enhanced detection of staphylococcus aureus-related hospitalizations using administrative databases, united states

1. Enhanced Detection of Staphylococcus aureus-related Hospitalizations Using Administrative Databases, United States�1999-2005 Jhung MA, Banerjee SN, Fridkin S, Tenover FC, McDonald LC Division of Healthcare Quality Promotion Centers for Disease Control and Prevention

2. Background Administrative Data ICD-9-CM discharge diagnoses Nationwide Inpatient Sample (NIS) National Hospital Discharge Survey (NHDS) Other Data Sources Sentinel surveillance of HAIs (NHSN) Population based surveillance (ABCs) Prevalence survey (APIC) Recently, several studies have estimated the national burden of Staphylococcus aureus infections using different data sources. Some have used administrative data, or ICD-9-CM codes, and examples of these are the Nationwide Inpatient Sample or NIS and the National Hospital Discharge Survey, or NHDS. Others have used sentinel surveillance for healthcare associated infections, population-based surveillance, and prevalence surveys. Recently, several studies have estimated the national burden of Staphylococcus aureus infections using different data sources. Some have used administrative data, or ICD-9-CM codes, and examples of these are the Nationwide Inpatient Sample or NIS and the National Hospital Discharge Survey, or NHDS. Others have used sentinel surveillance for healthcare associated infections, population-based surveillance, and prevalence surveys.

3. Background Administrative databases are attractive for estimating the national burden of infections because they are relatively available, reliable, and inexpensive. However, their data may be inconsistent across providers, lack elements important for surveillance, and have highly variable sensitivity and positive predictive value. Administrative databases are attractive for estimating the national burden of infections because they are relatively available, reliable, and inexpensive. However, their data may be inconsistent across providers, lack elements important for surveillance, and have highly variable sensitivity and positive predictive value.

4. Background Some commonalities of recently published estimates using administrative data are that they have varied in database, methodology, and results. They often use few ICD-9-CM codes, provide a single estimate for each infection type, and make no comparison to other estimatesSome commonalities of recently published estimates using administrative data are that they have varied in database, methodology, and results. They often use few ICD-9-CM codes, provide a single estimate for each infection type, and make no comparison to other estimates

5. Objectives Determine national estimates of S. aureus-related hospitalizations Pilot enhanced detection algorithm Establish a range of estimates Explore reasons for incidence trends Compare to other analyses The objective of this study was to determine a national estimate for Staphylococcus aureus infections in the US, and in the process, attempt to identify an enhanced detection scheme that would define a range of estimates for each infection type, and provide enough granularity to explore reasons for any trends in incidence observed. We also sought to compare results from our study to other published analyses.The objective of this study was to determine a national estimate for Staphylococcus aureus infections in the US, and in the process, attempt to identify an enhanced detection scheme that would define a range of estimates for each infection type, and provide enough granularity to explore reasons for any trends in incidence observed. We also sought to compare results from our study to other published analyses.

6. Methods Nationwide Inpatient Sample (NIS) dataset The Surveillance Network (TSN) for resistance Categories of S. aureus infections Bloodstream infections (septicemia) Respiratory infections (pneumonia) Skin infections �All other� Conservative, Moderate, Liberal estimates �Case definitions� include healthcare and community associated disease We used the Nationwide Inpatient Sample (NIS) for ICD-9 data from 1999-2005 and obtained resistance data from published estimates from The Surveillance Network (TSN). We defined 3 categories of Staphylococcus aureus infections Bloodstream infections, which we labeled Septicemia, Respiratory infections, which we labeled Pneumonia, and Skin infections. This resulted in a 4th category of remaining suspected Staphylococcus aureus infections, which we designated, an �all other� category We then identified combinations of ICD-9-CM discharge codes to derive three levels of estimates: conservative, moderate, and liberal. Note that the case definitions we used included both healthcare and community-associated disease.We used the Nationwide Inpatient Sample (NIS) for ICD-9 data from 1999-2005 and obtained resistance data from published estimates from The Surveillance Network (TSN). We defined 3 categories of Staphylococcus aureus infections Bloodstream infections, which we labeled Septicemia, Respiratory infections, which we labeled Pneumonia, and Skin infections. This resulted in a 4th category of remaining suspected Staphylococcus aureus infections, which we designated, an �all other� category We then identified combinations of ICD-9-CM discharge codes to derive three levels of estimates: conservative, moderate, and liberal. Note that the case definitions we used included both healthcare and community-associated disease.

