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Health Indicators, vol. 2002, no. 1 >

Data quality, concepts and methodology

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These notes provide general comments to assist with accurate interpretation of the health indicators. Please see the descriptions for specific information on indicator definitions, sources, calculation methods, and other details. Additional information on interpretation, comparability, relevant standards/benchmarks, and other material is available upon request.

Regional health indicators

The methodology used for these indicators was designed to maximize inter-regional and inter-provincial comparability given the characteristics of available national datasets. For this reason, there may be differences between definitions, data sources, and extraction procedures used in some local, regional, or provincial/territorial reports when compared to those described here. In addition, discrepancies may exist due to on-going updates to databases.

Rates are standardized wherever possible to facilitate comparability across provinces/regions and over time.

Health region level rates and population estimates presented in this publication are based on the boundaries in effect as of January 1999.

Indicators based on hospitalization records produced by the Canadian Institute for Health Information (CIHI) are limited to health regions with population greater than 100,000.

Health region level population estimates:

Population estimates for health regions were produced by Statistics Canada (Demography Division) for all provinces, except Quebec, Alberta and British Columbia. Quebec health region population estimates were provided by l'Institut de la statistique du Québec, Alberta population estimates from Alberta Health and Wellness and British Columbia population estimates were provided by BC Stats. See Appendix 1 for methodology.


The Highlights contain a number of data comparisons for general understanding of each indicator. For all highlights (except where specified below), rate comparisons have been tested for statistical significance at the 95% confidence level to either their respective Canada rates or to other sub-groups, as the highlights dictate. These highlights contain terms such as "significantly high", "not significantly different" or "significantly low" and refer to statistical significance tests. For example, a rate for a given health region may be significantly higher than the Canada average. These statistical terms should not be confused with non-statistical terms such as "much higher" or "much lower", which refer strictly to numerical differences. All Labour Force Survey, Census (except average personal income) and Crime data highlights have been written according to comparisons of point estimates and do not take into account sampling variability or statistical significance.

Health status indicators based on vital statistics (Statistics Canada - STC)

Provincial vital and cancer statistics

Within the Health Indicators product are eight indicators based on vital and cancer statistics and that are produced at the Canada, province and territorial level only, with long time series. These indicators may have different methodologies compared to the regional health indicators (discussed below). Data on provincial health and on regional health may be the same indicator, but the numbers or rates may differ because of their methodologies. One key difference is that the provincial indicators are based on single years of data, whereas regional level data are based on three year averages (see below for details). For this reason, in addition to certain additional methodological differences, comparisons between these two sources is not recommended.

These provincial health indicators include the Canada/province/territory-only time series data for Life Expectancy, Low Birthweight, Age-standardized Mortality Rates, Infant Mortality, Potential Years of Life Lost and Cancer Incidence.

Age-standardized mortality and cancer incidence rates were based on place of residence. The formula for age-standardization is presented in a later section entitled "Age-standardized mortality rates". Cancer incidence data from 1998 to 2002 are estimates produced by Health Canada.

Life expectancy is calculated using the Greville method, a widely recognized method of constructing a life table (Greville TNE. Short methods of constructing abridged life tables. The Record of American Institute of Actuaries 1943; 32(65):29-42, Part 1). These provincial/territorial life expectancy data were based on single years of mortality and population and were abridged life tables (i.e., 5 year age-sex groupings). Although their methodologies differ, the Greville, Chiang and Keyfitz methods of calculating life expectancy yield similar results (Ng Edward and Gentleman Jane F, "The Impact of Estimation Method and Population Adjustment on Canadian Life Table Estimates", Health Reports 1995, Vol. 7, No.3, pp.15-22).

There are no special notes for the provincial vital statistics indicators of low birthweight and infant mortality outside of what is described in the Definitions and Data Sources document.

Potential years of life lost (PYLL) was calculated in the same fashion as the regional-level indicators of the same name, as described in a later section entitled "Potential years of life lost".

Regional-level vital statistics indicators

  • Rates are based on place of residence for indicators derived from birth and death events.
  • Indicators presented in this product (with the exception of province-only indicators, described above) which were derived from vital statistics, are based on three years of data in both numerator and denominator. For low birth weight, three years (e.g., 1995 to 1997) of the appropriate birth data are used in both the numerator and denominator. For infant and perinatal mortality, three years of death or stillbirth data are divided by the same three years of birth data. For mortality, three years of death data (e.g., 1995 to 1997) are divided by three times the mid-year (e.g., 1996) population estimate. In all vital statistics table titles, the year mentioned simply refers to the middle year (e.g., 1996).

