have needed to see a professional for problems with their emotions, nerves or mental health. Those who did not think they needed help or thought they needed help for less than 4 weeks were coded as ‘low perceived need’. Those with ‘perceived need’ were then asked about structural and attitudinal barriers (see Table A1 in the Supplementary online Appendix for a list of structural and attitudinal barriers for not seeking treatment).
Respondents who had accessed mental health treatment in the past 12 months were asked whether the treatment had stopped and, if so, whether they ‘quit before the [provider] wanted you to stop’. Those who saw a provider and ‘quit’ were then asked reasons for treatment drop-out from a list of potential reasons similar to the list of reasons for not seeking treatment (see Table A1). Those who ‘got better’ or ‘didn't need help anymore’ were not asked about structural or attitudinal reasons for dropping out. For the purposes of this study, only those who dropped out from all sectors and gave a reason for dropping out of treatment were included in the analysis. Respondents who endorsed more than one reason for not seeking help or drop-out were coded positively on each reason reported.
Sociodemographic predictor variables
Sociodemographic variables included age (18–34, 35–49, 50–64, ⩾65 years), sex, completed years of education (seven categories: no education, some primary, primary finished, some secondary, secondary finished, some college, college finished), income (classified into four categories based on country quartiles: low, low-average, high-average, high) and marital status (married/cohabitating, separated/widowed/divorced, never married).
Analytic approach
The distribution of barriers to seeking treatment was examined among respondents with any 12-month disorder who had not used services in the 12 months prior to interview and then repeated in the subsample of respondents who recognized the need for treatment. These analyses were carried out in subsamples defined by severity of disorder. Multivariate logistic regression models were then estimated to examine the association of sociodemographic variables and disorder severity with barriers, controlling for number of mood, anxiety, substance and disruptive behavior disorders and country. Models also examined interactions of sociodemographic variables with country. As model fit, assessed by Akaike's information criterion (AIC; Burnham & Anderson, Reference Burnham and Anderson2002), was best for the model without interaction in both cases, we present only models without interactions for all countries combined. The same analysis steps were repeated to study reasons for drop-out from treatment among respondents who received treatment but dropped out. Logistic regression coefficients and their standard errors were exponentiated to create odds ratios (ORs) and their 95% confidence intervals (CIs). Standard errors were estimated using the Taylor series method in SUDAAN (Research Triangle Institute, 2009) to adjust for clustering and weighting of data. Multivariate significance tests were conducted using Wald χ 2 tests based on coefficient variance–covariance matrices adjusted for design effects using the Taylor series method. Statistical significance was evaluated using two-sided design-based 0.05-level tests.
Results
Barriers to seeking treatment
Of the 63 678 Part 2 respondents, 11 471 met criteria for a 12-month disorder but reported no service use during that period. Of these, 4583 (38.5%) perceived a need for professional treatment, including 1124 of 2380 (48.1%) serious cases, 1930 of 4478 (42.8%) moderate cases, and 1529 of 4613 (31.0%) of mild cases.
Among respondents with serious disorders, low perceived need was the most commonly reported barrier to treatment in 15 of the 25 surveys (99.3–56.4% reporting this as a barrier) and attitudinal barriers in the other 10 surveys (80.3–52.2%) (Table 2). Among respondents with moderate/mild disorders, low perceived need was the most commonly reported barrier to treatment in 17 of the 25 surveys (99.3–62.1%) and attitudinal barriers in the other eight surveys (75.1–50.1%). Structural barriers were never most commonly reported, but were second most commonly reported among respondents with serious disorders in eight surveys (44.4–0.7%) and among respondents with moderate/mild disorders in three surveys (28.0–0.4%).
Table 2. Barriers for not seeking treatment among all respondents with 12-month mental disorders who did not use services in that period, according to the level of disorder severity
PRC, People's Republic of China; s.e., standard error.
a n shown is the denominator n of all respondents with 12-month mental disorders who did not use services in that period in each country.
