1. Manitoba Centre for Health Policy The Future of Information-Rich Environments Leslie L. Roos Lisa Lix CPS Meetings – June 2004

2. Entry Points 1)Use of Administrative Data from Defined Populations Small area analysis and population-based studies. 2)Development of Record Linkage Individual-based Permits: a.Building histories for each individual to track utilization before or after an index event b.Maintenance of registries c.Management of multifile databases d.Working across data sets

3. Figure 1. Administrative data in an information-rich environment

4. Producing Integrated Systems 1)The presence or generation of a common identifier across data files 2)Longitudinal data files with considerable amounts of individually-based information 3)A population registry with geographic codes 4)Tools to access them

5. Table 1. Manitoba Research Registry: Characteristics and Relevance CharacteristicsResearch Relevance Very Large NMany physical and statistical controls feasible Population-based for an entire province Heterogeneity on many variables Longitudinal data (going back over 30 years) Many types of longitudinal studies, drawing of cohorts, more reliable measurement of important variables

6. Table 1. Continued Specification of place of residence (by postal code) at any time point Neighborhood longitudinal studies; Permits analysis of small area variation Mobility/migration and loss to follow-up well specified Cohort studies; Mobility data allow capturing “length of exposure” Family and sibling information (ties in with neighborhood information) Non-experimental designs estimate importance of different factors control for unobserved / unmeasured background characteristics

7. Table 2. New Data Sets Data SetResearch Relevance Education Grade 3 enrollment and examination results for one year Studies based on birth cohorts can look not only at “pass/fail” outcomes but at school enrollment (grade level or not), non-enrollment but residence in province, and loss to follow- up. Grade 12 enrollment, examination results, and graduation for seven years

8. Table 2. Continued Family Services Received “income assistance” at any time in a seven year period: month by month data Income assistance can be a dependent or independent variable Neighborhood Information Compositional: aggregated upwards from Canadian Census Better understanding of neighborhoods; Characteristics can be entered as independent variables in analyses of health and health care Contextual – collected from various sources (statistics on crime, social programs, etc.)

9. Table 3. Social Variables as Independent Predictors of Human Development Social VariablesData Sets Number of years received family assistance FA Average household income (number of years recorded, partial coverage) P Average household income (from Census enumeration / dissemination area) CS Number of children in the familyRR Data sets: FA (Family Assistance), P (Pharmaceutical), RR (Research Registry), H (Hospital Abstracts), PC (Physician Claims), CS (Canadian Census)

10. Table 3. Continued Mother’s marital status at birth of first childRR Number of household location movesRR Number of years living in a single-parent family RR Age of mother at birth of first childRR Number of “family structure” changes (parental separations, remarriages) RR Data sets: FA (Family Assistance), P (Pharmaceutical), RR (Research Registry), H (Hospital Abstracts), PC (Physician Claims), CS (Canadian Census)

11. Table 3. Continued Individual was first-born childRR Number of years living with a disabled parent H, PC, RR Neighborhood characteristics number of years living in neighborhood with “bad” characteristics RR, CS Data sets: FA (Family Assistance), P (Pharmaceutical), RR (Research Registry), H (Hospital Abstracts), PC (Physician Claims), CS (Canadian Census)

12. Table 4. Quality of Diagnostic Information from Hospital Abstracts and Physician Claims DiagnosisDetailed chart review 1 Abstracts / claims compared with clinical information Abstracts / claims compared with population- based survey Prevalence estimate 5 (survey as base) Asthma (Adult).60 2 Recent Acute Myocardial Infarction 0.750.82 4 Diabetes0.740.72 5 +5% Chronic Pulmonary Disease 0.72 Hypertension0.60 3 0.59 5 -19% Congestive Heart Failure 0.800.58 4 Liver Disease0.75

13. 1 Alberta Chart Review (Quan et al., 2002). 2 Canadian MultiCentre Asthma Study (Manitoba Component) (Huzel et al., 2002). 3 Manitoba Heart Health Project (Muhajarine et al., 1997). 4 Ontario Fastrak 11 Acute Coronary Syndromes Registry (Austin et al., 2002). 5 Manitoba Heart Health Project (Robinson et al., 1997). Table 4. Notes

14. Statistics for Information- Rich Environments Random effects models Models for multiple outcome variables Spatial regression models

15. Random Effects Models Regression coefficients can vary across subjects Components 1)Within-individual component –Individual’s change over time is described by a regression model with a population-level intercept and slope 2)Between-individual component –Variation in individual intercepts and slopes is captured

16. Advantages of Random Effects Models Ability to incorporate time-varying covariates –Severity of illness –Presence of co-morbid conditions –Location of residence Development of non-linear models –Count data –Binary data

17. Random Effects Models: Example Health service use and cost at the end of life Measurements compiled each month of the last six months of life Partition between-individual and within-individual variation –Between-individual variation in trends over time accounts for a substantial portion of variation in the data Explanatory variables –Age, gender, income quintile, region of residence, location of death, cause of death

18. Spatial Regression Models Account for spatial auto-correlation in the data, which can lead to biased regression parameter estimates Example: Geographically weighted regression (GWR) –Individual parameter estimates are produced for each geographical area, along with the associated standard errors, test statistics, and p-values

19. Models for Multiple Outcome Variables Simultaneous models for two or more outcome variables Controls Type 1 errors arising from multiple hypothesis testing on individual dependent variables Accounts for correlation between outcomes, which may increase statistical power