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Article
April 5, 2010

Metabolic Screening in Children Receiving Antipsychotic Drug Treatment

Author Affiliations

Author Affiliations: Department of Health Systems, Management, and Policy, Colorado School of Public Health (Dr Morrato), Department of Pediatrics, School of Medicine (Drs Morrato and Maahs), Department of Clinical Pharmacy, School of Pharmacy (Drs Morrato and Valuck), University of Colorado at Denver, and Children's Outcome Research Program, The Children's Hospital (Dr Morrato and Ms Campagna), Denver, and Barbara Davis Center for Childhood Diabetes (Dr Maahs), Aurora, Colorado; Departments of Psychiatry and Medicine and the Center for Clinical Studies, School of Medicine, and Department of Psychology, Washington University, St Louis, Missouri (Drs Nicol and Newcomer); Department of Health Policy and Management, Rollins School of Public Health, Emory University, Atlanta, Georgia (Dr Druss); and Department of Pharmacy Practice, College of Pharmacy, Oregon Health & Science University Campus, Oregon State University, Portland (Dr Hartung).

Arch Pediatr Adolesc Med. 2010;164(4):344-351. doi:10.1001/archpediatrics.2010.48
Abstract

Objectives  To estimate metabolic screening rates, predictors of screening, and incidence of metabolic disturbances in children initiating second-generation antipsychotic (SGA) drug treatment.

Design  A retrospective, new-user cohort study (between July 1, 2004, and June 30, 2006) using Medicaid claims data.

Settings  California, Missouri, and Oregon.

Patients  A total of 5370 children (aged 6-17 years) without diabetes mellitus taking SGA drugs and 15 000 children without diabetes taking albuterol (control) but no SGA drugs.

Intervention  Findings 1 year after recommendations from the American Diabetes Association and American Psychiatric Association called for metabolic screening of patients receiving SGA drugs.

Outcome Measures  Serum glucose and lipid testing, 6-month incidence of diabetes, and dyslipidemia disturbances.

Results  Glucose screening was performed in 1699 (31.6% [95% confidence interval (CI), 30.4%-32.9%]) SGA-treated children vs 1891 (12.6% [12.1%-13.2%]) control individuals. Lipid testing was performed in 720 (13.4% [95% CI, 12.5%-14.4%]) SGA-treated children vs 458 (3.1% [2.8%-3.3%]) controls. In multivariate logistic regression analysis, children with serious and/or multiple psychiatric diagnoses and those who used health care services more intensively were more likely to receive metabolic screening. The case incidence of glucose and lipid disorders was higher in SGA-treated vs albuterol-treated children (8.9 per 1000 children [95% CI, 6.6%-11.8%] vs 4.9 per 1000 children [3.9%-6.2%]; and 9.7 per 1000 children [95% CI, 7.2%-12.7%] vs 4.6 per 1000 children [95% CI, 3.6%-5.8%], respectively).

Conclusion  Most children starting treatment with SGA medications in this public sector sample did not receive recommended glucose and lipid screening.

Antipsychotic drugs are approved for the treatment of schizophrenia and other neuropsychiatric disorders,1,2 including indications for the treatment of bipolar disorder,3-7 adjunctive therapy for the treatment of depression,3 and treatment of children and adolescents.8 Children with mental disorders have been increasingly prescribed antipsychotic medication,9-13 primarily second-generation (atypical) antipsychotics (SGAs). One multistate Medicaid study14 found that one-quarter of new prescriptions for antipsychotics were filled for children. In a national study,10 1% to 2% of all outpatient visits by children to office-based physicians included a prescription for an antipsychotic medication. During the past decade, use of antipsychotic medications has increased an estimated 5-fold in US children9,10 and is mainly being used for behavioral control (in autistic and nonautistic children).10 Although there has been a parallel increase in antipsychotic prescription rates in Denmark,15 Germany,16 the United Kingdom,17 the Netherlands,18 Italy,19 and France,20 the rates of prescription of antipsychotics in these countries have remained consistently lower than in the United States.9,10,16

In 2003, the Food and Drug Administration required that a class warning be added to product labeling for all SGAs regarding the risk of hyperglycemia and diabetes mellitus, including the potential for extreme hyperglycemia associated with ketoacidosis, hyperosmolar coma, or death.21 The warning further stated that glucose levels should be monitored in patients with an established diagnosis of diabetes, risk factors for diabetes, or symptoms of hyperglycemia. Since then, greater specificity has been added to some SGA product labeling regarding adverse effects on weight gain and lipid profiles.5,22,23 In addition, the American Diabetes Association, the American Psychiatric Association, the North American Association for the Study of Obesity, and the American Association of Clinical Endocrinologists issued a joint consensus statement in early 200424 recommending metabolic screening and monitoring, consisting of weight and body mass index (calculated as weight in kilograms divided by height in meters squared), waist circumference, blood pressure, and fasting serum glucose and lipid profiles for all patients receiving these agents, regardless of age. The evidence base supporting metabolic testing for SGA-treated patients consisted of a synthesis of the literature and presentations from 14 experts drawn from the areas of psychiatry, obesity, and diabetes and from the Food and Drug Administration and representatives from the pharmaceutical industry.24

