mmpc - National Indian Health Board

Age and Sex
Methods
Medical care payments increase with age, and yet the differences in age of populations among AIAN
groups and between AIAN and Whites cannot be changed unless a Medicaid eligibility policy change
occurs affecting age groups differently. Age can be coded continuously (in years), or in intervals (5
or 10 year age cohorts). Usually an analytical definition is used to avoid unnecessarily large
numbers of interval groups: irregular intervals are used to define age groups with substantially
higher payments than the immediate younger group. We will test the use of a Continuous Age
variable and an analytical Interval Age Group variable. The adjustment that explains most of the
variation in the Mean Total Payment will be used in the Core Model.
Medical care payments are generally higher for women than men when the services include
reproductive care, and especially higher in Medicaid for women who are pregnant or give birth
during the year. Alternatives for gender determinants are 1) Male, Female pregnant, & Female non-
Pregnant; or 2) Male, Female, with adjustment for pregnancy and childbirth services in the Service
Coverage variables. The adjustment that explains most of the variation in the Mean Total Payment
will be used in the Core Model.
Disability as Basis of Medicaid Eligibility and Dual Eligibility with Medicare
Medical care payments are generally higher for blind or disabled individuals. We provided a list of
the blind and disabled eligibility categories for Medicaid and for Dual Eligibility with Medicare in
AIAN Medicaid Program and Policy Statistics: Summary Report, 2009 . The proportions of AIAN
Medicaid eligibles who are Disabled or Dual eligibles in each IHS Area are documented in AIAN
Medicaid Program and Policy Data, 2010 .
Risk Adjustment: Morbidity
Medical care costs rise as the number and severity of medical conditions increase. To determine if
there are differences in payments not related to illness requires adjusting costs for differences in the
morbidity burden among the study groups. This is possible with Medicaid claims tested Risk
Adjustment software systems (Winkelman and Dammler, 2008). We used the Adjusted Clinical
Groups® (ACG) system developed at The Johns Hopkins University to characterize the concurrent
morbidity burden in the AIAN and White Medicaid populations in our study for the time period of
the claims (http://www.acg.jhsph.org). With this software demographic, diagnostic and procedure
code combinations on 12 continuous months of claims and encounter records were reduced to a
fixed number of health status categories called Aggregated Diagnosis Groups.
The ACG system captures interrelationships between co-occurring morbidities that pose the greatest
demands for health care resources. ACG actuarial cells are based on Aggregated Diagnosis Groups
that are similar in terms of severity and likelihood of persistence of the health condition over time.
Diagnostic codes are assigned to one of the 32 clusters of Aggregated Diagnosis Groups. Since
individuals can have more than one diagnosis, they may have more than one Aggregated Diagnosis
Group. Individual diseases or conditions are placed into a single Aggregated Diagnosis Groups based
14