Profile of patients lost to follow-up in the burn injury rehabilitation model systems' longitudinal database

Journal of Burn Care and Research

Volumn 27, Issue 5, 2006, Pages 703-712

  Holavanahalli, Radha K ^a   Lezotte, Dennis C ^b   Hayes, Michael P ^c   Minhajuddin, Abu ^a   Fauerbach, James A ^c   Engrav, Loren H ^d   Helm, Phala A ^a   Kowalske, Karen J ^a  

^a UNIVERSITY OF TEXAS SOUTHWESTERN MEDICAL CENTER   (United States)

^b UNIVERSITY OF COLORADO   (United States)

^c JOHNS HOPKINS UNIVERSITY SCHOOL OF MEDICINE   (United States)

^d UNIVERSITY OF WASHINGTON SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADULT; AGE; ARTICLE; ASSAULT; BURN; DATA BASE; DRUG ABUSE; EDUCATION; EMPLOYMENT; FEMALE; FOLLOW UP; HEALTH INSURANCE; HUMAN; LOGISTIC REGRESSION ANALYSIS; LONGITUDINAL STUDY; MAJOR CLINICAL STUDY; MALE; MODEL; PATIENT COMPLIANCE; PREDICTION; RISK FACTOR;

ADULT; AGE FACTORS; BURNS; DATABASES; DATABASES AS TOPIC; EDUCATIONAL STATUS; FEMALE; FOLLOW-UP STUDIES; HUMANS; LENGTH OF STAY; LOGISTIC MODELS; MALE; MARITAL STATUS; MEDICALLY UNINSURED; PROSPECTIVE STUDIES; RISK FACTORS; SUBSTANCE-RELATED DISORDERS; UNEMPLOYMENT; UNITED STATES; VIOLENCE;

EID: 33748993141     PISSN: 1559047X     EISSN: 15590488     Source Type: Journal    
DOI: 10.1097/01.BCR.0000238085.87863.81     Document Type: Article

References (37)

