BMC Psychiatry volume 21, Article number: 542 [2021] Cite this article
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Abstract
Background
Major depressive disorder [MDD] is a common recurrent mental disorder and one of the leading causes of disability in the world. The recurrence of MDD is associated with increased psychological and social burden, limitations for the patient, family, and society; therefore, action to reduce and prevent the recurrence of this disorder or hospital readmissions for depression among the patients is essential.
Methods
The data of this retrospective cohort study were extracted from records of 1005 patients with MDD hospitalized in Farshchian hospital in Hamadan city, Iran [2011–2018]. The hospital readmissions rate due to depression episodes was modeled using generalized Poisson regression [GPR]. Demographic and clinical characteristics of the patients were considered as explanatory variables. SAS v9.4 was used [P < 0.05].
Results
A majority of the patients were male [66.37%]. The mean [standard deviation] of age at onset of MDD and the average number of hospital readmissions were 32.39 [13.03] years and 0.53 [1.84], respectively [most patients [74.3%] did not experience hospital readmissions]. According to the results of the GPR, the lower age at the onset of the disease [IRR = 1.02;P = 0.008], illiteracy [IRR = 2.06;P = 0.003], living in urban areas [IRR = 1.56;P = 0.015], history of psychiatric illnesses in the family [IRR = 1.75;P = 0.004], history of emotional problems [IRR = 1.42;P = 0.028] and having medical disorders [IRR = 1.44;P = 0.035] were positively associated with the number of hospitalizations.
Conclusion
According to our findings, urbanization, early onset of the disease, illiteracy, family history of mental illness, emotional problems, and medical disorders are among major risk factors associated with an increased number of hospital readmissions of MDD.
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Introduction
Major depression is a common mental disorder and one of the leading causes of disability and the burden of diseases in the world. About 300 million people are affected by depression worldwide and there was an increase of more than 18% between 2005 and 2015 [1]. Depression is the leading cause of early retirement and absence from work due to illnesses in many European countries [2]. Depression is very common in people with chronic diseases such as diabetes, heart disease, rheumatoid arthritis, kidney disease, the acquired immunodeficiency syndrome [AIDS], and multiple sclerosis, and studies have shown that about one-third of these patients suffer from depression [3].
According to the World Health Organization [WHO], depression is the second most important and serious disorder after cardiovascular disease and ranks fourth among health problems. Depression is expected to impose a heavy burden on communities by 2020 [4]. Depression can be long-term and recurrent, significantly impairing the ability of people to work at school or in everyday life and feel that life is worthless [5,6,7]. This significant violation of daily functioning is equal to or greater compared to other chronic medical conditions such as diabetes and congestive heart failure [8]. As a result, MDD can have a significant impact on the quality of life and work productivity [9, 10]. The average onset age of major depressive disorder [MDD] is around forty, in which approximately 50% of the cases begin between the ages of 20 and 50 [11]. Some new epidemiological data suggest that the incidence of MDD may be increasing among people under 20 [12, 13].
MDD is both a chronic and recurrent disease. Recurrence after recovery is common in MDDs [13]. Patients who are at least in their second period of depression are in the recurrent type of MDD. Although relieving the signs and symptoms of depression is the main goal of treatment, maintaining this state of health is one of the current challenges in mental health. Both natural and therapeutic studies of MDD have highlighted the high rate of recurrence after recovery [14,15,16,17,18,19]. Persistence or recurrence is the most important clinical problem associated with depression that leads to significant consequences for the individual and general health [20, 21]. The higher the number of previous courses, the faster the recurrence, bringing about not only a shorter interval between cases but also a greater intensity of each one [15, 17, 18]. As studies have shown, approximately 50% of those who recover from the first MDD would experience another period in their lifetime, roughly 80% of those with a history of two periods would relapse [20, 22,23,24,25,26], and finally, on average, each depressed patient experiences 3 to 4 periods of depression in his or her lifetime [27]. Gender, level of education, employment status, and marital status are all the probable risk factors that may lead to a recurrence in a depressed patient [28,29,30,31,32]. One of the goals of the WHO in promoting mental health and treatment of mental illness is to reduce the duration and number of hospitalizations and also to expand social services, which can reduce the psychological, emotional, economic, and social burden and improve physical health and quality of life of family members [33, 34]. Therefore, it is necessary not only to identify risk factors of depression but also to deal with them [35].
