Health Policy 115 (2014) 165–171

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Initial impact of Medicare’s nonpayment policy on catheter-associated urinary tract infections by hospital characteristics Kristin Schuller a,∗ , Janice Probst b,1 , James Hardin c,2 , Kevin Bennett b,1 , Amy Martin b,1 a b c

University of North Dakota, 501 N. Columbia Road, Stop 9037, Grand Forks, ND 58202, United States University of South Carolina, 220 Stoneridge Drive, Suite 204, Columbia, SC 29210, United States University of South Carolina, 1600 Hampton Street, Suite 507, Room 539, Columbia, SC 29208, United States

a r t i c l e

i n f o

Article history: Received 29 January 2013 Received in revised form 19 November 2013 Accepted 26 November 2013

Keywords: Medicare’s nonpayment policy Catheter-associated urinary tract infections Hospital-acquired infections Quality of care Patient safety

a b s t r a c t Aims and objectives: The goal of this study was to evaluate the trend in urinary tract infections (UTIs) from 2005 to 2009 and determine the initial impact of Medicare’s nonpayment policy on the rate of UTIs in acute care hospitals. Background: October 2008 commenced Medicare’s nonpayment policy for the additional care required as a result of hospital-acquired conditions, including catheter-associated urinary tract infections (CAUTIs). CAUTIs are the most common form of hospital-acquired infections. Methods: Rates of CAUTIs were analyzed by patient and hospital characteristics at the hospital level on a quarterly basis, yielding 20 observation points. October 2008 was used as the intervention point. A time series analysis was conducted using the 2005–2009 Nationwide Inpatient Sample datasets. A repeated measures Poisson regression growth curve model was used to analyze the rate of CAUTIs by hospital characteristics. Results: The annual rate of CAUTIs continues to rise; however the annual rate of change is starting to decline. The change in rate of CAUTIs was not significantly different before and after the policy’s payment change. The results of the adjusted time series analysis show that various hospital characteristics were associated with a significant decline in rate of CAUTIs in quarters 16–20 (after the policy implementation) compared to the rate in time 1–15 (before the policy implementation), while other characteristics were associated with a significant increase in CAUTIs. Conclusions: Medicare’s nonpayment policy was not associated with a reduction in hospitals’ CAUTI rates. The use of administrative data, improper coding of CAUTIs at the hospital level, and the short time period post-policy implementation were all limitations in this study. © 2013 Elsevier Ireland Ltd. All rights reserved.

1. Introduction ∗ Corresponding author. Tel.: +1 701 777 6077. E-mail addresses: [email protected] (K. Schuller), [email protected] (J. Probst), [email protected] (J. Hardin), [email protected] (K. Bennett), [email protected] (A. Martin). 1 Tel.: +1 803 251 6317. 2 Tel.: +1 803 777 3191. 0168-8510/$ – see front matter © 2013 Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.healthpol.2013.11.013

Hospital-acquired conditions (HACs) accounted for an estimated 100,000 deaths and $17–45 billion in additional hospital costs in 2002 [4,5], and have been identified as a major safety and public health concern due to their significant impact on health. Approximately two million hospitalized patients will incur some type of HAC each year,

