1. Michael Wolfson Statistics Canada
Reshaping Official Health Statistics: Evolution of Administrative Health Data in Canada
Michael Wolfson
Statistics Canada

2. Three Major Phases
Canadian context: constitution gives jurisdiction for health care to provinces
up to mid-1990s – direct uses of routinely collected administrative data
recent past to present – growth of record linkage
future – introduction of electronic health records; debate over “secondary use”
n.b. some provinces much more advanced

3. Phase 1 – Direct Uses of Administrative Data in Health Statistics
birth and death registration – since 19th century
mortality rates, life expectancy, ecological analysis
hospital in-patient admissions – since 1960s
basic prevalences of biomedically-defined disease
small area surgical procedure rate variations
partial exception: cancer registry

4. Phase 2 – Broadened and more Powerful Use of Administrative Data via Record Linkage
move from each administrative encounter to each individual person as basic unit of analysis → “trajectory” of encounters
n.b. Manitoba Centre for Health Policy actually the pioneer; longitudinally linked hospital + physician + nursing home + other records dating from late 1970s
three examples: census ↔ mortality, hospitals ↔ survey, hospitals ↔ hospitals

5. Health Inequalities – Urban Life Expectancy at Birth, By Income Quintile, Canada
3.3 years
Let me start with a simple example, from one of the many studies led by Russell Wilkins on differences in LE by income quintiles, 5 equal-sized groups of the population ranked by income.
without record linkage, the best that Russell has been able to do over the years is to use the postal codes on the death certificates to link each death to the average income in the area where they live based on census data; note that this approach only works for urban postal codes.
As should by now be well known, we see a clear gradient in health with respect to income. For every step up the income ladder, there is a noticeable increase in LE, though the increase is greatest for the first step, from the bottom to the second quintile.
While the distances between individual curves has shifted somewhat over the 25 year period covered, the measured gap has remained, though it has declined from about 4.6 years in 1971 to about 3.3 years in 1996.
Source: Wilkins et al, Statistics Canada, mortality and census data

6. Health Inequalities – Household Life Expectancy by Sex and Income Decile (assuming survival to age 25; from 1991 Census + mortality follow-up to 2001)
4.8 years
7.6 years
This graph is brand new. It is based on a major data development funded by the CPHI – the linkage of about 2.8 million long form census returns from 1991 to death certificates up to the end of We are now able to estimate mortality differentials by income, deciles or tenths of the population in this case, with unprecedented precision.
The differences are 4.8 years for women, and 7.6 for men. These gaps are more than double those we have been producing using only ecological data as in the previous slide.
Moreover, to put these differences in LE by income decile in context, they are more than three times as large as those ascribed to all cancers combined.

7. Hospitalization Rate (%) by Body Mass Index ( ; excluding pregnancy and childbirth; excluding Quebec)
Age-sex standardized to Canadian population
My second example is very simple, but essentially brand new.
This chart shows the relationship between self-reported obesity, from the 2001 CCHS [ where we know BMI to be biased downward ] and the proportions of the population who are hospitalized at least once in the year following the interview.
The main result is that the 17% of Canadians who were obese had a statistically significant 50% higher rate of hospitalization. We can know this only because an overwhelming proportion of CCHS respondents gave us permission to link their survey information to their provincial health records.
This may be a BGO = blinding glimpse of the obvious, but please understand, we have never had the data to show this rigorously for Canada ever before, and we have been able to do it only by using record linkage.
33%
17% 2%
48%

8. Variation in Hospitalization Rates Across Health Regions with and without Adjustments (visits per 1,000)
We can push this kind of linkage analysis further. Here we see a very familiar result, in this case using only unlinked administrative data – the one year hospitalization rates for 116 health regions across Canada. These vary from a low of less than 40 visits per thousand population to over To be a bit more conservative, and to ensure the results are robust, the arrows in this chart point to the 10th and the 90th percentiles of these health regions ranked by their rates of hospitalization, and the 90th percentile region had 2.3 times as many hospitalizations as the 10th percentile.
Of course, you might say that some of the high rate regions had an older or more female population, and both of these factors would clearly account for a higher hospitalization rate.

9. Variation in Hospitalization Rates Across Health Regions with and without Adjustments (visits per 1,000)
So here is the result adjusted for age and sex. The ratio of hospitalization rates drops marginally to 2.2.
Still, some of these regions might have more individuals suffering from chronic disease, or they might have more smokers and obese individuals. Indeed, you might conjecture that some of these regions have physicians who are more inclined to admit their patients to hospitals.
But it is impossible to assess these hypotheses without record linkage of some sort.

