MODULE TWO: Epidemiologic Measurements: An Overview

Describing Disease Occurrence First, the types of data Nominal Ordinal Discrete Continuous 4-2

Four types of data DescriptionExamples NominalCategorical – unordered categories Two levels – dichotomous More than two levels – multichotomous Sex, disease (yes, no) Race, marital status, education status OrdinalCategorical – ordering informative Preference rating (e.g., agree, neutral, disagree) DiscreteQuantitative – IntegersNumber of cases ContinuousQuantitative – Values on a continuum Dose of ionizing radiation © 2010 Jones and Bartlett Publishers, LLC

Types of Variables Categorical: No numerical scale, just groups Male/Female Yes/No Married/Single/Divorced Quantitative: Takes on numerical values Discrete variables take on values that tend to be “choppy” or integers. Examples are number of children, number of times married. Continuous variables take on many values on a finely- grained scale. Examples are temperature, pressure, or speed measurements.

Categorical data Nominal Ordinal 4-5

Quantitative: Discrete vs. Continuous 4-6

Counts, ratio, proportion, and rate © 2010 Jones and Bartlett Publishers, LLC

Counts – Limited Interpretation New CasesReporting Locationof Disease PeriodPopulation City A City B Annual Rate of Occurrence = Count ÷ Population City A:20 / 100 = 1 / 5 City B:100 / 1000 = 1 / 10

* Simplest/most frequently performed measure in epidemiology Refers to the number of cases of a disease or other health phenomenon being studied i.e. cases of influenza in Allegheny county in January, 2002 i.e. number of persons involuntarily referred for psychiatric crisis intervention during the summer months of 2009 Useful for allocation of health resources Limited usefulness for epidemiologic purposes without knowing size of the source population Counts

Epidemiological Outcomes Ratio: Relationship between two numbers Example: males/females In a ratio the values of x and y are independent such that the values of x are not contained in y Proportion: A ratio where the numerator is included in the denominator Example: males/total births Rate: A proportion with the specification of time Example: (deaths in 1999/population in 1999) x 1,000

Like a proportion, a ratio is a fraction, BUT without a specified relationship between the numerator and denominator Example: Occurrence of Major Depression Female cases = = ----2:1 female to male Male cases = Ratios

Proportion In a proportion, x is contained in y A proportion is typically expressed as a percentage, such that the rate base is 100 © 2010 Jones and Bartlett Publishers, LLC

In epidemiology, tell us the fraction of the population that is affected. Persons included in the numerator are always included in the denominator: A Proportion: A + B Indicates the magnitude of a part, related to the total. Proportions

Proportions - Example ABTotal (A + B) # persons with hypertension # persons without hypertension Total study population 1,4009,65011,050 P = A / (A + B) = (1,400 / 11,050) = For ease of usage, you multiply a proportion by 100 to get a percentage: p = = 12.7%

Rate A rate may be thought of as a proportion with the addition that it represents the number of health- related states or events in a population over a specified time period © 2010 Jones and Bartlett Publishers, LLC

A ratio in which TIME forms part of the denominator Epidemiologic rates contain the following elements: * a defined time interval (day, week, month year, decade, century, etc) * number of events occurring in that time interval * estimate (or count) of the population at risk in the time interval * a multiplier or constant (x 10; x 100; x 1000; etc) Rates

Calculate crude annual death rate in the US: Annual death count Crude death rate = x 1,000 Reference population (during midpoint of year) Death count in U.S. during 1990: 2,148,463 U.S. population on June 30, 1990: 248,709,873 2,148,463 Crude death rate = x 1,000 = 8.64 per 1, ,709,873 Rates – Example

In epidemiology, the occurrence of a disease or condition can be measured using rates and proportions. We use these measures to express the extent of these outcomes in a community or other population. Rates tell us how fast the disease is occurring in a population. Proportions tell us what fraction of the population is affected. (Gordis, 2000)

Morbidity Measures Incidence is always calculated for a given period of time An attack rate is an incidence rate calculated for a specific disease for a limited period of time during an epidemic Population at risk X 1,000 Number of new events during a time period Incidence Rate =

Morbidity Measures Prevalence is not a rate Point prevalence measures the frequency of all current events (old and new) at a given instant in time Period prevalence measures the frequency of all current events (old and new) for a prescribed period of time Population at risk X 1,000 Number of existing events, old and new Prevalence =

High prevalence may reflect: High risk Prolonged survival without cure Low prevalence may reflect: Low risk Rapid fatal disease progression Rapid cure Examples: Ebola, Common cold

Relationship Between Incidence and Prevalence (cont.) Cancer of the pancreas Incidence low Duration short Prevalence low Adult onset diabetes Incidence low Duration long Prevalence high Roseola infantum Incidence high Duration short Prevalence low Essential hypertension Incidence high Duration long Prevalence high

Calculation Practice Skin Cancer on Nitro beach: 1. Point prevalence on 9/28/20 2. Period prevalence for year Incidence rate for year 2005 What information will you need?

To Review: Morbidity Measures Prevalence is not a rate Point prevalence measures the frequency of all current events (old and new) at a given instant in time Period prevalence measures the frequency of all current events (old and new) for a prescribed period of time Population at risk X 1,000 Number of existing events, old and new Prevalence =

                                                        Diagnosed cases of Skin Cancer On Nitro Beach, 9/28/2004 Point Prevalence (9/28/2004) = (10/450)*1000 = 22 per 1000 # of existing cases = 10 Total population at risk = 450 

                                                              Diagnosed cases of Skin Cancer on Nitro Beach, 2005 Average population at risk = 500 Incidence rate (year 2005) = (5/500)*1000 = 10 per 1000 Period prevalence (year 2005) = (15/500)*1000 = 30 per 1000 # of new cases = 5 # Existing cases (10) + New cases (5)

Cumulative incidence rate (attack rate) Diseases or events that affect a larger proportion of the population than the conventional incidence rate. © 2010 Jones and Bartlett Publishers, LLC

Difference between crude and age-adjusted rates The crude rate of an outcome is calculated without any restrictions, such as by age or sex, on who is counted in the numerator or denominator These rates are limited if we try to compare them between subgroups of the population or over time because of potential confounding influences, such as differences in the age-distribution between groups © 2010 Jones and Bartlett Publishers, LLC

Example of the importance of age-adjustment In 2002, the crude mortality rate in Florida was 1,096 per 100,000 compared with 579 per 100,000 in Utah The crude mortality rate ratio is 1.9, meaning the rates in Florida are 1.9 times (or 90%) higher than in Utah However, the age distribution differs considerably between Florida and Utah. In Florida 6.3% of the population is under five years of age and 16.7% of the population is 65 years and older. Corresponding percentages in Utah are 9.8% and 8.5%. © 2010 Jones and Bartlett Publishers, LLC

Example of the importance of age-adjustment (continued) Using the direct method of age-adjustment based on the 2000 US standard population yielded rates of 762 in Florida and 782 in Utah per 100,000 Thus, after adjusting for differences in the age distribution, the rate in Florida is 0.97 times that in Utah © 2010 Jones and Bartlett Publishers, LLC

Objective: Be familiar with tables, graphs, and numerical methods for describing epidemiologic data Tables Line listing Frequency distribution Graphs Bar chart, pie chart Histogram Epidemic curve Box plot Two-way (or bivariate) scatter plot Spot map Area map Line graph © 2010 Jones and Bartlett Publishers, LLC