Chapter 3 Methods

3.1 Reporting Requirements

The 2007 amendment to the MCARE Act (Act 52) requires all hospitals to report all healthcare-associated infections (HAI) associated with any inpatient location using the Patient Safety Module of CDC’s National Healthcare Safety Network ([NHSN][https://www.cdc.gov/nhsn/index.html]).

3.2 Data

This report contains data from inpatient facilities reported to NHSN. Facilities are required to follow the National Healthcare Safety Network (NHSN) Patient Safety Protocol (Centers for Disease Control and Prevention 2016b) to identify, classify, and report infections and patients at risk. The descriptions below aim to faithfully summarize procedures and case definitions extensively detailed in the 2016 NHSN Patient Safety Protocol. The 2016 NHSN Patient Safety Protocol is the authoritative reference.

3.2.1 Benchmarked Infections

Previous Pennsylvania HAI reports included unadjusted crude HAI rates for many infection types, this report only includes device- and procedure-associated HAIs for which CDC calculates standardized infection ratios (SIRs). These include CLABSIs, CAUTIs and seven procedure-related SSIs (hip and knee replacements, abdominal hysterectomies, colon surgeries, cardiac surgeries and cardiac bypass surgeries with one or two incisions) benchmarked by DOH in previous reports (Centers for Disease Control and Prevention 2016b).

3.2.2 Reporting Period

This report includes contains device-associated HAIs that occurred in 2016. It also includes SSIs resulting from procedures performed in 2016; however, the infection might have been diagnosed in 2017, since SSI surveillance case definitions include a 30- or 90-day follow-up period (Centers for Disease Control and Prevention 2016b). Data was downloaded from NHSN and current as of Sept. 5, 2017.

3.2.3 Included Facilities

This report includes data from acute care, women’s, children’s, long-term acute care (LTAC), rehabilitation, psychiatric and critical access hospitals. Importantly, this year’s report includes both free-standing rehabilitation hospitals and inpatient rehabilitation units in hospitals, collectively known as inpatient rehabilitation facilities (IRFs). Per CMS guidance, IRF units reported data to NHSN under their own NHSN identifier, independent from the hospital in which they are located. Facility data was excluded if the facility was not open for all 12 months of 2016.

3.2.4 Data Validation

Act 52 requires facilities to have an HAI surveillance plan and to apply NHSN case criteria. Case criteria are published in, The NHSN Manual: Patient Safety Protocol (Centers for Disease Control and Prevention 2016b). Case finding and case classification are the responsibility of the facility.

The NHSN online collection tool has several automated data validation algorithms. When inconsistent or out-of-range data is entered, a pop-up message appears alerting the user to correct the data value. The tool also has defined algorithms to detect data problems. These are identified as alerts and appear upon login thereby prompting the user to fix the identified problems. Clicking on the alert brings the user to the identified problem so that it can be corrected.

In addition to the built-in NHSN data validation tools, DOH performs several activities to promote reporting of quality data. First, DOH provides education and consultation to infection preventionists. Second, DOH analyzes NHSN data quarterly to identify missing data or data values that are unusual, inconsistent or duplicative. DOH provides each facility with a tailored listing of data elements to validate. Hospitals have 30 days from the end of the analysis month to make corrections to their data.

DOH does not currently perform external validation or direct chart audits.

3.3 Catheter-Associated HAIs

Catheters are tubes inserted into the body that allow solutions to drain from or be infused into the body. Health care workers follow medical guidelines to insert and care for catheters to keep them clean. When not put in correctly or kept clean, they can become a pathway for germs to enter the body. Bacteria or other germs could travel along the catheter and enter the body site where they were placed. The bacteria could grow in the body fluids and cause a serious infection. These infections are classified as ‘associated’ with use of a catheter.

Catheter-associated HAIs can only be diagnosed among hospitalized patients who have catheters in place during their hospitalization for three or more consecutive days. The catheter must have been in place on the day that the infection was found or the day before. Hospitals are not required to follow-up with discharged patients to determine whether they stayed infection free. However, infections that get diagnosed after discharge are counted if they are reported to the hospital and meet the NHSN criteria. For more information refer to these resources [Frequently Asked Questions about Catheters] (https://www.cdc.gov/hai/bsi/catheter_faqs.html).

Infections that occur after the use of two types of catheters are tracked in this report and nationally. They are infections associated with central lines and urinary catheters. CAUTI and CLABSI case definitions are detailed in the NHSN Patient Safety Component Manual (Centers for Disease Control and Prevention 2016b). The number of CLABSIs and CAUTIs are aggregated monthly by hospital location and entered into NHSN each month.