Regional level data quality measures: Confidence Intervals

  • All data presented have an associated 95% confidence interval (CI). The confidence interval illustrates the degree of variability associated with a rate. Wide confidence intervals indicate high variability, thus, these rates should be interpreted and compared with due caution. Some age-standardized rates were suppressed due to both a very small underlying count plus extremely high variability. Confidence intervals can also be used to determine whether a rate in one health region is statistically below, above or no different than the rate for the same indicator in another health region.
  • The confidence intervals for the age-standardized rates were produced using the Spiegelman method as presented below. Reference: Spiegelman M. Introduction to Demography, Revised Edition. Cambridge Massachusetts: Harvard University Press, 1968. p 113, Formula 4.29

    where Ps is the standard population (see below), Psx is the age-specific standard population, x is the age group (using 5-year age groups) and mx is the age-specific crude mortality rate and Px is the population estimate for the corresponding age group. Note that when using three years of data, mx is actually sum/ Px*3 where sum equals three years of mortality data for the specific age group.
  • The confidence intervals for the crude count, crude rate and birth-related data were produced via the Fleiss method. Reference: Fleiss JL, Statistical Methods for Rates and Proportions, 2nd Ed, Wiley and Sons, NY, 1981. Take note that the lower confidence interval (CI) in this formula is constrained by zero, which means the difference between the rate and the lower CI is not always equal to the difference between the rate and the upper CI.

where n=the number of events, p=the proportion or rate, SE=the standard error (1.96 at 95% confidence), sq= the square root, q=1-p. Remember that n is comprised of three years worth of data, and p=n/pop, where pop is three years worth of life-years.

Age-standardized rates

  • Mortality rates, with the exception of crude rates, potential years of life lost (PYLL) and infant and perinatal mortality, as well as cancer incidence and certain CIHI-based data, are age-standardized using the direct method, and the 1991 Canadian Census population structure. The use of a standard population results in more meaningful rate comparisons, because it adjusts for variations in population age distributions over time and across different geographic areas.

    Age (in years) Standard Population Age (in years) Standard Population
    <1 403,061 45-49 1,674,153
    1-4 1,550,285 50-54 1,339,902
    5-9 1,953,045 55-59 1,238,441
    10-14 1,913,115 60-64 1,190,217
    15-19 1,926,090 65-69 1,084,588
    20-24 2,109,452 70-74 834,024
    25-29 2,529,239 75-79 622,221
    30-34 2,598,289 80-84 382,303
    35-39 2,344,872 85-89 192,410
    40-44 2,138,891 90+ 95,467

    Source: Statistics Canada Cat. No. 84F0208XPB, Causes of Death 1997, Appendix 3

    The formula for age-standardized death rate r is:

    Where for age group i, di and pi are, respectively, the age-sex specific death count and population size for a given cause of death and geographical area, and Wi is the weight for that group. Note that the same weight is used for each sex. To yield a rate per 100,000 population, r is multiplied by 100,000.

Geographic coding (geo-coding) to health regions

  • Birth and death data for 1996 and beyond (in addition to 950 birth records from Ontario in 1995 and all 1995 birth and death data from Alberta) have been linked to health regions using postal codes reported with place of residence and converted to enumeration area (EA) using the automated geo-coding system developed in Health Statistics Division. These data were then aggregated to health region based on the EA level correspondence developed in Health Statistics Division with the cooperation of provincial Ministries of Health, Alberta Treasury and BC Stats.
  • All 1995 birth and death data (except where noted above) and the small portion of records from 1996 onwards which had no postal code information were linked to health regions using at least one of two methods:
    1. For most records (where health regions are comprised of complete census subdivisions) the standard geographical classification codes recorded on the vital statistics database were used to link records to health region.

    2. Selected records (those linked to census subdivisions which are associated with more than one health region) were extracted from the data base. Using the registration numbers of these events, birth registration records and death certificates were accessed and the postal codes for place of residence were captured. These records were then geo-coded to enumeration areas (same as for data years 1996 and 1997), linked to health regions using the EA-to-HR correspondence, then merged with the remaining data for that year to get the most accurate health region link.

