Estimating the prevalence of any impairing childhood mental disorder in the national health interview survey

Abstract

This study investigates whether the six‐item Strengths and Difficulties Questionnaire SDQ (five symptoms and one impact item) included in the National Health Interview Survey (NHIS) can be used to construct models that accurately estimate the prevalence of any impairing mental disorder among children 4–17 years old as measured by a shortened Child/Adolescent or Preschool Age Psychiatric Assessment (CAPA or PAPA). A subsample of 217 NHIS respondents completed a follow‐up CAPA or PAPA interview. Logistic regression models were developed to model presence of any child mental disorder with impairment (MDI) or with severe impairment (MDSI). Models containing only the SDQ impact item exhibited highly biased prevalence estimates. The best‐performing model included information from both the five symptom SDQ items and the impact item, where absolute bias was reduced and sensitivity and concordance were increased. This study illustrates the importance of using all available information from the six‐item SDQ to accurately estimate the prevalence of any impairing childhood mental disorder from the NHIS. Copyright © 2015 John Wiley & Sons, Ltd.

Introduction

In 1998, key recommendations in a report by a National Advisory Mental Health Council (NAMHC) board called for an ongoing national survey to monitor youth's mental health disorders, impairment, and service use (US Department of Health and Human Services, 2000). More than a decade later, this goal is only partially realized. Assessment of mental disorders during childhood and adolescence is challenging. Typical behavior during these developmental stages ranges widely. Early childhood assessments can be the most challenging, partly explaining why there are comparatively few national estimates of mental disorders among children younger than eight years old. While two national studies provide prevalence estimates of specific mental disorders for children 8–15 years (Merikangas et al., 2010) and 13–17 years (Kessler et al., 2012) neither provides ongoing surveillance of mental disorders. Certain national surveys, such as the National Health Interview Survey (NHIS), produce estimates of select disorders based on parent report (e.g. attention deficit hyperactivity disorder, ADHD); however, these surveys do not include esitmates of any childhood mental disorder using a standardized tool.

In response to the NAMHC recommendation, the National Institutes of Health National Intitute of Mental Health (NIMH) partnered with the Centers for Disease Control and Prevention's National Center for Health Statistics (NCHS) to incorporate variations of the Strengths and Difficulties Questionnaire (SDQ), a widely used tool to screen for child mental health problems, into the NHIS in 2001 (Bourdon et al., 2005). The SDQ extended version (25 symptom items; five impact items) was included in the 2001, 2003, and 2004 NHIS; the six‐item version of the SDQ (five symptom items; one impact item) was included in 2002, 2005–2007 and 2010–2013; and the impact item alone appeared in the 2008–2009 NHIS. NHIS data are cited regularly in federal reports such as America’s Children in Brief: Key National Indicators of Well‐being (Federal Interagency Forum on Child and Family Statistics, 2012). SDQ data have been analyzed in multiple ways over the past decade. Bourdon et al. (2005) established US normative scoring bands for the extended SDQ, defining children with high difficulties as those in the top 10% band. The researchers then used a measure of mental health service contact to test the predictive validity of SDQ scores and found that, indeed, higher SDQ scores correlated with greater service use. Pastor et al. (2012) examined (six‐item) versions of the SDQ and used “high symptom score” (i.e. total SDQ score ≥ 6 from the five symptom items) and/or “serious overall difficulties” (i.e. report of definite or severe difficulties on the impact item) as indicators of mental health problems. They identified a total of 7.4% of children as having mental health problems, using a combination of scoring methods: 2.1% had both high symptom and serious overall difficulties scores, 2.2% had a high symptom score alone, and 3.1% had only the serious overall difficulties rating. Both Bourdon and Pastor used service use and other child characteristics to compare children who were rated as having or not having mental health problems – a criteria‐related type of measure validation. However resulting population prevalence estimates varied substantially in size and demographic profile depending on the type of SDQ scoring method used.

