KEYWORDSbreast cancer, depression, delivery of diagnosis, psychocial, risk factors, support 1 | BACKGROUND Breast cancer is the second most common cancer in the world and the most frequent cancer among women. 1 This disease is one of the most stressful events in someone's life, what can trigger a depression. 2 In investigations made worldwide tendency was observed, that older, not employed, low educated and single patients are more prone to depression. [3][4][5][6] Lithuania remains a country where depression in cancer patients is under studied. Taking into account that the suicidal rate of the general population of Lithuania is one of the biggest in Europe since 1993, 7 it is very important to investigate the risk factors for depression. Lithuania is a country in which many economic, political, and social changes have occurred during the last two decades. We had a hypothesis that risk factors of depression in Lithuanian breast cancer patients will differ from other European non post-soviet countries because of economic level and social-cultural features. 8 The main aims of this study were to evaluate prevalence and risk for depression in Lithuanian women diagnosed with breast cancer, and to identify the influence of social, demographic, psychological, and clinical factors on the depression. 2 | METHODS 2.1 | Design and data collection The questionnaire survey was performed in the National Cancer Institute between 2012 and 2014. Participants were newly diagnosed breast cancer patients, age 18 to 80 years with T1-3N0-3M0 stage breast cancer. Patients completed questionnaires before beginning cancer treatment and at one-year follow-up. Completed questionnaires included: The Beck Depression Inventory Second Edition (BDI-II) 9 ; Vrana &Lauterbach Traumatic Events Scale-Civilian version (TEQ) 10 ; a questionnaire on patients' satisfaction about the communication of the diagnosis, and necessity of psychological support.
| Statistical analysisDescriptive statistics were used to characterize the sample. Categorical variables were expressed as absolute and relative frequencies.Mann-Whitney U was used for between-group comparisons of BDI-II scores and Pearson's chi-square test for categorical variables. Categorical variables were compared using Pearson's chi-square test. The odds ratios (OR) and 95% confidence intervals (95% CI) for depression and risk variables were estimated using multiple logistic regression.The BDI-II cut-off of ≥13 score was used to dichotomize patients into cases and non-cases. Unvariate logistic regression was conducted to explore the unadjusted association between variables and outcome.Likelihood ratio test was used for models fit. Summary measures of goodness of fit (likelihood ratio chi-square and Hosmer and Lemeshow's goodness-of-fit test) were conducted for assessing fit of the models. Receiver operating characteristics curve was used to assess discrimination power of the model. For assessing changes of prevalence of depression symptoms over time, McNemar's chi-square