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Principles of Epidemiology

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Presentation on theme: "Principles of Epidemiology"— Presentation transcript:

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

2 About the Author Dona Schneider
Throughout her research on maternal and child health, Dr. Schneider has conducted studies evaluating data quality and how it can affect interpretation of study results. This study of concurrence between maternal reports and medical records on children’s chronic health conditions is the latest of her data quality evaluation papers. Links: Dona Schneider’s home page at the E J Bloustein School of Planning and Public Policy, Rutgers University, NJ, USA Environmental and Occupational Health Sciences Institute, New Brunswick, NJ, USA

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 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 Race Categories in the Census 1860-2000
1870 1900 1970 20002 White White White White White Black Black Black of Negro decent Negro or Black Black, African American, or Negro Quadroon1 Quadroon Chinese Chinese Chinese Chinese Indian Indian Indian (Amer.) American Indian or Alaska Native Japanese Japanese Japanese Japanese Filipino Filipino Asian Indian Korean Korean Hawaiian Native Hawaiian Vietnamese Source: 200 Years of U.S. Census Taking: Population And Housing Questions U.S. Department of Commerce. U.S. Bureau of the Census. 1 In 1890, mulatto was defined as a person who was three-eighths to five-eighths black. A quadroon was one-quarter black and an octoroon one-eighth black. 2 Categories printed in the 2000 Census Dress Rehearsal questionnaire. Guamanian or Chamorro Samoan Other Asian Other Pacific Islander Other Some other race

7 Office of Management and Budget (OMB)
Revision of Statistical Policy Directive No. 15, Race and Ethnic Standards for Federal Statistics and Administrative Reporting 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

8 OMB Race Categories 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

9 OMB Race Categories (continued)
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

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 U.S. Census Bureau

12 Vital Statistics Maintained by the National Center for Health Statistics (http://www.cdc.gov/nchs/nvss.htm) States report the following to NCHS: Birth data (Natality) Death data (Mortality) Marriage data (no longer collected) Divorce data (no longer collected)

13 CDC Wonder

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

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

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

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

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 Incidence/Prevalence Example
For male residents of Connecticut: The incidence rate for all cancers in 1982 431.9 per 100,000 per year The prevalence of all cancers on January 1, 1982 1,789 per 100,000 (or 1.8%)

23 Proportional cancer incidence by gender, US 2000

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

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 Calculating Outcome Measures
Exposure Disease (cases) No Disease (controls) Incidence IE = A / (A+B) Exposed A B IN = C / (C+D) Not Exposed C D Relative Risk = IE / IN Attributable Risk = IE - IN

27 Lung Cancer Relative Risk = IE / IN = 189 / 41 = 4.61
Exposure Yes No Total Incidence Smoker 70 300 370 70/370 = 189 per 1000 Non-smoker 30 700 730 30/730 = 41 per 1000 100 1,100 1,000 Relative Risk = IE / IN = 189 / 41 = 4.61 Attributable Risk = IE - IN = = 148 per 1000

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

29 RR < 1 RR = 1 RR > 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 Coronary Heart Disease
Annual Death Rates for Lung Cancer and Coronary Heart Disease by Smoking Status, Males Annual Death Rate / 100,000 Coronary Heart Disease Exposure Lung Cancer Smoker 127.2 1,000 Non-smoker 12.8 500 RR 127.2 / 12.8 = 9.9 1000 / 500 = 2 AR 127.2 – 12.8 = per 100,000 1000 – 500 = 500 per 100,000

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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