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Original Investigation |

Risk for Incident Diabetes Mellitus Following Initiation of Second-Generation Antipsychotics Among Medicaid-Enrolled Youths Online Only FREE

David M. Rubin, MD, MSCE1,2,3; Amanda R. Kreider, BS, BA1; Meredith Matone, MHS1; Yuan-Shung Huang, MS3,4; Chris Feudtner, MD, PhD, MPH1,2,3; Michelle E. Ross, PhD5; A. Russell Localio, PhD5
[+] Author Affiliations
1PolicyLab, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
2Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia
3Division of General Pediatrics, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
4Healthcare Analytics Unit, The Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
5Department of Biostatistics and Epidemiology, The Perelman School of Medicine, University of Pennsylvania, Philadelphia
JAMA Pediatr. 2015;169(4):e150285. doi:10.1001/jamapediatrics.2015.0285.
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Published online

Importance  Second-generation antipsychotics (SGAs) have increasingly been prescribed to Medicaid-enrolled children, either singly or in a medication combination. Although metabolic adverse effects have been linked to SGA use in youths, estimating the risk for type 2 diabetes mellitus, a rarer outcome, has been challenging.

Objective  To determine whether SGA initiation was associated with an increased risk for incident type 2 diabetes mellitus. Secondary analyses examined the risk associated with multiple-drug regimens, including stimulants and antidepressants, as well as individual SGAs.

Design, Setting, and Participants  Retrospective national cohort study of Medicaid-enrolled youths between January 2003 and December 2007. In this observational study using national Medicaid Analytic eXtract data files, initiators and noninitiators of SGAs were identified in each month. Included in this study were US youths aged 10 to 18 years with a mental health diagnosis and enrolled in a Medicaid fee-for-service arrangement during the study. Those with chronic steroid exposure, a diagnosis of diabetes mellitus, or SGA use during a 1-year look-back period were ineligible. The mean follow-up time for all participants was 17.2 months. Youths were followed up until diagnosis of diabetes mellitus or end of follow-up owing to censoring caused by the transition into a Medicaid managed care arrangement or Medicaid ineligibility (the end of available data). Propensity weights were developed to balance observed demographic and clinical characteristics between exposure groups. Discrete failure time models were fitted using weighted logistic regression to estimate the risk for incident diabetes mellitus between initiators and noninitiators.

Exposure  A filled SGA prescription.

Main Outcomes and Measures  Incident type 2 diabetes mellitus identified through visit and pharmacy claims during the observation period.

Results  Among 107 551 SGA initiators and 1 221 434 noninitiators, the risk for incident diabetes mellitus was increased among initiators (odds ratio [OR], 1.51; 95% CI, 1.35-1.69; P < .001). Compared with youths initiating only SGAs, the risk was higher among SGA initiators who used antidepressants concomitantly at the time of SGA initiation (OR, 1.54; 95% CI, 1.17-2.03; P = .002) but was not significantly different for SGA initiators who were concomitantly using stimulants. As compared with a reference group of risperidone initiators, the risk was higher among those initiating ziprasidone (OR, 1.61; 95% CI, 0.99-2.64; P = .06) and aripiprazole (OR, 1.58; 95% CI, 1.21-2.07; P = .001) but not quetiapine fumarate or olanzapine.

Conclusions and Relevance  The risk for incident type 2 diabetes mellitus was increased among youths initiating SGAs and was highest in those concomitantly using antidepressants. Compared with risperidone, newer antipsychotics were not associated with decreased risk.

Figures in this Article

During the last decade, the use of second-generation antipsychotics (SGAs) among children has grown, even as the use of other psychotropic medications has declined.14 Between 2002 and 2007, SGA use among Medicaid-enrolled youths increased by 62%, exceeding rates among commercially insured youths, with nearly 350 000 youths having filled prescriptions in 2007.47

There are certain conditions for which SGAs have regulatory approval in youths such as schizophrenia, bipolar disorder, and irritability associated with autism8,9; however, the growth in SGA prescribing is attributable mostly to shifts in the management of disruptive behaviors among youths without psychotic disorders.4,5,1012 By the mid-2000s, half of all youths prescribed SGAs had a diagnosis of attention-deficit/hyperactivity disorder, the most prevalent externalizing disorder, and 1 in 7 had a diagnosis of attention-deficit/hyperactivity disorder in the absence of comorbidity.4 Prescribing behavior to youths in foster care, who have higher rates of disruptive behaviors than other youths, included SGAs in most cases.13 The use of SGAs has also reflected a shift toward more complicated treatment regimens; 85% of youths receiving SGAs between 2004 and 2008 were concurrently receiving other psychotropic medications. By 2008, 1 in 5 youths taking stimulants and 1 in 3 taking antidepressants also received SGAs.3

The increased prescribing of SGAs has occurred without sufficient safety and efficacy data, and concerns of adverse metabolic effects have emerged.1417 Evidence of rapid weight gain and deleterious metabolic changes have prompted concerns for development of type 2 diabetes mellitus in youths exposed to SGAs.18,19 While trial data have assessed short-term metabolic indicators, the rare outcome of diabetes mellitus has been challenging to study. Larger observational data sets, including data from the Tennessee Medicaid program, 3 regional health plans, and Denmark, have suggested an association of SGAs with diabetes mellitus.2022 However, given the geographic variability in prescribing practices and underlying risk for type 2 diabetes mellitus in youths, these findings should be assessed in a geographically diverse population.2325 A larger data set would also permit the evaluation of whether concomitant use of other medications (eg, stimulants or antidepressants) or a selection of certain SGAs alters the risk for diabetes mellitus; thus far, such relationships have been explored only in smaller, facility-based samples.18 Therefore, we conducted a longitudinal study of a national sample of Medicaid-enrolled youths at risk for SGA exposure from January 2003 to December 2007.

Box Section Ref ID

At a Glance
  • This large national cohort study examined whether the initiation of second-generation antipsychotics (SGAs) was associated with an increased risk for incident type 2 diabetes mellitus in youths. Secondary analyses examined the risk associated with multiple-drug regimens, including stimulants and antidepressants, as well as individual SGAs.

  • Second-generation antipsychotic use in youths was associated with a 50% increase in the risk for incident type 2 diabetes mellitus.

  • Compared with youths initiating only SGAs, the risk was approximately 50% higher for SGA initiators who were concurrently using antidepressants but was not significantly different for SGA initiators who were concurrently using stimulants.

  • Such results warrant increased attention to the safety of the use of SGAs in pediatric populations, including the identification and adoption of best prescribing practices, as well as ways to enhance adherence to metabolic screening policies when initiating treatment with SGAs.

