BACKGROUND AND AIMS: Longitudinal population studies are a keystone in describing the course of back pain over time. Yet, potential bias because of repeated attrition has received little attention. This study aims to identify those back pain related indicators most susceptible to bias and to discuss practical consequences for back pain research. METHODS: Analyses were based on a population-based longitudinal multi-centre postal back pain survey with two postal follow-up measurements within 2 years. The baseline sample comprised 9263 subjects. Different sets of measures at entry were used to predict subsequent attrition: Socio-demographic variables, indicators of back pain, health related measures, and response behaviour. Back pain related indicators comprised prevalence estimates, pain intensity, disability, and radiating pain. Weighted and unweighted back pain outcomes were compared at the first and second follow-up to assess bias. RESULTS: Only 52.3% of the eligible participants at baseline continued participation till the second follow-up. Age and prior response behaviour were the best predictors of attrition while health and back pain related variables were of less importance. Differences between weighted and unweighted estimates of back pain related indicators were small to negligible, thus indicating little bias in point estimates. Unexpectedly, the reported back pain burden slightly declined over time. CONCLUSION: The representativeness of the sample is consecutively reduced because of differential attrition over the different measurement points. Despite this, bias due to attrition has a marginal impact on the point estimates of virtually all back pain related outcomes.
BACKGROUND AND AIMS: Longitudinal population studies are a keystone in describing the course of back pain over time. Yet, potential bias because of repeated attrition has received little attention. This study aims to identify those back pain related indicators most susceptible to bias and to discuss practical consequences for back pain research. METHODS: Analyses were based on a population-based longitudinal multi-centre postal back pain survey with two postal follow-up measurements within 2 years. The baseline sample comprised 9263 subjects. Different sets of measures at entry were used to predict subsequent attrition: Socio-demographic variables, indicators of back pain, health related measures, and response behaviour. Back pain related indicators comprised prevalence estimates, pain intensity, disability, and radiating pain. Weighted and unweighted back pain outcomes were compared at the first and second follow-up to assess bias. RESULTS: Only 52.3% of the eligible participants at baseline continued participation till the second follow-up. Age and prior response behaviour were the best predictors of attrition while health and back pain related variables were of less importance. Differences between weighted and unweighted estimates of back pain related indicators were small to negligible, thus indicating little bias in point estimates. Unexpectedly, the reported back pain burden slightly declined over time. CONCLUSION: The representativeness of the sample is consecutively reduced because of differential attrition over the different measurement points. Despite this, bias due to attrition has a marginal impact on the point estimates of virtually all back pain related outcomes.
Authors: Henry Völzke; Till Ittermann; Carsten Oliver Schmidt; Sebastian E Baumeister; Sabine Schipf; Dietrich Alte; Reiner Biffar; Ulrich John; Wolfgang Hoffmann Journal: Dtsch Arztebl Int Date: 2015-03-13 Impact factor: 5.594
Authors: Markus Melloh; Achim Elfering; Cathy M Chapple; Anja Käser; Cornelia Rolli Salathé; Thomas Barz; Christoph Röder; Jean-Claude Theis Journal: Int Arch Occup Environ Health Date: 2012-03-21 Impact factor: 3.015