All sections for Final Flashcards
Why use indirect adjustment?
- When the age (or other characteristic) specific mortality rates are not available (Such as in countries with poor vital records keeping (e.g. ages are not well recorded))
- Very small populations that could create greater error
- Also used for occupational exposure (For example: Do people who work in a certain industry, such as mining or construction, have a higher mortality than people of the same age in the general population?)
With direct adjustment we…
apply mortality rates of out populations of interest to a 3rd hypothetical population
in indirect adjustment we do the opposite of what?
Adding a 3rd population with direct adjustment. In indirect adjustment, we apply mortatlity rates from a real population (country, state, national) that our populations of interest (pop 1 and 2) reside within to the age structure of our populations of interest
Steps of indirect adjustment
- Apply 3rd person population age-specific rates to the age distribution to our two comparison populations and calculate the expected number of deaths if they had the same risk for death as the standard pop. Then sum expected number of deaths
- Calculate SMR (Standardized Mortality Ration) for population 1 (SMR = Observed deaths divided by expected deaths)
- Calculate the indirect adjusted rate by multiplying the SMR of pop 1 by observed deaths
- do the same for pop 2
- Compare the adjusted populations
Complete the Indirect Adjustment Example
on the ipad (1)
What is study design?
Research study design:
Set of methods and procedures used to collect and analyze data on variables in a specific research problem
Many types with own advantages and disadvantages
How is the study design type detemined
Nature of research question
Goal of research
Availability of resources
Study Design Observational
Researchers observe individuals and record information about variable of interest.
No manipulation of events
The purpose is to describe some group or situation
Study Design Experimental
Researchers intentionally impose treatments on individuals and measure their responses
Determine ‘cause and effect’ relationship
Determine whether treatment leads to positive effect
Types of observational studies
Individual based
- cohort (longitudinal)
- case-control
- cross-sectional
Population based
- ecologucal
Type of experimental study
randomized controlled trial (RCT)
Evidence strength highest to lowest
- Systematic review/meta-analysis
- RCT (randomized controlled trail)
- cohort
- case control
- cross sectional
- case reports and series
- Ideas, opinions, editorials
- animal research
- invitro “test tube” research
What is prevalence data collection
Available data sources
- Birth records, death records, etc.
- Through studies
What is a cross sectional study design?
- prevalence or transverse study
- Helps us to investigate causes of disease
- Analyzes sample population at one point in time: (Collect data, but do NOT intervene in any way. Exposure and outcomes measured simultaneously)
- Gives us a “snapshot” of the population (
—–we CAN identify prevalent cases: we know they EXISTED at the time of the study
—–we CANNOT identify incident cases: we do NOT know WHEN the condition started
What are exposures
Broadly applied to any factor that is the primary explanatory variable of interest.
- Independent variable
- The cause in the cause and effect relationship
Also applied to other variables such as confounders which may also have to be addressed.
What is an outcome
Disease, state of health, or health related event or death that we think the exposure impacts in some way
- Dependent variable
- Its value or presence depends on another variable
- The effect in the cause-and-effect relationship
Example of exposure and outcome: Does exposure increase the risk of lung cancer?
Exposure: smoking
Outcome: lung cancer
Example of exposure and outcome: Is caffeine consumption during pregnancy associated with an increased risk of low brith weight?
Exposure: caffeine consumption
Outcome: low birth weight
How to determine is there is an association in a cross-sectional study
- Calculate the prevalence of the disease/outcome
- Calculate the prevalence of exposure/risk factor
How to compare prevalence of disease
- who has been exposed with who does and does not have outcome
- who has NOT been exposed with who does and does not have outcome
A/(A+B) vs. C/(C+D)
EX: Smokers 72.5% and Non Smokers 35.8%. SUBTRACT these to get “Smokers have a 36.7% higher prevalence of lung cancer than do non smokers”
Should prevalence be a decimal, whole number, or percentage
Percentage
How to compare prevalence of EXPOSURE
- disease outcome with who has and has not been exposed
- no disease outcome with who has and has not been exposed
A/(A+B) vs. C/(C+D)
EX: Lung cancer 68.8% and no lung cancer 31.8%. SUBTRACT these to get “Among people who have lung cancer, there was a 37% higher prevalence of smoking than those without lung cancer”
What are some issues with cross-sectional studies?
The prevalent cases in the study may not be generalizable - They don’t represent all people with that disease/illness
Identifying prevalent cases could exclude people who already died from the disease - Because both exposure and outcome are determined at the same time it is hard to determine the temporal relationship
what are some benefits of cross-sectional studies
Can be very suggestive of a possible exposures or risk factors for disease - After cross-sectional the next step would be to determine if there is a temporal causal step (This requires a case-control study design)
Cross-sectional studies and causality
Cross-sectional studies are not enough to determine causality.
Only mathematical associations
Case-Control designs are the next step in order to determine causality. - They themselves do not conclusively determine causality.
Cross-sectional studies and causality
Cross-sectional studies are not enough to determine causality.
Only mathematical associations
Case-Control designs are the next step in order to determine causality. - They themselves do not conclusively determine causality.
Advantages of cross-sectional studies
Relatively feasible and not too time-consuming
Relatively inexpensive to conduct
Helps to estimate burden of diseases within a population
- Useful for health planning
We can study multiple outcomes/exposures at once
- Demographics of the people they occurs in/does not occur in
- Conditions in which the health outcomes/exposures occurs
- What exposures are near the outcome at the time that the snapshot was taken.