Sample and Outcomes

The intent of this analysis was to identify youths with mental health diagnoses initiating SGAs and compare their risk for incident diabetes mellitus with that of comparable youths who did not initiate SGAs. To accomplish this task, we treated an observational national cohort of Medicaid-enrolled youths as a sequence of nonrandomized trials (termed pseudotrials) beginning each month, mirroring an approach described by Hernán and colleagues.26 This approach was previously applied to a reanalysis of the Women’s Health Initiative.27 Our methods appear in a separate report28 and in eAppendix 1 in the Supplement.

The institutional review board at The Children’s Hospital of Philadelphia approved this study. Patient consent was waived because access to identifiable data was limited.

National Centers for Medicare and Medicaid Services Medicaid Analytic eXtract files allowed for identification of a target population of youths aged 10 to 18 years enrolled in Medicaid between January 2003 and December 2007, the latest complete Medicaid files available for longitudinal study. Child-level demographic, eligibility, encounter, and pharmacy data were linked across years using a common Medicaid child identifier.

Inclusion criteria for this study consisted of (1) a behavioral or psychiatric diagnosis and (2) coverage in a Medicaid fee-for-service arrangement for at least 10 of 12 consecutive months prior to study entry (ie, a look-back period). The fee-for-service sample, 40% of the Medicaid population during this period, was chosen to ensure ascertainment of diagnoses and prescriptions owing to concerns regarding the reliability of Medicaid managed care data in some states.29,30 Youths were excluded for the following criteria: (1) an SGA prescription in the 1-year look-back period or prior diagnosis of type 1 or type 2 diabetes mellitus to ensure a clean cohort without preexisting exposure or diagnosis; (2) chronic inhaled corticosteroid or oral steroid exposure during the look-back period, which would have independently raised the risk for diabetes mellitus; (3) a diagnosis of schizophrenia, for which comparison youths who were noninitiators of SGAs would have been difficult to identify; or (4) a diagnosis of polycystic ovary syndrome, for which metformin, a medication also used for type 2 diabetes mellitus, is often prescribed (Figure). Details appear in eAppendix 2 in the Supplement.

Place holder to copy figure label and caption
Figure.
Study Participant Flow Diagram for US Medicaid Population, 2003-2007

PCOS indicates polycystic ovary syndrome.

aContinuously is defined as at least 10 of 12 consecutive months.

bFor information on identification of Medicaid payment arrangements, see eAppendix 2 in the Supplement.

cYouths with PCOS, a diagnosis for which metformin (a type 2 diabetes mellitus medication) is indicated, were excluded from analysis. Polycystic ovary syndrome was identified in the Medicaid encounter data using the International Classification of Diseases, Ninth Revision code 256.4.

dEligible youths satisfied additional inclusion and exclusion criteria at this stage. For further details, see main text and eAppendix 2 in the Supplement.

Graphic Jump Location

The primary outcome was incident type 2 diabetes mellitus identified through either of 2 algorithms: (1) an inpatient or outpatient visit with a type 2 diabetes mellitus diagnosis (International Classification of Diseases, Ninth Revision codes 250.x0 or 250.x2) or unspecified diabetes mellitus (codes250 or 250.x), plus a filled prescription for a noninsulin antidiabetic drug (eg, metformin, metformin combination, or oral hypoglycemic), within a 4-month window or (2) 2 inpatient or outpatient visits, one with a type 2 diabetes mellitus diagnosis and the second with either a type 2 diabetes mellitus diagnosis or an unspecified diabetes mellitus diagnosis within a 4-month window.3136 In a 10% sample, 1450 youths satisfied at least 1 of these algorithms; 238 (16.4%) satisfied algorithm 1, 620 (42.8%) satisfied algorithm 2, and 592 (40.8%) satisfied both. Youths were identified as having type 1 diabetes mellitus if they did not satisfy the algorithms for type 2 diabetes mellitus and had either a type 1 diabetes mellitus diagnosis (codes250.x1 or 250.x3) or an insulin prescription.

Separately, we conducted 2 sensitivity analyses of the algorithm for ascertaining type 2 diabetes mellitus. The first allowed a wider time window to satisfy algorithm requirements (6 months instead of 4 months). Second, to address potential misclassification of diabetes type, we included in our outcome all children with a diagnosis of type 1 diabetes mellitus with no filled prescription for insulin during the child’s observation window. The results changed little in each analysis (eAppendix 3 in the Supplement).

Statistical Analysis

In each month between November 2003 and December 2007, eligible children were classified as either initiators or noninitiators of SGAs based on a filled prescription in the month. Children were then followed up until diagnosis of diabetes mellitus or end of follow-up owing to censoring caused by transition into a Medicaid managed care arrangement or Medicaid ineligibility (the end of available data). Children could be eligible in multiple months as initiators or noninitiators provided they satisfied the eligibility criteria in that month. Because of the exclusion criteria, SGA initiators could not become eligible for 12 months after ceasing SGA use, while noninitiators were eligible for subsequent trials until either later initiation or censoring.

A conventional intention-to-treat (or as randomized) analysis was used to compare the risk for incident diabetes mellitus between SGA initiators and noninitiators. In this framework, children were followed up as originally exposed without regard to treatment termination or switching.26 In an initial step, to mimic randomization and to balance on baseline characteristics, we used propensity score models to estimate the probability of exposure (initiation) in each month. These models included sex, age, race/ethnicity, Medicaid eligibility status, US Census division, mental health diagnoses, a prior diagnosis of a lipid or metabolic disorder or hypertension, a history of complex chronic conditions,37 and other psychotropic medication classes used in that month or in the 3 previous months (eTable in the Supplement). We then weighted the noninitiators by the odds of being an initiator, so that their weighted characteristics would resemble those of the initiators.38 The weighted balance checks are described in eAppendix 1 in the Supplement.38,39

In the next step, a response model estimated the association of SGA initiation with incident diabetes mellitus. Specifically, a discrete failure time model was fitted using weighted logistic regression (eAppendix 1 in the Supplement). Because propensity score–based weights balanced exposure groups on all observed covariates, the time elapsed since pseudotrial entry (categorized as 0-6 months, 7-12 months, 13-18 months, 19-24 months, and ≥25 months) was the sole covariate in this model. An additional analysis included a set of interaction terms for follow-up time and SGA initiation to assess whether the association was modified by length of follow-up. Robust variance estimates accounted for repeated observations of patients across pseudotrials, defined as each month of initiation and its subsequent follow-up.26 The cumulative incidence of diabetes mellitus over 20 months was then calculated from the monthly estimates of incident risk, which incorporated the average length of follow-up.