Relatively little information exists regarding the short-term and long-term metabolic effects of antipsychotic use in children,25 although initial observations indicate that metabolic adverse effects of antipsychotic treatment in pediatric populations are readily detected.26-31 Compared with adults, children who take olanzapine, risperidone, and quetiapine fumarate are particularly at risk for weight gain.28-31 Type 2 diabetes and cardiovascular adverse events have also been identified in children and adolescents exposed to antipsychotics, especially when multiple antipsychotics are prescribed or antipsychotics are coprescribed with mood stabilizers and antidepressants.27 Moreover, the association between antipsychotic medication use and diabetes has been stronger in children and adolescents than adults.26 However, secondary analyses in some recent studies14,32-34 suggest that children are less likely to receive metabolic screening and monitoring compared with adults.

The present study aimed to estimate population-based rates of serum glucose and lipid testing in children from 3 state Medicaid programs who initiated SGA drug treatment compared with a pediatric reference population after the availability of Food and Drug Administration warnings and the American Diabetes Association, American Psychiatric Association, North American Association for the Study of Obesity, and American Association of Clinical Endocrinologists joint recommendations.24 Using this sample, we also sought to identify demographic and clinical predictors of glucose and lipid testing in SGA-treated children and to estimate the incidence of early detection of metabolic disorders, with the goal of informing strategies for improving metabolic monitoring of this at-risk population.

Methods

Study population

A new-user cohort was defined as Medicaid fee-for-service clients (aged 6-17 years) enrolled in California, Missouri, and Oregon who had started taking antipsychotic medication in 2005. Children were included if they had a prescription claim for 1 of 5 commonly prescribed SGA drugs (aripiprazole, olanzapine, quetiapine, risperidone, or ziprasidone hydrochloride) and continuous enrollment eligibility 180 days before and after the date of the first (index) antipsychotic pharmacy claim. Use of clozapine in children was so infrequent that it was not included in this study. Study participants had a unique encrypted identifier from which to identify their complete medical, pharmacy, and laboratory claims for the 180 days before and after the index date (representing an observation range of July 1, 2004, through June 30, 2006).

Children were not included in the study if they were Medicare dual eligible or in a managed care plan because complete laboratory and medical claims were not available. Because our aim was to estimate rates of glucose and lipid screening rather than monitoring related to existing disease, children with preexisting diabetes or dyslipidemia (n = 100), defined by a diagnosis in the medical claims or a pharmacy claim for an oral antidiabetic, insulin, or antidyslipidemic drug in the 180 days before antipsychotic drug therapy initiation, were not included in the study.

To provide a relative comparison, we compared absolute rates in our sample of SGA-treated children with a reference population of chronically ill children for whom metabolic screening is not clinically indicated. A comparison cohort of children initiating albuterol, but not receiving antipsychotic medication, was identified (N = 15 000). Albuterol is a commonly prescribed medication for children with asthma and has been used to identify a control group for other claims-based research in psychiatry14,35 because it selects for individuals across a broad age range typically receiving health care in a nonpsychiatric setting. We hypothesized that rates of screening in SGA-treated children would be higher than in an age-matched comparison group of albuterol-treated children receiving care during the same period.

Main outcome measures

Glucose testing was defined as a medical claim with an American Medical Association Current Procedural Terminology (CPT) code for a metabolic or general health panel (80048, 80050, or 80053) or glucose-specific serum test (82947, 82948, 82950, or 82951) occurring 30 days before through 180 days after the index date.14 Lipid testing was defined as a claim with a CPT code for a lipid panel (80061) or lipid-specific serum test (82465, 83700, 83701, 83715, 83716, 83721, or 84478), using the same time frame.

Diagnoses were recorded in the medical claims according to the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM).36 Incident glucose disturbances were identified when a diagnosis in the medical claims or a prescription claim for an oral antidiabetic drug or insulin was present in the 180 days after the index date. Incident lipid disturbances were identified, using the Clinical Classifications Software (CCS) classification scheme developed by the Agency for Healthcare Research and Quality,37 when a prescription claim for an antidyslipidemic drug or a diagnosis for disorders of lipid metabolism (CCS code 53) was present in the 180 days after the index date. Studies38,39 that examine the validity of using claims data to identify patients with chronic disease suggest our algorithm would yield specificity levels greater than 0.90.

Demographic and clinical characteristics of patients

Age (at the index date), sex, state of residence, and race/ethnicity (white, black, and other [including Hispanic and Asian]) were available from the Medicaid beneficiary records from the state in question for each child. Physiologic and developmental differences associated with puberty were accounted for by categorizing the study population into 2 age groups (6-12 and 13-17 years).

