Difference between revisions of "Interaction"
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OR<sub>AB</sub> = (0,91-1) + (2,63-1) + 1 = 2,74 | OR<sub>AB</sub> = (0,91-1) + (2,63-1) + 1 = 2,74 | ||
| − | However, the observed value for | + | However, the observed value for OR<sub>AB</sub> is 6,75. |
Therefore, 6.75 - 2.74 = 4,01 of the effect is due to biological interaction when both exposures are present. The biological interaction represents 60.7% (4.01/6.75 * 100) of the effect when both exposures are present. | Therefore, 6.75 - 2.74 = 4,01 of the effect is due to biological interaction when both exposures are present. The biological interaction represents 60.7% (4.01/6.75 * 100) of the effect when both exposures are present. | ||
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The above represents a simplified explanation of additive and multiplicative models when testing for interaction. Further explanation can be found in major textbooks <ref name="Rothman"/>. | The above represents a simplified explanation of additive and multiplicative models when testing for interaction. Further explanation can be found in major textbooks <ref name="Rothman"/>. | ||
| − | Using a statistical package for multivariable analysis (based on a multiplicative assumption) could have led to different conclusions. However, it is possible to assess biological interaction with such a package if using an additive model. To do so, we need to create four levels of exposure (dummy variables) exposure to A and B, exposed to A and not B, exposed to B but not A, and exposed to neither. The later exposure is used as a reference for the 3 others, and variables and biological interaction can be measured using additive assumptions as in the above examples. | + | Using a statistical package for multivariable analysis (based on a multiplicative assumption) could have led to different conclusions. However, it is possible to assess biological interaction with such a package if using an additive model. To do so, we need to create four levels of exposure (dummy variables) exposure to A and B, exposed to A and not B, exposed to B but not A, and exposed to neither. The later exposure is used as a reference for the 3 others, and variables and biological interaction can be measured using additive assumptions as in the above examples. |
=References= | =References= | ||
<References/> | <References/> | ||
| + | |||
| + | <div style="display: inline-block; width: 25%; vertical-align: top; border: 1px solid #000; background-color: #d7effc; padding: 10px; margin: 5px;"> | ||
| + | '''FEM PAGE CONTRIBUTORS 2007''' | ||
| + | ;FEM Editor 2007 | ||
| + | :Naomi Boxall | ||
| + | ;Original Author | ||
| + | :Alain Moren | ||
| + | ;FEM Contributors | ||
| + | :Arnold Bosman | ||
| + | :Sabrina Bacci | ||
| + | :Lisa Lazareck | ||
| + | :Naomi Boxall | ||
| + | </div> | ||
| + | [[Category:Effect Modification and Confounding]] | ||
Latest revision as of 21:21, 10 April 2023
Contents
Statistical and biological interaction
The term interaction is frequently used alternatively with effect modification. Statistical packages for multivariable analysis offer to test for interaction. They often are based on a multiplicative assumption. Interaction is present only if the joint effect of A and B is more than the multiplication of the respective effect of A without B by the effect of B without A. This is called "statistical interaction". It is assessed after logarithmic transformation.
Epidemiologists are interested in "biological interaction". Where there is no biological interaction between two exposures (A and B), the risks related to A and B are added to each other when both A and B are present. If biological interaction occurs, we expect the joint risk to be higher than the sum of A and B risks. The difference is attributable to the joint effect.
Formulae
In cohort studies, the risk due to the interaction between two exposures A and B can be calculated as follows [1]:
Interaction = RAB - RA - RB + RO
In which RA is the risk when only exposed to A, RB is the risk when only exposed to B, RAB is the risk when exposed to both, and RO the risk when exposed neither to A nor B.
If there is no interaction (i.e. the exposures are independent of one another) the expected risk when exposed to both factors can be computed as:
RAB = RA + RB - RO
Cohort Studies
During an outbreak of Salmonella enteritidis gastroenteritis, two risk factors were suggested by the data, consumption of undercooked chicken (exposure A) and taking anti-acid medications (exposure B). The risk of illness was respectively 5/1000 among those who were not exposed to any of the 2 risk factors, 10/1000 among those who took anti-acid medication but did not eat undercooked chicken, 20 among those who ate undercooked chicken but did not use anti-acid medication and 100/1000 among people eating undercooked chicken and taking anti-acid medication.
| Exposures | Cases | Total | Risk | |
|---|---|---|---|---|
| neither A nor B | No chicken, no antiacids | 1 | 1000 | 0.001 |
| A but not B | Chicken but not antiacids | 20 | 1000 | 0.020 |
| B but not A | Antiacids but not chicken | 10 | 1000 | 0.010 |
| A and B | Chicken and antiacids | 100 | 1000 | 0.100 |
If there was no interaction between exposure to undercooked chicken and antiacids, the expected risk when exposed to both risk factors would be:
RAB = 0.02 + 0.01 - 0.001 = 0.029
However, the observed risk is 0.100, suggesting a biological interaction between the consumption of undercooked chicken and taking anti-acid medications. The joint risk is more than the simple addition of the two risks. The additional risk linked to exposure to undercooked chicken and antiacids is potentially responsible for 71 cases per 1000, potentially explaining 71% of disease occurrence.
