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ArticleUnmet Need for Mental Health Care Among U.S. Children: Variation by Ethnicity and Insurance StatusSheryl H. Kataoka, M.D., M.S.H.S., Lily Zhang, M.S., Kenneth B. Wells, M.D., M.P.H.

 

Full Text  http://dx.doi.org/10.1176/appi.ajp.159.9.1548Abstract

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OBJECTIVE: Policy discussions regarding the mental health needs of children and adolescents

emphasize a lack of use of mental health services among youth, but few national estimates are

available. The authors use three national data sets and examine ethnic disparities in unmet need

(defined as having a need for mental health evaluation but not using any services in a 1-year period)

to provide such estimates. METHOD: The authors conducted secondary data analyses in three

nationally representative household surveys fielded in 1996–1998: the National Health Interview

Survey, the National Survey of American Families, and the Community Tracking Survey. They

determined rates of mental health service use by children and adolescents 3–17 years of age and

differences by ethnicity and insurance status. Among the children defined as in need of mental health

services, defined by an estimator of mental health problems (selected items from the Child Behavior

Checklist), they examined the association of unmet need with ethnicity and insurance

status.RESULTS: In a 12-month period, 2%–3% of children 3–5 years old and 6%–9% of children and

adolescents 6–17 years old used mental health services. Of children and adolescents 6–17 years old

who were defined as needing mental health services, nearly 80% did not receive mental health care.

Controlling for other factors, the authors determined that the rate of unmet need was greater among

Latino than white children and among uninsured than publicly insured children. CONCLUSIONS:These

findings reveal that most children who need a mental health evaluation do not receive services and

that Latinos and the uninsured have especially high rates of unmet need relative to other children.

Rates of use of mental health services are extremely low among preschool children. Research

clarifying the reasons for high rates of unmet need in specific groups can help inform policy and

clinical programs.

It is estimated that one of every five children and adolescents in the United States has a mental

disorder (1–3); left untreated, these disorders are often debilitating (4–7). Empirically validated

treatments exist for many mental disorders, including attention deficit hyperactivity disorder (ADHD),

conduct disorder, mood disorders, and anxiety disorders (4–9). However, recent health policy

discussions concerning the mental health needs of children and adolescents have been limited by the

lack of national data on rates of mental health service use and unmet need for such services (10).

Populations that may be particularly vulnerable to lower rates of use of mental health services include

ethnic minority youth and the uninsured. Previous studies on child mental health service use have

largely been based on regional data or data from insured populations and have yielded a mixed

pattern of results regarding possible disparities in service use. For example, in a sample of children in

New Haven, Conn. (11), African American and Latino children had lower rates of mental health service

use than did Caucasian children. A study of a group of insured children (12) found lower rates of

service use for African American but not Latino children than for white children. However, in the Great

Smoky Mountains Study (13), African American children did not differ from Caucasian children in

service use. How insurance status affects use of child mental health services is also unclear (14, 15).

Recent national studies of adults (16, 17) suggest that, compared with whites, African Americans have

lower rates of access, African Americans and Latinos with mental health or substance abuse problems

have lower rates of active treatment, and African Americans have lower rates of appropriate care for

depressive or anxiety disorders. In addition, uninsured adults have lower rates of access to mental

health care and lower rates of active treatment for mental health or substance abuse problems than

insured adults (18).

To study rates of use and unmet need for children and adolescents in the United States, we primarily

used data from the National Survey of American Families, a large nationally representative sample,

and attempted when possible to confirm findings across several national data sets. We hypothesized

that the rate of overall mental health service use for children is low and that most children who need a

mental health evaluation do not receive any services (our definition of unmet need). In addition, we

hypothesized that minority and uninsured children have greater rates of unmet need for a mental

health evaluation than their white and insured counterparts.

MethodSection:

Data Sources

We provide separate cross-sectional analyses of three nationally representative household samples of

U.S. civilian, noninstitutionalized children 3–17 years old. Full descriptions of the study designs are

available elsewhere (19–21). Because methods differ across surveys, we examined rates of use and

ethnic and insurance status within each survey and then considered the consistency of conclusions

across data sets. The main data set for this study is the National Survey of American Families (19),

with supporting analyses from the National Health Interview Survey (20) and the Community Tracking

Survey (21) when applicable.

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The main data set for the study, the 1997 National Survey of American Families (19, 22, 23), sampled

more than 44,000 households and 28,867 children, with larger shares of the sample from 13 states

that account for more than half of the U.S. population and a smaller sample of the balance of the

nation to permit national estimates. The most knowledgeable adult in the household (95% were

parents) provided information about the sampled child, but emancipated minors provided their own

information. The survey oversampled people with low incomes. Interviews were conducted in Spanish

and English. The response rate was 65.4%.

The 1998 National Health Interview Survey (20) sampled 38,209 households, including 11,017

children, using a multistage stratified cluster design and oversampling African Americans and Latinos.

A knowledgeable adult in the household provided information about the randomly selected child. The

response rate was 82.4%. The 1996–1997 Community Tracking Survey (21) sampled 32,732 family

insurance units, which included 8,852 children and adolescents aged 3–17. Sixty sites were randomly

selected on the basis of metropolitan statistical areas, and sites were stratified by region and size.

Households were randomly selected. To increase the precision of the national estimates, a smaller

supplemental sample was included in this survey that randomly selected households from the 48

continental United States. An adult informant from each family insurance unit provided information

about the randomly selected child. Interviews were conducted in English and Spanish. The overall

response rate was 65.0%.

Outcome Measures

Each survey elicited information on use of child mental health services over the preceding 12 months.

For each data set, we derived a dichotomous variable indicating whether the child had received any

mental health care. The National Survey of American Families respondents were asked how many

times the child had received mental health services from a doctor, mental health counselor, or

therapist, excluding visits for smoking cessation and treatment for substance abuse. The National

Health Interview Survey asked whether the adult respondent had seen or talked to a mental health

professional about the child. The Community Tracking Survey asked if the child had received any care

from a mental health professional.

Children were categorized as having unmet need if they had exceeded a cutoff point on a mental

health screening measure (described later in this article) but had not received any mental health

services in the past 12 months. Need was not measured in the Community Tracking Survey.

