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Principles of Epidemiology Dona SchneiderDona Schneider, PhD, MPH, FACE E J Bloustein School of Planning and Public Policy Rutgers University, NJ, USA.

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Presentation on theme: "Principles of Epidemiology Dona SchneiderDona Schneider, PhD, MPH, FACE E J Bloustein School of Planning and Public Policy Rutgers University, NJ, USA."— Presentation transcript:

1 Principles of Epidemiology Dona SchneiderDona Schneider, PhD, MPH, FACE E J Bloustein School of Planning and Public Policy Rutgers University, NJ, USA

2 2 About the Author Dona Schneider

3 3 Known Risk Factors for Cancer Smoking Dietary factors Obesity Exercise Occupation Genetic susceptibility Infectious agents Reproductive factors Socioeconomic status Environmental pollution Ultraviolet light Radiation Prescription Drugs Electromagnetic fields

4 4 Preliminary Topics Data sources and limitations for cancer epidemiology How much cancer is occurring? How does occurrence vary within the population? How do cancer rates in your area compare to that in other areas?

5 Data sources and limitations for cancer epidemiology Review U.S. Census, U.S. Vital Statistics, SEER and NJCR data

6 6 Some other raceOther Other Pacific Islander Other Asian Samoan Guamanian or Chamorro Vietnamese Native HawaiianHawaiian Korean Asian Indian Filipino Japanese American Indian or Alaska Native Indian (Amer.) Indian Chinese QuadroonQuadroon 1 Black, African American, or Negro Negro or Black Black of Negro decent Black White Race Categories in the Census

7 7 Revised racial and ethnic standards (effective as of the 2000 decennial census) have 5 minimum categories for data on race and 2 for ethnicity Other Federal programs should adopt standards no later than January 1, 2003 Revision of Statistical Policy Directive No. 15, Race and Ethnic Standards for Federal Statistics and Administrative Reporting Office of Management and Budget (OMB)

8 8 American Indian or Alaska Native A person having origins in any of the original people of North and South America (including Central America) and who maintain tribal affiliation or community attachment Asian A person having origins in any of the original people of the Far East, Southeast Asia of the Indian subcontinent including for example, Cambodia, China, India, Japan, Korea, Malaysia, Pakistan, the Philippine Islands, Thailand and Vietnam OMB Race Categories

9 9 Black or African American A person having origins in any of the black racial groups of Africa. Terms such as “Haitian” or “Negro” can be used in addition to “Black or African American” Native Hawaiian or Other Pacific Islander A person having origins in any of the original peoples of Hawaii, Guam, Samoa or other Pacific Islands White Persons having origins in any of the original peoples of Europe, the Middle East or North Africa OMB Race Categories (continued)

10 10 Census Data Changes to the Race Question in the 2000 Census: The Asian and Pacific Islander (API) category was split: a) Asians b) Native Hawaiian and Other Pacific Islanders (NHOPI) The category American Indian, Eskimo, Aleut (AIEA) was changed to American Indian or Alaskan Native (AIAN) Respondents could select more than one race.

11 11 U.S. Census Bureau

12 12 Vital Statistics Maintained by the National Center for Health Statistics ( ) States report the following to NCHS: Birth data (Natality) Death data (Mortality) Marriage data (no longer collected) Divorce data (no longer collected)

13 13 CDC Wonder

14 14 Registries for Morbidity Data New Jersey Cancer Registry SEER: Surveillance, Epidemiology, and End Results

15 15 Data Limitations Little data is available at the local level Problem of small numbers Data may not be collected uniformly (race category differences, etc.) People are mobile Cancer has a long lag time

16 How much cancer is occurring? Understand incidence rates and prevalence

17 17 Measuring Epidemiological Outcomes A proportion with the specification of time (e.g. deaths in 2000 / population in 2000) Rate A ratio where the numerator is included in the denominator (e.g. males / total births) Proportion Relationship between any two numbers (e.g. males / females) Ratio

18 18 Definitions Incidence is the rate of new cases of a disease or condition in a population at risk during a time period Prevalence is the proportion of the population affected

19 19 Incidence Incidence is a rate Calculated for a given time period (time interval) Reflects risk of disease or condition Incidence = Number of new cases during a time period Population at risk during that time period

20 20 Prevalence Prevalence is a proportion Point Prevalence: at a particular instant in time Period Prevalence: during a particular interval of time (existing cases + new cases) Prevalence = Number of existing cases Total number in the population at risk

21 21 Prevalence = Incidence  Duration Prevalence depends on the rate of occurrence (incidence) AND the duration or persistence of the disease At any point in time: More new cases (increased risk) yields more existing cases Slow recovery or slow progression increases the number of affected individuals

22 22 Incidence/Prevalence Example For male residents of Connecticut: The incidence rate for all cancers in per 100,000 per year The prevalence of all cancers on January 1, ,789 per 100,000 (or 1.8%)

23 23 Proportional cancer incidence by gender, US 2000

24 How does occurrence vary within the population? Understand measures of association and difference

25 25 Outcome Measures Compare the incidence of disease among people who have some characteristic with those who do not The ratio of the incidence rate in one group to that in another is called a rate ratio or relative risk (RR) The difference in incidence rates between the groups is called a risk difference or attributable risk (AR)

26 26 Calculating Outcome Measures Outcome D B No Disease (controls) I N = C / (C+D) C Not Exposed I E = A / (A+B) AExposed Incidence Disease (cases) Exposure Relative Risk = I E / I N Attributable Risk = I E - I N

27 27 1,100 1, Total Lung Cancer No 30/730 = 41 per Non-smoker 70/370 = 189 per Smoker IncidenceYesExposure Relative Risk = I E / I N = 189 / 41 = 4.61 Attributable Risk = I E - I N = = 148 per 1000

28 28 Smokers are 4.61 times more likely than nonsmokers to develop lung cancer 148 per 1000 smokers developed lung cancer because they smoked Relative Risk = I E / I N = 189 / 41 = 4.61 Attributable Risk = I E - I N = = 148 per 1000

29 29 RR < 1RR = 1RR > 1 Risk comparison between exposed and unexposed Risk for disease is lower in the exposed than in the unexposed Risk of disease are equal for exposed and unexposed Risk for disease is higher in the exposed than in the unexposed Exposure as a risk factor for the disease? Exposure reduces disease risk (Protective factor) Particular exposure is not a risk factor Exposure increases disease risk (Risk factor)

30 30 Annual Death Rates for Lung Cancer and Coronary Heart Disease by Smoking Status, Males 1000 – 500 = 500 per 100, – 12.8 = per 100,000 AR 1000 / 500 = / 12.8 = 9.9RR Non-smoker 1, Smoker Coronary Heart Disease Lung CancerExposure Annual Death Rate / 100,000

31 31 Summary The risk associated with smoking is lower for CHD (RR=2) than for lung cancer (RR=9.9) Attributable risk for CHD (AR=500) is much higher than for lung cancer (AR=114.4) In conclusion: CHD is much more common (higher incidence) in the population, thus the actual number of lives saved (or death averted) would be greater for CHD than for lung cancer


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