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Increasing hospital costs for Clostridium difficile colitis: Type of hospital matters Li Wang, PhD,a and David B. Stewart, MD,b Hershey, PA Background. To assess differences in hospital costs for inpatients with Clostridium difficile (CD) colitis based on hospital size, rural or urban hospital setting, and hospital designation as a teaching institution. Methods. Data from the Pennsylvania Health Care Cost Containment Council (PHC4) were reviewed for 2005 to 2008. Cost-to-charge ratios were used to derive costs from hospital charges, adjusting costs for inflation. Propensity score–matched CD colitis and non–CD colitis cohorts were compared. Costs were modeled by generalized linear regression. Results. A total of 78,273 patients with CD colitis were identified. The average hospital cost per admission for patients with CD colitis was $22,094 compared to $10,865 for non–CD colitis patients. The 2005 to 2008 admission costs for CD colitis rose 9%, while the prevalence of CD colitis decreased by 13%. The costs for patients with CD colitis were consistently twice as much as those for non–CD colitis patients (P < .0001). Small facilities had the greatest costs overall (P < .0001). Urban and teaching facilities had greater costs than rural and nonteaching facilities (P < .0001), which corresponded to a greater proportion of patients with greater comorbidities (P < .0001). Among rural hospitals, the smallest facilities had the greatest costs (P < .0001), as did urban nonteaching hospitals (P < .0001). By contrast, costs did not differ among urban teaching hospitals of varying sizes (P = .35). Conclusion. Costs for inpatient CD colitis in Pennsylvania have been increasing. Teaching and urban hospitals treat the group of patients with CD colitis with the greatest comorbidity, accounting for their greater cost of care. The cost of treating CD colitis is comparable among different sizes of teaching hospitals, which may reflect a more standardized approach toward treatment choices. (Surgery 2011;150:727-35.) From the Department of Public Health Sciences,a The Pennsylvania State University, and the Division of Colon and Rectal Surgery,b Department of Surgery, Pennsylvania State Hershey Medical Center, Hershey, PA OVER THE PREVIOUS 2 DECADES, the incidence of Clostridium difficile (CD) colitis has increased among hospitalized patients in the United States and Europe.1-3 During this time, the demographic of patients with CD colitis has broadened to include immunocompetent patients who harbor none of the risk factors historically associated with developing CD colitis.4-6 A concomitant increase in the severity of CD colitis reported in the United States has resulted in an increase in both disease morbidity and mortality.7 Associated with the increasing severity of CD colitis is the growing need for Presented at the 68th Annual Meeting of the Central Surgical Association, Detroit, MI, March 17–19, 2011. Accepted for publication August 18, 2011. Reprint requests: Li Wang, PhD, Department of Public Health Sciences, The Pennsylvania State College of Medicine, 600 Centerview Drive, A210, Hershey, PA 17033. E-mail: [email protected] 0039-6060/$ - see front matter Ó 2011 Mosby, Inc. All rights reserved. doi:10.1016/j.surg.2011.08.019 colectomy in those patients who fail to respond to antibiotic therapy.1 Each of these factors contributes substantially to cost of care through the need for antibiotics, the need for operative treatment, and an increased duration of hospital stay. The magnitude of this excess cost, however, has not been studied sufficiently. One issue that has been discussed with respect to other diseases8-12 but that has yet to be studied with respect to CD colitis centers on whether a disparity exists in terms of cost as related to different hospital settings for CD colitis of varying severity. It is unknown if hospital size or hospital designation as a teaching or academic institution has any bearing on cost of care. Issues related to cost of CD colitis outcomes are particularly complex considering the different patient populations encountered among different types of hospitals. Based on the findings that institutional volume and personal expertise are associated with improved outcomes in the treatment of other diseases, a similar expectation regarding the management of CD colitis would also seem justifiable, considering the SURGERY 727 728 Wang and Stewart complexity involved in deciding on the need for and timing of colectomy. The purpose of this study was to describe the trend in hospital costs attributable to CD colitis within the state of Pennsylvania and to describe how factors such as hospital size, rural or urban care settings, and status as a teaching facility affected the cost of treating CD colitis. METHODS This study was performed with institutional review board approval from the authors’ institution. Data