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In epidemiology, preventable fraction for the population (PFp), is the proportion of incidents in the population that could be prevented by exposing the whole population.. It is calculated as = /, where is the incidence in the exposed group, is the incidence in the populati
In epidemiology, attributable fraction among the exposed (AF e) is the proportion of incidents in the exposed group that are attributable to the risk factor. The term attributable risk percent among the exposed is used if the fraction is expressed as a percentage. [ 1 ]
For example, in 1953 Levin's paper estimated that lung cancer has a relative risk of 3.6–13.4 in smokers compared to non-smokers, and that the proportion of the population exposed to smoking was 0.5–0.96, resulting in the high AF p value of 0.56–0.92. [6]
The formula for calculating the NEPP is = where N = population size,; P d = prevalence of the disease,; P e = proportion eligible for treatment,; r u = risk of the event of interest in the untreated group or baseline risk over appropriate time period (this can be multiplied by life expectancy to produce life-years),
Incidence is usually more useful than prevalence in understanding the disease etiology: for example, if the incidence rate of a disease in a population increases, then there is a risk factor that promotes the incidence. For example, consider a disease that takes a long time to cure and was widespread in 2002 but dissipated in 2003.
In epidemiology, the relative risk reduction (RRR) or efficacy is the relative decrease in the risk of an adverse event in the exposed group compared to an unexposed group. It is computed as ( I u − I e ) / I u {\displaystyle (I_{u}-I_{e})/I_{u}} , where I e {\displaystyle I_{e}} is the incidence in the exposed group, and I u {\displaystyle I ...
To derive the formula for the one-sample proportion in the Z-interval, a sampling distribution of sample proportions needs to be taken into consideration. The mean of the sampling distribution of sample proportions is usually denoted as μ p ^ = P {\displaystyle \mu _{\hat {p}}=P} and its standard deviation is denoted as: [ 2 ]
The number of infections equals the cases identified in the study or observed. An example would be HIV infection during a specific time period in the defined population. The population at risk are the cases appearing in the population during the same time period. An example would be all the people in a city during a specific time period.