Hospitals are responsible for reporting infections that meet NHSN case definition inclusion and exclusion criteria (Centers for Disease Control and Prevention 2016b). To reduce the likelihood that an infection is erroneously attributed to the hospital, infections that occur prior to hospital admission or within the first two days of admission are excluded. Repeat infections are infections at the same site that return within 14 days of the initial date of onset; these are not counted again. A blood stream infection associated with a catheter that is secondary to a recently documented infection at another site, with the same organism, is also not counted again.

3.3.1 CLABSIs

CLABSIs only occur among patients who have had a central line (or central venous catheter). This is a tube placed in a large vein to allow access to the bloodstream and provides the patient with important medicine. It is typically located close to the center of the body (e.g., the heart). The NHSN Patient Safety Component Manual (Centers for Disease Control and Prevention 2016b) defines a CLABSI as a lab confirmed infection of the blood. Inpatients that receive dialysis through a central line are eligible to get a CLABSI.

3.3.2 CAUTIs

CAUTIs only occur among patients who have had a urinary catheter (Foley catheter), which is a tube placed in the bladder that remains in place (indwelling) to drain urine into a bag. The NHSN Patient Safety Component Manual (Centers for Disease Control and Prevention 2016b) defines a CAUTI as lab confirmed infection in the urine from a patient with at least one symptom (e.g., fever, vomiting). An infection is defined as bacterial growth of two or fewer species at least one of which is extremely plentiful (i.e., more than 100,000 colony forming unite per milliliter urine). For more information refer to these resources [Frequently Asked Questions about Catheter-associated Urinary Tract Infections] (https://www.cdc.gov/hai/ca_uti/cauti_faqs.html).

3.3.3 Counting Catheter-Days

Because CAUTIs and CLABSIs only occur among people with a catheter in place, it is important to track and report the number of people that have an indwelling catheter each day. The process of counting the total number of at-risk patients can be performed in one of several fashions. Hospitals can perform a daily census by hand, a once a week manual count that is extrapolated to represent the month, or through use of a counting algorithm in the hospital’s electronic medical record. This is performed separately for each hospital location (e.g., oncology ICU, pediatric ward, burn unit) and also entered each month into NHSN.

The number of persons at risk is measured as urinary catheter days or central line days. They are defined as follows:

  • Urinary catheter days: the total number of hospitalized patients with a urinary catheter in place per day over the month (total number of patients with a urinary catheter in place multiplied by the number of days a catheter was used for each patient).

  • Central line days: the total number of hospitalized patients with a central line in place per day over the month (total number of patients with at least one central line in place multiplied by the number of days a central line was in place for each patient).

3.3.4 Admitted Patients

The number of hospitalized patients is counted every day or week using similar processes as those described for counting device days. This is performed separately for each hospital location (e.g., oncology ICU, pediatric ward, burn unit). The counts are aggregated by location and month and entered into NHSN each month.

3.4 Surgical Site Infections

A surgical site infection (SSI) occurs after surgery in the part of the body where the surgery took place. These infections may involve only the skin or may be more serious and involve tissue under the skin or organs. SSIs sometimes take days or months after surgery to develop (Council of State and Territorial Epidemiologists 2015).

3.4.1 Counting Surgeries

Each surgical procedure that occurred in calendar year 2016 and met reporting criteria described in Chapter 9 of the Patient Safety Component Manual (Centers for Disease Control and Prevention 2016b) was eligible for inclusion in this report. Only surgeries that occurred in inpatient locations from within hospitals are included in this report.

This report includes SSI data from the below seven most common surgeries:

  • Cardiac (CARD);
  • Cardiac bypass graft surgery with one incision (CBGC);
  • Cardiac bypass graft surgery with two incisions (CBGB);
  • Knee prosthesis (KPRO);
  • Hip prosthesis (HPRO);
  • Abdominal hysterectomy (HYST); and
  • Colon surgery (COLO).

Since diabetes is an important risk factor for infection and should be accounted for in statistical models, surgical patients in whom the diagnosis of diabetes was undetermined were excluded. Similarly, surgical patients who received insulin for perioperative control of hyperglycemia but had no diagnosis of diabetes were also excluded.

3.4.2 Counting SSIs

The NHSN Patient Safety Component Manual requires hospitals to use active, patient-based, prospective surveillance to identify superficial incisional and deep incisional SSIs (Centers for Disease Control and Prevention 2016b). Post-discharge and before-discharge surveillance methods might include:

  1. Direct examination of patients’ wounds in the hospital or during follow-up visits to either surgery clinics or physicians’ offices;
  2. Review of medical records or surgery clinic patient records;
  3. Surgeon surveys by mail or telephone; and
  4. Patient surveys by mail or telephone.

