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Health Services Research & Development

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2012 HSR&D/QUERI National Conference Abstract

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2012 National Meeting

3045 — Assessing Bias in Current Methods for Estimating Disease-Attributable Costs across 31 Common Conditions in VA Users

Zeliadt SBYee LBatten AJ, and Chapko MK, VA Puget Sound; Wagner TH, HERC; Zhao XH, VA Puget Sound;

Objectives:
Quantifying how much individual conditions add to healthcare costs is of considerable interest to decision makers. We compared two commonly used methods – matching and regression – to estimate the disease-attributable (DA) costs of 31 conditions in the VA.

Methods:
A 20% random sample of VA users from FY2008 was identified. Encounter-level costs for FY2008 were extracted using inpatient TRT files and outpatient OPAT files from Decision Support Systems, and were linked to files recording all encounter-level ICD9 diagnosis and procedure codes. The matching approach compared costs for patients with each condition of interest and a demographically similar group of patients without the condition. The regression approach included indicators for each condition and potential confounding factors.

Results:
Among 996,869 VA users in FY2008, 83.9% of VA users had at least one of our 31 priority conditions and 67.9% had two or more conditions. The four most prevalent conditions were hypertension (55%), diabetes (26%), dyspepsia (22%) and ischemic heart disease (19%). Among this sample of VA users with at least one chronic condition, the actual FY2008 expenditure was $6.7 billion with a mean cost of $7,039. All approaches yielded unrealistically high estimates. For example, matching estimated the DA costs of hypertension to be $3,507 per person, which would mean hypertension accounts for over $1.9 billion (28%) of the actual FY2008 budget. Regression approaches yielded inconsistent results with negative costs for some conditions. The combined cost estimates for only the 31 conditions significantly exceeded the total FY2008 expenditure; matching by 252%, demographic-adjusted regression by 231%, and comorbidity-adjusted regression by 110%. This is notable given the DA cost estimates for the 31 conditions should not include costs for care beyond this limited group of conditions.

Implications:
Commonly used and widely cited methods for estimating disease-attributable costs are significantly biased when applied to VA users who have multiple conditions. Adjusting for comorbidity in regression models helps avoid double-counting some components of care for complex patients, although these models appear to also result in over-estimates. Alternative methods, including attributable-fraction, should be explored.

Impacts:
Decision makers should use caution when interpreting cost studies for individual conditions among VA users.


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