Epidemiology 2 Flashcards
1
Q
Define
Observational Studies
A
- Uncontrolled studies/natural experiments
- Viewed as less definitive than clinical trials
- By the end of experiment, cohort might flake → your sample size will be lower
2
Q
What can/can’t you control in observational studies?
A
- Only thing you control is finding the boundary in which you will observe
- Cannot control certain factors (or confounders)
3
Q
List
Types of Observational Studies
3
A
- Cohort studies
- Case-control studies
- Cross-sectional studies
4
Q
Define
Cohort Study
A
- Starts with exposed & non-exposed group, both w/o disease
- Follow groups to observe disease incidence or mortality rates
- How many people develop the disease based on who are & who are not exposed?
5
Q
In Cohort Studies, how are disease rates in both groups compared?
A
rate ratio or rate difference(??) lol
6
Q
List
Types of Cohort Study
2
A
- Retrospective (past to present)
- Prospective (present to future)
7
Q
Define
Retrospective studies
A
- Faster & cheaper
- Getting people now, and asking their past lifestyle
- Info on exposure & confounders from historical information
8
Q
Define
Issues w/ Retrospective Studies
A
- Bias, will they tell you accurately? Or too much/too little/incorrect info?
- Di ka sure if your medical records are accurate
9
Q
Define
Prospective Studies
A
- Take longer, more expensive
- Measure exposure levels & confounding variables at baseline
- Good when biological samples are required
- Unreasonable to keep historical biological samples
- To study diseases difficult to ascertain in retrospect
10
Q
Define
Issues w/ Prospective Studies
A
- Lifestyle can change throughout the study: Takes longer, can be multifactorial
- Inconsistent statistically
- Tip: get too much at first, para kung umonti u arent screwed
11
Q
List
What can cohort studies consider?
2
A
Cumulative incidence or risk (disease events per person)
* This person had disease event, this person didn’t
Incidence rates or mortality
rates (disease events per person per time)
* Over a month, x people had this disease
12
Q
Cohort studies are good for ____ exposures & ____ diseases
A
rare exposures & common diseases
13
Q
Define
Case Control Studies
A
- Start: diseased & non-diseased groups
- Look backward in time
- More subject to bias than cohort
- Case: has disease
- Control: no disease
- Compares odds of exposure in each group
14
Q
Define
Recall bias
A
- Cases recall past exposures more often than controls
- typical of retrospective exposure assessment
- some people may recall more, some less
- no such thing as perfect recall
- Results biased towards finding association
15
Q
Case Control studies are good for ____ exposures & ____ diseases.
why?
A
common exposures & rare diseases
- Since ure starting with a bias looking into the rare disease, might as well look into those patients
- Cohorting a rare disease needs a huuuge cohort
- Unicorn disease: hard to catch
16
Q
Proportion Exposed vs Odds of Exposure
A
Proportion exposed = a / (a+b)
Odds of exposure = a / b
a is no. of exposed
b is no. of non-exposed
17
Q
What conclusion can we draw when comparing odds of exposure and control?
A
odds of exposure > control → exposure is associated with disease
18
Q
Describe
Cross-sectional Studies (Prevalence Studies)
A
- Measures exposure & disease at the same time
- Done when outcome of interest is subclinical/asymptomatic disease
- Difficult to determine whether exposure DID precede disease
- looking at 1 point in time & getting samples (bio & envi) in that point in time
- Tend to be weaker in design (compared to cohort & case-control)
19
Q
Define
subclinical disease
A
illness that is staying below the surface of clinical detection
- “under” the clinic, Clinic doesn’t see it bc you don’t go to the hospital bc it’s not that bad
- Ex. blood has the lead due to low exposure, but won’t show disease associated with high exposure (neurotoxicity)
20
Q
Relationship bet. Cross sectional studies vs. cohort or case-control studies
A
Cross-sectional is Often only possible design
* Can be confirmed in cohort or case-control studies
* Often first step before cohort or case-control studies
21
Q
Define
bias
A
Distortion of the true relationship bet. exposure & disease
22
Q
List
Kinds of bias
3
A
Selection Bias
Confounding Bias
Information Bias
23
Q
Define
Selection bias
A
- Selecting a study population not representative of general population
- Cannot be corrected during analysis
- Easy to do, but difficult to correct
- Once you selected your sample, kahit anong sample test won’t explain why you selected your samples
24
Q
Describe
Healthy Worker Effect
A
- Workers compared to general population
- Results based against finding adverse health effects among workers
- Typically ‘labour’ work (physical work)
- What representation means may be different based on what you’re looking at
25
# Explain
how did the study on ethylene oxide & breast cancer obtain selection bias?
* Only 20% of individuals in target pop answered questionnaire on how breast cancer occurs
* Bc of **how they designed where/how the interview will be,** nagkakaselection bias agad
26
# Describe
Confounding Bias
**Distortion of exposure-disease relationship** by a **third variable association** with both exposure & disease
* Ex. Age, smokers
* Adjustment for effect can be made during analysis
* Might show association even when there isn’t
* Influences both independent and dependent variables – will show association even if there isn’t
* **Even if you select your cohort really well, there can still be confounding bias** – important to always talk to your cohort
27
# Describe
How does Confounding Bias impact results?
* **Might show association even when there isn’t**
* Influences both independent and dependent variables – will show association even if there isn’t
* Can change positively or negatively(???? what)
28
How can the error of confounding bias be adjusted during analysis?