Patients with serious mental disorders have been a particular focus for cardiometabolic screening and intervention.40,41 Serious mental illness was defined as having schizophrenia (CCS code 70), other psychoses (CCS code 71), or affective disorders (CCS code 69), which include bipolar disorder and major depression. Patients with medical claims associated with 2 or more diagnosed mental disorders (ICD-9-CM codes 290-319) were classified as having a comorbid mental disorder.10 Coprescribed psychotropic medications during the study period were identified from the pharmacy claims records and categorized as stimulants, antidepressants, mood stabilizers, and anxiolytics and hypnotics.10 Persistent antipsychotic users, defined as a maximum gap in therapy no greater than 30 days during the first 180 days of therapy,42 were identified to evaluate whether they had higher rates of metabolic monitoring because they would theoretically be at greater risk for developing metabolic adverse effects than patients receiving time-limited therapy.

As an indicator of the degree to which each child accessed general medical care, the number of medical claims for office visits (CPT codes 99201-99205, 99211-99215, or 99241-99245) occurring in the 180 days after drug initiation were determined and categorized as none, 1, or 2 or more. Children with a medical claim for an emergency department visit or a hospitalization were also identified because this may indicate greater use of medical services, greater severity of disorder, and increased access to general laboratory testing.

Statistical analysis

The distribution of patient demographic and clinical characteristics and health care use patterns were stratified by age group, and proportions were compared between SGA-treated and albuterol-treated children using χ2 tests. The percentage of SGA-treated children who received glucose and lipid testing was stratified by age group and compared with the albuterol-treated comparison group using χ2 tests. Multivariate logistic regression was used to identify predictors of the likelihood of receiving testing among SGA-treated children after adjusting for age group, ethnicity, state, presence of serious mental illness and mental disorder comorbidity, persistent antipsychotic use, use of other psychotropic medications, number of medical office visits, emergency department use, and hospitalization. Glucose testing and lipid testing were modeled independently.

Incidence rates of glucose and lipid disturbances per 1000 treated children were calculated by age group and compared between the SGA-treated and albuterol-treated comparison groups using χ2 tests. All analyses were performed with SAS statistical software, version 9.2 (SAS Institute Inc, Cary, North Carolina). The study received approval from the Colorado Multiple Institutional Review Board and the California Committee for the Protection of Human Subjects.

Results

Characteristics of sga-treated children

Among the 5370 children initiating SGA therapy, the most commonly prescribed antipsychotic was risperidone, which was prescribed to 2714 children and adolescents (50.5%). New prescriptions to children aged 6 to 12 years totalled 1729 (60.7%), and new prescriptions to adolescents aged 13 to 17 years totalled 985 (39.1%), followed by quetiapine, aripiprazole, olanzapine, and ziprasidone. Median days of index drug supplied for all children initiating antipsychotic therapy was 30 days (interquartile range, 30-90 days). Persistent use at 84 days was 62.7%, which decreased to 37.8% at 180 days. Persistence rates varied by 3.3% for each index antipsychotic drug. Children treated with SGAs were more often male, white, and hospitalized during the study period than the age-matched group of albuterol-treated children (Table 1). A total of 38.9% of children aged 6 to 12 years and 58.6% of adolescents aged 13 to 17 years who were initiating antipsychotic drug treatment had a recorded diagnosis for a serious mental illness. As indicated in Table 1, other psychotropic drug use was 4 to 6 times more prevalent among antipsychotic-treated children.

Table 1. 
Select Patient Characteristicsa
Select Patient Characteristicsa

Rates of serum glucose and lipid testing

Glucose screening was performed in 1699 (31.6% [95% confidence interval (CI), 30.4%-32.9%]) SGA-treated children vs 1891 (12.6 [12.1%-13.2%] control individuals [< .001]). Lipid testing was performed in 720 (13.4% [95% CI, 12.5%-14.4%]) SGA-treated children vs 458 (3.1% [2.8%-3.3%]) controls. Glucose and lipid testing rates for the antipsychotic and albuterol cohorts, stratified by age group, are presented in the Figure. Glucose screening was 2 to 3 times more common than lipid screening in both age groups, and screening was more common in older children.

Figure. 
Serum glucose and lipid screening among children receiving second-generation antipsychotic and albuterol drug treatment. Mean estimates and 95% confidence intervals (error bars) are presented. Data are from California, Missouri, and Oregon state Medicaid programs of children initiating drug treatment in 2005. Antipsychotic-treated (n = 5370) and albuterol-treated (n = 15 000) patients had no recognized diabetes mellitus or dyslipidemia at the start of therapy.

Serum glucose and lipid screening among children receiving second-generation antipsychotic and albuterol drug treatment. Mean estimates and 95% confidence intervals (error bars) are presented. Data are from California, Missouri, and Oregon state Medicaid programs of children initiating drug treatment in 2005. Antipsychotic-treated (n = 5370) and albuterol-treated (n = 15 000) patients had no recognized diabetes mellitus or dyslipidemia at the start of therapy.