7. MethodsICD-9-CM Diagnosis Codes 041.11 Staphylococcus aureus 482.41 Staphylococcus aureus pneumonia 038.11 Staphylococcus aureus septicemia The ICD-9-CM codes we used are shown here, separated into Staphylococcus aureus specific codes shown in the top box, and non-specific codes in the larger, bottom box.The ICD-9-CM codes we used are shown here, separated into Staphylococcus aureus specific codes shown in the top box, and non-specific codes in the larger, bottom box.

8. ICD-9-CM Diagnosis CodesConservative Estimate 041.11 Staphylococcus aureus 038.11 Staphylococcus aureus septicemia 482.41 Staphylococcus aureus pneumonia For our conservative estimate, we used only specific codes for Staphylococcus aureus septicemia and pneumonia. The conservative estimate of skin infections consisted of discharges containing the organism code for Staphylococcus aureus (041.11) plus one of the skin infection codes listed here and no additional organism codes. Once placed into an infection category, discharges were removed from the database and were not therefore able to be counted again.For our conservative estimate, we used only specific codes for Staphylococcus aureus septicemia and pneumonia. The conservative estimate of skin infections consisted of discharges containing the organism code for Staphylococcus aureus (041.11) plus one of the skin infection codes listed here and no additional organism codes. Once placed into an infection category, discharges were removed from the database and were not therefore able to be counted again.

9. ICD-9-CM Diagnosis CodesModerate Estimate 041.11 Staphylococcus aureus 482.41 Staphylococcus aureus pneumonia 038.11 Staphylococcus aureus septicemia The moderate estimates for septicemia and pneumonia were obtained by starting with the conservative estimates and adding to those discharges with the Staphylococcus aureus organism code and one of the non-specific bloodstream or respiratory infection codes and no other organism code. The moderate skin infection estimate contained the Staphylococcus aureus organism code and one of the skin infection codes. Other organism codes were allowed in this estimate. The moderate estimates for septicemia and pneumonia were obtained by starting with the conservative estimates and adding to those discharges with the Staphylococcus aureus organism code and one of the non-specific bloodstream or respiratory infection codes and no other organism code. The moderate skin infection estimate contained the Staphylococcus aureus organism code and one of the skin infection codes. Other organism codes were allowed in this estimate.

10. ICD-9-CM Diagnosis CodesLiberal Estimate 041.11 Staphylococcus aureus 482.41 Staphylococcus aureus pneumonia 038.11 Staphylococcus aureus septicemia The liberal estimates for septicemia, pneumonia and skin infections were obtained by starting with the moderate estimates and adding to those discharges containing non-specific respiratory, bloodstream, and skin infection codes with no organism code multiplied by a proportion, estimated from the literature, of each infection type that could be reasonably attributed to Staphylococcus aureus.The liberal estimates for septicemia, pneumonia and skin infections were obtained by starting with the moderate estimates and adding to those discharges containing non-specific respiratory, bloodstream, and skin infection codes with no organism code multiplied by a proportion, estimated from the literature, of each infection type that could be reasonably attributed to Staphylococcus aureus.

11. This slide shows rates of Staphylococcus aureus related discharges from 1999-2005. Total rates, depicted in blue, and rates for each infection type increased significantly over the time period. To the right of the screen are 2005 discharge counts for each category, which show that by 2005, skin infections contributed substantially more to the total count than did septicemia or pneumonia. This slide shows rates of Staphylococcus aureus related discharges from 1999-2005. Total rates, depicted in blue, and rates for each infection type increased significantly over the time period. To the right of the screen are 2005 discharge counts for each category, which show that by 2005, skin infections contributed substantially more to the total count than did septicemia or pneumonia.

12. Skin infections made up nearly half of all Staphylococcus aureus related discharges in 2005. Since they comprised such a large portion of all infections, we took a closer look to see what these skin infections were.Skin infections made up nearly half of all Staphylococcus aureus related discharges in 2005. Since they comprised such a large portion of all infections, we took a closer look to see what these skin infections were.

13. For each year in the time frame we studied, three major classes of infections comprised the vast majority of all discharges falling into the skin infection category. Shown here are data from 2004, when chronic ulcers, surgical site infections, and cellulitis and abscesses made up 98% of all skin infections identified.For each year in the time frame we studied, three major classes of infections comprised the vast majority of all discharges falling into the skin infection category. Shown here are data from 2004, when chronic ulcers, surgical site infections, and cellulitis and abscesses made up 98% of all skin infections identified.

14. Looking at these three classes of skin infections over time, we found significant changes in the proportion each contributed to the overall skin infection category. This graph shows the proportion of all Staphylococcus aureus skin infections attributed to the three main classes identified on the previous slide. Note that the proportion of chronic ulcers and SSIs decreased, and that of the cellulitis and abscess class increased significantly over time. By 2005, 73% of all skin infections fell into the cellulitis and abscess class. Looking at these three classes of skin infections over time, we found significant changes in the proportion each contributed to the overall skin infection category. This graph shows the proportion of all Staphylococcus aureus skin infections attributed to the three main classes identified on the previous slide. Note that the proportion of chronic ulcers and SSIs decreased, and that of the cellulitis and abscess class increased significantly over time. By 2005, 73% of all skin infections fell into the cellulitis and abscess class.