Secondary analyses of multiple-drug regimens with stimulants and antidepressants, and of individual SGA medications, used similar methods (eAppendix 1 in the Supplement). Multiple-drug regimens were defined by the use of stimulants or antidepressants in the month of initiation or in the 3 months prior.

In the first of several sensitivity analyses, we assessed the potential confounding of unmeasured exposures using an approach described by Lin and colleagues40 (eAppendix 4 in the Supplement). The second addressed surveillance, or lead-time, bias arising out of possibly increased glucose screening among SGA initiators.41,42 This analysis used empirical estimates of prevalence, timing, and frequency of glucose screening to assess the magnitude of bias associated with observed differences in these testing measures between SGA initiators and noninitiators during follow-up (eAppendix 5 in the Supplement). An additional analysis to address lead-time bias examined the degree to which the observed covariates in the propensity score model were predictive of glucose screening and thus accounted for differential screening between exposure groups (eAppendix 6 in the Supplement).

Analyses were conducted using SAS version 9.3 (SAS Institute) and Stata version 13.1 (Stata Corp).

Description of the Cohort

Between November 2003 and December 2007, 1 328 985 children satisfied eligibility criteria in at least 1 month. This included 107 551 children who ever initiated an SGA and 1 221 434 who never initiated (Table 1). At first eligibility for study, 65% of children were aged 10 to 14 years and 57% were male; 54% of children were white, 27% were black, 12% were Hispanic, and less than 10% had race identified as other or unknown. Fifteen percent of children were Medicaid eligible based on foster care status, 18% were in the Supplemental Security Income (disability) program, and the remainder were income eligible through Temporary Assistance for Needy Families or had another basis for eligibility. The mean (SD) length of follow-up was 19.1 (12.7) months for SGA initiators and 17.2 (12.2) months for noninitiators. The sample included children from all 9 US Census divisions.

Table Graphic Jump LocationTable 1.  Demographic Characteristics of Eligible Children at First Entry Into Study

Table 2 describes the changes in the demographic and clinical characteristics of the participants across all months of initiation, representing the characteristics of participants as they were treated repeatedly as initiators or noninitiators in our study design. Over time, an increasing number of patients received diagnoses of attention-deficit/hyperactivity disorder or bipolar or conduct disorder or were identified as having complex chronic medical conditions. Risperidone was the most commonly used SGA at initiation (38.9%); aripiprazole use increased and the use of olanzapine decreased substantially over time. Comparing exposure groups, initiators were more likely to qualify for Medicaid owing to foster care or disability (Supplemental Security Income) than noninitiators. With the exception of developmental delay, mental health diagnoses were more prevalent among SGA initiators, for whom use of all other psychotropic medication classes was twice as common (82% vs 32%). After weighting, no important differences remained between initiators and noninitiators.

Table Graphic Jump LocationTable 2.  Change Over Time in Demographic and Clinical Characteristics Among Children and Adolescents Initiating SGA Medication, 2003-2007
Outcomes

In total, 0.4% of SGA initiators and 0.2% of noninitiators developed diabetes mellitus (Table 2). Among those who developed diabetes mellitus, the mean (SD) time to diagnosis was 13.5 (9.2) months for initiators and 14.6 (10.1) months for noninitiators. After standardizing for differences in demographic and clinical characteristics, initiators were more likely to develop diabetes mellitus than noninitiators (odds ratio [OR], 1.51; 95% CI, 1.35-1.69; P < .001) (Table 3). The risk for developing diabetes mellitus over 20 months was 38 per 10 000 children for SGA initiators compared with 25 per 10 000 for noninitiators, a difference of 13 per 10 000 children. The association of initiation and diabetes mellitus did not vary over the follow-up time.

Table Graphic Jump LocationTable 3.  Association of SGAs and Incident Type 2 Diabetes Mellitus in US Medicaid-Enrolled Children Ages 10 to 18 Yearsa

Next, we examined the risk associated with simultaneous use of other common psychotropic medications at the time of SGA initiation. We compared a standardizing population of children receiving both an antidepressant and an SGA with 3 other groups. First, compared with children who received neither SGAs nor antidepressants, the OR of incident diabetes mellitus was 1.94 (95% CI, 1.54-2.44; P < .001), an increased risk of 23 per 10 000 children over 20 months of follow-up. Second, compared with children initiating an SGA in the absence of antidepressants, the OR was 1.54 (95% CI, 1.17-2.03; P = .002). Finally, compared with SGA-naive antidepressant users, the OR was 1.55 (95% CI, 1.35-1.77; P < .001).

We similarly compared 3 groups of children with a standardizing population of children concomitantly receiving a stimulant when initiating SGAs. First, compared with children with neither medication, exposure to both drugs was associated with an increased risk for incident diabetes mellitus of 1.47 (95% CI, 1.19-1.81; P < .001), an increased risk of 10 per 10 000 children. Second, compared with the children who received only SGAs, there was no significant difference in the risk for diabetes mellitus (OR, 0.84; 95% CI, 0.64-1.11; P = .22). Finally, compared with users of only stimulants, the OR of incident diabetes mellitus was 1.41 (95% CI, 1.18-1.68; P < .001).

Comparisons of individual SGA medications appear in Table 4. As compared with a reference group and standardizing population of risperidone initiators, the OR of incident diabetes mellitus among ziprasidone initiators was 1.61 (95% CI, 0.99-2.64; P = .06) and among aripiprazole initiators was 1.58 (95% CI, 1.21-2.07; P = .001). There was no significant difference in risk between children using risperidone and children using either quetiapine fumarate or olanzapine.

Table Graphic Jump LocationTable 4.  Association of Individual Antipsychotic Medications and Incident Type 2 Diabetes Mellitus in US Medicaid-Enrolled Children Ages 10 to 18 Years
Sensitivity Analyses

A sensitivity analysis of the potential bias from unobserved confounding suggested that a covariate would need to have double the prevalence among SGA initiators compared with noninitiators and a 2- to 3-fold stronger association with diabetes mellitus among SGA initiators to cause the observed OR of 1.51 to lose statistical significance.40

In a separate sensitivity analysis for lead-time bias, we found that even extreme acceleration of diagnosis owing to increased frequency of glucose screening among initiators compared with noninitiators could produce an OR of no more than 1.26. This estimate falls below the lower bound of the confidence interval of the primary treatment effect (eAppendix 5 in the Supplement). Furthermore, while initiators were more likely to be screened than noninitiators, the propensity score model covariates were predictive of glucose screening and thus partially accounted for observed differences in screening prevalence (eAppendix 6 in the Supplement).

