Incorporating Caregiver and Family Effects in Economic Evaluations of Child Health

Patients are not isolated individuals, but have friends and families who care about them and, often, care for them. This is especially true for children. Parents' well-being and quality of life is likely to be affected by the illness of their child, especially when the illness is severe, even if they do not provide informal care. This chapter deals with the inclusion of the costs and effects on caregivers and other family members in economic evaluations, with a focus on informal care. Key topics are illustrated using data from previous studies. In the first part of the chapter, the burden of providing informal care to children and its valuation for economic evaluations is described. Next, attention is turned to family effects. Finally, recommendations and conclusions are provided for incorporating caregiver and family effects in economic evaluations of child health

Preference-based health-related quality-of-life qutcomes in children with autism spectrum disorders: A comparison of generic instruments

Background: Cost-effectiveness analysis of pharmaceutical and other treatments for children with autism spectrum disorders (ASDs) has the potential to improve access to services by demonstrating the value of treatment to public and private payers, but methods for measuring QALYs in children are under-studied. No cost-effectiveness analyses have been undertaken in this population using the cost-per-QALY metric. Objective: This study describes health-related quality-of-life (HR-QOL) outcomes in children with ASDs and compares the sensitivity of two generic preference-based instruments relative to ASD-related conditions and symptoms. Methods: The study design was cross-sectional with prospectively collected outcome data that were correlated with retrospectively assessed clinical information. Subjects were recruited from two sites of the Autism Treatment Network (ATN) in the US: a developmental centre in Little Rock, Arkansas, and an outpatient psychiatric clinic at Columbia University Medical Center in New York. Children that met Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) criteria for an ASD by a multidisciplinary team evaluation were asked to participate in a clinical registry. Families of children with an ASD that agreed to be contacted about participation infuture research studies as part of the ATN formed the sampling frame for the study. Families were included if the child with the ASD was between 4 and 17 years of age and the family caregiver spoke English. Eligible families were contacted by mail to see if they would be interested in participating in the study with 150 completing surveys. HR-QOL outcomes were described using the Health Utilities Index (HUI) 3 and the Quality of Well-Being Self- Administered (QWB-SA) scale obtained by proxy via the family caregiver. Results: Children were diagnosed as having autistic disorder (76%), pervasive developmental disorder-not otherwise specified [PDD-NOS] (15%), and Asperger's disorder (9%). Average HUI3 and QWB-SA scores were 0.68 (SD 0.21, range 0.07-1) and 0.59 (SD 0.16, range 0.18-1), respectively. The HUI3 score was significantly correlated with clinical variables including adaptive behaviour (ρ = 0.52; p < 0.001) and cognitive functioning (ρ = 0.36; p < 0.001). The QWB-SA score had weak correlation with adaptive behaviour (ρ = 0.25; p < 0.001) and cognitive functioning (r = 0.17; p < 0.005). Change scores for the HUI3 were larger than the QWB-SA for all clinical measures. Scores for the HUI3 increased 0.21 points (95%CI 0.14, 0.29) across the first to the third quartile of the cognitive functioning measure compared with 0.05 (95% CI -0.01, 0.11) for the QWB-SA. Adjusted R2 values also were higher for the HUI3 compared with the QWB-SA across all clinical measures. Conclusions: The HUI3 was more sensitive to clinical measures used to characterize children with autism compared with the QWB-SA score. The findings provide a benchmark to compare scores obtained by alternative methods and instruments. Researchers should consider incorporating the HUI3 in clinical trials and other longitudinal research studies to build the evidence base for describing the cost effectiveness of services provided to this important population. Adi

169 Association of Asthma Specialty Care and Adverse Outcomes for Children Enrolled in the Arkansas Medicaid Program

OBJECTIVES/GOALS: Specialty care for asthmatic children should prevent adverse asthma outcomes. This study of children receiving care in the Arkansas Medicaid program used a comparative effectiveness research design to test whether allergy specialty care was associated with reduced adverse asthma outcomes. METHODS/STUDY POPULATION: Using the Arkansas All Payer Claims Database we studied Medicaid-enrolled children with asthma using a propensity score greedy nearest neighbor one-to-one matching algorithm. We matched children with (treatment) and without (comparison) an allergy specialist visit in 2018. The propensity score model included 26 covariates (demographic, clinical, and social determinants of health). Multivariable adjusted logistic regression was used to estimate adverse asthma events (AAE: emergency department visit or inpatient hospitalization with a primary or secondary diagnosis of asthma in 2019). RESULTS/ANTICIPATED RESULTS: We identified 3,031 children with an allergy specialist visit in 2018, and successfully propensity-score matched 2,910 of the treatment group with a non-allergy specialist visit comparison group. The rate of AAEs in 2019 was 9.5% for individuals with an allergy specialist visit versus 10.1% among those without a specialist visit (p=0.450). The adjusted regression analysis showed 20.3% lower rates of AAEs (aOR: 0.797; 95% Confidence Interval: 0.650, 0.977; p=0.029) in 2019 for children with an allergy specialist visit in 2018 compared to those that did not. DISCUSSION/SIGNIFICANCE: Utilizing allergy specialist care was associated with better asthma outcomes in our statewide study of Arkansas Medicaid-enrolled children with asthma. Asthma quality metrics based on guideline-based recommendations for allergy specialist care should be considered in population health management programs

Predicting pediatric severe asthma exacerbations: an administrative claims-based predictive model

