2.0 Health System Performance 2013

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Accessibility

2.1    Wait Time for Hip Fracture Surgery

Definition

Proportion with surgery within 48 hours: The risk-adjusted proportion of hip fractures that were surgically treated within 48 hours of patient’s admission to hospital, among patients age 65
and older.

Method of Calculation

Denominator: The number of hip fractures among patients age 65 and older that were surgically treated in an acute care hospital.

Numerator: A subset of the denominator and represents the number of hip fractures that were surgically treated within 48 hours.

Wait time is measured from the date/time of the first admission with hip fracture (index admission) to the date/time when hip fracture surgery was received.

Refer to the Technical Notes (Appendix II) for the case selection and inclusion/exclusion criteria. A logistic regression model was fitted with age, sex and selected pre-admission comorbid
diagnoses (heart failure, ischemic heart disease, hypertension, chronic obstructive pulmonary
disease, diabetes with complications and cardiac dysrhythmia) as independent variables, modelling the probability of having hip fracture surgery within 48 hours. Coefficients derived from the logistic model were used to calculate the probability for each case. The expected number of patients in a region is the sum of these case probabilities for that region. The risk-
adjusted proportion was calculated by dividing the observed number by the expected number of cases and multiplying by the Canadian average. A 95% confidence interval was also calculated; the method used to calculate confidence intervals is available upon request. Refer to the Model Specifications (Appendix III) for a list of variables entered in the model and coefficient values.

Interpretation

Operative delay in older patients with hip fracture is associated with a higher risk of post-operative complications and mortality. Wait time for surgery following hip fracture provides a measure of access to care. The wait time may be influenced by comorbid conditions, hospital transfers and practice differences related to certain types of medications, like blood thinners. However, longer waits may indicate lack of resources, physician unavailability and/or other issues related to access to care.

Standards/Benchmarks

A benchmark of hip fracture fixation within 48 hours was set by federal, provincial and territorial governments in December 2005.

Data Source

Discharge Abstract Database (DAD), CIHI

Reference Period

April 1, 2011, to March 31, 2012

Comprehensiveness

Available for all provinces and territories, except Quebec. Rates for Quebec are not available due to differences in data collection.

Comments

Beginning with 2009–2010 data, information on procedure start time is available in the DAD; therefore, the proportion of hip fracture patients receiving surgery within 48 hours can be calculated. Prior to that, the indicator was calculated in days rather than hours.

A person can have more than one hip fracture and one repair in the reference period; therefore, a person can be included in the indicator more than once.

Note: Due to differences in methodology, this indicator may differ from similar indicators developed and reported by jurisdictions.

Bibliography

Bergeron, E. et al. “Is the Delay to Surgery for Isolated Hip Fracture Predictive of Outcome in
Efficient Systems?” The Journal of Trauma 60 (2006): pp. 753–757.
Canadian Institute for Health Information. Health Indicators 2007. Ottawa, Ont.: CIHI, 2007. Canadian Institute for Health Information.Waiting for Heath Care in Canada: What We Know
and What We Don’t Know. Ottawa, Ont.: CIHI, 2006.

Health Canada. Final Report of the Federal Advisor on Wait Times. Ottawa, Ont.: Health Canada, 2006.

Ministry of Health and Long-Term Care. First Common Benchmarks Will Allow Canadians to Measure Progress in Reducing Wait Times(press release). Toronto, Ont.: MOHLTC, December 12, 2005. Accessed from <http://news.ontario.ca/archive/en/2005/12/12/First-ever-common-benchmarks-will-allow-Canadians-to-measure-progress-in-reducin.html>. 

Vidal, E. I. et al. “Hip Fracture in the Elderly: Does Counting Time From Fracture to Surgery or From Hospital Admission to Surgery Matter When Studying In-Hospital Mortality?” Osteoporosis International 20 (2009): pp. 723–729.

Weller, I. et al. “The Effect of Hospital Type and Surgical Delay on Mortality After Surgery for
Hip Fracture.” Journal of Bone and Joint SurgeryBritish Volume 87 (2005): pp. 361–366.

Appropriateness

2.2    Caesarean Section Rate

Definition

Proportion of women delivering babies in acute care hospitals by Caesarean section.

Method of Calculation

(Number of Caesarean sections ∕ number of deliveries ) × 100

Denominator (Delivery)

Inclusion:
Delivery coded in any diagnosis field:

ICD-9

641–676 with a fifth digit of 1 or 2; 650 or V27

ICD-10-CA

O10–O16, O21–O29, O30–O37, O40–O46, O48, O60–O69, O70–O75, O85–O89, O90–O92, O95, O98, O99 with a sixth digit of 1 or 2; or Z37

Exclusion:
Delivery in which an abortive procedureiv was recorded:

CCP

78.52, 86.3, 86.4, 87.0, 87.1 or 87.2

CCI

5.CA.88^^, 5.CA.89^^ or 5.CA.93^^

Numerator (Caesarean Section)

The numerator is a subset of the denominator. Caesarean section is identified as any of the following procedure codes:iv

iv.  Code may be recorded in any position. Procedures coded as out of hospital and abandoned after onset are excluded.

CCP

86.0–86.2, 86.8 or 86.9

CCI

5.MD.60^^

Interpretation

Caesarean section rates provide information on the frequency of surgical birth delivery relative to all modes of birth delivery. Since Caesarean section delivery increases maternal morbidity/mortality and is associated with higher costs, Caesarean section rates are often used to monitor clinical practices, with an implicit assumption that lower rates indicate more appropriate, as well as more efficient, care.