Main Independent Variables

The adult respondent reported the race of each child, which was categorized as white, black, Hispanic,

or other; the white and black groups excluded subjects of Hispanic background.

The National Survey of American Families defined current child health insurance coverage as public

insurance (Medicaid, Medicare, military insurance, and Indian Health Service insurance), private

insurance (employment related or directly purchased), or no insurance. We grouped the National

Health Interview Survey and Community Tracking Survey data into similar categories. For children with

multiple coverages, a hierarchy was used to assign a child to the applicable category (private

insurance, then public insurance, followed by no insurance). Whether a child’s health insurance

included mental health coverage was not elicited in the surveys.

The measure that estimates need for a mental health evaluation, included in both the National Survey

of American Families and the National Health Interview Survey, is the Mental Health Indicator (24),

which comprises selected items from the Child Behavior Checklist (25). The original Child Behavior

Checklist is a standardized questionnaire of parent-rated child behavior over the preceding 6 months.

Child Behavior Checklist items that best discriminated between demographically similar children who

were or were not referred for mental health services were chosen for inclusion in the Mental Health

Indicator, which is thus a measure of the need for clinical evaluation. The Mental Health Indicator

includes four age- and gender-specific items from the Child Behavior Checklist rated on a scale of 0, 1,

or 2, for a total possible score of 8; higher scores represent greater need.

Validation of the Mental Health Indicator was based on receiver operator characteristic analysis of

Mental Health Indicator scores compared with external criteria such as a lifetime history of ADHD,

mental retardation, learning disability, and past use of mental health services (26). The recommended

cutoff point for defining a level of need meriting evaluation is a score of 2, but to avoid concerns about

overinclusion of minor or transient symptoms, we relied on a more stringent criterion of 3 or higher for

analyses of unmet need, with specificity ranging from 88% to 90% for each age-gender category (26).

Mental Health Indicator scores are available in the National Health Interview Survey for children 4 and

older and in the National Survey of American Families for children 6 and older. The Community

Tracking Survey has no child mental health need indicator, so we used the Community Tracking

Survey to describe rates of services use only.

Demographic and Parent Characteristic Variables

Demographic information included the child’s age and gender and the household income. The U.S.

Census poverty ratio was used to assess total family income in each data set. Poor was defined as a

ratio below 1.0, indicating that the family income was below the poverty level. In the National Health

Interview Survey, this information was missing for 20% of the children, but we imputed the variable

using other sociodemographic information.

The designations of West, South, Midwest, and Northeast for regional location were based on the U.S.

Census statistical groupings (27).

The adult’s level of education was categorized as less than high school, high school, some college, or 4

or more years of post-high-school education. Household composition was defined as single-parent

household or non-single-parent household. Emancipated minors (0.2% of the youth) were grouped with

non-single-parent households for analyses. Mental health functioning of the adult respondent was

determined by using the five-item Mental Health Inventory (28), which assesses psychological well-

being in adults. Scores on this instrument range from 25 to 100, and higher scores indicate better

mental health. A cutoff of 67 or below indicates poor mental health, approximately the lowest 20% of

the general population (29).

Statistical Analysis

Descriptive analyses of service use by age group, ethnicity, and insurance status were conducted for

each data set separately. We used statistical tests appropriate for the complex sampling design of

each data set. We used a modified Pearson’s chi-square statistic (30) to test the independence of two

categorical variables in the National Survey of American Families data, and a Wald statistic (31) was

used to test independence for each two-way table in the National Health Interview Survey and

Community Tracking Survey data. For each data set, standard errors of individual coefficients were

calculated by using a jackknife replication method (32), which took into account the complex weighting

schemes.

We used logistic regression in both the National Survey of American Families and the National Health

Interview Survey for the unadjusted analyses of unmet need, which estimated the probability of having

no mental health care in a year for those children 6–17 years old who met criteria for need (Mental

Health Indicator score of 3 or higher). To examine the effect of race and insurance characteristics on

unmet need while controlling for other factors, we performed multiple logistic regression using only the

main data set, the National Survey of American Families. Following the Aday and Andersen health

service model (33), we included covariates shown in previous studies (11, 12,15, 34 –39)  to be

significantly associated with service use such as predisposing factors (age, gender, race, parental

education, single-parent household, parental mental health), enabling resources (income, insurance,

and regional location), and child’s mental health need.

Statistical analyses were performed by using WesVar software (40) for the National Survey of

American Families and SUDAAN software (41) for the National Health Interview Survey and Community

Tracking Survey. All estimates are weighted to be nationally representative.

ResultsSection:

Prevalence of Mental Health Service Use and Need

For children aged 3–17, the rate of having any mental health service use varied from 6.0% (both

National Survey of American Families and National Health Interview Survey) to 7.5% (Community

Tracking Survey) (data not shown). Rates were lower for preschool children (2%–3% for children 3–5

years old) (Table 1). Across data sets, a higher percentage of children with public insurance used

services (9%–13%) than did the uninsured (4%–5%) and privately insured (5%–7%) children. In these

unadjusted analyses, ethnic minority status was significantly associated with lower rates of use in the

Community Tracking Survey and National Health Interview Survey data sets, but not in the National

Survey of American Families data. Across data sets, male children had higher rates of mental health

use than did female children.

Table 2 shows the use of mental health services among children with different levels of need for these

services. The weighted percentage of 6–17-year-olds with mental health problems was 15.2% in the

National Health Interview Survey (1,350 out of 8,679) and 20.8% in the National Survey of American

Families (4,793 out of 21,260). Data from the National Survey of American Families showed that

greater levels of mental health need were associated with higher rates of having any mental health

care among children 6–17 years old: 2% of youth with a Mental Health Indicator score of 0, 6% with

scores of 1 or 2, and 21% with scores of 3 or greater used mental health services (χ2=151.014, df=1.3,

p<0.001) (data not shown). The National Health Interview Survey had similar associations between

level of need and service use: 1.6% of youth with a Mental Health Indicator score of 0, 8.3% with

scores of 1 or 2, and 24.8% with scores of 3 or greater used mental health services (χ2=332.35, df=2,

p<0.000) (data not shown).