from the Pennsylvania Health Care Cost Containment Council (PHC4) were reviewed for 2005 to 2008. The PHC4 is an independent state agency responsible for addressing the problem of escalating health costs, ensuring the quality of care, and increasing access to health care. The PHC4 database contains statewide inpatient discharge data and data from outpatient and freestanding ambulatory surgery centers. Patients with CD colitis were identified if their primary or secondary diagnoses included the International Classification of Diseases, 9th edition, Clinical Modification (ICD-9-CM) diagnosis code for Clostridium difficile colitis: 008.45. Only patients whose residence was in Pennsylvania (approximately 95% of all the admissions) were included in this study, which excluded heterogeneous out-of-state travelers. This was done in part to allow for a meaningful analysis regarding patient travel distance from their home residence to the treating hospital. Cost-to-charge ratios were used to derive costs from hospital charges, and costs were inflation adjusted to 2008 dollars. Propensity score–matched CD colitis and non– CD colitis cohorts were compared. The propensity score matching was performed for each year based on the following variables: sex, race, age, admission type, payor type, patient comorbidities, hospital status as a rural or urban facility, hospital status as a teaching or nonteaching facility, hospital size, and the yearly quarter at the time of admission. Hospital size (small, medium, or large) was stratified according to the size criteria used by the Healthcare Cost and Utilization Program (http://www.ahrq.gov/data/hcup). The categorization of hospital size was based on the number of hospital beds, and was specific to the urban/rural and teaching status of the hospital. For rural hospitals, small size was defined as having 1 to 49 beds, medium as 50 to 99 beds, and large as $100 beds. For urban nonteaching hospitals, a bed count of 1 to 124 was considered small, 125 to 199 beds was medium, and $200 beds was Surgery October 2011 considered large. For urban teaching hospitals, 1 to 249 beds was considered small, 250 to 424 was medium, and $425 eds was large. The urban or rural status of a hospital was determined by whether or not the location of the hospital was found within a Metropolitan Statistical Area (MSA). The teaching status was determined by both PHC4 data entry and by publically available online lists of Pennsylvania teaching hospitals. Patient comorbidity was derived based on the diagnosis codes for each patient, and a patient’s overall degree of comorbidity was stratified by using a Charlson Comorbidity Index (CCI). Information regarding the number of colectomies performed within the CD colitis cohort was also collected. The procedural codes contained in PHC4 data were ICD-9 procedural codes; no Current Procedural Terminology codes were available. The codes used for partial colectomy were 45.7, 45.71 to 45.76, and 45.79, while the code for total colectomy was 45.8. Colectomy rates were not the primary focus of this study, but given their potential influence on cost and an interest in assessing how common colectomy was required for CD colitis, these data were included. The propensity score matching did not involve colectomy as a factor, because the colectomy rate was extremely low (<2%) as described in the Results section, and therefore the mean costs for patients with CD colitis were hardly affected by the cost of colectomy. In addition, the large number of disease processes other than CD colitis for which colectomy might have been performed would not have allowed for comparison of similar patient cohorts, especially when considering such diseases as inflammatory bowel disease and cancer. Non–CD colitis indications for colectomy would have introduced bias into cost assessment, as seen, for example, in the additional costs for an ulcerative colitis patient undergoing colonoscopies and inflammatory bowel disease medical therapy before colectomy. Such diseases, disanalogous to each other and to CD colitis, would have produced groups inappropriate for comparison. Considering the extremely low number of colectomies performed in the CD colitis cohort, the influence of colectomy on cost was negligible. To test whether patients with CD colitis differed from the matched non–CD colitis cohort, v2 tests were performed. Mann-Whitney-Wilcoxon testing was used to compare differences in median costs. Given the non-normal distribution of cost data, generalized linear regression with multiple covariates was performed to study costs, with a log link function and the error term having a Gamma Wang and Stewart 729 Surgery Volume 150, Number 4 Table I. Mean hospital costs per admission for Clostridium difficile colitis vs matched non– Clostridium difficile colitis patients* Year No. of patients Rate of CD colitis Mean cost per admission for CD colitis Mean