Standards/Benchmarks

Guidelines defining the appropriate indications for Caesarean section are available.

Data Sources

Discharge Abstract Database (DAD), CIHI

Fichier des hospitalisations MED-ÉCHO, ministère de la Santé et des Services sociaux du Québec

Reference Period

April 1, 2011, to March 31, 2012

Comprehensiveness

Available for all provinces and territories.

Comments

Prior to the 2001–2002 rate, deliveries were based on adjusted newborn counts. Beginning with the 2002–2003 rate, stillbirths are included in the delivery count. Prior to that, stillbirths were excluded.

Bibliography

Canadian Institute for Health Information. Giving Birth in Canada: A Regional Profile. Ottawa, Ont.: CIHI, 2004.

Canadian Institute for Health Information. Giving Birth in Canada: Regional Trends From
2001–2002 to 2005–2006. Ottawa, Ont.: CIHI, 2007.

Joseph, K. S. et al. “Determinants of Pre-Term Birth Rates in Canada From 1981 Through 1983 and From 1992 Through 1994.” New England Journal of Medicine 339 (1998): pp. 1434–1439.

Liu, S. et al. “Recent Trends in Caesarean Delivery Rates and Indications for Caesarean Delivery in Canada.” Journal of Obstetrics and Gynaecology Canada 26 (2004): pp. 735–742.

Liu, S. et al. “Risk of Maternal Postpartum Readmission Associated With Mode of Delivery.”
Obstetrics and Gynecology 105 (2005): pp. 836–842.

National Collaborating Centre for Women’s and Children’s Health. Caesarean Section (clinical guidelines). London, U.K.: RCOG Press, 2004. Accessed December 14, 2010, from
<www.nice.org.uk/nicemedia/live/13620/57162/57162.pdf>.

Shearer, E. L. “Cesarean Section: Medical Benefits and Costs.” Social Science & Medicine 37 (1993): pp. 1223–1231.

2.3 Percentage of Patients With Repeat Hospitalizations for a Mental Illness

Definition

Risk-adjusted percentage of individuals that had three or more episodes of care for a selected mental illnessover all those who had at least one episode of care for a selected mental illness in general hospitalsvi within a given year. An episode of care refers to all contiguous hospitalizations and same-day surgery visits in general hospitals.

Method of Calculation

         Total number of individuals who had at least three episodes
                of care for a selected mental illness over a year                   × 100 
Total number of individuals with at least one episode of care 
for a selected mental illness over a year

Refer to the Technical Notes (Appendix II) for the episode building and case selection criteria. A logistic regression model was fitted with age, sex, type of mental illness and discharged against medical advice or did not return from a pass (yes/no) as independent variables. These factors were captured on the index episode of care. Coefficients derived from the logistic model were used to calculate the probability of repeat hospitalizations for each patient. The expected number of repeat hospitalizations for a region is the sum of these probabilities in that region. The risk-adjusted percentage was calculated by dividing the observed number of repeat hospitalizations in each region by the expected number of repeat hospitalizations in the region and multiplying by the Canadian average repeat hospitalizations percentage. A 95% confidence interval was also calculated; the method used to calculate confidence intervals is available upon request. Refer to the Model Specifications (Appendix III) for a list of variables entered in the model and coefficient values.

v.   The mental illnesses selected for this indicator are substance-related disorders; schizophrenia, delusional and non-organic psychotic disorders; mood/affective disorders; anxiety disorders; and selected disorders of adult personality and behaviour.
vi.  Refer to the General Methodology Notes section for more information.

Interpretation

This indicator is considered an indirect measure of appropriateness of care, since the need for frequent admission to hospital depends on the person and the type of illness. Challenges in getting appropriate care/support in the community and/or the appropriate medication often lead to frequent hospitalizations. Variations in this indicator across jurisdictions may reflect differences in the services that help individuals with mental illness remain in the community for a longer period of time without the need for hospitalization.1

This indicator may help to identify a population of frequent users, and further investigations could provide a description of the characteristics of this group. Understanding this population can aid in developing/enhancing programs that may prevent the need for frequent rehospitalization.

Standards/Benchmarks

Benchmarks have not been identified for this indicator.

Data Sources

Discharge Abstract Database (DAD), CIHI

National Ambulatory Care Reporting System (NACRS), CIHI Ontario Mental Health Reporting System (OMHRS),vii CIHI 
Fichier des hospitalisations MED-ÉCHO, ministère de la Santé et des Services sociaux du Québec

Reference Period

April 1, 2010, to March 31, 2012

Comprehensiveness

Available for all provinces and territories.

Comments

Each individual has a 12-month follow-up after his or her first episode of care in a given year. Repeat hospitalizations over a 12-month period can occur at more than one facility.

vii.   In Ontario, facilities are mandated to submit data on discharges from adult designated mental health beds to OMHRS. As a result, inpatient cases from Ontario are extracted from both the DAD and OMHRS. Note that only general hospitals are included (that is, specialty mental health facilities are excluded).

 

When building episodes of care, the exclusion of psychiatric hospitals might introduce a bias. It is possible that the wrong discharge date might be used to track repeat hospitalizations, or two hospitalizations that belong to the same episode might be erroneously attributed to two different episodes. Further analyses demonstrated that this bias is minimal and does not affect the indicator results.