Weighed estimates of unmet need for children under age 6 were available only in the National Health

Interview Survey. Of 1,499 children 4–5 years old, 131 (8.5%) were estimated to have mental health

problems, but of the 131 children in need, only nine (6.0%) used any mental health services in the

preceding year.

Among children 6–17 years old in the National Survey of American Families estimated to have need

(Mental Health Indicator score ≥3), 79% had not used mental health services in the past 12 months

(data not shown). A higher percentage of Hispanic children in need (88%) than white children in need

(76%) did not receive care (Table 3). In addition, a higher percentage of uninsured children (87%) than

those with public insurance (73%) received no care (Table 3). Likewise, in the National Health

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Interview Survey, 75% of children who had mental health problems had not received services: 80% of

blacks, 82% of Hispanics, and 72% of whites had unmet need (χ2=14.6, df=3, p<0.01) (data not

shown). Fewer children with public or private insurance than uninsured children had unmet need

(χ2=38.0, df=2, p<0.001).

After adjusting for other demographic factors and parent characteristics and using multiple logistic

regression with the National Survey of American Families data only, we found that Hispanic children

with mental health problems had greater odds of having no care, or unmet need, than white children

(odds ratio=2.66) (Table 3). The adjusted odds of having unmet need were significantly lower for the

publicly insured than for uninsured children (odds ratio=0.39) (Table 3), but there was no significant

difference in unmet need between uninsured and privately insured children.

DiscussionSection:

To our knowledge, this study is the first to provide national estimates of use of child mental health

services and the unmet need for such services. Overall, the data suggest that 6.0%–7.5% of U.S.

children receive mental health services, a result confirmed across three data sets. We also found that

most children and adolescents who need a mental health evaluation do not get any mental health care

in a year, and this was more pronounced for Latinos and the uninsured. “Need” in this study is based

on a screening measure that estimates need for clinical evaluation, not necessarily indicating that

treatment or intensive services are warranted. Nevertheless, we applied a more stringent cutoff point

than recommended for these national surveys specifically to avoid concerns that we would comment

on high unmet need for a group largely consisting of milder cases of need.

Another issue is whether we could have underestimated levels of use or need being met because of

the brief utilization question in these surveys and emphasis on specialty mental health services.

Although this is a general estimate of service use for mental health problems, the service use item in

one survey did encompass primary care and nonpsychiatric care for mental health problems, yet

conclusions were similar about levels of use and unmet need across data sets even though they

differed in methodology. In addition, even if the rate of overall unmet need is somewhat lower, there is

no reason to assume that comparative estimates of use or unmet need (i.e., across ethnic groups) are

biased.

With these caveats in mind, we found that only 21% of the children who need a mental health

evaluation receive services. This suggests that about 7.5 million children have an unmet need for

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mental health services in the United States, largely confirming findings from others (37). At a time of

continuing concern over the high costs of health care, including mental health care, it may be difficult

to focus national policy debates on the implications of such a high rate of unmet need among children.

However, the implications for children and adolescents with untreated mental health problems can

have major developmental consequences. For example, longitudinal studies have found that

depressed children are at greater risk for later suicidal behavior, poor academic functioning, substance

abuse, and unemployment (42), necessitating improvement in access to effective mental health

prevention and early intervention for youth and their families.

Our finding that Latino children have even greater rates of unmet need than white children is

particularly concerning given the national estimates suggesting that Latino adolescents have higher

rates of suicidal thoughts, depression, and anxiety symptoms and greater rates of dropping out of high

school than white adolescents (43). As described in the Surgeon General’s Report on Mental Health:

Culture, Race, and Ethnicity (44), services could be improved for Latinos by dealing with barriers such

as financial constraints and lack of bilingual bicultural mental health providers. In examining birthplace

of parent, we found that unmet need did not significantly differ between children with non-U.S.-born

parents (N=79, 93%) and those with U.S.-born parents (N=515, 88%) (χ2=1.42, df=1, p>0.05).

Additional research is needed to clarify the mechanisms underlying disparities in unmet need for

mental health care among children because these different mechanisms have unique intervention and

policy implications. For example, differences in parental attitudes may require educational

interventions, but community differences in service availability require policy targeted at developing

resources. Although Latino children had the highest rates of unmet need, the rates for white children

were also quite high, emphasizing the general conclusion that there is a high rate of unmet need

nationally for mental health care among children and adolescents.

We also found that uninsured children had higher rates of unmet need than publicly insured children,

suggesting that Medicaid and other public insurance programs offer an important safety net, but we

cannot comment as to whether the uninsured children in these studies were eligible for but not

enrolled in public programs such as Medicaid or the State Children’s Health Insurance Program.

Expanding insurance coverage to currently uninsured children, as proposed by the State Children’s

Health Insurance Program (45), could be an important avenue for addressing unmet need (46).

However, implementation of the State Children’s Health Insurance Program has been variable across

states (47), with lower enrollment among black and Hispanic children than among white children (48).

Our findings for children differ from those found in adults (18), in which the uninsured had less access

than the privately insured, but the lower differences by insurance status among children could be

partly due to the high level of unmet need across insurance groups among children.

We provide only a first sketch of national mental health care among preschoolers, who are referred

most often for behavioral problems such as aggression, defiance, and overactivity (49, 50). Our

descriptive findings suggest that the vast majority of preschool children with mental health needs in

the United States do not receive services for those problems. The more talked-about public concern is

the high rate of psychotropic medication use in young children (51, 52), but an even greater problem

may be the lack of any evaluation or care for children with mental health problems. Further research is

needed to confirm these findings on larger samples of young children and to determine whether unmet

need is due to parent preference, unavailability of services, or problems in recognizing problems or

identifying specialists for this age group (53).

As we have suggested, this study is limited by the use of parent-reported screening measures of need

and service use; national studies using diagnostic measures for specific childhood disorders, level of

impairment, and type and quality of service use are needed. In addition, only noninstitutionalized

children were surveyed, which excludes those living in out-of-home placements and the homeless, who

may have higher rates of mental health need than the general population (54–56). However, our

overall estimate of unmet need is similar to estimates based on diagnostic measures (37), and our

findings concerning the use of services were consistent across three independent national data sets.

