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The American Journal of Managed Care January 2017
Alignment of Breast Cancer Screening Guidelines, Accountability Metrics, and Practice Patterns
Tracy Onega, PhD; Jennifer S. Haas, MD; Asaf Bitton, MD; Charles Brackett, MD; Julie Weiss, MS; Martha Goodrich, MS; Kimberly Harris, MPH; Steve Pyle, BS; and Anna N. A. Tosteson, ScD
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The Social Value of Childhood Vaccination in the United States
Tomas J. Philipson, PhD; Julia Thornton Snider, PhD; Ayman Chit, PhD; Sarah Green, BA; Philip Hosbach, BA; Taylor Tinkham Schwartz, MPH; Yanyu Wu, PhD; and Wade M. Aubry, MD
Value-Based Payment in Implementing Evidence-Based Care: The Mental Health Integration Program in Washington State
Yuhua Bao, PhD; Thomas G. McGuire, PhD; Ya-Fen Chan, PhD; Ashley A. Eggman, MS; Andrew M. Ryan, PhD; Martha L. Bruce, PhD, MPH; Harold Alan Pincus, MD; Erin Hafer, MPH; and Jürgen Unützer, MD, MPH, MA
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An Assessment of the CHIP/Medicaid Quality Measure for ADHD
Justin Blackburn, PhD; David J. Becker, PhD; Michael A. Morrisey, PhD; Meredith L. Kilgore, PhD; Bisakha Sen, PhD; Cathy Caldwell, MPH; and Nir Menachemi, PhD, MPH

An Assessment of the CHIP/Medicaid Quality Measure for ADHD

Justin Blackburn, PhD; David J. Becker, PhD; Michael A. Morrisey, PhD; Meredith L. Kilgore, PhD; Bisakha Sen, PhD; Cathy Caldwell, MPH; and Nir Menachemi, PhD, MPH
Concerns regarding the quality measure for attention-deficit/hyperactivity disorder may limit its usefulness and its ability to promote improvement efforts.

Objectives: We analyzed a standard children’s quality measure for attention-deficit/hyperactivity disorder (ADHD) using data from a single state to understand the characteristics of those meeting the measure, potential barriers to meeting the measure, and how meeting the measure affected outcomes.

Study Design: Retrospective study using claims from Alabama’s Children’s Health Insurance Program from 1999 to 2012.

Methods: We calculated the quality measure for ADHD care, as specified within CMS’ Child Core Set and with an expanded denominator. We described the eligible population meeting the measure, assessed potential barriers, and measured the association with health expenditures using logit regressions and log-Poisson models.

Results: Among those receiving ADHD medication, 11% of enrollees were eligible for annual measure calculation during our study period. Calculated as specified by CMS, 38% of enrollees met the measure. Using an expanded denominator of 7615 eligible medication episodes, 14% met all aspects of the measure. Primary reasons for failing to meet the measure were lacking medication coverage (64%) and lacking a follow-up visit within 30 days (62%). The rate of meeting the measure decreased with age and was lower for black enrollees. Health service utilization and costs were greater among children meeting the measure.

Conclusions: Too few children are eligible for inclusion, and systematic differences exist among those who meet the measure. The measure may be sensitive to arbitrary criteria while missing potentially relevant clinical care. Refinements to the measure should be considered to improve generalizability to all children with ADHD and improve clinical relevance. States must consider additional analyses to direct quality improvement.

Am J Manag Care. 2017;23(1):e1-e9
Takeaway Points

We investigated measure adherence, barriers to adherence, and the association with health expenditures. We found: 
  • A small fraction of children treated with attention-deficit/hyperactivity disorder medication are eligible for inclusion, and that number is declining. 
  • Population characteristics are associated with meeting the ADHD quality measure and should be considered when making comparisons. 
  • Alternative but reasonable specifications for the measure can generate different results. 
  • The measure may fail to capture clinically relevant and evidence-based care quality. 
  • The measure fails to capture symptom relief, school performance, and other key outcomes.
Attention-deficit/hyperactivity disorder (ADHD) is the most commonly diagnosed behavioral disorder among children and youth,1 at an estimated prevalence of 5% to 11%, and is a growing public health concern.2 Children with ADHD can be inattentive, disruptive, and impulsive, and have been shown to experience more injuries3-5 and incur higher overall healthcare costs.6-9 ADHD diagnosis and treatment is often parent- or teacher-initiated and typically consists of psycho-stimulant medications and/or behavior therapy—both of which require careful clinical monitoring to ensure proper dosing and adherence.10-12 Currently, there is no consensus on the relevant population-based outcomes of ADHD treatment to assess quality of care.13 Clinicians and parents have individual treatment goals,14 and previous studies have focused on symptom assessment from the child and their parents and teachers.15,16