cost per non–CD colitis admission P value 2005–2008 2005 2006 2007 2008 78,273 21,581 19,634 18,849 18,209 1.083% 1.175% 1.081% 1.051% 1.023% $22,094 $21,325 $21,428 $22,624 $23,173 $10,865 $10,470 $10,928 $10,893 $11,234 <.0001 <.0001 <.0001 <.0001 <.0001 *Costs were adjusted to 2008 dollars. The sample size was the same between the Clostridium difficile (CD) colitis group and the non–CD colitis group within each year. The significance value refers to a comparison of mean hospital costs between the CD colitis group and non–CD colitis group within the given period. distribution. To explore whether rural patients with CD colitis traveled greater distances to receive treatment, travel distances were compared between urban and rural patients. The travel distances between the residence of a patient and the treating facility was defined as the geodetic distance between the centroids of the patient’s ZIP code and the treating facility’s ZIP code, calculating the shortest distance between the center points of the 2 ZIP code areas with the aid of geospatial mapping. The travel distance was determined through a built-in function within SAS statistical software (SAS Institute, Inc., Cary, NC). RESULTS A total of 7,227,788 inpatient admissions were recorded in the PHC4 dataset from 2005 to 2008. Of these, a total of 78,273 admissions for CD colitis (including primary and secondary admission diagnoses for CD colitis) were found, equaling approximately 1% of the total admissions for the study period. For the CD colitis cohort, >70% were 65 years of age or older and had Medicare as their primary payor. More than half (57%) of the patients with CD colitis were female. Caucasian patients comprised 87% of all admissions for CD colitis, while black patients comprised 11%. Approximately 68% of patients were treated at large hospitals, 27% in teaching hospitals, and 10% in rural hospitals. An equal number of propensity score–matched, non–CD colitis patients from the same time period were also included for comparison. No statistically significant differences existed between the CD colitis and the matched non–CD colitis cohorts with respect to any demographic category (all P values >.8). A total of only 450 total colectomies were performed for patients with CD colitis (0.57% of all CD colitis admissions) during the study period, and a total of 1,061 partial colectomies (1.36% of all CD colitis admissions) were performed for patients with CD colitis. Fig 1. Comparison of hospital costs by year between Clostridium difficile colitis (CDC) and matched, non-CDC patients. Patients with either a primary or secondary admission diagnosis of CD colitis comprised 1.1% of all admissions during the study period (Table I), with a consistent decrease from 1.2% in 2005 to 1.0% in 2008. The mean hospital costs per admission for patients with CD colitis during the study period, as calculated in 2008 dollars, was $22,094 compared to a mean hospital cost of $10,865 for the propensity score–matched, non–CD colitis cohort (P < .0001). Median hospital costs for the same period were $10,931 for the CD colitis cohort and $6,485 for the non–CD colitis cohort (P < .0001). For each study year, the mean hospital costs per admission for patients with CD colitis were consistently twice as high compared to the matched, non–CD colitis cohort (P < .0001). The mean per admission costs for the CD colitis cohort increased by 9% from 2005 to 2008 (P < .0001), but the number of CD colitis-related admissions during the same time period decreased by 13% (P < .0001). Fig 1 plots the time trend of mean hospital costs for both CD colitis and the non–CD colitis cohort across the 4-year study period and shows a consistent difference in costs between the 2 groups during the study period. 730 Wang and Stewart Surgery October 2011 Table II. Analysis for costs of Clostridium difficile colitis per admission (2005–2008) Variable Age (y) <18 18–64 $65 Sex Male Female Race White Black Other Admission type Emergent Urgent Other Comorbidity 0 1–2 $3 Payor type Medicare Commercial Other Hospital size Small Median Large Teaching Yes No Rural hospital Rural Urban Year 2005 2006 2007 2008 Unadjusted mean ($) P value Adjusted mean ($) SE P value 48,874 27,449 19,412 Ref. <.0001 <.0001 40,536 25,142 18,132 1019 166 73 Ref. <.0001 <.0001 24,899 20,008 Ref. <.0001 22,576 18,389 118 83 Ref. <.0001 20,292 34,081 34,520 Ref. <.0001 <.0001 18,630 33,253 32,260 68 343 768 Ref. <.0001 <.0001 20,055 21,040 31,955 Ref. <.0001 <.0001 18,729 18,343 30,167 80 139 263 Ref. <.0001 <.0001 21,975 21,797 25,548 Ref. .32 <.0001 19,769 20,113 24,307 84 124 376 Ref. <.0001 <.0001 19,942 26,360 32,536 Ref. <.0001 <.0001 18,569 23,198 29,917 73 192 352 Ref. .73 <.0001 25,465 20,160 22,068 Ref. <.0001 <.0001 22,847 18,819 20,001 231 146 82 Ref. .02 <.0001 33,528 17,854 Ref. <.0001 32,831 16,705 215 67 <.0001 Ref. 11,969 23,194 <.0001 Ref. 10,922 21,448 119 77 <.0001 Ref. 21,325 21,428 22,624 23,173 Ref. .63 <.0001 <.0001 19,443 19,699 20,522 20,798 126 134 143 147 Ref. .86 .002 <.0001 Generalized linear modeling with multiple covariates was conducted to determine the factors associated with increased mean hospital costs among patients with CD colitis. Table II lists the results of a cost analysis within the CD colitis cohort. The unadjusted mean hospital costs represented the raw mean for each stratum of each variable included in the analysis, while the adjusted mean was calculated based on the predicted mean derived from the estimated linear regression model by controlling for other variables that were held constant at the sample mean. The unadjusted analysis of the CD colitis cohort revealed that greater costs occurred in patients <18 years of age, males, nonCaucasian, non–African American race, elective hospital admissions, non-Medicare, noncommercial payor type, a CCI score $3, teaching hospital status, urban hospital status, and small-sized hospitals (all P <.0001). The adjusted cost analysis produced similar results, with 2 exceptions. Compared to patients with a CCI score of 0, a CCI score of 1 or 2 produced no cost difference based on an unadjusted analysis, while greater costs (P < .0001) were noted at this level of CCI when other variables were adjusted for. A second exception involved costs associated with commercial insurance payors, where greater costs occurred by unadjusted analysis (P < .0001), but not when other variables were adjusted for (P = .73). Fig 2 provides a map of Pennsylvania by county, with a color code indicating various 1-way distances of travel measured from the patient’s residence Wang and Stewart 731 Surgery Volume 150, Number 4 Table III. Mean costs (all years) for patients with Clostridium difficile colitis by types of hospitals Size of hospital Status All types Rural Urban, teaching Urban, nonteaching Fig 2. Mean patient travel distance in miles from patient residence to treating hospital based on the patient’s county of residence. to the hospital where they received treatment. Pennsylvania has approximately 50% of its counties designated as rural. As shown in Fig 2, patients living in northern Pennsylvania travelled greater mean distances than those in the south, and patients close to the 2 largest metropolitan areas (Philadelphia and Pittsburgh) travelled the least distance. Classifying each patient’s place of residence as either rural or urban, patients living in rural areas had a greater mean 1-way distance of travel compared to patients living in urban areas (rural, 17.1 miles vs urban, 7.2 miles; P < .0001). Costs for patients with CD colitis were further studied by stratifying treating hospitals according to the size of the facility and based on each facility’s designation as a rural hospital, an urban teaching hospital, or an urban nonteaching hospital (Table III). Patients with CD colitis were treated in >200 hospitals, with only 25 of those facilities being designated as teaching hospitals. Teaching hospitals performed a disproportionately large number of admissions for CD colitis compared to their number, involving 27% of admissions for the study period. Small facilities had the greatest mean hospital costs overall (P < .0001).Urban and teaching facilities had greater costs than rural and nonteaching facilities (P < .0001). When facilities were differentiated only by teaching status, the mean hospital costs were $33,528 for teaching facilities compared to $17,854 for nonteaching facilities (P < .0001). When only urban/rural status was considered, the mean costs of patients with CD colitis were $23,194 for urban facilities vs $11,969 for rural hospitals (P < .0001). The greater costs for patients with CD colitis at urban and teaching hospitals was associated with the finding that those Small ($) Medium ($) Large ($) P value 25,465 18,117 37,100 20,160 7,888 32,726 22,068 12,274 34,039 <.0001 <.0001 .35 25,239 20,128 16,557 <.0001 hospitals had a higher proportion of patients with higher CCI scores (P < .0001). Among rural hospitals, the smallest facilities had the greatest costs (P < .0001), as was the case with urban nonteaching hospitals (P < .0001). By contrast, costs did not differ among urban teaching hospitals of varying sizes (P = .35). The patterns of cost difference by hospital type were similar to patterns in duration of stay by hospital type, which may be associated with this difference in cost. For small rural hospitals, the average duration of stay among patients with CD colitis was 14 days vs 7 days for medium rural hospitals and 9 days for large rural hospitals (P < .05). For urban nonteaching hospitals, the mean duration of stay was 22 days for small hospitals vs 12 days for medium facilities and 11 days for large hospitals (P < .05); however, there was no statistically significant difference in duration of stay among various sized urban teaching