These studies offer some reassurance that the extended and shorter SDQ versions are identifying key populations of interest (i.e. children who may have mental disorders). However, the method recommended for validating brief mental health screening measures is administering the instrument of interest and gold standard clinical assessments in close succession so the brief measure can be calibrated to the clinical assessment for maximum concordance (Kessler et al., 2004). This approach was used to estimate the prevalence of adult serious mental illness (SMI) in the National Survey on Drug Use and Health (NSDUH) (Aldworth et al., 2010; Kessler et al., 2003). Similar work has been conducted with adolescents. In the National Comorbidity Survey – Adolescent Supplement (NCS‐A), Kessler et al. (2006) examined associations between the five‐symptom item SDQ and Schedule for Affective Disorders and Schizophrenia for School‐age Children (K‐SADS) interviews (Kaufman et al., 1997). Observed K‐SADS prevalence estimates of adolescent mental disorders with serious impairment were reproduced with little model‐based bias (6.1% in the five‐symptomm [no impact] item SDQ versus 4.8% in the K‐SADS) and with good individual‐level concordance (area under the curve, AUC* = 0.85) (Kessler et al., 2006). The researchers concluded that the best scoring approach was to sum responses into a total score and use a cut point of ≥ 6 to estimate prevalence of mental disorders with serious impairment.

Screening measures are typically used to identify cases in need of further clinical assessment; consequently, these scales are typically short, easy‐to‐score and designed to be clinically relevant. To be most helpful screening scales need to be sensitive enough to identify the majority of relevant cases and also have high positive predictive value (PPV), or a low false positive rate (Glover and Albers, 2006; Kessler et al., 2002). Several short screening scales exist to examine specific disorders in children, but far fewer short, comprehensive screeners are available to identify a more broad risk for any mental disorder. This article examines the utility of the very short global six‐item SDQ included in the NHIS for constructing models that can be used to predict the prevalence of any impairing child mental disorder as measured by the Child and Adolescent or Preschool‐age Psychiatric Asessements (CAPA or PAPA). Using similar methods to those described by Kessler et al. (2003) and Aldworth et al. (2010), this study expands upon the NCS‐A findings (Kessler et al., 2006) to examine prediction models for childhood mental disorders across a broad age range of children and adolescents included in the NHIS. We compare and contrast four predictive models that mirror the variety of scoring methods previously used (Federal Interagency Forum on Child and Family Statistics, 2012; Pastor et al., 2012). We also examine how child age (4–11 versus 12–17 years) impacts prediction methods for any impairing childhood mental disorder.

Methods

Sample

This study uses data collected from a follow‐up study to the NHIS (Aldworth et al., 2012). NHIS data are collected through in‐person household interviews among a nationally representative sample of the civilian non‐institutionalized US population. When children are present in a family, one sample child is selected. Parent participants for this study were recruited from the final three quarters of 2011 and first quarter of 2012 of NHIS. Only sample children whose parents completed the NHIS interview in English, provided complete contact information and SDQ responses, and indicated the child had no history of mental retardation (now referred to as “intellectual disability”), developmental delay, autism, or Down syndrome were eligible for participation. In the current study, parents were the only respondents for children 4–11; for children 12–17, parent/child pairs were required. Cases were selected based on the Neyman optimal allocation design; sampling strata were defined by SDQ scores and then sampled proportionally to the size of the standard error of a proxy measure of child mental disorder distributed across the strata. In addition, the oversampling of minorities in the NHIS sampling design was partially reversed by a race/ethnicity adjustment factor applied to the design in order to control the variability in analysis weights.

Of the 1187 identified parent respondents, 195 were ineligible due to a competing study using the NHIS sample, 277 were not locatable and 239 not contactable; and 200 refused or broke off the interview. The final sample size was 217 (18.3%), including 139 completed parent interviews for children aged 4–11 and 78 completed parent/child pairs for children aged 12–17. (Another 50 parents of 12–17 year old children responded, but the children did not.) Non‐response and post‐stratification adjustments were applied to the weights to lessen bias due to non‐response or the lack of population coverage meeting eligibility requirements.

