Relationship Between Adverse Childhood Experience Survey Items and Psychiatric Disorders

David Cawthorpe, PhD; Brian Marriott, MSc;
Jaime Paget; Iraj Moulai; Sandra Cheung

Perm J 2018;22:18-001 [Full Citation]

https://doi.org/10.7812/TPP/18-001

E-pub: 10/05/2018

ABSTRACT

Context: Developmental psychopathology theory suggests a relationship between early childhood adversity and mental disorder.
Objective: To examine the relationship between the specific items on the Adverse Childhood Experiences (ACE) survey and the International Classification of Diseases, Tenth Revision (ICD-10) categories of psychiatric diagnoses in a pediatric sample.
Design: The sample included patients enrolled in the Child and Adolescent Addiction Mental Health and Psychiatry Program with both a completed ACE survey and at least 1 diagnosis of record (per admission). These criteria yielded 2 samples for each sex (ACE survey item frequencies and values in collapsed and multiple-admission groups). Data were analyzed employing tetrachoric correlation, hierarchical regression, and polychoric factor analysis.
Results: Hierarchical regression analysis identified that ICD-10 diagnostic categories, except for substance disorders, were not consistently related to ACE total score and tended to reduce the magnitude of the ACE total score in the multiple-admission group. Tetrachoric correlation revealed very low (< 0.4) positive and negative correlations between ICD-10 categories and ACE items in both multiple-admission and collapsed sample groups. Polychoric factor analysis indicated that the ACE survey items and the ICD-10 categories for both sexes were independent, with only the diagnostic ICD-10 category substance disorders being marginally associated with the ACE items factor for females.
Conclusion: The nominal relationship between ACE items and ICD-10 diagnostic categories indicates the need to include ACE assessment in advance of differential diagnosis and implementation of conventional mental health interventions for children and adolescents.

INTRODUCTION

The Adverse Childhood Experiences (ACE) survey has been a standard component of assessment in the Child and Adolescent Addiction Mental Health and Psychiatry Program (CAAMHPP) in Alberta, Canada, since September 1, 2016.¹ Implementation of the ACE survey advances CAAMHPP’s strategic direction of developing a trauma-informed and trauma-focused standard of care. To date, research has identified a relationship between ACE survey total score and clinical urgency and severity.¹ In keeping with results from the original population-based ACE study,² regional findings have shown a relationship between physical disorders and mental disorder as a potential endpoint for early adverse experience in children, adolescents, and adults,^1,3,4 in addition to the relationship of mental disorder with chronic and preventable disease.^5,6

As formulating psychiatric diagnoses is a cornerstone of practice, it stands to reason that an examination of its relationship to individual ACE items is warranted. This is particularly the case, given a conclusion of the article examining the relationship between ACE scores and clinical urgency and severity,¹ being that those with an ACE score of 0 and a particular diagnosis might require fundamentally different treatments than those with the same diagnosis and a high ACE score.

Psychiatric diagnosis, misdiagnosis, and comorbidity are interrelated constructs with a longstanding body of supporting literature.^7-13 The need for trauma-informed and trauma-focused models of care adds a layer of complexity.^14,15 Identification of ACEs is required to optimize treatment and outcomes.^16,17 However, general medical education related to developmental psychopathology remains an area in considerable need of applied clinical pedagogical innovation.¹⁸ The ACE survey criteria brings this to the foreground of diagnostic considerations. In the present article, we examined the relationship between the specific ACE items and the International Classification of Diseases, Tenth Revision (ICD-10) categories of psychiatric diagnoses. The results are discussed in terms of the role of the ACE survey in assessment, diagnosis, and care planning.

METHODS

This research was conducted under The University of Calgary Research Ethics Board approval (REB15-1057). Staff training on the collection of the ACE survey, the details of data collection, storage, and retrieval, as well as the relationship of ACE scores to clinical and demographic variables have been described.¹ This article focuses on the relationship of individual ACE survey items to psychiatric diagnoses of record. For each separation from service, where applicable, at least 1 and often several psychiatric diagnoses assigned by the attending resident or psychiatrist were recorded in each patient’s file and entered into the electronic Regional Access and Intake System (RAIS).

