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Principles of Epidemiology. Dona Schneider, PhD, MPH, FACE. Epidemiology Defined. Epi + demos + logos = “that which befalls man” The study of the distribution and determinants of disease frequency in human populations (MacMahon and Pugh, 1970). Epidemiology Defined.
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Principles of Epidemiology Dona Schneider, PhD, MPH, FACE
Epidemiology Defined • Epi + demos + logos = “that which befalls man” • The study of the distribution and determinants of disease frequency in human populations (MacMahon and Pugh, 1970)
Epidemiology Defined • The study of the distribution and determinants of health-related states or events in specified populations and the application of this study to the control of health problems (John Last, 1988)
Uses of Epidemiology • Identifying the causes of disease • Legionnaire’s disease • Completing the clinical picture of disease • Tuskegee experiment • Determining effectiveness of therapeutic and preventive measures • Mammograms, clinical trials • Identifying new syndromes • Varieties of hepatitis
Uses of Epidemiology • Monitoring the health of a community, region, or nation • Surveillance, accident reports • Identifying risks in terms of probability statements • DES daughters • Studying trends over time to make predictions for the future • Smoking and lung cancer • Estimating health services needs
Life Table of Deaths in London Source: Graunt’s Observations 1662
Graunt’s Observations • Excess of male births • High infant mortality • Seasonal variation in mortality
Yearly Mortality Bill for 1632:Top 10 Causes of Death Chrisomes & Infants Consumption Fever Collick, Stone, Strangury Flox & Small Pox Bloody Flux, Scowring & Flux Dropsie & Swelling Convulsion Childbed Liver Grown 0 500 1000 1500 2000 2500 Number of deaths
Leading Causes of Death in US: 1990 Heart disease Cancer Stroke Unintentional injury Lung diseases Pneumonia and influenza Diabetes Suicide Liver disease HIV/AIDS 0 50 100 150 200 250 300 Death Rates per 100,000
Endemic Vs. Epidemic No. of Cases of a Disease Epidemic Endemic Time
1900 1940 1960 1980 2000
Statistics • Statistics: A branch of applied mathematics which utilizes procedures for condensing, describing, analyzing and interpreting sets of information • Biostatistics: A subset of statistics used to handle health-relevant information
Statistics (cont.) • Descriptive statistics: Methods of producing quantitative summaries of information • Measures of central tendency • Measures of dispersion • Inferential statistics: Methods of making generalizations about a larger group based on information about a subset (sample) of that group
Populations and Samples • Before we can determine what statistical test to use, we need to know if our information represents a population or a sample • A sample is a subset which should be representative of a population
Samples • A sample should be representative if selected randomly (i.e., each data point should have the same chance for selection as every other point) • In some cases, the sample may be stratified but then randomized within the strata
Example We want a sample that will reflect a population’s gender and age: • Stratify the data by gender • Within each strata, further stratify by age • Select randomly within each gender/age strata so that the number selected will be proportional to that of the population
Populations and Samples • You can tell if you are looking at statistics on a population or a sample • Greek letters stand for population parameters (unknown but fixed) • Arabic letters stand for statistics (known but random)
Classification of Data Qualitative or Quantitative • Qualitative: non-numeric or categorical • Examples: gender, race/ethnicity • Quantitative: numeric • Examples: age, temperature, blood pressure
Classification of Data Discrete or Continuous • Discrete: having a fixed number of values • Examples: marital status, blood type, number of children • Continuous: having an infinite number of values • Examples: height, weight, temperature
Hint • Qualitative (categorical) data are discrete • Quantitative (numerical) data may be • discrete • continuous
Qualitative Data: Nominal • Data which fall into mutually exclusive categories (discrete) for which there is no natural order • Examples: • Race/ethnicity • Gender • Marital status • ICD-10 codes • Dichotomous data such as HIV+ or HIV-; yes or no
Qualitative Data: Ordinal • Data which fall into mutually exclusive categories (discrete data) which have a rank or graded order • Examples: • Grades • Socioeconomic status • Stage of disease • Low, medium, high
Quantitative Data: Interval • Data which are measured by standard units • The scale measures not only that one data point is different than another, but by how much • Examples • Number of days since onset of illness (discrete) • Temperature in Fahrenheit or Celsius (continuous)
Quantitative Data: Ratio • Data which are measured in standard units where a true zero represents total absence of that unit • Examples • Number of children (discrete) • Temperature in Kelvin (continuous)
Review of Descriptive Biostatistics • Mean • Median • Mode and range • Variance and standard deviation • Frequency distributions • Histograms
Mean • Most commonly used measure of central tendency • Arithmetic average • Formula: x = x / n • Sensitive to outliers
Example: Number of accidents per week 8, 5, 3, 2, 7, 1, 2, 4, 6, 2 x= (8+5+3+2+7+1+2+4+6+2) / 10 = 40 / 10 = 4
Median • The value which divides a ranked set into two equal parts • Order the data • If n is even, take the mean of the two middle observations • If n is odd, the median is the middle observation
Given an even number of observations (n=10): Example: 1, 2, 2, 2, 3, 4, 5, 6, 7, 8 Median = (3+4) / 2 = 3.5 Given an odd number of observations (n=11): Example: 1, 2, 2, 2, 3, 4, 5, 6, 7, 8, 10 Median = 4 (n+1)/2 = (11+1)/2 = 6th observation
Mode • The number which occurs the most frequently in a set • Example: 1, 2, 2, 2, 3, 4, 5, 6, 7, 8 • Mode = 2
Range • The difference between the largest and smallest values in a distribution • Example: 1, 2, 2, 2, 3, 4, 5, 6, 7, 8 • Range = 8-1 = 7
Variance and Standard Deviation • Measures of dispersion (or scatter) of the values about the mean • If the numbers are near the mean, variance is small • If numbers are far from the mean, the variance is large
V = [S(x-x)2] / (n-1) V = [(8-4) 2 +(5-4) 2 +(3-4) 2 +(2-4) 2 +(7-4) 2 +(1-4) 2 + (2-4) 2 +(4-4) 2 +(6-4) 2 +(2-4) 2] / (10-1) = V = 5.7777 Variance
Standard Deviation SD = ÖV SD = Ö5.777 = 2.404
Symmetric and Skewed Distributions Symmetrical Skewed Mean Mean Median Mode Median Mode
Frequency Diagrams of Symmetric and Skewed Distributions Skewed Symmetric
Frequency Diagram for 12 Psychiatric Patients Frequency Score
Histogram Frequency Number of accidents per week
Frequency Polygon Frequency Number of accidents per week
B C Frequency B C A D A D Number of accidents per week Frequency Polygon and Histogram Note: area A = A; B = B; C = C; D = D; area under histogram = to area under polygon
Descriptive Statistics • Used as a first step to look at health-related outcomes • Examine numbers of cases to identify an increase (epidemic) • Examine patterns of cases to see who gets sick (demographic variables) and where and when they get sick (space/time variables)