A practical approach to assess depression risk and to guide risk reduction strategies in later life.
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.
Almeida
Osvaldo P
OP
Western Australian Centre for Health & Ageing, Centre for Medical Research of the University of Western Australia, Perth, Australia. osvaldo.almeida@uwa.edu.au
Alfonso
Helman
H
Pirkis
Jane
J
Kerse
Ngaire
N
Sim
Moira
M
Flicker
Leon
L
Snowdon
John
J
Draper
Brian
B
Byrne
Gerard
G
Goldney
Robert
R
Lautenschlager
Nicola T
NT
Stocks
Nigel
N
Scazufca
Marcia
M
Huisman
Martijn
M
Araya
Ricardo
R
Pfaff
Jon
J
D000368:Aged; D000369:Aged, 80 and over; D001315:Australia; D003430:Cross-Sectional Studies; D003866:Depressive Disorder / Q000175:diagnosis / Q000517:prevention & control* / Q000523:psychology; D003865:Major Depressive Disorder / Q000175:diagnosis / Q000517:prevention & control / Q000523:psychology; D005260:Female; D006801:Humans; D008019:Life Style; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D018570:Risk Assessment; D012307:Risk Factors; D040242:Risk Reduction Behavior; D011795:Surveys and Questionnaires