Sample

Diagnosis formed the basis of the sample construction, and the proportions in specific ICD-10 diagnostic categories were different for each sex. The sample was selected from CAAMHPP enrollments who had a completed ACE survey collected between November 2015 and February 2018 linked to an admission with 1 or more diagnoses of record (n = 72,714) between July 2012 (when discharge diagnosis was implemented in the information system) and February 2018. Only ACE surveys noted as completed (ie, items assigned the value of 0 or 1, as per ACE survey completion instructions) were included in the analysis, and missing items were not included. There were 33,886 diagnoses (37% in males) linked to ACE survey scores, representing 3116 unique females and 2124 unique males. Each ACE score and index diagnosis was counted only once in the relevant ICD-10 categories for each patient. CAAMHPP serves primarily children and youth aged 0-18 years, with specialized areas (eg, Eating Disorders and Transitional Youth) also serving young adults up to about age 24 years. Males had a mean age of 11 years (standard deviation = 7 years) and a mean ACE total score of 2.4. Females had a mean age of 14 years (standard deviation = 8 years) and a mean ACE total score of 2.9. A total of 3221 ACE surveys (35% in males) were not linked to diagnoses. There were, respectively, 4.5 and 4.8 diagnoses on average for males and females (eg, discharge diagnoses are recorded for each patient admission); hence, the data were reduced to represent a single count for each ACE item or total score associated with each unique index patient diagnosis in each ICD-10 category. Thus, an individual patient was counted only once for each ACE item for each distinct diagnosis in each ICD-10 category and could be counted in more than one ICD-10 category (eg, for comorbid or diagnoses). Two groups were constructed for each sex, representing an ACE survey-linked group collapsed by unique diagnosis by each patient and an ACE survey-linked group linked by unique diagnoses by patient to the multiple admissions for each patient.

Variables

The analysis examined the relationship between ICD-10 categories and the 10 individual ACE items and the total score on the original ACE survey. The following ICD-10 diagnostic categories were included: Organic, including symptomatic, mental disorders (F00-F09); mental and behavioral disorders caused by psychoactive substance use (F10-F19); schizophrenia, schizotypal, and delusional disorders (F20-F29); mood (affective) disorders (F30-F39); neurotic, stress-related, and somatoform disorders (F40-F48); behavioral syndromes associated with physiologic disturbances and physical factors (F50-F59); disorders of adult personality and behavior (F60-F69); mental retardation (F70-F79); disorders of psychological development (F80-F89); behavioral and emotional disorders with onset usually occurring in childhood and adolescence (F90-F98); and unspecified mental disorder (F99). An additional variable was constructed that represented the frequency of unique diagnoses for each unique patient across all admissions.

In addition to total ACE score (sum of ACE items), ACE survey items included the following: 1) emotional abuse; 2) physical abuse; 3) sexual abuse; 4) lack of love/support; 5) neglect; 6) parental divorce/separation; 7) spousal abuse; 8) parental substance abuse; 9) parental mental illness; and 10) parental prison. Additionally, because of the effect of sex, males and females were analyzed separately.

Data Analysis

Tetrachoric correlation compared ACE items and ICD-10 categories by admission and collapsed samples by sex. Hierarchical regression and polychoric factor analysis for binary data were employed to describe and explicitly test the relationship between ACE total score and ICD-10 categories.¹⁹ 

RESULTS

Table 1 shows the distribution of ACE scores ranging from 0 to 10 for the 2 main groups stratified by sex. Of note is that the first group representing the collapsed data maximizes the number of diagnoses for each individual in the ACE score distribution, whereas the multiple-admissions dataset maximizes the number of diagnoses over admissions for each individual in the ACE score distribution. The collapsed and multiple-admissions datasets are presented in tandem throughout Tables 1-6 and are also identified in relation to the sample sizes in each. Note in Table 1 that the 2 distributions for each group (males and females) are representative of one another for values greater than 0, with the greatest difference being under 5% in Value 1 items for males and females. Also, the ratio comparing the 2 groups in each sex increases with the total score value, representing additional admissions for individuals with higher ACE score totals.

Table 2 shows the frequency distributions for the individual ACE items in the 2 groups (collapsed vs multiple admissions) for males and females. The highest frequencies are in Item 9 (parental mental illness) in both groups. Note between groups, the difference in the percentage total sample recorded by sex for each group. For example, for females, Item 1 (emotional abuse), there is a 14% difference, indicating that this group had additional admissions.

Table 3 shows the frequency of ICD-10 category counts in the 2 groups. Note that the group with multiple admissions included the total number of diagnoses in the sample, not just those linked to ACE scores. This is because diagnoses have been recorded for a longer period than ACE surveys have been collected.

Tables 4A and 4B provide for each sex a crosstabulation of the count for each ACE item by each ICD-10 category. The most important percentage total is that of the whole sample because each individual in the sample is equipotent for positive membership in any given cell, with the denominator including 0 value counts. The highest percentage totals (Table 4A) in the crosstabulation for females are mood disorders (F30-F39) and neurotic, stress-related, and somatoform disorders (F40-F48). The highest percentage totals in the crosstabulation for males (Table 4B) are neurotic, stress-related, and somatoform disorders (F40-F48); mood (affective) disorders (F30-F39); behavioral syndromes associated with psychological disturbances and physical factors (F50-F59); and childhood adolescent behavioral emotional disorders (F90-F98). Although the foregoing descriptive tables are necessary, the associations between ACE items and ICD-10 categories are better revealed in the 2 groups on the basis of tetrachoric correlation analyses and polychoric factor analyses.

