Predictors of follow-up completeness in longitudinal research on traumatic brain injury: Findings from the national institute on disability and rehabilitation research traumatic brain injury model systems program

Jason W. Krellman, Stephanie A. Kolakowsky-Hayner, Lisa Spielman, Marcel Dijkers, Flora Hammond, Jennifer Bogner, Tessa Hart, Joshua B. Cantor, Theodore Tsaousides

Research output: Contribution to journalArticle

17 Citations (Scopus)

Abstract

Objective To identify baseline participant variables in the domains of demographics, medical/psychosocial history, injury characteristics, and postinjury functional status associated with longitudinal follow-up completeness in persons with traumatic brain injury (TBI) using the TBI Model Systems (TBIMS) National Database (NDB). Design Exhaustive chi-square automatic interaction detection was used to identify factors that classified participants according to level of follow-up completeness. Setting Retrospective analysis of a multi-center longitudinal database. Participants Individuals (N=8249) enrolled in the TBIMS NDB between 1989 and 2009 who were eligible for at least the first (year 1) follow-up up to the fifth (year 15) follow-up. Interventions None. Main Outcome Measures Follow-up completeness as defined by 6 different longitudinal response patterns (LRPs): completing all follow-ups, wave nonresponse, dropping out, completing no follow-ups without formally withdrawing, formally withdrawing before completing any follow-ups, and formally withdrawing after completing some follow-ups. Results Completing all follow-ups was associated with higher levels of education, living with parents or others, and having acute care payer data entered in the NDB. Subgroups more vulnerable to loss to follow-up (LTFU) included those with less education, racial/ethnic minority backgrounds, those with better motor functioning on rehabilitation discharge, and those for whom baseline data on education, employment, and acute care payer were not collected. No subgroups were found to be more likely to have the LRPs of dropping out or formal withdrawal. Conclusions These data identify subgroups in which retention strategies beyond those most commonly used might reduce LTFU in longitudinal studies of persons with TBI, such as the TBIMS, and suggest future investigations into factors associated with missing baseline data.

Original languageEnglish
Pages (from-to)633-641
Number of pages9
JournalArchives of Physical Medicine and Rehabilitation
Volume95
Issue number4
DOIs
StatePublished - 2014

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Databases
Education
Research
Longitudinal Studies
Rehabilitation
Parents
History
Demography
Outcome Assessment (Health Care)
Rehabilitation Research
Traumatic Brain Injury
Wounds and Injuries

Keywords

  • Bias (epidemiology)
  • Brain injuries
  • Follow-up studies
  • Lost to follow-up
  • Outcome assessment (health care)
  • Rehabilitation

ASJC Scopus subject areas

  • Rehabilitation
  • Physical Therapy, Sports Therapy and Rehabilitation

Cite this

Predictors of follow-up completeness in longitudinal research on traumatic brain injury : Findings from the national institute on disability and rehabilitation research traumatic brain injury model systems program. / Krellman, Jason W.; Kolakowsky-Hayner, Stephanie A.; Spielman, Lisa; Dijkers, Marcel; Hammond, Flora; Bogner, Jennifer; Hart, Tessa; Cantor, Joshua B.; Tsaousides, Theodore.

In: Archives of Physical Medicine and Rehabilitation, Vol. 95, No. 4, 2014, p. 633-641.

Research output: Contribution to journalArticle

Krellman, Jason W. ; Kolakowsky-Hayner, Stephanie A. ; Spielman, Lisa ; Dijkers, Marcel ; Hammond, Flora ; Bogner, Jennifer ; Hart, Tessa ; Cantor, Joshua B. ; Tsaousides, Theodore. / Predictors of follow-up completeness in longitudinal research on traumatic brain injury : Findings from the national institute on disability and rehabilitation research traumatic brain injury model systems program. In: Archives of Physical Medicine and Rehabilitation. 2014 ; Vol. 95, No. 4. pp. 633-641.
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T2 - Findings from the national institute on disability and rehabilitation research traumatic brain injury model systems program

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AU - Kolakowsky-Hayner, Stephanie A.

AU - Spielman, Lisa

AU - Dijkers, Marcel

AU - Hammond, Flora

AU - Bogner, Jennifer

AU - Hart, Tessa

AU - Cantor, Joshua B.

AU - Tsaousides, Theodore

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N2 - Objective To identify baseline participant variables in the domains of demographics, medical/psychosocial history, injury characteristics, and postinjury functional status associated with longitudinal follow-up completeness in persons with traumatic brain injury (TBI) using the TBI Model Systems (TBIMS) National Database (NDB). Design Exhaustive chi-square automatic interaction detection was used to identify factors that classified participants according to level of follow-up completeness. Setting Retrospective analysis of a multi-center longitudinal database. Participants Individuals (N=8249) enrolled in the TBIMS NDB between 1989 and 2009 who were eligible for at least the first (year 1) follow-up up to the fifth (year 15) follow-up. Interventions None. Main Outcome Measures Follow-up completeness as defined by 6 different longitudinal response patterns (LRPs): completing all follow-ups, wave nonresponse, dropping out, completing no follow-ups without formally withdrawing, formally withdrawing before completing any follow-ups, and formally withdrawing after completing some follow-ups. Results Completing all follow-ups was associated with higher levels of education, living with parents or others, and having acute care payer data entered in the NDB. Subgroups more vulnerable to loss to follow-up (LTFU) included those with less education, racial/ethnic minority backgrounds, those with better motor functioning on rehabilitation discharge, and those for whom baseline data on education, employment, and acute care payer were not collected. No subgroups were found to be more likely to have the LRPs of dropping out or formal withdrawal. Conclusions These data identify subgroups in which retention strategies beyond those most commonly used might reduce LTFU in longitudinal studies of persons with TBI, such as the TBIMS, and suggest future investigations into factors associated with missing baseline data.

AB - Objective To identify baseline participant variables in the domains of demographics, medical/psychosocial history, injury characteristics, and postinjury functional status associated with longitudinal follow-up completeness in persons with traumatic brain injury (TBI) using the TBI Model Systems (TBIMS) National Database (NDB). Design Exhaustive chi-square automatic interaction detection was used to identify factors that classified participants according to level of follow-up completeness. Setting Retrospective analysis of a multi-center longitudinal database. Participants Individuals (N=8249) enrolled in the TBIMS NDB between 1989 and 2009 who were eligible for at least the first (year 1) follow-up up to the fifth (year 15) follow-up. Interventions None. Main Outcome Measures Follow-up completeness as defined by 6 different longitudinal response patterns (LRPs): completing all follow-ups, wave nonresponse, dropping out, completing no follow-ups without formally withdrawing, formally withdrawing before completing any follow-ups, and formally withdrawing after completing some follow-ups. Results Completing all follow-ups was associated with higher levels of education, living with parents or others, and having acute care payer data entered in the NDB. Subgroups more vulnerable to loss to follow-up (LTFU) included those with less education, racial/ethnic minority backgrounds, those with better motor functioning on rehabilitation discharge, and those for whom baseline data on education, employment, and acute care payer were not collected. No subgroups were found to be more likely to have the LRPs of dropping out or formal withdrawal. Conclusions These data identify subgroups in which retention strategies beyond those most commonly used might reduce LTFU in longitudinal studies of persons with TBI, such as the TBIMS, and suggest future investigations into factors associated with missing baseline data.

KW - Bias (epidemiology)

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