hospitals, with small and medium urban teaching facilities having a mean duration of stay of 13 days vs 14 days for large hospitals for patients with CD colitis. DISCUSSION During the 4-year time period analyzed in this study, inpatient admissions for CD colitis in Pennsylvania comprised only 1% of the >7 million admissions during that time span. With the recent increase in the frequency of severe forms of CD colitis reported in the literature, including those patients who either die from the infection or who require colectomy, CD colitis has become a disease that now involves more commonly surgeons in its treatment. These factors may lend to a perception among surgeons that CD colitis is more common or more severe than it actually is when considered in a broader context, and the statewide data presented in this study underscore the fact that while previous papers13-15 have shown that the disease is more common relatively than it used to be, 732 Wang and Stewart that nonetheless, in more absolute terms and while looking beyond a single institutional experience, CD colitis still represents a small number of hospital admissions overall. The incidence of CD colitis and the rate of admission for CD colitis has been reported to be increasing,16,17 but these reports cover much greater periods of time than the present study, and they are not based on the most recent data. Reports that describe increasing rates of CD colitis are based on data no more current than 2005 to 2006, and to the best of the authors’ knowledge, there have been no studies that have included data from more recent time periods. Although our study is oriented toward issues related to cost over a short period of time, the authors noted a decrease in the rate of inpatient CD colitis in Pennsylvania from 2005 to 2008. Of note is that the incidence of CD colitis reported in this study was a measurement recorded carefully in a statewide dataset, and was not a number reached through computational maneuvers or methods that would have introduced selection bias. PHC4 has been quite active in documenting the incidence of CD colitis and reporting this to state legislators, with reports published by PHC4 indicating that the hospital-acquired infection rates (including CD colitis) have decreased by as much as 8% from 2007 to 2008.18 The percentage of patients with CD colitis requiring total colectomy was quite low at only 450 patients, representing 0.56% of the total number of CD colitis–related admissions in the study period. Partial colectomy was performed for 1.4% of all CD colitis–related admissions. Cost and not colectomy rates was the primary focus of this study, but the low colectomy rate helps provide a more balanced view of the status of the disease over a large population. While any single institution may experience a period during which an outbreak of severe CD colitis occurs and that may be associated with a greater rate of colectomy during that outbreak, caution is needed in generalizing those experiences to patients at large. While the authors do not wish to diminish the importance of CD colitis as a potentially lethal infectious disease, statewide data such as that presented in the present study help maintain balance in gauging the true scope of the disease in terms of its incidence, severity, and need for colectomy. Colectomy was not included in the criteria for matching for reasons given in the Methods section of the paper. Given that only 2% of patients with CD colitis underwent a colectomy, these patients did not require exclusion while Surgery October 2011 computing mean CD colitis costs. Sensitivity analysis revealed that excluding patients with CD colitis who underwent a colectomy would have decreased the mean CD colitis costs by only $700, far less than the cost difference of approximately $10,000 between the CD colitis and non–CD colitis cohorts. Therefore, despite the fact that a small percentage of patients with CD colitis underwent a colectomy, while a corresponding number of patients from the non–CD colitis cohort may not have undergone colectomy, this scenario would not mitigate the findings of the present study. The cost of CD colitis during the study period increased for each year evaluated, being twice as great as that of a propensity score–matched non– CD colitis control group. Although the number of CD colitis–related admissions decreased during the same period by 13%, hospital costs in the CD colitis group increased simultaneously by 9%. Increasing costs during this period were undoubtedly affected by the national trend in increasing costs for drugs including antibiotics. Additional reasons for the rising costs of CD colitis are left for future research that would focus on what components of total cost are increasing. Adjusted cost analysis revealed several interesting findings. Based on the type of admission, the greatest costs measured while controlling for other factors were