Using national claims records of Medicaid-enrolled children from 2003 to 2007, this study estimated a 50% increase in the risk for incident type 2 diabetes mellitus among youths initiating SGAs compared with similar youths not initiating SGAs. Concurrent stimulant use at initiation did not reduce this risk, while concurrent antidepressant use might have elevated it. Exposure to aripiprazole and ziprasidone demonstrated a higher association with incident diabetes mellitus than exposure to risperidone.

A proper interpretation of these findings requires balancing empirical evidence of efficacy of treatment against the potential risk for harm to children and adolescents. The increased prescribing of SGAs to youths over time reflects a perceived value of these medications in treating youths with disruptive and aggressive behaviors; however, efficacy data supporting the use of SGAs for these indications are lacking,57,43 as are data to substantiate the benefit of concurrent treatment with stimulants and antidepressants.6 At the same time, we acknowledge that the absolute increase in diabetes mellitus risk in SGA-exposed youths remains small (risk difference of 1 per 800 over 20 months of observation). However, the limited follow-up time in this cohort may have understated the cumulative risk. Moreover, the risk for metabolic complications, such as weight gain and obesity, is an order of magnitude larger than the risk for diabetes mellitus; weight gains of 4.4 to 8.5 kg following a median SGA exposure of 11 weeks have been reported in a facility-based sample.18

This study had several strengths. Beyond the large national sample, we paid careful attention to adjustment for confounding and conducted sensitivity analyses. Nevertheless, we acknowledge limitations. A potential limitation of any observational study lies in unobserved confounding. Our sensitivity analyses suggest that only a confounder with both a strong positive association with diabetes mellitus and a large disparity in prevalence between SGA initiators and noninitiators could explain the observed treatment effect. It is improbable that the 2 most salient unmeasured confounders, obesity and altered hypothalamic-pituitary-axis regulation following chronic or toxic stress exposure, would satisfy these criteria4450; while they may be associated with type 2 diabetes mellitus, a disparity in prevalence between exposure groups of the magnitude required to nullify our results is unlikely. Conversely, clinicians may choose not to prescribe SGAs in children with risk factors for diabetes mellitus, such as obesity, in which case, we might have underestimated the true association. Another limitation was surveillance, or lead-time, bias that might arise from greater physician screening for diabetes mellitus among SGA initiators. However, during this period, other research reported low metabolic screening rates; guidelines for metabolic screening in youth did not emerge until 2008, after our observation period.51 In addition, our sensitivity analyses suggest that lead-time bias is unlikely to explain our findings, and our statistical models included covariates that partially explained differences in screening frequencies between exposure groups (eAppendices 5 and 6 in the Supplement). Finally, owing to our use of an intention-to-treat design, we did not consider whether SGA initiators and noninitiators remained on their assigned treatment after initiation. Therefore, this study design might understate the risk of sustained treatment with SGAs.28

Although unlikely to explain the aggregate SGA effect, premorbid and unmeasured obesity might confound the analysis of differential risk for diabetes mellitus by specific SGA medications. Previous trials in smaller clinical samples suggested that newer SGAs (aripiprazole and ziprasidone) might carry fewer metabolic adverse effects, particularly weight gain, than other SGA medications.18,52,53 In our study, the newer agents were not associated with a lower risk for diabetes mellitus compared with risperidone. Although an emerging clinical practice of choosing aripiprazole or ziprasidone for obese patients might have overstated the risk for youths receiving those medications in our study (confounding by indication), our results nevertheless suggest an elevated risk for diabetes mellitus across all SGAs.

This national analysis of Medicaid-enrolled youths adds to a literature of adverse events related to SGA use, particularly with the rising trend of prescribing SGAs concurrently with other psychotropic medications. Such results warrant continued scrutiny of SGA safety in pediatric populations, the identification and adoption of best prescribing practices, and ways to enhance adherence to metabolic screening policies when initiating SGA treatment.

Corresponding Author: David M. Rubin, MD, MSCE, The Children’s Hospital of Philadelphia, 34th and Civic Center Boulevard, CHOP North, Room 1533, Philadelphia, PA 10194 (rubin@email.chop.edu).

Accepted for Publication: February 3, 2015.

Published Online: April 6, 2015. doi:10.1001/jamapediatrics.2015.0285.

Author Contributions: Dr Rubin and Ms Huang had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: All authors.

Acquisition, analysis, or interpretation of data: Rubin, Kreider, Matone, Huang, Ross, Localio.

Drafting of the manuscript: Rubin, Kreider, Matone, Ross, Localio.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Rubin, Kreider, Matone, Ross, Localio.

Obtained funding: Rubin, Matone, Huang, Ross, Localio.

Administrative, technical, or material support: Kreider, Matone.

Study supervision: Rubin, Localio.

Conflict of Interest Disclosures: None reported.