Tables 5A and 5B present for each sex the tetrachoric correlations for the collapsed and multiple-admission samples. Overall, the correlations were low. Thirty-two product correlations were greater than 0.2. Only 4 of these correlations were greater than 0.3 for females, and only 3 were greater than 0.3 for males. All correlations were weak, with none greater than 0.34, which was for female mood (affective) disorders (F30-F39).

Tables 6A and 6B present for females and males, respectively, the hierarchical regression models describing the relationship in both collapsed and multiple-admission groups: ICD-10 categories and the ACE total score. For females in the multiple-admission group, membership in the following diagnostic categories significantly, but marginally, reduced the ACE total score in the model: Substance disorder (F10-F19); mood (affective) disorders (F30-F39); neurotic, stress-related, and somatoform disorders (F40-F48); mental retardation (F70-F79); developmental disorders (F80-F89); child/adolescent behavioral emotional disorders (F90-F98); and unspecified mental disorder (F99).

For females in the multiple-admission group, membership in the following diagnostic categories significantly, but marginally, increased the ACE total score: Schizophrenia, schizotypal, and delusional disorders (F20-F29). For females in the collapsed-admission group, membership in the following diagnostic categories significantly, but marginally, reduced the ACE total score in the model: Developmental disorders (F80-F89) and unspecified mental disorder (F99).

For females in the collapsed-admission group, membership in the following diagnostic categories significantly, but marginally, increased the ACE total score: Mood (affective) disorders (F30-F39), child/adolescent behavioral emotional disorders (F90-F98), behavioral syndromes associated with physiological disturbances and physical factors (F50-F59), and substance disorder (F10-F19).

For females there was sign reversal between the significant-collapsed group and multiple-admission group in the following diagnostic categories: Mood (affective) disorders (F30-F39) and child/adolescent behavioral emotional disorders (F90-F98). The remaining categories for females were nonsignificant. For females the frequency of comorbid diagnoses significantly, but marginally increased the ACE total score.

For males in the multiple-admission group membership in the following diagnostic categories significantly, but marginally, reduced the ACE total score in the model: Organic mental disorders (F00-F09); schizophrenia, schizotypal, and delusional disorders (F20-F29); mood (affective) disorders (F30-F39); neurotic, stress-related, and somatoform disorders (F40-F48); behavioral syndromes associated with physiological disturbances and physical factors (F50-F59); personality/behavior disorders (F60-F69); developmental disorders (F80-F89); and unspecified mental disorder (F99).

For males in the multiple-admission group, membership in none of the diagnostic categories significantly increased the ACE total score. For males in the collapsed-admission group, membership in the following diagnostic categories significantly reduced the ACE total score in the model: Neurotic, stress-related, and somatoform disorders (F40-F48); personality/behavior disorders (F60-F69); and developmental disorders (F80-F89).

For males in the collapsed-admission group, membership in the following diagnostic categories significantly, but marginally, increased the ACE total score: Substance disorder (F10-F19) and child/adolescent behavioral emotional disorders (F90-F98).

For males there was no sign reversal between the significant-collapsed group and the multiple-admission group. The remaining categories for males were nonsignificant. For males in both the collapsed group and multiple-admission group, the frequency of comorbid diagnoses marginally increased the ACE total score.

Tables 7A and 7B, and 8A and 8B present, respectively, for females and males the polychoric factor analysis results in the collapsed group only because the tetrachoric results were nearly identical for both groups. For both sexes, with the exception of substance disorders for females, the ICD-10 categories and the individual ACE items were independent, having no significant shared variance (ie, loadings < 0.35). Furthermore, each group of variables loaded on different factors, with the ACE items accounting for 50% of the variance for females and 37% of the variance for males.

Results Summary

Simple descriptive bivariate analysis of the ACE item and ICD-10 category frequencies identified potentially distinct relationships among these groups of variables for both sexes. Hierarchical regression indicated only marginal (weak) significant relationships between the ACE total score and the categories of psychiatric disorder, which for the most part reduced the magnitude of the ACE total score and in the case of frequency of diagnosis only marginally increased the ACE total score for both sexes. Tetrachoric correlation indicated low (weak) correlations between ICD-10 categories and ACE items, with no value greater than 0.34. The highest value was for male and female substance disorders in the collapsed group across about 6 of the ACE items: Emotional abuse, physical abuse, sexual abuse, neglect, parental substance abuse, and parental prison. Female substance disorders was the only ICD-10 category with a significant relationship in the polychoric factor analyses. Hierarchical regression produced results that were similar to, although less precise than, the polychoric factor analyses, likely because of the weak positive and negative correlations between the ACE items and the ICD-10 categories.