The proportion of respondents who reported low perceived need is significantly lower among those with serious than moderate/mild disorders in nine surveys (24.3–86.4% v. 42.0–95.8%, χ 2 1 = 4.0–37.4, p = 0.045–0.001) and significantly higher in none. The proportion of respondents who reported structural barriers, in comparison, is significantly higher among those with serious than moderate/mild disorders in eight surveys (12.3–44.4% v. 3.8–28.0%, χ 2 1 = 3.9–50.6, p = 0.048–0.001) and significantly lower in none. Finally, the proportion of respondents who reported attitudinal barriers is significantly higher among those with serious than moderate/mild disorders in eight surveys (14.5–73.6% v. 5.0–56.5%, χ 2 1 = 4.2–34.0, p = 0.040–0.001) and significantly lower in none.
The vast majority (96.3%) of respondents recognizing a need for treatment but who did not receive treatment reported at least one attitudinal barrier (Table 3). This was true regardless of level of disorder severity (95.1–96.9%). By far the most common attitudinal barrier was wanting to handle the problem on their own (63.8% overall; 57.9–66.5% across subgroups defined by disorder severity). The next most common attitudinal barriers were related to perceived need: the belief that the problem was not severe (24.4% overall; 22.9–26.3% across subgroups defined by disorder severity) and that it would get better on its own (16.0% overall; 10.6–23.6% across subgroups defined by disorder severity). Wanting to handle the problem on their own was somewhat less likely to be reported by respondents with serious than moderate or mild disorders, but several other attitudinal barriers were more likely to be endorsed by those with serious than moderate or mild disorders. Of structural barriers, financial barriers and lack of availability were the most often mentioned.
Table 3. Barriers for not seeking treatment among the subgroup with 12-month mental disorders who perceived a need for mental health care but did not access any, according to level of severity (all countries)
s.e., Standard error.
The pattern of endorsement of each barrier was examined by calculating Pearson's correlation coefficient. All structural barriers were highly positively correlated with each other, as were attitudinal barriers. The exception to this pattern occurred with ‘Want to handle on own’ and ‘Problem was not severe’. These two barriers were negatively correlated with each other (−0.80). It seems that respondents who endorsed any of those two barriers were less likely to report any other attitudinal or structural barrier, as the majority of pair-wise correlations were below 0.30 (data not shown, but available upon request).
Correlates of barriers to treatment
Low perceived need for treatment was more common at older ages, among men and among milder cases (Table 4). Among respondents with perceived need, structural barriers were more common among the youngest than oldest respondents (OR 2.0, 95% CI 1.1–3.5, χ 2 3 = 9.3, p = 0.026). Respondents with the two lowest levels of education (OR 3.2, 95% CI 1.9–5.3 and OR 1.5, 95% CI 1.1–2.2) were more likely to report structural barriers than those with the highest level of education (χ 2 6 = 27.2, p < 0.001). Married/cohabitating respondents were marginally more likely to endorse such barriers. Respondents with a serious disorder were more likely than respondents with a mild disorder to report a structural barrier (OR 1.6, 95% CI 1.2–2.2, χ 2 2 = 12.2, p = 0.002).
Table 4. Multivariable analyses of the sociodemographic correlates of not seeking treatment because of low perceived need, any structural barriers or any attitudinal barriers among respondents with 12-month DSM-IV disorders (all countries) a
OR, Odds ratio; CI, confidence interval.
a Analyses adjusted for number of 12-month mood, anxiety, substance and disruptive behavior disorders, and country.
b Degrees of freedom (df) for χ 2 = k – 1, where k is the number of categories on the correlate variable.
* Significant at the 0.05 level, two-sided test.