Funding/Support: This study was supported by the Agency for Healthcare Research and Quality and the Stoneleigh Foundation.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Olfson  M, Blanco  C, Liu  SM, Wang  S, Correll  CU.  National trends in the office-based treatment of children, adolescents, and adults with antipsychotics. Arch Gen Psychiatry. 2012;69(12):1247-1256.
PubMed   |  Link to Article
Cooper  WO, Arbogast  PG, Ding  H, Hickson  GB, Fuchs  DC, Ray  WA.  Trends in prescribing of antipsychotic medications for US children. Ambul Pediatr. 2006;6(2):79-83.
PubMed   |  Link to Article
Kreider  AR, Matone  M, Bellonci  C,  et al.  Growth in the concurrent use of antipsychotics with other psychotropic medications in Medicaid-enrolled children. J Am Acad Child Adolesc Psychiatry. 2014;53(9):960-970.e2.
PubMed   |  Link to Article
Matone  M, Localio  R, Huang  YS, dosReis  S, Feudtner  C, Rubin  D.  The relationship between mental health diagnosis and treatment with second-generation antipsychotics over time: a national study of US Medicaid-enrolled children. Health Serv Res. 2012;47(5):1836-1860.
PubMed   |  Link to Article
Crystal  S, Olfson  M, Huang  C, Pincus  H, Gerhard  T.  Broadened use of atypical antipsychotics: safety, effectiveness, and policy challenges. Health Aff (Millwood). 2009;28(5):w770-w781.
PubMed   |  Link to Article
Dosreis  S, Yoon  Y, Rubin  DM, Riddle  MA, Noll  E, Rothbard  A.  Antipsychotic treatment among youth in foster care. Pediatrics. 2011;128(6):e1459-e1466.
PubMed   |  Link to Article
Zito  JM, Safer  DJ, Zuckerman  IH, Gardner  JF, Soeken  K.  Effect of Medicaid eligibility category on racial disparities in the use of psychotropic medications among youths. Psychiatr Serv. 2005;56(2):157-163.
PubMed   |  Link to Article
Correll  CU, Kratochvil  CJ, March  JS.  Developments in pediatric psychopharmacology: focus on stimulants, antidepressants, and antipsychotics. J Clin Psychiatry. 2011;72(5):655-670.
PubMed   |  Link to Article
Christian  R, Saavedra  L, Gaynes  BN,  et al. Appendix A, Tables of FDA-Approved Indications for First- and Second-Generation Antipsychotics: Future Research Needs for First- and Second-Generation Antipsychotics for Children and Young Adults: Future Research Needs Papers, No. 13. Rockville, MD: Agency for Healthcare Research and Quality; 2012.
Findling  RL.  Dosing of atypical antipsychotics in children and adolescents. Prim Care Companion J Clin Psychiatry. 2003;5(suppl 6):10-13.
Wiechers  IR, Leslie  DL, Rosenheck  RA.  Prescribing of psychotropic medications to patients without a psychiatric diagnosis. Psychiatr Serv. 2013;64(12):1243-1248.
PubMed   |  Link to Article
Birnbaum  ML, Saito  E, Gerhard  T,  et al.  Pharmacoepidemiology of antipsychotic use in youth with ADHD: trends and clinical implications. Curr Psychiatry Rep. 2013;15(8):382.
PubMed   |  Link to Article
Zito  JM, Safer  DJ, Sai  D,  et al.  Psychotropic medication patterns among youth in foster care. Pediatrics. 2008;121(1):e157-e163.
PubMed   |  Link to Article
Correll  CU.  Monitoring and management of antipsychotic-related metabolic and endocrine adverse events in pediatric patients. Int Rev Psychiatry. 2008;20(2):195-201.
PubMed   |  Link to Article
Correll  CU.  Assessing and maximizing the safety and tolerability of antipsychotics used in the treatment of children and adolescents. J Clin Psychiatry. 2008;69(suppl 4):26-36.
PubMed   |  Link to Article
De Hert  M, Dobbelaere  M, Sheridan  EM, Cohen  D, Correll  CU.  Metabolic and endocrine adverse effects of second-generation antipsychotics in children and adolescents: a systematic review of randomized, placebo controlled trials and guidelines for clinical practice. Eur Psychiatry. 2011;26(3):144-158.
PubMed   |  Link to Article
Panagiotopoulos  C, Ronsley  R, Davidson  J.  Increased prevalence of obesity and glucose intolerance in youth treated with second-generation antipsychotic medications. Can J Psychiatry. 2009;54(11):743-749.
PubMed
Correll  CU, Manu  P, Olshanskiy  V, Napolitano  B, Kane  JM, Malhotra  AK.  Cardiometabolic risk of second-generation antipsychotic medications during first-time use in children and adolescents. JAMA. 2009;302(16):1765-1773.
PubMed   |  Link to Article
Teff  KL, Rickels  MR, Grudziak  J, Fuller  C, Nguyen  H-L, Rickels  K.  Antipsychotic-induced insulin resistance and postprandial hormonal dysregulation independent of weight gain or psychiatric disease. Diabetes. 2013;62(9):3232-3240.
PubMed   |  Link to Article
Nielsen  RE, Laursen  MF, Vernal  DL,  et al.  Risk of diabetes in children and adolescents exposed to antipsychotics: a nationwide 12-year case-control study. J Am Acad Child Adolesc Psychiatry. 2014;53(9):971-979.e6.
PubMed   |  Link to Article
Andrade  SE, Lo  JC, Roblin  D,  et al.  Antipsychotic medication use among children and risk of diabetes mellitus. Pediatrics. 2011;128(6):1135-1141.
PubMed   |  Link to Article
Bobo  WV, Cooper  WO, Stein  CM,  et al.  Antipsychotics and the risk of type 2 diabetes mellitus in children and youth. JAMA Psychiatry. 2013;70(10):1067-1075.
PubMed   |  Link to Article
Rubin  D, Matone  M, Huang  Y-S, dosReis  S, Feudtner  C, Localio  R.  Interstate variation in trends of psychotropic medication use among Medicaid-enrolled children in foster care. Child Youth Serv Rev. 2012;34(8):1492-1499. doi:10.1016/j.childyouth.2012.04.006.
Link to Article
Centers for Disease Control and Prevention. Obese youth over time: 2013, 2011, 2009, 2007, 2005, and 2003. Adolescent and School Health 2014. http://www.cdc.gov/healthyyouth/obesity/obesity-youth.htm. Accessed January 27, 2015.
Liese  AD, Lawson  A, Song  HR,  et al.  Evaluating geographic variation in type 1 and type 2 diabetes mellitus incidence in youth in four US regions. Health Place. 2010;16(3):547-556.
PubMed   |  Link to Article
Hernán  MA, Robins  JM, García Rodríguez  LA.  Discussion on 'statistical issues arising in the Women's Health Initiative.' Biometrics. 2005;61(4):922-930. doi:10.1111/j.0006-341X.2005.454_7.x.
Link to Article
Prentice  RL, Pettinger  M, Anderson  GL.  Statistical issues arising in the Women’s Health Initiative. Biometrics.2005;61(4):899-911; discussion 911-941.
PubMed   |  Link to Article
Ross  ME, Kreider  AR, Huang  Y-S, Matone  M, Rubin  DM, Localio  AR.  Propensity score methods for analyzing observational data like randomized experiments: challenges and solutions for rare outcomes and exposures. Am J Epidemiol. In press.
Nysenbaum  J, Bouchery  E, Malsberger  R. The Availability and Usability of Behavioral Health Organization Encounter Data in MAX 2009. Washington, DC: Mathematica Policy Research; 2013.
Dodd  AH, Nysenbaum  J, Zlatinov  A. Assessing the Usability of the MAX 2007 Inpatient and Prescription Encounter Data for Enrollees in Comprehensive Managed Care. Washington, DC: Mathematica Policy Research; 2012.
Bobo  WV, Cooper  WO, Stein  CM,  et al.  Positive predictive value of a case definition for diabetes mellitus using automated administrative health data in children and youth exposed to antipsychotic drugs or control medications: a Tennessee Medicaid study. BMC Med Res Methodol. 2012;12:128.
PubMed   |  Link to Article
Chen  G, Khan  N, Walker  R, Quan  H.  Validating ICD coding algorithms for diabetes mellitus from administrative data. Diabetes Res Clin Pract. 2010;89(2):189-195.
PubMed   |  Link to Article
Guo  JJ, Keck  PE  Jr, Corey-Lisle  PK,  et al.  Risk of diabetes mellitus associated with atypical antipsychotic use among Medicaid patients with bipolar disorder: a nested case-control study. Pharmacotherapy. 2007;27(1):27-35.
PubMed   |  Link to Article
Kemper  AR, Dombkowski  KJ, Menon  RK, Davis  MM.  Trends in diabetes mellitus among privately insured children, 1998-2002. Ambul Pediatr. 2006;6(3):178-181.
PubMed   |  Link to Article
Lambert  BL, Chou  CH, Chang  KY, Tafesse  E, Carson  W.  Antipsychotic exposure and type 2 diabetes among patients with schizophrenia: a matched case-control study of California Medicaid claims. Pharmacoepidemiol Drug Saf. 2005;14(6):417-425.
PubMed   |  Link to Article
McIntyre  RS, Jerrell  JM.  Metabolic and cardiovascular adverse events associated with antipsychotic treatment in children and adolescents. Arch Pediatr Adolesc Med. 2008;162(10):929-935.
PubMed   |  Link to Article
Feudtner  C, Christakis  DA, Connell  FA.  Pediatric deaths attributable to complex chronic conditions: a population-based study of Washington State, 1980-1997. Pediatrics. 2000;106(1, pt 2):205-209.
PubMed
Sato  T, Matsuyama  Y.  Marginal structural models as a tool for standardization. Epidemiology. 2003;14(6):680-686.
PubMed   |  Link to Article
Vittinghoff  E. Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models. New York, NY: Springer; 2012.
Lin  DY, Psaty  BM, Kronmal  RA.  Assessing the sensitivity of regression results to unmeasured confounders in observational studies. Biometrics. 1998;54(3):948-963.
PubMed   |  Link to Article
Duffy  SW, Nagtegaal  ID, Wallis  M,  et al.  Correcting for lead time and length bias in estimating the effect of screen detection on cancer survival. Am J Epidemiol. 2008;168(1):98-104.
PubMed   |  Link to Article
Hutchison  GB, Shapiro  S.  Lead time gained by diagnostic screening for breast cancer. J Natl Cancer Inst. 1968;41(3):665-681.
PubMed
Pringsheim  T, Gorman  D.  Second-generation antipsychotics for the treatment of disruptive behaviour disorders in children: a systematic review. Can J Psychiatry. 2012;57(12):722-727.
PubMed
Abdullah  A, Peeters  A, de Courten  M, Stoelwinder  J.  The magnitude of association between overweight and obesity and the risk of diabetes: a meta-analysis of prospective cohort studies. Diabetes Res Clin Pract. 2010;89(3):309-319.
PubMed   |  Link to Article
Ehlert  U.  Enduring psychobiological effects of childhood adversity. Psychoneuroendocrinology. 2013;38(9):1850-1857.
PubMed   |  Link to Article
Finkelhor  D, Turner  HA, Shattuck  A, Hamby  SL.  Violence, crime, and abuse exposure in a national sample of children and youth: an update. JAMA Pediatr. 2013;167(7):614-621.
PubMed   |  Link to Article
Johnson  SB, Riley  AW, Granger  DA, Riis  J.  The science of early life toxic stress for pediatric practice and advocacy. Pediatrics. 2013;131(2):319-327.
PubMed   |  Link to Article
Joung  KE, Park  KH, Zaichenko  L,  et al.  Early life adversity is associated with elevated levels of circulating leptin, irisin, and decreased levels of adiponectin in midlife adults. J Clin Endocrinol Metab. 2014;99(6):E1055-E1060.
PubMed   |  Link to Article
Ogden  CL, Lamb  MM, Carroll  MD, Flegal  KM. Obesity and Socioeconomic Status in Children and Adolescents: United States, 2005–2008. NCHS Data Brief No. 51. Hyattsville, MD: National Center for Health Statistics; 2010.
Rosmond  R.  Role of stress in the pathogenesis of the metabolic syndrome. Psychoneuroendocrinology. 2005;30(1):1-10.
PubMed   |  Link to Article
Findling  RL, Drury  SS, Jensen  PS,  et al. Practice Parameter for the Use of Atypical Antipsychotic Medications in Children and Adolescents. Washington, DC: American Academy of Child and Adolescent Psychiatry; 2011.
Correll  CU, Carlson  HE.  Endocrine and metabolic adverse effects of psychotropic medications in children and adolescents. J Am Acad Child Adolesc Psychiatry. 2006;45(7):771-791.
PubMed   |  Link to Article
Fraguas  D, Merchán-Naranjo  J, Laita  P,  et al.  Metabolic and hormonal side effects in children and adolescents treated with second-generation antipsychotics. J Clin Psychiatry. 2008;69(7):1166-1175.
PubMed   |  Link to Article