      Due to the small population of Churchill health region (4690), Manitoba (pop. 1,110 in 1996) and the number of deaths, virtually all vital statistics data for this health region would, in the absence of any adjustment, need to be suppressed. As such, in this product all vital statistics data presented for region 4680 (Burntwood) are an aggregate of Burntwood and Churchill regions. For census-related data, however Burntwood and Churchill are presented both separately as well as combined (i.e., Burntwood/Churchill).

Birth statistics
Birth data on our Vital Statistics Database for Ontario are underestimated for data years 1995, 1996 and 1997 due to incomplete files. Birth data for those same years for some other provinces may also be affected by this incompleteness. Thus, birth-related data (low birth weight, infant mortality and perinatal mortality), particularly for Ontario, should be interpreted with caution.

Life expectancy
This variable was calculated using the Chiang methodology for abridged life tables. The estimates are based on three years (e.g., 1995-1997) of mortality data and the mid-year population estimates, as described above. Abridged life tables use five-year age groupings of both population and mortality rate inputs (as opposed to single year age breakdown). Since there is more variability in the number of events by age in smaller geographic areas, abridged life tables are more suitable for the adaptation to a sub-provincial level (health region). Chiang's method in particular was chosen because it was relatively easy to adapt to the health region level data and included the calculation of standard error (in this case, addressing the variability of deaths from one year to the next).

Life expectancy revisions (Vol. 2001, No. 3 and beyond):
A methodological adjustment was made to the calculation of life expectancy. The change only affects the life expectancy values for both sexes combined. Users are encouraged to use the life expectancy data found in Health Indicators issues from this release onwards.

Disability-free life expectancy
Estimates of disability-free life expectancy are calculated using Sullivan's method (Sullivan, DF. A single index of mortality and morbidity. HSMHA Health Reports 86 (April 1971) : 347-354). Essentially, the latter generalizes Chiang's method (Chiang, CL. The Life Table and its Applications. Robert E. Krieger Publishing Company, Malabar, Florida, 1984: 316).

Sullivan's method is based on activity limitation rates within a population, according to sex and age group, in the calculation of life expectancy with disability. In the case of people living in health institutions, it was assumed that everyone had at least one activity limitation. For people living in other types of institutions, the hypothesis established is that the activity limitation rate by age group and sex was identical to the population in private households.

Disability-free life expectancy represents the difference between life expectancy and life expectancy with disability. The standard deviations of disability-free life expectancy estimates (and consequently the upper and lower limits of the confidence intervals associated with these estimates) are based on Colin Mathers' method (Mather, C. Health Expectancies in Australia 1981 and 1988. Australian Government Publishing Service, Canberra, 1991: 117). This method takes into account both the stochastic fluctuations in the observed death rates and the sampling variability of the activity limitation rates.

NOTE: The disability data for DFLE came from the 1996 Census of Population. Questions on disability in the Census of Population are generally used to capture the sample of post-censal Health and Activity Limitations Survey. Because of the decision not to conduct this survey in 1996, data on disability from the Census of population of 1996 were neither verified nor imputed. More precisely, no validation was undertaken to check the completeness or consistency of the data, and as a result, no corrections to the data were made. In addition, the data were not adjusted to account for population undercounts.

DFLE estimates will vary according to both the concepts from which they are based and the surveys from which the data are extracted.

DFLE (Volume 2001, No's. 1 and 2): For these issues, disability was defined as "having any activity limitation or handicap".

DFLE (Volume 2001, No. 3 and beyond): For this issue and for future issues, disability is defined as "having an activity limitation that affects activities at home, work or at school". This differs from previous Health Indicators issues by excluding limitations that only affect activities other than home, work or school as well as respondents who stated that they had some form of handicap other than an activity limitation.

Deaths due to medically treatable diseases

  • The definitions the medically treatable diseases were taken from a paper written by JRH Charlton (Charlton JRH, "Avoidable deaths and diseases as monitors of health promotion", pp. 467-479, in Measurement in health promotion and protection, Copenhagen and Albany NY: World Health Organization and the International Epidemiological Association, 1987). The types of medically treatable diseases mentioned in Charlton originally came from a paper by DD Rutstein (Rutstein DD, "Monitoring progress and failure: sentinel health events (unnecessary diseases, disabilities and untimely deaths", pp. 195-212, in Measurement in health promotion and protection, Copenhagen and Albany NY: World Health Organization and the International Epidemiological Association, 1987).
  • All results were age-standardized according to the age group considered for reasonable odds of survival. These age-standardized rates per 100,000 reflect these age groups, not the total population.
  • The method of calculating confidence intervals was the Spiegelman method (described earlier).