This study reinforces the finding of the recent Surgeon General’s report (10) that there is substantial

unmet need for child mental health care in the United States, which is particularly acute for some

minority and uninsured groups.

TABLE 1

TABLE 2

TABLE 3View larger version

Presented at the Robert Wood Johnson Clinical Scholars Program 2000 National Meeting, Fort

Lauderdale Fla., Nov. 8–11, 2000. Received Oct. 9, 2001; revision received April 15, 2002; accepted

May 8, 2002. From the Department of Psychiatry and Biobehavioral Sciences, Child and Adolescent

Psychiatry Division, and the Research Center on Managed Care for Psychiatric Disorders, University of

California, Los Angeles; and RAND, Santa Monica, Calif. Address reprint requests to Dr. Kataoka, UCLA

Health Services Research Center, 10920 Wilshire Blvd., Suite 300, Los Angeles, CA

90024;skataoka@ucla.edu (e-mail). Funded by the Robert Wood Johnson Foundation Healthcare for

Communities grant 031280 and Clinical Scholars Program grant 037098 and by NIMH grant MH-01170

from the Research Center on Managed Care for Psychiatric Disorders. The authors thank Ruth Klap,

Ph.D., Arlene Fink, Ph.D., and Bonnie Zima, M.D., M.P.H., for their reviews of this article; Susan

Stockdale, Ph.D., for assistance with a previous version; and Lingqi Tang, Ph.D., and Naihua Duan,

Ph.D., for statistical assistance.

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adolescents with depression. Arch Gen Psychiatry 2002; 59:225-231 CrossRef, Medline43. Centers for Disease Control and Prevention: Youth Risk Behavior Surveillance—United

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the Surgeon General. Rockville, Md, US Department of Health and Human Services, Substance Abuse and Mental Health Services Administration, Center for Mental Health Services, National Institutes of Health, National Institute of Mental Health, 2001

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Socioeconomic Status and Adolescent Mental DisordersKatie A. McLaughlin, PhD,  E. Jane Costello, PhD, William Leblanc, PhD, Nancy A. Sampson, BA, and Ronald C. Kessler, PhD

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This article has been cited by other articles in PMC.

Abstract

Significant associations between low socioeconomic status (SES) and mental disorder have been found throughout the developed world in studies of both adults and children.1–3 However, low SES can be defined in numerous ways. Existing studies have been relatively unsystematic in their selection of indicators and have seldom compared results across indicators, making it impossible to know from the available evidence which of the several components of SES accounts for the overall association between low SES and mental disorder. A family may be poor according to a governmental definition of adequate income (absolute poverty) or, alternatively, may have low income relative only to that of others in the community (relative deprivation). A family may live in an area of high poverty,4 in an area of high income inequality (community inequality), or in a community in which a high proportion of the population lives in both poverty and high inequality.5Relative deprivation can also be measured as a subjective state, as in the individual’s sense of whether he or she is better off or worse off than other people (subjective social status). There is reason to think that subjective social status might be important in and of itself, as previous research has shown that subjective social status is associated with health independent of income or education.6

Although socioeconomic gradients in health are well documented using this range of indicators,6–8 we know of no studies that have simultaneously examined the relative importance of absolute and relative SES, subjective social status, and community level inequality in predicting mental health. We also are not aware of any studies examining how associations between different aspects of SES and mental health vary across sociodemographic groups. Such variations are likely, given that the association between low SES and mental illness has been shown to vary in different racial/ethnic groups.9–11

These distinctions have important implications for intervention. Some researchers have argued that social factors such as poverty and income inequality are “fundamental causes” of mental disorders because they limit access to important health-promoting resources.4 If that is the case, then prevention efforts should target factors operating at the societal level. If, however, deprivation increases the risk of mental disorder only to the extent that individuals perceive their social status to be low, then changing the social environment will not be enough unless such changes also lead to changes in subjective social status. Although factors operating at multiple levels are likely to influence the development of mental health problems in adolescents, the relative contribution of these various aspects of SES remains unknown.

In this study we used data from the National Comorbidity Survey Adolescent Supplement (NCS-A),12,13 a national survey of US adolescents, to examine the associations between 5 aspects of SES and mental disorders across 3 racial/ethnic groups in the United States: parent educational attainment, family income, relative deprivation, subjective social status, and community level inequality.

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METHODS

The NCS-A was carried out between February 2001 and January 2004. Adolescents aged 13 to 17 years were interviewed face-to-face in dual-frame household and school samples selected to be representative of the US population.12,13 The household sample included 904 adolescents (879 in school and 25 who had dropped out of school) from households that participated in the National Comorbidity Survey Replication (NCS-R), a national survey of adults.14 The adolescent response rate, conditional on NCS-R participation, was 86.8%. The school sample included 9244 adolescents from a representative sample of schools in the NCS-R counties. The adolescent response rate, conditional on school participation, was 82.6%. The proportion of schools initially selected to participate in the NCS-A was low (28.0%). Matched replacement schools were selected for schools that declined to participate. A comparison between household sample respondents who attended nonparticipating schools and school sample respondents from replacement schools found no evidence of bias in estimates of either prevalence or mental disorder correlates resulting from the use of replacement schools.13

One parent or guardian was asked to complete a self-administered questionnaire (SAQ) about the participating adolescent’s developmental history and mental health. The self-administered questionnaire response rate, conditional on adolescent participation, was 82.5% in the household sample and 83.7% in the school sample. This article focuses on the 6483 adolescent-parent pairs for whom data were available from both adolescent interviews and self-administered questionnaires (Table 1).

TABLE 1—Weighted Distribution of Sociodemographic Factors and SES Indicators by Ethnicity: National Comorbidity Survey Adolescent Supplement, February 2001–January 2004

Written informed consent was obtained from parents or guardians before approaching adolescents. Written assent was then obtained from adolescents before surveying either adolescents or parents. Each respondent was given $50 for participation. These recruitment and consent procedures were approved by the Human Subjects Committees of both Harvard Medical School and the University of Michigan. Once the survey was completed, cases were weighted for variation in within-household probability of selection in the household sample and residual discrepancies between sample and population sociodemographic and geographic distributions.