DISCUSSION

The results identified female substance disorder as the only significant diagnostic category related to both ACE items and the ACE total score. This finding related to substance disorders is most closely aligned with that of a recent article examining ACEs and substance use among young adults,²⁰ which found that ACEs accumulated over development and were associated with young-adult outcomes, particularly substance disorders. Most importantly, the tetrachoric correlations and polychoric factor analysis in the current study provided evidence that the ICD-10 diagnostic categories and ACE items are independent.

Consistent with transactional epigenetic models of developmental psychopathology,^21-24 the main ACE items for males and females were parental divorce/separation, parental mental illness, and emotional abuse, which combine variance under the rubrics of heredity and transgenerational transmission of coping styles, notwithstanding the influence of these vicissitudes on the zone of proximal development.^25-27 Females appeared to more frequently endorse the ACE items lack of love/support and parental substance abuse.

Psychiatry is not an exact science or medicine.²⁸ Nevertheless, it is heavily prescriptive, pharmacotherapy-focused, and controlled in terms of treatment on the basis of diagnosis.^29,30 Given the level of error inherent in psychiatric diagnostic formulation, overdiagnosis of some disorders,^31-33 and the issues attending misdiagnosis,^13,34 particularly in children,³⁵ any opportunity to add precision is imperative.

The diagnoses made were not associated with high endorsement of or correlation with the ACE items. However, both males and females substantially endorsed ACE items (Table 1), such as parental divorce/separation and parental mental disorder, factors often associated with enduring family adversity and mental disorders.^36-40 By necessity, these family adversities are embedded in the micro-interactions within familial relationships that originally establish neuronal architecture and subsequently constitute the basis of an individual’s memory and propensity.⁴¹

Furthermore, there was a low base rate of ACE item endorsement across all ICD-10 diagnostic categories. Possibly, ACEs account for more dimensional core aspects of mental disorder that permeate all categorical constructs of diagnosis, possibly illustrating the limits of psychiatric diagnosis and may be related to poorly understood phenomena, such as treatment resistance.^42,43 Hence, first including ACE assessment as an axis in differential diagnosis before labeling a child with a diagnosis is warranted in advance of implementing conventional interventions.^44-46 Prior ACE assessment may at least help to rule out misdiagnosis, and identify comorbidity and significant underlying diatheses to better inform care planning before treatment. The present results hold the potential to inform direct use of ACE survey information to focus treatment planning and to create the clinical “space” required to develop, implement, and test trauma-focused care.

This study has a number of limitations. There was a preponderance of diagnoses for each individual. Psychiatrists in Alberta, Canada, are remunerated in part on the basis of making diagnoses; hence, some diagnoses may be overrepresented in this sample and in the population and could bias the relationship with ACE survey items and total score. Additionally, some diagnoses might go unidentified, and hence be under-represented. Similarly, some ACE items might also have gone unreported, possibly because of stigma. Additionally, the ACE survey may not include all categories underpinning the development of subsequent trauma.

There is also the requirement for one or more exit diagnoses for each admission. These diagnoses can change over time for each individual. Individuals could have both the same or different diagnoses for each distinct admission and/or multiple concurrent comorbid diagnoses in any particular admission. Hence, it was necessary to link multiple diagnoses and/or admissions for each patient to a smaller number of ACE surveys. This effect was minimized by counting each diagnosis only once for each individual with an ACE survey in the collapsed group. Even so, individuals could be counted more than once for each distinct diagnosis in 1 ICD-10 category and more than once between the 11 ICD-10 categories.

Comparison of the collapsed group with the multiple-admission group provided a basis to understand the differences that arise when the same analyses were applied to datasets constructed somewhat differently, yet consisting of the same variables. To some extent, this approach provided a means that validated the findings, especially with respect to the hierarchical, tetrachoric, and polychoric analyses. Although one might expect relatively consistent results across these analyses in and between each of the 2 data groups, the multiple-admission group could be biased in at least 2 ways. First, for more serious disorders such as schizophrenia, increased frequency of admission could inflate the membership in the multiple-admission group. This also holds true for a greater number of individuals having fewer admissions with common disorders, such as neurotic, stress-related, and somatoform disorders. This bias is possibly evident where the results are significant in the multiple-admission linked data but not the collapsed data, or as with child/adolescent behavioral emotional disorders where the results are significant in both groups but have a reversed sign in the coefficient (eg, Tables 6A and 6B).

Secondly, the larger sample size in the multiple-admission data group tended to dampen the correlations of ICD-10 categories and ACE items, which were also low in the collapsed data group. Tallying the frequency of all diagnoses for each patient provided a variable that could be employed as a covariate of analysis in the hierarchical regression analyses. Of note, the frequency of diagnosis variable for each patient was significantly positively related to the ACE total score in the collapsed and multiple-admission datasets, but it accounted for less than 0.07 increase in total ACE score for both sexes. Polychoric factor analysis resolved this issue in the collapsed group (the group with the least bias in respect to the relationship between ACE score and diagnosis), demonstrating overall a nonsignificant level of shared variance between ICD-10 categories and ACE items, with only a marginal shared variance for female substance disorders.