Studying reversible diseases, identifying the factors affecting their recurrence or hospital readmission, and finally preventing them are all among the most important issues in medicine. Since MDD is a recurrent disease and its recurrence or hospital readmission is associated with increased psychological, social, and other burdens that the patients and their family are faced with, the use of appropriate statistical methods to model the recurrence of the disease has a significant role in identifying the factors affecting it. Most studies on recurrent depression have used logistic regression to model recurrent as a binary response, so that the absence/presence of a single episode of the disease is modeled [25, 36]. This approach is faced with limitations because it neglects the rate of recurrent episodes as a crucial outcome. In this regard, considering outcomes like the number of re-hospitalizations or readmissions due to the disease or the degree of recurrence as count outcomes [that somehow reflects the recurrent rate] and its modeling are of particular importance. This is especially important because MDD has a highly recurrent nature and its recurrence varies across individuals substantially [37].
Due to the high cost of treatment, maintenance, and the costs imposed by crimes caused by depression disorder, its damages cannot be calculated as it affects all aspects of patients’ lives and functions negatively and often requires long-term care and follow-up. It has also been shown that the incidence of MDD may be increasing among people aged under 20. This study aimed to compare different count regressions in modeling hospital readmissions due to MDD and to investigate the factors associated with MDD in Hamadan province. Our results would help to control the disease and to prevent recurrence or hospital readmission in these patients in Hamadan province.
Materials and methods
Data
In this retrospective cohort study, information of all 1005 patients with MDD admitted to Sina [Farshchian] Educational hospital [the referral hospital and the only psychiatric hospital across Hamadan Province] in Hamadan, Iran was extracted from their records from 2011 to 2018. In this study, only the patients who were admitted for treatment of depression are considered. MDD was diagnosed through a clinical interview which was conducted by a psychiatrist based on Diagnostic and Statistical Manual of Mental Disorders [DSM-5] [13]. Data were collected using a checklist prepared by the authors and clinical consultant. The checklist included demographic characteristics and clinical characteristics of the patients. The information related to the explanatory variables [recorded in the patients’ cases] had been recorded by a medical doctor based on patients’ and their companions’ statements.
The response variable [outcome]
The number of hospital readmissions from the beginning of the disease to the end of the study [2011–2018] of patients with MDD was considered as the response variable. As the Sina hospital was the only place for admitting the patients with major depression for hospitalization throughout the Hamadan province, most of the patients were hospitalized only in this place. However, records of hospital readmissions in places outside of the province were also extracted and pooled with the response variable.
Explanatory variables
Information was used as explanatory variables as follows: 1] Demographic characteristics including gender [male/female], marital status [single [including never married, divorced, widow [er]]/married], education level [illiterate/literate], occupation [unemployed/employed], type of residence [city/village], the number of siblings, history of emotional problems including death/disease of a family member or relatives, divorce of parents, marriage, pregnancy, having a new member in the family, an emotional failure [breaking up with a girlfriend or boyfriend, breaking up the engagement or marriage, divorce, infidelity, etc.], having a girl or boy abandoning the house and military service [yes/no]; 2] Variables related to patients’ clinical characteristics including age at the onset of the disease, history of psychiatric illnesses/mental disorders in family members [yes/no], history of medical disorders including cardiovascular, visual and auditory diseases, skin problems, hypertension, hyperlipidemia, glands [including thyroid and diabetes], gastrointestinal problems [including gastric ulcer and pancreatitis], neurological problems including stroke, headache and seizures], rheumatic problems [including lung and asthma] and urological problems [including kidney and urinary tract diseases], having a history of suicide attempt [yes/no], having a history of smoking [yes/no], having a history of substance abuse including opium, cannabis, glass and psychotropic substances, etc., [yes/no]. All variables were selected based on previous studies and information available. A description of the selected explanatory variables was provided in Table 1.
Table 1 Demographic and personal characteristics of the college patients
Full size table
Statistical analysis
The hospitalization rate among patients with MDD was modeled as a function of gender, age of onset, and other explanatory variables using several count regression models including Poisson, negative binomial [NB], generalized Poisson [GP], exponentiated-exponential geometric [EEG], and their zero-inflated [ZI] counterpart regressions [Please see the Additional file 1 for model desciption]. All variables were the same considered in all models. Vuong test based on Bayesian Information criteria [BIC] was used to compare non-nested models [All tests were conducted with α ≤ 0.05] to see which one provides a better fit. This test produces a z-statistic where a value > 1.96 supports the alternative that the first model fits the data better and a value