associated with elective admissions. This observation reflects probably the fact that elective admissions associated with CD colitis are usually admissions for other diagnoses, with the development of CD colitis during the admission as a secondary problem (secondary CD colitis). This would combine the cost of the primary indication for admission with the cost of management of CD colitis, the latter of which would increase drug and hospital charges through an increased duration of stay. Urgent and emergent admissions may include those patients with more severe CD colitis who may have undergone earlier colectomy. In those patients who are elderly and have a greater number of medical problems, less aggressive care may lead to lesser costs by withholding operative management and maximally aggressive medical therapy related to an anticipated poor outcome. Greater costs were seen with smaller hospital sizes, urban hospitals, and teaching facilities. The finding of greater costs at smaller hospitals as opposed to larger facilities is interesting, considering that one would expect that smaller facilities would have a lesser threshold for transferring patients who might be more ill and who would require more intensive and therefore more expensive care. Smaller facilities that were also rural were Wang and Stewart 733 Surgery Volume 150, Number 4 found to have a lesser CCI than larger facilities. This finding may indicate that the patients who are kept at smaller and/or rural facilities are those who tend to be less ill, but who may have greater hospital stays requiring greater durations of antibiotics and in-hospital observation. Smaller hospitals may also lack physician specialists as well as multidisciplinary coordination of patient care, which may further contribute to issues related to efficiency of care, which could affect cost. The effect on cost based on hospital designation also demonstrated that teaching status brought stability to hospital costs. While only a small percentage (10.6%) of the hospitals in this study were designated as teaching facilities, this minority of hospitals cared for a disproportionately large percentage (27%) of all CD colitis–related admissions during the study period and cared for patients with a greater number of comorbidities than their nonteaching counterparts. The reason that teaching status brought homogeneity to CD colitis–related hospital costs may reflect an institutional commitment to education, greater adherence to national quality guidelines, and institutional mechanisms for quality and peer review such as regular morbidity and mortality discussions and review of national guideline compliance. Our study has weaknesses and limitations. As with all administrative data, there is no detailed clinical information to explore certain specific aspects of treatment. It would be interesting to evaluate the frequency with which rural hospitals transfer patients with CD colitis to tertiary care facilities, which would provide more information regarding the complex relationship between patient characteristics and greater costs at tertiary care centers. This type of information is not available in the PHC4 dataset. While this is a definite limitation in the data presented, it would certainly apply to only a minority of patients. The impact produced by hospital transfers on the study results would be minimized by the large number of patients included in the study, as well as the fact that hospital transfers would also be possible in the propensity score–matched cohort, so that the present analysis does not deal with a confounding element that would preferentially affect only 1 cohort. Another potential weakness is related to its use of discharge data, which may not be as accurate as data obtained from reviewing medical charts. There may also be concerns about possible ‘‘upcoding’’ performed by hospitals to increase charges by using less strict criteria for diagnosing various infectious diseases. While this potential is always present in studies of this kind, upcoding alone would not be serious enough to affect our conclusions. This study used both primary and secondary diagnosis codes for inpatient admissions to identify patients with CD colitis. The diagnosis codes for inpatient data are much more accurate than the diagnosis codes used at outpatient office visits, because of strict criteria used to justify the admission of patients. The rate of CD colitis in small hospitals was not appreciably different than at larger hospitals. For example, when analyzing 2008 data, the CD colitis rate among all admissions was 1.31% for small hospitals, 0.96% for medium hospitals, and 1.01% for large hospitals. The similarity of CD colitis rates across various sizes of hospitals does not point to an obvious upcoding problem. REFERENCES 1. Dallal RM, Harbrecht BG, Boujoukas AJ, et al. Fulminant Clostridium difficile: an underappreciated and increasing cause of death and complications. Ann Surg 2002;235:363-72. 