CHIP reporting in the CPS.

OBJECTIVE: To assess the quality of the Current Population Survey's (CPS) Child Health Insurance Program (CHIP) data. DATA SOURCES: Linked 2000-2004 Medicaid Statistical Information System (MSIS) and the 2001-2004 CPS. DATA COLLECTION METHODS: Centers for Medicare & Medicaid Services provided the Census Bureau with its MSIS file. The Census Bureau linked the MSIS to the CPS data within its secure data analysis facilities. STUDY DESIGN: We compared responses to the CPS health insurance items with Medicaid and CHIP status according to the MSIS. PRINCIPAL FINDINGS: CHIP reporting in the CPS is unreliable. Only 10-30 percent of those with CHIP (but not Medicaid) report this type of coverage in the CPS. Many with CHIP report Medicaid coverage, so the reporting error for a Medicaid-CHIP composite is smaller, but still substantial. CONCLUSIONS: The quality of the CPS CHIP information renders it effectively unusable for health policy analysis. Analysts should consider using a Medicaid-CHIP composite for CPS-based analyses.

How does Medicaid-managed care impact reporting of Medicaid status?

OBJECTIVE: To resolve a conflict in the literature on whether Medicaid-Managed Care (MMC) impacts the Medicaid Undercount. DATA SOURCES/STUDY SETTING: California county-level data (1995-1997) on MMC penetration, public use data from the Current Population Survey (CPS) (1995-1997), and restricted CPS data matched to administrative records on Medicaid enrollment (2001-2002). STUDY DESIGN: We explore the robustness of previous results from the literature first using aggregate data and alternative models. We then examine CPS data linked to Medicaid enrollment data to estimate models of Medicaid reporting errors related to MMC. DATA COLLECTION/EXTRACTION METHODS: The Census Bureau linked administrative data on Medicaid enrollment to the CPS. Other data used were public use. PRINCIPAL FINDINGS: We find similar results to a previous study using aggregate data that suggest that MMC worsens reporting of Medicaid enrollment. However, using alternative methods we find those results are not statistically significant and can have opposite signs. Our linked CPS microdata analysis suggests that MMC improves reporting. The article concludes with implications of these results for policy makers. CONCLUSION: It is unlikely that increased MMC penetration explains the increased Medicaid Undercount.

Effect of patient's life expectancy on the cost-effectiveness of treatment for ocular hypertension.

OBJECTIVE: To assess the influence of expected life span on the cost-effectiveness of treating ocular hypertension to prevent primary open-angle glaucoma. METHODS: We used a Markov simulation model to estimate the cost and benefit of ocular hypertension treatment over a person's remaining life. We examined the influence of age on the cost-effectiveness decision in 2 ways: (1) by evaluating specific age cohorts to assess the influence of age at the initiation of treatment; and (2) by evaluating the influence of a specific life span. RESULTS: At a willingness to pay $50,000/quality-adjusted life year to $100,000/quality-adjusted life year, treatment of people with a 2% or greater annual risk of developing glaucoma was cost-effective for people aged 45 years with a life expectancy of at least 18 remaining years. However, to be cost-effective, a person aged 55 years must have a life expectancy of 21 remaining years and someone aged 65 years must have a life expectancy of 23 remaining years. CONCLUSIONS: A person with ocular hypertension must have a life expectancy of at least 18 remaining years to justify treatment at a threshold of a 2% or greater annual risk of developing glaucoma. Persons at higher levels of risk require a life expectancy of 7 to 10 additional years to justify treatment.

The increased cost of medical services for people diagnosed with primary open-angle glaucoma: a decision analytic approach.

PURPOSE: Glaucoma accounts for more than 11% of all cases of blindness in the United States, but there have been few studies of economic impact. We examine incremental cost of primary open-angle glaucoma considering both visual and nonvisual medical costs over a lifetime of glaucoma. DESIGN: A decision analytic approach taking the payor's perspective with microsimulation estimation. METHODS: We constructed a Markov model to replicate health events over the remaining lifetime of someone newly diagnosed with glaucoma. Costs of this group were compared with those estimated for a control group without glaucoma. The cost of management of glaucoma (including medications) before the onset of visual impairment was not considered. The model was populated with probability data estimated from Medicare claims data (1999 through 2005). Cost of nonocular medications and nursing home use was estimated from California Medicare claims, and all other costs were estimated from Medicare claims data. RESULTS: We found modest differences in the incidence of comorbid conditions and health service use between people with glaucoma and the control group. Over their expected lifetime, the cost of care for people with primary open-angle glaucoma was higher than that of people without primary open-angle glaucoma by $1688 or approximately $137 per year. CONCLUSIONS: Among Medicare beneficiaries, glaucoma diagnosis not found to be associated with significant risk of comorbidities before development of visual impairment. Further study is necessary to consider the impact of glaucoma on quality of life, as well as aspects of physical and visual function not captured in this claims-based analysis.