Epidemiology Chapter 4 quiz

Descriptive epidemiology

Descriptive epidemiology Means of organizing, summarizing, and describing epidemiologic data by person, place, and time Descriptive statistics can take on various forms, including tables, graphs, and numerical summary measures Application of statistical methods makes it possible to effectively describe the public health problem 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 Objective: Describe uses, strengths, and limitations of descriptive study designs Four types of descriptive studies: 1. Ecologic studies 2. Case reports 3. Case series 4. Cross-sectional surveys Ecologic study Involves aggregated data on the population level Ecologic fallacy Case reports and case series A case report involves a profile of a single individual A case series involves a small group of patients with a similar diagnosis Provide evidence for larger scale studies (hypothesis generating) Cross-sectional survey (sometimes called prevalence survey) Conducted over a short period of time (usually a few days or weeks) and the unit of analysis is the individual There is no follow-up period Cross-sectional survey Strengths Can be used to study several associations at once Can be conducted over a short period of time Produces prevalence data Biases due to observation (recall and interviewer bias) and loss-to-follow-up do not exist Can provide evidence of the need for analytic epidemiologic study Cross-sectional study Weaknesses Unable to establish sequence of events Infeasible for studying rare conditions Potentially influenced by response bias Serial surveys Cross-sectional surveys that are routinely conducted U.S. Census Behavior Risk Factor Surveillance System National Health Interview Survey National Hospital Discharge Survey China: 1.32 billion (19.84%) India: 1.13 billion (16.96%) United States: 304.0 million (4.56%) Indonesia: 231.6 million (3.47%) Brazil: 186.5 million (2.8%) Pakistan: 163 million (2.44%) Bangladesh: 158.6 million (2.38%) Nigeria: 148 million (2.22%) Russia: 142 million (2.13%) Japan: 127.8 million (1.92%) Mexico: 106.5 million (1.6%) Philippines: 88.7 million (1.33%) Vietnam: 87.4 million (1.31%) Germany: 82.2 million (1.23%) Ethiopia: 77.1 million (1.16%) Approximately 4.3 billion people live in these 15 countries, representing roughly two-thirds of the world's population. Objective Define the four general types of data Nominal data (dichotomous or binary) Ordinal data Discrete Continuous Types of data Description Examples Nominal Categorical - unordered categories Two levels - dichotomous More than two levels - multichotomous Sex, disease (yes, no) Race, marital status, education status Ordinal Categorical - ordering informative Preference rating (e.g., agree, neutral, disagree) Discrete Quantitative - Integers Number of cases Continuous Quantitative - Values on a continuum Dose of ionizing radiation Objective: Define ratio, proportion, and rate Ratios, proportions, and rates are commonly used measures for describing dichotomous data The general formula for a ratio, proportion, or rate is x/y*10n 10n is called the rate base, with typical values of n = 0, 1, . . ., 5 Ratio In a ratio the values of x and y are independent such that the values of x are not contained in y The rate base for a ratio is typically 1 Proportion In a proportion, x is contained in y A proportion is typically expressed as a percentage, such that the rate base is 100 Rate A rate may be thought of as a proportion with the addition that it represents the number of healthrelated states or events in a population over a specified time period Rate equations n Population from which deathoccurred Deathsoccuring during a given time period Mortality Rate 10 n Populationa t risk during the sametime period Newcases occuring during a given time period IndicenceRate 10 Rate equations n Time each personobserved;totalled for all persons Number o f cases during observation period Person time rate 10 n populationa t risk a t thebeginningo f thetime period newcases occuring during a shorttime period Attack Rate 10 n (population a t beginningo f thetime period) (primary cases) newcases amongcontactso f primary cases during a shorttime period Secondary Attack Rate 10 n Total study populationa t a point in time New and existing cases o f the diseaseo r event a t a point in time Prevalence proportion 10 Cumulative incidence rate (attack rate) Diseases or events that affect a larger proportion of the population than the conventional incidence rate. Objective: Distinguish 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 agedistribution between groups Example of the importance of ageadjustment 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%. Example of the importance of ageadjustment (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 Two methods for calculating ageadjusted rates Direct Indirect Direct method Suppose that we want to know the rate for females assuming they had the same age-distribution as males. To do this we multiply the age-specific female cancer rates by the age-specific population values for males to get expected number of cases for females for each age group, assuming they had the same age distribution as males. These expected counts are then summed and divided by the total male population. Direct method , per 100,000 , , , Age adjusted rate 100 000 293 57 565 613 168 853 The resulting malignant cancer rate for females age-adjusted to the male population is: The crude rate is 1.09 times (or 9%) higher for males than females The age-adjusted rate ratio for males to females is now 1.28. This means that if females had the same age-distribution as males, malignant cancer incidence would be 28% higher for males than females, as opposed to 9% higher found using crude rates. Example 2 Population A Age (years) Population # deaths Attack Rate 15-19 1000 24 24/1000=.024 20-24 4000 16 16/4000=.004 25-29 6000 121 121/6000=.020 Total 11000 161 161/11000=.0146 Population B Age (years) Population # deaths Attack Rate 15-19 5000 120 120/5000=.024 20-24 2000 10 10/2000=.005 25-29 500 10 10/500=.020 Total 7500 140 140/7500=.0187 Example 2 Continued Population A Age (years) Population Attack Rate Pop. B Expected 15-19 1000 x .024 = 24 20-24 4000 x .005 = 20 25-29 6000 x .020 = 120 11000 164 Age-adjusted rate: 164/11000= .0149 Crude rate ratio: .0146/.0187=.78 22% lower in population A Adjusted rate ratio: .0146/.0149=.98 2% lower in population A Objective: Define the standardized morbidity (or mortality) ratio 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 Standardized morbidity (or mortality) ratio (SMR) Expected Observed SMR Interpretation of the SMR 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. Example of SMR Suppose that some or all of the female age-specific counts are unavailable, but that the total count is available Further suppose that the age-specific rates for males can be calculated Now multiply the age-specific rates in the male (standard) population by the age-specific female population values to obtain the expected number of all malignant cancer cases per age-specific group (see following table) Example of SMR 0.746 272,554 203,337 SMR Sum the expected counts to obtain the total number of expected malignant cancers in the comparison population This ratio indicates that fewer malignant cancer cases (about 25%) were observed in females than expected from the age-specific rates of males Example 2 - Indirect Method Population A Age (years) Population # deaths Attack Rate 15-19 1000 12 12/1000=.012 20-24 2000 20 20/2000=.010 25-29 3000 91 91/3000=.030 Total 6000 123 Population B Age (years) Population # deaths 15-19 4000 85 20-24 250 Not Available 25-29 750 Not Available Total 5000 95 Example 2 Continued Age (years) Population B Attack Rate A Expected Deaths 15-19 4000 .012 48 20-24 250 .010 2.5 25-29 750 .030 22.5 5000 73.0 SMR = Observed/Expected = 95/73 = 1.3 The ratio indicates 30% more deaths than expected, based on the age-specific rates of population A (standard population) 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 What cancer is more common? Breast cancer in women Prostate cancer in men For U.S. Whites, 2003-2005 Female breast cancer rate = 129.4 per 100,000 Male prostate cancer rate = 155.3 per 100,000 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 Objective: Be familiar with measures for evaluating the strength of the association between variables For discrete and continuous variables Correlation coefficient (denoted by r) Coefficient of determination (denoted by r2 ) Spearman's rank correlation coefficient Slope coefficient based on regression analysis Slope coefficient based on multiple regression analysis For nominal and ordinal variables Spearman's rank correlation coefficient Slope coefficient based on logistic regression analysis Slope coefficient based on multiple logistic regression analysis Other measures of association Under analytic epidemiologic studies, the risk ratio (also called relative risk) and odds ratio are commonly used to measure association, as will be discussed in a later chapter