Figures

Place holder to copy figure label and caption
Figure.
Study Participant Flow Diagram for US Medicaid Population, 2003-2007

PCOS indicates polycystic ovary syndrome.

aContinuously is defined as at least 10 of 12 consecutive months.

bFor information on identification of Medicaid payment arrangements, see eAppendix 2 in the Supplement.

cYouths with PCOS, a diagnosis for which metformin (a type 2 diabetes mellitus medication) is indicated, were excluded from analysis. Polycystic ovary syndrome was identified in the Medicaid encounter data using the International Classification of Diseases, Ninth Revision code 256.4.

dEligible youths satisfied additional inclusion and exclusion criteria at this stage. For further details, see main text and eAppendix 2 in the Supplement.

Graphic Jump Location

Tables

Table Graphic Jump LocationTable 1.  Demographic Characteristics of Eligible Children at First Entry Into Study
Table Graphic Jump LocationTable 2.  Change Over Time in Demographic and Clinical Characteristics Among Children and Adolescents Initiating SGA Medication, 2003-2007
Table Graphic Jump LocationTable 3.  Association of SGAs and Incident Type 2 Diabetes Mellitus in US Medicaid-Enrolled Children Ages 10 to 18 Yearsa
Table Graphic Jump LocationTable 4.  Association of Individual Antipsychotic Medications and Incident Type 2 Diabetes Mellitus in US Medicaid-Enrolled Children Ages 10 to 18 Years

