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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
The Challenge of Paying for Cost-Effective Cures
Patricia J. Zettler, JD, and Erin C. Fuse Brown, JD, MPH
An Expanded Portfolio of Survival Metrics for Assessing Anticancer Agents
Jennifer Karweit, MS; Srividya Kotapati, PharmD; Samuel Wagner, PhD; James W. Shaw, PhD, PharmD, MPH; Steffan W. Wolfe, BA; and Amy P. Abernethy, MD, PhD
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,
Patient-Centered Care: Turning the Rhetoric Into Reality
Joel S. Weissman, PhD; Michael L. Millenson, BA; and R. Sterling Haring, DO, MPH
The Effect of Massachusetts Health Reform on Access to Care for Medicaid Beneficiaries
Laura G. Burke, MD, MPH; Thomas C. Tsai, MD, MPH; Jie Zheng, PhD; E. John Orav, PhD; and Ashish K. Jha, MD, MPH
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Joanna P. MacEwan, PhD; Wes Yin, PhD; Satyin Kaura, MSci, MBA; and Zeba M. Khan, PhD
Electronic Health Records and the Frequency of Diagnostic Test Orders
Ibrahim Hakim, BBA; Sejal Hathi, BS; Archana Nair, MS; Trishna Narula, MPH; and Jay Bhattacharya, MD, PhD
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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.
We calculated the CMS measure, and reported it as 2 rates: rate 1, the Initiation Phase, and rate 2, C&M Phase (Table 1). For rate 1, we identified 10,822 eligible IPSDs among 9693 children who met the definition of age, negative medication history, continuous enrollment through 30 days, and no mental health/substance abuse hospitalizations, of whom 4142 (38%) had a follow-up within 30 days. Of the 4142 IPSDs meeting rate 1, 2897 IPSDs among 2801 children met the continuous enrollment requirements for calculation of rate 2. Of those, 1083 (37%) had 2 additional follow-up visits and 210 days of medication coverage during the C&M Phase (days 31-300 from IPSD), thereby fulfilling the measure criteria. In our expanded denominator, we identified 7615 IPSDs among 6924 children that met all the measure eligibility requirements for rate 1 and were continuously enrolled for a minimum of 300 days. For this specification, the same 1083 IPSDs that met rate 2 were the numerator, but 4718 additional IPSDs were included despite not receiving an initial follow-up visit within 30 days, which was a requirement for the rate 2 denominator. Allowing an additional 15 days would increase rate 1 by 13 percentage points and increase the number of IPSDs eligible for rate 2 by 1421.

The reasons for failing to meet the measure are described for the expanded denominator of IPSDs (Table 1). Although 691 children were included with multiple IPSDs ranging from 2 (n = 429; 62%) to 7 (n = 1; 0.1%), henceforth we describe the unit of analysis as “children,” rather than IPSDs, for ease of interpretation. The lack of complete medication coverage was a primary reason for failing to meet the measure (65%). However, the majority of children continued to file ADHD medication claims beyond the initial prescription (90%), and 60% did so for at least 150 days.

Characteristics of children meeting and not meeting the measure are shown in Table 2. Among black children eligible for measure calculation, 9.8% met the measure compared with 15.8% of white children. Otherwise, characteristics between children meeting and not meeting the measure did not differ substantially.

Children meeting the measure were observed to have a greater frequency of ED visits (26.1%) than those who did not (21.9%) (P = .002), but they were not significantly more likely to have an ED- or inpatient-treated injury: 14.4% versus 12.8%, respectively (P = .154) (Table 3). Mean annual prescription drug costs were higher among children meeting the measure ($1678 vs $916; P <.001), as were medical costs, excluding pharmacy claims ($1698 vs $1160; P <.001), and therefore, overall expenditures ($3376 vs $2076; P <.001).

After adjustment for covariates in the logit model, the likelihood of meeting the measure declined monotonically with age (Table 4). Relative to 6-year-olds, 12-year-old children were 13.0 percentage points less likely to meet the measure (P <.001). Children with a prior mental health diagnosis were 5.8 percentage points more likely to meet the measure (P <.001). Compared with white children, black children were 5.5 percentage points less likely to meet the measure (P <.001). Meeting the measure was associated with a 2.2 percentage point–increased likelihood of an ED visit or hospitalization for an injury (P = .032). Similarly, meeting the measure was associated with a 4.3 percentage point–increased likelihood of an ED visit during the measurement year (P = .001). We observed no relationship between meeting the measure and hospitalization (P = .214). In an additional analysis of 2019 children who had an additional year of follow-up after completion of the measure calculation, there were no statistically significant differences observed in the rates of injury, ED visits, or hospitalizations between those meeting versus not meeting the measure (available on request).

The average annual predicted total expenditures per child were $2261 for the measurement year (eAppendix Table). Children meeting the measure had total expenditures that were $909 higher (P <.001). In particular, prescription drug expenditures were $514 higher and medical expenditures were $375 higher (P <.001 for each) for children meeting the measure.


