Illinois Department of Public HealthPat Quinn, Governor

Quality Process Measures

The Centers for Medicare & Medicaid Services (CMS) publishes a set of data every quarter covering several measures each for heart failure, heart attack and pneumonia care. This data is submitted to CMS by the hospitals using clinical chart data.

IDPH compiles these data into one composite measure for each condition.  A weighted average is computed by adding up all the numerators and dividing by all the denominators. The numerator for the composite score is calculated by summing the number of patients who were both candidates for and received any of the aspects of care. If a patient received all four aspects of heart failure care, the patient would be counted four times. If the patient was a candidate for only three aspects of care, he/she would only be counted three times.

The denominator for the composite scores is calculated by summing the number of patients who were eligible for any of the four aspects of heart failure care. If a patient was eligible to receive all four aspects of heart failure care, the patient would be counted four times in the denominator.

The individual measures for heart failure, heart attack and pneumonia care published by CMS are linked to the composite measure with results for each displayed.
 

Readmission rates

Readmission rates includes patients readmitted to a hospital within 30 days of discharge from a previous hospital stay for heart attack, heart failure, or pneumonia. Readmission rates are reported for Medicare patients only.  Readmissions rates displayed on this site reflect 3 years of data. For more information, visit the CMS Hospital Compare Web site.

Thirty Day Mortality

CMS 30-day mortality rates take into account deaths within 30 days from all causes after an initial hospitalization with a principal diagnosis of heart attack, heart failure, or pneumonia. Mortality rates are reported for Medicare patients only.  Mortality rates displayed on this site reflect 3 years of data.  For more information, visit the CMS Hospital Compare Web site.

 

Inpatient Quality Indicators

The inpatient quality indicators (IQIs) are a set of measures that can be used with hospital inpatient discharge data to provide a perspective on quality.   Mortality indicators for inpatient procedures include procedures for which mortality has been shown to vary across institutions and for which there is evidence that high mortality may be associated with poorer quality of care.  Utilization indicators examine procedures whose use varies significantly across hospitals and for which questions have been raised about overuse, underuse, or misuse.

Discharge data submitted by the hospitals are entered by IDPH into software provided by the Agency for Healthcare Research and Quality (AHRQ). The measures of care listed are authored by AHRQ. 

AHRQ Version 4.2 software is utilized with risk adjustment as appropriate.  For additional information about AHRQ measures and risk adjustment, read about the Quality Indicators at http://www.qualityindicators.ahrq.gov/.
 


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Pediatric Quality Indicators

The Pediatric Quality Indicators (PDIs) are a set of measures that reflect quality of care inside hospitals and identify potentially avoidable hospitalizations among children (PDI). They focus on potentially preventable complications and iatrogenic events for pediatric patients treated in hospitals, and on preventable hospitalizations among pediatric patients.

Discharge data submitted by the hospitals are entered by IDPH into software provided by the Agency for Healthcare Research and Quality (AHRQ). The measures of care listed are authored by AHRQ. 

AHRQ Version 4.2 software is utilized with risk adjustment as appropriate.  For additional information about AHRQ measures and risk adjustment, read about the Pediatric Quality Indicators at http://www.qualityindicators.ahrq.gov/.
 


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Surgical Care Improvement Project (SCIP) Process Measures

Hospitals submit monthly data on a quarterly submission cycle from specified surgical measure categories to the Illinois Department of Public Health and the federal Centers for Medicare & Medicaid Services (CMS). Some of these measures were selected for display and summed to numerator and denominator totals. These totals are then presented in the mouse-over or hover box while the percentage shown in the performance band is the percentage calculated by dividing the numerator (n) by the denominator (of N) and multiplying by 100. These measures and rates can be an indicator of how well the hospitals care for their patients. It is important to know that small differences in the percentages usually don't mean that one hospital is significantly better or worse. It is better to look at larger differences. Percentages may be affected by such factors as how many patients are included in the calculation of the rate. This doesn't necessarily reflect the quality of the care you will receive.

Psychiatric, rehabilitation and long-term care hospitals currently are not reported on this Web site, although many have agreed in principle to provide data using standard quality measures. The conditions currently measured - care of adults with a heart attack, heart failure, or pneumonia or adults having surgery - are not commonly treated in these settings.

For technical information about where the data comes from and how the measures are calculated, see Information for Professionals on Data Collection.

Health Care-associated Infection Methodology

Central Line-Associated Bloodstream Infections

The National Healthcare Safety Network (NHSN), a secure, Internet-based surveillance system managed by the Division of Healthcare Quality Promotion at the Centers for Disease Control and Prevention, is used by all Illinois hospitals with adult, pediatric and neonatal intensive care units to collect data on central line-associated bloodstream infections.

The reporting criteria and methods required by NHSN and a summary of the national level data are detailed on the NHSN Web site.

While the data collection methodology is identical in adult and pediatric intensive care settings, in neonatal intensive care unit (NICU) locations (level III or level II/III), data on central line-associated bloodstream infections are collected for each of five birth-weight categories  (<750 g, 751-1000 g, 1001-1500 g, 1501-2500 g, and >2500 g) and for catheter type (central line and umbilical).  The risk of bloodstream infections in neonates varies by birth-weight and type of catheter.

A summary measure for central line-associated bloodstream infection (CLABSI) is calculated as follows:

Standardized Infection Ratio:

The Standardized Infection Ratio (SIR) is a summary measure used to compare the central line-associated bloodstream infection (CLABSI) experience among one or more groups of patients to that of a standard population.  It is the observed number of infections divided by the expected number of infections. 

For Health Care-Associated Infection (HAI) reports, the standard population comes from NHSN data reported from all hospitals using the system.  "Expected" is based on historical data for CLABSI rates at the national level.  The expected number of infections for a given intensive care unit is calculated by multiplying the national CLABSI rate from the standard population by the observed number of central line days for that unit.  If the expected number of infections is less than 1.0, the SIR value is not calculated for a given intensive care unit.  This stipulation allows for a more precise measure of the SIR.

Effective June 6, 2011, the national CLABSI rates used to calculate the expected number of infections for a given intensive care unit no longer include clinical sepsis cases.  Although clinical sepsis (CSEP) cases were excluded from the NHSN surveillance definition of CLABSI in January 1, 2010 the pooled means did not reflect this.  The implementation of CSEP decremated pooled means will allow for more appropriate comparisons.

View a detailed description of the calculation of the SIR.

A general interpretation of the SIR is as follows:

If the SIR equals 1.0 (observed number of CLABSIs equals the expected number of CLABSIs based on the NHSN data) there is no difference between the observed number and the expected number.

If the SIR is greater than 1.0, the number of CLABSIs observed in a specific hospital is greater than the number of CLABSIs expected based on the NHSN pooled data.

If the SIR is less than 1.0, the number of CLABSIs observed in a specific hospital is less than the number of CLABSIs expected based on the NHSN pooled data.

To assess whether the difference between the observed number of CLABSIs is significantly different from the expected number of CLABSIs, a 95% confidence interval for the SIR is calculated.  The confidence interval for a hospital’s SIR is the range of possible SIRs within which there is a 95% confidence that the real SIR for that hospital lies, given the number of infections and procedures that were observed in that hospital in a specific time period.  The lower bound of the 95% confidence interval is only calculated if the observed infection count is greater than 0.

The formula used to calculate the confidence interval is:

SIR Formula

If the 95% confidence interval includes 1.0, the hospital’s infection rate is similar (not statistically significantly different) from the “expected” (predicted).

If the SIR is greater than 1.0 and the 95% confidence interval does not include 1.0, the hospital’s infection rate is statistically significantly higher than “expected” (predicted).

If the SIR is less than 1.0 and the 95% confidence interval does not include 1.0, the hospital’s infection rate is statistically significantly lower than “expected” (predicted).

All conclusions are based on the assumption that the hospital’s patient population is similar to the NHSN pooled patient population.

Calculation of SIR for Neonatal Intensive Care Units (NICUs)
The SIR for NICUs is comprised of both central line associated blood stream infections as well as umbilical catheter-associated blood stream infection (UCABSIs).  To calculate the SIR, the expected number of infections is calculated for each of the five birth-weight categories  (<750 g, 751-1000 g, 1001-1500 g, 1501-2500 g, and >2500 g) by catheter type, using the methodology referenced above, summed and compared to the observed number of infections (CLABSI and UCABSI) in all five birth-weight categories.

The interpretation of the SIR is identical to that referenced above.  

 

Methicillin-Resistant Staphylococcus aureus (MRSA) and Clostridium difficile Infections in Illinois Hospitals

As of January 1, 2012 all acute care hospitals in Illinois began reporting Methicillin-resistant Staphylococcus aureus (MRSA) bloodstream infections and Clostridium difficile infections (CDI) using the National Healthcare Safety Network's (see above) Multidrug-Resistant Organism (MDRO) Laboratory-Identified (LabID) event module.  The LabID event surveillance method enables facilities to report proxy measures for healthcare acquisition of infections based on data obtained from the laboratory without clinical evaluation of the patient.  Based on when an individual is admitted to a hospital as a patient and when the lab specimen was obtained, positive lab results (Lab ID events) are categorized as hospital onset or community onset.  Positive lab results are categorized as hospital onset (HO) if the specimen was collected on or after the fourth day of an inpatient hospital stay. Positive lab results from specimens obtained during the first 3 days of an inpatient stay are categorized as community onset (CO). 
 
Methicillin-resistant Staphylococcus aureus (MRSA) bloodstream infections- NHSN Surveillance Reporting
 
 
The summary measure used to quantify MRSA blood stream infections is the Healthcare Facility Onset Incidence Rate of Methicillin-resistant Staphylococcus aureus (MRSA) bloodstream infections. This rate is calculated as the number of positive lab culture results (from non-duplicate unique blood source specimens) occurring hospital-wide identified 4 or more days after a patient was admitted to the facility, divided by total number of patient days, multiplied by 1,000. The standardized infection ratio for Healthcare Facility Onset Incidence Rate of MRSA bloodstream infections adjusts for medical school affiliation, facility bed size, and the prevalence rate of Community Onset MRSA using a risk model.
 
The risk model for MRSA bacteremia is as follows: 
 
Number of predicted (expected) HO MRSA bacteremia LabID events = 
exp [ -10.2368 
+ 0.3672(bedsize > 400*) 
+ 0.3248(medical school affiliation = major*) 
+ 2.2760(CO MRSA prevalence rate)] x patient days 
 
*For these risk factors, if present = 1; if not = 0 
 
The facility’s CO prevalence rate and patient days is entered directly into the formula shown above. If the facility meets the description for bedsize and medical school affiliation listed in the formula, a value of 1 or 0 replaces the two variable names in parenthesis.  The MRSA bacteremia LabID SIR is calculated by dividing the number of observed HO MRSA bacteremia LabID events by the predicted (expected) HO MRSA bacteremia LabID events. SIR values are calculated only if the number of predicted events is greater or equal to 1.
 

Clostridium difficile infections (CDI) - NHSN Surveillance Reporting
 

The summary measure used to quantify CDI is Healthcare Facility Onset Incidence Rate of Clostridium difficile infections (CDI). This rate is calculated as the number of lab results (from non-duplicate specimens) positive for Clostridium difficile identified 4 or more days after a patient was admitted to the facility, divided by number of patient days, multiplied by 10,000. For calculation of CDI rates, patient days for newborn populations (neonatal intensive care units, special care nurseries, well-baby nurseries, and newborns in labor and delivery units) are not included in the denominator. The standardized infection ratio for Healthcare Facility Onset Incidence Rate of CDI adjusts for the type of testing used at the facility, medical school affiliation, facility bed size, and the prevalence rate of Community Onset CDI using a risk model.
 
The risk model for CDI is as follows: 
 
Number of predicted (expected) HO CDI LabID events = 
exp [ -7.8983 
+ 0.3850(CDI test type = NAAT*) 
+ 0.1606(CDI test type = EIA*) 
+ 0.3338(CO CDI prevalence rate) 
+ 0.2164(bedsize > 245*) 
+ 0.0935(bedsize = 101-245 beds*) 
+ 0.1870(medical school affiliation = major*) 
+ 0.0918(medical school affiliation = graduate*)] x CDI patient days 
 
*For these risk factors, if present = 1; if not = 0 
 
The CDI LabID SIR is calculated by dividing the number of observed HO CDI LabID events by the predicted (expected) HO CDI LabID events. SIR values are calculated only if the number of predicted events is greater or equal to 1.
 