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with 20% of those occurrences being preventable [1–5]. The main problems associated with HACs are increases in cost of care, length of stay, morbidity and mortality, complication rates, and the overall negative impact on quality of care [3]. The HACs category encompasses a multitude of infections and conditions, including surgical site infections, urinary tract infections, pneumonia, deep vein thrombosis and pulmonary embolism, and so forth [3,6]. Of the two million HACs that occur annually, the most common is catheter-associated urinary tract infections (CAUTIs) representing approximately 30–40% of all HACs or 1–1.5 million CAUTIs annually [7–10]. Eighty percent of all UTIs are caused by urinary catheters with an estimated 25% of catheterized patients at increased risk of infection associated with catheterization from 2 to 10 days [8,9,11–14]. Even though there is a low mortality rate associated with CAUTIs, the high incidence rate creates a significant burden on the healthcare system [13]. To curtail the rising incidence and costs associated with all HACs, countries across the world have implemented policies and regulations to reduce HACs [3]. In the United States, Medicare implemented a nonpayment policy for HACs on October 1, 2008 known as the “Hospital-Acquired Conditions Initiative” [6]. This policy eliminated Medicare reimbursement to hospitals for the extra care needed as a result of conditions deemed hospital-acquired, which included CAUTIs [6]. Two of the major goals of Medicare’s nonpayment policy were to save lives and reduce costs [15], each of which could be accomplished by motivating hospitals to implement changes in their care delivery process to improve their organizational structure [16]. Specific procedural changes targeted the avoidance of unnecessary use of catheters and implementing prevention strategies for catheterized patients [17]. To provoke these changes, Medicare’s nonpayment policy uses reimbursement as the financial motivator by shifting the costs associated with a patient’s additional healthcare services resulting from a CAUTI to the hospital [6,16–18]. The overall goal of the present study was to evaluate the rate of decline in CAUTIs based on the implementation of Medicare’s nonpayment policy. It was hypothesized that the rate of CAUTIs would decline at a greater rate after the implementation of Medicare’s nonpayment policy in 2008 compared to the years prior to the policy change. 2. Methods The 2005–2009 Nationwide Inpatient Sample (NIS) datasets were used to conduct a time series analysis on the rate of CAUTIs. The rates of CAUTIs were calculated on a quarterly basis for the five-year period, yielding 20 observation points. There were an estimated 1050 participating hospitals, with approximately 8 million discharges recorded in the NIS annually. The initial dataset contained information on 40,082,431 patient-level hospitalizations. The rate of CAUTIs was calculated over a subset of all hospitalizations. The dependent variable is the rate of CAUTIs. For each hospital, this is calculated as the number of occurrences of each CAUTI over the total number of patients, expressed as CAUTIs per 1000 patient discharges.

Community-acquired UTIs were identified using primary diagnosis codes and were excluded from the rates. The ICD-9 code used to measure UTIs was 996.64 [19]. October 2008 was used as the intervention point. This point was used instead of the Deficit Reduction Act in 2005 because the goal of this paper is to measure how effective hospitals were at decreasing the rate of CAUTIs once the nonpayment policy was nationally enforced. Starting in 2005, hospitals could begin to prepare for the payment policy change, but the actual payment change did not occur until October 2008. Time was measured in quarters over the five year study period, resulting in 20 quarters of observation. Quarters 1–15 comprise the period before the policy’s payment change (October 2008), and quarters 16–20 comprise the period after the policy’s payment change. There are two sets of control variables. The first set of hospital structural variables represent hospital characteristics including, bed size (small, medium, or large), teaching status, (nonteaching or teaching), control and ownership (government-owned, private not-for-profit, private investor-owned), location (rural or urban), and region (Northeast, Midwest, South, or West). Critical Access Hospitals (CAHs) were excluded from this analysis because they are not included in Medicare’s nonpayment policy. All small (1–50 beds), rural hospitals were excluded since the NIS did not have a variable designated to represent CAHs. The NIS compiled bed size based on region, location, and teaching status. However, in the NIS bed size was only coded as small, medium, and large and since individual bed counts were not included in the analysis, all small, rural hospitals were excluded. The second set of control variables reflects information on the patient characteristics including age and primary payer. This analysis only assessed the rate of CAUTIs in adults 65 and older with Medicare. All other patient ages and primary payers were excluded. Patients with immunesuppressant conditions (HIV/AIDS, renal failure, metastatic cancer, and lymphoma) were excluded from the analysis due to the severity of their condition and treatment, as well as the potential to skew the results. Patients transferred from another hospital or health care facility were also excluded due to uncertainty regarding the origin of the UTI. All analyses used NIS weights to allow generalization of inference from our sample to the nation as a whole. Bivariate adjusted analysis for the two time periods was used to determine the rates of CAUTIs and Chi-square tests were obtained to determine significance. These analyses were performed at the patient level (approximately 14 million observations) for the rate of CAUTIs overall. The data were then summarized from the patient level to means at the hospital level (n = 14,187). Once aggregated to the hospital level, only the observations resulting in a CAUTI were included in the dataset, which yielded 4758 observations. Only CAUTI observations were analyzed due to the small number of CAUTIs in the dataset. By only analyzing the discharges that resulted in a CAUTI, we can more clearly see the trend of CAUTIs over time and the hospital characteristics associated with higher rates of CAUTIs. The number of hospitals per quarter varied from 512 to 778 because the hospital sample changed over the study period.

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Table 1 Mean rate and annual change in rate of CAUTIs per 1000 CAUTI discharges per year, aggregated at hospital level. Year

Total discharges (weighted)

Total observations (CAUTIs)

Mean rate per 1000 dischargesa

Annual rate change

2005 2006 2007 2008 2009

4065 4298 3314 5732 5765

835 881 682 1177 1183

0.0012 0.0017 0.0003 0.0027 0.0007

NA 0.0005 −0.0014 0.0024 −0.0020

a

Mean rate per 1000 discharges = (#CAUTIs/total CAUTI discharges) × 1000.

