epi 9/29

-Controlling confounding

control for confounding factors in the study design: restriction or matching.

If you were not able to control in the design, you can control in the analysis through

stratified analysis( involves the division of participants into subgroups) direct and indirect adjustment, or multivariable analysis. You can control for multiple confounding factors, for this you use multivariable analysis.

-leading causes of death

these are measures of proportionate mortality.

Proportionate mortality

this does not give you risk of death. it is a proportion. Denominator is total number of deaths. Numerator is deaths due to specific cause.

-Direct adjustment

use an internal standard population, form a standard population from the two groups you are comparing. Ex: death rates at two times. Difference in number of people within age groups. Form a standard population by adding them together and apply the age specific death rates to the new population to get expected deaths if they were dying at the rate of "early" and "late" Divide total number expected deaths by population (standard) to get age-adjusted rate. Once you hold constant the effect of age, find out that the age adjusted rate differs due to age distribution variance.

-issues with mortality data

-code underlying the cause of death may exclude contributing diseases-codes and definitions of death and disease vary by country, code is changed by ICD every 10 years

Host determinants

Age, sex, resistance, exposure, vaccinations, breed, physiologic state

Interpreting change in proportionate mortality

Change in proportionate mortality from certain disease, or in populations may not be due to an actual change, but due to changes in the difference in mortality from other diseases

Real changes in mortality

Changes in survival without changing incidence, Change in incidence, Change in age make up of population, A ccombination of all of these factors

Agents of disease

Infectious- bacteria, parasites, fungi, viruses, Non infectious- nutrition, chemical, physical , Consider dose, virulence, survivability, infectivity, toxicity

-When is mortality rate a good indicator of risk?

Not when disease is mild or not fatal, better when case fatality is high and survival is short. Why does this matter? Its hard to get incidence rates, much harder than getting mortality rates. If disease fatality is high and survival is short, can use mortality rate in replacement of incidence.

Artifactual changes in mortality

Numerator: diagnosis error, error in age, differences in coding or classifying Denominator- errors in counting population, classifying population, and differnces in percentage of the population at risk

Environmental determinants

Physical, biological, sociological

Considerations in adjusted rates

The rates calculated are hypothetical because they depend on the standard population. The standard population, although arbitrary, should resemble the population as much as possible.

-Case-fatality

a proportion!! Numerator is deaths from a specific cause. Denominator is everybody who had the disease. This is a measure of severity. Of those who get the disease, how many people die? Ex: West Nile provides a risk of dying once a person has the disease

-Indices of Mortality

all of these causes interrelate. A represent everyone in the population at risk from diseases from all causes, D. shows ways of calculating various rates of mortality and fatality. See diagram!

-indirect standardization (SMR's)

another form of adjustment, but using an external standard population. We can then apply the rates to the population to get expected number of deaths ex: get expected number of deaths if miners were dying at same population as males in the general population. Calculate SMR which is observed deaths/expected deaths. Here, tells us that we the risk of death in miners is greater than in general population. SMR >1 death risk is higher SMR<1 death risk is lower SMR=1 death risk is identical. Choice of standard population is arbitrary, but it is important because it affects the rates that you calculate. The goal is to make the standard population as close to the population being studied as possible.

-Confounding

has to be related to exposure ie: factor must be a risk factor for the disease, ,must be associated with the epxosure, and not on causal pathway.

-Deaths from Cholera

individual deaths were used as numerator, number of houses was denominator. This was before germ theory of infectious disease was ever around

-Mortality (death) rate

number of deaths during a specified time period/ total population time at risk during that time period. Provides risk of death in population, can be specific Mortality rate is an easy way to see how we are doing in terms of prevention, and is very easily accessible.

crude rate

overall for entire population

adjusted rate

overall rate adjusted for confounding factors. Often adjusted to a standard population. Attempt to remove confounding effect

-specific rates

people in denominator must be at risk to be in numerator. You can restrict rate specifically by age, sex, racial/ethnic group, etc. Gives risk of death in an age specified group if age specific mortality rate, gives risk of death from a specific cause if it is a cause mortality rate.

specific

rates with subpopulations ie: age, sex, cause