epidemiology 101: basic concepts - njaihaepidemiology 101: basic concepts jack glass rowan...

© 2013 American Industrial Hygiene Association, New Jersey Section, Inc.

Epidemiology 101: Basic Concepts

Jack Glass Rowan University, J Tyler Scientific

[email protected] 856-256-4154 NJA

IHA

mailto:[email protected]

NJAIHA Summer Review Course: Epidemiology

About the Presenter: Jack Glass

• Director of Environmental Health and Safety, Rowan University; Cooper Medical School of Rowan; Rowan School of Osteopathic Medicine

• Principal Investigator, J Tyler Scientific Company, Inc.

• 25 Years of EHS Consulting Practice • MS-Environmental Health-Temple University • MPH/DrPH-UMDNJ-In Progress

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Course Objectives

At the end of this course, you should be able to:

• Solve Basic Epidemiological Formulas

• Identify Type and Purpose of Study Designs

• Calculate and Describe Attributable Risks

• Understand How Demographics will impact Studies and Results

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Epidemiology and Industrial Hygiene

• Epidemiology tends to rest with the Public Health Professional - may or not be an IH.

• An IH needs to understand the basics, know the calculations, apply the findings, pass the exam!

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What is Epidemiology?

• The study of epidemics? • The study of diseases? • The study of diseases of the skin? • Something scientists and academics use

to confuse other people?

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Definition of Epidemiology

“The study of the distribution and determinants

of health-related states or events in specified populations

and the application of this study to control health problems.”

- James Last A Dictionary of Epidemiology

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Unpacking that Definition

• Study: observing, recording, experimenting • Distribution : who, where, when • Determinants: why? • Health-related states • Specified populations • Application

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Specified Populations

• How many people in this room are infected with the HIV virus?

• How many people in New York are infected?

• How many people in the US are infected?

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Why is it Important to Specify the Population

• In order to be able to compare between two populations, we need to know what the defined population is.

• For example, if we say 50 people in this room have an infection compared with 100 people in the next room, does it mean that infections are less common in this room?

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Numerators and Denominators

• (n / d) • Numerator – the top half of the fraction • Denominator- the bottom number in the

fraction • Think Notre / Dame!

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Numerators and Denominators 2

• There may be fewer people in this room than in the next room.

• Let’s assume that there are 100 people in this room and 1000 people in the next room: – So 50 people with infections out of 100 people in this

room means half (50/100) of the people in this room have infections.

– 100 people with infections out of a 1000 in the next room means only a tenth (100/1000) of the next room have colds.

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Measuring Disease Frequency

• There are 2 main measures used: – Prevalence – Incidence

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Distinguishing Between Incidence and Prevalence

• Prevalence includes both old and new cases and is usually expressed as a percentage.

• Incidence includes only NEW cases and is expressed as the number of cases per population per year.

• Time period and population must be specified.

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Prevalence

• Prevalence of colds in this class • Number of cases (people with colds) = 3 • Population of class = 30 • Prevalence = 3 / 30 • Expressed as a percentage = 3 / 30 X 100 = 10%

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Incidence

• Number of cases of newly diagnosed HIV infection in a city in 2003 is 900.

• Population of the city is 100,000. • Incidence of HIV is 900 per 100,000 in

2003.

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Defining Risk

• Probability that an event will occur. • Different from causation. • Chance that if exposed to certain risk

factors will develop condition.

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Risk and Risk Factors

• Risk factors are factors that increase the probability that a disease will occur.

• Risk factors could be: – environmental – behavioral / lifestyle – genetic

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Differentiating between Risk and Causation

• Risk is about probability or likelihood. • Causation is about “certainty.” • Identifying a risk may be the first step to

understanding causation (e.g., smoking and lung cancer).

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Types of Risk

• Absolute risk

• Relative risk

• Attributable risk

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Measures of Risk: Absolute Risk

• Number of cases in a defined population. • Similar to incidence. • If 100 people are infected with HIV in a town of

1000 people, the absolute risk of HIV in the town is 100 per 1000.

• But the people in the town have different lifestyles, genes, living conditions which absolute risk does not take note of.

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Measures of Risk: Relative Risk

• Going back to our example, we could divide the population of the town into injecting drug users (IDUs) and non-injecting drug users non-IDUs).

• Count the number of cases of HIV in IDUs and count the number in non-IDUs.

• Relative risk (risk ratio) is the ratio between the two. – i.e., risk in the exposed / risk in the unexposed

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Relative Risk

• In our example, there were 400 IDUs in the town, and 80 of them were diagnosed with HIV in the year of our study. The risk of HIV in IDUs was therefore 80 / 400 = 0.2.

• There were 20 diagnoses of HIV in the non-IDU population of 600, so the risk of HIV in non-IDUs was 20 / 600 = 0.033.

• The relative risk is therefore 0.2 divided by 0.033 = 6.06.

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What does the Relative Risk Mean?

• From the example, we obtained a relative risk of 6.06.

• In simple terms, it means that IDUs in the town in that year were 6.06 times more likely to be diagnosed with HIV than non-IDUs.

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Attributable Risk

• Difference between risk in the exposed and risk in the unexposed.

• Risk in exposed minus risk in unexposed. • From our example, the attributable risk for

smokers in the town was: 0.2 - 0.033 = 0.167

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Rates

• Rates are another means of expressing measurement.

• 3 broad types of rates commonly used in epidemiology: – crude rates – specific rates – standardized rates

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Crude Rates

• Looking at the death records in Youngtown which has a population of 100,000, we find that 500 people died in 2005.

• In neighbouring Oldtown with the same population of 100,000, there were 800 deaths in 2005.