As part of the Children’s Health Insurance Program (CHIP) Reauthorization Act,17 an initial core set of quality measures for CHIPs and state Medicaid agencies was developed to foster quality improvement efforts over time.18-27 Evaluation of these quality measures, which are collectively known as the Child Core Set, is ongoing, with measures being retired if they fail to meet criteria of importance, scientific acceptability, feasibility, and usability, as set forth by the National Quality Forum (NQF).28 Among these measures is one for follow-up care for ADHD, calculated from administrative data. The measure is designed to capture the extent to which publicly insured children (ie, CHIP, Medicaid) newly treated for ADHD have adequate medication adherence and follow-up visits indicative of physician monitoring. A recent review of child mental health quality measures by Zima and colleagues gave a “D” rating to this measure, asserting there was no conclusive supporting evidence.29 Similar measures are a focus of the Agency for Healthcare Research and Quality and CMS' Pediatric Quality Measures Program.30

Our purpose was to describe the ADHD quality measure in the Child Core Set using data from a single state. First, we identified the proportion of children meeting the measure. We evaluated whether population characteristics, such as race/ethnicity, age, or rural status, were challenges to meeting the measure. Second, we identified potential barriers to meeting the measure. We measured the specific components that were the most difficult to meet, such as timeliness of follow-up visits, and we explored whether exclusion criteria would be useful to understand the measured population. Third, we measured the association of ADHD treatment with selected outcomes. Although cognizant of the difficulties of accurately capturing the effectiveness of ADHD treatment using administrative data, we determined whether meeting the measure conferred any benefit on outcomes, such as injury rates and healthcare costs.


Data and Study Population

This study used 1999 to 2012 claims data from Alabama’s stand-alone CHIP program, “ALL Kids,” to calculate the measure of ADHD quality treatment. Throughout this time period, ALL Kids coverage was available in 12-month enrollment periods to Alabama children younger than 19 years with family incomes between 100% and 200% of the federal poverty level (FPL). In October 2009, income eligibility was expanded to include up to 300% FPL. Annual premiums and co-payments for services were determined primarily by FPL category. Family incomes at 100% to 150% FPL were categorized as the “low-fee group.” Depending on year, this group faced co-payments ranging from $0 to $3 for a physician visit. Those children at 150% to 200% FPL (“fee group”) faced co-payments of $5. Children received exemption from cost sharing (“no-fee group”) if they met federal criteria, such as Native American heritage. The October 2009 expanded-eligibility group (“expansion group”) had incomes of 200% to 300% FPL and resembled the cost-sharing structure of the fee group.

Enrollee characteristics were derived from ALL Kids claims and enrollment data, which are maintained through a contract with Blue Cross/Blue Shield of Alabama, as well as from enrollment files provided by ALL Kids. Race/ethnicity was self-reported during the enrollment application. We grouped children into 3 racial/ethnic categories: white, black, and other (includes Hispanic/Latino children of any race/ethnicity). To identify children in rural areas, we used rural urban commuting area codes based on enrollees’ zip codes.

Construction of the ADHD Quality Measure

The ADHD Measure, “Measure ADD: Follow-up Care for Children Prescribed Attention-Deficit/Hyperactivity Disorder (ADHD) Medication,” was constructed according to the CMS technical manual as follows.18 First, children aged 6 to 12 years who received medications specified by the Healthcare Effectiveness Data and Information Set (HEDIS) for the treatment of ADHD were identified from claims data. Eligible children must have been continuously enrolled in the 120 days prior and 300 days following their initial ADHD prescription date (the index prescription start date [IPSD]) (eAppendix Figure [eAppendices available at]). This includes a minimum of 120 days prior to the IPSD without claims for ADHD medications (the “negative medication history”), 30 days after the IPSD (the Initiation Phase), and 300 days following the IPSD (the Continuation and Management Phase [C&M Phase]). Information about follow-up visits and medication use were used to determine whether children met the measure’s criteria.