Reference

1.  E. Lin et al., Hospital Report Card: Mental Health 2007 Briefing Pages, accessed from
<http://www.oha.com/KnowledgeCentre/Library/HospitalReports/Documents/Hospital%20 Reports%202007/Mental%20Health.pdf>. 

Continuity

2.4    30-Day Mental Illness Readmission Rate

Definition

Risk-adjusted rate of readmission following discharge for a mental illness. A case is counted as
a readmission if it is for a selected mental illness diagnosisviii and if it occurs within 30 days of the index episode of inpatient care. An episode of care refers to all contiguous hospitalizations and same-day surgery visits in general hospitals.ix

Method of Calculation

Total number of episodes with a 30-day readmission
                for a selected mental illness in a given fiscal year                   × 100 
Total number of episodes with a selected mental illness 
in the first 11 months of the same fiscal year

Refer to the Technical Notes (Appendix II) for the episode building and case selection criteria.

A logistic regression model was fitted with age, sex, type of mental illness, discharged against medical advice or did not return from a pass (yes/no) and multiple previous admissions for a selected mental illness (two and more) during the past 12 months as independent variables. These factors were captured on the index episode of care. Coefficients derived from the logistic model were used to calculate the probability of readmission for each episode. The expected number of readmissions for a region is the sum of these probabilities in that region. The risk- adjusted readmission rate was calculated by dividing the observed number of readmissions in each region by the expected number of readmissions in the region and multiplying by the Canadian average readmission rate. A 95% confidence interval for the risk-adjusted readmission rate was also calculated; the method used to calculate confidence intervals is available upon request. Refer to the Model Specifications (Appendix III) for a list of variables entered in the model and coefficient values.

viii.  The mental illnesses selected for this indicator are substance-related disorders; schizophrenia, delusional and non-organic psychotic disorders; mood/affective disorders; anxiety disorders; and selected disorders of adult personality and behaviour.
ix.    Refer to the General Methodology Notes section for more information.

Interpretation

Readmission to inpatient care may be an indicator of relapse or complications after an inpatient stay. Inpatient care for people living with a mental illness aims to stabilize acute symptoms. Once stabilized, the individual is discharged, and subsequent care and support are ideally provided through outpatient and community programs in order to prevent relapse or complications. High rates of 30-day readmission could be interpreted as a direct outcome of poor coordination of services and/or an indirect outcome of poor continuity of services after discharge.

Standards/Benchmarks

Benchmarks have not been identified for this indicator.

The following results were found in the literature. According to the Hospital Mental Health Services in Canada 2005–2006 report, the 30-day readmission rate, including general hospitals only, was 9.2%.1 According to the National Association of State Mental Health Program Directors, in 1995, the 30-day readmission rate for psychiatric hospitals in America was between 8.1% and 10.2%.2 For patients from Department of Veterans Affairs medical centres in the United States, the percentage of patients readmitted within 30 days was between 13.1%
and 15.3%.3

Data Sources

Discharge Abstract Database (DAD), CIHI

Ontario Mental Health Reporting System (OMHRS),x CIHI National Ambulatory Care Reporting System (NACRS), CIHI Fichier des hospitalisations MED-ÉCHO, ministère de la Santé et des Services sociaux du Québec

Reference Period

April 1, 2011, to March 31, 2012

Comprehensiveness

Available for all provinces and territories.

Comments

A 30-day readmission can occur in the same facility as the index episode or in a different facility. A readmission can be a planned or unplanned admission. Planned versus unplanned admissions cannot be distinguished in all available data sources. For jurisdictions where comprehensive information was available, rates including both planned and unplanned readmissions and only unplanned readmissions were compared, and they were not statistically significantly different. Published work has shown that few planned readmissions for mental illness within 30 days are scheduled by practitioners.4

When building episodes of care, the exclusion of psychiatric hospitals might introduce a bias. It is possible that the wrong discharge date might be used to track readmissions, or two hospitalizations that belong to the same episode might be erroneously attributed to two different episodes. Further analyses demonstrated that this bias is minimal and does not affect the indicator results.

References

1.  Canadian Institute for Health Information, Hospital Mental Health Services in Canada
2005–2006 (Ottawa, Ont.: CIHI, 2008).

2.  R. Hermann and S. Mattke, Selecting Indicators for the Quality of Mental Health Care at the Health System Level in OECD Countries(Paris, France: Organisation for Economic Co-operation and Development, 2004).

3.  D. L. Leslie and R. A. Rosenheck, “Comparing Quality of Mental Health Care for Public- Sector and Privately Insured Populations,”Psychiatric Services 51, 5 (2000): pp. 650–655.

4.  E. Lin et al., Hospital Report Card: Mental Health 2007 Briefing Pages, accessed from
<http://www.oha.com/KnowledgeCentre/Library/HospitalReports/Documents/Hospital%20 Reports%202007/Mental%20Health.pdf>. 

x.   In Ontario, facilities are mandated to submit data on discharges from adult designated mental health beds to OMHRS. As a result, inpatient cases from Ontario are extracted from both the DAD and OMHRS. Note that only general hospitals are included (that is, specialty mental health facilities are excluded).

Effectiveness

2.5    Ambulatory Care Sensitive Conditions Hospitalization Rate

Definition

Age-standardized acute care hospitalization rate for conditions where appropriate ambulatory care prevents or reduces the need for admission to hospital, per 100,000 population younger than age 75.

