Julia F-M Gilmartin-Thomas1, Andrew Forbes1, Danny Liew1, John J McNeil1, Flavia M Cicuttini1, Alice J Owen1, Michael E Ernst2,3, Mark R Nelson1,4, Jessica Lockery1, Stephanie A Ward1,5,6, Ljoudmila Busija1. 1. Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia. 2. Department of Pharmacy Practice and Science, College of Pharmacy, The University of Iowa, Iowa City, Iowa, U.S.A. 3. Department of Family Medicine, Carver College of Medicine, The University of Iowa, Iowa City, Iowa, U.S.A. 4. Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia. 5. Centre for Healthy Brain Ageing (CHeBA), University of New South Wales, Sydney, New South Wales, Australia. 6. Department of Geriatric Medicine, Prince of Wales Hospital, Randwick, New South Wales, Australia.
Abstract
OBJECTIVE: Evaluate the Pain Impact Index, a simple, brief, easy-to-use, and novel tool to assess the impact of chronic pain in community-dwelling older adults. METHODS: A Rasch modelling analysis was undertaken in Stata using a partial credit model suited to the Likert-type items that comprised the Index. The Index was evaluated for ordering of category thresholds, unidimensionality, overall fit to the Rasch model, measurement bias (Differential Item Functioning, DIF), targeting, and construct validity. RESULTS: The four-item Pain Impact Index was self-completed by 6454 community-dwelling Australians who were aged at least 70 years and experienced pain on most days. Two items showed evidence of threshold disordering, and this was resolved by collapsing response categories (from 5 to 3) for all items. The rescored Index conformed to the unidimensionality assumption and had satisfactory fit with the Rasch model (analyses conducted on a reduced sample size to mitigate the potential for overpowering: n = 377, P > 0.0125, power > 77%). When considering uniform DIF, the most frequent sources of measurement bias were age, knee pain, and upper back pain. When considering nonuniform DIF, the most frequent source of measurement bias was knee pain. The Index had good ability to differentiate between respondents with different levels of pain impact and had highest measurement precision for respondents located around the average level of pain impact in the study sample. Both convergent and discriminant validity of the Index were supported. CONCLUSION: The Pain Impact Index showed evidence of unidimensionality, was able to successfully differentiate between levels of pain impact, and had good evidence of construct validity.
OBJECTIVE: Evaluate the Pain Impact Index, a simple, brief, easy-to-use, and novel tool to assess the impact of chronic pain in community-dwelling older adults. METHODS: A Rasch modelling analysis was undertaken in Stata using a partial credit model suited to the Likert-type items that comprised the Index. The Index was evaluated for ordering of category thresholds, unidimensionality, overall fit to the Rasch model, measurement bias (Differential Item Functioning, DIF), targeting, and construct validity. RESULTS: The four-item Pain Impact Index was self-completed by 6454 community-dwelling Australians who were aged at least 70 years and experienced pain on most days. Two items showed evidence of threshold disordering, and this was resolved by collapsing response categories (from 5 to 3) for all items. The rescored Index conformed to the unidimensionality assumption and had satisfactory fit with the Rasch model (analyses conducted on a reduced sample size to mitigate the potential for overpowering: n = 377, P > 0.0125, power > 77%). When considering uniform DIF, the most frequent sources of measurement bias were age, knee pain, and upper back pain. When considering nonuniform DIF, the most frequent source of measurement bias was knee pain. The Index had good ability to differentiate between respondents with different levels of pain impact and had highest measurement precision for respondents located around the average level of pain impact in the study sample. Both convergent and discriminant validity of the Index were supported. CONCLUSION: The Pain Impact Index showed evidence of unidimensionality, was able to successfully differentiate between levels of pain impact, and had good evidence of construct validity.
Authors: Marianne Jensen Hjermstad; Peter M Fayers; Dagny F Haugen; Augusto Caraceni; Geoffrey W Hanks; Jon H Loge; Robin Fainsinger; Nina Aass; Stein Kaasa Journal: J Pain Symptom Manage Date: 2011-06 Impact factor: 3.612
Authors: Regina L M van Boekel; Kris C P Vissers; Rob van der Sande; Ewald Bronkhorst; Jos G C Lerou; Monique A H Steegers Journal: PLoS One Date: 2017-05-10 Impact factor: 3.240
Authors: Julia Fm Gilmartin-Thomas; Flavia M Cicuttini; Alice J Owen; Rory Wolfe; Michael E Ernst; Mark R Nelson; Jessica Lockery; Robyn L Woods; Carlene Britt; Danny Liew; Anne Murray; Barbara Workman; Stephanie A Ward; John J McNeil Journal: Arch Gerontol Geriatr Date: 2020-08-13 Impact factor: 4.163