**Stratification** of groups or **multivariate analysis**
* Can see odds ratio/incidence go down when your remove a confounder
**However, you need to actively correct it**
* Be aware that you have a confounding variable present
* pag di mo alam na may confounding variable, edi di mo maadjust sa stat
29
# Define
Effect modification
Third variable modifies the effect of the exposure variable of interest
30
# Define
Information Bias
* Occurs when **information obtained** about either exposure or disease is incorrect
* **Mismeasurement** or **misclassification**
31
How does information bias impact results?
| Towards what
Usually to bias results **towards no association**
32
# List
Kinds of Data Analysis Variables
| 2
1. Categorical variable (or dichotomous variable)
2. Continuous variable (on a spectrum)
33
How do we analyze data when both exposure & disease variables are categorical?
* Calculate **rate ratio** & **odds ratio**
* Can be **stratified** to control for confounding
34
How do we analyze data when both exposure & disease variables are continuous?
Regression analysis
35
What are the implications of regression (R)?
What is an acceptable R?
R coefficient **significantly different from 0** = exposure is **significant** predictor of disease
* Acceptable R: usually +- 0.8, depends on nature of study
* Minsan nga .5 relationship is strong enough eh (wtf)
36
How do we analyze data when exposure & disease variables area mix of cat & cont?
**Linear regression** for outcome
* Can be used w/ categorized exposure in regression
**Logistic regression** - measure of interest is odds ratio
* Even when disease variable is categorical
* Either categorical or continuous variables can be included among disease predictors
37
Why does epidemiology use a lot of regression analysis?
kasi you’re always looking at **correlation,** and regression analysis shows the quantitative relationship bet 2 variables
38
____ sample sizes = ____ statistical power
Large sample sizes = greater statistical power
39
# Define
Confidence Interval
What is the usual CI set?
Range of **plausible values** for **measure of effect**
Usually 95%, if more sure edi 99%
40
# Idk this maica edit before long exam
Precision of estimate = reflection of random error
* Error likely to result form choosing sample of total population of interest
* Error from biases etc.
41
Why does epidemiology have the risk of being less precise/accurate?
How do we account for the lower precision/accuracy?
nature of data collection makes precision difficult
* So your stat has to remove/control your data
* But also don’t overly control your data
42
# List
Other Aspects of Epidemiology
| 2
1. Environmental Epidemiology
2. Occupational Epidemiology
43
What does Environmental Epidemiology include and exclude?
Includes **environmental agents**
* Large number of people are exposed involuntarily
* Looks at explicit connection w/ environment
Excludes **voluntary exposures**
44
Describe
Environmental Epidemiology exposure
1. Exposure is usually **low level** and **homogenous**
2. **Difference in risk** bet. more & less exposure is usually small
* Needs large sample size to see the small differences
45
# Differentiate
Pandemic vs Epidemic vs Endemic
**Pandemic: global**
**Epidemics:** **unusual** outbreaks of disease significantly **above normal** levels
regional
* Sudden urge in incidences
* Often caused by known agents
**Endemic**: exist at **constant, low or bg** levels
* Always there
* COVID is endemic: it’ll be there for a long time
46
# Define
Occupational Epidemiology & nature of its exposure
Studies of illness or iniury associated w/ **workplace exposures**
Relatively **high exposures** in relatively **small numbers of people**
* Often geographically isolated in a worksite
47
How were we able to discover carcinogens in Occupational Epidemiology?
Historical studies - needed time to identify long term effects
48
# T/F
Current workplace exposures to suspected toxins lower than in past
T
49
What is the goal of measuring exposure?
* **Detecting and quantifying** dose-response relationship
* Support a **causal** relationship
50
What do we measure when measuring exposure?
1. **External exposures** (the actual thing)
2. **Biomarkers** (ex. Proteins produced by antigens)
51
What is the impact of a mismeasured exposure?
* Skews true dose-response to a **weaker(?) relationship**
* Which is why we need to understand which biomarker to get to definitively measure exposure & disease
52
What is the impact of Misclassification of an exposure?
* severely bias toward null hypothesis (no relationship)
* Part of information/selection bias
53
# Describe
Job-Exposure Matrix (JEM)
Cross classification of jobs and exposure levels across time
54
How are jobs on JEM categorized?
Categorized based on presumably **same exposure level**
* Workers in all jobs in one category assigned same exposure level at any given point in time
55
# Describe
Biomarkers
Some are **measure of internal dose**
* Can **account for variation** in absorption and metabolism
* Estimate of **biologically relevant dose**
56
# T/F
Many Biomarkers persist long enough for use in retrospective studies
Why/why not?
F
few persist long enough for use in retrospective studies
* Bc we can metabolize hooray
* Ex. opioid addict who doesnt take opioids for a week will pass the drug test
57
# Define
Quantitative Meta-analysis
* Combine results from different study designs
* Provides **weighted average** of results across studies
* Studies with the **most weight = lowest variance**
* Do not need access to raw data (can access from other papers)
58
# Define
Pooled Analysis
* Raw data from several studies obtained and **re-analyzed**
* More time consuming
* More flexibility for analysis
59
# Differentiate
Quantitative Meta-analysis vs Pooled Analysis
* Quanti: combine results, weighted average lang (easier, no need for raw data)
* Pooled: literally re-analyze raw data (more time-consuming, but also more flexibility for analysis)
