Dose Effect

Explained

So far we have only considered that persons could be either exposed or not, but exposure can frequently be quantified and a gradient established. Examples may include the number of glasses (or volume) of water consumed per day, the number of pizza slices eaten, or the degree of completion of a vaccination course etc. The risk of illness can then be calculated according to amount drunk, eaten or completed.

Amount drunk	Exposed	Cases	Risk (%)	RR
None	100	5	5	ref
1 glass	100	11	11	2.2
2 glasses	100	16	16	3.2
3 glasses	100	22	22	4.4
≥4 glasses	100	28	28	5.7
	500	82	82	c

The Χ2 test for trend across these strata = 23.24, p≤0.001 - indicating that there is a trend across the exposure strata.

If instead a case control study had been conducted (note the similarity between the OR and the RR above):

Amount drunk	Exposed	Cases	OR
None	5	30	ref
1 glass	11	30	2.2
2 glasses	16	30	3.2
3 glasses	22	30	4.4
≥4 glasses	28	30	5.6
	82	150

For which the Χ2 test for trend = 12.77, p≤0.001

In the above examples, the risk of disease occurring increases with the amount of water consumed (i.e. with the dose of water). This is frequently called a dose response or a dose effect relationship.

To compute the risk ratio or the odds ratio the same reference level of exposure is used. Here the non exposed group is used as a reference. Alternatively the group with the lowest incidence could be used.

Dose response is particularly interesting when in an outbreak (often in water borne or a food borne outbreak) everybody is exposed to the same factor but the level of exposure differs among individuals. Choosing the lowest category of exposure as a reference still allows identification of a food item or a beverage as the vehicle for the outbreak. To test that the trend observed in risk or odds ratio differs from no trend, we can use a chi square for trend , , . Dose response is also an important criterion to consider when providing evidence for causality proposed by Bradford Hill .