The household and school samples were then merged with sums of weights proportional to relative sample sizes, adjusted for design effects in estimating disorder prevalence. These weighting procedures are detailed elsewhere.12,13 The weighted sociodemographic distributions of the composite sample closely approximate those of the US census population.15 The weighted distribution of sociodemographic factors in the NCS-A is shown in Table 1.

Measures

Diagnostic Assessment.

Adolescents were administered a modified version of the Composite International Diagnostic Interview, a fully structured interview designed for use by trained lay interviewers.16 For these analyses, diagnoses were grouped into 4 classes: mood disorders (major depressive disorder or dysthymia, and bipolar I-II disorder), anxiety disorders (panic disorder with or without agoraphobia, agoraphobia without history of panic disorder, social phobia, specific phobia, generalized anxiety disorder, posttraumatic stress disorder, and separation anxiety disorder), disruptive behavior disorders (attention-deficit/hyperactivity disorder, oppositional-defiant disorder, conduct disorder, and intermittent explosive disorder), and substance disorders (alcohol and drug abuse and alcohol and drug dependence with abuse).

Parents and adolescents provided diagnostic information about major depressive disorder, attention-deficit/hyperactivity disorder, oppositional-defiant disorder, and conduct disorder, those disorders for which parent reports have previously been shown to play the largest part in diagnosis.17 Parent and adolescent reports were combined at the symptom level using an “or” rule, such that a symptom was considered present if it was endorsed by either respondent. All but 2 symptoms were diagnosed using Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) diagnostic hierarchy rules. The exceptions were oppositional-defiant disorder, which was defined with or without conduct disorder, and substance abuse, which was defined with or without dependence. The current report focuses on disorders that were present during the 12 months prior to the interview.

Socioeconomic Status.

We examined associations of adolescent mental disorders with indicators of absolute and relative SES and with a community level measure of income distribution. Absolute SES variables included highest parental educational attainment and gross household income.

Parent education was coded in 4 categories: college graduate or advanced degree (the reference group), some college, high school graduation, and less than high school graduation.

Family household income was defined in relation to the poverty line, consistent with previous epidemiological surveys,18,19 using the standard approach of welfare economics.20 Low income was defined as less than 1.5 times the official federal poverty line; low-average income

was defined as 1.5 to 3 times the poverty line; high-average income as 3 to 6 times the poverty line; and high income (reference group) was defined as 6 or more times the poverty level.

Relative deprivation was defined as the difference between each adolescent’s total household income and the mean income of all households in the adolescent’s census tract, divided by the standard deviation of tract-level income of those households,21 calculated using all households in the participating adolescent’s census tract, with higher values indicating greater income deprivation.

Community-level income inequality was estimated using the Gini coefficient in each adolescent’s census tract. The Gini coefficient is among the most widely used measures of income inequality22–25 and ranges from 0 in situations of complete equality (i.e., everyone has the same income) to 1 in situations of complete inequality (i.e., 1 person has all the income). The association between the Gini coefficient and health is similar to associations observed for other markers of income inequality.23 We standardized the Gini coefficient to have a mean of 0 and a standard deviation of 1 in the current analysis.

Subjective social status was assessed using a questionnaire developed for use with adolescents by the MacArthur SES and Health Network26 to determine where individuals believe they rank in the social hierarchy.6,27 Respondents were shown a drawing of a ladder with 10 rungs and told to

(t)hink of this ladder as representing where young people stand in your school, neighborhood, or community. At the top of the ladder are the young people who have the highest standing. At the bottom are those who have the worst standing.

Respondents were then instructed to place an X on the rung that best represented their perception of where they stood relative to other young people. The original adult scale was developed to capture multiple aspects of socioeconomic position simultaneously.6,27 It is strongly associated with objective indicators of SES and the primary predictors of where an individual places himself or herself on the ladder are socioeconomic factors (occupation, income, education, satisfaction with standard of living, and financial security).26–28 The scale has been associated with a wide range of health outcomes, including obesity, hypertension, diabetes, and depression, in both adolescents and adults.27–33 We standardized the subjective social status variable to have a mean of 0 and a standard deviation of 1.

Analysis

We examined the association between adolescent SES and mental disorders during the previous 12 months by using logistic regression. For each outcome, we first estimated a base model that included absolute SES indicators (parental education and income) as predictors of disorders and then estimated successively more complex models that added relative and community level SES

indicators to the base model 1 at a time. The most complex model included all indicators simultaneously. Models were estimated first in an overall data array, that is, a consolidated data file that stacked the 20 separate disorder-specific files and included 19 dummy variables to distinguish among these files, thereby forcing the estimated slopes of disorders on SES indicators to be constant across disorders. This model was then estimated again in subsamples defined by disorder class (mood, anxiety, disruptive behavior, and substance disorders) to investigate the possibility of variation in associations of the predictors with the different kinds of outcome disorders, and race/ethnicity (non-Hispanic White [White], non-Hispanic Black [Black], and Hispanic). Controls for respondent age (coded in 4 dummy variables corresponding to ages 13 or 14, 15, 16, and 17) and gender were included in all models. Relative deprivation, community level inequality, and subjective social status were treated as linear variables in the analyses. Because each of the relative SES indicators was standardized, coefficients for these variables represented the change in odds of a mental disorder associated with 1 standard deviation change in that indicator. Logistic regression coefficients and standard errors were exponentiated to create odds ratios (ORs) with 95% confidence intervals (CI). Standard errors were estimated using the Taylor series linearization method to account for sample weights and clustering. The Wald χ2 test was used to evaluate the joint significance of sets of predictors. Statistical significance was consistently evaluated using .05-level 2-sided tests.

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RESULTS

All but 1 of the bivariate correlations among SES indicators was significant (Table 2). However, correlations between subjective social status and the other indicators of SES were generally smaller in magnitude than those among the “objective” indicators.

TABLE 2—Correlations Among Measures of Socioeconomic Status: National Comorbidity Survey Adolescent Supplement, February 2001–January 2004

Socioeconomic Status Indicators and Adolescent Mental Disorder

We first examined a series of bivariate models including each of the 5 SES indicators considered alone in predicting past-year mental disorder, adjusting for demographics (Table 3, model 1).