Finally, mandatory completion of ACE surveys has been implemented only since September 2016, after commencement of training in November 2015, with voluntary completion during the training period. Although the best efforts have been made to ensure completeness, some staff may still not complete ACE surveys for all patients. Staff may also complete surveys on the basis of incomplete information or, depending on their practice model, variations may also arise in the staff’s approach to collecting the information required to complete the ACE survey.

CONCLUSION

The previously published article, drawn from the same dataset at an earlier date, identified a strong relationship of the ACE total score with standardized, reliable, and valid measures of clinical severity and urgency.¹ In the present study, ICD-10 categories were largely independent of the ACE survey items and total score. There were mostly only marginal and often negative ICD-10 category relationships with either ACE items or the ACE total score. The findings illustrate the imperative requirement to reorganize the structural approach to diagnosis and pedagogy associated with psychiatric assessment and care planning. On the basis of the present findings, it is recommended that clinical assessment practice include ACE total scores and items as a formal dimension of differential diagnosis, in advance of intervention planning, rather than focusing treatment based solely on traditional categorical psychiatric diagnostic formulation.

For an institution to become trauma informed, it must make use of trauma-related information. Making the ACE survey (trauma information) available for assessment before diagnosis and in advance of care planning are necessary process steps on the path to developing trauma-focused care.

Next steps may include implementation of trauma-informed practice guidelines, which are available in many regions across North America.^47,48 Transforming treatment from trauma-informed into trauma-focused practice is in the early stages. Fortunately, there are the targeted, globally accessible, and extensive resources of the Alberta Family Wellness Initiative, the primary focus of which is on brain growth and the effects of toxic stress on developmental trajectory. Many individuals and professionals have been exposed to their exceptional education programs.

The implementation of the ACE survey in our regional CAAMHPP services is relatively recent and was motivated by the international Alberta Family Wellness Initiative Accelerating Innovation Symposia. This step oriented our regional child and adolescent mental health services to the importance of early adverse experiences and their place in assessment and treatment, permitting us to become more trauma informed. Although not all-encompassing with respect to trauma, the ACE survey items provide a guide to formally identifying information that may be used to establish a care plan that is trauma focused. During the implementation phase, it was acknowledged that staff has been dealing with trauma in the served population for many years as a standard of care. The ACE survey provided a mechanism to organize and make use of its trauma-specific information. Furthermore, community physician education has taken place with respect to the contextual importance of past trauma and the use of the ACE survey.^49,50 

Nevertheless, much work remains to be done. For example, defining and linking interventions to clinical outcomes on the basis of identifying specific interventions that are more effective than others in respect to the number and type of ACE survey items endorsed. Becoming trauma focused at a system level will take years and necessarily involves integrating the best information from multiple sources as evidence-based research emerges.

Becoming trauma informed at the system level is a complex innovation, requiring change management that takes place against the background of attempting to improve the capacity to serve the long-term unmet need in the community.⁵¹ Employing the adverse childhood experience survey fits well into a strategy under development termed “shaping demand.” Emergencies aside, one component of this strategy involves empowering and educating families that are actively seeking access to publicly funded, ambulatory, and elective mental health services. In short, rather than waiting weeks to months for an appointment to gain access to specialized knowledge from professionals, by employing online resources, families would engage in orientation to treatment and problem definition⁵² (including ACE surveys) with linkages to vetted resources, such as those provided nationally by organizations such as Teen Mental Health.⁵³ On the basis of past research,⁵⁴ one might expect some portion of the families who would otherwise be waiting for services to be able to gather and implement targeted information that would help them to some extent resolve their difficulties. This approach might prove useful to many organizations, such as insurance companies as well as privately and publicly funded mental health services.

Disclosure Statement

The author(s) have no conflicts of interest to disclose.

Acknowledgments

Kathleen Louden, ELS, of Louden Health Communications provided editorial assistance.

How to Cite this Article

Cawthorpe D, Marriott B, Paget J, Moulai I, Cheung S. Relationship between adverse childhood experiences survey items and psychiatric disorders. Perm J 2018;22:18-001. DOI: https://doi.org/10.7812/18-001