Predictors of glucose and lipid testing

Table 2 presents the results from the multivariate logistic regression analysis of patient characteristics associated with the likelihood of receiving glucose and lipid screening among SGA-treated children. The strongest determinants of glucose testing were having an emergency department visit (adjusted odds ratio [AOR], 2.52), having multiple mental health comorbidities (2.42), and having been hospitalized (2.15). The strongest determinants of lipid testing were having multiple mental health comorbidities (2.85), having 2 or more medical office visits (1.88), and having serious mental illness (1.51). Children receiving antipsychotic treatment in Missouri and Oregon were less likely to receive glucose testing (0.62 and 0.81, respectively) and lipid testing (0.29 and 0.57, respectively) than those in California. The adjusted likelihood of metabolic testing was not associated with the index antipsychotic.

Table 2. 
Factors Predicting Glucose and Lipid Testing Among Children Receiving SGA Drug Treatmenta
Factors Predicting Glucose and Lipid Testing Among Children Receiving SGA Drug Treatmenta

Incidence of detected glucose and lipid disturbances

The incidence of glucose disturbances during the first 6 months of treatment were more common in antipsychotic-treated compared with albuterol-treated children (0.9% vs 0.5%, P = .001) as were lipid disturbances (1.0% vs 0.5%, P < .001). The incidence of metabolic disturbances was not statistically different between children prescribed olanzapine, risperidone, or quetiapine (glucose, 0.8%; lipids, 1.2%) compared with children prescribed aripiprazole or ziprasidone (glucose, 0.9%; lipids, 1.3%). Table 3 presents the incidence of glucose and lipid disturbances and source of ascertainment stratified by the child's age. Glucose and lipid disturbances were more common in older than younger children. The overall difference in observed rates of metabolic disturbances between the antipsychotic-treated and albuterol-treated children appears to be primarily attributable to differences in rates of new diagnoses rather than rates of new antidiabetic and antidyslipidemic drug prescriptions.

Table 3. 
Six-Month Incidence of Metabolic Disorders Among 20 370 Children Receiving Second-Generation Antipsychotic and Albuterol Drug Treatmenta
Six-Month Incidence of Metabolic Disorders Among 20 370 Children Receiving Second-Generation Antipsychotic and Albuterol Drug Treatmenta

Comment

The results of this multistate study of publicly insured children indicate that most children prescribed antipsychotic medication did not receive metabolic screening. Screening rates were approximately one-quarter to one-half lower than rates reported for adults.32,33 To our knowledge, this is the first study to compare rates of metabolic screening in antipsychotic-treated children with screening rates in an age-matched comparison pediatric population. As hypothesized, rates of glucose and lipid testing were significantly higher among antipsychotic-treated children; however, those rates fell short of consensus screening recommendations and they point to a substantial missed opportunity for metabolic screening in the vulnerable population of antipsychotic-treated children. This finding is disturbing, given the general environment of public health concern surrounding increasing rates of obesity and type 2 diabetes in adolescents and given specific concerns about metabolic risk for children with psychiatric conditions. Recent approvals of pediatric indications for 2 additional SGA drugs,43,44 with approval of a third drug still pending,45 suggest that the number of children receiving antipsychotic medication, and therefore requiring metabolic monitoring, will increase.

The current results further indicate that children with serious and/or multiple psychiatric diagnoses and those who use health care services more intensively (eg, multiple medical office visits, emergency department use, and hospitalization) are the most likely to receive metabolic screening. This finding is consistent with determinants of metabolic testing in adults,46,47 which indicate that patients with greater severity of mental illness, higher levels of medical and psychiatric comorbidity, and higher use of health care services are more likely to receive metabolic testing. However, even among children with mental disorder comorbidity, who were persistent in taking their antipsychotic medication and who had 2 or more medical office visits after drug initiation, only 45.2% received glucose testing and only 22.1% received lipid testing. One patient-level factor that notably did not influence screening rates in this study was race/ethnicity, despite certain racial/ethnic groups clearly being at higher risk for developing type 2 diabetes48 and evidence that race/ethnicity can influence risk of antipsychotic-induced metabolic adverse events.49 More work is needed to confirm this finding because race/ethnicity measures in claims data can be imperfect. In addition to these patient-level factors, the observation of higher testing rates for glucose and lipids in children in the California Medicaid systems (vs in Missouri and Oregon) suggests that system-level or system-wide physician-level factors may also influence screening rates.

More research is needed to discern the root causes of the low metabolic screening rates observed in our study population; however, it may be useful to consider several potential obstacles to metabolic monitoring in children. First, pediatricians and primary care physicians may remain unconvinced of the need to monitor children receiving antipsychotic medication. Instead, some physicians may have decreased antipsychotic prescribing and/or switched to lower-risk agents in response to the metabolic warnings, as has been shown in some studies.13,14 Alternatively, the low rates of monitoring found in this study and others may reflect adoption of targeted screening for those with clinical changes (eg, those with significant weight gain) rather than mass screening. This hypothesis should be evaluated in future studies because our claims data do not include weight or body mass index measures. Furthermore, many physicians might order metabolic testing, but many families may be noncompliant with follow-through because of the child's refusal to have blood drawn or the added hassle factor of fasting and traveling to another health care facility for testing. Finally, challenges to interpreting metabolic measures in children and disagreement in general about what constitutes a metabolic abnormality requiring intervention50 could dissuade physicians from collecting laboratory data altogether.