Potential Years of Life Lost

  • In this publication, death was considered premature if the person died before age 75. This is more reflective of life expectancies in recent years and is more reflective of international standards. Many previous Statistics Canada publications provide PYLL data based on death before age 70. Additionally, PYLL can be presented as an age-standardized rate or as a crude rate; in this publication, it is presented as a crude rate. As well, the denominator can be based on population aged 0 to 74 or for the total population. In this publication, the denominator is based on the former. Thus, the data in this publication cannot be easily compared with prior analyses.
  • In this publication, a PYLL rate was produced, where the weights are taken as proportions of the years lost per death within each age group over the total years lost in all age groups. Each death event is multiplied by its age-specific weight. The sum of all these values represents the total PYLL. The PYLL rate is PYLL per 100,000 population aged 0 to 74. The use of weights allows for the calculation of confidence intervals. The confidence intervals for each PYLL rate were produced by the Spiegelman method (described earlier).




Under 1



















































This publication only presents PYLL rates based on the sum of all age groups. Thus, the rate is calculated as follows:

Where PYLL is the sum of PYLL for ages 0 to 74 for the three years of data, WT is a weight of 1.0 and POP is the population aged 0-74 for the middle year of the three years.

If a user wanted to calculate age-specific PYLL rates based on their own data, the formula would be:

where i is the specific age group.

Indicators based on Cancer Incidence (STC)

Cancer Incidence

The Canadian Cancer Registry (CCR) is a central database located at Statistics Canada that contains patient-oriented information about diagnosis of cancers in Canada. Data on the incidence of cancer are collected by the provincial and territorial cancer registries. The information is used for descriptive and analytic epidemiological studies: to identify risk factors for the cancer; to plan, monitor and evaluate a broad range of cancer control programs (e.g., screening); and for health services and economic research and planning.

  • Cancer incidence is based on place of residence at time of diagnosis.
  • Data contained in this table have been tabulated using the October 24, 2001 file in addition to a special 1997 Ontario data file (*) and the International Agency for Research on Cancer (IARC) rules for determining multiple primaries sites. Included are cancer sites 140 to 208 from the International Classification of Diseases, Ninth Edition (ICD-9).
  • Cancer incidence data in this product are based on three years of data (e.g., 1994 to 1996) averaged over the population estimate of the middle year (e.g., 1995). Table titles associated with these data reflect the mid-point of the three-year period being averaged (i.e., 1995).
  • All data presented have an associated 95% confidence interval (CI). The confidence interval illustrates the degree of variability associated with a rate. Wide confidence intervals indicate high variability, thus, these rates should be interpreted and compared with due caution. Some age-standardized rates were suppressed due to both a very small underlying count plus extremely high variability. Confidence intervals can also be used to determine whether a rate in one health region is statistically below, above or no different than the rate for the same indicator in another health region.
  • The confidence intervals for the age-standardized cancer incidence rates were produced via the Spiegelman method (described earlier).
  • Due to the small population of Churchill health region (4690), Manitoba (pop. 1,110 in 1996) and the number of events, virtually all cancer incidence statistics data for this health region would, in the absence of any adjustment, need to be suppressed. As such, in this product all cancer incidence data presented for region 4680 (Burntwood) are an aggregate of Burntwood and Churchill regions.
  • Cancer incidence rates are age-standardized using the direct method and the 1991 Canadian Census population structure. See "Age-standardized rates" section for details.
  • Over 95% of the cancer incidence data for 1994 and beyond have been linked to health regions using postal codes reported with place of residence and converted to enumeration area (EA) using the automated geo-coding system developed in Health Statistics Division.
  • The remaining 5% of cancer incidence data (for which there were no postal codes available) were linked to health regions using the census subdivision (CSD) of residence using the automated geo-coding system developed in Health Statistics Division.
  • Approximately 7% of Ontario cancer incidence records over the 1994-1996 and 1995-1997 time periods could not be linked to health region geography. As such, cancer incidence data for Ontario are only published at the provincial level.
  • Cancer incidence data for the three northern health regions in Quebec (2410, 2417 and 2418) have been suppressed at the request of the Quebec Cancer Registry.
  • Cancer incidence data for health regions in Prince Edward Island and Alberta have been suppressed at the request of their respective Cancer Registries.

Indicators based on National Population Health Survey, the Canadian Community Health Survey, and the National Longitudinal Survey of Children and Youth (STC)

National Population Health Survey

The National Population Health Survey (NPHS), which began in 1994/95, collects information about the health of the Canadian population every two years. It covers household and institutional residents in all provinces and territories, except persons living on Indian reserves, Canadian Forces bases, and in some remote areas. The NPHS has both a longitudinal and a cross-sectional component. Respondents who are part of the longitudinal component will be followed for up to 20 years.

The Health Indicators data are based on both the longitudinal and cross-sectional components for household residents (institutional excluded) living in the provinces (territories excluded). Data are available for the first three cycles (1994/95, 1996/97 and 1998/99).

The 1994/95 and 1996/97 cross-sectional samples are made up of longitudinal respondents and their household members and individuals who were selected as part of supplemental samples, or "buy-ins", in some provinces. The 1998/99 cross-sectional sample is made up mostly of longitudinal respondents and their cohabitants. No buy-ins were added to 1998/99 data. However, to keep the sample representative, infants born in 1995 and thereafter and immigrants who entered Canada since the beginning of 1995 were randomly selected and added to the NPHS sample.

The 1994/95 provincial, non-institutional cross-sectional sample consisted of 27,263 households, of which 88.7% agreed to participate in the survey. After application of a screening rule to maintain the representativeness of the sample, 20,725 households remained in scope. In 18,342 of these households, the selected person was aged 12 or older. Their response rate to the in-depth health questions was 96.1% or 17,626 respondents. In 1996/97, the overall response rate at the household level was 82.6%. The response rate for the randomly selected individuals aged 2 or older in these households was 95.6%. In 1998/99, the overall response rate was 88.2% at the household level. The response rate for the randomly selected respondents 0 or older in these households was 98.5%.

The 1994/95 provincial, non-institutional longitudinal sample consisted of 17,276 respondents. A response rate of 93.6% was achieved in 1996/97, and a response rate of 88.9% was achieved in 1998/99.

National Population Health Survey -- Northern Component (1994/95 and 1996/97)

Statistics Canada conducted the northern component of the NPHS in conjunction with the statistical bureaus in Yukon and NWT. Data were obtained through a separate survey due to the special challenges of survey taking in Canada's North.

The target population of the Yukon/NWT integrated NPHS/NLSCY survey included household residents living in private occupied dwellings located in the two territories, with the exclusion of populations on Indian Reserves, Canadian Forces Bases and in institutions. Moreover, persons living in unorganized areas in the Yukon (13% of the population) and persons living in unorganized areas, very small or extreme northern communities of the NWT (4.9% of the population) were also excluded from the target population.

Most of the core content from the 1994-95 NPHS main survey is included in the northern survey; however, special "focus content" on stress was excluded. In each selected household in the North, demographic information was collected from all household members, then one person, aged 12 years and over, was randomly selected for a more in-depth interview. The questionnaire included components on health status, use of health services, risk factors and demographic and socio-economic status. Some content changes were made in the 1996/97 NPHS North survey.

Collection operations ran from November 1994 to March 1995 (and again from November 1996 to March 1997). Computer-assisted personal interviewing (CAPI), used for the NPHS in the provinces, was not available in the territories at the time of the survey. A paper and pencil questionnaire designed to replicate the CAPI application was used instead. Telephone interviews were conducted where available, otherwise personal interviews were done.

The selected person response rate for the NPHS 1994/95 was 94.2% at the North level (2,020 respondents). For the Yukon this rate was 94.8%, while the rate for the NWT was 93.1%. The cross-sectional response rate at the North level (both territories) for the NPHS 1996/97 was 86.2% (1,499 respondents). For the Yukon, this rate was 83.9% while the rate for the NWT was 89.8%.

Heavy drinking, 1994/95: Due to a high proportion (42.8%) of refusals/non-stated responses to the question on frequency of heavy drinking in the 1994/95 NPHS-North, these data were deemed unreleasable/unreliable. Heavy drinking has been defined as the number of times current drinkers drank 5 or more alcoholic beverages on one occasion.

Diabetes: Due to a high level of data suppression for the proportion of residents 12 and over living in the territories diagnosed by a health professional as having diabetes (even at high levels of aggregation), no Highlight was written for this indicator.

