Brain injury · 2024
Who enrolls and why? Examining center-specific underlying patterns behind enrollment: a New York City-based traumatic brain injury model systems study.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Ahmed Asim, Smith Michelle, Mandal Soutrik, Bushnik Tamara
Аннотация
BACKGROUND: To elucidate the sociodemographic and study factors involved in enrollment in the Traumatic Brain Injury Model System (TBIMS) database, this study examined the effect of a variety of variables on enrollment at a local TBIMS center.
METHODS: A sample of 654 individuals from the local TBIMS center was studied examining enrollment by age, gender, race, ethnicity, homelessness status at date of injury, history of homelessness, health insurance status, presence of social support, primary language, consenting in hospital or after discharge, and the need for an interpreter. Binary logistic regression was conducted to identify variables that predict center-based enrollment into TBIMS.
RESULTS: Results demonstrated that older age was associated with decreasing enrollment (OR = 0.99, p = 0.01), needing an interpreter made enrollment less likely (OR = 0.33, p < 0.01), being primarily Spanish speaking predicted enrollment (OR = 3.20, p = 0.02), Hispanic ethnicity predicted enrollment (OR = 7.31, p = 0.03), and approaching individuals in the hospital predicted enrollment (OR = 6.94, p < 0.01). Here, OR denotes the odds ratio estimate from a logistic regression model and P denotes the corresponding p-value.
CONCLUSIONS: These results can be useful in driving enrollment strategies at this center for other similar TBI research, and to contribute a representative TBI sample to the national database.
METHODS: A sample of 654 individuals from the local TBIMS center was studied examining enrollment by age, gender, race, ethnicity, homelessness status at date of injury, history of homelessness, health insurance status, presence of social support, primary language, consenting in hospital or after discharge, and the need for an interpreter. Binary logistic regression was conducted to identify variables that predict center-based enrollment into TBIMS.
RESULTS: Results demonstrated that older age was associated with decreasing enrollment (OR = 0.99, p = 0.01), needing an interpreter made enrollment less likely (OR = 0.33, p < 0.01), being primarily Spanish speaking predicted enrollment (OR = 3.20, p = 0.02), Hispanic ethnicity predicted enrollment (OR = 7.31, p = 0.03), and approaching individuals in the hospital predicted enrollment (OR = 6.94, p < 0.01). Here, OR denotes the odds ratio estimate from a logistic regression model and P denotes the corresponding p-value.
CONCLUSIONS: These results can be useful in driving enrollment strategies at this center for other similar TBI research, and to contribute a representative TBI sample to the national database.