2. Pepin J, Valiquette L, Alary ME, et al. Clostridium difficile –associated diarrhea in a region of Quebec from 1991 to 2003: a changing pattern of disease severity. CMAJ 2004; 171:466-72. 3. Karlstrom O, Fryklund B, Tullus K, et al. Swedish C. difficile study group. A prospective nationwide sudy of Clostridium difficile-associated diarrhea in Sweden. Clin Infect Dis 1998;26:141-5. 4. Gerding DN, Johnson S, Peterson LR, Mulligan ME, Silva J Jr. Clostridium difficile-associated diarrhea and colitis. Infect Control Hosp Epidemiol 1995;16:459-77. 5. Centers for Disease Control and Prevention. Severe Clostridium difficile-associated disease in populations previously at low risk---four states, 2005. MMWR Morb Mortal Wkly Rep 2005;54:1201-5. 6. Johnson S, Gerding DN. Clostridium difficile-associated diarrhea. Clin Infect Dis 1998;26:1027-34. 7. Archibald LK, Banerjee SN, Jarvis WR. Secular trends in hospital-acquired Clostridium difficile disease in the United States, 1987-2001. J Infect Dis 2004;189:1585-9. 8. Musher DM, Aslam S, Logan N, et al. Relatively poor outcome after treatment of Clostridium difficile colitis with metronidazole. Clin Infect Dis 2005;40:1586-90. 9. Birkmeyer JD, Warshaw AL, Finlayson SRG, et al. Relationship between hospital volume and late survival after pacreaticoduodenectomy. Surgery 1999;126:178-83. 10. Sosa JA, Bowman HM, Gordon TA, et al. Importance of hospital volume in the overall management of pancreatic cancer. Ann Surg 1998;228:429-38. 11. Bach PB, Cramer LD, Schrag D, et al. The influence of hospital volume on survival after resection for lung cancer. N Engl J Med 2001;345:181-8. 12. Roohan PJ, Bickell NA, Baptiste MS, et al. Hospital volume differences and five-year survival from breast cancer. Am J Pub Health 1998;88:454-7. 13. McDonald LC, Kilgor GE, Thompson A, et al. An epidemic, toxin gene-variant strain of Clostridium difficile. New Engl J Med 2005;353:2433-41. 14. Loo VG, Poirier L, Miller MA, et al. A predominantly clonal multi-institutional outbreak of Clostridium difficile-associated 734 Wang and Stewart 15. 16. 17. 18. diarrhea with high morbidity and mortality. New Engl J Med 2005;353:2442-9. Kuijper EJ, Barbut F, Brazier JS, et al. Update of Clostridium difficile infection due to PCR ribotype 027 in Europe, 2008. Euro Surveill 2008;13:18942. Ricciardi R, Rothenberger DA, Madoff RD, Baxter NN. Increasing prevalence and severity of Clostridium difficile colitis in hospitalized patients in the United States. Arch Surg 2007;142:624-31. Riccardi R, Ogilvie JW, Roberts PL, Marcello PW, Concannon TW, Baxter NN. Epidemiology of Clostridium difficile colitis in hospitalized patients with inflammatory bowel diseases. Dis Colon Rectum 2009;52:40-5. Pennsylvania Cost Containment Council web site. Hospitalacquired Infections in Pennsylvania 2007---news release. Available from: http://www.phc4.org/reports/hai/07/ nr012209.htm. Accessed August 28, 2011. DISCUSSION Dr. Frederick Luchette (Maywood, IL): Dr Stewart, very nice presentation. This study used a large state databank that has inherent strengths and weaknesses. Dr Stewart describes a trend in inpatient cost for management of Clostridium difficile colitis as increasing over the 3-year study period. It concludes that, as one would expect, teaching and urban hospitals treat patients with more comorbidities, which he suggests accounts for the higher cost of care. I’m not sure that one can make this conclusion based on the data used in this study. You touched on this in the introduction: in recent years, there has been both an increased incidence and severity of Clostridium difficile colitis evolving during the last decade. This current epidemic may be driven by a specific strain, NAP1/B1, which has been well documented in the numerous reports in North America and Europe. It is characterized by significant mobility and mortality associated with an increased rate of medical failure to antibiotic treatment, leading to colectomy. So is the increased cost for the treatment at small rural hospitals related to protracted stays for treatment? Similarly, is the higher cost of the care at the urban teaching facilities because of the more complex patient population at these hospitals and possibly more frequent colectomies for the more virulent strain of Clostridium difficile seen in these institutions? Another concern I have is with the accuracy of the data in the Pennsylvania Health Care Cost Containment Council, which is using discharge coding. Many hospitals will not use strict criteria for diagnosing various infectious diseases, which is referred to as upcoding. This aggressive coding allows the hospitals to increase charges, and thus reimbursement, particularly at small rural hospitals. Could the higher cost at the small rural hospital facilities just be inaccurate because of the upcoding? Stated another way, how do