Context Rates of health may need to be calculated for small geographical areas

1. Does it matter what estimation method I use to provide small area populations at risk in standardised mortality ratios? CCSR Seminar: 16th December 2003 Paul Norman

2. Context • Rates of health may need to be calculated for small geographical areas • Census years we have age-sex population counts for a range of geographical areas, but outside census years … • Annual age-sex disaggregated mid-year estimates only available down to local authority level • Various small area population estimation methods commonly used • Studies have shown variation in population sizes & age structures • Lunn et al. (1998) • Middleton (1996) • Simpson et al. (1996 and 1997) • Rees (1994) • Differently estimated small area ‘populations at risk’ may lead to different SMRs if different size &/or age-sex structure

3. Deaths in a standard area population Population in the standard area Population in the location of interest X Indirect Standardised Mortality Ratio (SMR) SMR = Observed mortality events Expected mortality events SMR = 100 x Deaths in a location of interest Observed Expected

4. Deaths in a standard area population Population in the standard area Population in the location of interest X Data sources for indirect SMRs at ward level SMR = 100 x Deaths in a location of interest Mortality data at national level Mortality data for the ward (VS4) By matching age-sex information Population estimate at national level Population estimate for the ward

5. This work … • Estimate a time-series of ward populations using various methods • Use outputs in SMRs • Address denominator uncertainties • Research definitions • Small area: electoral wards (caveat) • Mortality measure: indirect SMRs (caveat) • Time period: annual mid-year estimates 1990-1998 • Geography: 1998 wards in GOR East • Output detail: age-groups (11) and sex (2) • Data acquisition: nationally consistent, public domain sources • Base population: Estimating with Confidence Populations (EwCPOP) based on 1991 Census (caveat)

6. Steps to achieve this … • Input data preparation • Geographical harmonisation • Temporal harmonisation • Single year of age • Estimation methods • Indicator of sub-district, ward level change (electorate) • Cohort-component • Optional enhancements • Allowances for special sub-populations • Hybrid methods • Constraints • Standardised mortality ratios • Use ward age-sex estimates as populations at risk • 2001 Census implications?

7. Geographical harmonisation

8. Postcode locations as building-bricks: assumptions • Residential postcode distribution is a proxy for population distribution (enhanced by household or address counts) • At a point in time a set of postcodes constitutes a ward Haldens & Panshanger 1998 Haldens 1991

9. Temporal harmonisation Population estimates needed for the mid-year Census ONS mid-year estimates Electorate Vital statistics

10. Disaggregation to single year of age • For annual ageing-on • For aggregation into appropriate age-groups

11. Estimation methods data scheme Ward totals Males Females Data at time t Wards (within LA district) Age 0-90+ Age 0-90+   LA district totals Data at time t + 1 ? ?  

12. Indicators of change • ONS MYEs • Annual mid-year time-series available • Age-sex detail, but • Only district level • Electorates as sub-district indicator • Annual time-series available, but • Collected 10th October • Only adult ages • Variable enumeration space & time

13. Apportionment, additive & ratio methods Data at time t Electorate derived ward totals Change between t & t + 1 ONS district MYEs Data at time t + 1 Electorate derived ward totals Apply previous age structure &/or constrain to MYE ONS district MYEs

14. Births Deaths In-migration Out-migration Ageing-on + - + - Cohort-component method (includes Vital Statistics) Data at time t Data at time t + 1 Electorate derived ward totals ONS district MYEs

15. Cohort-component enhancement: Suppressed aging-on of special populations • Students • Armed forces • Communal establishments

16. Method option: Constraints • Ward age-sex estimates are controlled to sum to district-level age-sex information, ONS annual MYE • Larger area estimates tend to be more reliable • Ensures consistency with ONS published data & thus … • More acceptable, but … • Some LAs disagree with the ONS MYE

17. Ages 0 90+ 0 90+ Constraints and Iterative Proportional Fitting (IPF) Ward (row) totals 1 Males Females Wards in LA district t + 1 initial age-sex estimates n Ward constraints Age-group (column) totals Age-group district-level constraints

18. Estimation methods & options

19. Many method / option combinations … Strategy for the choice of population at risk Differences in estimate outputs …

20. Differences in outputs (1991 cf 1998) Newnham: simpler methods constrained Coggeshall: simpler methods constrained Newnham: cohort-component, plus migration and special populations Coggeshall: cohort-component, plus migration and special populations

21. abs(1991 - 1998) 1991 Differences in outputs (1991 cf 1998) * 100 • Most variation in estimate outputs for: • Youngest ages • Young adults • Most elderly

22. Using 1998 outputs in SMR calculations (Newnham)

23. Using 1998 outputs in SMR calculations (Newnham) Smaller base population leads to lower expected Student ages suppressed, elderly enhanced Similar structure to base, total & elderly enhanced Structure erroneously aged-on Students enhanced, elderly suppressed

24. Using 1998 outputs in SMR calculations (Newnham) Smaller base population leads to lower expected Student ages suppressed, elderly enhanced Similar structure to base, total & elderly enhanced Structure erroneously aged-on Students enhanced, elderly suppressed Lower expected leads to higher SMR Higher expected leads to lower SMR Youthful population leads to lower expected & higher SMR

25. Comparison of 1998 SMRs: cf no population change

26. Are the differences enough to make a difference?!? • Overlapping SMR confidence intervals? • Yes, but observations small numbers leading to wide CIs • Do wards fall in the same SMR quintile? • Ranking by SMR: • Quintile 1: 29% wards consistently most healthy • Quintile 5: 6% wards least healthy

27. Differently estimated populations at risk and SMRs … • If a larger population is estimated by a method compared with another, but with the same age-sex structure, a lower SMR results because more events are expected (and vice versa) • If a method estimates an older population structure than another, a higher expected is calculated, resulting in lower SMRs (and vice versa) • Population size is more critical in simpler methods (as little or no new age information) • Poorly specified cohort-component models tend to result in lower SMRs, because incorrectly aged-on populations lead to higher expected mortality • Fully specified cohort-component models tend to result in greater range of SMRs, due to populations kept youthful in certain locations by migration data and suppressed ageing of sub-groups (proxy for migration) • Areas with the best health consistently have lowest SMRs calculated • Areas with the very worst health similarly identified but not the same consistency • Fair level of tolerance in SMRs for all-ages • Not necessarily the case with age-specific mortality rates (Rees et al., 2003a)

28. Following 2001 Census outputs (& rebased MYEs) … • Uncertainty in the EwCPOP base population used • Uncertainty in the annual district level ONS MYEs used as constraints

29. In the light of the 2001 Census outputs … • Uncertainty in the annual national level ONS MYEs used for ASMRs National ASMRs differ Populations at risk differ Thus: Expected changes Events don’t change SMRs alter

30. Deaths in a standard area population Population in the standard area Population in the location of interest X Uncertainty in SMR calculations … SMR = 100 x Deaths in a location of interest Mortality data at national level Mortality data for the ward (VS4) Population estimate at national level Population estimate for the ward

31. Uncertainty in estimated populations at risk • By total size & by age • Newnham • Maximum • Average • Minimum • CC-mig-sp-IPF • Coggeshall • Maximum • Average • Minimum • CC-mig-sp-IPF No consideration here for rebasing MYEs!

32. Uncertainty in SMR calculations … How confident can we be in our SMR results? Confidence limits (c. 95%) are calculated using: The assumption is that the ‘expected’ is reliable But it is not! Event counts may well be more reliable!! (or Byar’s approximation)