The future excess fraction model for calculating burden of disease
Abstract Background Estimates of the burden of disease caused by a particular agent are used to assist in making policy and prioritizing actions. Most estimations have employed the attributable fraction approach, which estimates the proportion of disease cases or deaths in a specific year which are...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2016-05-01
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| Series: | BMC Public Health |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12889-016-3066-1 |
| _version_ | 1811263498891558912 |
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| author | Lin Fritschi Jayzii Chan Sally J. Hutchings Tim R. Driscoll Adrian Y. W. Wong Renee N. Carey |
| author_facet | Lin Fritschi Jayzii Chan Sally J. Hutchings Tim R. Driscoll Adrian Y. W. Wong Renee N. Carey |
| author_sort | Lin Fritschi |
| collection | DOAJ |
| description | Abstract Background Estimates of the burden of disease caused by a particular agent are used to assist in making policy and prioritizing actions. Most estimations have employed the attributable fraction approach, which estimates the proportion of disease cases or deaths in a specific year which are attributable to past exposure to a particular agent. While this approach has proven extremely useful in quantifying health effects, it requires historical data on exposures which are not always available. Methods We present an alternative method, the future excess fraction method, which is based on the lifetime risk approach, and which requires current rather than historical exposure data. This method estimates the future number of exposure-related disease cases or deaths occurring in the subgroup of the population who were exposed to the particular agent in a specific year. We explain this method and use publically-available data on current asbestos exposure and mesothelioma incidence to demonstrate the use of the method. Conclusions Our approach to modelling burden of disease is useful when there are no historical measures of exposure and where future disease rates can be projected on person years at risk. |
| first_indexed | 2024-04-12T19:46:22Z |
| format | Article |
| id | doaj.art-3f8eba563b3449c9821da394e9c91157 |
| institution | Directory Open Access Journal |
| issn | 1471-2458 |
| language | English |
| last_indexed | 2024-04-12T19:46:22Z |
| publishDate | 2016-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Public Health |
| spelling | doaj.art-3f8eba563b3449c9821da394e9c911572022-12-22T03:18:57ZengBMCBMC Public Health1471-24582016-05-011611810.1186/s12889-016-3066-1The future excess fraction model for calculating burden of diseaseLin Fritschi0Jayzii Chan1Sally J. Hutchings2Tim R. Driscoll3Adrian Y. W. Wong4Renee N. Carey5School of Public Health, Curtin UniversityDepartment of Mathematics and Statistics, Curtin UniversityDepartment of Epidemiology and Biostatistics, Imperial College LondonSchool of Public Health, University of SydneyDepartment of Mathematics and Statistics, Curtin UniversitySchool of Public Health, Curtin UniversityAbstract Background Estimates of the burden of disease caused by a particular agent are used to assist in making policy and prioritizing actions. Most estimations have employed the attributable fraction approach, which estimates the proportion of disease cases or deaths in a specific year which are attributable to past exposure to a particular agent. While this approach has proven extremely useful in quantifying health effects, it requires historical data on exposures which are not always available. Methods We present an alternative method, the future excess fraction method, which is based on the lifetime risk approach, and which requires current rather than historical exposure data. This method estimates the future number of exposure-related disease cases or deaths occurring in the subgroup of the population who were exposed to the particular agent in a specific year. We explain this method and use publically-available data on current asbestos exposure and mesothelioma incidence to demonstrate the use of the method. Conclusions Our approach to modelling burden of disease is useful when there are no historical measures of exposure and where future disease rates can be projected on person years at risk.http://link.springer.com/article/10.1186/s12889-016-3066-1Burden of diseaseMethodologyPolicyPrevention |
| spellingShingle | Lin Fritschi Jayzii Chan Sally J. Hutchings Tim R. Driscoll Adrian Y. W. Wong Renee N. Carey The future excess fraction model for calculating burden of disease BMC Public Health Burden of disease Methodology Policy Prevention |
| title | The future excess fraction model for calculating burden of disease |
| title_full | The future excess fraction model for calculating burden of disease |
| title_fullStr | The future excess fraction model for calculating burden of disease |
| title_full_unstemmed | The future excess fraction model for calculating burden of disease |
| title_short | The future excess fraction model for calculating burden of disease |
| title_sort | future excess fraction model for calculating burden of disease |
| topic | Burden of disease Methodology Policy Prevention |
| url | http://link.springer.com/article/10.1186/s12889-016-3066-1 |
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