These models showed significant associations between past-year disorders and parent educational attainment (χ2

3 = 17.3; P < .001), such that respondents whose parents had less than a college degree had higher odds of disorder (ORs = 1.2–1.5), and subjective social status, such that adolescents who rated themselves as being 1 step higher on the ladder had 14% lower odds of meeting criteria for a mental disorder (OR = 0.8; χ2

1 = 42.3; P < .001).

TABLE 3—Associations of Absolute and Relative SES Indicators With 12-Month DSM-IV Mental Disorders: National Comorbidity Survey Adolescent Supplement, February 2001–January 2004

Five subsequent models were examined: the first including income and education, the next 3 models added a measure of relative SES (tract-level relative deprivation, tract-level Gini coefficient, and subjective social status), and the fifth model was a fully adjusted model. In the first of these models, family income was not associated with past-year adolescent disorders (χ2

3 = 1.3; P = .723), but parent educational attainment was (χ2

3 = 11.1; P = .011) (Table 3; model 2). Adolescents whose parents did not complete high school (OR = 1.4) or attended some college but did not graduate (OR = 1.4) had elevated odds of past-year disorder relative to those of adolescents whose parents graduated college. Controlling for parental education and income, neither relative deprivation (OR = 1.0; χ2

1 = 2.8; P = .094) nor tract-level Gini coefficient (OR = 1.0; χ2

1 = 1.6; P = .211) were associated with adolescent disorders. In contrast, subjective social status was associated with past-year disorders, such that adolescents who rated themselves as being 1 step higher on the ladder had 14% lower odds of meeting criteria for a mental disorder (OR = 0.8; χ2

1 = 35.3; P < .001). In the fully adjusted model including all SES indicators simultaneously, only parental education (χ2

3 = 9.0; P = .029) and subjective social status (OR = 0.8; χ2

1 = 33.8; P < .001) were associated with past-year adolescent disorders. Associations between SES and mental disorders in the final multivariate model were quite similar to the patterns observed in the bivariate models. Subsequent analysis focused on this final multivariate model (Table 3).

Differential Associations by Class of Disorder

A test for variation associations of the 9 SES coefficients in predicting the 4 disorder classes was significant (χ2

27 = 250.6; P < .001), indicating that these associations differed across disorder

classes (Table 4). Associations between SES indicators and anxiety disorders showed a pattern similar to that of the general model. In the fully adjusted model, parental education (χ2

3 = 8.7; P = .034) and subjective social status (OR = 0.8; χ2

1 = 13.9; P < .001) were associated with past-year anxiety disorders. For mood disorders, relative deprivation (OR = 1.1; χ2

1 = 3.9; P = .048) and subjective social status (OR = 0.8; χ2

1 = 25.3; P < .001) were significant in the fully adjusted model. Subjective social status was the only SES indicator significantly associated with disruptive behavior disorders (OR = 0.7; χ2

1 = 26.8; P < .001) and substance use disorders (OR = 0.7; χ2

1 = 23.3; P < .001) in the fully adjusted models. Tests for variations in the associations of individual SES indicators across disorders revealed significant differences for family income (χ2

3 = 25.7; P = .002) and subjective social status (χ2

3 = 14.0; P = .003) (Table 4).

TABLE 4—Multivariate Associations of SES Indicators With 12-Month DSM-IV Mood, Anxiety, Disruptive Behavior, and Substance Disorders: National Comorbidity Survey Adolescent Supplement, February 2001–January 2004

Socioeconomic Status and Adolescent Mental Disorder by Race/Ethnicity

A test for variations in the associations of the 9 SES coefficients predicting mental disorders across the 3 racial/ethnic groups was significant (χ2

18 = 101.1; P < .001; Table 5). The pattern of associations among White adolescents mirrored the findings in the total sample: parent education (χ2

3 = 10.6; P = .014) and subjective social status (OR = 0.7; χ21 = 36.6; P < .001) were associated

with past-year disorders in the fully adjusted model. Among Hispanic adolescents, by comparison, only subjective social status was associated with mental disorder (OR = 0.8; χ2

1 = 7.9; P = .005, in the fully adjusted model), whereas none of the SES indicators considered here was associated with mental disorders among Black adolescents. A test result for racial/ethnic variations in the associations among individual SES indicators with past-year disorder was significant only for subjective social status (χ2

2 = 21.3; P < .001). The association between subjective social status and mental disorder for Black adolescents differed compared with those of both White (χ2

1 = 20.9; P < .001) and Hispanic adolescents (χ21 = 5.5; P = .020) but did not

differ for Hispanic versus non-Hispanic White adolescents (χ21 = 0.8; P = .678; Table 5).

TABLE 5—Multivariate Associations of Absolute and Relative SES Indicators With 12-Month DSM-IV Mental Disorders, by Ethnicity: National Comorbidity Survey Adolescent Supplement, February 2001–January 2004

Subjective Social Status and Other Socioeconomic Status Indicators

Finally, we examined interactions between subjective social status and each of the other SES indicators in predicting past-year mental disorder. (Detailed results are not shown but are available on request). This analysis revealed a significant interaction between parental education and subjective social status in predicting behavior disorders (χ2

3 = 16.6; P < .001). Higher subjective status was associated with reduced odds of behavior disorders among most respondents but not among those whose parents had the lowest level of education. A similar pattern was observed for non-Hispanic White and Hispanic adolescents, whereas among Black adolescents, subjective status was associated with reduced odds of mental disorder only among those whose parents had a college degree.

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DISCUSSION

In this article, we examined the associations of 5 aspects of SES with adolescent mental disorders in a representative population-based sample. We were particularly interested in determining the relative importance of absolute measures of SES (parental education and income), relative measures of SES (relative deprivation and community level income inequality), and subjective social status.6,26,28 Nearly all measures of SES are significantly intercorrelated among themselves but not so strongly that it is not possible to study the associations of each aspect of SES with adolescent mental disorders while controlling for the others.