Canadian Community Health Survey

Starting with data year 2000/01, the Canadian Community Health Survey (CCHS) replaces the cross-sectional aspect of the NPHS.

The primary objective of the CCHS is to provide timely cross-sectional estimates of health determinants, health status and health system utilization at a sub-provincial level (health region or combination of health regions).

The CCHS collects information from individuals aged 12 or older who are living in private dwellings. People living on Indian reserves or Crown lands, residents of institutions, full-time members of the Canadian Armed Forces, and residents of certain remote regions are excluded. The CCHS covers approximately 98% of the Canadian population aged 12 or older.

Each two-year collection cycle is comprised of two distinct surveys: a health region-level survey in the first year with a total sample of 130,000 and a provincial-level survey in the second year with a total sample of 30,000. Sample sizes in any particular month or year may increase due to provincial or health region-level sample buy-ins.

The response rate for the first cycle of the CCHS at the national level was 84.7% (131,535 respondents).

For more information about the CCHS, see: /health_surveys.

National Longitudinal Survey of Children and Youth

The National Longitudinal Survey of Children and Youth (NLSCY), developed jointly by Human Resources Development Canada and Statistics Canada, is a comprehensive survey which follows the development of children in Canada and paints a picture of their lives. The survey monitors children's development and measures the incidence of various factors that influence their development, both positively and negatively.

The first cycle of the NLSCY, conducted in late 1994 and early 1995, interviewed parents of approximately 23,000 children up to the age of 11. They shared information not only about their children, but also about themselves and the children's families, schools and neighbourhoods.

The second cycle, carried out in winter and spring of 1996-97, interviewed parents of the same children and provides unique insights into the evolution of children and their family environments over a two-year period. A new sample of newborn and 1-year-old children was added to cycle 2 to allow for cross-sectional estimates.

Collection of cycle 3 began in the fall of 1998 and was carried until June 1999. In addition to the original sample of children, who were aged 2 to 13 years at the time of the second data collection, a new sample of newborn and 1-year-old children was added to cycle 3 to allow for cross-sectional estimates. An extra cross-sectional sample of children 5 years old was also added to allow some provincial estimates for that age group.


To ensure high data quality for estimates from the NPHS, the CCHS and NLSCY, a weighted bootstrap resampling procedure (and for the NPHS-North, a modified bootstrap procedure) was used to calculate coefficients of variation (CVs) for totals and rates. If the CV was greater than 33.3% or the sample size was less than 10, the data were suppressed and an 'F' symbol appears in the data cell. If the CV is greater than 16.5% and no greater than 33.3%, the data should be interpreted with caution and an 'E' symbol appears in the same cell as the data. Data with CVs of 16.5% or less are presented without restrictions.

Sampling theory dictates that sample survey results of exactly 100% or 0% must have a coefficient of variation of exactly 0. In reality it is possible that in rare circumstances the true estimate may be lower than 100% or conversely greater than 0% and results should be interpreted as such.

Indicators based on 1996 to 2000 crime data (STC)

  • Health region level data are not available for the crime-related indicators.

  • Data on crime incidents that come to the attention of the police are captured and forwarded to the Canadian Centre for Justice Statistics (CCJS) via the Uniform Crime Reporting (UCR) survey according to a nationally-approved set of common scoring rules, categories and definitions.

  • The UCR is a summary or aggregate-based survey that records the number of criminal incidents reported to the police. The survey does not gather information on the victims, but does collect information on the number of persons charged by sex and by an adult/youth breakdown. For all violent crimes (except robbery), a separate incident is counted for each victim. For non-violent crimes, one incident is counted for each distinct occurrence. Incidents that involve more than one infraction are counted under the most serious violation. As a result, less serious offences are under-counted. The survey has been in operation since 1962 and has full national coverage.

  • The aggregate UCR Survey records the total number of adults and youths (aged 12 to 17) charged by sex. When a person is charged with more than one offence, they are counted only once, under the most serious offence.

  • The most serious offence is generally the offence that carries the longest maximum sentence under the Criminal Code of Canada. In categorizing incidents, violent offences always take precedence over non-violent offences. As a result, less serious offences are under-represented by the UCR survey.