Method of Calculation

(Total number of acute care hospitalizations for ambulatory care sensitive conditions younger than age 75 ∕ total mid-year population younger than age 75) × 100,000 (age adjusted)

 

Inclusion criteria:

Based on a list of conditions developed by Billings et al., any one most responsible diagnosis code of

  • Grand mal status and other epileptic convulsions;
  • Chronic obstructive pulmonary diseases;
  • Asthma;
  • Heart failure and pulmonary edema;
  • Hypertension;
  • Angina; or
  • Diabetes.

See the Technical Notes (Appendix II) for codes used. Exclusion criteria:

  1. Individuals age 75 and older
  2. Death before discharge

Interpretation

Hospitalization for an ambulatory care sensitive condition is considered to be a measure of access to appropriate primary health care. While not all admissions for these conditions are avoidable, it is assumed that appropriate ambulatory care could prevent the onset of this type of illness or condition, control an acute episodic illness or condition, or manage a chronic disease or condition. A disproportionately high rate is presumed to reflect problems in obtaining access to appropriate primary care.

Standards/Benchmarks

The right level of utilization is not known, and large regional variations in the rate of hospitalization for these conditions exist.

Data Sources

Discharge Abstract Database (DAD), CIHI

Fichier des hospitalisations MED-ÉCHO, ministère de la Santé et des Services sociaux du Québec

Reference Period

April 1, 2011, to March 31, 2012

Comprehensiveness

Available for all provinces and territories.

Comments

Beginning with the 2006–2007 rate, the definition of the ambulatory care sensitive conditions indicator was refined to better align it as a measure of primary health care. In the revised definition, the diabetes component includes diabetes with short-term complications and diabetes without mention of complication; angina, hypertension and heart failure components exclude records where cardiac procedures were also coded. Rates based on the new definition were calculated for the previous years to allow for comparisons over time.

Bibliography

Anderson, G. M. “Common Conditions Considered Sensitive to Ambulatory Care.” In Patterns of Health Care in Ontario, 2nd Ed. Eds. V. Goel et al. Ottawa, Ont.: Canadian Medical Association, 1996, pp. 104–110.

Billings, J., G. M. Anderson and L. S. Newman. “Recent Findings on Preventable
Hospitalizations.” Health Affairs 15 (1996): pp. 239–249.

Billings, J. et al. “Impact of Socio-Economic Status on Hospital Use in New York City.”
Health Affairs 12 (1993): pp. 162–173.

Manitoba Centre for Health Policy and Evaluation. Concept: Ambulatory Care Sensitive (ACS) Conditions. Accessed December 14, 2010, from 
<http://mchp-appserv.cpe.umanitoba.ca/ viewConcept.php?conceptID=1023>.

2.6    30-Day Acute Myocardial Infarction In-Hospital Mortality Rate

Definition

The risk-adjusted rate of all-cause in-hospital death occurring within 30 days of first admission to an acute care hospital with a diagnosis of acute myocardial infarction (AMI).

Method of Calculation

Numerator: Number of deaths from all causes occurring in hospital within 30 days of admission for AMI.

Denominator: Total number of AMI episodes in an 11-month period.

Refer to the Technical Notes (Appendix II) for the episode building and case selection criteria.

A logistic regression model was fitted with age, sex and select pre-admission comorbid diagnoses as independent variables. Coefficients derived from the logistic model were used to calculate the probability of in-hospital death following AMI for each case (episode). The expected number of in-hospital deaths in a region is the sum of the case probabilities of that region. The risk-adjusted mortality rate (RAMR) was calculated by dividing the observed number of in-hospital deaths for each region by the expected number of in-hospital deaths for the region and multiplying by the Canadian average in-hospital death rate. A 95% confidence interval for the RAMR was also calculated; the method used to calculate confidence intervals is available upon request. Refer to the Model Specifications (Appendix III) for a list of variables entered in the model and coefficient values.

Interpretation

A lower risk-adjusted mortality rate following AMI may be related to quality of care or other factors. It has been shown that the 30-day in-hospital mortality rate is highly correlated (r = 0.9) with total mortality (death in and out of hospital) following AMI.1 Inter-regional variations in 30-day in-hospital mortality rates may be due to jurisdictional and institutional differences in standards of care, as well as other factors that were not included in the adjustment.

Standards/Benchmarks

Benchmarks have not been identified for this indicator.

Data Source

Discharge Abstract Database (DAD), CIHI

Reference Period

Rates are based on three years of pooled data: April 1, 2009, to March 31, 2012

Comprehensiveness

Available for all provinces and territories, except Quebec. Rates for Quebec are not available due to differences in data collection.

Comments

Beginning with the rates based on 2003–2004 to 2005–2006 data, AMI case selection criteria were revised to account for the fact that an increasing number of AMI patients are undergoing a revascularization procedure (percutaneous coronary intervention or coronary artery bypass) at their index admission. In the case of revascularization procedures, AMI may not be coded as the most responsible diagnosis; these cases were previously excluded from the indicator. In addition, exclusion criteria were revised so patients with a length of stay of less than three days who are discharged alive are no longer excluded. Comparison of rates with those of previous years should be made with caution.

These rates should be interpreted with caution due to potential differences in the coding of comorbid conditions across provinces and territories.

Reference

1.  J. V. Tu et al., “Acute Myocardial Infarction Outcomes in Ontario,” in Cardiovascular Health
& Services in Ontario: An ICES Atlas, edsC. D. Naylor and P. M. Slaughter (Toronto, Ont.: Institute for Clinical Evaluative Sciences, 1999): pp. 84–100.