Subjective social status is the SES indicator most consistently related to mental disorders. This association is significant in almost all models in which it was tested. Lower subjective status is associated with higher odds of each of the 4 disorder classes. The association between subjective social status and mental disorders varies, though, by parental education. Subjective social status was less strongly associated with past-year disorder among adolescents whose parents had low levels of education. Of the absolute SES measures, family income was unassociated with all

mental disorder classes, whereas lower parental education was associated with higher odds of mental disorder. Parental education was associated only with anxiety disorders in the fully adjusted model, but adolescents whose parents had some college education but did not complete a degree were more likely to have a mental disorder across all diagnostic groups. Relative SES measures were not associated with adolescent mental disorders. Relative deprivation was associated only with odds of mood disorder, and community level inequality was unassociated with adolescent mental disorder in all models.

These findings suggest that among adolescents, one’s perception of social status is the aspect of SES most strongly associated with mental health. Some previous studies have shown associations between material disadvantage that were stronger than those perceived for social status and mental disorders, whereas other studies have found the opposite.5,33–35 The measure used in the current study required adolescents to mark a rung on a ladder corresponding to “where you think you stand relative to other young people in your school, neighborhood, or community.” The acute sensitivity of adolescents to their social status at school is well documented,36 and these status perceptions may be more important determinants of mental health than traditional SES indicators in this age group. Although the primary factors associated with subjective social status ratings in adults are socioeconomic, including income, education, and occupation,27 future research needs to explore how status dimensions other than parental income and education, such as membership in social groups and possession of desirable objects, are involved in these judgments among adolescents.37 The interaction between subjective social status and education indicates that perceptions of status are less strongly associated with mental health among adolescents whose parents have the lowest levels of education, suggesting that status perceptions are linked to mental health only among adolescents who have surpassed a certain threshold of objective SES. In other words, perceptions of one’s status are less important if one’s objective status is quite low. We are unaware of previous research that has examined interactions between subjective social status and other SES indicators; this warrants further examination in future research.

In the United States, racial/ethnic differences in income often make it difficult to disentangle associations of race/ethnicity from associations of poverty with mental disorders. Using a large sample representative of the United States, we found clear racial/ethnic differences among associations between objective and subjective social status and adolescent mental health. First, parental education is associated with mental disorders only among non-Hispanic White adolescents. Second, none of the SES indicators in these analyses is associated with mental disorders in Black adolescents, despite the fact that only half as many Black participants have parents with a college degree as White participants and that the average household income of Black families is less than 60% of that of non-Hispanic White families in the study.15 Many,9,10,38 but not all,1 studies of child and adolescent mental health have noted a

stronger association between poverty and mental disorders among non-Hispanic White youths than among youths of other racial/ethnic backgrounds.

Finally, subjective social status is associated with increased odds of mental disorders in non-Hispanic White and Hispanic adolescents but not in Black youths, although racial/ethnic differences in subjective social status are smaller than differences in income or education (Table 1). It has been suggested that minority racial status influences the goals and outlook of Black children beginning at a very early age, lowering their expectations for future success.27 Low perceptions of social status have been linked to low expectations for oneself and one’s future.28,39,40 Given that perceptions of status incorporate judgments of future prospects and opportunities, SES may be less strongly associated with mental health among Black adolescents because their status expectations are lower. Lower status expectations may attenuate the relationship between SES and mental health among Black adolescents because being raised in a low-SES family does not violate their status expectations. Indeed, previous studies have observed stronger associations between subjective social status and health in non-Hispanic White than in Black individuals.31,41 Another possibility is that Black adolescents rely on criteria to rank themselves on the social ladder that are different from White adolescents. Previous research has shown that determinants of subjective status vary as a function of cultural identity and community context,39 raising the possibility that this measure captures different dimensions of status across racial/ethnic groups. Goodman et al.28 found that objective indicators of SES are less strongly linked to subjective social status rankings in Black adolescents than in non-Hispanic White adolescents, suggesting that factors beyond SES contribute to their perceptions of social status. A final explanation for the lack of association between subjective status and mental disorder in Black adolescents relates to parental education. Less than one fourth of Black adolescents in the survey had parents with a college degree, and we found that status perceptions were associated with mental health most strongly for those with high parental education. Identifying mechanisms explaining racial/ethnic differences in the association of SES with adolescent mental health is an important goal for future research.

The study is limited by the cross-sectional design, which prevents drawing firm conclusions about temporal priority. Although prospective studies find that subjective social status predicts changes in health over time,28,33 our findings could also reflect reverse causation, whereby adolescents with mental disorders rate their status lower than adolescents without mental illness. Another potential limitation is that we limited analysis of aggregate income effects to relatively small geographic areas. Previous research has shown stronger effects of income inequality when aggregated over larger areas (e.g., states or countries).42,43However, we also examined inequality at the state level and found no association with adolescent disorders. Additional limitations include the use of lay interviewers, rather than clinicians, to administer the diagnostic interviews and potential informant biases in the reporting of psychopathology by adolescents. The NCS-A relies mostly on adolescent reports of symptoms. Some research suggests that SES

is more strongly associated with parent and teacher reports than adolescent reports of adolescent psychopathology.44 Our use of adolescent reports as the mainstay of the assessment consequently might have attenuated SES–mental health associations in our study. A final noteworthy limitation is that the participation rate of initially selected schools was low. Methodological analysis, however, shows that school refusal is unlikely to have influenced our results, because the household sample included adolescents from schools that did not participate and comparison of disorder prevalence in participants from refusal schools and replacement schools revealed no differences either between rates of disorder or between associations of basic predictors with disorder.12

Results reported here suggest that the association between SES and adolescent mental health results most directly from individual perceptions of social status. We find virtually no associations between absolute income, inequality, or relative deprivation and past-year mental disorders; a modest association with parental education; and a consistent association with subjective social status. These findings mean that subjective judgments of status might be one mechanism by which SES gets “under the skin” to influence health outcomes, highlighting a potentially modifiable target for interventions aimed at reducing status-based disparities in health.

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Acknowledgments

The National Comorbidity Survey Replication Adolescent Supplement (NCS-A) is supported by the National Institute of Mental Health (NIMH; U01-MH60220 and R01-MH66627) with supplemental support from the National Institute on Drug Abuse (NIDA), the Substance Abuse and Mental Health Services Administration (SAMHSA), the Robert Wood Johnson Foundation (RWJF; Grant 044780), and the John W. Alden Trust.

