Citation

Davies JC, Kemp GJ, Frostick SP, Stevens G, Manning DP. Safety Sci. 2003; 41(1): 65-76.

Copyright

(Copyright © 2003, Elsevier Publishing)

DOI

unavailable

PMID

unavailable

Abstract

The aim of this study was to investigate the effects of gender and age in the population of patients attending hospital as a result of 'underfoot' accidents. Patients attending hospital for the diagnosis and treatment of injuries were interviewed using a portable computer-based questionnaire, the Merseyside Accident Information Model (MAIM). Two studies were used, based on 2516 (1555 men and 961 women) and 1306 (751 men and 575 women) patients, interviewed while attending AandE, orthopaedic wards and fracture clinics in a large teaching hospital. The age range of patients in both studies was 15-90 years. The philosophy underlying MAIM is to record all the available information about the immediate circumstances of the accident. This allows us to identify (1) sources of injuries and (2) incidence of injuries associated with these variables: age, gender and underfoot events (classified by slips, trips, missed footing, turned ankle, unintended step on and loss of balance as the first unexpected event). Even though the criteria for patient selection differed in the two studies, the analysis of both revealed similar characteristics. In both studies, logistic regression analysis showed underfoot accidents were predicted by (1) gender (odds ratios 1.85 and 2.83) and (2) age (odds ratios 1.30 and 1.22) per 10 year increase (all P less than 0.001). Patients suffering underfoot accidents are increasingly likely to be female as their age increases. This is true both for 'fracture' and 'non-fracture' accidents, and in the latter at least this cannot be due to osteoporosis. We conclude that older people are increasingly likely to suffer fractures in underfoot accidents. Women are more likely to have underfoot accidents, increasingly with advancing age, and this cannot be explained by gender and ageing effects associated with fractures. We believe these data are potentially important for cost-effective targeting of preventative measures.