cmulative incidence rate

attack rate it tends to describe diseases or events that affect a larger proportion of the p opulation that the conventional incidence rate. The denominator includes the population at risk at the beginning of the time period. By convention the rate base for the attck rate is 100

Serial surveys

conducted annually, a cross-sectional survey that is routinely conducted. these surveys reveal changing patterns of heat-related states or events over time. bRFSS survey collected natihonal data on the percentage of adults who are overweight or obese, according to BML

ratios, proportions and rates

in epidemiology it is common to deal with data that indicate whether an individual was exposed to an illness, has an illness, experienced an injury, is disabled, or is dead. Ratios, proportions, and rates are commonly used measures for describing dichotomous data. The general formula for a ratio proportion or rate is x/yx10pgpg 73

Ratios, proportions and rates

in epidemiology, it is common to deal with data that indicate whether an individual was exposed to an illness, has an illness experienced an injury s disabled or is dead. Rations proportions and rates are commonly used measures for describing dichotomous data. The general formula for a ratio proportion or rate is x/yx10n 10 n is called the rate base, with typical values of n=0,1,2,3,4,5 In a ratio, the values of x and y are independent such that the values of x are not contained in y. The rate base for a ration is 100 =1. for example, in 2012 there were 40,596 suicides in the US of which 32,7777 were male and 8,819 were female. The ration of males to females indicates that males were 3.60 times more likely than females to commit suicide.