Reasons and correlates for dropping out of treatment
Of the 16 518 respondents with 12-month disorders, 27.9% reported receiving mental health treatment in the past year. Of those 5047 respondents, 3917 dropped out of treatment, but the vast majority of these patients continued treatment in another section, with only 466 (12.8%) dropping out of all treatment. The distribution of reasons for dropping out of treatment in the latter group was examined only in the total sample because of sparse data (Table 5). Attitudinal reasons predominate, with 83.9% of respondents reporting at least one attitudinal reason. ‘Wanted to handle on my own’ was the most commonly reported (50.2%), followed by ‘perceived ineffectiveness’ of treatment (39.3%). Negative experience with a treatment provider was the only reason for drop-out that varied across severity level, with 26.9% of those with severe conditions compared to 11.2% of those with moderate and 15.9% with mild disorders reporting this as a reason for drop-out (χ 2 2 = 6.9, p = 0.032). Structural barriers were reported by 41.8% of drop-outs, with no difference across severity levels (χ 2 2 = 2.7, p = 0.26). Financial barriers and inconvenience/transportation were reported by around 25% of drop-outs, again with no difference across severity levels (χ 2 2 = 2.1, p = 0.35 and χ 2 2 = 3.4, p = 0.18 respectively). No strong correlations were found among reasons for dropping out of treatment.
Table 5. Reasons for dropping out of treatment among respondents with 12-month mental disorders who recognized the need for treatment according to level of severity (all countries)
s.e., Standard error.
Only exploratory analysis was possible in examining country-specific reasons for dropping out of treatment because of the small numbers of drop-outs in the sample (see Table A2 in the Supplementary online Appendix), but this analysis confirmed that attitudinal barriers were predominant in most countries with sufficient sample size for analysis, although structural barriers were important reasons for severe cases in some high-income countries, including New Zealand (49%), Portugal (32.3%) and the USA (30.2%), and also in some upper-middle-income countries, including Brazil (29.6%) and Mexico (37.1%). In multivariate analyses (Table 6), age was found to be inversely related to structural barriers (χ 2 3 = 4.6, p = 0.033), with respondents with moderate conditions more likely than those with mild conditions to report structural barriers (OR 3.5, 95% CI 1.3–9.3).
Table 6. Multivariable analyses of the sociodemographic correlates of dropping out of treatment because of any structural barriers among respondents with 12-month DSM-IV disorders who recognized the need for treatment (all countries) a
OR, Odds ratio; CI, confidence interval.
a Controls: number of 12-month mood, anxiety, substance and disruptive behavior disorders, and country.
* Significant at the 0.05 level, two-sided test.
Discussion
Several important study limitations merit attention before interpreting these results. First, the cross-sectional design of the WMH surveys prevents us from capturing the complexity of representation in the sequence of help-seeking (Mechanic, Reference Mechanic2002). Second, response rates varied widely across WMH surveys, with some surveys with response rates below acceptable standards. This could bias the report of perceived need and barriers because survey response could be related to severity of psychopathology (Kessler et al. Reference Kessler, Little and Groves1995). Third, the list of barriers to treatment and reasons for drop-out used, based on previous research in Western countries, was the same in all countries participating in the WMH surveys even though customization of questions to different national contexts might have yielded more nuanced information. Questions about barriers to treatment were structured in a way that prevented those with low perceived need from endorsing other reasons, which might have led to an underestimate of other reasons. Fourth, disorder-specific needs were not assessed, as we grouped all 12-month disorders together. There is reason to believe that perceived need is not uniform across diagnoses (Mojtabai et al. Reference Mojtabai, Olfson and Mechanic2002). In addition, some of the most incapacitating disorders, such as schizophrenia, were not evaluated.
Despite these limitations, the results clearly show that low perceived need for treatment is an extremely important barrier for seeking treatment worldwide. This result is consistent with previous studies (van Beljouw et al. Reference van Beljouw, Verhaak, Prins, Cuijpers, Penninx and Bensing2010). Although low perceived need would be expected in mild cases, a substantial number of severe cases think that they do not need help. Low perceived need was also high in countries that differ widely in levels of development, although it is possible that a deeper analysis might show that these perceptions differ in important ways across cultural settings. The absence of more textured information makes it impossible to obtain deeper insights from these data, but it is certainly plausible that variation in mental health literacy, that is in knowledge and beliefs about mental disorders, could be involved. As mental disorders are still highly stigmatized, social and cultural factors might contribute to biased perceptions of need (Leventhal et al. Reference Leventhal, Nerenz, Steele, Baum, Taylor and Singer1984; Jorm, Reference Jorm2000; Gureje et al. Reference Gureje, Lasebikan, Kola and Makanjuola2006). Biased judgment due to the illnesses themselves might also be involved along with stigma and inaccurate beliefs (Mechanic, Reference Mechanic2002; Prins et al. Reference Prins, Verhaak, Bensing and van der Meer2008; Schomerus & Angermeyer, Reference Schomerus and Angermeyer2008).
