Association of Quality Metrics and Adverse Outcomes for Children with Asthma
Summary
Asthma is the most common chronic condition among children in the United States, affecting more than 5.5 million U.S. children, and roughly 1 in 10 children in Arkansas. Asthma management guidelines focus on important asthma outcome measures — asthma control, asthma exacerbations, and quality of life. Identifying linkages between asthma control measures and asthma exacerbations can lead to new algorithms for predicting at risk children. This project uses the Arkansas All-Payer Claims Database (APCD) to explore relationships between asthma quality metrics and adverse outcomes in children and then build predictive algorithms to identify children at risk of an adverse outcome based on poor quality care.
The APCD is a large-scale claims database from various payers operating in Arkansas that includes medical and pharmacy records. Claims from the Arkansas APCD will be used to develop asthma quality metrics and asthma outcomes in children with an ultimate goal of identifying at-risk children. Measurement of care quality uses the National Committee for Quality Assurance HEDIS system. In particular, the HEDIS care quality measure for asthma, the asthma medication ratio (AMR), can be used to measure asthma control. The AMR is a ratio of asthma controller medications to total number of asthma medications where a threshold of <0.5 identifies patients with poorly controlled asthma. We will use the AMR to assess relationships between asthma control and asthma exacerbations using novel methods.
The major study hypothesis is that children with better quality metrics will be less likely to experience an adverse outcome. A study sample of children 5 to 18 years of age with a HEDIS defined condition of persistent asthma will be constructed from the APCD who have at least 24-consecutive months of insurance enrollment. Patients with chronic obstructive pulmonary disease, emphysema, cystic fibrosis, or acute respiratory failure will be excluded. To improve causal interpretation, quality assessment will occur in a premeasurement period (calendar year 2018) and outcomes assessment will occur in the following year (calendar year 2019). Finally, the research team will identify factors accounting for a low AMR to identify at-risk children. These algorithms have the potential to be commercialized.
Specific Aim 1: Examine the association between asthmatic quality of care and adverse outcomes using the APCD where asthma-specific acute inpatient admissions, urgent care visits, or emergency department (ED) visits, and/or asthma exacerbations defined by oral corticosteroid (OCS) burst (prescription for 3 or more days and <14 days) define adverse events.
Hypothesis 1: Children with AMR <0.5 will have more asthma-specific adverse outcomes compared to children with a higher AMR.
Specific Aim 2: Predict children at risk for AMR <0.5 across insurance payer classes using traditional and machine learning approaches.
Hypothesis 1: Predictive models can be developed to identify children at risk of poor quality care leading to excessive adverse events and higher costs of care within classes of insurance. These predictive models have a high probability of commercialization.
Keywords:
- Translational Research
- Health Care
- Medication
- Patient
- adverse events
- Community
- machine learning