descriptive epidemiology

involves observation, definitions, measurements, interpretations, and dissemination of health related states or even by person, place and time. pg 68

Decriptive Study Designs

pg 68 influde case reports and case series, cross sectional surveys and xploratory ecologic designs. a description of these study designs, their strenghts and weaknesses is presented in table 4-1

Study design

pg 68 program that directs the researcher along the path of systematically collecting, analyzing and interpreting data. It is a formal approach of scientific or scholarly investigations. There are both descriptive and anlytic study designs.

Ecology study

pg 69 involves making comparisons between variables where the unit of analysis is aggregated data on the population level rather than on the individual level.

ecologic fallacy

pg 69 is an error that results when an association between aggregated level variables is used to draw a conclusion about the association between individual-level variables when this association does not actually exist. Ecologic studies are often appropriate in environmental settings injuries are often associated with characteristics in the environment and may best be controlled by group focused interventions modifications to physical a, social technological, political, economic and organizational environments. rather than efforts to change individual behaviors. It is possible that although higher levels of fruit and vegetable consumption may occur in states and territories with lower levels of obesity, those eating five or more servings of fruit and vegetables per day may mot be the ones with the lower BMI

case reports and case studies

pg 70 a profile of a single individual it includes qualitative descriptive research of the facts in chronological order. a recent report described a 74-year-old woman who experienced airway obstruction when a piece of meat became lodged in her trachea. a bystander was unsuccessful at practicing the Heimlich maneuver and the

discrete data

pg 73 integers or counts that differ by fixed amounts, with no intermediate values possible of lung cancer reported in the US in a given year, a number of children. a number of sick days taken in a month.

ordinal data

pg 73 the order among categories provides additional information e.g, stage or grade of cancer. ordinal scale data are commonly used in health behavior research.

person, time, rate

pg 74 if the denominator of the incidence rate is the sum of the time each person was observed is the person-time rate. this measure is also referred to as an incidence density rate. this measure is also referred to as an incidence density rate. the denominator is the time each person is observed instead of the number of people. time can be assured in minutes, day, months or years. for example, if 100 people were followed for 1 years, there are 100 persons-years in the denominator person - time rate = new cases occuring during an observation period/time each person observed would for all persons x 10 n

A rate

pg 74 is a type of frequency measure where the numerator involves nominal data that represent the presence or absence of a health-related state or event.

mortality rate

pg 74 is the total number of deaths reported during an given time interval divided by the population from which the deaths occurred. it is calculated as mortality rate = deaths occurring during a given time/population from which deaths occurred x10 pg

incidence density rate

pg 74 the denominator is the time each person is observed instead of the number of people. time can be measured in minutes, days months or years. for example if 100 people were followed for 1 year there are 100 person years in the denominator

Cross sectional surveys

pg. 71 is conducted over a short period of time usually a few days or weeks and the unit of analysis is the individual. there is no follow-up period. cross-sectional surveys are useful for examing associations among health-related states or events and personal characteristics such as age, gender, race and ethnicity, marital status, education, occupation, access to health care and so on. they reveal who is at greatest risk and provide clues as to the causes of disease. 1956 congress passed the National health survey act. established periodic health surveys to collect information on health-related states or events.

continuous data

pg. 73 measurable quantities not restricted to taking on integer values e.g., age, weight, temperature when data are collected they ae typically entered into a spreadsheet in which each row represents a case and each column represents personal characteristics, clinical details, descriptive epidemiologic factors, and so on. a partial line listing containing information from a breast cancer study is shown in table 4-2. Examples of nominal data are in columns 3,4,5 and 8. An example of ordinal data is in column 9. Discrete data are in column 6, and continuous data are in columns 2 and 7.

person-time rate

pg. 74 the measure is also referred to s an incidence density rate. the denominator is the time eaach person is observed instead of the number of people. time can be measured in minutes, days, months, or years. for example if 100 people were followed for one yeard there are 100 person-years in the denominator

proportions

pg. 74 x is contained in y a proportion is typically expressed as a percentage such that the rate base in 10 2 - 100 thus in 2012 the proportion of suicide cases in the US who were male was o.78 or 78%j.

Nominal data

unordered categories or classes gender, race, ethnicity, marital status, occupation. Nomial data that take on one of two distinct values is referred to as dichotomous. Nominal data that takes on more than two distinct values are called multichotomous.