Case-control studies
In case-control studies, biological interaction can be measured using the following formula [2]. As risk cannot be computed in a case-control study, the odds ratio (OR) is used instead:
Interaction = (ORAB - 1) - (ORA-1) - (ORB-1)
Given no interaction (independence between the two exposures):
ORAB = (ORA-1) + (ORB-1) + 1
In a national case-control study looking at Salmonella enteritidis in children in France [3]
| Season | Eggs | Cases | Controls | OR |
|---|---|---|---|---|
| Not Summer | < 2 weeks storage | 32 | 36 | ref |
| Not Summer | > 2 weeks storage | 7 | 3 | 2.63 |
| Summer | < 2 weeks storage | 52 | 64 | 0.91 |
| Summer | > 2 weeks storage | 12 | 2 | 6.75 |
The joint effect of A and B is therefore computed as:
ORAB = (0,91-1) + (2,63-1) + 1 = 2,74
However, the observed value for ORAB is 6,75.
Therefore, 6.75 - 2.74 = 4,01 of the effect is due to biological interaction when both exposures are present. The biological interaction represents 60.7% (4.01/6.75 * 100) of the effect when both exposures are present.
In 1976, an Ebola viral haemorrhagic fever outbreak occurred in the Bumba zone of Zaire (now the Democratic Republic of Congo). The disease was amplified by exposure to a large, active hospital [4].
| Hospital | Case | Cases | Controls | OR |
|---|---|---|---|---|
| Unexposed | Unexposed | 41 | 266 | ref |
| Exposed | Unexposed | 85 | 22 | 25.1 |
| Unexposed | Exposed | 149 | 26 | 37.2 |
| Exposed | Exposed | 43 | 4 | 69.1 |
If there was no interaction between "exposure to hospital" and "exposure to a case", the OR associated with contact to both exposures:
ORAB = (37.2 - 1) + (25.1-1) + 1 = 61.3
This is slightly different from 69.1, as shown in the table. We could conclude the presence of very little additive interaction between the two risk factors. In addition, we would need to explain the biological meaning of such interaction when it seems unlikely that cases in the hospital would do much mixing - being too ill to socialize in that setting.
The above represents a simplified explanation of additive and multiplicative models when testing for interaction. Further explanation can be found in major textbooks [1].
Using a statistical package for multivariable analysis (based on a multiplicative assumption) could have led to different conclusions. However, it is possible to assess biological interaction with such a package if using an additive model. To do so, we need to create four levels of exposure (dummy variables) exposure to A and B, exposed to A and not B, exposed to B but not A, and exposed to neither. The later exposure is used as a reference for the 3 others, and variables and biological interaction can be measured using additive assumptions as in the above examples.
References
- ↑ 1.0 1.1 K.J.Rothman, S.Greenland, T.L.Lash. Modern Epidemiology. Third ed. Philadelphia, USA: Lipincott Williams and Wilkins; 2008.
- ↑ Kalilani L, Atashili J. Measuring additive interaction using odds ratios. Epidemiologic Perspectives & Innovations 2006;3(1):5.
- ↑ Delarocque-Astagneau, E., Desenclos JC, Bouvet P, Grimont PA. Risk factors for the occurrence of sporadic Salmonella enterica serotype enteritidis infections in children in France: a national case-control study. Epidemiol Infect 1998;121(3):561-7.
- ↑ Ebola haemorrhagic fever in Sudan, 1976. Bulletin of the World Health Organisation 1978; 56 (2): 271:93.
FEM PAGE CONTRIBUTORS 2007
- FEM Editor 2007
- Naomi Boxall
- Original Author
- Alain Moren
- FEM Contributors
- Arnold Bosman
- Sabrina Bacci
- Lisa Lazareck
- Naomi Boxall
Root > Assessing the burden of disease and risk assessment > Field Epidemiology > Measurement in Field Epidemiology > Problems with Measurement > Bias > Effect Modification and Confounding