We also found evidence that the development of glucose and lipid disturbances among antipsychotic-treated children during the first 180 days after drug therapy initiation was more common than in our pediatric reference group. The findings are consistent with results from controlled trials25-27 and epidemiology studies31 that demonstrated early onset and rapid progression of metabolic risk factors after antipsychotic treatment in children. The fact that only a few children were screened suggests that actual rates of metabolic abnormalities in these settings may be higher and that claims-based estimates available to state policy makers may underestimate true rates of these problems. A recent Food and Drug Administration Psychopharmacologic Drugs Advisory Committee evaluating antipsychotic use in children strongly recommended that registries, or some other means, be established toward the goal of better safety surveillance in children.45

Although psychiatrists have expertise in prescribing antipsychotic medications, increased attention to metabolic screening by all health care professionals is required. Pediatricians and other primary care physicians are uniquely positioned to identify early signs of cardiometabolic dysfunction related to antipsychotic medications. Increasing numbers of primary care physicians are treating children with psychotropic medications; some have reported nearly 85% of all prescriptions for psychotropic medications are provided by pediatricians and primary care physicians51 and as many as one-third of prescriptions for antipsychotic medications are provided by nonpsychiatrists.52 Primary care physicians are likely to encounter such children in their practices because data regarding effectiveness for treatment of major childhood-onset mental illnesses continue to accumulate and antipsychotic drug use increases.

Several limitations of this study deserve mention. For the purposes of this analysis, testing refers to laboratory testing alone and does not encompass other clinical measures of risk commonly performed in the primary care setting, such as weight, height, body mass index percentile, and family history of diabetes and cardiovascular disease. Furthermore, this study assessed only whether laboratory tests were ordered and obtained, not what the results were or what the clinical response was in terms of adjusting treatment plans to mitigate medication-associated risk. Furthermore, the results of testing performed more than 30 days before the index prescription or more than 180 days after prescription were not evaluated. However, monitoring recommendations call for baseline evaluation, which should fall within the period studied.

Caution should also be exercised when interpreting these results because they may not be generalizable to other populations or reimbursement settings, even though they represent a large sample from 3 states. Theoretically, metabolic screening rates may have improved since the time of data collection in response to either the prevalence of high body mass index among children and adolescents in the United States53 or specific communication about the metabolic risks associated with antipsychotic medication. However, little evidence is available with regard to substantial improvements over time in available Medicaid or managed care data sets.14,33,54

In conclusion, children who are treated with antipsychotic medications are recognized as a higher-risk group for metabolic disorders because of potential adverse metabolic effects of these medications and other predisposing factors. Results from this study suggest that serum glucose and lipid monitoring, recommended tools for assessing metabolic risk, are greatly underused in pediatric populations receiving antipsychotic drug treatment. Despite the result that use of glucose and lipid screening was higher in the antipsychotic-treated group than in an age-matched reference pediatric population, screening rates are markedly low, are lower than rates reported in antipsychotic-treated adults, and are well below the recommended goal that “all patients”24 taking antipsychotics be monitored. Given evidence of short-term adverse metabolic changes and the potential long-term metabolic risk burden, greater effort is needed to ensure consistent screening for all children receiving antipsychotic medications.

Correspondence: Elaine H. Morrato, DrPH, MPH, University of Colorado at Denver, Mailstop B119, Bldg 500, 13001 E 19th Pl, Aurora, CO 80045 (Elaine.Morrato@ucdenver.edu).

Accepted for Publication: November 12, 2009.

Author Contributions: Drs Morrato and Druss had full access to all data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: Morrato, Druss, Valuck, Campagna, and Newcomer. Acquisition of data: Hartung and Campagna. Analysis and interpretation of data: Morrato, Nicol, Maahs, Druss, Hartung, Valuck, Campagna, and Newcomer. Drafting of the manuscript: Morrato, Nicol, Campagna, and Newcomer. Critical revision of the manuscript for important intellectual content: Morrato, Nicol, Maahs, Druss, Hartung, Valuck, Campagna, and Newcomer. Statistical analysis: Valuck, Campagna, and Newcomer. Obtained funding: Morrato. Administrative, technical, and material support: Morrato, Nicol, and Hartung. Study supervision: Morrato and Newcomer.