Respondent characteristics for the NHIS main sample and this study sample are shown in Table 1. Demographic characteristics of the study sample based on the final study sample weights are identical to those of the main NHIS sample because both were post‐stratified to the same US Census‐based totals. The SDQ weighted mean scores are also similar across the study and NHIS samples.

Table 1.

Child population estimates (percentage distributions)

Variable	NHIS main sample (NHIS sample child weights)	Final study sample (design weightsa)	Final study sample (final sample weightsb)
Age group
4–11	57.2	66.2	57.2
12–17	42.8	33.8	42.8
Gender
Male	51.2	47.4	51.2
Female	48.8	52.6	48.8
Race/ethnicity
Non‐Hispanic White	54.2	55.8	54.2
Non‐Hispanic Black	13.1	19.9	13.1
Non‐Hispanic Other	9.9	16.4	9.9
Hispanic	22.8	7.9	22.8
NHIS 5‐item SDQ Score 3
0	32.0	30.4	31.6
1	23.6	23.1	20.1
2	20.0	17.4	19.5
3	10.3	14.3	13.0
4	6.1	7.1	8.3
5	3.7	4.0	3.9
6	2.4	2.0	2.1
7	0.9	0.6	0.5
8	0.6	0.6	0.5
9	0.2	0.4	0.5
10	0.0	0.0	0.0
NHIS SDQ Impact Item Score c
0	80.1	81.4	78.6
1	14.4	15.4	17.0
2	4.1	2.2	3.1
3	1.3	1.1	1.3

NHIS = National Health Interview Survey; SDQ = Strengths and Difficulties Questionnaire.

^aDesign weight is the NHIS sample child weight multiplied by reciprocal of study sample selection probability.

^bFinal sample weight is the design weight modified by nonresponse adjustments and post‐stratification adjustments to match the NHIS control totals by age group, gender, and race/ethnicity.

^cCases with missing SDQ score and impact item score values were excluded from this table.

Measures

Strengths and difficulties questionnaire (SDQ)

For the NHIS, parents report on children's mental health by responding to the six‐item SDQ (Goodman, 2001). Parents are asked the degree to which five items describe the sample child by responding “not true,” “somewhat true,” or “certainly true” to each item (e.g. “he is often unhappy, depressed, or tearful”). Each item is scored 0–2; the total SDQ score sums the five items (range 0–10). These five items are supplemented by one “impact” item that assesses functional impairment: “Overall, do you think that [child] has difficulties in any of the following areas: emotions, concentration, behavior, or being able to get along with other people?” The parent is asked to respond “no” or “yes” to minor, definite, or severe difficulties, yielding a score 0–3. This six‐item SDQ was developed in consultation with the scale developer to fit within the time constraints of the NHIS (US Department of Health and Human Services/Centers for Disease Control and Prevention, 2003). Kessler et al. (2006) examined the parent‐reported 25‐ and five‐item SDQ measures in a model predicting clinical interview results from the K‐SADS (Kaufman et al., 1997). High item‐total correlations (0.64–0.76) were found between the single items selected from the five SDQ subscales and scores from the full subscales included in the 25‐item version (Kessler et al., 2006).

Clinical interview

The clinical interview used in this study for children aged 8–17 was the electronic, shortened CAPA (Angold and Costello, 2000). The CAPA is a semi‐structured interview recommended for use with children aged 9–18. It is based on the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM‐IV) (American Psychiatric Association, 1994) and is administered by trained lay interviewers. In the CAPA, respondents report symptoms for lifetime and current (past three months) time periods. The shortened CAPA instruments include five modules to assess anxiety disorders, mood disorders, ADHD, oppositional defiant disorder (ODD), and conduct disorder (CD). The CAPA has established clinical validity and test–retest reliability across various mental disorder diagnoses ranges from kappa = 0.55 to 1.0 (Angold and Costello, 1995, 2000).