References

1.    Rahman A, Perri A, Deegan A, Kuntz J, Cawthorpe D. On becoming trauma-informed: Role of the Adverse Childhood Experiences survey in tertiary child and adolescent mental health services and the association with standard measures of impairment and severity. Perm J 2018;22:17-054. DOI: https://doi.org/10.7812/TPP/17-054.
    2.    Felitti VJ, Anda RF, Nordenberg D, et al. Relationship of childhood abuse and household dysfunction to many of the leading causes of death in adults. The Adverse Childhood Experiences (ACE) Study. Am J Prev Med 1998 May;14(4):245-58. DOI: https://doi.org/10.1016/s0749-3797(98)00017-8.
    3.    Cawthorpe D. A novel population-based health index for mental disorder. Perm J 2013 Spring;17(2):50-4. DOI: https://doi.org/10.7812/TPP/12-081.
    4.    Ghuttora HK, Cawthorpe D. Treatment of physical disorder in children with mental disorder: A health care utilization study. J Hosp Adm 2014;3(2):24-31. DOI: https://doi.org/10.5430/jha.v3n2p24.
    5.    Chartier G, Cawthorpe D. From “Big 4” to “Big 5”: A review and epidemiological study on the relationship between psychiatric disorders and World Health Organization preventable diseases. Curr Opin Psychiatry 2016 Sep;29(5):316-21. DOI: https://doi.org/10.1016/j.eurpsy.2017.01.592.
    6.    Cawthorpe D, Davidson M. Temporal comorbidity of mental disorder and ulcerative colitis. Perm J 2015 Winter;19(1):52-7. DOI: https://doi.org/10.7812/TPP/14-120.
    7.    Witztum E, Margolin J, Levy A. [Misdiagnosis and labeling in psychiatry and their consequences: Part II]. [Article in Hebrew]. Harefuah 1995 Jul;129(1-2):15-20, 79.
    8.    Altamura AC, Buoli M, Caldiroli A, et al. Misdiagnosis, duration of untreated illness (DUI) and outcome in bipolar patients with psychotic symptoms: A naturalistic study. J Affect Disord 2015 Aug 15;182:70-5. DOI: https://doi.org/10.1016/j.jad.2015.04.024.
    9.    Novak T, Scanlan J, McCaul D, MacDonald N, Clarke T. Pilot study of a sensory room in an acute inpatient psychiatric unit. Australas Psychiatry 2012 Oct;20(5):401-6. DOI: https://doi.org/10.1177/1039856212459585.
    10.    Aggarwal S, Angus B. Misdiagnosis versus missed diagnosis: Diagnosing autism spectrum disorder in adolescents. Australas Psychiatry 2015 Apr;23(2):120-3. DOI: https://doi.org/10.1177/1039856214568214.
    11.    Knežević V, Nedić A. Influence of misdiagnosis on the course of bipolar disorder. Eur Rev Med Pharmacol Sci 2013 Jun;17(11):1542-5.
    12.    Chilakamarri JK, Filkowski MM, Ghaemi SN. Misdiagnosis of bipolar disorder in children and adolescents: A comparison with ADHD and major depressive disorder. Ann Clin Psychiatry 2011 Feb;23(1):25-9. [Password protected].
    13.    Stone J, Zeidler M, Sharpe M. Misdiagnosis of conversion disorder. Am J Psychiatry 2003 Feb;160(2):391; author reply 391-2. DOI: https://doi.org/10.1176/appi.ajp.160.2.391.
    14.    Gilbert LK, Breiding MJ, Merrick MT, et al. Childhood adversity and adult chronic disease: An update from ten states and the District of Columbia, 2010. Am J Prev Med 2015 Mar;48(3):345-9. DOI: https://doi.org/10.1016/j.amepre.2014.09.006.
    15.    Schüssler-Fiorenza Rose SM, Xie D, Stineman M. Adverse childhood experiences and disability in US adults. PM R 2014 Aug;6(8):670-80. DOI: https://doi.org/10.1016/j.pmrj.2014.01.013.
    16.    Kerker BD, Storfer-Isser A, Szilagyi M, et al. Do pediatricians ask about adverse childhood experiences in pediatric primary care? Acad Pediatr 2016 Mar;16(2):154-60. DOI: https://doi.org/10.1016/j.acap.2015.08.002.
    17.    Marie-Mitchell A, O’Connor TG. Adverse childhood experiences: Translating knowledge into identification of children at risk for poor outcomes. Acad Pediatr 2013 Jan-Feb;13(1):14-9. DOI: https://doi.org/10.1016/j.acap.2012.10.006.
    18.    Cawthorpe D. Primary care physician ability to identify pediatric mental health issues. Can Child Adolesc Psychiatr Rev 2005 Nov;14(4):99-102.
    19.    Holgado-Tello FP, Chacón-Moscoso S, Barbero-García I, Vila-Abad E. Polychoric versus Pearson correlations in exploratory and confirmatory factor analysis of ordinal variables. Qual Quant 2010 Jan;44(1):153-66. DOI: https://doi.org/10.1007/s11135-008-9190-y.
