Modeling the determinants of Medicaid home care payments for children with special health care needs: A structural equation model approach

Omolola E. Adepoju, Ph.D., M.P.H., Yichen Zhang, M.S., Charles D. Phillips, Ph.D., M.P.H.

Texas A&M University, School of Rural Public Health, Department of Policy and Management, 1266 TAMU, College Station, TX 77843, USA

Disability and Health Journal, Vol. 7, Issue 4, p426–432
Published online: June 7, 2014


The management of children with special needs can be very challenging and expensive.


To examine direct and indirect cost drivers of home care expenditures for this vulnerable and expensive population.


We retrospectively assessed secondary data on children, ages 4–20, receiving Medicaid Personal Care Services (PCS) (n = 2760). A structural equation model assessed direct and indirect effects of several child characteristics, clinical conditions and functional measures on Medicaid home care payments.


The mean age of children was 12.1 years and approximately 60% were female. Almost half of all subjects reported mild, moderate or severe ID diagnosis. The mean ADL score was 5.27 and about 60% of subjects received some type of rehabilitation services. Caseworkers authorized an average of 25.5 h of PCS support per week. The SEM revealed three groups of costs drivers: indirect, direct and direct + indirect. Cognitive problems, health impairments, and age affect expenditures, but they operate completely through other variables. Other elements accumulate effects (externalizing behaviors, PCS hours, and rehabilitation) and send them on a single path to the dependent variable. A few elements exhibit a relatively complex position in the model by having both significant direct and indirect effects on home care expenditures – medical conditions, intellectual disability, region, and ADL function.


The most important drivers of home care expenditures are variables that have both meaningful direct and indirect effects. The only one of these factors that may be within the sphere of policy change is the difference among costs in different regions.