Bibliography

Hosmer, D. W. and S. Lemeshow. “Confidence Interval Estimates of an Index of Quality Performance Based on Logistic Regression Models.” Statistics in Medicine 14 (1995): pp. 2161–2172.

Tu, J. V. et al. “Acute Myocardial Infarction Outcomes in Ontario (Methods Appendix).”
In Cardiovascular Health & Services in Ontario: An ICES Atlas (Technical and Methods Appendices). Eds. C. D. Naylor and P. M. Slaughter. Toronto, Ont.: Institute for Clinical Evaluative Sciences, 1999.

2.7    30-Day Stroke In-Hospital Mortality Rate

Definition

The risk-adjusted rate of all-cause in-hospital death occurring within 30 days of first admission to an acute care hospital with a diagnosis of stroke.

Method of Calculation

Numerator: Number of deaths from all causes occurring in hospital within 30 days of admission for stroke.

Denominator: Total number of stroke episodes in an 11-month period.

Refer to the Technical Notes (Appendix II) for the episode building and case selection criteria. A logistic regression model was fitted with age, sex, type of stroke and select pre-admission comorbid diagnoses as independent variables. Coefficients derived from the logistic model were used to calculate the probability of in-hospital death following stroke for each case (episode). The expected number of in-hospital deaths for a region is the sum of these case probabilities in that region. The risk-adjusted mortality rate (RAMR) was calculated by dividing the observed number of in-hospital deaths for each region by the expected number of in-hospital deaths for the region and multiplying by the Canadian average in-hospital death rate. A 95% confidence interval for the RAMR was also calculated; the method used to calculate confidence intervals is available upon request. Refer to the Model Specifications (Appendix III) for a list of variables entered in the model and coefficient values.

Interpretation

Stroke is a leading cause of death and long-term disability. Adjusted mortality rates following stroke may reflect the underlying effectiveness of treatment and quality of care. Inter-regional variations in stroke mortality rates may be due to jurisdictional and institutional differences in standards of care, as well as other factors that are not included in the adjustment.

Standards/Benchmarks

Benchmarks have not been identified for this indicator.

Data Source

Discharge Abstract Database (DAD), CIHI

Reference Period

Rates are based on three years of pooled data: April 1, 2009, to March 31, 2012.

Comprehensiveness

Available for all provinces and territories, except Quebec. Rates for Quebec are not available due to differences in data collection.

Comments

Beginning with rates based on 2003–2004 to 2005–2006 data, case selection criteria for stroke were revised to include patients transferred to rehabilitation during their index admission. In this case, stroke may not be coded as the most responsible diagnosis; these cases were previously excluded from the indicator. In addition, stroke resulting from occlusion of pre-cerebral
arteries is now included in the indicator. These cases were previously excluded because their
identification was not possible in the ICD-9 coding system. Comparison of rates with those of previous years should be made with caution.

This indicator is based on the methodology used to calculate the 30-day acute myocardial infarction in-hospital mortality rate. Rates should be interpreted with caution due to potential differences in the coding of comorbid conditions across provinces and territories.

Bibliography

Hosmer, D. W. and S. Lemeshow. “Confidence Interval Estimates of an Index of Quality Performance Based on Logistic Regression Models.” Statistics in Medicine 14 (1995): pp. 2161–2172.

Mayo, N. E. et al. “Changing Rates of Stroke in the Province of Quebec, Canada: 1981–1988.”
Stroke 22 (1991): pp. 590–595.

Mayo, N. E. et al. “Hospitalization and Case-Fatality Rates for Stroke in Canada From 1982
Through 1991: The Canadian Collaborative Study Group of Stroke Hospitalizations.”
Stroke 27 (1996): pp. 1215–1220.

Weir, N. and M. S. Dennis. “Towards a National System for Monitoring the Quality of
Hospital-Based Stroke Services.” Stroke 32 (2001): pp. 1415–1421.

 

2.8    30-Day Acute Myocardial Infarction Readmission Rate

Definition

Risk-adjusted rate of urgent readmission following discharge for acute myocardial infarction (AMI). Non-elective return to an acute care hospital for any cause is counted as a readmission if it occurs within 30 days of discharge from the index episode of inpatient care. An episode of care refers to all contiguous inpatient hospitalizations and same-day surgery visits.

Method of Calculation

Numerator: Number of cases within the denominator with an urgent readmission within 30 days of discharge in the reference period.

Denominator: Total number of AMI episodes in an 11-month period.

Refer to the Technical Notes (Appendix II) for the episode building and case selection criteria.

A logistic regression model was fitted with selected patient characteristics as independent variables. Coefficients derived from the logistic model were used to calculate the probability of readmission for each case (episode). The expected number of readmissions in a region is the sum of the case probabilities of that region. The risk-adjusted readmission rate (RARR) was calculated by dividing the observed number of readmissions for each region by the expected number of readmissions for the region and multiplying by the Canadian average readmission rate. A 95% confidence interval for the RARR was also calculated; the method used to calculate confidence intervals is available upon request. Refer to the Model Specifications (Appendix III) for a list of variables entered in the model and coefficient values.

Interpretation

Readmissions to acute care facilities are increasingly being used to measure institutional or regional quality of care and care coordination. Readmission rates after AMI can be influenced by a variety of factors, including the quality of inpatient and outpatient care, effectiveness of the care transition and coordination, or the availability of appropriate diagnostic or therapeutic technologies during the initial hospital stay. While not all urgent readmissions are avoidable, interventions during and after a hospitalization can be effective in reducing readmission rates.