An SSI is classified as being either superficial incisional, deep incisional or organ space (only among patients who had cardiac surgery). Superficial and deep incisional infections are further classified into those identified at the primary or secondary incision site among surgeries that had two or more incisions.

A superficial incisional infection involves only the skin or subcutaneous tissue, whereas a deep incisional infection involves deep soft tissues of the incision.1 Superficial incisional infections must have one of the following:

  1. Purulent drainage;
  2. Identification of organism from wound;
  3. Re-opening of the incision by the provider, without obtaining a culture, and clinical symptoms consistent with infection; or
  4. Diagnosis of a superficial incisional SSI by the surgeon or other designee.

Deep incisional SSIs involve deep soft tissues of the incision (e.g., fascial and muscle layers), and the patient must have at least one of the following:

  1. Purulent drainage from the deep incision;
  2. A deep incision that spontaneously dehisces, or is deliberately opened or aspirated by a surgeon or other designee and organism identified by a culture or non-culture-based test; if culture or non-culture testing is not performed, then the patient must have consistent symptoms of infection (i.e., objective fever or localized pain or tenderness); or
  3. An abscess involving the deep incision that is detected on gross anatomical, histopathologic exam or imaging test.

For patients undergoing cardiac surgery, the infection could also be classified as an organ space infection. An organ space infection occurs when tissue deeper than fascial/muscle layers is infected.

Irrespective of the procedure type, superficial SSIs must occur within 30 days of the procedure. The surveillance period for deep incisional and organ/space SSIs varies by procedure type. Patients who had a COLO or HYST procedure were monitored for 30 days, whereas other patients were monitored for 90 days.

Two different perspectives exist regarding SSI surveillance. Some suggest that surveillance should monitor SSIs that affect any part of the incision, both deep and superficial. Others suggest that only infections located in deeper tissues should be monitored. These are more serious and are easier to correctly identify. To address these different viewpoints, NHSN calculates two SIRs. The ‘All’ model includes superficial and deep tissue infections whereas the ‘Complex’ model includes only deep tissue infections.

As per NHSN guidelines, infections with well-known community-associated organisms and/or organisms associated with latent infections were not classified as SSIs (Centers for Disease Control and Prevention 2016b).

3.5 Data Analysis

In this report, the DOH transitioned to using performance measures calculated directly by CDC’s NHSN.

In previous years, DOH reported its own statistical analyses of NHSN data. Pennsylvania was an early adopter of the NHSN system and required comprehensive reporting of each HAI-type from all inpatient facilities (i.e., acute care, long-term acute care, rehabilitation, psychiatric, critical access, women’s and children’s hospitals). At the time, national comparison data was limited; therefore, DOH reported HAI measures relative to other facilities in the state.

With more national data now available, CDC refreshed its case definitions, updated its statistical methods and set new baselines for HAI rate comparisons. The new national reference data was reported to NHSN in 2015 from 6,000 acute care, critical access, long term acute care and inpatient rehabilitation hospitals. As detailed below, these changes allow comparison of a hospital’s HAI experience to that of the rest of the country and will enable tracking of trends from year-to-year relative to the new 2015 baseline.

3.5.1 Standardized Measures of Performance

CDC calculates standardized infection or utilization ratios (SIR or SUR) to evaluate a facility’s HAI performance relative to similar facilities in the rest of the country. Both quantify the degree to which the actual practice differs from what normally occurs.

The SIR measures occurrence of infections, whereas the SUR measures frequency of either central line or urinary catheters among patients admitted into the hospital. The statistical methods used to calculate the SIR and SUR are virtually identical, and the process is described below. The word ‘SIR’ is used in the below description for the sake of consistency, although, the same basic method applies to creation and interpretation of the SUR. The differences between SIR and SUR will be discussed when applicable.

The word ‘standardized’ or ‘adjusted’ means that the data has been statistically adjusted for the factors that make hospitals different. When comparing two SIRs, those factors are removed as a reason for explaining why hospitals with lower SIRs are doing better than those with higher SIRs. The differences are due to other causes, such as adherence to infection control practices.