15. Cellulitis and abscess in Younger Age Groups Taking a closer look at cellulitis and abscess infections, we also found significant changes across age groups. This graph contains information on the cellulitis and abscess class only and shows the proportion contributed to this class by each of 4 age groups. We found that while the percent contribution by the two youngest age groups increased significantly, proportions for the two oldest age groups either stayed constant or decreased over time. Taking a closer look at cellulitis and abscess infections, we also found significant changes across age groups. This graph contains information on the cellulitis and abscess class only and shows the proportion contributed to this class by each of 4 age groups. We found that while the percent contribution by the two youngest age groups increased significantly, proportions for the two oldest age groups either stayed constant or decreased over time.

16. S. aureus Septicemia Increases from Conservative to Liberal Estimates We turn now to our estimates by Staphylococcus aureus infection category. As expected, liberal estimates were larger than moderate which were larger than conservative. An example of this is shown here in a graph depicting these 3 levels for 2005 Staphylococcus aureus septicemia discharges. For septicemia, the moderate estimate was 21% greater than the conservative, and the liberal estimate was 54% greater than the conservative. Notice that the liberal estimate of nearly 170,000, when multiplied by the overall percent of methicillin resistance obtained from TSN, corresponds well to the national estimate from ABC surveillance for invasive MRSA infections (Klevens, et al. JAMA, October 2007).We turn now to our estimates by Staphylococcus aureus infection category. As expected, liberal estimates were larger than moderate which were larger than conservative. An example of this is shown here in a graph depicting these 3 levels for 2005 Staphylococcus aureus septicemia discharges. For septicemia, the moderate estimate was 21% greater than the conservative, and the liberal estimate was 54% greater than the conservative. Notice that the liberal estimate of nearly 170,000, when multiplied by the overall percent of methicillin resistance obtained from TSN, corresponds well to the national estimate from ABC surveillance for invasive MRSA infections (Klevens, et al. JAMA, October 2007).

17. NIS Estimate Larger than NHDS Estimate The last result slide demonstrates a potential sensitivity benefit that using the NIS database might have. This graph shows total Staphylococcus aureus related discharge numbers for both the Nationwide Inpatient Sample and the National Hospital Discharge Survey, and you can see that in each year from 1999 to 2005, the Nationwide Inpatient Sample estimate is substantially larger than the National Hospital Discharge Survey estimate. By 2005, there is a 25% difference between the Nationwide Inpatient Sample and the National Hospital Discharge Survey estimates. This may be due in part to the fact that the Nationwide Inpatient Sample database contains up to 15 discharge diagnosis codes while the National Hospital Discharge Survey is limited to seven.The last result slide demonstrates a potential sensitivity benefit that using the NIS database might have. This graph shows total Staphylococcus aureus related discharge numbers for both the Nationwide Inpatient Sample and the National Hospital Discharge Survey, and you can see that in each year from 1999 to 2005, the Nationwide Inpatient Sample estimate is substantially larger than the National Hospital Discharge Survey estimate. By 2005, there is a 25% difference between the Nationwide Inpatient Sample and the National Hospital Discharge Survey estimates. This may be due in part to the fact that the Nationwide Inpatient Sample database contains up to 15 discharge diagnosis codes while the National Hospital Discharge Survey is limited to seven.

18. Summary Staphylococcus aureus-related discharges have increased significantly from 1999-2005 Estimates based on this algorithm are higher than other published estimates Septicemia liberal estimates may be more accurate than conservative Majority of increase in Staphylococcus aureus-related discharges is due to skin infections (cellulitis and abscess) in patients < 45 years of age Community associated disease? In summary, we found that Staphylococcus aureus -related discharges increased significantly from 1999-2005 across all infection types. Even our conservative estimates were higher than other published values that used administrative data sources. Using ABC surveillance as a de facto gold standard, we found evidence that our liberal estimate, at least for septicemia, may be more accurate than the conservative value. Finally, the bulk of the increase in Staphylococcus aureus -related discharges over the six year period appeared to be due to increases in skin infections, particularly cellulitis and abscesses, among patients less than 45 years of age. This raises the possibility that the majority of the overall increase in Staphylococcus aureus -related discharges is to due to community-associated disease. In summary, we found that Staphylococcus aureus -related discharges increased significantly from 1999-2005 across all infection types. Even our conservative estimates were higher than other published values that used administrative data sources. Using ABC surveillance as a de facto gold standard, we found evidence that our liberal estimate, at least for septicemia, may be more accurate than the conservative value. Finally, the bulk of the increase in Staphylococcus aureus -related discharges over the six year period appeared to be due to increases in skin infections, particularly cellulitis and abscesses, among patients less than 45 years of age. This raises the possibility that the majority of the overall increase in Staphylococcus aureus -related discharges is to due to community-associated disease.

