1. Instructor Resource Chapter 9 Copyright © Scott B. Patten, 2015. Permission granted for classroom use with Epidemiology for Canadian Students: Principles, Methods & Critical Appraisal (Edmonton: Brush Education Inc. www.brusheducation.ca).

2. Chapter 9. Confounding in descriptive studies

3. Objectives Define confounding. Define and describe strategies to adjust for confounding in descriptive studies: stratification direct standardization indirect standardization Describe the concept of weighting.

4. Confounding in context So far, we have discussed 2 main categories of error: random error systematic error We have identified 2 categories of systematic error: misclassification bias selection bias Confounding is an additional category of error.

5. Confounding Confounding is the intermixing of an effect of interest with the effect of an independent causal factor. In descriptive epidemiology, confounding is important to consider when comparing parameters such as prevalence or mortality. In direct comparisons of mortality, for example, the effect of modifiable determinants might be mixed together with the effects of nonmodifiable (and therefore less interesting) determinants such as age and sex.

6. Confounding and standardization Modifiable determinants of health outcomes are more important than nonmodifiable determinants. Only modifiable determinants can lead to effective preventive interventions. In descriptive epidemiology, the most important procedure to handle confounding involves: stratification standardization

7. Notes on terminology Stratification and standardization are procedures for adjusting an estimate. Saying that an estimate is “crude” is a way of saying that it has not been adjusted.

8. Crude mortality rates For example, crude mortality is: 4.85 / 1000 years -1 in Nunavut 6.72 / 1000 years -1 in Ontario When comparing these rates, the goal is to determine whether where you live affects mortality, but in these crude rates this effect is confounded by age and sex.

9. Stratification Stratification is way to deal with the problem of confounding. Confounding mixes effects and stratification is a way to unmix them. Mortality, for example, could be calculated within age-sex groups. Stratified rates are called “specific rates.” Comparison of age- and sex-specific rates would no longer be confounded by age and sex.

10. Nunavut versus Ontario OntarioNunavut Male6.75.6 Female6.64.2 Sex-stratified mortality rates per 1000 years -1 for Ontario and Nunavut are as follows: With stratification for sex, mortality still appears to be higher in Ontario

11. Stratification by age OntarioNunavut under 1 year4.626.3 1 to 4 years0.10.3 5 to 9 years0.10.9 10 to 14 years0.10.3 15 to 19 years0.33.1 20 to 24 years0.42.9 25 to 29 years0.41.4 30 to 34 years0.51.5 35 to 39 years0.72.8 40 to 44 years1.13.1 45 to 49 years1.85.3 50 to 54 years3.05.2 55 to 59 years4.75.5 60 to 64 years7.314.1 65 to 69 years11.525.7 70 to 74 years18.273.9 75 to 79 years30.465.9 80 to 84 years52.184.3 85 to 89 years89.5290.3 90 years and over173.1125 Now, the (age stratified) rates tend to be higher in Nunavut!

12. Confounding in a stratified table How can the crude mortality be higher in Ontario, but the age-stratified estimates be higher in Nunavut? The effect of where you live is intermixed with how old you are. People in Nunavut tend to be younger, so the crude mortality is lower—but this is not due to where they live, it is due to their age.

13. Direct standardization Direct standardization takes a set of stratum-specific rates and weights them by the stratum-specific structure of a chosen standardizing population. Using the national population of men as the standardizing population, direct standardization for age leads to / 1000 year -1 rates (in men) of: 6.66 in Ontario 12.09 in Nunavut Comparison of these standardized rates provides information about the effect of place of residence (in this case, in men), adjusted for age. Commonly, direct standardization would account for age and sex simultaneously.

14. Age at time of death Male Weights* Weighted rates (Ontario) Weighted rates (Nunavut) OntarioNunavut 0–4 years1.17.60.0571710.070.43 5 to 9 years0.100.0545520.010.00 10 to 14 years0.10.60.0579930.010.03 15 to 19 years0.450.0673830.030.34 20 to 24 years0.64.80.0701390.040.34 25 to 29 years0.50.70.0698620.030.05 30 to 34 years0.71.50.0682270.050.10 35 to 39 years0.93.40.0666660.060.23 40 to 44 years1.35.60.0702510.090.39 45 to 49 years2.150.0804880.170.40 50 to 54 years3.66.30.0793470.290.50 55 to 59 years5.87.10.0689690.400.49 60 to 64 years9.120.80.059580.541.24 65 to 69 years1428.20.043780.611.23 70 to 74 years22.487.60.0319030.712.79 75 to 79 years37.132.30.0244350.910.79 80 to 84 years62.431.30.0170791.070.53 85 to 89 years108214.30.0087780.951.88 90 years and over186.590.90.0033970.630.31 Directly standardized rate /1000 population 6.6612.09 * proportion of the national male population in each age category Direct standardization for age, in men: Ontario & Nunavut

15. Indirect standardization As with direct standardization, indirect standardization uses a standardizing population. In indirect standardization, stratum-specific (usually age- and sex-specific) rates from the standardizing population are multiplied by (you could say weighted by) the proportion of the exposed cohort falling into each of those age-sex categories. This calculation leads to an expected number of deaths (or other incident event). The observed-to-expected ratio is the SMR.

16. Indirect standardization In a mortality analysis, SMR is a standardized mortality ratio. In an analysis of incidence or prevalence, it may stand for standardized morbidity ratio, but in the case of incidence many would prefer to say “standardized incidence ratio” (SIR).

17. Indirect standardization This is the SMR, expressed as a percentage:

18. Weighting Direct and indirect standardization use weighting to facilitate or enhance comparisons of different populations. Weights are also used in studies that have more complicated sampling procedures than a simple random sample—in this case they are needed to make correct estimates.

19. Stratified sampling and weighting Some studies oversample subgroups so that estimates can be made in those groups. For example, Canadian national surveys often oversample small provinces so that better estimates can be made in those provinces. If national estimates did not account for this, the national rates would be unduly affected by the smaller provinces. In data analysis, the use of weighting can offset design effects that are due to stratified sampling.

20. Survey sampling weights A common sampling weight is the inverse of the selection probability for a person—this is called a probability weight. Another type of survey sampling weight is a frequency weight—an integer depicting the number of people in the target population represented by a respondent. Frequency weights are produced by Statistics Canada for many of their national surveys.

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