The clinical interview for children aged 4–7 was the electronic Preschool Age Psychiatric Assessment (E‐PAPA) (Egger et al., 2006), the CAPA younger‐age companion. It was developed for parents of children aged 2–5, although it has been used for children as old as seven. The test–retest reliability of the PAPA has been examined in a pediatric clinical sample with results comparable to diagnostic interviews with parents with older children (Egger et al., 2006). A shortened five‐module version (anxiety, mood, ODD, ADHD, and CD) was developed specifically for use in this study.

The CAPA and PAPA instruments include an incapacities module that assesses impairment for all endorsed symptoms. The interviewer is asked to distinguish three levels of impaired functioning – absent, partial or severe. “Partial incapacity” refers to a notable reduction of function. “Severe incapacity” refers to a complete, or almost complete, inability to function in a particular area. Two measures were derived from the CAPA and PAPA clinical interviews: mental disorder with impairment (MDI) and mental disorder with severe impairment (MDSI). MDI indicates a presence of any disorder assessed from the five modules with at least one partial or severe impairment rating; MDSI indicates the presence of any assessed disorder accompanied by one or more severe impairment rating. For children aged 12–17, MDI or MDSI was based on disorder and impairment information from both parent and child interviews.

Data collection

Data collection occurred from February to August 2012. Verbal parent consent and adolescent assent were obtained from all respondents. To maintain a consistent reporting period between the clinical interview and screening measure, the six‐item SDQ was re‐administered at the start of the clinical interview. Parent respondents of children aged 4–7 were administered the PAPA (Egger et al., 2006). Parents of children aged 8–17 and children aged 12–17 were administered the CAPA (Angold and Costello, 2000). Interviews were conducted by telephone by trained lay interviewers using tablet computers. Interviews were recorded for quality review. Parent interviews were conducted first. Parents were provided a $25 renumeration; children aged 12–17 received a $25 gift card. Recruitment and consent procedures were approved by the contracting organization's Institutional Review Board.

Statistical analysis

For each of the two age groups (4–11 and 12–17), weighted logistic regression models were applied to the study sample data in which the response variable was either CAPA/PAPA‐based MDI or MDSI (positive or negative), and the explanatory variables included various formulations of the five symptom/one impact item SDQ scores. The study sample final analysis weights were used in the models.

Receiver operating characteristic (ROC) analyses were applied to each of the models to identify an appropriate cut point to dichotomize the predicted probability of MDI based on the model, for each of the two age groups. A cut point on the SDQ was needed so that prevalence estimates based on the SDQ were as close as possible to those based on the CAPA or PAPA. With a coarse instrument such as the SDQ with very few levels, it is not always possible to do this exactly, especially when dealing with weighted data. Therefore, any differences that result between the SDQ and CAPA estimates (positive or negative) represent the model‐based bias. If a child's predicted probability of MDI was greater than or equal to the cut point, then that child was predicted to be MDI positive; otherwise, he or she was predicted to be MDI negative. The cut point was selected to (approximately) equalize the weighted false positive (i.e. predicted MDI positive, but CAPA/PAPA‐based MDI negative) and false negative (i.e. predicted MDI negative, but CAPA/PAPA‐based MDI positive) counts. This is equivalent to minimizing the model‐based bias in prevalence estimates of MDI based on CAPA/PAPA determinations versus those based on predictions derived from the model and cut point. An identical approach was used to analyze MDSI.

Results

Models using the SDQ impact item only

As in America’s Children (Federal Interagency Forum on Child and Family Statistics, 2012), the four‐level SDQ impact item can be collapsed into a two‐level variable to describe none or minor versus definite or severe difficulties. This variable has only one (informative ) discriminating cut point; consequently, this is the only way it can predict childhood mental disorder (i.e. it predicts both MDI and MDSI with the same cut point).