10. Variation in Hospitalization Rates Across Health Regions with and without Adjustments (visits per 1,000)
And as in the obesity slide I just showed you, we can assess this conjecture by drawing again on our linked CCHS – hospital data. And yes, this mixture of factors reduces the 90 – 10 hospitalization ratio a bit more, to 2.0.
But let us recall the first example I just showed, where we saw very substantial differences in life expectancy by income. We might therefore also conjecture that these factors too might account for some of these large differences in hospitalization rates across health regions in Canada.

11. Variation in Hospitalization Rates Across Health Regions with and without Adjustments (visits per 1,000)
And indeed they do. When SES factors like income, education, race, and immigration status are factored in, we see as large a decline in the 90 – 10 hospitalization ratio, from 2.0 to 1.7, as we saw for age and sex and illness and risk factors and other health care use combined.

12. one year observation window one year follow-up window
Underlying Person-Oriented Information for Heart Attack / Revascularization Analysis
Heart Attack (AMI)
Treatment (revascularization = bypass or angioplasty)
Death
one year observation window
one year follow-up window
(excluded)
Let me give one more example of results that are simply unattainable without pan-Canadian record linkage of health records – this time focusing only on hospitalizations.
In this diagram, each line represents a highly stylized view of one patient’s trajectory through the health care system, with three kinds of events – light blue for AMI, dark blue(?) for revascularization, and black for death.
I want to show you an analysis that brings together an acute medical event – a heart attack or AMI, a treatment – revascularization, and an outcome – death within 30 days. We are going to look at all individuals who were hospitalized with an AMI, though we want to have a “cleaner” analysis by focusing on “first AMIs”, which we approximate by excluding any AMI hospitalizations that were preceded by at least one viist in the previous year for a heart-related reason. The top row illustrates the trajectory of an individual who was rejected from the analysis just this reason.
The second line represents an individual who was revascularized, and survived for one year, but not much longer. The other lines give examples of other patterns.
time

13. Heart Attack Survival in Relation to Treatment by Health Region, Seven Provinces, 1995/96 to 2003/04
Here are the results for this analysis. The red dots show the situation in 1995/6, while the blue are 8 years later.
Overall, there has been a dramatic increase in treatment rates – more than a tripling from an average of 12.8 to 39.8%. We might therefore expect a similar improvement in outcomes. And we do see some improvement in survival, but compared to the increase in treatments, the reduction in mortality is more modest, about a 3.6 percentage point drop – from 13.2 to 9.4%.
Moreover, and to my mind even more importantly, the scatter of dots shows a very wide variation among health regions. In 2003/4, a number of health regions had 30 day mortality rates around 8%, yet treatment rates varied more than three-fold, from under 20% to over 60%.
Note that this kind of plot is much more sophisticated than the small area variations traditionally being reported, for example in ICES practice atlases. It shows not only practice variation, but also how this variation is linked to one, admittedly crude, outcome measure.

14. Important Caveats for the AMI → Revascularization → Mortality Results
revascularization is also intended to relieve symptoms
other clinical aspects of treatment not taken into account, e.g. thrombolysis, post discharge Rx
no risk factors – obesity, physical fitness, smoking, hypertension, lipids – considered
no socio-economic factors considered
n.b. in related analysis, co-morbidity (Charlson Index) was included, with one-year mortality follow-up – results essentially unchanged
The impression given by this graph is that health care practice is all over the map – there is no consensus on how to treat AMI.
Of course, there are some important caveats, and herein lie our challenges.
No account has been taken here of other clinical factors – e.g. thrombolysis. Nor have we taken any account of the broader determinants of health, which we just saw can be highly significant. It’s not that we don’t want to include these covariates; we just did not have the data!
Also, the intended benefits of revasc are much more than a reduction in 30 day mortality, we are also interested in quality of life, and longer term survival.
I should note that with a smaller number of provinces, we were able to do a one year mortality follow-up, and include adjustments for comorbidity, and the results were essentially unchanged.