Multivariate analysis was performed using a repeated measures Poisson regression growth curve model to analyze the rate of CAUTIs over time. This repeated measures analysis used multiple data points to assess the trend of CAUTIs over time before and after the policy implementation. An offset variable was incorporated to adjust the number of CAUTIs for the variation in the total number of hospital discharges per hospital in the specific time period. Correlations among repeated observations within hospitals were addressed using the modified sandwich variance estimator to adjust Wald tests of coefficients in the pooled model. This Poisson regression model was used to calculate the mean rates and change in rates of CAUTIs before and after Medicare’s policy change. Poisson regression was used due to the size and complexity of the data to analyze the rate and change in rate of CAUTIs by hospital characteristics to determine the initial impact of the policy. 3. Results The overall mean rate of CAUTIs increased from 0.0012 per 1000 discharges in 2005 to 0.0027 per 1000 discharges in 2008 and then dropped to 0.0007 per 1000 discharges in 2009 (Table 1). The rate of change increased each year, except for 2007 and then again in 2009.

When adjusting for the rate of CAUTIs by hospital characteristics assessed at the hospital level, significant differences were found in the mean rate of CAUTIs by bed size, control or ownership, teaching status, and location for all five years (Table 2). For differences by size, hospitals with more beds (large hospitals) had higher mean rates of CAUTIs all five years compared to small and medium sized hospitals. Teaching and urban hospitals had significantly higher mean rates of CAUTIs from 2005 to 2009 than nonteaching and rural hospitals. Government-owned hospitals had significantly higher mean rates of CAUTIs for all five years compared to private for-profit hospitals and nonprofit hospitals. For regional differences, hospitals located in the South had higher mean rates of CAUTIs compared to hospitals in the Northeast and the West, except for 2007 when hospitals in the Midwest had the highest mean rates of CAUTIs. Multivariate time series analysis was used to analyze each of the hospital characteristics to determine the change in rate of CAUTIs. The overall change in rate of CAUTIs decreased after the policy change (0.0307); however, the change in rate was not significant (p = 0.3577) (Table 3). A visual representation of the change in growth of CAUTI is shown in Fig. 1. Comparable to the annual change in rate of CAUTIs seen in Table 1, the growth in rate of CAUTIs inclined slowly over the first 15 quarters (before the policy

Table 2 Mean rate of CAUTIs by hospital characteristics at the hospital level per 1000 discharges.a Year

2005

2006

2007

2008

2009

Total observations Weighted observations

835 4065

881 4298

682 3314

1177 5732

1183 5765

Size Small Medium Large

0.16 0.28 0.55

0.13 0.28 0.58

0.15 0.30 0.55

0.15 0.29 0.55

0.18 0.28 0.54

0.64 0.36

0.62 0.38

0.60 0.40

0.63 0.37

0.65 0.35

0.16 0.84

0.19 0.81

0.14 0.86

0.20 0.80

0.18 0.82

Ownership Government Nonprofit Private

0.64 0.22 0.14

0.63 0.21 0.16

0.69 0.20 0.10

0.66 0.19 0.15

0.62 0.21 0.18

Region Northeast Midwest South West

0.21 0.26 0.33 0.20

0.18 0.26 0.36 0.21

0.26 0.31 0.21 0.22

0.19 0.29 0.33 0.18

0.18 0.28 0.33 0.20

Teaching status Teaching Nonteaching Location Rural Urban

a

All hospital characteristics each year were significant based on 95% confidence intervals.

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Table 3 Poisson regression estimates of the change in rate of CAUTIs before and after Medicare’s nonpayment policy by hospital characteristics (interacted with time). Parameter

Estimate

Standard error

95% confidence limits

Pr > |Z|

Intercept Slope before the policy Intercept at policy change Slope after the policy

−15.5609 0.0422 0.4736 0.0307

0.1171 0.0156 0.1577 0.0334

−15.7905 0.0117 0.1645 −0.0347

−15.3313 0.0728 0.7827 0.0961

Initial impact of Medicare's nonpayment policy on catheter-associated urinary tract infections by hospital characteristics.

The goal of this study was to evaluate the trend in urinary tract infections (UTIs) from 2005 to 2009 and determine the initial impact of Medicare's n...
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