  • The aggregate UCR survey scores violent incidents (except robbery) differently from other types of crime. For violent crime, a separate incident is recorded for each victim (i.e. if one person assaults three people, then three incidents are recorded; but if three people assault one person, only one incident is recorded). Robbery, however, is counted as if it were a non-violent crime in order to avoid inflating the number of victims (e.g. for a bank robbery, counting everyone present in the bank would result in an over-counting of robbery incidents). For non-violent crimes, one incident (categorized according to the most serious offence) is counted for every distinct or separate occurrence.

  • With UCR charge data it is possible for someone to be charged (and counted) more than once in a year. As a result, it is likely that the actual number of persons charged is less than the figure reported for a given time period.

  • The comparison between youth and adult crime rates poses some difficulties. The entire youth population represents a high-risk group for becoming involved in criminal activity. By contrast, the level of risk among adults is not consistent across the entire age group. Almost half of the adult population is 45 years and older; this age group is affected by fewer risk factors and as a result, is rarely involved in crime. A more direct comparison would look at youths and young adults. Unfortunately, data are not currently available to make this comparison.

  • Rates are calculated on the basis of 100,000 population. The population estimates for 2000 are preliminary postcensal estimates as of July 1 and are provided by Statistics Canada's Demography Division, Population Estimates Section.

Indicators based on labour force data (STC)

  • Regional unemployment rates and youth unemployment rates where calculated as annual averages from the Canadian Labour Force Survey (LFS). The estimates were derived by linking, at the enumeration area (EA) level, the LFS geography to health regions.
  • Some health regions could not be published as the estimated rate did not meet the minimum requirements for quality and confidentiality.
  • The LFS is a monthly sample of approximately 52,000 households. The survey is designed to represent the Canadian population aged 15 years and older. The survey excludes Indian reserves, full time members of the Canadian Forces, and persons living in institutions. The survey also excludes the Territories.
  • The unemployment rate is the number of unemployed persons divided by the labour force population, expressed as a percentage. An unemployed person is someone who:
    • was without work and had looked for work
    • was on temporary layoff and available for work
    • had a new job to start in the future.

The labour force population consists of the unemployed people plus the employed persons. To be employed, a person

    • worked at any job at all
    • had a job but was not at work during the reference week.

Indicators based on 1996 Census data (STC)

  • Regional data on non-medical determinants of health indicators and certain community characteristics were extracted from the 1996 Census, based on enumeration areas (EA). A correspondence file, linking EAs to current health regions has been developed in the Health Statistics Division of Statistics Canada with the cooperation of provincial Ministries of Health, Alberta Treasury and BC Stats.
  • Income-related indicators from 1996 Census are based on 1995 income.
  • Low income rate, children in low income families: Low income data were not derived for economic families or unattached individuals in the Territories or on Indian reserves. For health regions containing Indian reserves, analysis of low income data should only be done with this caveat explicitly noted.
  • Housing affordability: Farm homes and band housing on Indian reserves were not included in the calculation of housing affordability. For health regions containing Indian reserves, analysis of housing affordability should only be done with this caveat explicitly noted.
  • Proportion Aboriginal population: This variable is derived from three questions asked in the 1996 Census (20% sample). Aboriginal population refers to those persons who reported identifying with at least one Aboriginal group, i.e. North American Indian, Métis or Inuit and/or those who reported being a treaty Indian or a Registered Indian as defined by the Indian Act of Canada and/or who were members of an Indian Band or First Nation. Census coverage studies were used to adjust these data with the population estimates for incompletely enumerated Indian Bands or reserves. The 1996 demographic population estimates (which adjusts for census undercoverage and refusal reserves) were used as the denominator for these percentages.
  • Owner-occupied dwellings: Band housing on Indian reserves and collective dwellings were not included. For health regions containing Indian reserves, analysis of owner-occupied dwellings should only be done with this caveat explicitly noted.
  • Average expected dwelling values: The same exclusions for owner-occupied dwellings apply for this variable, in addition to farms.

For more information on census concepts, please refer to the 1996 Census Dictionary, Statistics Canada, Catalogue no. 92-351-XPE.

Health System Indicators (Canadian Institute for Health Information - CIHI)

  • CIHI's Privacy and Confidentiality policy does not permit the publication of data that might reasonably identify an individual, whether a patient or care provider, without consent. As a result, measures were taken to protect against residual disclosure from the dissemination of the regional rates including the suppression of small cell sizes. In addition, reporting data based on the region of the patient's residence (not hospitalization) reduces opportunities for identifying individual care providers.