we know that all the patients discharged with a diagnosis of Clostridium difficile colitis from the small rural hospitals actually had that diagnosis and disease? Dr. David Stewart (Hershey, PA): Let me start with the last question first. As with any discharge data, upcoding Surgery October 2011 may exist; however, it is unlikely to have caused the higher costs of small hospitals in our study. Upcoding may increase the cost of a patient who does not have CD colitis, but its effect on the average cost for CD colitis can be in either direction, depending on whether the patient included from upcoding has higher or lower cost compared to the average cost of the correctly coded CD colitis patients. Also as we see, the rate of CD colitis was similar across small, medium and large sized hospitals. So even if upcoding may exist, it does not differentially affect small hospitals. You mentioned NAP1 strain. I have an IRB-approved tissue bank at our hospital. I collect Clostridium difficile samples and then I analyze them for NAP1 strain using PFGE. I use PCR to identify TCDC mutations. I look at MICs for vancomycin and flagyl. And this just began less than a year ago. So we have about 25 samples that have been isolated, strained, and typed. And almost every one of those samples was NAP1, had binary toxin, had toxins A and B, had basement-low MICs for flagyl and vancomycin. And in addition, had other virulence factors that aren’t as well publicized and aren’t as well understood. So I tend to think that the higher incidence and severity of Clostridium difficile colitis might not be because of a particular strain. I’m more convinced now, seeing some of this preliminary small-size data, that maybe NAP1 designation is important because it covaries with some other unidentified virulence factor. Because none of the NAP1 strains in our sample had any longer length of stay when compared to a matched cohort, none of them required surgery, none of them required ICU care, and yet 82% of the samples we’ve collected thus far were NAP1. At least at our hospital, NAP1 is the strain that people develop with all of the associated virulence factors. And yet, for some reason, most of our patients don’t end up needing colectomy, even at a tertiary referral center. We average about 10 a year. Dr. Thomas Hayward (Indianapolis, IN): How did you handle the data irregularities inherent in administrative cost data? There are commonly extreme values from zero cost to very high charges, just because of low sampling errors as a file. In common practice, you often substitute the average if they differ more than a standard deviation or two from the mean. Question number 2: how did you handle transfers, or was there a way to pull out transfers from the triage hospital from the rural to the tertiary care facility? Was that data available in your data set? Thirdly, the incidence of Clostridium difficile acquired disease decreased over your study, although it had been increasing for most of the decade. Why do you think this happened? And could it be a result of the SCIP measures that were first introduced in 2005, where perioperative antibiotics were limits to a 24-hour course or less for the first time among many of our colleagues. And of course, the PQI supplement became instituted, and that should be available in a coded database. Or Surgery Volume 150, Number 4 do you think it is because of upcoding or decreased coding for complications because of publication of complication data? Or do you think it’s something else? Dr. David Stewart (Hershey, PA): In terms of costs, the possible effect of outliers on costs is minimal due to the large sample size in our study (78,273 CD colitis patients). In addition, several ways were also used to clean data to ensure the cost accuracy, such as equating the cost-to-charge ratio outliers to the nearest reasonable values, and excluding patients with zero charges. Secondly, this study did not examine transfers, for the average cost analyzed was for per admission not per patient. If a patient was transferred and had two discharge records from two facilities, then this patient contributed two observations to the study sample. Wang and Stewart 735 Lastly, you mentioned decreasing incidence. I tried to be very careful not to couch this study in terms of an epidemiologic study, which I would define the latter as being looking at disease shifts at the population level over broad sweeps of time. I only looked at a very small cross section of time, more for the purpose of evaluating cost. And I just wanted to have incidence and costs put on par there so that you could see that our incidence is going down over this very short time period, which matches other in-state markers of healthcare-acquired infections as well as making my point that, even with the incidence decreasing, our costs are going up, which is something that the PHC4 has been commenting on for the last decade.