Factors Related to Return to Work in Women After Breast Cancer in Iran

Abstract

Background:Most women are diagnosed with breast cancer (BC) when they are still at the appropriate age for employment. The increasing survival rates of patients with BC call for more attention to their ability to return to work.Objectives:The aim of this study was to identify factors related to the return to work in Iranian women underwent BC treatment.Patients and Methods:A total of 175 women with BC, who met the inclusion criteria, were enrolled in this cross-sectional study. The subjects were recruited from four hospitals affiliated to Mashhad university of medical sciences. These hospitals are oncology referral centers in eastern Iran. All records of employed women with BC were studied in four hospitals of Mashhad city, Iran, during 2000 - 2010. The researchers designed a questionnaire, which consisted of questions regarding social/demographic, health/disease, and work characteristics. The questionnaires were completed through interviews. Data were analyzed using SPSS software, version 11.5.Results:The mean age of the patients at the time of interview was 44.3 ± 6.72 years. Mean age of “Return- to-work” group was 42.71 and “No return-to-work” group was 51.06. Most women (80%) were married. At the time of the interview, 80% had returned to work after a BC diagnosis. Older patients (OR = 0.796; 95% CI, 0.625 - 0.907, P = 0.002), and those with a great deal of work experience (OR = 0.861; 95% CI, 0.752 - 0.986, P = 0.030) were less likely to return to work. Also, women who had no pain or surgery scar (OR = 23.03; 95% CI, 4.53 - 117.02, P < 0.001) as well as those who had no lymphedema after the BC treatment were more likely to return to work (OR = 22.373; 95% CI, 4.04 - 23.892, P < 0.001).Conclusions:The results of this study show the factors related to the return-to-work after BC treatment in working women in Mashhad city, Iran. These predictors should be taken into account in order to improve the patient’s life quality.

Predictors of resignation and sick leave after cancer diagnosis among Japanese breast cancer survivors

“…Secondly, patients with a lower educational attainment were at higher risk for resignation compared with those with higher educational attainment. Our ndings are consistent with previous studies in non-Asian populations [12,[24][25][26][27][28]. However, comparison of resignation rates with other countries studies warrants careful consideration, given important differences in socioenvironmental factors, including widely differing national systems regulating provision of medical leave, as well as availability of companybased healthcare resources [29].…”

“…These ndings are consistent with previous studies showing a correlation between length of SL and RTW, with longer SL making RTW and continued employment more di cult [39,40]. Conversely, Azarkish et al reported no relationship between taking SL and job loss [27]. Longer SL is reported to be associated with more invasive treatment, advanced breast cancer, and economic deprivation, all of which are factors related to unemployment [25,40,41].…”

Breast Cancer as a Major Concern Among Iranian Women

“…Return-to-work can be considered a type of social recovery that enhances the patient's life quality (9). There- fore, it is necessary for health policies to consider issues of return to work for these patients.…”

A Comparative Study for Modelling the Survival of Breast Cancer Patients in the West of Iran

“…Certainly, the prerequisites for the disease prevention and proficient treatment findings are the precise detection of affective risk factors in diseases formation and progression (Mert, Kılıç, Bilgili, & Akan, 2015). For a long time ago, conventional survival models have been used to achieve this aim and to provide necessary requisites for breast cancer prognostication (Ahmadinejad, Movahedinia, Movahedinia, Naieni, & Nedjat, 2013;Azarkish, Najmabadi, Roudsari, & Shandiz, 2015;Sadoughi, Afshar, Olfatbakhsh, & Mehrdad, 2016). Although the prominent interpretability of these models has made them pioneer for simple medical explanations, but the lower bias and accurate prediction of recently introduced learning algorithms, has stimulated the statistical focus on machine learning methods.…”