3 Methods

3.1 Reporting Requirements

The 2007 amendment to the MCARE Act (Act 52) requires all hospitals to report all HAIs into CDC’s NHSN.9 This report contains data from inpatient hospitals and only includes patients that had overnight hospital stays. Facilities are required to follow the NHSN Patient Safety Component (PSC) Manual to identify, classify, and report infections and patients at risk. Act 52 also requires facilities to have an HAI surveillance plan to quickly identify and report HAIs. Case finding and case classification are the responsibility of the facility.

The Department released guidance for reporting C. difficile and MRSA infections that permits certain types of hospitals to report these infections in one of two ways. Acute care hospitals, long-term acute care hospitals (LTAC) and inpatient rehabilitation facilities (IRF) are required to report positive laboratory results for C. difficile found in stool and MRSA found in the blood. These are called LabID events. Inpatient psychiatric facilities (IPFs), children’s hospitals, and critical access hospitals (CAH) are not required to report LabID events; therefore, LabID event data from these hospital types are not included in this report. These facilities have an option to report either LabID events or MRSA bloodstream infections and CDIs that meet surveillance infection definitions. These infection surveillance definitions require both clinical symptoms and a positive laboratory test, whereas LabID events only require a positive laboratory test.

The descriptions below summarize procedures and case definitions that are extensively detailed in the 2020 NHSN PSC Manual, which is the authoritative source for the classification of HAIs included in this report.9

3.2 Benchmarked Infections

This report includes device-associated, procedure-associated HAIs and LabID events. HAIs included in this report are CLABSIs, CAUTIs, and seven procedure-related SSIs (HPRO and KPRO, HYST, COLO, CARD, CBGB and CBGC), as well as CDI LabID events and MRSA bloodstream LabID events.

3.3 Reporting Period

This report includes infections that were reported in 2020. The report also includes SSIs resulting from procedures performed in late 2020 that had an infection diagnosed in 2021 per NHSN timing criteria.9 The 2020 and 2019 NHSN data were created from data saved on June 1, 2021 and July 1, 2020, respectively. The 2019 data from UPMC Magee-Womens Hospital in Pittsburgh and Conemaugh Memorial Medical Center were downloaded in February 2021 and January 2023, respectively.

3.4 Included Facilities

This report includes data from ACHs, children’s, LTAC, IRFs, IPFs, and CAHs. Free-standing rehabilitation hospitals and inpatient rehabilitation units in hospitals are classified as IRFs. Facility data were excluded from this report if the facility was not open for all 12 months of 2020.

3.5 Data Validation

The NHSN online data collection tool has several automated data validation algorithms. When inconsistent or out-of-range data are entered, an alert is displayed in NHSN with a message prompting the user to correct the entered value.

In addition to the built-in NHSN tool, the Department analyzes the data quarterly to identify missing, duplicative, or unusual data. The Department provides each facility with a facility-specific list of data elements to correct. Facilities have 30 days from the end of the analysis month to correct their data.

3.6 CLABSIs

The NHSN PSC Manual9 defines a CLABSI as a laboratory-confirmed infection of the blood in a patient who had had a central line for three or more consecutive days. The catheter must have been in place on the day that the infection was identified or the day before. A blood stream infection in a patient with a central line that is secondary to a recently documented infection at another body site, that meets the criteria for a listed NHSN HAI with the same organism, is not classified as a CLABSI.

Hospitals are not required to follow-up with discharged patients to conduct infection surveillance. However, infections that are diagnosed after discharge are reported to NHSN if they are reported to the hospital and meet the NHSN criteria.

3.7 CAUTIs

The NHSN PSCM9 defines a CAUTI as a laboratory-confirmed infection in the urine from a patient with at least one of the symptoms specified by NHSN who had had a urinary catheter for three or more consecutive days. The catheter must have been in place on the day that the infection was identified or the day before. An infection is defined as bacterial growth of two or fewer species at least one of which is heavy growth (i.e., more than 100,000 colony forming unit per milliliter urine). Repeat infections are infections at the same site that return within 14 days of the initial date of onset; these are not counted again. For more information refer to these resources Frequently Asked Questions about Catheter-Associated Urinary Tract Infections.