3.5.2 Interpreting SIRs and SURs

The word ‘ratio’ is a mathematical method used to compare two numbers. It is calculated as one number divided by another. The SIR is a ratio in which one number is the count of infections that occurred and the other is the number of infections predicted by a statistical model. The statistical model adjusts for risk factors that vary among hospitals that may underlie differences in the number of reported infections. The predicted number of infections is derived from the statistical model and is interpreted as the number of infections a nearly identical hospital would have. The SIR and SUR are useful metrics to compare one facility to similar facilities in the rest of the country. Below are rules to interpret SIRs by themselves:

  • If the SIR > 1.0, then more HAIs were observed than predicted, based on the 2015 national aggregate data.
  • If the SIR= 1.0, then the same number of HAIs were observed as predicted, based on the 2015 national aggregate data.
  • If the SIR < 1.0, then fewer HAIs were observed than predicted, based on the 2015 national aggregate data.

SIRs do not estimate the chance, or risk, of getting an HAI. It only compares the risk relative to the national experience among similar hospitals. A few problems arise when using the SIR as an indicator of HAI risk. The biggest problem is that hospitals with few patients and/or beds are not predicted to have one HAI. CDC and statisticians agree that SIRs among facilities with less than one predicted infection are not an accurate estimate of the infection risk. Calculation of a SIR in these hospitals could result in a very large SIR value if the facility reported even one infection, and this may be misleading. In these cases, CDC recommends that a SIR be calculated over a two-year time span. This optimizes the estimation of the SIR because the number of observed and predicted infections will increase. After combining 2015 and 2016 surveillance data, some hospitals still did not have the requisite predicted infections of 1.0 or more. For some hospitals in Pennsylvania, the SIR was not calculated because the predicted number of infections was less than 1.0. This problem does not occur for standardized utilization ratios (SURs) because the predicted number of patients with a catheter was never less than one.

Another problem pointed out above is that low values of predicted infections leads to SIRs that are unstable. To further evaluate the SIRs value, it’s important to consider if it is statistically meaningful. For a small hospital that performs few operative procedures or uses few urinary or central line catheters, the level of confidence in the stability of the calculated measure is lower than for a hospital with many procedures or device days. This is reflected in a calculation known as the confidence interval (CI), for which a lower value and an upper value are calculated and displayed. The true SIR for the hospital is found somewhere between the lower value and the upper value with 95% certainty. In general, the smaller the facility, the wider the confidence interval, meaning that confidence in the calculated measure is low. Larger facilities tend to have narrower confidence intervals, meaning the true value is likely to be in a tighter range of values.

To fully interpret SIRs, it’s best to see not only how large or small the SIR value is when compared to 1.0, but also examine the 95% confidence interval. If a hospital has a 95% confidence interval that does not include 1.0, it is considered “statistically significant.” The combination of both approaches is considered the best way to evaluate a single SIR. Below are rules for interpreting the 95% Confidence Interval.

95% Confidence Interval:

  • The 95% confidence interval is a range of values in which a high degree of confidence exists that the true SIR (or SUR) lies within that range.
  • If the confidence interval does not include 1.0, then the SIR is significantly different than 1 (i.e., the number of observed infections is significantly different than the number predicted).
    • Example: 95% confidence interval = (0.85, 0.92)
  • If the confidence interval includes the value of 1.0, then the SIR is not significantly different than 1.0 (i.e., the number of observed infections is not significantly different than the number predicted).
    • Example: 95% confidence interval = (0.85, 1.24)
  • If the SIR is 0.000 (i.e., the infection count is 0 and the number of predicted infections is ≥ 1.0), the lower bound of the 95% confidence interval will not be calculated.
    • Example: 95% confidence interval = (, 1.49)

The SIR is calculated by dividing the number of observed infections by the number of predicted infections.

\[\text{SIR} = \frac{\text{Observed HAIs}}{\text{Predicted}}\]

If a hospital reported 10 CAUTIs during 2016 and, based on the 2015 re-baseline data, only five CAUTIs were predicted in that hospital, the SIR would be 10/5 or 2.00. If another hospital reported five CAUTIs and, based the 2015 re-baseline data, 10 CAUTIs were predicted, the SIR would be 5/10 or 0.50 and the 95% confidence interval is 0.183 - 1.108. This means that although the hospital appears to have half of the predicted infections, it’s 95% likely that it has just as many CAUTIs as a similar hospital.

3.5.3 CLABSI Statistical Models

Negative binomial regression was used to adjust for risk factors and estimate the number of predicted CLABSIs. The strongest risk factor associated with CLABSIs is type of hospital. Because this could not be adequately adjusted for through statistical methods, separate models were used for each type of facility: Acute care hospitals (ACH), critical access hospitals (CAH), long-term acute care (LTAC) facilities, neonatal intensive care units (NICUs) and inpatient rehabilitation facilities (IRF). A separate model for children’s, women’s and psychiatric hospitals was not necessary, and they were included in the ACH model. A separate model was also used among patients in the neonatal intensive care unit (NICU). Twenty-two hospitals reported data from this ward location.

