Table 4.

Results of the Analysis of Model of 3 And 6 Months Post-Injury Medical Expenditures Accounting For Sharps versus Non-Sharps Injuries.

	Model With Indicators For Sharps Vs. Non-Sharps Injury		Model Excluding All Sharps Injuries
	3 Months (N=2696)	6 Months (N=2696)	3 Months (N=2044)	6 Months (N=2044)
Sharps injury
Part one: odds ratio for any expenditure	1.40 [0.85, 2.29]	2.99 [1.31, 6.84]	--	--
Part two: change in expenditures given any expenditure	−$215 [−$540, $319]	−$129 [−$756, $649]	--	--
Overall: change in expenditures	−$136 [−$427, $334]	$7 [−$584, $771]	--	--
Non-sharps injury
Part one: odds ratio for any expenditure	2.57 [1.80, 3.66]	2.94 [1.86, 4.65]	2.57 [1.79, 3.69]	2.86 [1.80, 4.55]
Part two: change in expenditures given any expenditure	$311 [−$19, $679]	$665 [$168, $1384]	$278 [−$72, $681]	$696 [$162, $1366]
Overall: change in expenditures	$420 [$120, $779]	$777 [$300, $1455]	$396 [$77, $768]	$806 [$293, $1460]

Notes: All models controlled for quartile of pre-injury medical expenditures, health insurance plan type, age group, and job category. Each model also included a constant term and was estimated using a two part model. The first part of the model used logit regression to predict whether an individual had any expenditures and the second part predicted the amount of expenditures for individuals who had a least some expenditures using log-transformed expenditures and ordinary least squares. Dollar amounts were rounded to the nearest dollar. Estimates using the log-OLS portion of the model were obtained using the appropriate retransformation algorithm for normal heteroskedastic residuals and bias-corrected bootstrapped 95% Confidence intervals with 1000 replications.