Analytical Epidemiology And Observational Studies Flashcards

Question

What does Prevalence indicate?

Answer 1

Prevalence indicates the probability that a member of the population has a given condition at a point in time. It includes all cases who still have the condition and are still members of the population.

Answer 2

When planning health services

Answer 3

Prevalence is not a useful measure for establishing the determinants of disease in a population (causes and other factors that influence the occurrence of disease and other health-related events)

Answer 4

Incidence risk is the total number of new cases divided by the population at risk at the beginning of the observation period

Answer 5

Risk is a way of quantifying the probability of a particular outcome within a specified period of time. Risk is the proportion of individuals in a population (initially free of disease) who develop the disease within a specified time interval. Incidence risk is expressed as a percentage (or if small as per 1000 people).

Answer 6

Incidence Risk = 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑛𝑒𝑤 𝑐𝑎𝑠𝑒𝑠/𝑇𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑝𝑒𝑜𝑝𝑙𝑒 𝑎𝑡 𝑟𝑖𝑠𝑘(𝑑𝑖𝑠𝑒𝑎𝑠𝑒 𝑓𝑟𝑒𝑒 𝑝𝑒𝑜𝑝𝑙𝑒) 𝑎𝑡 𝑡ℎ𝑒 𝑏𝑒𝑔𝑖𝑛𝑛𝑖𝑛𝑔 )

Answer 7

The incidence risk assumes that the entire population at risk at the beginning of the study period has been followed for the specified time period for the development of the outcome under investigation.

Answer 8

Develop the outcome under investigation Refuse to continue to participate in the study Migrate Die Enter the study some time after it starts

Answer 9

Incidence rates also measure the frequency of new cases of disease in a population. However, incidence rates take into account the sum of the time that each person remained under observation and at risk of developing the outcome under investigation.

Answer 10

Incidence rate

Answer 11

𝑰𝒏𝒄𝒊𝒅𝒆𝒏𝒄𝒆 𝑹𝒂𝒕𝒆=𝑵𝒖𝒎𝒃𝒆𝒓 𝒐𝒇 𝑵𝒆𝒘 𝑪𝒂𝒔𝒆𝒔 𝒅𝒖𝒓𝒊𝒏𝒈 𝒔𝒑𝒆𝒄𝒊𝒇𝒊𝒆𝒅 𝒑𝒆𝒓𝒊𝒐𝒅/𝑻𝒊𝒎𝒆 𝒆𝒂𝒄𝒉 𝒑𝒆𝒓𝒔𝒐𝒏 𝒘𝒂𝒔 𝒐𝒃𝒔𝒆𝒓𝒗𝒆𝒅, 𝒕𝒐𝒕𝒂𝒍𝒍𝒆𝒅 𝒇𝒐𝒓 𝒂𝒍𝒍 𝒑𝒆𝒐𝒑𝒍𝒆

Answer 12

If Cure rate increases If Recurrence decreases

Answer 13

50/800= 0.0625 62.5 per 1000 resident or 6.25 per 100 residents or 0.0625%

Answer 14

25/750= 0.0333 or about 3.3 per 100 or 3.3% over a year

Answer 15

Biases are systematic differences between the data that has been collected and the reality in the population.

Answer 16

Randomisation (experimental analytical studies) Masking

Answer 17

error in the process of selecting participants for the study (non representative to the real population) or/and assigning them to particular arm of the study (e.g. in RCTs)

Answer 18

when those patients who are lost to follow-up differ in a systematic way to those who did return for assessment or clinic.

Answer 19

when variables are reported differently between assessors

Answer 20

subjects in different arms of the study are treated differently (other than the exposure or intervention)

Answer 21

occurs when the research materials (e.g., questionnaires) prompt participants to answer or act in inauthentic ways through leading questions.