Financial Disclosure: Dr Morrato has received past research funding from the National Institutes of Health, Pfizer Inc, and Eli Lilly and Company and is a consultant to the Food and Drug Administration. Dr Nicol has received research funding from the National Alliance for Research on Schizophrenia and Depression. Dr Maahs has received research funding from Merck/Schering-Plough for an investigator clinical trial of lipid-lowering medications and is supported by grant K23 DK075360. Dr Druss has received grant funding from the National Institute of Mental Health and the Agency for Healthcare Research and Quality and has consulted with Pfizer Inc. Dr Valuck has received research grant support from the Agency for Healthcare Research and Quality, the American Foundation for Suicide Prevention, Eli Lilly and Company, Pfizer Inc, and Ortho-McNeil-Janssen Pharmaceutical Ltd and has served as a consultant to Eli Lilly and Company, Forest Laboratories, and H. Lundbeck A/S. Dr Newcomer has received research grant support from the National Institute of Mental Health, the National Alliance for Research on Schizophrenia and Depression, the Sidney R. Baer, Jr Foundation, Ortho-McNeil-Janssen Pharmaceutical Ltd, Bristol-Myers Squibb, Wyeth Pharmaceuticals, and Pfizer Inc; has served as a consultant to AstraZeneca Pharmaceuticals, Bristol-Myers Squibb, Janssen Pharmaceuticals, Pfizer Inc, Solvay Pharmaceuticals, Otsuka Pharmaceutical Group, Wyeth Pharmaceuticals, Forest Laboratories, Sanofi Aventis, H. Lundbeck A/S, Tikvah Therapeutics Inc, Otsuka Pharmaceuticals, and Vanda Pharmaceuticals; has been a member of data and safety monitoring boards for Organon Pharmaceuticals, Merck/Schering-Plough, Dainippon Sumitomo Pharma Co Ltd, and VIVUS Inc; has been a consultant to litigation; and has received royalties from Compact Clinicals/Jones and Bartlett Publishers for a metabolic screening form.

Additional Contributions: We acknowledge the collective efforts of our Medicaid collaborators from California, Missouri, and Oregon for their assistance in data acquisition and manuscript review.