1. Deeg DJH. Attrition in longitudinal studies: Does it affect the generalizability of the findings? An introduction to the series. J Clin Epidemiol 2002;55:213-15.
2. Van Beijsterveldt CEM, van Boxtel MPJ, Bosma H, et al. Predictors of attrition in a longitudinal cognitive aging study: The Masstrict Aging Study. J Clin Epidemiol 2002;55:216-23.
3. Frack SA, Woodruff SI, Candelaria J, Elder JP. Correlates of compliance with measurement protocols in a Latino nutrition-intervention study. Am J Prev Med 1997;13:131-6.
4. Siddiqui O, Flay BR, Hu FB. Factors affecting attrition in a longitudinal smoking prevention study. Prev Med 1996;25:554-60.
5. Edwards AG, Rollnick S. Outcome studies of brief alcohol intervention in general practice: the problem of lost subjects. Addiction 1997;92:1699-704.
6. Mihelic AH, Crimmins EM. Loss to follow-up in a sample of Americans 70 years of age and older: the LSOA 1984-1990. J Gerontol 1997;52B:S37-48.
7. Laursen P. The impact of aging on cognitive functions: an 11 year follow-up study of four age cohorts. Acta Neurol Scand Suppl 1997;172:7-86.
8. Agosti V, Nunes E, Ocepeck-Welikson K. Patient factors related to early attrition from an outpatient cocaine research clinic. Am J Drug Alcohol Abuse 1996;22:29-39.
9. Corrigan JD, Bogner JA, Mysiw WJ, Clinchot D, Fugate L. Systematic bias in outcome studies of persons with traumatic brain injury. Arch Phys Med Rehabil 1997;78:132-7.
10. Hansten ML, Downey L, Rosengren DB, et al. Relationship between follow-up rates and treatment outcomes in substance abuse research: more is better but when is "enough" enough? Addiction 2000;95:1403-16.
11. Corrigan JD, Harrison-Felix C, Bogner J, et al. Systematic bias in traumatic brain injury outcome studies because of loss to follow-up. Arch Phys Med Rehabil 2003;84:153-60.
12. Walton MA, Ramanathan CS, Reischl TM. Tracking substance abusers in longitudinal research: understanding follow-up contact difficulty. Am J Community Psychol 1998;2:233-53.
13. Cottler LB, Compton WM, Ben-Abdallah A, et al. Achieving a 96.6 percent follow-up rate in a longitudinal study of drug abusers. Drug Alcohol Depend 1996;41:209-17.
14. Fauerbach JA, Lawrence J, Stevens S, Munster A. Work status and attrition from longitudinal studies are influenced by psychiatric disorder. J Burn Care Rehabil 1998;19:247-52.
15. Richards JS, Go BK, Rutt RD, Lazarus PB. The National Spinal Cord Injury Collaborative Database. In: Stover SL, DeLisa JA, Whiteneck GG, editors. Spinal cord injury: clinical outcomes from the model system. Gaithersburg, MD: Aspen Publishers Inc.; 1995. p. 10-20.
16. Nishimoto RH. Who drops out of drug-user treatment research on women? Substance Use Misuse 1998;33:1291-313.
17. Zunzunegui MV, Beland F, Guiterrez-Cuadra P. Loss to follow-up in a longitudinal study on aging in Spain. J Clin Epidemiol 2001;54:501-10.
18. Spotts D, Hindin R, Garfield F, et al. Strategies for follow-up of drug users: field research guidelines. Substance Use and Abuse 1996;31:1891-927.
19. Scott CK. A replicable model for achieving over 90% follow-up rates in longitudinal studies of substance abusers. Drug Alcohol Dependence 2004;74:21-36.
20. Canupp KC, Waites KB, DeVivo MJ, et al. Predicting compliance with annual follow-up evaluations in persons with spinal cord injury. Spinal Cord 1997;35:314-9.
21. Derogatis LR, Melisaratos N. The Brief Symptom Inventory: an introductory report. Psyhcol Med 1983;13:595-605.
22. Willer B, Ottenbacher KJ, Coad ML. The community integration questionnaire. Am J Phys Med Rehabil 1994;73:103-11.
23. Diener E, Emmons RA, Larsen RJ, Griffin S. The satisfaction with life scale. J Person Assess 1985;49:71-5.
24. Ware JE, Snow KK, Kosinski M, Gandek B. SF-36 Health survey: manual and interpretation guide. Boston, MA: Nimrod Press; 1993.
25. Granger CV, Hamilton BB, Keith RA, Zielezny M, Sherwin FS. Advances in functional assessment for medical rehabilitation. Top Geriatric Rehabil 1986;1:59-74.
26. Jebsen RH, Taylor N, Trieschmann RB, Trotter MJ, Howard LA. An objective and standardized test of hand function. Arch Phys Med Rehabil 1969; June:311-9.
27. Psaty BM, Cheadle TD, Koepsell TD, et al. Race and ethnicity-specific characteristics of participants loss to follow-up in a telephone cohort. Am J Epidemiol 1994;140:161-71.
28. Altman DG. Statistics in medical journals: some recent trends. Stat Med 2000;19:3275-89.
29. Kristman V, Manno M, Cote P. Loss to follow-up in cohort studies: how much is too much? Eur J Epidemiol 2004;19:751-60.
30. Boys A, Marsden J, Stillwell G, et al. Minimizing respondent attrition in longitudinal research: Practical implications from a cohort study of adolescent drinking. J Adolescence 2003;26:363-73.
31. Minder C, Muller T, Gillman G, et al. Subgroups of refusers in a disability prevention trial in older adults: baseline and follow-up Analysis. Am J Public Health 2002;92:445-50.
32. Rubin DB. Multiple Imputation for Non-response in Surveys. New York: Wiley; 1987.
33. Vach W, Blettner M. Biased estimation of odds ratios in case-control studies due to the use of ad-hoc methods of correcting for missing values in confounding variables. Am J Epidemiol 1991;134:895-907.
34. Laird NM, Ware JH. Random-effects models for longitudinal data. Biometrics 1982;38:963-74.
35. Littell RC, Milliken GA, Stroup WW, Wolfinger RD. SAS System for Mixed Models. Cary, NC: SAS Institute Inc.; 1996. p. 633.
36. SAS/STAT User's Guide, Version 8. Cary, NC: SAS Institute Inc.; 1999. p. 3884.
37. Fairclough DL. Design and analysis of quality of life studies in clinical trials. New York, NY: Chapman and Hall/CRC; 2002.