References

Olfson  M, Blanco  C, Liu  SM, Wang  S, Correll  CU.  National trends in the office-based treatment of children, adolescents, and adults with antipsychotics. Arch Gen Psychiatry. 2012;69(12):1247-1256.
PubMed   |  Link to Article
Cooper  WO, Arbogast  PG, Ding  H, Hickson  GB, Fuchs  DC, Ray  WA.  Trends in prescribing of antipsychotic medications for US children. Ambul Pediatr. 2006;6(2):79-83.
PubMed   |  Link to Article
Kreider  AR, Matone  M, Bellonci  C,  et al.  Growth in the concurrent use of antipsychotics with other psychotropic medications in Medicaid-enrolled children. J Am Acad Child Adolesc Psychiatry. 2014;53(9):960-970.e2.
PubMed   |  Link to Article
Matone  M, Localio  R, Huang  YS, dosReis  S, Feudtner  C, Rubin  D.  The relationship between mental health diagnosis and treatment with second-generation antipsychotics over time: a national study of US Medicaid-enrolled children. Health Serv Res. 2012;47(5):1836-1860.
PubMed   |  Link to Article
Crystal  S, Olfson  M, Huang  C, Pincus  H, Gerhard  T.  Broadened use of atypical antipsychotics: safety, effectiveness, and policy challenges. Health Aff (Millwood). 2009;28(5):w770-w781.
PubMed   |  Link to Article
Dosreis  S, Yoon  Y, Rubin  DM, Riddle  MA, Noll  E, Rothbard  A.  Antipsychotic treatment among youth in foster care. Pediatrics. 2011;128(6):e1459-e1466.
PubMed   |  Link to Article
Zito  JM, Safer  DJ, Zuckerman  IH, Gardner  JF, Soeken  K.  Effect of Medicaid eligibility category on racial disparities in the use of psychotropic medications among youths. Psychiatr Serv. 2005;56(2):157-163.
PubMed   |  Link to Article
Correll  CU, Kratochvil  CJ, March  JS.  Developments in pediatric psychopharmacology: focus on stimulants, antidepressants, and antipsychotics. J Clin Psychiatry. 2011;72(5):655-670.
PubMed   |  Link to Article
Christian  R, Saavedra  L, Gaynes  BN,  et al. Appendix A, Tables of FDA-Approved Indications for First- and Second-Generation Antipsychotics: Future Research Needs for First- and Second-Generation Antipsychotics for Children and Young Adults: Future Research Needs Papers, No. 13. Rockville, MD: Agency for Healthcare Research and Quality; 2012.
Findling  RL.  Dosing of atypical antipsychotics in children and adolescents. Prim Care Companion J Clin Psychiatry. 2003;5(suppl 6):10-13.
Wiechers  IR, Leslie  DL, Rosenheck  RA.  Prescribing of psychotropic medications to patients without a psychiatric diagnosis. Psychiatr Serv. 2013;64(12):1243-1248.
PubMed   |  Link to Article
Birnbaum  ML, Saito  E, Gerhard  T,  et al.  Pharmacoepidemiology of antipsychotic use in youth with ADHD: trends and clinical implications. Curr Psychiatry Rep. 2013;15(8):382.
PubMed   |  Link to Article
Zito  JM, Safer  DJ, Sai  D,  et al.  Psychotropic medication patterns among youth in foster care. Pediatrics. 2008;121(1):e157-e163.
PubMed   |  Link to Article
Correll  CU.  Monitoring and management of antipsychotic-related metabolic and endocrine adverse events in pediatric patients. Int Rev Psychiatry. 2008;20(2):195-201.
PubMed   |  Link to Article
Correll  CU.  Assessing and maximizing the safety and tolerability of antipsychotics used in the treatment of children and adolescents. J Clin Psychiatry. 2008;69(suppl 4):26-36.
PubMed   |  Link to Article
De Hert  M, Dobbelaere  M, Sheridan  EM, Cohen  D, Correll  CU.  Metabolic and endocrine adverse effects of second-generation antipsychotics in children and adolescents: a systematic review of randomized, placebo controlled trials and guidelines for clinical practice. Eur Psychiatry. 2011;26(3):144-158.
PubMed   |  Link to Article
Panagiotopoulos  C, Ronsley  R, Davidson  J.  Increased prevalence of obesity and glucose intolerance in youth treated with second-generation antipsychotic medications. Can J Psychiatry. 2009;54(11):743-749.
PubMed
Correll  CU, Manu  P, Olshanskiy  V, Napolitano  B, Kane  JM, Malhotra  AK.  Cardiometabolic risk of second-generation antipsychotic medications during first-time use in children and adolescents. JAMA. 2009;302(16):1765-1773.
PubMed   |  Link to Article
Teff  KL, Rickels  MR, Grudziak  J, Fuller  C, Nguyen  H-L, Rickels  K.  Antipsychotic-induced insulin resistance and postprandial hormonal dysregulation independent of weight gain or psychiatric disease. Diabetes. 2013;62(9):3232-3240.
PubMed   |  Link to Article
Nielsen  RE, Laursen  MF, Vernal  DL,  et al.  Risk of diabetes in children and adolescents exposed to antipsychotics: a nationwide 12-year case-control study. J Am Acad Child Adolesc Psychiatry. 2014;53(9):971-979.e6.
PubMed   |  Link to Article
Andrade  SE, Lo  JC, Roblin  D,  et al.  Antipsychotic medication use among children and risk of diabetes mellitus. Pediatrics. 2011;128(6):1135-1141.
PubMed   |  Link to Article
Bobo  WV, Cooper  WO, Stein  CM,  et al.  Antipsychotics and the risk of type 2 diabetes mellitus in children and youth. JAMA Psychiatry. 2013;70(10):1067-1075.
PubMed   |  Link to Article
Rubin  D, Matone  M, Huang  Y-S, dosReis  S, Feudtner  C, Localio  R.  Interstate variation in trends of psychotropic medication use among Medicaid-enrolled children in foster care. Child Youth Serv Rev. 2012;34(8):1492-1499. doi:10.1016/j.childyouth.2012.04.006.
Link to Article
Centers for Disease Control and Prevention. Obese youth over time: 2013, 2011, 2009, 2007, 2005, and 2003. Adolescent and School Health 2014. http://www.cdc.gov/healthyyouth/obesity/obesity-youth.htm. Accessed January 27, 2015.
Liese  AD, Lawson  A, Song  HR,  et al.  Evaluating geographic variation in type 1 and type 2 diabetes mellitus incidence in youth in four US regions. Health Place. 2010;16(3):547-556.
PubMed   |  Link to Article
Hernán  MA, Robins  JM, García Rodríguez  LA.  Discussion on 'statistical issues arising in the Women's Health Initiative.' Biometrics. 2005;61(4):922-930. doi:10.1111/j.0006-341X.2005.454_7.x.
Link to Article
Prentice  RL, Pettinger  M, Anderson  GL.  Statistical issues arising in the Women’s Health Initiative. Biometrics.2005;61(4):899-911; discussion 911-941.
