Objectives of epidemiology.2-7
Step 1. Understanding the etiology or cause of a disease (risk factors)
Step 2. Finding out the extent that a disease or health problem affects a community or population.
Step 3. Determine the natural history or prognosis
Step 4. Evaluate existing and newly developed preventative therapeutic measures and modes of healthcare delivery
Step 5. Provide the foundation for developing public policy relating to environmental problems, genetic issues, and other considerations regarding disease prevention and health promotion.
Define, compare, calculate, and interpret Measures of Morbidity .41.58
(# of NEW cases of a disease occurring in the population during a specified period of time / # of persons who are at risk of developing the disease during that period of time) x 1000 = Incidence rate per 1,000
: the NEW cases of a disease over a period of time.
- special form of cumulative incidence
- Used for diseases of short observation time period
- Not a true rate because the time dimension is often uncertain (food borne outbreaks)
= (# of new cases among population during period / population at risk at beginning of period) x 100 (expressed as %)
: the EXISTING cases of a disease over a period of time.
(# of cases of a disease present in the population at a specified time / # of persons in the population at that specified time) x 1,000 = Prevalence per 1,000
Understand why incidence data are important for measuring risk.
Measures risk (probability of developing disease)
Useful for investigating determinants of disease (not survival)
To investigate causes of disease
Need to know population ‘at risk’ or ‘person-time’ at risk
Define, compare, calculate, and interpret Measures of Mortality
Cause-specific mortality rate.64.65
=(Total number of deaths due to x cause / total population at mid year) x 1,000
=(No.of deaths from leukemia in one year in children younger than 10 years of age / No.of children in the population younger than 10 years of age at midyear) x 1,000
Annual mortality rate .64
-annual death rate, or mortality rate from all causes.
(total # of deaths from all causes in 1 year / total population at mid year ) x 1,000
--what percentage of people who have a certain disease die within a certain time after their disease was diagnosed?
=(# of deaths due to a certain disease/total # suffering from that disease) x 100 %
-Measure of the severity of the disease
-As therapy improves, case-fatality would be expected to decline.
Proportionate mortality. This is Not a rate. 66
-the proportionate mortality from cardiovascular disease in the U.S. in 2010 (percent, %)
-of all deaths in the US, what proportion was caused by cardiovascular disease?
=( # of deaths from cardiovascular diseases in US in 2010 / total deaths in the US in 2010) x 100
Assess the Validity.89 and Reliability105.110 of Diagnostic and Screening Tests
Define, compare and calculate measures of validity, including sensitivity and specificity.
-Validity of test: its ability to distinguish between who has a disease and who does not.
-Sensitivity of test is defined as the ability of the test to identify correctly those who have the disease.
-Specificity of test is defined as the ability of the test to identify correctly those who do not have the disease.
Have a Disease
Do not have Disease
True Positive (TP)
Have the disease
And test positive
False Positive (FP)
Do not have the disease
But test positive
Positive predictive value
False Negative (FN)
Have the disease
But test negative
True Negative (TN)
Do not have the disease
And test negative
= TP /TP+FN
Define and calculate positive predictive value.100.
-what is the probability that the patient has the disease?
-PPV is proportion of people who screened positive and actually have the disease
(True positive/All positives)x100
Relationship between Positive predictive value (PPV) and disease prevalence.101
: the higher the prevalence, the higher the predictive value. Therefore, a screening program is most productive and efficient if it is directed to a high-risk target population.
Sensitivity = 99%, Specificity = 95%
Positive Predictive Value
Relationship between Positive predictive value and Specificity of the test. 104.
: an increase in specificity resulted in a much greater increase in predictive value than did the same increase in sensitivity.
Prevalence =10% , Sensitivity =100%
Positive Predictive value
Describe a development phase which is marked by a sudden and abrupt increase in population growth rates of living organisms. This increase might be brought on by medical innovation in therapy and treatment of sickness or disease.
It may be followed by a re-leveling of population growth after subsequent declines in fertility rates. (how medical issues affect the population, how sickness spreads and to which population)
Broad change in epidemiological transition: shift from acute communicable diseases to chronic non-communicable diseases.
Causes of change in epidemiological transition: increase in food supply, clean water, environmental pollutants-smog, herbicides, radiation, migration of population, overuse of drugs.
Study designs (case-control.197, cross sectional.210, prospective and retrospective cohort.182, clinical trial)
How findings from each study may be used to measure risks of disease association.
Compare and contrast study designs, including advantages/disadvantages and methodologic considerations of each.
Understand which study design is best suited to address the study hypothesis.
Understand why cases and controls are matched in a case control study design.
Understand common biases with each study design.
Incidence density sampling in case-control study.
-Are those studies where cases are incident cases.
-In other words, they are recruited over a period of time as they develop.
-Allow for testing of hypothesis;
-they use explicit comparison group
-Study causes of disease and test hypothesis
-assess disease etiology (cause and effect)
- systematically determine whether exposure is greater in diseased than non-diseased
-made up of population and individual level studies
-Measure distribution of disease across population.
-Do not assess disease etiology (cause and effect)
-describe distribution of disease
-Used to generate hypothesis rather than to test them
-Used to allocate resources and plan programs.
-limitation: cannot use to test hypothesis
-less expensive, less time consuming b/c they use info already available
-useful for describing patterns of disease occurrence
-useful for formulating research questions
Compare to Interventional study, there is no manipulation by the researcher.
1. alert us to possible relationships and epidemics
2. Inexpensive b/c often utilize easy to obtain data
3. only ecological variables can allow us to examine ecological relationships
Limitations cannot test hypothesis about determinants of disease due to:
1. lack of comparison group (control groups)
2. lack of info about time course of variables
3. lack of control for confounding variables
- This solution has not purchased yet.
- Submitted On 15 Feb, 2020 11:28:06