Attributable fraction and related measures: Conceptual relations in the counterfactual framework
The attributable fraction (population) has attracted much attention from a theoretical perspective and has been used extensively to assess the impact of potential health interventions. However, despite its extensive use, there is much confusion about its concept and calculation methods. In this arti...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2023-02-01
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| Series: | Journal of Causal Inference |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/jci-2021-0068 |
| _version_ | 1797832438581297152 |
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| author | Suzuki Etsuji Yamamoto Eiji |
| author_facet | Suzuki Etsuji Yamamoto Eiji |
| author_sort | Suzuki Etsuji |
| collection | DOAJ |
| description | The attributable fraction (population) has attracted much attention from a theoretical perspective and has been used extensively to assess the impact of potential health interventions. However, despite its extensive use, there is much confusion about its concept and calculation methods. In this article, we discuss the concepts of and calculation methods for the attributable fraction and related measures in the counterfactual framework, both with and without stratification by covariates. Generally, the attributable fraction is useful when the exposure of interest has a causal effect on the outcome. However, it is important to understand that this statement applies to the exposed group. Although the target population of the attributable fraction (population) is the total population, the causal effect should be present not in the total population but in the exposed group. As related measures, we discuss the preventable fraction and prevented fraction, which are generally useful when the exposure of interest has a preventive effect on the outcome, and we further propose a new measure called the attributed fraction. We also discuss the causal and preventive excess fractions, and provide notes on vaccine efficacy. Finally, we discuss the relations between the aforementioned six measures and six possible patterns using a conceptual schema. |
| first_indexed | 2024-04-09T14:07:47Z |
| format | Article |
| id | doaj.art-9b372a22fb9e4351b9611b23257d1d9c |
| institution | Directory Open Access Journal |
| issn | 2193-3685 |
| language | English |
| last_indexed | 2024-04-09T14:07:47Z |
| publishDate | 2023-02-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Journal of Causal Inference |
| spelling | doaj.art-9b372a22fb9e4351b9611b23257d1d9c2023-05-06T15:58:55ZengDe GruyterJournal of Causal Inference2193-36852023-02-01111395010.1515/jci-2021-0068Attributable fraction and related measures: Conceptual relations in the counterfactual frameworkSuzuki Etsuji0Yamamoto Eiji1Department of Epidemiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama 700-8558, JapanOkayama University of Science, Okayama 700-0005, JapanThe attributable fraction (population) has attracted much attention from a theoretical perspective and has been used extensively to assess the impact of potential health interventions. However, despite its extensive use, there is much confusion about its concept and calculation methods. In this article, we discuss the concepts of and calculation methods for the attributable fraction and related measures in the counterfactual framework, both with and without stratification by covariates. Generally, the attributable fraction is useful when the exposure of interest has a causal effect on the outcome. However, it is important to understand that this statement applies to the exposed group. Although the target population of the attributable fraction (population) is the total population, the causal effect should be present not in the total population but in the exposed group. As related measures, we discuss the preventable fraction and prevented fraction, which are generally useful when the exposure of interest has a preventive effect on the outcome, and we further propose a new measure called the attributed fraction. We also discuss the causal and preventive excess fractions, and provide notes on vaccine efficacy. Finally, we discuss the relations between the aforementioned six measures and six possible patterns using a conceptual schema.https://doi.org/10.1515/jci-2021-0068attributable fractioncounterfactual modelexcess fractionpreventable fractionprevented fractionvaccine efficacy62d2062p10 |
| spellingShingle | Suzuki Etsuji Yamamoto Eiji Attributable fraction and related measures: Conceptual relations in the counterfactual framework Journal of Causal Inference attributable fraction counterfactual model excess fraction preventable fraction prevented fraction vaccine efficacy 62d20 62p10 |
| title | Attributable fraction and related measures: Conceptual relations in the counterfactual framework |
| title_full | Attributable fraction and related measures: Conceptual relations in the counterfactual framework |
| title_fullStr | Attributable fraction and related measures: Conceptual relations in the counterfactual framework |
| title_full_unstemmed | Attributable fraction and related measures: Conceptual relations in the counterfactual framework |
| title_short | Attributable fraction and related measures: Conceptual relations in the counterfactual framework |
| title_sort | attributable fraction and related measures conceptual relations in the counterfactual framework |
| topic | attributable fraction counterfactual model excess fraction preventable fraction prevented fraction vaccine efficacy 62d20 62p10 |
| url | https://doi.org/10.1515/jci-2021-0068 |
| work_keys_str_mv | AT suzukietsuji attributablefractionandrelatedmeasuresconceptualrelationsinthecounterfactualframework AT yamamotoeiji attributablefractionandrelatedmeasuresconceptualrelationsinthecounterfactualframework |