The first component required a follow-up visit with a practitioner with prescribing authority within the first 30 days after the IPSD. The second component required at least 2 follow-up visits with practitioners with prescribing authority, as well as medication claims equating to at least 210 of 300 days (70%) with medication coverage. We calculated the measure for children with IPSDs from March 1, 1999, through December 31, 2011, in order to ensure complete observable follow-up time for the measure calculation. As specified by CMS, children were excluded if they received inpatient treatment for mental health or substance abuse at any point during follow-up.18 Additionally, we excluded 24 outlying children with greater than 38 mental health outpatient follow-up visits, representing extreme values of less than 1% of all children.


We calculated the number of children who failed to meet the specific components of the measure outlined in the CMS technical specification manual criteria. As specified, the measure was calculated and reported as 2 components with different denominators: rate 1 covering the first 30 days following the IPSD and rate 2 covering 300 days following the IPSD. Rate 2 is calculated only for those meeting rate 1 with a follow-up visit within 30 days. Although we calculated both measures as specified, we focused on children with 300 days of continuous enrollment after the IPSD (regardless of follow-up within 30 days) in order to comprehensively assess characteristics of meeting and not meeting the measure. Thus, our denominator included children with an IPSD who met the age requirement, negative medication history requirement, and mental health and substance abuse hospitalization exclusion criteria, and were continuously enrolled for 300 days.

We then determined which component of the measure was not met by holding the denominator constant and calculating the proportion of children that: 1) had no 30-day follow-up, 2) had <210 days of medication coverage, and 3) did not have 2 follow-up visits between days 31 and 300 of follow-up. For example, if an eligible child had no follow-up visit within 30 days, he or she did not meet the measure. However, we still determined if the child had medication coverage of 210 days or 2 follow-up visits in days 31 to 300 following the IPSD. Furthermore, we calculated alternative measure specifications to illustrate the sensitivity of the measure to its definitions. For example, although the Initiation Phase measure specifies follow-up within 30 days, we calculated rates at 45 and 60 days. Likewise, we calculated different medication coverage periods (ie, 210, 150, 90, and 30 days).

We used claims data to construct covariate and outcome variables, including health expenditures and injury-related utilization. We used primary International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) diagnosis codes from the HEDIS Mental Health Diagnosis Value Set to determine if a child had any mental health diagnoses during the 120-day negative medication history. We examined total expenditures and expenditures for emergency department (ED) visits, hospitalizations, all medical costs (excluding pharmacy claims), and pharmacy claims during the measurement period. Health expenditures were defined as payments made on behalf of ALL Kids, excluding co-payments. All expenditures were inflation-adjusted to 2012 dollars using the Consumer Price Index (all items).

ED visits were defined as claims with Revenue Center codes 450 to 452, 456, or 459, or Current Procedure Terminology–4 procedure codes from 99281 to 99285 that did not result in hospital admission. Hospitalization was defined by place of service codes. To identify children with injuries, we used primary ICD-9-CM diagnosis codes 800 to 999 in any position to create a binary indicator for injuries treated in the ED or inpatient setting. Outpatient injury visits are considered unreliable, and similar methods have been used for comparisons of ADHD treatment.31

Statistical Analysis

Bivariate comparisons of measure specifications used the 2-sample test of proportions. Expenditure category means were compared with t tests. Multivariable comparisons were estimated with logit regression models to predict differences in characteristics of meeting the measure as well as the association of meeting the measure with any injury, ED, or hospitalization, clustered at the individual level to account for correlated errors from children with multiple IPSDs. We report marginal effects, which represent the percentage point change in the likelihood of the outcome for each predictor, holding all others factors constant at their mean values. For health service expenditures, we estimated Poisson models with a log-link function to account for skewness of the data. Marginal effects from this model reflect the predicted incremental costs or savings resulting from meeting the measure.


Between 1999 and 2012, ALL Kids had an average enrollment of 61,251 children annually. The average annual rate of children with ADHD medication claims was 7.6%, and this increased from 1.7% (389 children) in 1999 to 11.1% (9604 children) in 2012 (Figure). Among children receiving ADHD medication each year, on average, 10.8% (544 children) were eligible for inclusion in measure calculation on the basis of age, negative medication history, continuous enrollment, and no mental health/substance abuse hospitalizations. Eligibility among children receiving ADHD medications declined over time from the peak of 17.0% in 2000 to 7.8% in 2012 (not shown).

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