Development of a case-mix funding system for adults with combined vision and hearing loss
1 Department of Kinesiology and Physical Education, Wilfrid Laurier University, 75 University Ave. W., Waterloo, ON, N2L 3C5, Canada
2 School of Public Health and Health Systems, University of Waterloo, 200 University Ave. W., Waterloo, ON, N2L 3G1, Canada
BMC Health Services Research 2013, 13:137 doi:10.1186/1472-6963-13-137Published: 15 April 2013
Adults with vision and hearing loss, or dual sensory loss (DSL), present with a wide range of needs and abilities. This creates many challenges when attempting to set the most appropriate and equitable funding levels. Case-mix (CM) funding models represent one method for understanding client characteristics that correlate with resource intensity.
A CM model was developed based on a derivation sample (n = 182) and tested with a replication sample (n = 135) of adults aged 18+ with known DSL who were living in the community. All items within the CM model came from a standardized, multidimensional assessment, the interRAI Community Health Assessment and the Deafblind Supplement. The main outcome was a summary of formal and informal service costs which included intervenor and interpreter support, in-home nursing, personal support and rehabilitation services. Informal costs were estimated based on a wage rate of half that for a professional service provider ($10/hour). Decision-tree analysis was used to create groups with homogeneous resource utilization.
The resulting CM model had 9 terminal nodes. The CM index (CMI) showed a 35-fold range for total costs. In both the derivation and replication sample, 4 groups (out of a total of 18 or 22.2%) had a coefficient of variation value that exceeded the overall level of variation. Explained variance in the derivation sample was 67.7% for total costs versus 28.2% in the replication sample. A strong correlation was observed between the CMI values in the two samples (r = 0.82; p = 0.006).
The derived CM funding model for adults with DSL differentiates resource intensity across 9 main groups and in both datasets there is evidence that these CM groups appropriately identify clients based on need for formal and informal support.