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Partitioning the population attributable fraction for a sequential chain of effects.

Mason CA, Tu S - Epidemiol Perspect Innov (2008)

Bottom Line: While the population attributable fraction (PAF) provides potentially valuable information regarding the community-level effect of risk factors, significant limitations exist with current strategies for estimating a PAF in multiple risk factor models.These strategies can result in paradoxical or ambiguous measures of effect, or require unrealistic assumptions regarding variables in the model.In addition, the approach can also be used to statistically control for confounding by other variables, while avoiding the potential pitfalls of attempting to separately differentiate direct and indirect effects.

View Article: PubMed Central - HTML - PubMed

Affiliation: College of Education and Human Development, University of Maine, Orono, ME, USA. craig.mason@umit.maine.edu

ABSTRACT

Background: While the population attributable fraction (PAF) provides potentially valuable information regarding the community-level effect of risk factors, significant limitations exist with current strategies for estimating a PAF in multiple risk factor models. These strategies can result in paradoxical or ambiguous measures of effect, or require unrealistic assumptions regarding variables in the model. A method is proposed in which an overall or total PAF across multiple risk factors is partitioned into components based upon a sequential ordering of effects. This method is applied to several hypothetical data sets in order to demonstrate its application and interpretation in diverse analytic situations.

Results: The proposed method is demonstrated to provide clear and interpretable measures of effect, even when risk factors are related/correlated and/or when risk factors interact. Furthermore, this strategy not only addresses, but also quantifies issues raised by other researchers who have noted the potential impact of population-shifts on population-level effects in multiple risk factor models.

Conclusion: Combined with simple, unadjusted PAF estimates and an aggregate PAF based on all risk factors under consideration, the sequentially partitioned PAF provides valuable additional information regarding the process through which population rates of a disorder may be impacted. In addition, the approach can also be used to statistically control for confounding by other variables, while avoiding the potential pitfalls of attempting to separately differentiate direct and indirect effects.

No MeSH data available.


Two risk factors with no interaction. (Continued).
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Figure 4: Two risk factors with no interaction. (Continued).

Mentions: The first example involves two risk factors, A and B, where A is believed to lead to increased rates of B, and both are believed to result in elevated rates of MMR. A and B are related but have no interaction effect. For example, smoking may lead to higher rates of low birthweight, and low birthweight may lead to higher rates of MMR; but the effect of being born low birthweight may be identical for all children, regardless of whether or not their mother smoked during pregnancy. Data for this example are presented in Figures 3 and 4.


Partitioning the population attributable fraction for a sequential chain of effects.

Mason CA, Tu S - Epidemiol Perspect Innov (2008)

Two risk factors with no interaction. (Continued).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2572052&req=5

Figure 4: Two risk factors with no interaction. (Continued).
Mentions: The first example involves two risk factors, A and B, where A is believed to lead to increased rates of B, and both are believed to result in elevated rates of MMR. A and B are related but have no interaction effect. For example, smoking may lead to higher rates of low birthweight, and low birthweight may lead to higher rates of MMR; but the effect of being born low birthweight may be identical for all children, regardless of whether or not their mother smoked during pregnancy. Data for this example are presented in Figures 3 and 4.

Bottom Line: While the population attributable fraction (PAF) provides potentially valuable information regarding the community-level effect of risk factors, significant limitations exist with current strategies for estimating a PAF in multiple risk factor models.These strategies can result in paradoxical or ambiguous measures of effect, or require unrealistic assumptions regarding variables in the model.In addition, the approach can also be used to statistically control for confounding by other variables, while avoiding the potential pitfalls of attempting to separately differentiate direct and indirect effects.

View Article: PubMed Central - HTML - PubMed

Affiliation: College of Education and Human Development, University of Maine, Orono, ME, USA. craig.mason@umit.maine.edu

ABSTRACT

Background: While the population attributable fraction (PAF) provides potentially valuable information regarding the community-level effect of risk factors, significant limitations exist with current strategies for estimating a PAF in multiple risk factor models. These strategies can result in paradoxical or ambiguous measures of effect, or require unrealistic assumptions regarding variables in the model. A method is proposed in which an overall or total PAF across multiple risk factors is partitioned into components based upon a sequential ordering of effects. This method is applied to several hypothetical data sets in order to demonstrate its application and interpretation in diverse analytic situations.

Results: The proposed method is demonstrated to provide clear and interpretable measures of effect, even when risk factors are related/correlated and/or when risk factors interact. Furthermore, this strategy not only addresses, but also quantifies issues raised by other researchers who have noted the potential impact of population-shifts on population-level effects in multiple risk factor models.

Conclusion: Combined with simple, unadjusted PAF estimates and an aggregate PAF based on all risk factors under consideration, the sequentially partitioned PAF provides valuable additional information regarding the process through which population rates of a disorder may be impacted. In addition, the approach can also be used to statistically control for confounding by other variables, while avoiding the potential pitfalls of attempting to separately differentiate direct and indirect effects.

No MeSH data available.