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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.
Quality measures must balance the use of currently available data with ideal measure components. Given the importance of improving the quality of care, measures with minimally acceptable evidence may be used; however, it is imperative that these suboptimal measures evolve as health data change and new data, such as electronic medical records, become more widely available.49 We find that the current ADHD quality measure lacks specific information to guide state public health insurance agencies on where to target interventions to improve the care. For example, failure to meet this measure may be related to a variety of factors, including access to care, medication adherence, scheduling constraints, or attitudes toward ADHD among parents and children. Furthermore, the lack of measurable outcomes that are relevant on a clinical and/or population scale further inhibit the usefulness of this measure. To improve the usability of this measure, we recommend development of additional components of the measure that will allow for more specific feedback to agencies to improve care and demonstrate a benefit of care consistent with the measure.

Limitations

Our assessment should be interpreted in light of its limitations. First, a single state analysis may not be representative of the experiences of other states or agencies with different policies and provider networks. Second, we acknowledge the possibility of selection bias in our analysis of injuries and healthcare costs among those meeting the measure. This could occur if children with more severe manifestations of ADHD are more or less likely to receive follow-up care and demonstrate adherence with medications. Alternatively, parents more likely to follow up may simply be high consumers of healthcare. Thus, estimates could be biased toward or away from the null. Finally, the measure calculation is based on treatment for ADHD, which may not be of uniform severity. Although claims data are unable to capture disease severity, we have controlled for previous mental health diagnoses and utilization in regressions for some risk adjustment. However, without more detailed information, we cannot be certain to have controlled for confounding by disease severity.

CONCLUSIONS

Monitoring the delivery and quality of care under state public health insurance agencies is an important process. State reporting of quality measures, including this measure for ADHD, has not yet been tied to incentives for improvement or penalties for performance failure. Before this can occur, thorough validation of all measures within the Child Core Set is necessary to ensure fairness of interagency comparisons. Assessment of the measures to ensure public health and clinical relevance is crucial, and as such, we found several concerns with the current ADHD measure reported by public health insurance agencies. Therefore, we recommend further empirically driven revisions to the measure. We recommend an improved understanding of the details behind why children fail to meet the measure, as well as improving clinical process and outcome metrics in order to better measure the true quality of ADHD treatment among publicly insured children. 

Author Affiliations: Department of Health Care Organization & Policy and Lister Hill Center for Health Policy, School of Public Health, University of Alabama at Birmingham (JB, DJB, MLK, BS), Birmingham, AL; Department of Health Policy & Management, School of Public Health, Texas A&M University (MAM), College Station, TX; Alabama Department of Public Health, Bureau of Children’s Health Insurance (CC), Montgomery, AL; Department of Health Policy and Management, Richard M. Fairbanks School of Public Health, Indiana University-Purdue University Indianapolis (NM), Indianapolis, IN.

Source of Funding: This work was supported by the Alabama Department of Public Health, Bureau of Children’s Health Insurance. The funding agency maintains the data used for this study, but the analysis and interpretation are the authors’ own.

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (DJB, JB, CC, MLK, NM, BS); acquisition of data (JB, CC, MLK, NM); analysis and interpretation of data (JB, MLK, BS); drafting of the manuscript (JB); critical revision of the manuscript for important intellectual content (DJB, CC, MLK, NM, BS); statistical analysis (JB, MLK); provision of patients or study materials (CC); obtaining funding (JB, CC); administrative, technical, or logistic support (MLK); and supervision (NM).

Address Correspondence to: Justin Blackburn, PhD, Assistant Professor, Department of Health Care Organization & Policy, University of Alabama at Birmingham, 1720 2nd Ave S, RPHB 330, Birmingham, AL 35294. E-mail: jblackburn@uab.edu.
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