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Epidemiology Kept Simple

Epidemiology Kept Simple. Chapter 7 Rate Adjustment. Goal. To reduce distortions and incomparability of rates when making comparison over time and among populations To encourage “like-to-like” comparisons. Illustrative Example Table 7.2 (p. 144).

Epidemiology Kept Simple

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1. Epidemiology Kept Simple Chapter 7 Rate Adjustment

2. Goal To reduce distortions and incomparability of rates when making comparison over time and among populations To encourage “like-to-like” comparisons

3. Illustrative ExampleTable 7.2 (p. 144) Rate in Population B is 9× that of Population A

4. Illustrative Example (cont.) Table 7.2 (p. 144) Within young, rates are identical

5. Illustrative Example (cont.)Table 7.2 (p. 144) Within old, rates are identical

6. Why the apparent paradox? Pop. A mostly old, Pop. B mostly young

7. And . . . rates are age-related

8. Confounding • Explanatory factor (population) associated with age • Extraneous factor (age) associated with disease rate • Age confounds relation between explanatory factor and disease rate • Biased comparison confounder Age Population Rate explanatory factor disease

9. Strata-specific comparisons You’re OK as long as you compare like-to-like

10. We can also adjust overall rate to compensate for confounding • Rate adjustment methods • Direct adjustment • Indirect adjustment • Other statistical method of adjustment • Mantel-Haenszel methods • Regression model

11. Terminology • “Rate” – any incidence or prevalence (economy of language) • Crude rate – rate for entire population • Strata-specific rate - rate within subgroup • Adjusted rate – overall rate compensated for extraneous factor • Two methods of adjustment • Direct • Indirect

12. §7.2 Direct Age-Adjustment • Study population – the population rate you want to adjust • Reference population - external population used as age norm, • Reference population may be • arbitrary • age distribution of some place at some time (“standard million”)

13. U.S. Standard Million, 1991

14. General Idea, Direct Adjustment • Apply strata-specific rates from study to a standard age age distribution • Adjusted rate is a weighted average of strata-specific rates (with weights from reference population)

15. Method where Ni = population size, reference population, strata i ri = rate, study population, strata I Note: caps denote reference pop. values, while lower case denotes study pop. values

16. Florida & Alaska Mortality Example (pp. 146 – 147) • Crude rates (per 100,000) • cRFlorida = 1026 • cRAlaska = 387 • See TABLE 7.5 for raw data

17. Age-Specific Rates

19. Comparing Adjusted Rates • Direct adjustment of Florida mortality rate using same standard million (Table 7.8, p. 147) derives aRFlorida = 784 • Recall, aRAlaska = 843 • Conclude: slight advantage goes to Florida

20. §7.3 Indirect Age-Adjustment • Same goal as direct adjustment • Based on multiplying crude rate by Standardized Mortality Ratio (SMR) whereA = observed number of cases in study population = the expected number of cases (next slide)

21. Expected Number of Cases () where Ri = rate, reference population, strata i ni = population size, study population, strata i Recall: caps denote reference pop. values and lower case denote study pop. values This is number of cases expected in study population if it had reference population’s rates

22. Illustrative ExampleZimbabwe & US Population (pp. 148 – 9)

23. Indirect adjustment of Zimbabwe rate

24. Zimbabwe SMR • Observed 98,808 deaths in Zimbabwe • Expected 36,381 (based on US rate) • SMR = 98,808 / 36,381 = 2.72 • Interpretation: Zimbabwe mortality rate is 2.72× that of US after adjusting for age