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Open Access Open Badges Research article

Unmet need and psychological distress predict emergency department visits in community-dwelling elderly women: a prospective cohort study

Jacqueline M Quail13*, Christina Wolfson12 and Abby Lippman1

Author Affiliations

1 Department of Epidemiology and Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada

2 Research Institute, McGill University Health Centre, Montreal, Quebec, Canada

3 Saskatchewan Health Quality Council, Saskatoon, Saskatchewan, Canada

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BMC Geriatrics 2011, 11:86  doi:10.1186/1471-2318-11-86

Published: 19 December 2011



Unmet need to perform activities of daily living (ADL) is associated with increased use of urgent health services by the elderly. However, the reported associations may be confounded by psychological distress. We examine the independent effects of unmet need and psychological distress upon emergency department (ED) visits.


We conducted a prospective study of randomly selected community-dwelling adults aged ≥ 75. We report here the results for women only (n = 530). In-person interviews collected data on self-reported unmet need and the 14-item l'Indice de détresse psychologique de Santé Québec psychological distress scale. ED visits were identified from an administrative database. Multivariable logistic regression was used to identify predictors of any ED visit in the 6 months following the baseline interview.


In multivariable analysis, unmet need in instrumental ADL was associated with subsequent ED visits (odds ratio = 1.57, 95% confidence interval = 1.02-2.41), as was psychological distress (odds rate = 1.30, 95% confidence interval = 1.02-1.67). The magnitude of the association between unmet need and ED visits was overestimated in statistical models that did not adjust for psychological distress.


Both unmet need and psychological distress were independent predictors of ED visits. Future investigations of unmet need and health services utilization should include psychological distress to control for confounding and improve the internal validity of statistical models.