It is striking that attitudinal barriers were more important among serious than moderate or mild cases in most of the countries. This presumably reflects the fact that serious cases are likely to recognize need and would seek care in the absence of attitudinal barriers. A desire to handle the problem by oneself was the second most common reason reported in respondents who recognized a need. Self-stigma and label avoidance can be related to the desire to handle the problem by oneself. Even in high-income countries, public attitudes towards mental illness (Mehta et al. Reference Mehta, Kassam, Leese, Butler and Thornicroft2009) and fear of being discriminated in the workplace for revealing a mental illness or psychiatric treatment restrain people from disclosing their own mental health history (Corrigan & Wassel, Reference Corrigan and Wassel2008; Wheat et al. Reference Wheat, Brohan, Henderson and Thornicroft2010). Stigma is an important reason for not having treatment in severe cases from low- and lower-middle-income countries (Gureje et al. Reference Gureje, Lasebikan, Kola and Makanjuola2006; Saxena et al. Reference Saxena, Thornicroft, Knapp and Whiteford2007; Brohan et al. Reference Brohan, Slade, Clement and Thornicroft2010).
Structural barriers such as finance and availability were commonly reported in severe cases that recognized need. Even in some developed countries that have health insurance to pay for treatment, a meaningful proportion of the population sometimes lacks this coverage (Mechanic, Reference Mechanic2002). In developing countries there is a gap between policy and financing (Saxena et al. Reference Saxena, Sharan and Saraceno2003), with underprovision and inefficiency in use of resources (Andrade et al. Reference Andrade, Viana, Tofoli and Wang2008; Seedat et al. Reference Seedat, Stein, Herman, Kessler, Sonnega, Heeringa, Williams and Williams2008). In some Latin American countries, where mental health reform has been implemented, community-based services are still insufficient, the integration with primary care is weak, and in-patient beds have been reduced to a level that might be inadequate to meet the needs (Romero-Gonzalez et al. Reference Romero-Gonzalez, Gonzalez and Rosenheck2003; Andreoli et al. Reference Andreoli, Almeida-Filho, Martin, Mateus and Mari Jde2007; Caldas de Almeida & Horvitz-Lennon, Reference Caldas de Almeida and Horvitz-Lennon2010). In many middle- and lower-income countries, geographic distance from services in rural areas, population density and lack of trained personnel produce service deficiencies (Jacob et al. Reference Jacob, Sharan, Mirza, Garrido-Cumbrera, Seedat, Mari, Sreenivas and Saxena2007).
The majority of respondents who dropped out of treatment wanted to handle the problem themselves. Perceived ineffectiveness was also common. Respondents from high-income countries who had previous treatment are skeptical about effectiveness of professional help for serious emotional problems (ten Have et al. Reference ten Have, de Graaf, Ormel, Vilagut, Kovess and Alonso2010). Negative experience with a provider is commonly reported by severe cases. Patients reject the passive role assigned to them, probably having a different evaluation of need than providers and little ability to evaluate the quality of services received (Prins et al. Reference Prins, Verhaak, Smolders, Laurant, van der Meer, Spreeuwenberg, van Marwijk, Penninx and Bensing2010). Structural factors and health beliefs could interact, therefore increasing the likelihood for dropping out (Ngui et al. Reference Ngui, Khasakhala, Ndetei and Roberts2010). Patients might prefer counseling rather than medication in primary care, when physicians are constrained by time and offer a pharmacological treatment (Ring et al. Reference Ring, Dowrick, Humphris, Davies and Salmon2005).
