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Methods Used in Descriptive Epidemiology Cheat Sheet by

The aspect of epidemiology concerned with organizing and summarizing data to identify patterns among cases or in populations by person, place, and time (who, where, and when). Used to develop hypotheses about the causes of the patterns or factors that increase the risk of disease.

Four types of descri­ptive studies

Ecologic studies-ecolo­gical studies are used to understand the relati­onship between outcome and exposure at a population level, where 'popul­ation' represents a group of indivi­duals with a shared charac­ter­istic such as geography, ethnicity, socio-­eco­nomic status of employ­ment.
Case reports-A case report is a detailed report of the symptoms, signs, diagnosis, treatment, and follow-up of an individual patient.
Case series-A case series is a type of medical research study that tracks subjects with a known exposure, such as patients who have received a similar treatment, or examines their medical records for exposure and outcome.
Cross-­sec­tional surveys-Are observ­ational studies that analyze data from a population at a single point in time. They are often used to measure the prevalence of health outcomes, understand determ­inants of health, and describe features of a popula­tion.

4 Types Of Data

Nominal Data is used to label variables without any order or quanti­tative value. The color of hair can be considered nominal data, as one color can’t be compared with another color.
Ordinal data have natural ordering where a number is present in some kind of order by their position on the scale. These data are used for observ­ation like customer satisf­action, happiness, etc., but we can’t do any arithm­etical tasks on them.
Discrete Data the term discrete means distinct or separate. The discrete data contain the values that fall under integers or whole numbers. The total number of students in a class is an example of discrete data. These data can’t be broken into decimal or fraction values.
Continuous data are in the form of fractional numbers. It can be the version of an android phone, the height of a person, the length of an object, etc. Continuous data represents inform­ation that can be divided into smaller levels. The continuous variable can take any value within a range.

4 Types Of Data

Qualit­ative Data
Quanti­tative Data
Nominal data
Discrete data
Ordinal data
Continuous data

Ratio, Propor­tion, and Rate

Ratio-A ratio is the relative magnitude of two quantities or a comparison of any two values. It is calculated by dividing one interval- or ratio-­scale variable by the other. The numerator and denomi­nator need not be related. Therefore, one could compare apples with oranges or apples with number of physician visits.
Proportion-A proportion is the comparison of a part to the whole. It is a type of ratio in which the numerator is included in the denomi­nator. You might use a proportion to describe what fraction of clinic patients tested positive for HIV, or what percentage of the population is younger than 25 years of age. A proportion may be expressed as a decimal, a fraction, or a percen­tage.
Rate-In epidem­iology, a rate is a measure of the frequency with which an event occurs in a defined population over a specified period of time. Because rates put disease frequency in the perspe­ctive of the size of the popula­tion, rates are partic­ularly useful for comparing disease frequency in different locations, at different times, or among different groups of persons with potent­ially different sized popula­tions; that is, a rate is a measure of risk.

Tables & Graphs

Line listing, Frequency distri­bution
Bar chart, pie chart, Histogram, Epidemic curve, Box plot, Two-way (or bivariate) scatter plot, Spot map, Area map, Line graph

Numerical Methods

Measures of central tendency
Measures of dispersion
Measures of central tendency refer to ways of design­­ating the center of the data.
Also called the spread or variab­­ility, are used to describe how much data values in a frequency distri­­bution vary from each other and from the measures of central tendency.
Mean, Median, Mode
Range, Inter-­qua­rtile range, Variance, Standard deviation, Coeffi­cient of variation, Empirical rule,C­heb­ychev’s inequality

Crude and Age-ad­justed Rates

Crude Rates
Age-Ad­justed Rates
Standa­rdized Morbidity
Rates allow for fairer compar­isons between geogra­phies with different population totals. Crude rates also account for the total burden of a health outcome to a community. This statistic is calculated as the number of events (numer­ator) divided by the population at risk (denom­ina­tor). The population at risk is “a term applied to all those whom an event could have happened, whether it did or not.” For many health statis­tics, the denomi­nator is simply the population total.
Age adjusting rates is a way to make fairer compar­isons between groups with different age distri­but­ions. For example, a county having a higher percentage of elderly people may have a higher rate of death or hospit­ali­zation than a county with a younger popula­tion, merely because the elderly are more likely to die or be hospit­alized. (The same distortion can happen when comparing races, genders, or time periods.) Age adjustment can make the different groups more compar­able.
In situations where age-sp­ecific rates are unstable because of small numbers or some are simply missing, age-ad­jus­tment is still possible using the indirect method SMR = 1 The health­-re­lated states or events observed were the same as expected from the age-sp­ecific rates in the standard popula­tion.  SMR > 1 More health­-re­lated states or events were observed than expected from the age-sp­ecific rates in the standard popula­tion.  SMR < 1 Less health­-re­lated states or events were observed than expected from the age-sp­ecific rates in the standard popula­tion.

Two Methods for Calcul­ating Age- adjusted Rates

Calculate the age-sp­ecific mortality rates for each age group in each popula­tion. Then choose the standard (refer­ence) population from one of the popula­tions (*Note: If the mortality rates of a specific community are compared to the national popula­tion, then the national population is considered as a “standard” popula­tion). Multiply the age-sp­ecific mortality rates of the other population under study to the number of persons in each age group of the standard popula­tion. By this way, you will get the expected deaths for each age group of each popula­tion. Add the number of expected deaths from all age groups. Finally to get the age-ad­justed mortality rates, divide the total number of expected deaths by the standard popula­tion. Now you can conclude by comparing the age-st­and­ardized mortality rates of two popula­tions
Choose a reference or standard popula­tion. Calculate the observed number of deaths in the population (s) of interest. Apply the age-sp­ecific mortality rates from the chosen reference population to the popula­tion(s) of interest. Multiply the number of people in each age group of the popula­tion(s) of interest by the age-sp­ecific mortality rate in the comparable age group of the reference popula­tion. Sum the total number of expected deaths for each population of interest. Divide the total number of observed deaths of the popula­tion(s) of interest by the expected deaths

Calcul­ation Rates

Incidence rate- is the number of new cases of a specified health­­-r­e­lated state or event reported during a given time interval
Incidence Rate= New cases occurring during a given time period­­/p­o­p­ul­­ation at risk during the same time period multiplied by 10z
Mortalilty Rate- is the total number of deaths reported during a given time
Mortality Rate = Deaths occurring during a given time period/ Population from which deaths occurred Multiplied by 10z
Person­­-Time Rate- When the denomi­­nator of the incidence rate is the sum of the time each person was observed
Person Time rate= New cases occurring during an observ­­at­i­o­np­­eri­­od­/Time each person observed, totaled for all persons multiply by 10z
Attack Rate- It involves a specific population during a limited time period, such as during a disease outbreak. It is also referred to as a cumulative incidence rate or risk
Attack Rate=New cases occurring during a shirt time period­­/P­o­p­ul­­ation at risk at the beginning of the time period multiplied by 100
Secondary Attack Rate- the rate of new cases occurring among contacts of known cases.
Secondary Attack Rate= New cases among contacts of primary cases during a short time period­­/(­P­o­pu­­lations at beginning of time period)- (primary cases) multiplied by 100
Point Preval­­ence- he frequency of an existing health­­-r­e­lated state or event during a time period.
Point Preval­­ence= Existing cases of a disease or event at a point in time/total study population at a point in time multiplied by 100


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