In contrast, models based on the four‐level impact item variable have three possible informative cut points and hence the capacity to distinguish between MDI and MDSI prediction. Results of an ROC analysis applied to the four‐level impact‐item model, for both MDI and MDSI and for each age group, are given in Table 2 (Model one). Because the four‐level impact item can score zero, one, two, or three, a cut point of x indicates that a score of x or greater will result in a positive MDI or MDSI prediction. Note that a cut point of two for the four‐level impact item is equivalent to a cut point of one for the dichotomized impact item. Therefore, the four rows of Table 2 in which the cut point is two exactly describe the ROC statistics of the dichotomized impact item; note that for these rows the cut point of two and modeled estimate are the same for MDI and MDSI (5.15% for children aged 4–11 and 7.32% for children aged 12–17). In the case of MDI prediction, the negative bias is large (–16.40 percentage points for children aged 4–11 and –13.68 percentage points for children aged 12–17). In contrast, models based on the four‐level impact item are able to distinguish between MDI and MDSI prediction. Even so, the large positive bias associated with MDI prediction using a cut point of one (15.33 percentage points for children aged 4–11 and 9.20 percentage points for children aged 12–17), suggests that even the four‐level impact item is gradated too coarsely to accurately predict MDI. The negative bias associated with MDSI prediction is much smaller (–3.67 percentage points for children aged 4–11 and –2.67 percentage points for children aged 12–17).

Table 2.

Receiver operating characteristic (ROC) model statistics

Age Group	Variable	Cut pointa	CAPA/PAPA estimateb	Modeled estimateb	Biasb	FPc (%)	FNc (%)	Sensd	Specd	PPVe	NPVe	AUCdf	AUCcf
Model one = four‐level impact item (0–3) predictor
4–11	MDI	1	21.55	36.88	15.33	25.09	9.76	0.547	0.680	0.320	0.845	0.614	0.714
4–11	MDI	2	21.55	5.15	−16.40	1.02	17.43	0.191	0.987	0.801	0.816	0.589
4–11	MDSI	2	8.81	5.15	−3.67	1.35	5.02	0.430	0.985	0.737	0.947	0.708	0.806
12–17	MDI	1	21.00	30.20	9.20	11.32	2.12	0.899	0.857	0.625	0.970	0.878	0.803
12–17	MDI	2	21.00	7.32	−13.68	1.78	15.46	0.264	0.977	0.756	0.833	0.621
12–17	MDSI	2	9.99	7.32	−2.67	1.93	4.60	0.539	0.979	0.736	0.950	0.759	0.866
Model two = SDQ score (0–10) predictor
4–11	MDI	3	21.55	25.07	3.52	12.37	8.85	0.589	0.842	0.507	0.882	0.716	0.764
4–11	MDSI	4	8.81	9.32	0.50	7.23	6.73	0.237	0.921	0.224	0.926	0.579	0.797
12–17	MDI	4	21.00	26.13	5.13	13.54	8.41	0.599	0.829	0.482	0.886	0.714	0.813
12–17	MDSI	5	9.99	11.04	1.05	5.41	4.35	0.564	0.940	0.510	0.951	0.752	0.862
Model three=SDQ score (0–10) and impact item (0 –3) predictors
4–11	MDI		21.55	25.18	3.63	12.47	8.85	0.589	0.841	0.505	0.882	0.715	0.785
4–11	MDSI		8.81	7.97	−0.85	3.77	4.62	0.476	0.959	0.526	0.950	0.717	0.834
12–17	MDI		21.00	21.14	0.14	7.11	6.97	0.668	0.910	0.664	0.912	0.789	0.847
12–17	MDSI		9.99	7.93	−2.06	2.15	4.21	0.579	0.976	0.729	0.954	0.777	0.913
Model four=subset of up to five individuaL SDQ item scores (0–2) and impact item (0–3) predictors
4–11	MDI9		21.55	21.12	−0.43	7.59	8.01	0.628	0.903	0.641	0.898	0.766	0.800
4–11	MDSI		8.81	8.92	0.11	4.77	4.66	0.471	0.948	0.466	0.949	0.710	0.873
12–17	MDI		21.00	21.24	0.24	7.84	7.60	0.638	0.901	0.631	0.904	0.770	0.851
12–17	MDSI		9.99	10.02	0.03	3.34	3.31	0.669	0.963	0.667	0.963	0.816	0.910

