Dataset Information

Operationalisation and validation of the Stopping Elderly Accidents, Deaths, and Injuries (STEADI) fall risk algorithm in a nationally representative sample.

ABSTRACT: BACKGROUND:Preventing falls and fall-related injuries among older adults is a public health priority. The Stopping Elderly Accidents, Deaths, and Injuries (STEADI) tool was developed to promote fall risk screening and encourage coordination between clinical and community-based fall prevention resources; however, little is known about the tool's predictive validity or adaptability to survey data. METHODS:Data from five annual rounds (2011-2015) of the National Health and Aging Trends Study (NHATS), a representative cohort of adults age 65 years and older in the USA. Analytic sample respondents (n=7392) were categorised at baseline as having low, moderate or high fall risk according to the STEADI algorithm adapted for use with NHATS data. Logistic mixed-effects regression was used to estimate the association between baseline fall risk and subsequent falls and mortality. Analyses incorporated complex sampling and weighting elements to permit inferences at a national level. RESULTS:Participants classified as having moderate and high fall risk had 2.62 (95% CI 2.29 to 2.99) and 4.76 (95% CI 3.51 to 6.47) times greater odds of falling during follow-up compared with those with low risk, respectively, controlling for sociodemographic and health-related risk factors for falls. High fall risk was also associated with greater likelihood of falling multiple times annually but not with greater risk of mortality. CONCLUSION:The adapted STEADI clinical fall risk screening tool is a valid measure for predicting future fall risk using survey cohort data. Further efforts to standardise screening for fall risk and to coordinate between clinical and community-based fall prevention initiatives are warranted.

SUBMITTER: Lohman MC

PROVIDER: S-EPMC5729578 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Operationalisation and validation of the Stopping Elderly Accidents, Deaths, and Injuries (STEADI) fall risk algorithm in a nationally representative sample.

Lohman Matthew C MC Crow Rebecca S RS DiMilia Peter R PR Nicklett Emily J EJ Bruce Martha L ML Batsis John A JA

Journal of epidemiology and community health 20170925 12

<h4>Background</h4>Preventing falls and fall-related injuries among older adults is a public health priority. The Stopping Elderly Accidents, Deaths, and Injuries (STEADI) tool was developed to promote fall risk screening and encourage coordination between clinical and community-based fall prevention resources; however, little is known about the tool's predictive validity or adaptability to survey data.<h4>Methods</h4>Data from five annual rounds (2011-2015) of the National Health and Aging Tren ...[more]

PMID: 28947669

Similar Datasets

Project description:Long working hours and a lack of sleep have been suggested to negatively affect the safety of workers. Here, we examined the association between long working hours/sleep-related problems and near-misses/injuries in industrial settings using a nationally representative sample of workers in Japan. Based on the composition ratio of workers by industry, sex, and age in Japan, data from a web-based cross-sectional survey for 18,682 participant full-time workers (7,098 female and 11,584 male; mean age, 43.7 [standard deviation 11.1] years) were analyzed. Nearly 30% and 5% of participants reported any types of near-misses during the past six months and injuries during the past year, respectively. For all types of near-misses and some types of injuries, a significant difference in frequency distribution was observed by industry. After adjustment for demographic, job-, and life-related variables, participants who worked long hours (i.e., more than 51 hours per week) were more likely to report job-related near-misses/injuries than those who worked 35-40 hours per week. The presence of sleep-related problems was also significantly related to near-misses and injuries. However, while sleep-related problems were significantly associated with near-misses/injuries in all industries, the association between long working hours and near-misses/injuries differed by industry. Odds ratios for near-misses/injuries were strongly significant in the "transport/postal services" industry for those who worked more than 51 hours per week compared to those who worked 35-40 hours per week. Comprehensive protective measures for workers, including (1) reducing total hours of service/job-related fatigue, (2) maintaining sufficient sleep hours/good sleep, and (3) increasing awareness about the impact of overwork/long working hours and sleep-related problems on workers' safety among employers, workers, clients/customers, and the general public might be effective for preventing near-misses and injuries in industrial settings among workers, especially those who work long hours in the "transport/postal services" industry.

Dataset Information

Operationalisation and validation of the Stopping Elderly Accidents, Deaths, and Injuries (STEADI) fall risk algorithm in a nationally representative sample.

Publications

Operationalisation and validation of the Stopping Elderly Accidents, Deaths, and Injuries (STEADI) fall risk algorithm in a nationally representative sample.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure