Ch 2 - Epidemiology & Data Presentation

how ancient egypt is related to tb

• there was a mummy found with it

how shakespear is related to tb

• wrote about tb (scraufula), believed that if the king touched your lymphnodes they would be cured, which was written in Macbeth

how corsets are related to tb

• women wore it to look restricted & slim as if they had tb

how monet & munich are related to tb

• artists that painted their sister (monet) and wife (munich) because of how romanticized tb was

how dracula is related to tb

• people believed tb was a vampire (someone who already passed) was feeding off sick humans, which caused them to look pale & thing

difference between a population & a sample

• a sample is a portion of a population

population

• collection of people who share a characteristic

• measurable quality = parameter

• complete set

• contain all members of this group

• reports are a true representation of opinion

sample

• measurable quality = statistic

• incomplete set

• a subset of the entire population

• reports have a margin of error and a confidence interval

• saves time, money & is more beneficial

types of samples

• probability sampling

• non-probability sampling

probability sampling

• every member of the population has a known population has a known probability of being sampled

• uses statistics, thus we measuring sampling error

• any study that wishes to produce statistics about the total population must use this method

non-probability sampling

• inherently biased, thus cannot calculate sampling error

• cannot measure sampling error

types of probability sampling types

• simple random sampling

• stratified sampling

• systematic sampling

• cluster sampling

simple random sampling

• gives every member of the population an equal chance of being included in the sample

• simple, often unrealistic, expensive, logistical difficulties

• poorly distributed variables = over or under-estimation

• basis of effective sampling techniques

stratified sampling

• target population divided into suitable, nonoverlapping, homogenous subpopulations or data

• random sample selected with each stratum to accurately represent all strat + reduce sampling error

systematic sampling

• when individuals, households can be ordered

• determines a selection interval (n), by dividing the total population by the sample size

• choosing every nth person (the length of the selection interval)

• good if pop’n listed by geographic area, other stratifying characteristic

• easy & popular method

cluster sample

• useful in saving resources in surveys of human populations when

  • the population is geographically

  • when sampling frame for the elements of the population studied is not available

• the units first sampled are not individual elements we are examining, but clusters or aggregates of those elements, can be space-based (state, county, block), organizational (school, grades), telephone based (area code)

convenience sampling

• use of a sample that is near at hand

• people out and about, on a road, engaged in a specific activity at the time of the survey

• route samples, street-corner political surveys, or clinic samples convenience samples when the target popuoation is residents of a give area

• inherently biased, often use to explore ideas and opinions of people about a new topic that may not be ready for a quantitative investigation

types of data

• quantitative

  • discrete

  • continuous

    • interval

    • ratio

• qualitative

  • nominal

  • ordinal

quantitative data

• data that can be measured with numbers, such as duration of speed

discrete data

• type of quantitative date

• whole numbers that can’t be broken down, such as a number of items

continuous data

• type of quantitative data

• numbers that can be broken down, such as height or weight

interval data

• type of continuous data

• numbers with known differences between variables, such as time

ratio data

• type of continuous data

• numbers that have measurable intervals where difference can be determined, such as height or weight

qualitative data

• non-numerical data that is categorical, such as yes/no responses or eye color

nominal data

• type of qualitative data

• data used for naming variables, such as hair color

ordinal data

• type of qualitative data

• data used to describe the order of values, such as 1 = happy, 2 = neutral, 3 = unhappy

Stevens’ measurement scales

• nominal

• ordinal

• interval

• ratio

nominal scale

• number of cases

• not ordered

• qualitative

• determines equality

ordinal scale

• median percentiles

• ordered/ ranked

• qualitative

• determines greater/less

interval scale

• mean

• standard deviation

• correlation

• continuous

• equal intervals between points

• no true zero scale

• determines equality of intervals

ratio scale

• coefficient of variation

• continuous true zero scale

• determination of equality of ratios

bar chart

• how we graphically represent this data

histogram

• display the frequency distributions for grouped categories of a continuous variable

line graph

• enables the reader to detect trends (ie: time)

pie charts

• circle that shows the proportion of cases according to several categories

once we have our data and/or visuals we ask

• where is the center of our data? (location)

• how is the data spread? (spread)

• how is the data distributed? (distribution)

location

• measured by

  • mode

  • median

  • mean

mode

• the number that occurs most frequently

median (m)

• when numbers are ordered, the middle (dividing the lower and upper half)

mean

• arithmetic average

spread

• measured by

  • range

  • midrange

  • variance

  • standard deviation

  • mean deviation

range

• difference between highest (H) and lowest (L)

midrange

• arithmetic mean of (H) and (L)

variance

• degree of variability

standard deviation

• square root of the variant

mean deviation

• the average of the absolute values of the deviations of each observation about the mean

distribution

• percentiles

• quartiles

• Q1=25%

• Q2=50%

• Q3=75%

• IQR=Q3-Q1

percentiles

• dividing the distribution into 100 parts or 100%

quartiles (Q)

• 25% of distribution

skewness

• negatively skewed

• normal (no skew)

• positively skewed

negatively skewed

• has an increasing slope from left to right

• “negative direction”

normal (no skew)

• symmetrical distributed in the center

• mean, median, mode are all similar/ the same

positively skewed

• negative slope from left to right

• “positive direction”

purpose of multimodal curves

• age related changes in

  • immune status

  • lifestyle of the host

• chronic diseases with long latency periods

epidemic curves

• graphic plotting of the distribution of cases by time of onset

• unimodal curve

• helps identify cause of a disease outbreak

• solid line = baseline cases of Salmonella Heidelberg. sporadic cases (four to eight per month) that typically occur

bivariate association

• examines relationships between two variables

• an association between two variables signifies only that they are related and not that the association is casual

• correlation not causation

pearson correlation coefficient

• measures strength of association

• aka pearson’s r/pearson product moment correlation

• range from -1 to 0 to 1

• if r is negative = inverse relationship

• if r is positive = positive relationship

• if r closer to +1 or -1 = the stronger association

• if r approaches 0 = the association becomes weaker

• if r is 0 = no association

non-linear correlations

• the linear correlation between X & Y is essentially 0 (-0.09)

• no linear association

• does not imply that there is no relationship between two variables, only that their relationship is non linear

dose-response curves

• correlative association between an exposure and effect

• toxic chemical & biological outcome

• dose = x-axis

• response = y-axis

• beginning, flat portion = subthreshold phase = low dosage = no/minimal effect occurs

• steep = threshold reached = increasing dose = increased response

• flattens = maximal response is reached

contingency tables

• case controlled study (something that already happened)

• a = exposure is present, and disease is present

• b = exposure is present, and disease is absent

• c = exposure is absent, and disease is present

• d = exposure is absent, and disease is absent

parameter estimation

• point estimate

  • single value used to estimate parameter (ie. using sample mean, to estimate population mean)

• interval estimate

  • range of values that with a certain level of confidence contains the parameter

• 95% confidence level

  • (most common) one is 95% certain the confidence interval contains the parameter

• for a more precise estimate of the confidence interval for population mean, one needs to increase the sample size, n