Descriptive Epidemiology
Organizes, summarizes, and describes epidemiologic data by person, place, and time |
Presents data in various formats inlcuding tables, graphs, and numerical summaries |
Utilizes statistical methods to effectively communicate public health issues |
Why is descriptive epidemiology helpful?
Provides information about a disease or condition |
Provides clues to identify a new disease or adverse health effect |
Identifies the extent of the public health problem |
Obtains a description of the public health problem that can be easily communicated |
Identifies the population at greatest risk |
Assists in planning and resource allocation |
Identifies avenues for future research |
Four types of descriptive studies:
Ecologic studies |
Case reports |
Case series |
Cross-sectional surveys |
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Four general types of data:
Nominal data (dichotomous or binary) |
Ordinal data |
Discrete |
Continuous |
Measures for describing dichotomous data:
Ratios |
Proportions |
Rates |
Crude and age-adjusted rates:
Crude rate: calculated without any restrictions, such as by age or sex |
Age-adjusted rate: calculated to account for differences in age distribution between groups |
Standardized morbidity (or mortality) ratio (SMR):
Used when age-specific rates are unstable or missing |
SMR = Observed / Expected |
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Types of Tables:
Line listing |
Frequency distribution |
Types of Graphs:
Bar chart, pie chart Histogram |
Histogram |
Epidemic curve |
Box plot |
Two-way (or bivariate) scatter plot |
Spot map |
Area map |
Line graph |
Numerical methods:
Measures of central tendency: Mean, Median, Mode |
Measures of dispersion: Range, Inter-quartile range, Variance, Standard deviation, Coefficient of variation, Empirical rule, Chebychev’s inequality |
Evaluating Variable Association Strength:
Correlation coefficient (r) |
Coefficient of determination (r^2) |
Spearman’s rank correlation coefficient |
Slope coefficient based on regression analysis |
Slope coefficient based on multiple regression analysis |
Cross-Sectional Studies
Cross-sectional studies analyze single-timepoint population data, assessing health outcomes, determinants, and population features. They're cost-effective for initial evidence but can't establish event sequences, study rare conditions, or avoid response bias.
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