Lecture 3 (Sept 20) Flashcards
What did American Psychologist E.L Thorndike (1874-1949) say about measurement
“If a thing exists, it exits in some amount; and if exists in some amount, it can be measured.”
Exposure Measurement considers
Available dose:
Cumulative vs. current
Administrated dose:
The amount that comes in contact
Absorbed dose (uptake):
The amount that enters the body
Active dose (biologically effective):
That actually affects the specific target organ
Count:
The number of occurrence of an event
In a major Canadian city, average annual number of homicides:
30 homicides per year in the 1970’s
50 per year in the 1990’s
90 per year in 2022-23
Ratio
Relationship between 2 numbers
Numerator NOT necessarily INCLUDED in the denominator
An example: (binary) sex ratio
Proportion
Relationship between 2 numbers
Numerator NECESSARILY INCLUDED in the denominator
Proportion always ranges between 0 and 1
Odds
The probability of an event occurring relative to it not occurring
In a population of 100 older people, 25 people are diabetic, what is:
a) the proportion of diabetes
b) odd of being a diabetic
a) = 25/100=0.25;
b) =25/75=0.33
Rate numerator vs denominator
Speed of occurrence of an event over time
Numerator
no. of EVENTS observed for a given time
Denominator
population in which the events occur (population at risk)
Incorporates a set period of time
Measures of Prevalence
Prevalence Rate: The proportion of the population (or population sample or sample subset) that has a given disease or other attribute at a specified time.
Obtainable from cross-sectional studies (Oct 25)
Two types of Measures of Prevalence:
Point prevalence rate
# with disease at specific time/Population at same time
Period prevalence rate
# with disease at specific time period/ Total defined population at same period
Example: Prevalence of meningitis in city A in 2023 (Jan 1-Dec 31) In city A (population 152,358) there were 6 case of meningitis in 2023. The Period PR of meningitis in this city during 2022 was ____ cases per 100,000 persons.
6 ÷ 152,358 X 100,000
Incidence Rate:
The proportion of the population at risk that develops a given disease or other attribute during a specified time period.
Obtainable from cohort (longitudinal) studies
# new events during specified time period/Population “at risk”
Example #1: IR cancer from 2010-2019 in county A
9,000 residents of county A were studied on Jan 1, 2010. Of these residents, 245 had a history of cancer. By Dec 31, 2019 a total of 179 new cases of cancer had been diagnosed in the “at risk” population. The 10-year cumulative incidence rate of cancer (first diagnosis) in this cohort was ___ cases per 10,000 people.
Answer 204
Because:
# new events = 179
at risk population = 9,000-245=8,755
Example #2: IR of Depression during 1st year of University
2,012 first year university students were sampled on Sept. 1st 2023. Of these, 112 had depression. On April 30th 2024, 345 of the sample had depression, including 79 of those who had depression in September.
What was the point prevalence rates of depression on Sept 1st 2023 and April 30th, 2024?
Sep 1st, 2023: (112/2012)=5.6%; April 30th, 2024 : 345/2012=17%
How many “resolution” cases were there? 112-79 (remained depressed until April)=33
The IR of new depression during the freshman year (Sept 1st 2023 to May 30th, 2024) in this sample was ____%.
Note: Let’s assume those who had depression (either resolved or remained depressed) at the inception of the study (n=112) are not at risk of new depression. We have to removed them from the denominator (population at risk).
(345 – 79) ÷ (2012 – 112) = 14.0%
Four Hallmarks of Health Studies
A research question/plausible theory
A well thought design to address the research question
Measurement of exposure and outcome
Analysis to compare groups
>i.e. rates of disease among exposed vs. in unexposed
Relative Risk
Tells us how many times as likely it is that someone who is ‘exposed’ to something will experience a particular health outcome compared to someone who is not exposed
Tells us about the strength of an association
Can be calculated using any measure of disease occurrence:
Prevalence
Incidence rate
Does not tell us anything about how much more disease is occurring
Random Error
Error due to “chance”
hitting all around the bullseye
Systematic Error
Error due to recognizable source
data hitting a specific location consistently but not meeting the bullseye
Precision vs accuracy
accuracy: close to the target
Precision: All hits are in close proximity to each other
Insufficient Precision creates random error and can happen because (3)
- The measurement tool is not precise enough (a ruler in cm is not precise when meaningful differences are in millimeter)
- Two (independent) interviewers rate the same person differently using the same scale (inadequate training?)
- The same interviewer rates the same person differently
A Valid Measure
Clearly conceptualizes the variable we want to measure
1) measures what it is supposed to measure, relevant information, underlying construct
- does not include unnecessary questions
- is able to do what it is designed to do: predict, discriminate, etc.
More relevant when a set of questions are used to measure a disease such as disability or depression, a behavioural traits such as gender role, etc.
Validity vs. Precision
precision is a lack of random error, validity refers to a lack of systematic error.
Sources of Measurement Error
Interviewer or observer
> Record abstracting (random) error
> Biased overestimation or underestimation
Participants
> Recall
> Random or systematic
