Factors Influencing the Risk of Depression among Working-age Population
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Abstract
The working-age population constitutes a major group of the country with the age range between 15 to 59 years old. They often face various situations that can impact their physical and mental health, particularly depression. Problems of depression and suicide among the working-age population have been frequently reported in the present day. Depression in this age group can be caused by numerous factors. However, the true causes leading to depression remain unknown. This research aims to study the factors that influence the risk of depression among the working-age population. The sample group consists of 760 working-age individuals residing in a northern province whose ages range from 40-59. A multi-stage sampling method was used. Data were collected using a developed questionnaire which went through an examination for its validity and reliability according to the criteria. A depression risk screening form which contains two questions was also used. Data were analyzed using descriptive statistics and binary logistic regression. Results show that the risk of depression among these working-age population was influenced by different factors: demographic, socio-economic, psychological, work-related, and health behaviors. First, demographic and socioeconomic factors include a family history of mental illness, average monthly income, emotional support, and access to information. Second, psychosocial factors include stress, being pessimistic, being cheated and having incurring debts, housing problems, overwork, and conflicts between marriage couples or life partners. Next, work-related factors are those that relate to job performance, recognition, salary and benefits, and teamwork also affect individuals to be at risk of having depression. Finally, health behaviors refer to factors of physical exercise, meditation, prayer, consumption of alcoholic drinks, and smoking. These variables together can predict the risk of depression among the working population with an accuracy of 65.4%. The obtained variable data can be used for surveillance and prevention of depression risk among working population.
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