As in previous surveys, we found that being female, being younger or middle-aged and having severe/moderate disorders are associated with perceived need for treatment, and with reporting more structural barriers to treatment-seeking (Mojtabai et al. Reference Mojtabai, Olfson and Mechanic2002; Cohen-Mansfield & Frank, Reference Cohen-Mansfield and Frank2008; Codony et al. Reference Codony, Alonso, Almansa, Bernert, de Girolamo, de Graaf, Haro, Kovess, Vilagut and Kessler2009; Mojtabai et al. Reference Mojtabai, Olfson, Sampson, Jin, Druss, Wang, Wells, Pincus and Kessler2011). Young and middle-aged adults were more likely than older adults to perceive need for treatment, and to report structural barriers to treatment-seeking after they perceived a need. Besides self-stigma and negative attitudes toward help-seeking (Jagdeo et al. Reference Jagdeo, Cox, Stein and Sareen2009), younger respondents may experience financial problems and time barriers to seeking treatment.
Conclusions
Our findings confirm that patients' lack of perceived need plays a major role in not receiving care worldwide (Prins et al. Reference Prins, Verhaak, Smolders, Laurant, van der Meer, Spreeuwenberg, van Marwijk, Penninx and Bensing2010). In addition, there is no agreement among cases on what should be considered need for mental health care (Alonso et al. Reference Alonso, Codony, Kovess, Angermeyer, Katz, Haro, de Girolamo, de Graaf, Demyttenaere, Vilagut, Almansa, Lepine and Brugha2007). There are many challenges to reduce this gap. Future research should focus on identifying categories of need among those with a diagnosis, in particular who would benefit from treatment and of what kind. Severe disorders, identified here as those associated with disabilities, are within the targets for mental health services, and are priorities in terms of delivering care. Motivating primary care physicians to recognize and treat mild and moderate disorders should be a goal for intervention (McCrone & Knapp, Reference McCrone and Knapp2007). Our results also suggest that there is need for community campaigns aimed at increasing public awareness, raising mental health literacy, and decreasing the distance between people's beliefs about different treatment options and what mental health professionals have to offer (Meadows & Burgess, Reference Meadows and Burgess2009; Khandelwal et al. Reference Khandelwal, Avode, Baingana, Conde, Cruz, Deva, Dumas, Gulbinat, Lopez, Mayeya, Mubbashar, Mohit, Ndeti, Puras, Saeed, Schilder, Silberberg, Tomov, Townsend, Iemmi and Jenkins2010). Stakeholders and health-care providers in countries with poor resources should target structural barriers by improving service availability and accessibility to reduce mental health service disparities.
Supplementary material
For supplementary material accompanying this paper visit http://dx.doi.org/10.1017/S0033291713001943.
Acknowledgments
The WHO WMH Survey Initiative is supported by the National Institute of Mental Health (NIMH; R01 MH070884), the John D. and Catherine T. MacArthur Foundation, the Pfizer Foundation, the US 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, GlaxoSmithKline and Bristol-Myers Squibb. We thank the staff of the WMH Data Collection and Data Analysis Coordination Centers for assistance with instrumentation, fieldwork and consultation on data analysis. None of the funders had any role in the design, analysis, interpretation of results or preparation of this paper. A complete list of all within-country and cross-national WMH publications can be found at www.hcp.med.harvard.edu/wmh/.