It is recommended to compare two CLABSI SIRs to each other only if both hospitals were included in the same adjustment model, such as children’s, psychiatric and acute care hospitals. SIRs from LTAC, IRF and CAH hospitals are not comparable to those from ACHs. Although not ideal, the observed and predicted CLABSIs from NICUs were added to those from the ACH model to create a single SIR or SUR for hospitals with NICUs.

The ACU CLABSI model adjusted for number of beds, affiliation with medical school and type of ward. Risk factors incorporated in other hospital models for other hospital types included patient population characteristics such as proportion of patients admitted with a stroke. Please refer to the SIR Guide Supplement for a list of all models and risk factors included in each model (Centers for Disease Control and Prevention 2017b).

3.5.4 CAUTI Statistical Models

Negative binomial regression was also used to adjust for risk factors and estimate the number of predicted CAUTIs. Similar to CLABSI models, separate models were used for each type of facility: acute care hospitals (ACH), critical access hospitals (CAH), long-term acute care (LTAC) facilities and inpatient rehabilitation facilities (IRF). Children’s, women’s and psychiatric hospitals were included in the ACH model. A separate NICU model was not necessary.

It is recommended to only compare two CAUTI SIRs to each other if both hospitals were included in the same adjustment model. Acute care, psychiatric and children’s hospitals were in the same model. Other hospital types were in different models.

The ACU CAUTI model adjusted for number of beds, affiliation with medical school and type of ward. Risk factors incorporated in other hospital type models included patient population characteristics such as average length of stay. Please refer to the SIR Guide Supplement for a list of all models and risk factors included in each model (Centers for Disease Control and Prevention 2017b).

3.5.5 SSI Statistical Models

Logistic regression models were used to estimate predicted SSIs. Models were built for each SSI separately. The SSI SIR is only calculated for ACHs and CAHs. Hospital type was not found to be a risk factor for SSIs.

Because age group was a risk factor for SSIs, each surgical procedure type had a separate model for adult and pediatric patients.

Only risk factors that were significantly related to SSIs are included in the models as risk factors. Models included factors related to patients and the hospital. Each model contains a separate set of risk factors. Generally, the statistical models adjust for patient-specific characteristics. Some models also adjust for medical school affiliation and hospital size. The observed and predicted SSIs from adult and pediatric patients were summed at the hospital level to calculate the single facility SIR.

The statistical process used to calculate the predicted number of infections required all risk factor data be present. Procedures without risk factor data were not included in the analysis. Procedures with risk factor data that were substantially more or less than the average (and likely caused by data entry problems) were also excluded from the analyses. Very few Pennsylvania procedures were excluded for these reasons due to the described previously validation process.

As mentioned in 3.4.2 (Counting SSIs), NHSN calculates SIRs for two different definitions of SSIs. In the All Model, both superficial and deep tissue infections are counted. In the Complex Model, only deep tissue infections are counted as SSIs. CDC publishes national and state-specific SSI SIRs using the Complex Model in its most recent 2015 HAI Progress report (Centers for Disease Control and Prevention 2017a). To facilitate comparison with these data, the results section of this report defines SSIs using the same Complex Model. Please refer to the SIR Guide Supplement for a list of all models and risk factors included in each model (Centers for Disease Control and Prevention 2017b).

Journal articles that report SSIs to assess the magnitude of the SSI problem count infections from both superficial and deep tissue sites (Magill et al. 2014, Klevens et al. (2007)). To evaluate these SSIs among Pennsylvania hospitals, Appendix A contains SIRs from the All Model.

The reader is encouraged to carefully interpret SSI SIRs from both the Results section and Appendix A. These SSI SIRs can only be compared with 2015 or later NHSN data that use the same adjustment model (All or Complex).

3.5.6 Limitations

Comparisons of SIRs from this report and all earlier Pennsylvania HAI reports are not valid due to the use of new 2015 national baseline data. Because the definition of HAIs changed over time, it is not recommended to compare case counts between the different reports either.

Although Pennsylvania hospitals are mandated to report all HAIs to NHSN, DOH does not currently perform any external data validation. Therefore, reported rates could be falsely low if facilities are underreporting infections. Because only the number of CLABSIs and CAUTIs were reported each month, differences between patients that may be related to risk of getting an infection (such as diabetes status) could not be accounted for in the statistical adjustment process.

  1. The provided case-definitions summarize extensive guidance in the 2016 NHSN Patient Safety Manual (Centers for Disease Control and Prevention 2016b). Please refer to it for additional detail.↩