19. Limitations Administrative data Not primarily intended for surveillance Unable to distinguish community from healthcare onset Unit of analysis is discharge not patient Analysis Codes may not represent S. aureus This study is subject to the following limitations: First, administrative data are primarily intended to assist with hospital billing, not disease surveillance Second, we were unable to determine community vs. healthcare acquisition of Staphylococcus aureus infections using these data Third, the unit of analysis for these databases is a hospital discharge, not a patient and it is unclear how many unique patients are represented in these discharges Finally, since Staphylococcus aureus organism codes are not linked directly to infection codes, the discharges identified by our algorithm may not represent true Staphylococcus aureus infectionsThis study is subject to the following limitations: First, administrative data are primarily intended to assist with hospital billing, not disease surveillance Second, we were unable to determine community vs. healthcare acquisition of Staphylococcus aureus infections using these data Third, the unit of analysis for these databases is a hospital discharge, not a patient and it is unclear how many unique patients are represented in these discharges Finally, since Staphylococcus aureus organism codes are not linked directly to infection codes, the discharges identified by our algorithm may not represent true Staphylococcus aureus infections

20. Conclusions Surveillance Using Administrative Data Multiple codes and a range of estimates may yield the most useful results A database with a large number of diagnosis fields may increase sensitivity Further study needed to determine appropriate estimate for pneumonia and skin infections Nonetheless, we may still be able to conclude the following about using administrative data to derive national estimates of Staphylococcus aureus infections First an approach employing multiple codes to identify a range of estimates may yield the most accurate and useful results Second, a database with a large number of diagnosis fields, perhaps greater than 15, may increase sensitivity Finally, further study is needed to determine which estimate from the range we identified is the best measure of the burden of Staphylococcus aureus pneumonia and skin infections.Nonetheless, we may still be able to conclude the following about using administrative data to derive national estimates of Staphylococcus aureus infections First an approach employing multiple codes to identify a range of estimates may yield the most accurate and useful results Second, a database with a large number of diagnosis fields, perhaps greater than 15, may increase sensitivity Finally, further study is needed to determine which estimate from the range we identified is the best measure of the burden of Staphylococcus aureus pneumonia and skin infections.

21. Acknowledgments Division of Healthcare Quality Promotion Centers for Disease Control and Prevention Katherine Ellingson Rachel Gorwitz Jeffrey Hageman John Jernigan Melissa Schaefer Agency for Healthcare Quality and Research Anne Elixhauser We would like to acknowledge the following individuals for their assistance.We would like to acknowledge the following individuals for their assistance.

22. Citations for Published Estimates Elixhauser A. Infections with Methicillin-Resistant Stapyhlococcus aureus (MRSA) in US Hospitals, 1993-2005. In: AHRQ/HCUP Statistical Brief #35; 2007:1-10 Jarvis WR. National prevalence of methicillin-resistant Staphylococcus aureus in inpatients at US health care facilities, 2006. Am Jour of Infect Cont. 2007 Dec;35(10):631-7. Klein E. Hospitalizations and deaths caused by methicillin-resistant Staphylococcus aureus, United States, 1999-2005. Emerg Infect Dis. 2007 Dec;13(12):1840-6. Klevens RM. Invasive methicillin-resistant Staphylococcus aureus infections in the United States. JAMA. 2007 Oct 17;298(15):1763-71. Kuehnert MJ. Methicillin-resistant-Staphylococcus aureus hospitalizations, United States. Emerg Infect Dis. 2005 Jun;11(6):868-72. Noskin GAl. The burden of Staphylococcus aureus infections on hospitals in the United States: an analysis of the 2000 and 2001 Nationwide Inpatient Sample Database. Arch Intern Med. 2005 Aug 8-22;165(15):1756-61. Noskin GA. National trends in Staphylococcus aureus infection rates: impact on economic burden and mortality over a 6-year period (1998-2003). Clin Infect Dis. 2007 Nov 1;45(9):1132-40. Styers D. Laboratory-based surveillance of current antimicrobial resistance patterns and trends among Staphylococcus aureus: 2005 status in the United States. Ann of Clin Micro and Antimicrobials. 2006;5:2 Authors for published estimates used in this study include the following.Authors for published estimates used in this study include the following.