Age-related mortality risk in cycling trauma: analysis of the National Trauma Databank 2017–2023
Abstract Background Cycling promotes health but carries significant injury risks, especially for older adults. In the U.S., cycling fatalities have increased since 1990, with adults over 50 now at the highest risk. As the population ages, the burden of cycling-related trauma is expected to grow, yet...
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BMC
2025-01-01
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| Series: | Injury Epidemiology |
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| Online Access: | https://doi.org/10.1186/s40621-024-00558-6 |
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| author | Marta A. W. Rowh Taylor A. Giller John N. Bliton Randi N. Smith Tim P. Moran |
| author_facet | Marta A. W. Rowh Taylor A. Giller John N. Bliton Randi N. Smith Tim P. Moran |
| author_sort | Marta A. W. Rowh |
| collection | DOAJ |
| description | Abstract Background Cycling promotes health but carries significant injury risks, especially for older adults. In the U.S., cycling fatalities have increased since 1990, with adults over 50 now at the highest risk. As the population ages, the burden of cycling-related trauma is expected to grow, yet age-specific factors associated with mortality risk remain unclear. This study identifies age-specific mortality risk thresholds to inform targeted public health strategies. Methods We conducted a cross-sectional analysis of the National Trauma Data Bank (NTDB) data (2017–2023) on non-motorized cycling injuries. A total of 185,960 records were analyzed using logistic regression with splines to evaluate the relationship between age and mortality risk. The dataset was split into training (80%) and testing (20%) sets. Age thresholds where mortality risk changed were identified, and models were adjusted for injury severity, comorbidities, and helmet use. Results The median patient age was 43 years (IQR 20–58). Four key age thresholds (12, 17, 31, and 69) were identified, with the largest mortality increase after age 69. Our model achieved an AUC of 0.93, surpassing traditional age cutoff models, with 84.6% sensitivity and 88.0% specificity. Conclusions Age is a significant predictor of mortality in cycling trauma, with marked increases in risk during adolescence and for adults over 69. These findings underscore the need for age-targeted interventions, such as improved cycling infrastructure for teens and enhanced safety measures for older adults. Public health initiatives should prioritize these vulnerable age groups to reduce cycling-related mortality. |
| format | Article |
| id | doaj-art-5299950b24474e9cbfc349345e999571 |
| institution | Kabale University |
| issn | 2197-1714 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | Injury Epidemiology |
| spelling | doaj-art-5299950b24474e9cbfc349345e9995712025-01-26T12:09:59ZengBMCInjury Epidemiology2197-17142025-01-011211810.1186/s40621-024-00558-6Age-related mortality risk in cycling trauma: analysis of the National Trauma Databank 2017–2023Marta A. W. Rowh0Taylor A. Giller1John N. Bliton2Randi N. Smith3Tim P. Moran4Department of Emergency Medicine, Emory UniversityDepartment of Emergency Medicine, Emory UniversityJamaica Hospital Medical CenterDepartment of Surgery, Emory UniversityDepartment of Emergency Medicine, Emory UniversityAbstract Background Cycling promotes health but carries significant injury risks, especially for older adults. In the U.S., cycling fatalities have increased since 1990, with adults over 50 now at the highest risk. As the population ages, the burden of cycling-related trauma is expected to grow, yet age-specific factors associated with mortality risk remain unclear. This study identifies age-specific mortality risk thresholds to inform targeted public health strategies. Methods We conducted a cross-sectional analysis of the National Trauma Data Bank (NTDB) data (2017–2023) on non-motorized cycling injuries. A total of 185,960 records were analyzed using logistic regression with splines to evaluate the relationship between age and mortality risk. The dataset was split into training (80%) and testing (20%) sets. Age thresholds where mortality risk changed were identified, and models were adjusted for injury severity, comorbidities, and helmet use. Results The median patient age was 43 years (IQR 20–58). Four key age thresholds (12, 17, 31, and 69) were identified, with the largest mortality increase after age 69. Our model achieved an AUC of 0.93, surpassing traditional age cutoff models, with 84.6% sensitivity and 88.0% specificity. Conclusions Age is a significant predictor of mortality in cycling trauma, with marked increases in risk during adolescence and for adults over 69. These findings underscore the need for age-targeted interventions, such as improved cycling infrastructure for teens and enhanced safety measures for older adults. Public health initiatives should prioritize these vulnerable age groups to reduce cycling-related mortality.https://doi.org/10.1186/s40621-024-00558-6CyclingGeriatricsTraumaMortality |
| spellingShingle | Marta A. W. Rowh Taylor A. Giller John N. Bliton Randi N. Smith Tim P. Moran Age-related mortality risk in cycling trauma: analysis of the National Trauma Databank 2017–2023 Injury Epidemiology Cycling Geriatrics Trauma Mortality |
| title | Age-related mortality risk in cycling trauma: analysis of the National Trauma Databank 2017–2023 |
| title_full | Age-related mortality risk in cycling trauma: analysis of the National Trauma Databank 2017–2023 |
| title_fullStr | Age-related mortality risk in cycling trauma: analysis of the National Trauma Databank 2017–2023 |
| title_full_unstemmed | Age-related mortality risk in cycling trauma: analysis of the National Trauma Databank 2017–2023 |
| title_short | Age-related mortality risk in cycling trauma: analysis of the National Trauma Databank 2017–2023 |
| title_sort | age related mortality risk in cycling trauma analysis of the national trauma databank 2017 2023 |
| topic | Cycling Geriatrics Trauma Mortality |
| url | https://doi.org/10.1186/s40621-024-00558-6 |
| work_keys_str_mv | AT martaawrowh agerelatedmortalityriskincyclingtraumaanalysisofthenationaltraumadatabank20172023 AT tayloragiller agerelatedmortalityriskincyclingtraumaanalysisofthenationaltraumadatabank20172023 AT johnnbliton agerelatedmortalityriskincyclingtraumaanalysisofthenationaltraumadatabank20172023 AT randinsmith agerelatedmortalityriskincyclingtraumaanalysisofthenationaltraumadatabank20172023 AT timpmoran agerelatedmortalityriskincyclingtraumaanalysisofthenationaltraumadatabank20172023 |