Note. The views and opinions expressed in this report are those of the authors and should not be construed to represent the views of any of the sponsoring organizations, agencies, or US Government. A complete list of NCS-A publications can be found at http://www.hcp.med.harvard.edu/ncs. Send correspondence toude.dravrah.dem.pch@scn. The NCS-A is carried out in conjunction with the World Health Organization World Mental Health (WMH) Survey Initiative.

We thank the staff of the WMH Data Collection and Data Analysis Coordination Centers for assistance with instrumentation, fieldwork, and consultation on data analysis.

The WMH Data Coordination Centers have received support from NIMH (R01-MH070884, R13-MH066849, R01-MH069864, R01-MH077883), NIDA (R01-DA016558), the Fogarty International Center of the National Institutes of Health (FIRCA R03-TW006481), the John D.

and Catherine T. MacArthur Foundation, the Pfizer Foundation, and the Pan American Health Organization. The WMH Data Coordination Centers have also received unrestricted educational grants from Astra Zeneca, Bristol-Myers Squibb, Eli Lilly & Company, GlaxoSmithKline, Ortho-McNeil, Pfizer, Sanofi-Aventis, and Wyeth. A complete list of WMH publications can be found at http://www.hcp.med.harvard.edu/wmh.

The funding organizations played no role in the design and conduct of the study; or in the collection, analysis, and interpretation of the data; or in the preparation, review, or approval of the manuscript.

Note. R. C. Kessler has been a consultant for AstraZeneca, Analysis Group, Bristol-Myers Squibb, Cerner-Galt Associates, Eli Lilly & Company, GlaxoSmithKline Inc., HealthCore Inc., Health Dialog, Integrated Benefits Institute, John Snow Inc., Kaiser Permanente, Matria Inc., Mensante, Merck & Co, Inc., Ortho-McNeil Janssen Scientific Affairs, Pfizer Inc., Primary Care Network, Research Triangle Institute, Sanofi-Aventis Groupe, Shire US Inc., SRA International, Inc., Takeda Global Research & Development, Transcept Pharmaceuticals Inc., and Wyeth-Ayerst; has served on advisory boards for Appliance Computing II, Eli Lilly & Company, Mindsite, Ortho-McNeil Janssen Scientific Affairs, Plus One Health Management and Wyeth-Ayerst; and has had research support for epidemiological studies from Analysis Group Inc., Bristol-Myers Squibb, Eli Lilly & Company, EPI-Q, GlaxoSmithKline, Johnson & Johnson Pharmaceuticals, Ortho-McNeil Janssen Scientific Affairs., Pfizer Inc., Sanofi-Aventis Groupe, and Shire US, Inc.

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Human Participant Protection

The institutional review boards of both Harvard Medical School and the University of Michigan approved the study.

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Mental health and suicidal behavior among graduate students.Garcia-Williams AG1, Moffitt L, Kaslow NJ.Author informationAbstractOBJECTIVE:The objective of this paper is to describe the mental health and service utilization of graduate students at a large southeastern university and identify psychological factors associated with their student suicidal behavior.

METHODS:E-mail invitations to complete the Interactive Screening Program, an online anonymous mental health questionnaire, were sent to graduate students. The questionnaire included the Patient Health Questionnaire (PHQ-9) as well as items assessing suicide behavior, anxiety, negative emotion, substance use, eating behavior, and service utilization.

RESULTS:A total of 301 graduate students responded to the questionnaires between 14 July 2010 and 24 January 2012. With regards to suicide, 7.3 % of the sample reported thoughts of suicide, 2.3 % reported having plans for suicide, and 1.7 % had hurt themselves in the past 2 weeks; while 9.9 % had ever made a suicide attempt in their lifetime. Graduate students had PHQ-9 scores indicating mild depression, and more than half endorsed feeling nervous, irritable, stressed, anxious, lonely, or having fights/arguments. In terms of service utilization, 22.2 % of the sample was currently taking some type of medication, and 18.5 % currently in counseling/therapy are females and those with higher PHQ-9 scores more likely to be using services. Those endorsing suicidal behavior in the past 2 weeks had significantly higher depression scores than those without such behavior and were characterized by more anxiety, negative emotions (such as loneliness, anger, hopelessness, desperation, and being out of control), substance use, and eating problems.

CONCLUSIONS:Graduate students experience significant amounts of stress and anxiety, and their suicidal behavior is strongly characterized bydepression, hopelessness, desperation, lack of control, and eating problems. Future work with this population should focus on the development and evaluation of mental health and wellness interventions and on ways to promote help-seeking, especially among male students.

PMID:

 

24711096

 

[PubMed - indexed for MEDLINE]

Mental health screening by self-report questionnaire among community adolescents in southern Brazil.Feijó RB1, Saueressig M, Salazar C, Chaves ML.Author informationAbstractPURPOSE:To evaluate the frequency of symptoms of depression, suicidal ideation, suicide behavior, and hopelessness among adolescents in southern Brazil.

METHODS:The Self-Report Questionnaire (SRQ) was administered to a random sample of 126 community youngsters to screen for mentalproblems, the Montgomery-Asberg Depression Rating scale for signs and symptoms of depression, an adapted version of DIS (Diagnostic Interview Schedule) for suicidal ideation and behavior, and the Back's Hopelessness scale (adapted version). Social class, cognitive performance, age, and sex were also analyzed.

RESULTS:Levels of symptoms of depression, suicidal ideation and behavior, and hopelessness were higher among those who were SRQ positive (8%). Female youngsters presented higher SRQ scores and on the Montgomery-Asberg scale. Cognitive performance was similar among all groups. Differences were not found according to social classes. The 15-17 year-old individuals (middle stage) presented higher hopelessness than the other stages of adolescence.

CONCLUSIONS:

The present findings suggest that screening for mental health conditions by self-report questionnaires may be of value of identify groups at major risk for violent, self-destructive behavior among community adolescents.

PMID:

 

9069024

 

[PubMed - indexed for MEDLINE]