    20.    Shin SH, McDonald SE, Conley D. Patterns of adverse childhood experiences and substance use among young adults: A latent class analysis. Addict Behav 2018 Mar;78:187-92. DOI: https://doi.org/10.1016/j.addbeh.2017.11.020.
    21.    Cicchetti D, Doyle C. Child maltreatment, attachment and psychopathology: Mediating relations. World Psychiatry 2016 Jun;15(2):89-90. DOI: https://doi.org/10.1002/wps.20337.
    22.    Hennessy KD, Rabideau GJ, Cicchetti D, Cummings EM. Responses of physically abused and nonabused children to different forms of interadult anger. Child Dev 1994 Jun;65(3):815-28. DOI: https://doi.org/10.2307/1131420.
    23.    Toth SL, Rogosch FA, Manly JT, Cicchetti D. The efficacy of toddler-parent psychotherapy to reorganize attachment in the young offspring of mothers with major depressive disorder: A randomized preventive trial. J Consult Clin Psychol 2006 Dec;74(6):1006-16. DOI: https://doi.org/10.1037/0022-006x.74.6.1006.
    24.    Kim J, Cicchetti D. Longitudinal trajectories of self-system processes and depressive symptoms among maltreated and nonmaltreated children. Child Dev 2006 May-Jun;77(3):624-39. DOI: https://doi.org/10.1111/j.1467-8624.2006.00894.x.
    25.    Sturge-Apple ML, Davies PT, Cicchetti D, Hentges RF, Coe JL. Family instability and children’s effortful control in the context of poverty: Sometimes a bird in the hand is worth two in the bush. Dev Psychopathol 2017 Aug;29(3):685-96. DOI: https://doi.org/10.1017/s0954579416000407.
    26.    Davies P, Cicchetti D, Hentges RF. Maternal unresponsiveness and child disruptive problems: The interplay of uninhibited temperament and dopamine transporter genes. Child Dev 2015 Jan-Feb;86(1):63-79. DOI: https://doi.org/10.1111/cdev.12281.
    27.    Adelstein DJ, Rice TW, Tefft M, et al. Aggressive concurrent chemoradiotherapy and surgical resection for proximal esophageal squamous cell carcinoma. Cancer 1994 Sep 15;74(6):1680-5. DOI: https://doi.org/10.1002/1097-0142(19940915)74:6<1680::AID-CNCR2820740607>3.0.CO;2-F.
    28.    Wakefield JC, First MB. Diagnostic validity and the definition of mental disorder: A program for conceptually advancing psychiatry. Can J Psychiatry 2013 Dec;58(12):653-5. DOI: https://doi.org/10.1177/070674371305801201.
    29.    Anand S. “Big pharma” and psychiatry: “The devil is in the dyad.” Aust N Z J Psychiatry 2012 Dec;46(12):1118-9. DOI: https://doi.org/10.1177/0004867412460596.
    30.    Oldani M. Deep pharma: Psychiatry, anthropology, and pharmaceutical detox. Cult Med Psychiatry 2014 Jun;38(2):255-78. DOI: https://doi.org/10.1007/s11013-014-9369-8.
    31.    Kato T, Sakai N, Watanabe Y, Nomura S. Possibility of over-diagnosis of bipolar disorder due to near-infrared spectroscopy. Psychiatry Clinical Neurosci 2017 Dec;71(12):843. DOI: https://doi.org/10.1111/pcn.12561.
    32.    Shukla D. Over-diagnosis of paroxysmal sympathetic hyperactivity. Neurol India 2017 May-Jun;65(3):683. DOI: https://doi.org/10.4103/neuroindia.NI_64_17.
    33.    Fisher EB, Chan JCN, Nan H, Sartorius N, Oldenburg B. Co-occurrence of diabetes and depression: Conceptual considerations for an emerging global health challenge. J Affect Disord 2012 Oct;142 Suppl:S56-66. DOI: https://doi.org/10.1016/s0165-0327(12)70009-5.
    34.    Rosenbaum M, McCarty T. The misdiagnosis of conversion disorder in a psychiatric emergency service. Gen Hosp Psychiatry 1992 Mar;14(2):145-8. DOI: https://doi.org/10.1016/0163-8343(92)90041-8.
    35.    Keitner GI. Misdiagnosis of affective disorders in adolescents. Am J Psychiatry 1982 Nov;139(11):1527. DOI: https://doi.org/10.1176/ajp.139.11.1527a.
    36.    Somers JA, Ibrahim MH, Luecken LJ. Biological sensitivity to the effects of childhood family adversity on psychological well-being in young adulthood. Child Maltreat 2017 Aug;22(3):236-44. DOI: https://doi.org/10.1177/1077559517711041.
    37.    Astrup A, Pedersen CB, Mok PLH, Carr MJ, Webb RT. Self-harm risk between adolescence and midlife in people who experienced separation from one or both parents during childhood. J Affect Disord 2017 Jan 15;208:582-9. DOI: https://doi.org/10.1016/j.jad.2016.10.023.