We used Alabama CHIP data from 1999 to 2012 to report trends in eligibility for inclusion in the CMS measure; disparities in measure adherence by age, race/ethnicity, and income level; sensitivity of meeting the measure to the definitions of its components; and the association with injuries and health expenditures. Our historical calculations reported here are slightly lower than other states’ most recently reported data, including those of Alabama.32 Using these data, we concluded there are substantial concerns with the measure in its current state based on the NQF major criteria for measure evaluation: importance, scientific acceptability, feasibility, and usability.28 Specifically, we found the measure lacks sufficient details, which limits its usefulness as a tool to improve ADHD treatment among publicly insured children. We present 5 main conclusions from this analysis, which may be useful in improving the measure.

First, the results indicate that only a small fraction of children with ADHD medications are eligible for inclusion in the measure, and that number is declining. In recent years, more than 10% of ALL Kids enrollees received ADHD medications, yet less than 2% of enrollees were eligible for measure calculation. Although the percent of enrollees aged 6 to 12 years has been increasing, a faster rate of growth is observed among children over age 12, and they are precluded from the measure. Additionally, the continuous enrollment requirement precluded many children from eligibility in the calculation of the measure. During this time period, the immediate renewal rate in ALL Kids was approximately 60%.33 Although program retention may vary among states, one objective of the Child Core Set Measures is to enhance state-by-state comparisons.34 Given that many children receiving ADHD medication are not eligible for calculation of the measure based on age or enrollment criteria, there are concerns that these children are not representative of the typical child on ADHD medications or that quality of care is not measured equitably. Thus, the population is important, but the measure appears to lack generalizability to the intended population, and so its scientific acceptability to capture changes in overall performance in the care of ADHD among children is decreasing.

Second, there are systematic differences in the characteristics of children who meet the measure, including age, prior mental health diagnoses, and race/ethnicity. Racial/ethnic disparities within ADHD have been previously observed; specifically, minorities have different perceptions about symptoms,35 are less likely to be diagnosed with ADHD,36,37 and have lower rates of ADHD medication use.38 Future consideration of the quality of care for ADHD among children in minority and other subgroups is needed in order to increase the usability of the measure’s goal of improving quality equally among all publicly insured children.

Third, meeting the measure is sensitive to both the allowable medication coverage criterion and to the time window for the initial follow-up visit. Failing to meet the measure was driven by lack of medication coverage and lack of 30-day follow-up visits. The proportion of children who received a follow-up visit within weeks of the 30-day window increased the number of those who met the measure by as much as 20 percentage points, suggesting the measure fails to capture clinically relevant, albeit untimely, follow-up care.

Conversely, the number of children taking ADHD medications without an initial follow-up visit is concerning because medication therapy is optimized when dosing is properly managed.39-43 For children, the range of dosing needed to achieve therapeutic effects is broad.39 Therefore, titrating the stimulant medication dose is widely accepted to achieve the desired response while balancing adverse side effects.39-44 Although having a 30-day follow-up visit does not guarantee proper dosing, at least some contact with a medical provider may be beneficial in monitoring side effects, including the potentially serious ones like aggression or emotional instability.44 Concerns with this measure have been present since the initial draft of the Child Core Set.45 For example, providers were concerned that only an in-person, and separately billable, visit counted as a follow-up. The CMS technical manual does permit C&M Phase follow-up visits to be conducted via telephone; however, like many public health insurance agencies nationwide, ALL Kids does not reimburse providers for such telephone-based services.

Fourth, our analysis raises issues regarding the validity of the measure to capture clinically relevant and evidence-based care quality. Given that the negative medication history period is 120 days, some fraction of children are not completely naïve to ADHD medication. It is unclear whether these children need the same level of clinical monitoring as first-time users, but it is well known that many children take medication “holidays” coinciding with school breaks, such as during the summer.46 Although clinical guidance regarding planned medication holidays is lacking and the long- and short-term effects of them are not well understood,10 the measure does not adequately account for this behavior.43 Furthermore, children directed by a physician to discontinue medication after less than 210 days fail to meet the measure. Thus, we believe that simply identifying the rate of children meeting or not meeting the measure may be insufficient for quality improvement.

Finally, as with any administrative data measure, claims data are unable to capture the presence of symptoms or school performance, which are key outcomes of ADHD treatment. Using information available from claims data in accordance with the NQF feasibility criterion, we calculated health service utilization, costs, and injury rates among children meeting and not meeting the measure as an attempt to assess whether the care received under the measure confers any measurable benefit. These outcomes are associated with ADHD diagnosis,3-6,47,48 but it is an open question whether injury rates are expected to decline among treated versus untreated children. We observed that injury rates were higher for those meeting the measure, as were total costs, largely driven by medical and pharmaceutical costs associated with measure criteria, including ADHD medication.

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