References
1.
Zuvekas  SH Prescription drugs and the changing patterns of treatment for mental disorders, 1996-2001.  Health Aff (Millwood) 2005;24 (1) 195- 205PubMedGoogle ScholarCrossref
2.
Walton  SMSchumock  GLee  KVAlexander  GMeltzer  DStafford  R Prioritizing future research on off-label prescribing: results of a quantitative evaluation.  Pharmacotherapy 2008;28 (12) 1443- 1452PubMedGoogle ScholarCrossref
3.
 Abilify (aripiprazole): US prescribing information. http://www.accessdata.fda.gov/drugsatfda_docs/label/2008/021436s21,021713s16,021729s8,021866s8lbl.pdf. Accessed February 8, 2010
4.
 Geodon (ziprasidone HCl): US prescribing information.  Pfizer Inc Web site. http://www.pfizer.com/files/products/uspi_geodon.pdf. Accessed February 8, 2010Google Scholar
5.
 Zyprexa (olanzapine): US prescribing information.  Eli Lilly and Company Web site. http://pi.lilly.com/us/zyprexa-pi.pdf. Accessed February 8, 2010Google Scholar
6.
 Seroquel (quetiapine fumarate): US prescribing information.  AstraZeneca Pharmaceuticals LP Web site. http://www.accessdata.fda.gov/drugsatfda_docs/label/2009/020639s045s046lbl.pdf. Accessed February 8, 2010Google Scholar
7.
 Risperdal (risperidone): US prescribing information.  Ortho-McNeil-Janssen Pharmaceuticals Inc Web site. http://www.risperdal.com/sites/default/files/shared/pi/risperdal_0.pdf. Accessed February 21, 2010Google Scholar
8.
 Pediatric Exclusivity Labeling Changes.  United States Food and Drug Administration Web site. http://www.fda.gov/downloads/ScienceResearch/SpecialTopics/PediatricTherapeuticsResearch/UCM163159.pdf. Accessed February 21, 2010Google Scholar
9.
Aparasu  RRBhatara  V Patterns and determinants of antipsychotic prescribing in children and adolescents, 2003-2004.  Curr Med Res Opin 2007;23 (1) 49- 56PubMedGoogle ScholarCrossref
10.
Olfson  MBlanco  CLiu  LMoreno  CLaje  G National trends in the outpatient treatment of children and adolescents with antipsychotic drugs.  Arch Gen Psychiatry 2006;63 (6) 679- 685PubMedGoogle ScholarCrossref
11.
Zito  JMSafer  DJdosReis  S  et al.  Psychotropic practice patterns for youth: a 10-year perspective.  Arch Pediatr Adolesc Med 2003;157 (1) 17- 25PubMedGoogle ScholarCrossref
12.
Zito  JMSafer  DJSai  D  et al.  Psychotropic medication patterns among youth in foster care.  Pediatrics 2008;121 (1) e157- e163http://pediatrics.aappublications.org/cgi/reprint/121/1/e157. Accessed February 8, 2010PubMedGoogle ScholarCrossref
13.
Constantine  RTandon  R Changing trends in pediatric antipsychotic use in Florida's Medicaid program.  Psychiatr Serv 2008;59 (10) 1162- 1168PubMedGoogle ScholarCrossref
14.
Morrato  EHDruss  BHartung  DM  et al.  Metabolic testing rates in three state Medicaid programs after FDA warnings and ADA/APA recommendations for second-generation antipsychotic drugs.  Arch Gen Psychiatry 2010;67 (1) 17- 24PubMedGoogle ScholarCrossref
15.
Deurell  MWeischer  MPagsberg  AKLabianca  J The use of antipsychotic medication in child and adolescent psychiatric treatment in Denmark: a cross-sectional survey.  Nord J Psychiatry 2008;62 (6) 472- 480PubMedGoogle ScholarCrossref
16.
Zito  JMSafer  DJBerg  LT  et al.  A three-country comparison of psychotropic medication prevalence in youth.  Child Adolesc Psychiatry Ment Health 2008;2 (1) 26PubMedGoogle ScholarCrossref
17.
Rani  FMurray  MLByrne  PJWong  IC Epidemiologic features of antipsychotic prescribing to children and adolescents in primary care in the United Kingdom.  Pediatrics 2008;121 (5) 1002- 1009PubMedGoogle ScholarCrossref
18.
Schirm  ETobi  HZito  JMde Jong-van den Berg  LT Psychotropic medication in children: a study from the Netherlands.  Pediatrics 2001;108 (2) e25http://pediatrics.aappublications.org/cgi/reprint/108/2/e25. Accessed February 8, 2010PubMedGoogle ScholarCrossref
19.
Clavenna  ARossi  EDerosa  MBonati  M Use of psychotropic medications in Italian children and adolescents.  Eur J Pediatr 2007;166 (4) 339- 347PubMedGoogle ScholarCrossref
20.
Sevilla-Dedieu  CKovess-Masfety  V Psychotropic medication use in children and adolescents: a study from France.  J Child Adolesc Psychopharmacol 2008;18 (3) 281- 289PubMedGoogle ScholarCrossref
21.
Rosack  J FDA to require diabetes warning on antipsychotics.  Psychiatr News 2003;38 (20) 1Google Scholar
22.
 Fanapt (iloperidone): US prescribing information.  Vanda Pharmaceuticals Inc Web site. http://www.fanaptus.com/assets/Newark%20Trade%20PI%2072483-10%20Fanapt%20full%20Master.pdf. Accessed February 8, 2010Google Scholar
23.
 Invega (paliperidone): US prescribing information.  Ortho-McNeil-Janssen Pharmaceuticals Inc Web site. http://www.invega.com/invega/shared/pi/invega.pdf#zoom=100. Accessed February 8, 2010Google Scholar
24.
American Diabetes Association; American Psychiatric Association; American Association of Clinical Endocrinologists; North American Association for the Study of Obesity, Consensus development conference on antipsychotic drugs and obesity and diabetes.  Diabetes Care 2004;27 (2) 596- 601PubMedGoogle ScholarCrossref
25.
Vitiello  BCorrell  Cvan Zwieten-Boot  BZuddas  AParellada  MArango  C Antipsychotics in children and adolescents: increasing use, evidence for efficacy and safety concerns.  Eur Neuropsychopharmacol 2009;19 (9) 629- 635PubMedGoogle ScholarCrossref
26.
Hammerman  ADreiher  JKlang  SHMunitz  HCohen  ADGoldfracht  M Antipsychotics and diabetes: an age-related association.  Ann Pharmacother 2008;42 (9) 1316- 1322PubMedGoogle ScholarCrossref
27.
McIntyre  RSJerrell  JM Metabolic and cardiovascular adverse events associated with antipsychotic treatment in children and adolescents.  Arch Pediatr Adolesc Med 2008;162 (10) 929- 935PubMedGoogle ScholarCrossref