AUCc=area under ROC curve (continuous predictor); AUCd=area under ROC curve (dichotomous predictor); FN=false negative; FP=false positive; MDI=mental disorder with impairment; MDSI=mental disorder with severe impairment; NPV=negative predictive value; CAPA/PAPA= child and adolescent psychiatric assessment (CAPA) or preschool age psychiatric assessment (PAPA); PPV=positive predictive value; ROC=receiver operating characteristic, Sens=sensitivity, Spec=specificity.

^aFor models with a single predictor, a cut point of x indicates that a score ≥ x will result in a positive MDI or MDSI prediction; for models with multiple predictors, the cut point is not given (see Table 2, footnotes 7 and 8).

^bCAPA/PAPA Estimate and Modeled Estimate refer to prevalence estimates of MDI or MDSI based on CAPA/PAPA determinations and model predictions, respectively. Bias is the difference between modeled and CAPA/PAPA estimates.

^cFP and FN are percentages of weighted false positive and false negative counts, respectively.

^dSens (i.e. sensitivity) is the weighted proportion of CAPA/PAPA MDI or MDSI positive cases also predicted MDI or MDSI positive (i.e. positive “hit” rate). Spec (i.e. specificity) is the weighted proportion of CAPA/PAPA MDI or MDSI negative cases also predicted MDI or MDSI negative (i.e. 1 – “false alarm” rate).

^ePPV is the weighted proportion of predicted MDI or MDSI positive cases that are also CAPA/PAPA MDI or MDSI positive. NPV is the weighted proportion of predicted MDI or MDSI negative cases that are also CAPA/PAPA MDI or MDSI negative.

^fAUCc is the concordance between the model (with continuous predictor) and CAPA/PAPA MDI or MDSI, and AUCd is the concordance between the model (with predictor dichotomized by the cut point) and CAPA/PAPA MDI or MDSI; AUCd = the average of sensitivity and specificity.

See Table 3 for cut points determined for combination of impact item score and SDQ score.

Cut point not given, because it would involve multiway tables or a detailed model description and cut point in terms of predicted probabilities.

Example formula: MDI (4–11 years) = if 1/(1 + exp[–(–3.7309 + 2.8261*Obed + 1.1503*Worry + 2.0109*Unhappy + 0.2049*Atten + 0.6565*Impact]) ≥ 0.46940 where Obed(iance), Worry, Unhappy, Adults, and Atten(tion) refer to the five items used in the abbreviated SDQ (this may require recoding of questionnaire raw scores) and impact refers to a sixth SDQ item. Other formulas available upon request.

Models using the SDQ score only

Results of an ROC analysis applied to models with the five‐item (no impact item) SDQ score (hereafter called “SDQ Score”) as a predictor (Kessler et al., 2006; Pastor et al., 2012) appear in Table 2 (Model two). Because the five‐item SDQ score has 11 levels (i.e. 0–10), it has a finer discriminating capacity than the four‐level impact item; this shows in the comparatively smaller levels of absolute bias in Table 2. Bias reduction is particularly noticeable in the case of MDSI prediction (0.50 percentage points for children aged 4–11 and 1.05 percentage points for children aged 12–17). However, for the 4–11 age group, sensitivity and AUCd (i.e. AUC based on the SDQ score dichotomized by the cut point) are very small (sensitivity = 0.237; AUCd = 0.579). This indicates that although the dichotomized SDQ score results in a bias reduction (in comparison with the impact item), it is not an accurate method to predict MDSI for this age group.