15. Key Messages re Phase 2
use of administrative data is much more powerful if combined with record linkage, both within admin data sets and across to health surveys
privacy and vested interests remain major challenges
especially last set of results suggest major potential in Canada’s health care sector to improve health outcomes without more resources – working smarter, not harder
“you can’t manage what you don’t measure”
national data essential to give both the needed sample sizes and to provide the breadth of “natural experiments”
I find this chart tremendously unsettling. We often hear that Canada’s health care sector is short of money. What any economist will immediately wonder is whether the system is suffering from major inefficiencies. How can some health regions do one-third as many bypass and angioplasty procedures as others, yet have the same 30 day mortality rates?
Perhaps one reason is that no one is asking this fundamental question; and one reason in turn is that no one has access to these data. As folks have been saying, “you can’t manage what you don’t measure”, and these kinds of measurements are not routinely or even periodically produced, let alone widely disseminated and discussed.
And again, these results are only possible with national level record linkage.

16. Phase 2.5 – LHAD: Longitudinal Health and Administrative Data Initiative
(simple) idea: build a more analytically powerful database of longitudinally linkable individual level data
bring together a wide range of administrative data on health care encounters – client registry, hospitals, Rx
plus over 500,000 Statistics Canada health survey responses (where consent to link with provincial health care records has been given) – NPHS, CCHS, CHMS
plus vital events (births, deaths) and cancer registry
using sophisticated record linkage methodology
extreme care to protect confidentiality
mechanism – governed by MoUs between Statistics Canada and each provincial health ministry
The obvious next step is to bring together the kinds of longitudinally linked hospitalization trajectories from the AMI-treatment example with the kinds of socio-economic and risk factor data from Canadians themselves, as in the CCHS example, as well as mortality data in general (not just in-hospital mortality) as in the 1991 census follow-up.
Statistics Canada is uniquely positioned to undertake this work, with our constitutional authority and the force of the Statistics Act to underpin such work, our strengths in analysis and statistical methodology, our excellent reputation with the people of Canada for integrity and unblemished record for maintaining the confidentiality of their data, and our various mechanisms for engaging the broader research community.
To this end, Statistics Canada recently launched the LHAD initiative. We are entering into a series of bilateral MoUs with the provinces. Each MoU specifically sets out the Statistics Act and Provincial Legislation under which the highly sensitive provincial health care data involves are being transferred to Statistics Canada.
Ongoing oversight is provided by the LHAD Steering Committee, with representatives from each province appointed by the Deputy Minister, + Vital + Cancer + CIHI. I co-chair this committee with Steini Brown of Ontario, and the steering committee has agreed on the initial set of priority analytical projects.

17. emerging electronic health record (EHR)
Phase 3 – Influencing the Content of Future Administrative Data for Statistical Purposes
emerging electronic health record (EHR)
so far, driven by patient care considerations
growing discussion of “secondary” or “health system” uses of EHR
significant privacy concerns
important counter-moves, e.g. research community and “health information summit”
idea: articulation of a carefully designed set of “use cases” / “killer examples”

18. Infoway – Conceptual Architecture

19. Infoway Use Cases – the Lamberts (1)
An overview of health issues and interventions of the members of a fictional extended family who are the subjects of care in all subsequent use-cases
This use of a persistent set of actors is intended to provide commonality for discussion of information requirements, and to effectively illustrate the need for relevant health information to be captured and reused:
in many different care settings
across many different disciplines
over time

20. Infoway Use Cases – the Lamberts (2)
narrative form describes:
the health services delivery context for each encounter,
who the principle actors are,
the specific expectation for information
capture and reuse across and between encounters – the major outcomes expected from the use of this information

21. Infoway Use Cases – the Lamberts (3)
Encounter (Clinical Use Case) ≡ narrative of interactions patient has with a provider in a health care setting such as the Emergency Room, an Outpatient Clinic, a Physician Office etc.
Clinical Activities ≡ lowest level of detail that describes the workflow event step for each actor’s (provider and patient) interactions with the Point of Service (PoS) systems and information sent or retrieved from the EHRi System.

22. Infoway and “Secondary Use”
so far, limited interaction (privacy chill, physician resistance)
idea: extend “use cases” to “health system” / secondary uses
e.g. cancer registry → many disease registries, e.g. AMI, diabetes
small area variations as a function of most relevant covariates
standardized and regular assessment of health outcomes
“continuity of care” metrics, e.g. GP → specialist → hospital → Rx, rehab → GP → home care, long term care etc.
Rx post-marketing surveillance
health care costs and outcomes as function of procedure volumes
etc., etc.

23. Concluding Comments
major growth over the past decade in use of administrative data in health statistics
excellent initiatives underway
growing use record linkage in partnership with provincial health care providers
growing efforts to influence future content of health care encounter data with broader statistical and “health system” uses in mind
concerns with “privacy chill” remain