Standards/Benchmarks

Benchmarks have not been identified for this indicator.

Data Sources

Discharge Abstract Database (DAD), CIHI

National Ambulatory Care Reporting System (NACRS), CIHI

Fichier des hospitalisations MED-ÉCHO, ministère de la Santé et des Services sociaux du Québec

 

Reference Period

April 1, 2011, to March 31, 2012

Comprehensiveness

Available for all provinces and territories.

Comments

Patients can appear in the denominator more than once if they have multiple episodes of care between April 1 and March 1 of the fiscal year.

Planned readmissions reported as urgent admissions are included in the readmission rate.

Bibliography

Ashton, C. M. and N. P. Wray. “A Conceptual Framework for the Study of Early Readmission as an Indicator of Quality of Care.” Social Science and Medicine 43 (1996): pp. 1533–1541.

Krumholz, H. M. et al. “Hospital 30-Day Acute Myocardial Infarction Readmission Measure.
Methodology.” Report prepared for Centers for Medicare & Medicaid Services, 2008. Assessed on October 10, 2012 from <http://www.qualitynet.org/dcs/ContentServer?c= Page&pagename=QnetPublic%2FPage%2FQnetTier4&cid=1219069855841>.

Hosmer, D. W. and S. Lemeshow. “Confidence Interval Estimates of an Index of Quality Performance Based on Logistic Regression Models.” Statistics in Medicine 14 (1995): pp. 2161–2172.

2.9    30-Day Obstetric Readmission Rate

2.10  30-Day Readmission—Patients Age 19 and Younger

2.11  30-Day Surgical Readmission Rate

2.12  30-Day Medical Readmission Rate

Definition

Risk-adjusted rate of urgent readmission for each of the following patient groups:

•  Obstetric

•  Patients Age 19 and Younger

•  Surgical

•  Medical

Non-elective return to an acute care hospital for any cause is counted as a readmission if it occurs within 30 days of discharge from the index episode of inpatient care. An episode of care refers to all contiguous inpatient hospitalizations and same-day surgery visits.

Method of Calculation

Numerator: Number of cases within the denominator with an urgent readmission within
30 days of discharge.

Denominator: Number of obstetric/patients age 19 and younger/surgical/medical episodes of care discharged between April 1 and March 1 of the fiscal year.

Records with pregnancy/childbirth (except for obstetric readmission), mental diseases and disorders, or palliative care (as most responsible diagnosis) are excluded. Refer to the Technical Notes of the obstetric, patients age 19 and younger, surgical and medical readmission rates (Appendix II) for the episode building and case selection criteria.

A logistic regression model was fitted with selected patient characteristics as independent variables. Coefficients derived from the logistic model were used to calculate the probability of readmission for each case (episode). The expected number of readmissions in a region is the sum of the case probabilities of that region. The risk-adjusted readmission rate (RARR) was calculated by dividing the observed number of readmissions for each region by the expected number of readmissions for the region and multiplying by the Canadian average readmission rate. A 95% confidence interval for the RARR was also calculated; the method used to calculate confidence intervals is available upon request. Refer to the Model Specifications of the obstetric, patients age 19 and younger, surgical and medical readmission (Appendix III) for a list of variables entered in the model and coefficient values.

Interpretation

Readmissions to acute care facilities are increasingly being used to measure institutional or regional quality of care and care coordination. Readmission rates can be influenced by a variety of factors, including the quality of inpatient and outpatient care, effectiveness of the care transition and coordination, and the availability and use of effective community-based disease management programs. While not all urgent readmissions are avoidable, interventions during and after a hospitalization can be effective in reducing readmission rates.

Standards/Benchmarks

Benchmarks have not been identified for this indicator.

Data Sources

Discharge Abstract Database (DAD), CIHI

National Ambulatory Care Reporting System (NACRS), CIHI

Fichier des hospitalisations MED-ÉCHO, ministère de la Santé et des Services sociaux du Québec

Reference Period

April 1, 2011, to March 31, 2012


Comprehensiveness

Available for all provinces and territories.

Comments

Patients can appear in the denominator more than once if they have multiple episodes of care between April 1 and March 1 of the fiscal year.

Planned readmissions reported as urgent admissions are included in the readmission rate.

Bibliography

Ashton, C. M. and N. P. Wray. “A Conceptual Framework for the Study of Early Readmission as an Indicator of Quality of Care.” Social Science and Medicine 43 (1996): pp. 1533–1541.

Feudtner, C. et al. “State-Level Child Health System Performance and the Likelihood of
Readmission to Children’s Hospitals.” The Journal of Pediatrics 157 (2010): pp. 98–102.

Jencks, S. F. et al. “Rehospitalizations Among Patients in the Medicare Fee-for-Service
Program.” New England Journal of Medicine 360 (2009): pp. 1418–1428.

Jiang, H. J. and L. M. Wier. All-Cause Hospital Readmissions Among Non-Elderly Medicaid Patients, 2007. (HCUP Statistical Brief #89.) Rockville, Maryland: Agency for Healthcare Research and Quality, 2010.

Liu, S. et al. “Risk of Maternal Postpartum Readmission Associated With Mode of Delivery.”
Obstetrics and Gynecology 105 (2005): pp. 836–842.