Models based on SDQ score and impact item

In the models including both the SDQ score and the impact item as predictors bias values are similar or slightly reduced relative to those from the SDQ score model (see Table 2, Model three); for the 4–11 year age group, sensitivity and AUCd are substantially increased (sensitivity = 0.476; AUCd = 0.717; Table 2). ROC statistics of the other three cases for this model are similar or better (sensitivity = 0.579–0.668; AUCd = 0.715–0.789). These results suggest that the impact item added as a predictor improves the prediction of MDI or MDSI for both age groups.

Because this model has two independent predictors, cut points need to be defined with respect to both the impact item score and SDQ score simultaneously (Table 3). For example, for the 4–11 age group, a combination of an impact item score of zero and an SDQ score ≥ 3 would be required to positively predict MDI, but in the case of MDSI prediction, no SDQ score cutpoint exists in combination with an impact item score of zero to positively predict MDSI (i.e. even an SDQ score of 10 would not be sufficient to positively predict MDSI). If the impact item score is one, then an SDQ score ≥ 3 would be required to positively predict MDI, but an SDQ score ≥ 5 would be required to positively predict MDSI. Table 3 shows some inconsistency in the MDI versus MDSI models for the 4–11 age group: if the impact item score is two or three, then a larger cut point with respect to the SDQ score would be required to positively predict MDI than MDSI. Also of interest is the fact that the cut points vary by age group; in other words, a positive MDI or MDSI prediction depends both on impact item score and SDQ score and on age group.

Table 3.

Cut points based on example three: Impact Item Score and SDQ Score

Age Group	Impact Item Score	Five‐item SDQ Score Cut Pointa
		MDI	MDSI
4–11	0	3	—b
	1	3	5
	2	2	0
	3	1	0
12–17	0	7	8
	1	3	6
	2	0	3
	3	0	1

MDI = mental disorder with impairment, MDSI = mental disorder with severe impairment, SDQ = Strengths and Difficulties Questionnaire.

^aA cut point of x indicates that an SDQ score ≥ x will result in a positive MDI or MDSI prediction.

^bNo SDQ score cutpoint combined with Impact score = 0 predicted MDSI status for the 4–11 age group.

Models based on subset of five individual SDQ items and impact item

To further reduce the absolute bias of modeled prevalence estimates, the five individual SDQ item scores and the impact item were included as predictors in the same model. These results are found in Table 2 (Model four). Exact cut points are not provided in Table 2; however, for illustration purposes, a sample equation for calculating MDI among 4–11 year olds is included in Table 2, footnote 9. In these models, some of the SDQ items had negative regression coefficients (i.e. an increase in the item score would result in a lower probability of MDI or MDSI prediction); consequently, these terms were dropped from those models. One item in particular (“gets along better with adults than with other children”) behaved like this in all models, except in the case of MDSI prediction for the 12–17 age group. Another item (“has many worries”) was dropped from the individual item model for MDSI prediction for the 4–11 age group. Some ROC statistics show a reduction in the bias of the modeled estimates, but others remain about the same (Table 2). Although not displayed, these similar models without the impact item score as a predictor performed more poorly, indicating the importance of including the impact item in the models.

Conclusion

This study investigates methods for using the six‐item SDQ included in the NHIS to estimate the prevalence of any impairing or severely impairing childhood mental disorder. Results demonstrate the importance of using all available information from the six‐item SDQ in accurately estimating the prevalence of childhood mental disorders from the NHIS. The two strongest models were those based on: (1) SDQ score and impact item; (2) the subset of five individual SDQ items and impact item. These models maximized the use of the SDQ items which, in turn, yielded a finer gradation of scores, a reduction of model‐based bias, and improved sensitivity and concordance than approaches based exclusively on the total SDQ score (Kessler et al., 2006; Pastor et al., 2012) or the impact item score alone (Federal Interagency Forum on Child and Family Statistics, 2012).