Prompts further research
Disadvantages of cross-sectional studies
Can establish an association, but NOT causation
- Temporal relationship between exposure and outcome may be difficult to establish
VERY susceptible to bias
- Impossible to ensure that confounders are distributed equally among the groups
- Often either exposure or outcome or both depend upon recall, which is fallible
- Groups could have different sample sizes, resulting in loss of statistical efficiency
Weaknesses of using aggregated data.
- Ecological Fallacy: Ecological inference fallacy or Population fallacy
- EX: falsely assumes that every individual’s IQ is high, since the class average IQ is high
Occurs when inferences about the nature of individuals are deduced from inferences about the group to which those individuals belong.
Is a cross-sectional study design an observational or experimental study type?
Observational – there is no manipulation of events/variables.
What are the advantages of cross-sectional study design?
- Can study multiple outcomes/exposures
- Inexpensive, feasibility, not too time-consuming
- Helps to estimate burden of disease
- Prompts further research
What are the disadvantages of cross-sectional study design?
- Cannot determine causality
- May not be generalizable
- Susceptible to other bias (confounders, recall, sample size)
What does a case control study examine?
the possible relation of an exposure to a certain disease
- We identify a group of individuals with that disease or outcome of interest called (cases)
- For comparison, a group of people without that disease or outcome of interest (controls)
Why do we collect data from case-control groups
to determine the proportion of exposure in each group
if there is an association between the exposure and disease, which groups prevalence should be higher
the prevalence of exposure should be higher in cases (disease) than in controls (no disease).
what are the steps in a case-control study?
- definition of cases
- selection of cases
- selection of controls
- ascertainment of exposure status
- analysis
- conclusion and interpretation
defining cases (case-control): How will you define who is included
Be specific
- what type of ling cancer?
- level of severity
- how long have they been diagnoses
- clinical characteristics
cases should be representative of all disabled
Correctly defining cases (outcome) helps us to select the cases and limit bias.
selecting cases (case-control)
Selecting our cases is important because it can introduce bias.
What is bias?
“systematic error introduced in to sampling or testing by selecting or encouraging one outcome or answer over another.”
Lots of different types
Bias is not always purposeful
What is selection bias?
selection of individuals, groups, or data for analysis in such a way that the sample is not representative of the population intended to be analyzed.
sources
- hospital patients, physicians practice patients, clinic patients, disease registries
what are the risks of selecting patients from hospitals?
- some tertiary and quarternary care facilities selectively admit severely ill patients
- Risk factors only in individuals with severe forms of the disease
- Not generalizable to all patients with the disease
if used would need to select from several hospitals in the community
primary, secondary, tertiary, quaternary
- Quarternary (experimental medicine and uncommon specializations)
- Tertiary (hospitalization and higher specialization like neurosurgery)
- Secondary (specialists like cardiologists, oncologists, ect.)
- Primary (essentials of screening, illness, injury, referral
what is the issue with using incident cases
- Must often wait for new cases to be diagnosed
- Prevalent cases have already been diagnosed and represent a larger number of cases available for the study
Tub example
- prevalence is in the tub
- mortality (death) is the drain
- recovery is evaporation
- incidence is the faucet
what is the issue with using prevelant cases
Risk factors that we identify in a study using prevalent cases may be related more to survival with the disease rather than to the development of the disease
Such a study is more likely to include longer-term survivors.
Non-representative group of cases
selecting controls (case-control)
One of the most difficult problems in epidemiology.
If we find more exposure in the cases than in the controls
- Would like to conclude that there is an association between the exposure and the disease.
The way controls are chosen is a major determinant of whether this conclusion is valid.
Fundamental conceptual issue:
- Should controls be similar to the cases in all respects other than having the disease in question?
- OR, should they be representative of all persons without the disease in the population from which the cases are selected.
selection of controls: non-hospital
School rosters
Birth records
Selective service lists
Insurance company lists
Residents of a defined areas (neighborhoods)
Random digit dialing
- Why is this a problem?
what is a best friend control?
A case is asked to provide the name for a friend who may be more likely to participate in a study
- Friends tend to be similar in age and sociodemographic variables
- Spouses and siblings
May be too similar, including the variables under investigation.
Hospital selection of controls
Hospital inpatients are a ‘captive population’ and are clearly identified.
Sick people go to hospitals
- Many of the risk factors that got them there may also be a risk factor for whatever disease is being studied.
- For example, cigarette smoking
Selecting from hospitals may mean that controls do not represent the general population.
If we use hospital controls, which do we use?
- Use a sample of all other admitted patients?
Or select patients with a specific ‘other’ diagnosis?
- Unlikely to be representative of the reference population.
- Perhaps have a lower-than-expected prevalence of the exposure of interest.
Hospital selection of controls
Hospital inpatients are a ‘captive population’ and are clearly identified.
Sick people go to hospitals
- Many of the risk factors that got them there may also be a risk factor for whatever disease is being studied.
- For example, cigarette smoking
Selecting from hospitals may mean that controls do not represent the general population.
If we use hospital controls, which do we use?
- Use a sample of all other admitted patients?
Or select patients with a specific ‘other’ diagnosis?
- Unlikely to be representative of the reference population.
- Perhaps have a lower-than-expected prevalence of the exposure of interest.
What is matching in case control?
Major concern is that cases and controls may differ in characteristics or exposures other than the one that has been targeted for study.
- Is an observed association due to factors other than the exposure being studied? Confounding
One approach is to match the cases and controls so that they are similar to the cases in certain characteristics.
Matching may be of two types:
- Group matching
- Individual matching
Match only on variables that we are convinced are risk factors for the disease and are not interested in investigating in this study
Matching variables other than these is called overmatching.