Each WMH country obtained funding for its own survey. The São Paulo Megacity Mental Health Survey is supported by the State of São Paulo Research Foundation (FAPESP) Thematic Project Grant 03/00 204-3. The Bulgarian Epidemiological Study of Common Mental Disorders (EPIBUL) is supported by the Ministry of Health and the National Center for Public Health Protection. The Beijing, Peoples Republic of China WMH Survey Initiative is supported by the Pfizer Foundation. The Shenzhen, People's Republic of China Mental Health Survey is supported by the Shenzhen Bureau of Health and the Shenzhen Bureau of Science, Technology and Information. The Colombian National Study of Mental Health (NSMH) is supported by the Ministry of Social Protection, with supplemental support from the Saldarriaga Concha Foundation. The ESEMeD project is funded by the European Commission (Contracts QLG5-1999-01 042; SANCO 2 004 123 and EAHC20081308), 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), Departament de Salut, Generalitat de Catalunya, Spain, 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 Epidemiology of Mental Disorders Study in Pondicherry, India was supported by the WHO/Directorate General of Health Services (DGHS) and helped by R. Chandrasekaran, JIPMER. Implementation of the Iraq Mental Health Survey (IMHS) and data entry were carried out by the staff of the Iraqi Ministry of Health (MOH) and Ministry of Planning (MOP) with direct support from the Iraqi IMHS team with funding from both the Japanese and European Funds through the United Nations Development Group Iraq Trust Fund (UNDG ITF). The Israel National Health Survey is funded by the Ministry of Health with support from the Israel National Institute for Health Policy and Health Services Research and the National Insurance Institute of Israel. The World Mental Health Japan (WMHJ) Survey is supported by the Grant for Research on Psychiatric and Neurological Diseases and Mental Health (H13-SHOGAI-023, H14-TOKUBETSU-026, H16-KOKORO-013) from the Japan Ministry of Health, Labor and Welfare. The Lebanese National Mental Health Survey (LEBANON) is supported by the Lebanese Ministry of Public Health, the WHO (Lebanon), the National Institute of Health/Fogarty International Center (R03 TW006481-01), the Sheikh Hamdan Bin Rashid Al Maktoum Award for Medical Sciences, anonymous private donations to IDRAAC, Lebanon, and unrestricted grants from AstraZeneca, Eli Lilly, GlaxoSmithKline, Hikma Pharm, Pfizer, Roche, Sanofi-Aventis, Servier and Novartis. 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). Te Rau Hinengaro: The New Zealand Mental Health Survey (NZMHS) is supported by the New Zealand Ministry of Health, Alcohol Advisory Council, and the Health Research Council. 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 Northern Ireland Study of Mental Health was funded by the Health and Social Care Research and Development Division of the Public Health Agency. The Portuguese Mental Health Study was carried out by the Department of Mental Health, Faculty of Medical Sciences, NOVA University of Lisbon, with the collaboration of the Portuguese Catholic University, and was funded by the Champalimaud Foundation, the Gulbenkian Foundation, the Foundation for Science and Technology (FCT) and the 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 the National School of Public Health and Health Services Management (former the National Institute for Research and Development in Health, present National School of Public Health Management and Professional Development, Bucharest), with technical support from Metro Media Transylvania, the National Institute of Statistics – National Center for Training in Statistics, Cheyenne Services SRL, Statistics Netherlands and were funded by the Ministry of Public Health (formerly the Ministry of Health) with supplemental support from Eli Lilly Romania SRL. The South Africa Stress and Health Study (SASH) is supported by the South African Department of Health and the University of Michigan. The Ukraine Comorbid Mental Disorders during Periods of Social Disruption (CMDPSD) study was funded by the US National Institute of Mental Health (RO1-MH61905). The US National Comorbidity Survey Replication (NCS-R) is supported by the NIMH (U01-MH60220), the Substance Abuse and Mental Health Services Administration (SAMHSA), the Robert Wood Johnson Foundation (RWJF; Grant044780) and the John W. Alden Trust.
The views and opinions expressed in this report are those of the authors and should not be construed to represent the views or policies of any of the sponsoring organizations, agencies, the WHO or the US Government.
Declaration of Interest
Dr D. Stein has received research grants and/or consultancy honoraria from Abbott, Astrazeneca, Eli-Lilly, GlaxoSmithKline, Jazz Pharmaceuticals, Johnson & Johnson, Lundbeck, Orion, Pfizer, Pharmacia, Roche, Servier, Solvay, Sumitomo, Takeda, Tikvah and Wyeth. Dr R. C. Kessler has been a consultant for GlaxoSmithKline Inc., Kaiser Permanente, Pfizer Inc., Sanofi-Aventis, Shire Pharmaceuticals and Wyeth-Ayerst; has served on advisory boards for Eli Lilly & Company and Wyeth-Ayerst; and has had research support for his epidemiological studies from Bristol-Myers Squibb, Eli Lilly & Company, GlaxoSmithKline, Johnson & Johnson Pharmaceuticals, Ortho-McNeil Pharmaceuticals Inc., Pfizer Inc. and Sanofi-Aventis.