Hospitals are not required to follow-up with discharged patients to conduct infection surveillance. However, infections that get diagnosed after discharge are reported to NHSN if they are reported to the hospital and meet the NHSN criteria.

3.8 Counting Device-Days and Patient-Days

Because CAUTIs and CLABSIs were only reported among people who had an indwelling catheter in place, facilities track and report the number of patients with a catheter in place each day. Counting is performed at the unit or ward level. Hospitals with more device-days are predicted to have more infections than hospitals with fewer numbers of device days.

The number of device-days for an infection 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).

The process of counting the total number of device days can be performed in one of several ways. Hospitals can perform a daily census by hand, or 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. Counting is performed separately for each hospital location (e.g., oncology ICU, pediatric unit, burn unit) and entered each month into NHSN.

The number of newly 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 unit, burn unit). The counts are aggregated by location and month and entered into NHSN monthly. These patients are at risk of acquiring CDI and MRSA bloodstream LabID events.

3.9 Surgical Site Infections

3.9.1 Counting Surgeries

Select surgical procedures that occurred in calendar year 2020 and met the reporting criteria described in Chapter nine of the PSCM9 were reported to NHSN and were included in this report. Corresponding patient information was also reported to NHSN for each of these surgical patients regardless of presence of infection. Only surgical patients for which the patient was in the same hospital overnight are included in this report. This report includes SSI data from the below seven benchmarked surgeries:

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

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.

3.9.2 Counting SSIs

The NHSN instructs hospitals to use active, patient-based, prospective surveillance to identify superficial incisional, deep incisional, and organ/space SSIs.9 Pre- and post-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. 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.9 Superficial incisional infections must have one of the following:

  1. Purulent drainage from the superficial incision;
  2. Identification of an organism from the superficial incision;
  3. Reopening of the incision by the provider, without obtaining a culture, and at least one of the following symptoms: localized pain or tenderness, localized swelling, erythema or heat; 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.

An organ/space infection is defined as an infection in tissue that is deeper than fascial/muscle layer, and the patient must have at least one of the following criterion for a specific organ/space infection described below as well as meeting the criteria for the specific type of organ/space infection:

  1. Purulent drainage from a drain that is placed in the organ/space;
  2. Identification of an organism from fluid or tissue in the organ/space; or
  3. An abscess involving the organ/space that is detected on gross anatomical histopathologic exam, or imaging test.

Fourteen different types of organ/space infections are recognized by NHSN, please reference the NHSN PSCM9 for a complete list of applicable infections.

Irrespective of the type of surgical procedure, 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 patients with other surgical procedures (CARD, CBGB, CBGC, HPRO, KPRO) were monitored for 90 days.

Two different methods are used to calculate a SSI adjusted metric. In one method, SSIs classified as superficial, deep, or organ/space are counted. This is called the “all” method. In the other method, only infections located in deep tissues or the organ/space are counted. This method is called the “complex” method. For each type of surgical procedure, an “all” and “complex” SSI SIR was computed in this report.

3.10 Laboratory Identified (LabID) Events

It is less labor intensive to use positive lab tests alone to identify and report infections compared to methods which require both clinical criteria and lab tests. It is for this reason that CDI in the stool and MRSA blood infections are reported as LabID events. This simple case definition ensures that the reported data are standardized and consistent between facilities. Unlike the other HAI types, both inpatient and select outpatient results (i.e., from the emergency department and 24-hour observation locations) are required to be reported to NHSN. These data are used to estimate the infection burden in the inpatient setting, the outpatient setting, and the community. LabID events are used by CMS for quality reporting programs.

3.10.1 Patient Days, Admissions, and Encounters

Facilities report a monthly count of patient days and admissions for each inpatient location separately regardless of admission and/or billing status. Acute care hospitals exclude patients in IRF locations and IPF locations with a unique CMS Certification Number from monthly facility counts. Hospitals with IRF units are classified as unique IRFs in this report.