Stone, J. and G. J. Hoffman. Medicare Hospital Readmissions: Issues, Policy Options and
PPACA. Washington, D.C.: Congressional Research Service, 2010.

2.13  Self-Injury Hospitalization Rate

Definition

Age-standardized rate of hospitalization in a general hospitalxi due to self-injury per
100,000 population.

Method of Calculation

Total number of discharges for a self-injury
                 for patients age 15 and older                   × 100,000 (age adjusted) 
Total mid-year population age 15 and older

Self-injury is identified by the following external cause of injury codes with a diagnosis type of 9: ICD-10-CA
X60 to X84

xi.  Refer to the General Methodology Notes section for more information.

Interpretation

Self-injury is defined as a deliberate bodily injury that may or may not result in death. This type of injury is the result of either suicidal or self-harming behaviours, or both. Self-injury can be prevented, in many cases, by early recognition, intervention and treatment of mental illnesses. While some risk factors for self-injury are beyond the control of the health system, high rates of self-injury hospitalization can be interpreted as the result of a failure of the system to prevent self-injuries that are severe enough to require hospitalizations.

Standards/Benchmarks

Benchmarks have not been identified for this indicator.

The following results were found in the literature. In Canada, in 2001–2002, the age-adjusted hospitalization rate due to self-injuries was 7.6 per 10,000.1

Data Sources

Discharge Abstract Database (DAD), CIHI

Ontario Mental Health Reporting System (OMHRS),xii CIHI National Ambulatory Care Reporting System (NACRS),xiii CIHI 
Fichier des hospitalisations MED-ÉCHO, ministère de la Santé et des Services sociaux du Québec

Reference Period

April 1, 2011, to March 31, 2012

Comprehensiveness

Available for all provinces and territories.

 

xii.   In Ontario, facilities are mandated to submit data on discharges from adult designated mental health beds to OMHRS. As a result, inpatient cases from Ontario are extracted from both the DAD and OMHRS. Note that only general hospitals are included (that is, specialty mental health facilities are excluded).
xiii.  To capture all hospitalized self-injury cases in Ontario, individuals identified in emergency departments with a self-injury code as the main reason for the visit and then transferred to a designated mental health bed were counted. More information is available upon request.

Comments

This indicator does not include cases of self-injury involving outpatient treatment in hospital emergency rooms or other medical facilities or completed suicide prior to hospital admission. Thus, this indicator cannot be used to estimate the prevalence of self-injury in the general population. Also not included are patients who were institutionalized in psychiatric hospitals
and were self-injured during their stay but did not require admission to a general hospital. For a broader estimate of self-injury, please refer to the In Focus section of Health Indicators 2012.

Using the available data sources, capturing intention is difficult. This indicator cannot distinguish whether or not the self-injury was intended to result in death (self-harming or suicidal behaviour). In addition, this indicator might provide biased estimates of the true number of hospitalizations
for self-injury, due to the manner in which intent is captured in the data sources available.
For example, poisoning can be coded as “unintentional”—an overdose—or “undetermined”— reflecting an uncertainty between unintentional and intentional motives. Both unintentional and undetermined injuries were not included in this indicator, even though it is assumed that a small number of these cases were, in fact, intentional.

Reference

1.  Canadian Institute for Health Information, National Trauma Registry Analytic Bulletin: Hospitalizations Due to Suicide Attempts and Self-Inflicted Injury in Canada, 2001–2002 (Ottawa, Ont.: CIHI, 2004).

2.14  Potentially Avoidable Mortality and Mortality From Preventable and Treatable Causes

Definitions

Potentially avoidable mortality: Premature deaths that could potentially have been avoided through all levels of prevention (primary, secondary, tertiary). Premature deaths are those that occur among individuals younger than age 75. Expressed as the age-standardized mortality rate and age-standardized potential years of life lost (PYLL) per 100,000 population.

Mortality from preventable causes: Premature deaths that could potentially have been prevented through primary prevention efforts. Mortality from preventable causes is a subset of potentially avoidable mortality. Expressed as the age-standardized mortality rate and age-standardized PYLL per 100,000 population.

Mortality from treatable causes: Premature deaths that could potentially have been avoided through secondary or tertiary prevention. Mortality from treatable causes is a subset of potentially avoidable mortality. Expressed as the age-standardized mortality rate and age-standardized PYLL per 100,000 population.

 

Method of Calculation

Mortality Rate:

   Number of deaths at age younger than 75
   from avoidable/preventable/treatable causes     × 100,000 (age adjusted) 
Total mid-year population younger than age 75

Potential Years of Life Lost (PYLL):

The sum of differences between 75 and age of deathxiv 
          from avoidable/preventable/treatable causes             × 100,000 (age adjusted) 
Total mid-year population younger than age 75

Refer to the Technical Notes (Appendix II) for the list of causes of death included in the indicators.

Interpretation

These indicators contribute to the measurement of health system performance. The potentially avoidable mortality indicator includes premature deaths that could be avoidable through all levels of prevention.

Mortality from preventable causes focuses on premature deaths from conditions that could potentially be avoided through primary prevention efforts, such as lifestyle modifications or population-level interventions (for example, vaccinations, injury prevention). The indicator informs efforts aimed at reducing the number of initial cases, or incidence reduction, as deaths are prevented by avoiding new cases altogether.

Mortality from treatable causes focuses on premature deaths that could potentially be avoided through secondary and tertiary prevention efforts, such as screening for and effective treatment of an existing disease. The indicator informs efforts aimed at reducing the number of people who die once they have the condition, or case-fatality reduction.