Study results varied by child age and mental disorder outcomes, but not consistently. The total SDQ score model (Kessler et al., 2006; Pastor et al., 2012) did not perform well in predicting the most seriously impairing mental disorders (MDSI) among children aged 4–11. This finding indicates the model used previously for adolescents aged 13 or older (Kessler et al., 2006) may not apply to younger children. The model that used both total SDQ score and impact items better predicted MDSI among children aged 4–11 (smaller absolute bias, 0.85) and showed an even smaller bias (0.14) in predicting a lower threshold of MDI among children aged 12–17. But this model still showed somewhat large biases in predicting MDI for children aged 4–11 and MDSI for children aged 12–17 (4–11 years MDI bias = 3.63; 12–17 years MDSI bias = −2.06). The model that included some individual SDQ item scores instead of the total SDQ score (in addition to the impact item) showed the least fluctuation in terms of bias by child age and mental disorder outcome; but the most fluctuation in the items retained in the models for each age group. These models consistently showed lower absolute bias (i.e. smallest absolute bias = 0.03 and largest absolute bias = 0.43) than other models. Findings demonstrate the importance of considering both child age and outcome of interest in model selection. Models built upon adolescent samples may not function similarly with young children, and models that predict highly impairing disorders may not function similarly in predicting any mental disorder.

Data from the NHIS are publicly available from the NHIS website. The analytic models developed in this study for estimating MDI and MDSI across two age groups offer preliminary guidance for NHIS users on how to improve upon previous SDQ scoring methods (Federal Interagency Forum on Child and Family Statistics, 2012; Kessler et al., 2006; Pastor et al., 2012). As Table 3 illustrates, cut points that depend upon the interaction of child age and outcome of interest can be reliably used when estimating the prevalence of MDI and MDSI. The online survey description document that accompanies the annual NHIS data release will include an appendix that gives further detail for users.

This study's results could be strengthened by future research. First, like many surveys, the NHIS uses a parent informant approach to child health data collection. The ability to predict childhood mental disorders within a national survey might improve with the addition of a child self‐reported screening tool. Second, the response rate of the study and sample sizes were substantially lower than desired; these should be expanded in future work. Third, the study's selection of a “gold standard” psychiatric assessment may yield different results from those generated by a different clinical assessment. This study used a shortened five‐module CAPA/PAPA diagnostic interview; further studies should validate these results with a full‐length clinical interview. CAPA results have been shown to be particularly powerful at eliminating false negative cases, but may yield lower overarching estimates of any mental disorders than a clinician‐administered interview (Angold et al., 2012). Consequently, these results should also be replicated using a clinician‐administered gold‐standard interview. Finally, the six‐item SDQ included in the NHIS could benefit from further psychometric testing and refinement. While recommended Models three and four from this study showed moderate concordance values (AUCd = 0.71–0.81), item refinement is needed to further strengthen concordance with clinical interview results. Furthermore, in the individual SDQ item models, some terms had negative regression coefficients and hence were excluded. Examining the ability of the six‐item SDQ to estimate the prevalence of individual mental disorders was beyond the scope of the current study; however, this too could be examined in future work. This study was the first to examine the ability of the six‐item SDQ to predict the prevalence of any impairing mental disorder among children four years and older as well as adolescents. The NHIS is the only national survey that annually includes an instrument to assess mental health broadly across a wide age range of children. Consequently, this research represents a necessary first step toward the goal of producing national prevalence estimates of any childhood mental disorder and associated impairment.

Declaration of interest statement

The authors have no competing interests.

Ringeisen, H. , Aldworth, J. , Colpe, L. J. , Pringle, B. , and Simile, C. (2015) Estimating the prevalence of any impairing childhood mental disorder in the national health interview survey. Int. J. Methods Psychiatr. Res., 24: 266–274. doi: 10.1002/mpr.1471.