Separate counts of patient visits to the emergency department and 24-hour observation unit are also reported monthly.

3.10.2 Classification of Positive Lab Events

The NHSN applies an automated process to the LabID event data to classify the events as either healthcare-facility onset or community-onset. Healthcare-facility onset cases occur on or after the fourth day following the admission date. These infections are considered to have developed due to an exposure in the healthcare facility. Community-onset cases occur among patients admitted to the hospital with a positive test on days one, two, or three and among all patients with a positive test who visit the 24-hour observation unit or emergency department affiliated with the hospital. A LabID event is classified as an incident if the patient never had a prior positive C. difficile test from the same hospital or if the previous positive CDI test was 57 or more days prior to the second test date. Only incident, healthcare-facility onset LabID events are reported in this report.

3.10.3 MRSA LabID events

Incident healthcare-facility onset MRSA bloodstream infections are defined as a positive test for MRSA from blood that met the above criteria. There are two instances in which a second positive MRSA bloodstream test collected after a hospitalization is counted as a second incident healthcare-facility onset infection from the same hospital visit: If the specimen is collected after the patient moved to a new hospital location or if the specimen is collected 14 or more days after the first collection date.

3.10.4 CDI LabID events

Tracking and reporting CDIs are difficult for three main reasons. First, C. difficile can live in the intestinal flora of people but not produce the toxins that cause clinical symptoms. These individuals are considered “colonized” and not infected with C. difficile. Second, CDI is difficult to treat and often reoccurs. This makes classifying recurrent and new onset cases difficult. In addition, diarrhea in hospitalized patients is common, and C. difficile is the cause in less than 30% of cases.

To ensure that positive test results reflect true infections, hospitals are instructed to test only unformed stool samples for CDI. The samples should conform to the shape of the container and have a fluid-like consistency. The patient should have three or more diarrhea episodes within a 24-hour period that meet this criterion.

Because healthy babies can carry C. difficile in their intestinal flora, results from babies in well-baby nurseries, neonatal ICUs (NICUs), special care nurseries, and labor, delivery, recovery, and postpartum units are excluded from the monthly admission and patient day monthly counts. Specimens from outpatient well-baby clinics are also excluded from the monthly encounter counts.

Microbiology labs can choose between four different categories of CDI tests, each of which has different sensitivities (ability to identify a true positive) and specificities (ability to identify a true negative). Two tests can detect the presence of C. difficile toxin. The cell cytotoxicity assay (ToxiCulture) is positive when stool added to a monolayer of an appropriate cell line produces a cytotoxic effect. The second test, in which both stool and neutralizing antitoxin are added, does not produce a cytotoxic effect. The enzyme immunoassay (EIA) test uses monoclonal antibodies to detect C. difficile toxin A and polyclonal antibodies to detect C. difficile toxin B. The EIA method is used to detect the glutamate dehydrogenase (GDH) antigen, which is specific to C. difficile (but not specific to a C. difficile toxin). The nucleic acid amplification test (NAAT) detects genes that make the C. difficile toxin. However, the NAAT cannot determine whether the toxin genes are turned on or off (to produce toxin). These different types of tests are used by labs to determine not only if C. difficile is present in the patient, but if they are colonized or infected as well.

Experts have not reached consensus on an optimal lab method to detect an active CDI case. There is a growing trend to use two laboratory tests on a single specimen, one to detect a toxin and the other to identify the organism. When this two-step process is used, the finding of the second or last test result determines whether the specimen is positive or not. Each facility reports the type of CDI lab test method most used during each quarter to NHSN. The type of CDI test is displayed in the hospital level CDI data.

Duplicate positive CDI tests are not reported and are defined in the 2020 NHSN PSCM9 as any C. difficile positive laboratory result from the same patient and hospital location/unit, following a previous C. difficile positive laboratory result within the past 14 days, even across calendar months and readmissions to the same facility. There should be 14 days with no C. difficile positive laboratory result for the patient and location/unit before an additional C. difficile LabID event is reported to NHSN for the patient and location. The date of specimen collection is considered day one.

3.11 Data Analysis

The number of predicted infections for each HAI is calculated in NHSN and derived from a statistical model. The statistical model adjusts for risk factors that vary among hospitals that may underlie differences in the number of reported infections. When these types of statistical models are used, the data are considered to be adjusted or standardized. The predicted value is interpreted as the number of infections that a similar hospital would have reported during 2015. The number of predicted device days for indwelling urinary catheters and central line is calculated in an identical fashion in NHSN.

3.11.1 Standardized Measures of Performance

Three metrics were calculated using SAS/STAT Software, version 9.4 of the SAS System.47 The SIR and the SUR both quantify the degree to which the actual number of HAIs or device days is different from the predicted number that were reported in the 2015 baseline data.

3.11.2 Interpreting SIRs and SURs

The SIR is the ratio of the number of HAIs reported to NHSN compared to the number that were predicted based on the 2015 national data, adjusting for risk factors that have been found to be significantly associated with the infection incidence. The SIR is calculated by dividing the number of reported infections by the number of predicted infections.

\[ SIR = \frac{Number \: of \: Reported \: HAIs}{Number \: of \: Predicted \: HAIs} \]

When comparing two SIR values, the risk factors included in the statistical model are unlikely to explain differences in SIRs. The number of patients at risk is adjusted for by the models and likewise cannot account for differences between SIR values. The differences are more likely due to other causes, such as adherence to infection prevention and control practices or patient characteristics.

The SIR is useful to compare one facility to similar facilities in the rest of the country or changes over time in the same facility or facility type, but caution should be used in these comparisons as surveillance efforts may differ across hospitals. Below are rules to interpret SIR values by themselves:

  • If the SIR is greater than 1.0, then more HAIs were reported than predicted, based on the 2015 national baseline data.
  • If the SIR equals 1.0, then the same number of HAIs were reported as predicted, based on the 2015 national baseline data.
  • If the SIR is less than 1.0, then fewer HAIs were reported than predicted, based on the 2015 national baseline data.

A SIR does not estimate the chance, or risk, of getting an HAI. It only compares the reported number of infections to the number predicted by the 2015 baseline data. A few problems arise when using the SIR as an indicator of HAI risk. The biggest problem is that small hospitals tend to have fewer than 1.0 predicted infection. CDC and statisticians agree that a SIR from a facility with less than 1.0 predicted infection is 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 because the denominator is so small. In this report, SIR values are only displayed when 1.0 or more infections are predicted.

An additional approach to evaluate a SIR is to determine whether it is statistically meaningful. For a small hospital that performs few operative procedures, the level of confidence in the estimate of the SIR is lower than for a hospital with many procedures. An additional metric is calculated, called the confidence interval (CI), for which a lower and an upper value are calculated and displayed. The true value of the SIR is found somewhere between the lower value and the upper value with 95% certainty. In general, the smaller the number of predicted infections, the wider the CI, meaning that confidence in the calculated SIR value is low. Larger facilities tend to have narrower CIs, meaning the true SIR value is likely to be within a tighter range of values.

To fully interpret a SIR, it is best to examine not only how large or small the SIR value is when compared to 1.0, but also examine the 95% CI. This is the third metric calculated using SAS/STAT Software, version 9.4 of the SAS System.47 If a hospital has a 95% CI that does not include 1.0, it is considered to be statistically significant. The combination of both approaches is the best way to evaluate a SIR. Below are rules for interpreting the 95% CI.

95% Confidence Interval:

  • The 95% CI 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 CI does not include 1.0, then the SIR or SUR is significantly different from 1.0 (i.e., the number of reported infections is significantly different from the number predicted).
    • Example: 95% CI = (0.85, 0.92). One can be 95% certain that the true SIR value is between 0.85 and 0.92 and statistically significantly less than 1.0.
    • Example: 95% CI = (1.04, 1.22). One can be 95% certain that the true SIR value is between 1.04 and 1.22 and statistically significantly more than 1.0.
  • If the CI includes the value of 1.0, then the SIR is not significantly different from 1.0 (i.e., the number of reported infections is not significantly different from the number predicted).
    • Example: 95% CI = (0.85, 1.24). One can be 95% certain that the true SIR value is not statistically significantly different from 1.0.
  • If the SIR is 0.000 (i.e., the infection count is 0 and the number of predicted infections is 1.0 or more), the lower bound of the 95% CI will be set at zero.
    • Example: 95% CI = (0, 1.49). One can be 95% certain that the true SIR value is between 0 and 1.49 and is not statistically significantly different from 1.0.
    • Example: 95% CI = (0, 0.85). One can be 95% certain that the true SIR value is between 0 and 0.85 and is statistically significantly less than 1.0.

3.11.3 CLABSI Statistical Models

Negative binomial regression was used to adjust for risk factors and estimate the number of predicted CLABSIs.48 The strongest risk factor associated with CLABSIs is type of hospital. Therefore, separate statistical models were created for each type of facility: ACHs, CAHs, LTAC facilities, NICUs and IRFs. A separate model for children’s hospitals and IPFs was not necessary, and those hospitals were included in the ACH model.

A SIR should only be compared with others over time within the same hospital or facility type. The reported 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 ACH CLABSI model adjusted for number of device days, number of beds, affiliation with a medical school, and type of unit. Risk factors incorporated in other hospital models 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.48

3.11.4 CAUTI Statistical Models

Negative binomial regression was also used to adjust for risk factors and estimate the number of predicted CAUTIs.48 Separate models were used for each type of facility: ACH, CAH, LTAC facility, and IRF. Children’s hospitals and IPFs were included in the ACH model. A separate NICU model was not necessary. A SIR should only be compared with others over time within the same hospital or facility type.

The ACH CAUTI model adjusted for number of device days, number of beds, affiliation with a medical school, and type of unit. 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.48

3.11.5 SSI Statistical Models

The statistical models that NHSN built to estimate then predicted number of SSIs can be found in the SIR Guide.48 In summary, models were built for each surgical procedure type separately. The SSI SIR was only calculated for ACHs and CAHs. Because age was such a strong risk factor, each surgical procedure type had a separate model for adult and pediatric patients.

Two models, “all” and “complex”, were produced for each of the seven surgical procedures. Generally, the statistical models adjusted for patient-specific characteristics. Some models also adjusted for medical school affiliation of the hospital and hospital size. The number of reported and predicted SSIs from adult and pediatric patients were summed at the hospital level to calculate a single facility SIR.

The statistical process used to calculate the predicted number of infections required the presence of all risk factor data. Surgical patients with missing data for one or more risk factors were excluded from the report. Patients with risk factor data that were substantially more or less than the average were also excluded from the analyses. Very few procedures were excluded for these reasons due to the validation process described previously.

As mentioned in the section entitled “Counting SSIs”, NHSN uses two different methods to examine SSIs. In the “all” model, superficial, deep tissue, and organ/space infections are counted. In the “complex” model, only deep tissue and organ/space infections are counted as SSI values. CDC published national and state-specific SSI SIR values using the complex model in their 2020 HAI Progress report.49 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 for a list of all models and risk factors included in each model.48

Journal articles that report SSIs to assess the magnitude of the SSI burden count infections from all three locations: superficial, deep tissue, and organ/space sites.21,22 To evaluate these SSIs among Pennsylvania hospitals, Appendix A contains SIR and related metrics from the “all” model. The reader is encouraged to carefully interpret SSI SIR data from both the Results section and Appendix A. Surgical site infection SIR data can only be compared with 2015 or later NHSN data that use the same adjustment model (“all” or “complex”).

