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Standards of Epidemiology Dona Schneider, PhD, MPH, FACE

Epidemiology Defined Epi + demos + logos = "that which comes upon man" The investigation of the appropriation and determinants of infection recurrence in human populaces (MacMahon and Pugh, 1970)

Epidemiology Defined The investigation of the dissemination and determinants of wellbeing related states or occasions in indicated populaces and the use of this study to the control of wellbeing issues (John Last, 1988)

Uses of Epidemiology Identifying the reasons for sickness Legionnaire's malady Completing the clinical picture of illness Tuskegee analyze Determining adequacy of restorative and preventive measures Mammograms, clinical trials Identifying new disorders Varieties of hepatitis

Uses of Epidemiology Monitoring the strength of a group, district, or country Surveillance, mishap reports Identifying dangers as far as likelihood articulations DES little girls Studying patterns after some time to make expectations for the future Smoking and lung growth Estimating wellbeing administrations needs

Life Table of Deaths in London Source: Graunt's Observations 1662

Graunt's Observations Excess of male births High newborn child mortality Seasonal variety 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 passings

Leading Causes of Death in US: 1900

Leading Causes of Death in US: 1990 Heart ailment Cancer Stroke Unintentional damage Lung ailments Pneumonia and flu Diabetes Suicide Liver ailment HIV/AIDS 0 50 100 150 200 250 300 Death Rates for every 100,000

Endemic Vs. Pandemic No. of Cases of a Disease Epidemic Endemic Time

Population Pyramid

1900 1940 1960 1980 2000

Statistics : A branch of connected science which uses methodology for consolidating, portraying, breaking down and deciphering sets of data Biostatistics : A subset of insights used to handle wellbeing pertinent data

Statistics (cont.) Descriptive insights : Methods of creating quantitative synopses of data Measures of focal inclination Measures of scattering Inferential insights : Methods of making speculations about a bigger gathering in light of data about a subset (test) of that gathering

Populations and Samples Before we can figure out what factual test to utilize, we have to know whether our data speaks to a populace or a specimen An example is a subset which ought to be illustrative of a populace

Samples A specimen ought to be illustrative if chose haphazardly (i.e., every information point ought to have an indistinguishable possibility for determination from each other point) now and again, the example might be stratified yet then randomized inside the strata

Example We need a specimen that will mirror a populace's sexual orientation and age: Stratify the information by sex Within every strata, promote stratify by age Select arbitrarily inside every sex/age strata so that the number chose will be corresponding to that of the populace

Populations and Samples You can tell on the off chance that you are taking a gander at measurements on a populace or a specimen Greek letters remain for populace parameters (obscure however altered) Arabic letters remain for insights (known yet arbitrary)

Classification of Data Qualitative or Quantitative Qualitative: non-numeric or straight out Examples: sex, race/ethnicity Quantitative: numeric Examples: age, temperature, circulatory strain

Classification of Data Discrete or Continuous Discrete: having a settled number of qualities Examples: conjugal status, blood classification, number of youngsters Continuous: having an unending number of qualities Examples: stature, weight, temperature

Hint Qualitative (clear cut) information are discrete Quantitative (numerical) information might be discrete ceaseless

Qualitative Data: Nominal Data which fall into fundamentally unrelated classes (discrete) for which there is no normal request Examples: Race/ethnicity Gender Marital status ICD-10 codes Dichotomous information, for example, HIV+ or HIV-; yes or no

Qualitative Data: Ordinal Data which fall into totally unrelated classes (discrete information) which have a rank or evaluated arrange Examples: Grades Socioeconomic status Stage of ailment Low, medium, high

Quantitative Data: Interval Data which are measured by standard units The scale measures not just that one information point is not quite the same as another, yet by the amount Examples Number of days since onset of sickness (discrete) Temperature in Fahrenheit or Celsius (nonstop)

Quantitative Data: Ratio Data which are measured in standard units where a genuine zero speaks to aggregate nonappearance of that unit Examples Number of kids (discrete) Temperature in Kelvin (persistent)

Review of Descriptive Biostatistics Mean Median Mode and range Variance and standard deviation Frequency appropriations Histograms

Mean Most regularly utilized measure of focal propensity Arithmetic normal Formula: x = x/n Sensitive to exceptions

Example: Number of mishaps every 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 esteem which separates a positioned set into two a balance of Order the information If n is even, take the mean of the two center perceptions If n is odd, the middle is the center perception

Given a much number of perceptions (n=10): Example: 1, 2, 2, 2, 3, 4 , 5, 6, 7, 8 Median = (3+4)/2 = 3.5 Given an odd number of perceptions (n=11): Example: 1, 2, 2, 2, 3, 4 , 5, 6, 7, 8, 10 Median = 4 (n+1)/2 = (11+1)/2 = 6 th perception

Mode The number which happens the most often in a set Example: 1, 2, 2, 2, 3, 4, 5, 6, 7, 8 Mode = 2

Range The distinction between the biggest and littlest values in a dissemination Example: 1, 2, 2, 2, 3, 4, 5, 6, 7, 8 Range = 8-1 = 7

Variance and Standard Deviation Measures of scattering (or scramble) of the qualities about the mean If the numbers are close to the mean, change is little If numbers are a long way from the mean, the difference is vast

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 = Ö V SD = Ö5.777 = 2.404

Symmetric and Skewed Distributions Symmetrical Skewed Mean Median Mode Median Mode

Frequency Diagrams of Symmetric and Skewed Distributions Skewed Symmetric

12 Patients' 5-point Anxiety Scale Scores

Frequency Diagram for 12 Psychiatric Patients Frequency Score

Accidents at a mid year camp requiring ER treatment

Histogram Frequency Number of mischances every week

Frequency Polygon Frequency Number of mishaps every week

B C Frequency B C A D A D Number of mischances every week Frequency Polygon and Histogram Note: territory A = A; B = B; C = C; D = D; region under histogram = to region under polygon

Descriptive Statistics Used as an initial step to take a gander at wellbeing related results Examine quantities of cases to recognize an expansion (pandemic) Examine examples of cases to see who becomes ill (demographic factors) and where and when they become ill (space/time factors)

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