Medicaid

The population information (e.g., enrollment, beneficiary characteristics) is included in the person summary files under the MAX library. Each beneficiary is uniquely identified by MSIS_ID, which is used to cross-reference data for each beneficiary across all MAX data files. Demographic characteristics, such as state, age, sex and race, are also available in person summary files for analysis. This population is the basis for calculating rates, and also serves as the sample denominator when calculating prevalence rates.

Below we describe the analytic steps for defining the study population:

1. Using the MAXDATA_PS _201X dataset, group the beneficiaries by MSIS_ID.
2. For each month, obtain active (i.e., living) beneficiaries that had FFS or MC coverage.
   For example, for January 201X,
   • Select beneficiaries who have FFS coverage (MAX_ELG_CD_MO_01 in “16”) or have MC (MAX_ELG_CD_MO_01 in “01”-“15”);
   • Excluded beneficiaries who are not enrolled in Dental Plan or Behavioral Plan (MAX_ELG_CD_MO_01 in “02”, “03”, “14”)
3. Using the twelve monthly enrollment information from previous step, combine beneficiaries into three groups:
   1. FFS only: have FFS enrollment for at least one month without MC enrollment
   2. MC only: have MC enrollment for at least one month without FFS enrollment
   3. FFS & MC: have combined MC and FFS enrollment (for at least two months)
4. Create a new variable “enrollment_type” to indicate the length of enrollment and split the groups by length of enrollment and: If enrolled in a given calendar year, then set enrollment_type = “full” , else set enrollment_type = “partial”

We found that most Medicaid patients have unknown race or ethnicity. Because we cannot control for any bias associated with race information being present in MAX data, we do not publicly release Medicaid MAX summary results by race or ethnicity.

A detailed description of the analytical steps is described in the report “VEHSS Claims & Registry Data Analysis Plan pdf icon[PDF – 579 KB]external icon.” Full analysis documentation is included in the “VEHSS Medicare Data Report pdf icon[PDF – 1.5 MB]external icon.”

Service Utilization

VEHSS analyzes the proportion of Medicaid enrollees who had at least one Medicaid claim indicating a specified service, as listed below in Table 1.

Medical Diagnoses

VEHSS categorizes diagnosis codes into medical diagnosis categories and subgroups. Individual Medicare beneficiaries were assigned to diagnosis categories and subgroups based on International Classification of Diseases (ICD) 9 (permissible in 2014) or ICD10 diagnosis codes on any patient claim during the year of observation. As reported in the “VEHSS Diagnosis Code Categories and Crosswalk,” we identified all eye and vision related ICD10 diagnosis codes, and organized them into a two-level categorization structure, including 17 categories and 78 subgroups, as listed in Table 2.