3.11.6 MRSA Bloodstream Infection Statistical Models

A negative binomial regression model was used to adjust for risk factors and estimate the number of predicted MRSA blood LabID events.48 Separate models were used for each type of facility: ACH, CAH, LTAC facilities, and IRF. It is not recommended to compare two or more SIR values in different hospitals. These should only be compared over time within the same hospital or hospital type.

The ACH MRSA LabID model adjusts for patient days at risk, both inpatient and outpatient community-onset MRSA prevalence rate, average length of stay, number of ICU beds, and affiliation with a medical school. Unit-specific information was not collected, so it could not be included in the model. No adjustment was made for CAHs or IRFs. Events in LTAC facilities were adjusted for percent of admissions on a ventilator. Please refer to the SIR Guide Supplement for a list of all models and risk factors included in each model.48

3.11.7 CDI Statistical Models

A negative binomial regression model was used to adjust for risk factors and estimate the number of predicted CDI LabID events.48 Separate models were used for each type of facility: ACH, CAH, LTAC facilities, and IRFs. It is not recommended to compare two or more SIR values in different hospitals. These should only be compared over time within the same hospital or hospital type.

The regression models were applied to quarterly data to estimate the number of predicted cases. This method is used to account for possible changes in CDI test method that may occur during the calendar year. The quarterly data were added together to compute the annual SIR.

Some CDI statistical models adjusted for type of CDI lab test by grouping the different one-step and two-step test methods into three categories. Those that used EIA for toxin, or GDH and EIA for toxin (two-step) or NATT and EIA (two-step) were in one group. Those that used EIA for toxin or GDH antigen and EIA for toxin (two-step), or NAAT and EIA (two-step) were in the second group. The remaining test types were placed in the third group.48

The ACH CDI model adjusted for the following factors:

  • patient days at risk;
  • inpatient community-onset CDI prevalence rate;
  • number of ICU beds;
  • type of CDI test;
  • number of licensed beds in the facility;
  • whether or not the hospital reported data from the 24-hour observation unit and emergency department;
  • whether or not the hospital is a teaching hospital;
  • whether or not the hospital was a specialty, non-specialty or oncology hospital; and
  • presence of an affiliation with a medical school.

The CAH model adjusted for inpatient community-onset CDI prevalence rate and patient days at risk. The LTAC facility model adjusted for inpatient community-onset CDI prevalence rate, type of CDI test, percent of patients on a ventilator, percent of rooms that are single occupancy and patient days at risk. The IRF model adjusted for type of CDI test, whether the facility/unit had more than 23.9% of admissions with orthopedic conditions, whether the facility/unit had greater than 5.2% of admissions with spinal cord disfunction, whether the facility/unit had more than 23.8% of admissions with a stroke and patient days at risk. The model also accounted for whether the facility was in a hospital or free standing, and, if free standing, whether community-onset CDI events were reported (or not). The SIR Guide Supplement lists all models and more details about the risk adjustment for in each model.48

3.12 Limitations

Comparisons of facility level SIR values from this report and those from the Pennsylvania HAI reports beginning in 2016 are encouraged. However, comparisons with Pennsylvanian HAI reports prior to 2016 are not recommended because the methods used to calculate the SIR used different comparison hospitals. Additionally, because the definitions of HAIs changed over time, it is not recommended to compare case counts between this report and those prior to 2016.

Comparisons of SIR values between different hospitals is discouraged. The statistical adjustment cannot account for differences between patients that are related to the risk of getting an infection (e.g., diabetes status). Patient characteristics were not collected for CLABSI, CAUTIs, and MRSA and CDI LabID events. Remnants of patient or hospital characteristics, such as measures of comorbidity, physician experience, and patient age may account for the differences in SIR values that are reported between two different hospitals.

The COVID-19 pandemic put a lot of strain on healthcare workers including infection preventionists who are responsible for HAI surveillance and prevention. This lead CMS to pause the quality reporting program in which several HAIs were a key component. In Pennsylvania, the law that required hospitals to report HAI data to NHSN remained in effect. The degree to which the pandemic impacted the occurrence, reporting and identification of HAIs and associated measures is unknown.