PubMed   |  Link to Article
Ross  ME, Kreider  AR, Huang  Y-S, Matone  M, Rubin  DM, Localio  AR.  Propensity score methods for analyzing observational data like randomized experiments: challenges and solutions for rare outcomes and exposures. Am J Epidemiol. In press.
Nysenbaum  J, Bouchery  E, Malsberger  R. The Availability and Usability of Behavioral Health Organization Encounter Data in MAX 2009. Washington, DC: Mathematica Policy Research; 2013.
Dodd  AH, Nysenbaum  J, Zlatinov  A. Assessing the Usability of the MAX 2007 Inpatient and Prescription Encounter Data for Enrollees in Comprehensive Managed Care. Washington, DC: Mathematica Policy Research; 2012.
Bobo  WV, Cooper  WO, Stein  CM,  et al.  Positive predictive value of a case definition for diabetes mellitus using automated administrative health data in children and youth exposed to antipsychotic drugs or control medications: a Tennessee Medicaid study. BMC Med Res Methodol. 2012;12:128.
PubMed   |  Link to Article
Chen  G, Khan  N, Walker  R, Quan  H.  Validating ICD coding algorithms for diabetes mellitus from administrative data. Diabetes Res Clin Pract. 2010;89(2):189-195.
PubMed   |  Link to Article
Guo  JJ, Keck  PE  Jr, Corey-Lisle  PK,  et al.  Risk of diabetes mellitus associated with atypical antipsychotic use among Medicaid patients with bipolar disorder: a nested case-control study. Pharmacotherapy. 2007;27(1):27-35.
PubMed   |  Link to Article
Kemper  AR, Dombkowski  KJ, Menon  RK, Davis  MM.  Trends in diabetes mellitus among privately insured children, 1998-2002. Ambul Pediatr. 2006;6(3):178-181.
PubMed   |  Link to Article
Lambert  BL, Chou  CH, Chang  KY, Tafesse  E, Carson  W.  Antipsychotic exposure and type 2 diabetes among patients with schizophrenia: a matched case-control study of California Medicaid claims. Pharmacoepidemiol Drug Saf. 2005;14(6):417-425.
PubMed   |  Link to Article
McIntyre  RS, Jerrell  JM.  Metabolic and cardiovascular adverse events associated with antipsychotic treatment in children and adolescents. Arch Pediatr Adolesc Med. 2008;162(10):929-935.
PubMed   |  Link to Article
Feudtner  C, Christakis  DA, Connell  FA.  Pediatric deaths attributable to complex chronic conditions: a population-based study of Washington State, 1980-1997. Pediatrics. 2000;106(1, pt 2):205-209.
PubMed
Sato  T, Matsuyama  Y.  Marginal structural models as a tool for standardization. Epidemiology. 2003;14(6):680-686.
PubMed   |  Link to Article
Vittinghoff  E. Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models. New York, NY: Springer; 2012.
Lin  DY, Psaty  BM, Kronmal  RA.  Assessing the sensitivity of regression results to unmeasured confounders in observational studies. Biometrics. 1998;54(3):948-963.
PubMed   |  Link to Article
Duffy  SW, Nagtegaal  ID, Wallis  M,  et al.  Correcting for lead time and length bias in estimating the effect of screen detection on cancer survival. Am J Epidemiol. 2008;168(1):98-104.
PubMed   |  Link to Article
Hutchison  GB, Shapiro  S.  Lead time gained by diagnostic screening for breast cancer. J Natl Cancer Inst. 1968;41(3):665-681.
PubMed
Pringsheim  T, Gorman  D.  Second-generation antipsychotics for the treatment of disruptive behaviour disorders in children: a systematic review. Can J Psychiatry. 2012;57(12):722-727.
PubMed
Abdullah  A, Peeters  A, de Courten  M, Stoelwinder  J.  The magnitude of association between overweight and obesity and the risk of diabetes: a meta-analysis of prospective cohort studies. Diabetes Res Clin Pract. 2010;89(3):309-319.
PubMed   |  Link to Article
Ehlert  U.  Enduring psychobiological effects of childhood adversity. Psychoneuroendocrinology. 2013;38(9):1850-1857.
PubMed   |  Link to Article
Finkelhor  D, Turner  HA, Shattuck  A, Hamby  SL.  Violence, crime, and abuse exposure in a national sample of children and youth: an update. JAMA Pediatr. 2013;167(7):614-621.
PubMed   |  Link to Article
Johnson  SB, Riley  AW, Granger  DA, Riis  J.  The science of early life toxic stress for pediatric practice and advocacy. Pediatrics. 2013;131(2):319-327.
PubMed   |  Link to Article
Joung  KE, Park  KH, Zaichenko  L,  et al.  Early life adversity is associated with elevated levels of circulating leptin, irisin, and decreased levels of adiponectin in midlife adults. J Clin Endocrinol Metab. 2014;99(6):E1055-E1060.
PubMed   |  Link to Article
Ogden  CL, Lamb  MM, Carroll  MD, Flegal  KM. Obesity and Socioeconomic Status in Children and Adolescents: United States, 2005–2008. NCHS Data Brief No. 51. Hyattsville, MD: National Center for Health Statistics; 2010.
Rosmond  R.  Role of stress in the pathogenesis of the metabolic syndrome. Psychoneuroendocrinology. 2005;30(1):1-10.
PubMed   |  Link to Article
Findling  RL, Drury  SS, Jensen  PS,  et al. Practice Parameter for the Use of Atypical Antipsychotic Medications in Children and Adolescents. Washington, DC: American Academy of Child and Adolescent Psychiatry; 2011.
Correll  CU, Carlson  HE.  Endocrine and metabolic adverse effects of psychotropic medications in children and adolescents. J Am Acad Child Adolesc Psychiatry. 2006;45(7):771-791.
PubMed   |  Link to Article
Fraguas  D, Merchán-Naranjo  J, Laita  P,  et al.  Metabolic and hormonal side effects in children and adolescents treated with second-generation antipsychotics. J Clin Psychiatry. 2008;69(7):1166-1175.
PubMed   |  Link to Article

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Multimedia

Supplement.

eAppendix 1. Statistical Analysis

eAppendix 2. Inclusion and Exclusion Criteria

eAppendix 3. Algorithm for Outcome Ascertainment: Sensitivity Analyses

eAppendix 4. Unobserved Confounding: Sensitivity Analysis

eAppendix 5. Lead Time Bias: Sensitivity Analysis

eAppendix 6. Lead Time Bias: Partial Adjustment for Bias

eTable. Propensity Score Covariates for Balancing

eReferences

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