International Psychogeriatrics

Research Article

A practical approach to assess depression risk and to guide risk reduction strategies in later life

Osvaldo P. Almeidaa1a2a3 c1, Helman Alfonsoa1a2, Jane Pirkisa4, Ngaire Kersea5, Moira Sima6, Leon Flickera1a7a8, John Snowdona9, Brian Drapera10, Gerard Byrnea11, Robert Goldneya12, Nicola T. Lautenschlagera1a2a13, Nigel Stocksa14, Marcia Scazufcaa15, Martijn Huismana16, Ricardo Arayaa17 and Jon Pfaffa1a2

a1 Western Australian Centre for Health & Ageing, Centre for Medical Research of the University of Western Australia, Perth, Australia

a2 School of Psychiatry & Clinical Neurosciences, University of Western Australia, Perth, Australia

a3 Department of Psychiatry, Royal Perth Hospital, Perth, Australia

a4 School of Population Health, University of Melbourne, Melbourne, Australia

a5 School of Population Health, University of Auckland, Auckland, New Zealand

a6 School of Nursing, Midwifery and Postgraduate Medicine, Edith Cowan University, Perth, Australia

a7 School of Medicine and Pharmacology, University of Western Australia, Perth, Australia

a8 Department of Geriatric Medicine, Royal Perth Hospital, Perth, Australia

a9 Department of Psychological Medicine, University of Sydney, Sydney, Australia

a10 School of Psychiatry, University of New South Wales, Sydney, Australia

a11 School of Medicine, University of Queensland, Brisbane, Australia

a12 Department of Psychiatry, University of Adelaide, Adelaide, Australia

a13 Academic Unit for Psychiatry of Old Age, St Vincent's Health, Department of Psychiatry, University of Melbourne, Melbourne, Australia

a14 Unit of General Practice, University of Adelaide, Adelaide, Australia

a15 Institute and Department of Psychiatry, Medical School, University of São Paulo, São Paulo, Brazil

a16 EMGO Institute for Health and Care Research, Department of Psychiatry, VU University Medical Center; Department of Sociology, VU University Amsterdam, Amsterdam, The Netherlands

a17 Department of Psychiatry, University of Bristol, Bristol, U.K.


Background: Many factors have been associated with the onset and maintenance of depressive symptoms in later life, although this knowledge is yet to be translated into significant health gains for the population. This study gathered information about common modifiable and non-modifiable risk factors for depression with the aim of developing a practical probabilistic model of depression that can be used to guide risk reduction strategies.

Methods: A cross-sectional study was undertaken of 20,677 community-dwelling Australians aged 60 years or over in contact with their general practitioner during the preceding 12 months. Prevalent depression (minor or major) according to the Patient Health Questionnaire (PHQ-9) assessment was the main outcome of interest. Other measured exposures included self-reported age, gender, education, loss of mother or father before age 15 years, physical or sexual abuse before age 15 years, marital status, financial stress, social support, smoking and alcohol use, physical activity, obesity, diabetes, hypertension, and prevalent cardiovascular diseases, chronic respiratory diseases and cancer.

Results: The mean age of participants was 71.7 ± 7.6 years and 57.9% were women. Depression was present in 1665 (8.0%) of our subjects. Multivariate logistic regression showed depression was independently associated with age older than 75 years, childhood adverse experiences, adverse lifestyle practices (smoking, risk alcohol use, physical inactivity), intermediate health hazards (obesity, diabetes and hypertension), comorbid medical conditions (clinical history of coronary heart disease, stroke, asthma, chronic obstructive pulmonary disease, emphysema or cancers), and social or financial strain. We stratified the exposures to build a matrix that showed that the probability of depression increased progressively with the accumulation of risk factors, from less than 3% for those with no adverse factors to more than 80% for people reporting the maximum number of risk factors.

Conclusions: Our probabilistic matrix can be used to estimate depression risk and to guide the introduction of risk reduction strategies. Future studies should now aim to clarify whether interventions designed to mitigate the impact of risk factors can change the prevalence and incidence of depression in later life.

(Received June 11 2010)

(Revised August 20 2010)

(Revised August 24 2010)

(Accepted August 26 2010)

(Online publication September 30 2010)


c1 Correspondence should be addressed to: Professor Osvaldo P. Almeida, WA Centre for Health & Ageing (M573), University of Western Australia, 35 Stirling Highway, Crawley, Perth, WA 6009, Australia. Phone: +61 8 9224 2720; Fax: +61 8 9224 8009. Email: