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.
Note that when hospital level reporting of the individual measures is low nation-wide, data for the corresponding composite measure(s) cannot be calculated.
For more information visit the CMS Hospital Compare Web site.
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.
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.
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 5.0.3 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/.
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 5.0.3 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/.
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.
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:
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
Clostridium difficile infections (CDI) - NHSN Surveillance Reporting
*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.
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 5.0.3 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/.
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.
Discharge data from the Illinois Department of Public Health is utilized to calculate information on utilization of services. Data on the number of inpatients, their length of stay and median charges 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. Data on outpatient procedure volume and charges includes the use of clinical classification software (CCS) for clustering patient diagnoses and procedures into a manageable number of clinically meaningful categories. The CCS was developed by the Healthcare Cost and Utilization Project http://www.hcup-us.ahrq.gov/toolssoftware/ccs/ccsfactsheet.jsp. Data on diagnostic procedure visit volume and charges includes the use of revenue codes from the discharge data set. Median charges shown for all displayed services are list prices that may be discounted and paid by health insurance companies.
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.
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.
The number of specially trained lactation consultants and the number of International Board Certified lactation consultants is gathered from data submitted by hospitals to the Illinois Department of Public Health through the Annual Hospital Profile survey. The number of live births for each hospital is calculated from hospital discharge data submitted by hospitals to the Illinois Department of Public Health.
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.
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.
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.
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 by county for the reporting period 2010 - 2014 (see Behavioral Risk Factor Surveillance System).
Data from the Illinois Behavioral Risk Factor Surveillance System (Illinois BRFSS) for years 2010 - 2014 were used to present prevalence of diabetes in Illinois counties. 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).
Data from the Illinois Behavioral Risk Factor Surveillance System (Illinois BRFSS) for years 2010 - 2014 were used to present prevalence of obesity in Illinois counties. Obesity prevalence is defined as the percentage of adults age 18 or older with a body mass index greater than or equal to 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).
Data from the Illinois Behavioral Risk Factor Surveillance System (Illinois BRFSS) for years 2010 - 2014 were used to present information on the percentage of current smokers in Illinois counties. Respondents to the BRFSS telephone survey indicated their current smoking status (see Behavioral Risk Factor Surveillance System).
Data from the Illinois Behavioral Risk Factor Surveillance System (Illinois BRFSS) for years 2010 - 2014 were used to present information on physical activity behaviors of residents of Illinois counties. 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).
Data from the Illinois Behavioral Risk Factor Surveillance System (Illinois BRFSS) for years 2010 to 2014 were used to present the percentage of the adult population (age 18 or older) who responded they had eight or more “poor mental health days” in the past 30 days. The data is collected through the BRFSS telephone interview and is based on responses to the question “Thinking about your mental health, which includes stress, depression, and problems with emotions, for how many days during the past 30 days was your mental health not good?”. Data is available at the Illinois county geographical level. For more information about the Illinois BRFSS see /contents/view/data_sources/#brfss.
Data from the Illinois Behavioral Risk Factor Surveillance System (Illinois BRFSS) for years 2010 – 2014 were used to present prevalence of diagnosis of heart attack in Illinois counties. Heart attack 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 heart attack (see Behavioral Risk Factor Surveillance System).
Data from the Illinois Behavioral Risk Factor Surveillance System (Illinois BRFSS) for years 2010 – 2014 were used to present prevalence of diagnosis of angina (coronary heart disease) in Illinois counties. Angina 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 angina (see Behavioral Risk Factor Surveillance System).
Data from the Illinois Behavioral Risk Factor Surveillance System (Illinois BRFSS) for years 2010 – 2014 were used to present prevalence of diagnosis of stroke in Illinois counties. Stroke 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 a stroke (see Behavioral Risk Factor Surveillance System).
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.
Type II Diabetes
The Clinical Classification Software (CCS) for ICD-9-CM was used to analyze the Behavioral Health measures for Mood Disorders, Alcohol-related Disorders, and Substance-related Disorders in the adult population (18 years and older). The CCS was developed as part of the Healthcare Cost and Utilization Project sponsored by the Agency for Health Research and Quality. The software was developed for use with hospital discharge data. The CCS tool collapses the multitude of ICD-9-CM codes (over 14,000 diagnosis codes and 3900 procedure codes) into a number of smaller clinically meaningful categories. It is often used in descriptive analyses. The CCS includes Mental Health and Substance Use disorder categories.
In this case, the Illinois Hospital Discharge data was utilized as the data source and includes all 2012, 2013, and 2014 outpatient discharges with an emergency department billing code falling within one of three CCS categories outlined above (for mood, alcohol or substance related disorders). These data currently exclude those cases known to have resulted in admission to a hospital as an inpatient.
Data for geographic areas (county, zip code, Cook County sub-regions and Illinois regions) and race/ethnicity categories with less than 20 discharges in 2012-2014 were suppressed due to statistical imprecision and patient confidentiality. Rates reported are average annual rates over 2012-2014 per 10,000 area population. Rate calculation for a given geographic area (zip/county/state region) or race/ethnicity category was suppressed when the 2012-2014 combined population estimate was less than 1000.
Further information about the Clinical Classification Software can be found at: https://www.hcup-us.ahrq.gov/toolssoftware/ccs/CCSUsersGuide.pdf.
Emergency Department visits for hypertension were defined as visits with a primary diagnosis of hypertension and are based on the Agency for Health Research and Quality prevention quality indicator PQI 7 (for hypertension). Specifications exclude all obstetric-related visits, visits associated with cardiac procedures, Stage IV kidney disease, or transfers from other health care facilities. The data source was the Illinois discharge data collection system and includes all 2012, 2013, and 2014 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 2012-2014 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 2012-2014 combined population estimate was less than 1000.
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. 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.
PQIs used in this report consist of the following 13 ambulatory care sensitive conditions, which are measured as rates of admission to the hospital. They are presented as a rate among the adult population (>=18 years of age):
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 AHRQ SAS QI 4.5a. As this software only defines the PQIs at the county level, the AHRQ SAS program was modified to allow for estimation of Illinois regions.
Race specific PQIs: The race specific PQI measures reflect crude, race-stratified rates. They are not risk adjusted. The rates are suppressed where case counts or populations are low.
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.
The supply of mental health providers represents the ratio of the county population to the number of mental health providers and comes from the County Health Rankings and Road Maps website (http://www.countyhealthrankings.org/). Mental health providers include psychologists, psychiatrists, licensed clinical social workers, counselors, marriage and family therapists and advance practice nurses specializing in mental health. Mental health providers that treat alcohol and other drug abuse disorders as well as marriage and family therapists were added to this measure in 2015. The data for mental health providers comes from the National Provider Identification registry. Further information about this methodology is available on the County Health Rankings website under “measure description “: < a href="http://www.countyhealthrankings.org/app/illinois/2016/measure/factors/62/description">http://www.countyhealthrankings.org/app/illinois/2016/measure/factors/62/description.
The readmission rates on the Illinois Public Health Community Map are based on the Centers for Medicare and Medicaid Services (CMS) 30-day readmission measures and are calculated using hospital discharge data received by the Illinois Department of Public Health. Readmission rates were calculated for three different conditions: Pneumonia, Heart Failure, and Heart Attack. Patients with a primary discharge of one of these three conditions during the index admission and associated all-cause readmissions within 30 days of discharge were examined. The study period was from 12/1/12 – 12/31/14. Only patients ages 18 and older were included. The ICD 9 codes for identifying Pneumonia, Heart Failure and Heart attack in the primary diagnosis spot of the index admission are outlined below. All causes for readmission were included, except patients with a planned readmission, such as readmissions for procedures or rehabilitation. Patients transferred from one acute care facility to another acute care facility and patients discharged against medical advice were excluded.
Rates were calculated per 100,000 population, as based on census population estimates. Rates were calculated per patient, rather than per admission, with each patient counted only once regardless of the number of admissions the patient had during the study period. Patient race and patient age were identified from the index admission.
The CMS 30-day readmission measures specifications are written for calculation by hospital, with hospital admissions for Pneumonia, Heart Failure and Heart Attack used as the measure denominators. The rates shown here are calculated by geographic area and demographic group and use population as the denominator. This may change interpretation of the data, including application of readmission rates to social determinants of health and disparities.
ICD 9 Diagnosis Codes
Concentrated disadvantage is a standardized measure of the economic strength of a community. While it is similar to measuring poverty, it encompasses more than just income level to assess a community’s economic standing. It acknowledges that some communities experience a concentration of economic disadvantages that adversely impact residents.
Concentrated disadvantage is one of 59 “life course indicators” developed by the Association of Maternal and Child Health Programs (AMCHP). More detailed information about the importance of and methodology for this indicator are available at: http://www.amchp.org/programsandtopics/data-assessment/LifeCourseIndicatorDocuments/LC-06_ConcentratedDisad_Final-4-24-2014.pdf
Concentrated disadvantage is calculated at the county level based on five variables collected in the U.S. Decennial Census and American Community Survey (ACS):
For this analysis, 2010 Census and 2008-2012 ACS data were used. The state mean for each indicator was computed by averaging the values across the 102 counties in Illinois. For each indicator separately, z-scores were computed to indicate the number of standard deviations each county’s value fell from the state mean. The five z-scores were averaged within each county to give an overall z-score. The overall z-scores were classified into quartiles to classify counties into four levels of concentrated disadvantage.
Quartile 1 = Low CD
Quartile 2 = Low-Medium CD
Quartile 3 = Medium-High CD
Quartile 4 = High CD
**NOTE: Z-scores are a statistical tool for comparison, but are not meaningful numbers in and of themselves. They indicate average distance of a county’s values from the state mean, so they are interpreted in comparison with other county values.
The measure of "severe housing problems" comes from the County Health Rankings and Road Maps website (http://www.countyhealthrankings.org/). This measure represents the percentage of households with at least one of four of the following housing problems:
The data is originally prepared by the U.S. Department of Housing and Urban Development (HUD). HUD gets custom data tables from the U.S. Census periodically that are not generally available and are known as the “Comprehensive Housing Affordability Strategy”. These data help highlight housing problems and needs, especially for low income households are used by local governments. Data presented represent the most recent update of this measure for reporting years 2008-2012.
For more information on this measure see: http://www.countyhealthrankings.org/app/illinois/2016/measure/factors/136/map or the CHAS website at https://www.huduser.gov/portal/datasets/cp.html.
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 4.5a and the 4.5a 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 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.