When calculating SIRs for facilities, a quarter will be excluded from the SIR (by way of not calculating the number of expected infections) if any of the exclusion criteria are met for that quarter (Table 1). 
 
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Table 1. Exclusion criteria applied prior to risk adjustment of CDI and MRSA bacteremia LabID event data. 
 
Incomplete/missing information on survey regarding facility bedsize, medical school affiliation, and/or CDI test type* 

Extreme outlier prevalence rate, defined as a prevalence rate greater than five times the interquartile range above the 75th percentile (IQR5) ** 

Incomplete/missing denominator data (i.e., patient days, admissions) 

Long Term Acute Care Hospitals 
----------

*exclusion based on CDI Test Type applied for CDI risk adjustment only.

**CO CDI prevalence rate IQR5 = 1.78; CO MRSA bacteremia prevalence rate IQR5 = 0.88

The interpretation of the SIR is identical to that referenced in the CLABSI section..  

Additional information regarding these output options and the risk models can be found at: http://www.cdc.gov/nhsn/PDFs/mrsa-cdi/RiskAdjustment-MRSA-CDI.pdf

 

Statewide Trends in Methicillin-Resistant Staphylococcus aureus (MRSA) and Clostridium difficile based on Hospital Discharge Data 

Methicillin-Resistant Staphylococcus aureus (MRSA) Trends in Illinois

Analysis in these sections was conducted using hospital discharge data, which are routinely collected and provided to the Illinois Department of Public Health for all acute care hospitals in Illinois.  The unit of analysis is the hospital discharge, not the person or patient.  If a person is admitted to the hospital multiple times during the course of a year, that person will be counted each time as a separate "discharge" from the hospital.

Up to 25 diagnosis codes for each discharge can be included in the analysis.

The ICD-9 codes  038.12, 041.12, and 482.42, appearing anywhere in the list of discharge diagnoses, was used to identify cases of MRSA infection.   

C. difficile Trends in Illinois

Analysis in these sections was conducted using hospital discharge data, which are routinely collected and provided to the Illinois Department of Public Health for hospitals in Illinois.  The unit of analysis is the hospital discharge, not the person or patient.  A person admitted to the hospital multiple times during the course of a year will be counted each time as a separate "discharge" from the hospital.

Up to 25 diagnosis codes for each discharge can be included in the analysis.

The ICD-9 code 008.45, appearing anywhere in the list of discharge diagnoses, was used to identify cases of C. difficile infection.


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Patient safety

The patient safety indicators (PSIs) are a set of measures that screen for adverse events that patients experience as a result of exposure to the health care system. These events are likely amenable to prevention by changes at the system or provider level. 

Discharge data submitted by the hospitals, are entered by IDPH into software provided by the Agency for Healthcare Research and Quality (AHRQ). The measures of care listed are authored by AHRQ. 

AHRQ Version 4.2 software is utilized with risk adjustment as appropriate.  For additional information about AHRQ measures and risk adjustment, read about the Patient Safety Indicators at http://www.qualityindicators.ahrq.gov/.

 

Breast Feeding

The breast feeding measures are calculated from birth certificate data submitted by birthing hospitals to the Vital Records program of the Illinois Department of Public Health. Questions about breast feeding were incorporated as part of the US Standard Certificate Revisions recommended by the National Center for Health Statistics (NCHS). In January 2013, the question for breast feeding was revised. The question asks:
 
How is the infant being fed? (breast milk only, formula only, both breast milk and formula, neither breast milk nor formula, unknown)
 
Breastfeeding is reported by hospital as the percentage of infants who are exclusively breastfed, exclusively formula fed, and infants who received any breastfeeding (those who are exclusively breastfed or who received both breast milk and formula). The denominator for all measures is the number of total births in the hospital.
 
These counts are based on self-reporting of the mothers and hospital staff. The rates are preliminary estimates and subject to ongoing and annual data revision. As such, these counts may vary for the same period based on final counts produced annually.
 

Satisfaction

The HCAHPS (Hospital Consumer Assessment of Healthcare Providers and Systems) survey is the first national, standardized, publicly reported survey of patients' perspectives of hospital care. HCAHPS (pronounced “H-caps”), also known as the CAHPS® Hospital Survey, is a standardized survey instrument and data collection methodology for measuring patients’ perceptions of their hospital experience.

The HCAHPS survey asks discharged patients 27 questions about their recent hospital stay. The survey contains 18 questions about critical aspects of patients’ hospital experiences (communication with nurses and doctors, the responsiveness of hospital staff, the cleanliness and quietness of the hospital environment, pain management, communication about medicines, discharge information, overall rating of hospital, and would they recommend the hospital). The survey also includes four items to direct patients to relevant questions, three to adjust for the mix of patients across hospitals, and two items that support Congressionally-mandated reports.

The HCAHPS survey is administered to a random sample of adult patients across medical conditions between 48 hours and six weeks following discharge; the survey is not restricted to Medicare beneficiaries. Participating hospitals may either use an approved survey vendor, or collect their own HCAHPS data (if approved by CMS to do so). To accommodate the needs of hospitals, HCAHPS can be implemented in four different survey modes: mail, telephone, mail with telephone follow-up, or active interactive voice recognition (IVR). Hospitals may either integrate HCAHPS with their own patient surveys, or use HCAHPS by itself. Hospitals must survey patients throughout each month of the year. The survey is available in official English, Spanish, Chinese, Russian and Vietnamese versions.

The survey itself, as well as detailed information on sampling, data collection and coding, and file submission, are contained in the HCAHPS Quality Assurance Guidelines, Version 4.0, found at the official HCAHPS Web site, www.hcahpsonline.org.


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Services

The number of patients, their length of stay and the charges associated with their visit are calculated from discharge data provided by the Illinois Department of Public Health. The collection of these data includes the use of DRG grouping software from 3M to combine these discharge data records into the various listed conditions. The median stay and charges are then calculated by listed service for each facility. A patient's length of stay for any listed condition will vary. The charges may be discounted and paid by the health insurance company.

Nurse Staffing

Hospitals gather internal data related to the type and number of monthly nurse hours worked.  They also gather data on the number of days patients spend within specific categories of service. This information is submitted quarterly to IDPH where the data is combined.  In addition, hospitals submit annual data on the number of nurse staffing position vacancies and turnover rates.

Infection Prevention Staffing

The number of infection prevention and control staff, both total and those certified in infection control, is gathered from data submitted by hospitals to the Illinois Department of Public Health through the Annual Hospital Profile survey.  The number of beds used in per 100 beds calculation reflects the total number of authorized beds established by certificate of need at the Illinois Department of Public Health.

Authorized Beds

The bed numbers refer to the number of authorized beds approved by the Health Facilities and Services Review Board. These bed numbers may change frequently as a result of the regular Board meetings, and will be updated regularly as other data is added to the site.

 

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Emergency Department Survices and Utilization

Timely and effective emergency department care is important because delays in receiving care in the emergency department and/or in being admitted as an inpatient can reduce the quality of care and increase risks and discomfort for patients with serious illnesses or injuries. 
 
Data for Emergency Department waiting times and the percentage of patients who left the Emergency Department without being seen come from Hospital Compare (hospitalcompare.hhs.gov).   Hospitals abstract the data for these measures from the medical records of their patients and submit the information to the Center for Medicare and Medicare Services (CMS).
 
Emergency room bypass hours are calculated from data submitted by hospitals to the Illinois Department of Public Health through its Web-based Hospital Bypass System.  Hospitals fulfill reporting requirements by accessing the system at the start of the bypass period, indicating that it is on bypass.  They then access the system at the end of the bypass period to indicate that the period has ended.  Not all hospitals experience the need to place their emergency room on "bypass".
 
The number of patients seen in the emergency room, both those admitted and those treated and released as outpatients, is calculated from discharge data provided by the Illinois Department of Public Health. 

Patient Insurance Mix

Hospitals gather data on the type and volume of insurance providers of the patients seen in their facility.  This information is submitted to the Illinois Department of Public Health annually, as part of the Annual Hospital Questionnaire (AHQ). Data on patient volume by insurance type was extracted as submitted to provide the percentage of hospital visits by major categories of insurance provider, with private pay and charity included where no insurance coverage exists.

 

 

Air Pollution Measure

Daily Fine Particulate Matter 

The fine particulate matter measure comes from the U.S. County Health Rankings website (http://www.countyhealthrankings.org/app/illinois/2013/measure/factors/125/datasource) and is made available from the CDC WONDER program.  CDC WONDER provides geographically aggregated daily measures of fine particulate matter in the outdoor air.  Fine particulate matter, or PM 2.5 particles, are air pollutants with an aerodynamic diameter less than 2.5 micrometers.  Data are available by place (combined 48 contiguous states plus the District of Columbia, region, division, state, county), time (year, month, day) and specified fine particulate matter (µg/m³) value. County-level and higher data are aggregated from 10 kilometer square spatial resolution grids.  Further information about methodology is available on http://wonder.cdc.gov/wonder/help/PM.html.

Disease Prevalence and Health Behavior Measures

Asthma Prevalence

Data from the Illinois Behavioral Risk Factor Surveillance System (Illinois BRFSS) were used to present prevalence of asthma.  Asthma prevalence is defined as adults 18 years of age or older having "Current Asthma".   "Current Asthma" means respondents to the BRFSS telephone survey indicated that asthma was current and active at the time of telephone interview.  Currently, data are available for ten Illinois counties for reporting year 2007 through 2009.  Data for Cook county and the Cook county sub-regions are available for reporting year 2010.  Data for remaining Illinois counties is forthcoming.(see Behavioral Risk Factor Surveillance System).

Diabetes Prevalence

Data from the Illinois Behavioral Risk Factor Surveillance System (Illinois BRFSS) for years 2009 and 2010 were used to present prevalence of diabetes in Illinois counties and Cook county sub-regions. Diabetes prevalence is defined as adults, 18 years of age or older, who responded to the BRFSS telephone interview that they had been told they had diabetes. (see Behavioral Risk Factor Surveillance System).

Obesity Prevalence

Data from the Illinois Behavioral Risk Factor Surveillance System (Illinois BRFSS) for years 2009 and 2010 were used to present prevalence of obesity in Illinois counties and Cook county sub-regions. Obesity prevalence is defined as the percentage of adults age 18 or older with a body mass index greater than 30. Data on height and weight is collected through BRFSS telephone interview and used to calculate body mass index. (see Behavioral Risk Factor Surveillance System). 

Current Smokers

Data from the Illinois Behavioral Risk Factor Surveillance System (Illinois BRFSS) for years 2009 and 2010 were used to present information on the percentage of current smokers in Illinois counties and Cook county sub-regions. Respondents to the BRFSS telephone survey indicated their current smoking status. (see Behavioral Risk Factor Surveillance System). 

Physical Inactivity

Data from the Illinois Behavioral Risk Factor Surveillance System (Illinois BRFSS) for years 2009 and 2010 were used to present information on physical activity behaviors of residents of Illinois counties and Cook county sub-regions. Respondents to the BRFSS telephone survey were categorized as physically inactive if they reported no leisure time physical activity within the last thirty days. (see Behavioral Risk Factor Surveillance System).

Fruit and Vegetable Consumption

Data from the Illinois Behavioral Risk Factor Surveillance System (Illinois BRFSS) for 2009 were used to present information on healthy eating behaviors of residents of Illinois counties and Cook county sub-regions. Respondents to the BRFSS telephone survey indicated their daily servings of fruits and vegetables. (see Behavioral Risk Factor Surveillance System).

Emergency Department Measures

Interest in the study of emergency department utilization is growing rapidly. Emergency department (ED) visit volume has surged in recent years, and many people are using these services as a primary means of obtaining medical care. Along with demographic components such as age, sex, race and ethnicity, payer and income level, it is useful to look at the type of visit associated with emergency department use (necessary or unnecessary) and ED use for ambulatory care-sensitive conditions such as asthma and diabetes.  

Emergency Department Visit Acuity Measures 

The Illinois Department of Public Health used an algorithm to help classify ED utilization developed by the New York University Center for Health and Public Service Research. Data used as input to this classification system comes from the Illinois discharge data collection system and includes all 2009, 2010, and 2011 outpatient discharges with an emergency department billing code. These data currently exclude those cases known to have resulted in admission to a hospital as an inpatient; however they do include cases where an outpatient surgery and/or observation care may have been given. The algorithm classifies ED cases into four major categories of visit necessity after isolating injury, drug, alcohol and mental illness cases for separate study. Any cases not meeting any of these criteria are labeled "unclassified".
 
The four major groups of ED care are as follows:
  1. non-emergent
  2. emergent but primary care treatable
  3. emergent but primary care avoidable 
  4. emergent and unavoidable
 
The first three groups are often combined, resulting in two final groupings labeled unnecessary and necessary ED visits.  Complete documentation on the development and use of the ED utilization algorithm is detailed by the New York University research team.
 
Data for geographic areas (county, zip code, Cook County sub-regions and Illinois regions) with less than 20 visits in a given emergency room category were suppressed due to statistical imprecision and patient confidentiality. Data concerning emergency room visits due to "alcohol" "drugs", and "mental health" are not presented due to very small numbers. 
 
Data by visit category is expressed as a percentage of all Emergency Department visits and crude rate per 10,000 population within a geographic locale. Charges shown are gross charges established by hospitals each year, not actual dollar amounts received in payment. All patients are charged the same gross charge (or list price) for the same services before applying any discounts.
 

Emergency Department Asthma and Diabetes Measures 

Asthma

The first-listed (or primary diagnosis) was used to identify the ED discharges for asthma (ICD-9-CM code 493).  The data source was the Illinois discharge data collection system and includes all 2009, 2010, and 2011 outpatient discharges with an emergency department billing code. These data currently exclude those cases known to have resulted in admission to a hospital as an inpatient; however they do include cases where an outpatient surgery and/or observation care may have been given.  
 
Asthma ED discharges were defined as follows: 
  1. pediatric, if patient age was less than 18 years; and 
  2. adult for patients 18 years or older  
Data for geographic areas (county, zip code, Cook County sub-regions and Illinois regions) and race/ethnicity categories with less than 20 discharges in 2009-2011 were suppressed due to statistical imprecision and patient confidentiality. Rates reported for pediatric and adult populations are three-year average crude rates per 10,000 area population.  Rate calculation for a given geographic area (zip/county/state region) or race/ethnicity category was suppressed when the 2009-2011 combined population estimate was less than 1000.  
 

Type II Diabetes

Type II Diabetes was defined as ICD-9-CM code 250.XX, which includes any listed diagnosis.  The data source was the Illinois discharge data collection system and includes all 2009, 2010, and 2011 outpatient discharges with an emergency department billing code. These data currently exclude those cases known to have resulted in admission to a hospital as an inpatient; however they do include cases where an outpatient surgery and/or observation care may have been given.  Only patients aged 18 years or older were included in the analysis.  Data for geographic areas (county, zip code, Cook County sub-regions and Illinois regions) and race/ethnicity categories with less than 20 discharges in 2009-2011 were suppressed due to statistical imprecision and patient confidentiality.  Three-year average crude rates are reported per 10,000 area population. Rate calculation for a given geographic area (zip/county/state region) or race/ethnicity category was suppressed when the 2009-2011 combined population estimate was less than 1000. 
 

Preventable Hospitalization Indicators

Prevention Quality Indicators (PQIs), developed by the federal Agency of Healthcare Research and Quality (AHRQ), identify hospital admissions that evidence suggests could have been avoided, at least in part, through high-quality outpatient care. They represent hospital admission rates for ambulatory care sensitive conditions, which are conditions for which good outpatient care can potentially prevent the need for hospitalization, further complications or more severe disease. Even though these indicators are based on hospital inpatient data, they provide insight into the community health care system or services outside the hospital setting. For Prevention Quality Indicators, lower rates usually represent better outpatient care which can potentially prevent the need for hospitalization.

PQIs used in this report consist of the following 13 ambulatory care sensitive conditions, which are measured as rates of admission to the hospital:

PQIs presented as a percentage rate of population over age 18 are:

  • Diabetes Short-term Complications Admission Rate (PQI 1)
  • Diabetes Long-term Complications Admission Rate (PQI 3)
  • Chronic Obstructive Pulmonary Disease Admission Rate (PQI 5)
  • Hypertension Admission Rate (PQI 7)
  • Congestive Heart Failure Admission Rate (PQI 8)
  • Dehydration Admission Rate (PQI 10)
  • Bacterial Pneumonia Admission Rate (PQI 11)
  • Urinary Tract Infection Admission Rate (PQI 12)
  • Angina Admission without Procedure (PQI 13)
  • Uncontrolled Diabetes Admission Rate (PQI 14)
  • Adult Asthma Admission Rate (PQI 15)
  • Rate of Lower-Extremity Amputation Among Patients with Diabetes (PQI 16)

PQIs presented as a percentage rate of total admissions for the specified condition are:

  • Perforated Appendix Admission Rate (PQI 2)

Both the observed and risk adjusted measures are reported. Risk adjustment is based on county specific age and sex distribution. Further details of the PQIs can be found at: http://www.qualityindicators.ahrq.gov/Modules/pqi_resources.aspx.

The PQIs are defined using AHRQ definitions and programs. The county level PQIs were created using the AHRQ Windows software Version 4.1a. As this software only defines the PQIs at the county level, the AHRQ SAS programs Version 4.1 were modified to allow for estimation of Illinois regions.

 

Categorization of Race/Ethnicity Data

Race and ethnicity data are collected according to Office of Management and Budget standards.  Race coding options are: American Indian/Alaska Native, Asian, Black, Hawaiian/Pacific Islander, White, Unknown/Not Provided.  Ethnicity was coded as Hispanic/Latin or Non-Hispanic/Latin.  For the purposes of the current analyses, race was categorized using the following method:  Ethnicity was assessed first and if "Hisp/Latin" was selected, race was assigned as Hisp/Latin.  If ethnicity was classified as "Non-Hispanic/Latin", race was assigned according to the race coding option selected: White, Black and any other options were coded as Other/Unknown.  This coding strategy resulted in 4.9% of the discharges being coded as race unknown. 
 

Primary Care Physicians

This measure provides the rate of primary care providers per 100,000 population. Primary care physicians are defined as including practicing physicians (M.D.'s and D.O.'s) under age 75 specializing in general practice medicine, family medicine, internal medicine, and pediatrics. The data comes from the U.S. County Health Rankings website (www.countyhealthrankings.org) and originates from the Health Resources and Services Administration (HRSA) Area Resource File (www.arf.hrsa.gov/index.htm). The Area Resource File, a national county level health resource file, collects data from an array of over fifty sources. It includes extensive data from the most recent year on physicians detailed by specialty and professional activity.

 

Data Issues

Computation of the "Deviation from the Statewide Benchmark"

The deviation from the statewide benchmark is defined in one of three categories: better than the state, worse than the state or no difference. To make this assessment, the county-specific PQI rates are compared against the benchmark derived from the population – the statewide PQI rate. If the benchmark falls within the 95% confidence interval (CI) of a given county, it is considered to be the same, i.e., there is no difference. If the benchmark lies outside of the county-specific CI then the county-specific rate is considered to be statistically different from the statewide benchmark. The direction of whether it is better or worse, is based on how the finding is scored (i.e., a lower PQI rates is better).

When a rate is a true zero (i.e., the numerator, in this instance the number of admissions for a certain condition is equal to 0) then the lower limit of the confidence interval is set to zero. The upper limit is set according to the denominator, the county-specific population. In the few instances where this has occurred to date, the deviation for that county-specific rate is that it performed better than the state (since a lower rate is desirable as opposed to a higher rate).

The AHRQ PQIs rates and confidence intervals are generated using the AHRQ Quality Indicator Windows 4.1a and the 4.1a SAS software.

Caution is advised in interpreting the rates and deviation from the statewide benchmark for those counties reporting a rate of 0 admissions per 100,000 population. A zero rate may truly reflect zero admissions for a specific condition or may reflect incomplete case ascertainment particularly in border counties.
 

Completeness of the Discharge Data

The IDPH discharge data set includes only Illinois residents discharged from Illinois hospitals and excludes discharge data from border states and beyond, as well as Veteran Administration medical centers.

Prevalence of Illness/Conditions

A less prevalent condition such as a perforated appendix or lower leg amputation may arise less frequently in a smaller county which could result in less stable estimates.

Data Suppression

Data for geographic areas (county, zip code, Cook County sub-regions and Illinois regions) with less than 20 visits in a given emergency room category were suppressed due to statistical imprecision and patient confidentiality. Data concerning emergency room visits due to "alcohol" "drugs", and "mental health" are not presented due to very small numbers.  

Rate calculation for a given geographic area (zip/county/state region) or race/ethnicity category was suppressed when the 2009-2011 combined population estimate was less than 1000.

Where the Data Came From

 

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