Hospitalization data and rates (CIHI)

  • Data are reported based on the region of the patient's residence, not region of hospitalization. Consequently, these figures reflect the hospitalization experience of residents of the region wherever they are treated, as opposed to the comprehensive activity of the region's hospitals (who will also treat people from outside of the region).
  • Regional estimates for British Columbia are derived from reported postal codes using a translation file developed by BC STATS, BC Ministry of Finance and Corporate Relations. Health region level data for other provinces were produced through a geo-coding process using correspondence files developed with input from each provincial health ministry and Alberta Treasury. The link between enumeration areas and health regions was first created to provide the best resolution to census geography, and a census subdivision link to health regions was derived from this file. The boundaries are those that were in effect in January 1999. Records with invalid, missing, or partial postal codes are not included in regional counts. The absence of complete postal codes from Quebec may affect rates for the Champlain District Health Council (Ottawa area) and other border regions.
  • Where possible, Canadian indicator values, based on data from all provinces and territories, are provided for comparison purposes.
  • At the national level and provincial levels, rates for health data that are based on a fiscal year (April to March) use October 1st population estimates. Unless otherwise specified, Canadian and provincial hospitalization rates are standardized using the same methodology as regional rates (see specific note below). Other rates are based on appropriate population figures. Canadian rates for physicians are based on July 1st population estimates.
  • Standardized rates are age adjusted using a direct method of standardization based on the July 1st, 1991 Canadian population. See "Age-standardized rates" section for details.

  • Unless otherwise specified, hospitalizations include discharges and deaths for inpatients in acute care hospitals for the reference period. Same day surgery (outpatient) cases and patients admitted to non-acute care hospitals (e.g. chronic care, psychiatric or rehabilitation facilities) are not included in the totals.

  • Data from the Discharge Abstract Database (DAD) include only jurisdictions that submit comprehensively to the database. Therefore, data from Quebec regions are not available for indicators derived from the DAD.
  • Cancelled and previous procedures: Where information is available, cancelled and previous procedures are excluded from the calculations. For Quebec data, cancelled procedures are not reported and therefore have not been excluded.
  • Bypass Surgery: In some cases, an alternative intervention to improve blood flow to the heart muscle is coronary angioplasty. Variations in the extent of this procedure may result in variations in bypass surgery.

  • Data quality, concepts and methodology notes for AMI and Stroke 30-day mortality, as well as the re-admission indicators (AMI, asthma, hysterectomy, prostatectomy) are available from CIHI upon request,
  • 1998 figures for indicators derived from the Discharge Abstract Database may vary in some regions from previous figures due to updates to the database for hospitalizations in Saskatchewan, Alberta, New Brunswick and Ontario.
  • Indicator values for Alberta, Newfoundland and Nova Scotia regions may vary from previously published figures as a result of revised population estimates, improved techniques to clarify boundaries or due to actual changes in health region boundaries.

Physician data (CIHI)

While physician density ratios are useful indicators of changes in physician numbers relative to the population, inference from total numbers or ratios as to the adequacy of provider resources should not be made. Various factors influence whether the supply of physicians is appropriate, such as: distribution and location of physicians within a region or province; physician type (i.e., family medicine physicians vs. specialists); level of service provided (full-time vs. part-time); physician age and gender; population's access to hospitals, health care facilities, technology and other types of health care providers; population needs (demographic characteristics and health problems); and society's perceptions and expectations.

The data reflect figures as of December of a given year and include full and part-time physicians in clinical and non-clinical practice (i.e., research, administration and teaching). Unless otherwise noted, data exclude both residents and physicians who are not licensed to provide clinical practice and have requested to the Southam Medical Group that their data not be published. As a result of enhancements to the methodology used to compile the data, historical figures presented in reports published after 1999 will differ slightly from figures previously published (by approximately 0.3%, depending on the year). Physician data published in CIHI reports may differ from other sources of physician information due to variations in methodologies used to define physicians, reporting periods or differences in the population data used to calculate ratios.

National Health Expenditure Database

  • Expenditure figures include spending by both the public and private sectors. For further information, see National Health Expenditure Trends, 1975-2001.
  • Provincial per capita figures are affected by numerous factors that will affect inter-provincial comparisons including, but not limited to, differing provincial inflation rates that are related to provincial differences in arbitration agreements between provincial governments and, for example, medical associations; different population distributions; geography; and differences in provincial purchasing power.

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Date Modified: 2002-05-28 Important Notices