Answer 22

occurs when a variable is classified incorrectly

Answer 23

systematic error due to differences in accuracy of recall to recall of past events / experiences; subjective interpretation

Answer 24

Random sampling methods help ensure that your sample doesn’t systematically differ from the population. In addition, if you’re doing an experiment, use random assignment to place participants into different treatment conditions. This helps counter bias by balancing participant characteristics across groups.

Answer 25

Wherever possible, you should hide the condition assignment from participants and researchers through masking (blinding). Participants’ behaviours or responses can be influenced by experimenter expectancies and demand characteristics in the environment, so controlling these will help you reduce systematic bias.

Answer 26

Cross sectional studies look at measurements on a subject at more or less one point in time.

Answer 27

A longitudinal study measures a subject over a period of time. Imagine you wanted to look at how many

Answer 28

- is the data valid? Sampling method: bias, representativeness

Answer 29

Bias might come from an unrepresentative sample, a badly worded questionnaire, unstandardized interviewers, non-response, conditions on that day that are unusual. The study may even be affected by things such as the weather! If we are to look at ED attendances on hot and rainy days then the type of patients presenting might be drastically different. If we are enticing patients to come on 1 day to the GP surgery for a study and it snowed that day it might affect the type of patient that turned up.

Answer 30

- higher costs - higher admin burden - subject migration or withdrawal - changes to medical practice

Answer 31

Cross-sectional studies

Answer 32

- Commonly conducted to find out the prevalence and to some extent, association of factors ability to rapidly generate data for policy makers or for generating hypotheses on the topic or for further research. - Contains multiple variables at the time of the data snapshot - The data can be used for various types of research - Many findings and outcomes can be analysed to create new theories/studies or generate hypothesis)) - Easy to conduct, require less time and financial resources

Answer 33

- Cannot be used to analyse behavior over a period to time - Does not help determine cause and effect - The timing of the snapshot is not guaranteed to be representative. Variables e.g. environmental factors change over time (seasons of the year) and this factor should also be considered at the time of conducting such a study design. - They are inefficient for rare diseases or diseases with a long latency period between exposure and disease manifestation. - Prone to different types of bias such as non-response bias, recall bias, interviewer bias - It measures exposure and outcome at a single point in time. Often it is problematic to assess the time-directionality of an effect – how can we be sure that an exposure did precede an outcome, if both are being measured at a single point in time? Also, it may even be that the outcome is causing the exposure, a problem in cross-sectional studies that we call reverse causality

Answer 34

Subjects are selected based on a known exposure to the factor of interest e.g. Babies whose mothers smoked whilst pregnant Patients with a certain disease given a certain medication Dentists exposed to drilling noise Subjects are followed up to a specific end point and the exposure groups compared Some groups may have graded exposure (e.g. smoking) Some cohorts may need a control group There should be objective evidence of exposure e.g. maternity records, disease register, certain profession

Answer 35

Measurement of risk (relationship of end point to proposed causal factor)

Answer 36

Relative risk

Answer 37

Is the measure of the incidence risk in one group, relative to the incidence risk in another (exposed vs unexposed).

Answer 38

There is a positive association between exposure and outcome (exposure is harmful)

Answer 39

There is a negative association between exposure and outcome (exposure is protective)

Answer 40

There is no association between exposure and outcome

Answer 41

Relative Risk = Incidence risk among exposure/ incidence risk among non exposed group Incidence risk amon an exposed group = A/ A+B Incidence risk among non exposed group= C/C+D

Answer 42

The risk of an individual developing a disease over a time period

Answer 43

A key to making an informed medical choice is weighing potential benefits versus potential harms.

Answer 44

Is the relative decrease in the risk of an adverse event in the exposed group compared to an unexposed group. RRR tells you by how much the treatment reduced the risk of bad outcomes relative to the control group who did not have the treatment.

Answer 45

RRR = IRunexposed – IRexposed / IR unexposed (IR-incidence rate or relative risk)

Answer 46

Is the actual amount that the risk is reduced by the treatment. The proportion of untreated (unexposed) persons who experience an adverse event minus the proportion of treated (exposed) persons who experience the event.