Previous machine learning approaches fail to consider race and ethnicity and social determinants of health (SDOH) to predict childhood asthma exacerbations. A predictive model for asthma exacerbations in children is developed to explore the importance of race and ethnicity, rural-urban commuting area (RUCA) codes, the Child Opportunity Index (COI), and other ICD-10 SDOH in predicting asthma outcomes. Insurance and coverage claims data from the Arkansas All-Payer Claims Database were used to capture risk factors. We identified a cohort of 22,631 children with asthma aged 5–18 years with 2 years of continuous Medicaid enrollment and at least one asthma diagnosis in 2018. The goal was to predict asthma-related hospitalizations and asthma-related emergency department (ED) visits in 2019. The analytic sample was 59% age 5–11 years, 39% White, 33% Black, and 6% Hispanic. Conditional random forest models were used to train the model. The model yielded an area under the curve (AUC) of 72%, sensitivity of 55% and specificity of 78% in the OOB samples and AUC of 73%, sensitivity of 58% and specificity of 77% in the training samples. Consistent with previous literature, asthma-related hospitalization or ED visits in the previous year (2018) were the two most important variables in predicting hospital or ED use in the following year (2019), followed by the total number of reliever and controller medications. Predictive models for asthma-related exacerbation achieved moderate accuracy, but race and ethnicity, ICD-10 SDOH, RUCA codes, and COI measures were not important in improving model accuracy.</p

Preference-Based Health-Related Quality-of-Life Outcomes in Children with Autism Spectrum Disorders

BACKGROUND: Cost-effectiveness analysis of pharmaceutical and other treatments for children with autism spectrum disorders (ASDs) has the potential to improve access to services by demonstrating the value of treatment to public and private payers, but methods for measuring quality adjusted life years (QALYs) in children are understudied. No cost-effectiveness analyses have been undertaken in this population using the cost per QALY metric. OBJECTIVE: This study describes health-related quality of life (HRQoL) outcomes in children with ASDs and compares the sensitivity of two generic preference-based instruments relative to ASD-related conditions and symptoms. METHODS: The study design was cross-sectional with prospectively collected outcome data that was correlated with retrospectively assessed clinical information. Subjects were recruited from two sites of the Autism Treatment Network (ATN): a developmental center in Little Rock, Arkansas, and an outpatient psychiatric clinic at Columbia University Medical Center in New York, NY. Children that met DSM-IV criteria for an ASD by a multi-disciplinary team evaluation were asked to participate in a clinical registry. Families of children with an ASD that agreed to be contacted about participation in future research studies as part of the ATN formed the sampling frame for the study. Families were included if the child with the ASD was between 4 and 17 years of age and the family caregiver spoke English. Eligible families were contacted by mail to see if they would be interested in participating in the study with N=150 completing surveys. HRQoL outcomes were described using the Health Utilities Index Mark III (HUI-3) and the self-administered Quality of Well-Being scale (QWB-SA) obtained by proxy via the family caregiver. RESULTS: Children were diagnosed as having autistic disorder (76%), pervasive developmental disorder (PDD-NOS) (15%), and Asperger's disorder (9%). Average HUI-3 and QWB-SA scores were 0.68 (SD=0.21, range of 0.07 to 1) and 0.59 (SD=0.16, range of 0.18 to 1) respectively. The HUI-3 score was significantly correlated with clinical variables including adaptive behavior (ρ=0.52; p<0.001) and cognitive functioning (ρ=0.36; p<0.001). The QWB-SA score had weak correlation with adaptive behavior (ρ=0.25; p<0.001) and cognitive functioning (ρ=0.17; p<0.005). Change scores for the HUI3 were larger than the QWB-SA for all clinical measures. Scores for the HUI3 increased 0.21 (95% CI: 0.14–0.29) points across the first to third quartile of the cognitive functioning measure compared to 0.05 (95% CI: −0.01–0.11) for the QWB-SA. Adjusted R(2)'s also were higher for the HUI3 compared to the QWB-SA across all clinical measures. CONCLUSIONS: The HUI-3 was more sensitive to clinical measures used to characterize children with autism compared to the QWB-SA score. The findings provide a benchmark to compare scores obtained by alternative methods and instruments. Researchers should consider incorporating the HUI-3 in clinical trials and other longitudinal research studies to build the evidence base for describing the cost-effectiveness of services provided to this important population

Predicting Health Utilities for Children With Autism Spectrum Disorders

Comparative effectiveness of interventions for children with autism spectrum disorders (ASDs) that incorporates costs is lacking due to the scarcity of information on health utility scores or preference-weighted outcomes typically used for calculating quality-adjusted life years (QALYs). This study created algorithms for mapping clinical and behavioral measures for children with ASDs to health utility scores. The algorithms could be useful for estimating the value of different interventions and treatments used in the care of children with ASDs. Participants were recruited from two Autism Treatment Network sites. Health utility data based on the Health Utilities Index Mark 3 (HUI3) for the child were obtained from the primary caregiver (proxy-reported) through a survey (N = 224). During the initial clinic visit, proxy-reported measures of the Child Behavior Checklist, Vineland II Adaptive Behavior Scales, and the Pediatric Quality of Life Inventory 4.0 (start measures) were obtained and then merged with the survey data. Nine mapping algorithms were developed using the HUI3 scores as dependent variables in ordinary least squares regressions along with the start measures, the Autism Diagnostic Observation Schedule, to measure severity, child age, and cognitive ability as independent predictors. In-sample cross-validation was conducted to evaluate predictive accuracy. Multiple imputation techniques were used for missing data. The average age for children with ASDs in this study was 8.4 (standard deviation = 3.5) years. Almost half of the children (47%) had cognitive impairment (IQ &lt; 70). Total scores for all of the outcome measures were significantly associated with the HUI3 score. The algorithms can be applied to clinical studies containing start measures of children with ASDs to predict QALYs gained from interventions