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Comparing Crude Rates

• Youngtown had a crude death rate of 500 per 100,000.

• Oldtown had a crude death rate of 800 per 100,000.

• Oldtown appears to have a higher death rate than Youngtown, but do the crude rates tell the whole story?

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Youngtown Oldtown Age group Number of deaths

10-20 200 30

20-30 150 20

30-40 50 40

40-50 20 20

50-60 15 90

60-70 10 150 70-80 20 250

80-90 35 200 TOTAL 500 800

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Delving Deeper: Specific Rates

• Looking at the number of deaths in different age groups, we get a different picture.

• The majority of deaths in Oldtown occurred in people over the age of 60.

• The majority of deaths in Youngtown occurred in people under the age of 40.

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Specific Rates

• Specific rates give us more detail by looking at the occurrence of events in a subgroup of the population.

• In the example, we used age groups, but could have used gender, ethnicity, occupation, etc.

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Comparing Rates: Standardization

• Going back to the example, we know that there were different patterns in the deaths recorded in the two towns.

• But we may find it difficult to compare rates between the two towns.

• Why?

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Why Standardize ?

• Perhaps Oldtown is a retirement town with many old people and few young people?

• Perhaps Youngtown has very few old people and is a barracks town consisting largely of soldiers going to Iraq?

• To enable valid comparison, we need to be comparing like with like – hence standardization.

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What are Standardized Rates?

• Standardized rates are rates that take into account the structure of the population and adjust for differences in population structure.

• Rates can be age-standardized, sex-standardized, etc.

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Causation

• Hills Postulates – strength of association – consistency of the observed association – specificity – temporality – biologic gradient – biologic plausibility – coherence – experiment – analogy

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Can We Trust Data?

“There are liars, damned liars, and statisticians.”

– Mark Twain

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Terms / Calculations

• Endemic / epidemic / pandemic • Morbidity / mortality • Error

– sources of error – Type 1 vs. Type 2

• Accuracy vs. precision • Study design • Risk

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Endemic / Epidemic / Pandemic

• Endemic - expected based on historical occurrence

• Epidemic - exceeds endemic rates in a given location

• Pandemic - exceeds endemic rates in various areas (WHO - 3 or more regions)

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Morbidity / Mortality

• Morbidity: sickness • Mortality: death • Pathogenicity: # ill / # infected • Infectivity: # infected / # exposed • Fatality Rate: # dead / # infected • Look at Exercise #1

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Error

• Sources of error – Random – Systematic

• Type 1 vs. Type 2

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Error

• Type 1 = Rejection Error = α Error – the probability of rejecting a true statement

• Type 2 = Acceptance Error = β Error – the probability of accepting a false statement

• Sensitivity = probability of a positive test given a positive condition - limit Type 1

• Selectivity = probability of a negative test given a negative condition - limit Type 2

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Accuracy vs. Precision

• Accuracy: represents the true value • Precision: repeatability

Accurate but not Precise Precise but not Accurate

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Study Design

• Case study • Case control • Cohort • Ecological • Experimental – clinical trials

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Risk

Disease Exposure True False

True A B A+B False C D C+D

A+C B+D

Relative Risk = A/(A+B) C/(C+D)

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Risk

Disease Exposure True False

True A B A+B False C D C+D

A+C B+D

Odds Ratio = A/C = AD B/D BC

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Problems

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Case 1: A total of 75 postal workers and 15 government employees handled two anthrax laced letters delivered to Congress offices in the Fall of 2001. Three postal workers and eight government staff members tested positive for anthrax exposure. Of the individuals who tested positive, one postal worker and three staff members displayed inhalation anthrax symptoms and were treated. One of the symptomatic staff members was hospitalized and passed away three days later.

• Calculate the pathogenicity of anthrax. • Calculate the infectivity of anthrax. • Calculate the case fatality rate of anthrax.

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Case 2: In a case control study of congenital heart defects, 600 newborns were studied with 300 cases and 300 controls. A total of 190 individuals who had heart defects were born to mothers who used isotretinoin (retinoic acid) to treat acne while they were pregnant. There were 210 individuals who did not have heart defects and were born to mothers who did not use isotretinoin while pregnant.

• Set up a 2 by 2 table, calculate the odds ratio, and interpret the results.

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Case 3: In a cohort study of 386 children living in South Jersey, a total of 16 developed brain cancer. Of the children in the study, 196 lived within 10 miles of a common water source; 10 of these children developed brain cancer.

• Set up a 2 x 2 table, calculate the relative risk, and interpret the results.

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References

• http://www.ted.com/talks/tags/epidemiology -Interesting Videos

• http://www.cdc.gov/epicasestudies/ - Application of Epi • http://publichealth.jbpub.com/aschengrau/Aschengrau06.pdf -

Full Chapter-Nice Overview. • http://www.epidemiologyschools.com/intro/index3.html - Intro

and Background Info. • http://www.med.uottawa.ca/sim/data/Study_Designs_e.htm -

All about Study Types • http://quizlet.com/18818047/small-animal-diseases-medical-

care-epidemiology-taxonomy-overview-of-microbes-flash-cards/ - Great Study Aid

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http://www.ted.com/talks/tags/epidemiology

http://www.cdc.gov/epicasestudies/

http://publichealth.jbpub.com/aschengrau/Aschengrau06.pdf

http://www.epidemiologyschools.com/intro/index3.html

http://www.med.uottawa.ca/sim/data/Study_Designs_e.htm

http://quizlet.com/18818047/small-animal-diseases-medical-care-epidemiology-taxonomy-overview-of-microbes-flash-cards/




Questions?

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Thank You! Drive Safe!

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