    38.    Turney K, Wildeman C. Adverse childhood experiences among children placed in and adopted from foster care: Evidence from a nationally representative survey. Child Abuse Negl 2017 Feb;64:117-29. DOI: https://doi.org/10.1016/j.chiabu.2016.12.009.
    39.    Kasehagen L, Omland L, Bailey M, Biss C, Holmes B, Kelso PT. Relationship of adverse family experiences to resilience and school engagement among Vermont youth. Matern Child Health J 2018 Mar;22(3):298-307. DOI: https://doi.org/10.1007/s10995-017-2367-z.
    40.    Bohman H, Låftman SB, Päären A, Jonsson U. Parental separation in childhood as a risk factor for depression in adulthood: A community-based study of adolescents screened for depression and followed up after 15 years. BMC Psychiatry 2017 Mar 29;17(1):117. DOI: https://doi.org/10.1186/s12888-017-1252-z.
    41.    Greenough WT, Black JE. Induction of brain structure by experience: Substrates for cognitive development. In: Gunnar MR, Nelson CA, editors. The Minnesota symposia on child psychology, vol. 24: Developmental behavioral neuroscience. Hillsdale, NJ: Lawrence Erlbaum Associates, Inc; 1992. p 155-200.
    42.    Nugent AC, Iadarola ND, Miller FG, Luckenbaugh DA, Zarate CA Jr. Safety of research into severe and treatment-resistant mood disorders: Analysis of outcome data from 12 years of clinical trials at the US National Institute of Mental Health. Lancet Psychiatry 2016 May;3(5):436-42. DOI: https://doi.org/10.1016/s2215-0366(16)00006-7.
    43.    Ociskova M, Prasko J, Latalova K, Kamaradova D, Grambal A. Psychological factors and treatment effectiveness in resistant anxiety disorders in highly comorbid inpatients. Neuropsychiatr Dis Treat 2016 Jun 24;12:1539-51. DOI: https://doi.org/10.2147/ndt.s104301.
    44.    Mi Z, Biswas K, Fairchild JK, et al. Repetitive transcranial magnetic stimulation (rTMS) for treatment-resistant major depression (TRMD) veteran patients: Study protocol for a randomized controlled trial. Trials 2017 Sep 2;18(1):409. DOI: https://doi.org/10.1186/s13063-017-2125-y.
    45.    Subramanian L, Bracht T, Jenkins P, et al. Clinical improvements following bilateral anterior capsulotomy in treatment-resistant depression. Psychol Med 2017 Apr;47(6):1097-106. DOI: https://doi.org/10.1017/s0033291716003159.
    46.    Arafat SM, Rahman SM, Haque MM, Shah MA, Algin S, Nahar JS. Clozapine can be the good option in resistant mania. Case Rep Psychiatry 2016;2016:3081704. DOI: https://doi.org/10.1155/2016/3081704.
    47.    Reeves E. A synthesis of the literature on trauma-informed care. Issues Ment Health Nurs 2015;36(9):698-709. DOI: https://doi.org/10.3109/01612840.2015.1025319.
    48.    Wiest-Stevenson C, Lee C. Trauma-informed schools. J Evid Inf Soc Work 2016 Sep-Oct;13(5):498-503. DOI: https://doi.org/10.1080/23761407.2016.1166855.
    49.    McCaffrey ESN, Chang S, Farrelly G, Rahman A, Cawthorpe D. Mental health literacy in primary care: Canadian Research and Education for the Advancement of Child Health (CanREACH). Evid Based Med 2017 Aug;22(4):124-31. DOI: https://doi.org/0.1136/ebmed-2017-110714.
    50.    Cawthorpe D. Children’s mental health 1954-2016—who cares? [Internet]. CMAJ Blogs; 2016 Sep 13 [cited 2018 Aug 16]. Available from: http://cmajblogs.com/childrens-mental-health-1954-2016/.
    51.    Mental health: A report of the Surgeon General [Internet]. Rockville, MD: Department of Health and Human Services, US Public Health Service; 1999 [cited 2018 Sep 20]. Available from: https://profiles.nlm.nih.gov/ps/access/NNBBHS.pdf.
    52.    Cawthorpe D. An evaluation of a computer-based psychiatric assessment: evidence for expanded use. Cyberpsychol Behav 2001 Aug;4(4):503–10. DOI: https://doi.org/10.1089/109493101750527060.
    53.    Kutcher S, Wei Y, Coniglio C. Mental health literacy: Past, present, and future. Can J Psychiatry 2016 Mar;61(3):154–8. DOI: https://doi.org/10.1177/0706743715616609.
    54.    Cunningham CE, Bremner R, Boyle M. Large group community-based parenting programs for families of preschoolers at risk for disruptive behaviour disorders: Utilization, cost effectiveness, and outcome. J Child Psychol Psychiatry 1995 Oct;36(7):1141–59.

Keywords: ACEs, adverse childhood experiences, assessment, care management, mental illness, pediatric care, psychiatric diagnosis, trauma informed