Standards/Benchmarks

Benchmarks have not been identified for this indicator.

Data Source

Vital Statistics—Death Database, Statistics Canada.

Reference Period

Rates are based on three years of pooled data: January 1, 2007, to December 31, 2009.

 

xiv.  The PYLL values for each of the five-year age groups are available at www.statcan.gc.ca/pub/82-221-x/2011002/quality-qualite/qua2-eng.htm#a229.

Comprehensiveness

Available for all provinces and territories.

Comments

Avoidable mortality indicators were developed based on the Australian Potentially Avoidable Deaths indicator and the U.K. Office for National Statistics’ list of causes of avoidable mortality, followed by expert review of the diagnosis codes and rationales for including each condition. Causes of death were assigned to preventable and treatable subcategories based on two main mechanisms of mortality reduction: incidence and case-fatality reduction. These subcategories are mutually exclusive. In cases where a prevention/treatment overlap exists, the case was assigned to the preventable category; the exceptions were ischemic heart disease and stroke, where a random half of cases were assigned as preventable and the other half assigned as treatable. However, the mutually exclusive nature of the subcategories does not imply that all cases assigned to the preventable group do not have a treatable component, and vice versa.

It is generally acknowledged that not all deaths from potentially avoidable causes can actually be avoided. For example, some deaths from treatable causes may be unavoidable due to late diagnosis or concurrent health problems, while some deaths from preventable causes could be due to unpredictable events against which no protective measures could have been taken.

An upper age limit of 75 should not imply that some deaths in the population older than 75 could not be avoided. However, multiple comorbidities are common among older adults, making the assignment of a single cause of death challenging.

The indicators will be reviewed periodically to assess the upper age limit and potential new avoidable conditions due to better understanding of disease etiology or advances in treatment.

Bibliography

Australian Government. National Healthcare Agreement: PI 20—Potentially Avoidable Deaths,
2010. Accessed on October 19, 2011, from <http://meteor.aihw.gov.au/content/index.phtml/ itemId/394495>. 

Ministry of Health. Saving Lives: Amenable Mortality in New Zealand, 1996–2006. Wellington, New Zealand: Ministry of Health, 2010.

Nolte, E. and C. M. McKee. Does Health Care Save Lives? Avoidable Mortality Revisited.
London, U.K.: The Nuffield Trust, 2004.

Office for National Statistics (United Kingdom). Definitions of Avoidable Mortality. Accessed on October 19, 2011, from <http://www.ons.gov.uk/ons/dcp171778_264958.pdf>.

Page, A. et al. Australian and New Zealand Atlas of Avoidable Mortality. Adelaide, Australia: PHIDU, University of Adelaide, 2006.

Rutstein, D. D. et al. “Measuring the Quality of Medical Care: A Clinical Method.” The New
England Journal of Medicine 294 (1976): pp. 582–588.

 

Safety

2.15  Hospitalized Hip Fracture Event Rate

Definition

Age-standardized rate of new hip fractures admitted to an acute care hospital, per 100,000 population age 65 and older. A new event is defined as a first-ever hospitalization for hip fracture or a subsequent hip fracture occurring more than 28 days after the admission for the previous event in the reference period.

Method of Calculation

(Total number of new hip fracture events for persons age 65 and older ∕ total mid-year population age 65 and older) × 100,000 (age adjusted)

Numerator inclusion criteria:

  1. Hip fracture present on admission
    ICD-10-CA: S72.0, S72.1 or S72.2; ICD-9/ICD-9-CM: 820.0–820.3, 820.8 or 820.9 coded as diagnosis type (1) or
  2. Age at admission 65 and older
  3. Sex recorded as male or female
  4. Admission to an acute care institution
  5. Canadian resident

Numerator exclusion criteria:

  1. Records with an invalid health card number or date of birth
  2. Records with an invalid admission date
  3. Hip fracture admissions within 28 days after the admission date of the previous hip fracture hospitalization
  4. Transfersxv

Interpretation

Hip fractures represent a significant health burden for seniors and for the health system. As well as causing disability or death, hip fracture may have a major effect on independence and quality of life. Measuring occurrence of hip fractures in the population is important for planning and evaluating preventive strategies, allocating health resources and estimating costs.

Standards/Benchmarks

Benchmarks have not been identified for this indicator.

xv.   If a subsequent hip fracture admission occurs on the same day as or prior to the discharge date of a previous hip fracture admission, it is considered a transfer.

Data Sources

Discharge Abstract Database (DAD), CIHI

Fichier des hospitalisations MED-ÉCHO, ministère de la Santé et des Services sociaux du Québec

Reference Period

April 1, 2011, to March 31, 2012

Comprehensiveness

Available for all provinces and territories.

Comments

This indicator includes all new hospitalized hip fractures in the reference period, encompassing first-ever and recurrent events. A person may have more than one hip fracture event in the reference period. Hip fractures not admitted to an acute care hospital and in-hospital hip fractures are not included in this indicator. Hip fractures occurring in a hospital are reported separately in the in-hospital hip fracture indicator.

Bibliography

Chevalley, T. et al. “Incidence of Hip Fracture Over a 10-Year Period (1991–2000): Reversal of a
Secular Trend.” Bone 40 (2007): pp. 1284–1289.

Marks, R. et al. “Hip Fractures Among the Elderly: Causes, Consequences and Control.” Ageing
Research Reviews 2 (2003): pp. 57–93.

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