Answer 47

ARR = IRunexposed – IRexposed

Answer 48

RRR = IRunexposed – IRexposed / IR unexposed (IR-incidence rate or relative risk) RRR= (3.0-1.9) / 3.0 = 0.36 = 36%

Answer 49

ARR = IRunexposed – IRexposed ARR= 3.0% -1.9% = 1.1%

Answer 50

This is the theoretical number needed to ‘treat’ (or enrol on intervention programme) in order to prevent one adverse outcome (e.g. death). It reframes the treatment’s benefits in a more user-friendly way.

Answer 51

People are recruited into cohort studies regardless of their exposure or outcome status. This is one of their important strengths. People are often recruited because of their geographical area or occupation, for example, and researchers can then measure and analyse a range of exposures and outcomes. Cohort studies should include groups that are identical EXCEPT for their exposure status. As a result, both exposed and unexposed groups should be recruited from the same source population. Incidence (temporal relationship between exposure and outcome) is clear. Best evidence for causality among observational studies. Good for assessing causality, prognosis, risk factors and harm. Important when RCT are unethical to conduct

Answer 52

Key exposures of interest may go hand-in-hand with confounding factors. Long follow-up period while waiting for events or diseases to occur. Often replaced by case-control studies Ineffective to investigate diseases with low incidence rate, outbreaks, long latency diseases (retrospective cohort or case-control studies are more suitable for this) Attrition. If a significant number of participants are not followed up (lost, death, dropped out) then this may impact the validity of the study. Not only does it decrease the study’s power, but there may be attrition bias – a significant difference between the groups of those that did not complete the study (increasing sample size could remedy this) Can be expensive and time-consuming, especially if a long follow-up period is chosen or the disease itself is rare or has a long latency. Subject to bias (confounding, associations, selection, observer, etc. bias)

Answer 53

A series of cases e.g. Each patient that comes to neuro ICU with an SAH is monitored for early cerebral blood flow then outcomes in 3 months OR For each patient that comes with SAH the relatives are asked about potential risk factors leading up to the haemorrhage

Answer 54

Problems are usually surrounding the selection of the matched control group. Study is stronger with several control groups. Questioner bias. Sequence of events may be unclear.

Answer 55

Usually cheaper than cohort and more than one variable/exposure could be tested The degree of matching of the participants will influence the results gained but may also may selection of participants much more difficult. If too much matching is achieved then this might affect the relative risk calculated. Results are likely to be better if the questioner is blinded (doesn’t know which group the patient is in) to avoid unintentional questioner bias

Answer 56

Useful when investigating cause of disease, because it helps determining if an exposure (risk factor) is associated with an outcome (disease, condition of interest) Useful to study conditions with low incidence rate e.g.<5% acute outbreaks, rare diseases, long latency diseases (time-lag between exposure & outcome) When it might be unethical to expose the patient or do nothing following exposure An example of a case-control study would be interviewing a group of mothers of children born with birth defects (cases, with the outcome of interest) to mothers of children born without birth defects (controls, without outcome the interest) and comparing their dietary and supplemental intakes of folic acid just prior to and during their pregnancies (exposure). If a lower level of exposure to folic acid is found in the case group as compared to the control group, the hypothesis that lower levels of folic acid are related to certain birth defects can be supported.

Answer 57

Odds of exposure in disease cases= Number of cases with exposure /Number of cases without exposure = A/C Odds of exposure in controls= Number of controls with exposure/ Number of controls without exposure = B/D Odds of exposure in cases/ Odds of exposure in controls = (A/C) / (B/D) = AD / BC = ODDS RATIO

Answer 58

OR= (60/40) / (100/300) = 4.5 OR- the odds of developing the disease is for 4.5 times higher in those who smoke compared to those who do not

Answer 59

On blackboard

Analytical Epidemiology And Observational Studies Flashcards

(89 cards)