28.
Correll  CUCarlson  HE Endocrine and metabolic adverse effects of psychotropic medications in children and adolescents.  J Am Acad Child Adolesc Psychiatry 2006;45 (7) 771- 791PubMedGoogle ScholarCrossref
29.
Arango  CRobles  OParellada  M  et al.  Olanzapine compared to quetiapine in adolescents with a first psychotic episode.  Eur Child Adolesc Psychiatry 2009;18 (7) 418- 428PubMedGoogle ScholarCrossref
30.
Khan  RAMican  LMSuehs  BT Effects of olanzapine and risperidone on metabolic factors in children and adolescents: a retrospective evaluation.  J Psychiatr Pract 2009;15 (4) 320- 328PubMedGoogle ScholarCrossref
31.
Correll  CUManu  POlshanskiy  VNapolitano  BKane  JMMalhotra  AK Cardiometabolic risk of second-generation antipsychotic medications during first-time use in children and adolescents.  JAMA 2009;302 (16) 1765- 1773PubMedGoogle ScholarCrossref
32.
Morrato  EHNewcomer  JWAllen  RRValuck  R Prevalence of baseline serum glucose and lipid testing in users of second-generation antipsychotic drugs: a retrospective, population-based study of Medicaid claims data.  J Clin Psychiatry 2008;69 (2) 316- 322PubMedGoogle ScholarCrossref
33.
Haupt  DWRosenblatt  LCKim  EBaker  RAWhitehead  RNewcomer  JW Prevalence and predictors of lipid and glucose monitoring in commercially insured patients treated with second-generation antipsychotic agents.  Am J Psychiatry 2009;166 (3) 345- 353PubMedGoogle ScholarCrossref
34.
Meltzer  HY Putting metabolic side effects into perspective: risks versus benefits of atypical antipsychotics.  J Clin Psychiatry 2001;62 ((suppl 27)) 35- 41PubMedGoogle Scholar
35.
Ma  JVaillancourt  RBoddam  RAuger  SSampalis  J Association between antidepressant use and prescribing of gastric acid suppressants.  Can J Psychiatry 2006;51 (3) 178- 184PubMedGoogle Scholar
36.
National Center for Health Statistics and Centers for Medicare and Medicaid Services, International Classification of Diseases, Ninth Revision Clinical Modification (ICD-9-CM). 6th ed. Hyattsville, MD National Center for Health Statistics2006;
37.
Agency for Healthcare Research and Quality, Healthcare Cost and Utilization Project (HCUP), Clinical Classifications Software (CCS) for ICD-9-CM Fact Sheet.  Healthcare Cost and Utilization Project Web site. 2007;http://www.hcup-us.ahrq.gov/toolssoftware/ccs/ccsfactsheet.jsp. Accessed December 29, 2009Google Scholar
38.
Rector  TSWickstrom  SLShah  M  et al.  Specificity and sensitivity of claims-based algorithms for identifying members of Medicare+ Choice health plans that have chronic medical conditions.  Health Serv Res 2004;39 (6, pt 1) 1839- 1857PubMedGoogle ScholarCrossref
39.
Quam  LEllis  LBVenus  PClouse  JTaylor  CGLeatherman  S Using claims data for epidemiologic research: the concordance of claims-based criteria with the medical record and patient survey for identifying a hypertensive population.  Med Care 1993;31 (6) 498- 507PubMedGoogle ScholarCrossref
40.
Newcomer  JWHennekens  CH Severe mental illness and risk of cardiovascular disease.  JAMA 2007;298 (15) 1794- 1796PubMedGoogle ScholarCrossref
41.
Marder  SREssock  SMMiller  AL  et al.  Physical health monitoring of patients with schizophrenia.  Am J Psychiatry 2004;161 (8) 1334- 1349PubMedGoogle ScholarCrossref
42.
Sikka  RXia  FAubert  RE Estimating medication persistency using administrative claims data.  Am J Manag Care 2005;11 (7) 449- 457PubMedGoogle Scholar
43.
 Seroquel labelling with pediatric indication.  AstraZeneca International Web site. http://www1.astrazeneca-us.com/pi/Seroquel.pdf. Accessed February 8, 2010Google Scholar
44.
 Zyprexa labelling with pediatric indication.  Eli Lilly and Company Web site. http://www.zyprexa.com/index.jsp. Accessed February 8, 2010Google Scholar
45.
Kuehn  BM FDA panel OKs 3 antipsychotic drugs for pediatric use, cautions against overuse.  JAMA 2009;302 (8) 833- 834Google ScholarCrossref
46.
Daniels  SRGreer  FRCommittee on Nutrition, Lipid screening and cardiovascular health in childhood.  Pediatrics 2008;122 (1) 198- 208PubMedGoogle ScholarCrossref
47.
American Diabetes Association, Standards of medical care in diabetes—2008.  Diabetes Care 2008;31 ((suppl 1)) S12- S54PubMedGoogle ScholarCrossref
48.
American Diabetes Association, Standards of medical care in diabetes—2009.  Diabetes Care 2009;32 ((suppl 1)) S13- S61PubMedGoogle ScholarCrossref
49.
Ader  MGarvey  WTPhillips  LS  et al.  Ethnic heterogeneity in glucoregulatory function during treatment with atypical antipsychotics in patients with schizophrenia.  J Psychiatr Res 2008;42 (13) 1076- 1085PubMedGoogle ScholarCrossref
50.
Correll  CU Monitoring and management of antipsychotic-related metabolic and endocrine adverse events in pediatric patients.  Int Rev Psychiatry 2008;20 (2) 195- 201PubMedGoogle ScholarCrossref
51.
Goodwin  RGould  MSBlanco  COlfson  M Prescription of psychotropic medications to youths in office-based practice.  Psychiatr Serv 2001;52 (8) 1081- 1087PubMedGoogle ScholarCrossref
52.
Cooper  WOArbogast  PGDing  HHickson  GBFuchs  DCRay  WA Trends in prescribing of antipsychotic medications for US children.  Ambul Pediatr 2006;6 (2) 79- 83PubMedGoogle ScholarCrossref
53.
Ogden  CLCarroll  MDCurtin  LRLamb  MMFlegal  KM Prevalence of high body mass index in US children and adolescents, 2007-2008.  JAMA 2010;303 (3) 242- 249PubMedGoogle ScholarCrossref
54.
Morrato  EHNewcomer  JWKamat  SBaser  OHarnett  JCuffel  B Metabolic screening after the American Diabetes Association's consensus statement on antipsychotic drugs and diabetes.  Diabetes Care 2009;32 (6) 1037- 1042PubMedGoogle ScholarCrossref
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