Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
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 (Schneider) 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) Epidemiology (Schneider) Uses of Epidemiology Identifying the causes of disease Completing the clinical picture of disease Tuskegee experiment Determining effectiveness of therapeutic and preventive measures Legionnaire’s disease Mammograms, clinical trials Identifying new syndromes Varieties of hepatitis Epidemiology (Schneider) Uses of Epidemiology Monitoring the health of a community, region, or nation Identifying risks in terms of probability statements Surveillance, accident reports DES daughters Studying trends over time to make predictions for the future Smoking and lung cancer Estimating health services needs Epidemiology (Schneider) Life Table of Deaths in London Age Deaths Survivors 0 -- 100 6 36 64 16 24 40 26 15 25 36 9 16 46 6 10 56 4 6 66 3 3 76 2 1 80 1 0 Source: Graunt’s Observations 1662 Epidemiology (Schneider) Graunt’s Observations Excess of male births High infant mortality Seasonal variation in mortality Epidemiology (Schneider) 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 Number of deaths Epidemiology (Schneider) 2000 2500 Leading Causes of Death in US: 1900 Pneumonia Tuberculosis Diarrhea and enteritis Heart disease Chronic nephritis Unintentional injury Stroke Diseases of early infancy Cancer Diptheria 0 50 100 150 200 Death rate per 100,000 Epidemiology (Schneider) 250 300 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 Death Rates per 100,000 Epidemiology (Schneider) 250 300 No. of Cases of a Disease Endemic Vs. Epidemic Endemic Time Epidemiology (Schneider) Epidemic Population Pyramid Epidemiology (Schneider) 1900 1980 Epidemiology (Schneider) 1940 1960 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 Epidemiology (Schneider) 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 Epidemiology (Schneider) 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 Epidemiology (Schneider) 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 Epidemiology (Schneider) Example We want a sample that will reflect a population’s gender and age: 1. Stratify the data by gender 2. Within each strata, further stratify by age 3. Select randomly within each gender/age strata so that the number selected will be proportional to that of the population Epidemiology (Schneider) 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) Epidemiology (Schneider) Classification of Data Qualitative or Quantitative Qualitative: non-numeric or categorical Examples: gender, race/ethnicity Quantitative: numeric Examples: age, temperature, blood pressure Epidemiology (Schneider) 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 Epidemiology (Schneider) Hint Qualitative (categorical) data are discrete Quantitative (numerical) data may be discrete continuous Epidemiology (Schneider) 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 Epidemiology (Schneider) 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 Epidemiology (Schneider) 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) Epidemiology (Schneider) 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) Epidemiology (Schneider) Review of Descriptive Biostatistics Mean Median Mode and range Variance and standard deviation Frequency distributions Histograms Epidemiology (Schneider) Mean Most commonly used measure of central tendency Arithmetic average Formula: x = x / n Sensitive to outliers Epidemiology (Schneider) 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 Epidemiology (Schneider) 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 Epidemiology (Schneider) 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 Epidemiology (Schneider) Mode The number which occurs the most frequently in a set Example: 1, 2, 2, 2, 3, 4, 5, 6, 7, 8 Mode = 2 Epidemiology (Schneider) 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 Epidemiology (Schneider) 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 Epidemiology (Schneider) Variance 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 Epidemiology (Schneider) Standard Deviation SD = V SD = 2.404 Epidemiology (Schneider) Symmetric and Skewed Distributions Symmetrical Skewed Mean Median Mode Mean Median Mode Epidemiology (Schneider) Frequency Diagrams of Symmetric and Skewed Distributions Symmetric Epidemiology (Schneider) Skewed 12 Patients’ 5-point Anxiety Scale Scores Patient 1 2 3 4 5 6 7 8 9 10 11 12 Anxiety score 4 3 5 1 4 4 2 5 4 3 4 5 Epidemiology (Schneider) Score Frequency 1 1 2 1 3 2 4 5 5 3 Total 12 Frequency Diagram for 12 Psychiatric Patients 5 4 3 2 1 0 1 2 3 Score Epidemiology (Schneider) 4 5 Accidents at a summer camp requiring ER treatment Week Frequency Percent 1 1 10 2 3 30 3 1 10 4 1 10 5 1 10 6 1 10 7 1 10 8 1 10 Epidemiology (Schneider) Histogram 4 Frequency 3 2 1 0 0 1 2 3 4 5 6 Number of accidents per week Epidemiology (Schneider) 7 8 9 Frequency Polygon 4 Frequency 3 2 1 0 0 1 2 3 4 5 6 Number of accidents per week Epidemiology (Schneider) 7 8 9 Frequency Polygon and Histogram Note: area A = A; B = B; C = C; D = D; area under histogram = to area under polygon Frequency 4 3 B C 2 B 1 C A D A 0 0 D 1 2 3 4 5 6 7 Number of accidents per week Epidemiology (Schneider) 8 9 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) Epidemiology (Schneider)