Patterns of antidepressant treatment changes among adults with major depressive disorder in Northwest Ethiopia: a hospital-based cross-sectional study

Abstract

Background:

Major depressive disorder (MDD) is a prominent cause of worldwide disability, severely affecting quality of life in Ethiopia. Despite a significant prevalence of treatment-resistant depression, challenges in treatment adherence, and a high prevalence of adverse drug reactions, the studies in Ethiopia have limited data on antidepressant treatment changes.

Objectives:

This research aims to identify patterns of antidepressant treatment changes, the reasons for treatment changes, and associated factors among patients with MDD in Ethiopia.

Design:

A hospital-based cross-sectional study.

Methods:

The present study was conducted at Debre Tabor Comprehensive Specialized Hospital, Northwest Ethiopia, from June 01, 2025, to August 30, 2025. The data was entered, cleaned, and analyzed by using SPSS Version 27. The antidepressant side-effect checklist was used to classify adverse effects as mild, moderate, or severe. The Naranjo adverse drug reactions (ADRs) probability scale assessed antidepressant-related adverse drug reactions; non-adherence was evaluated using a self-reported tablet count tool. A multivariable logistic regression model was utilized to identify factors associated with antidepressant treatment changes. The significance level was set at a p-value of 0.05, with a 95% confidence interval (CI).

Results:

Out of 220 respondents, 127 (57.7%) did not respond to their antidepressants, which impacted their ability to carry out daily responsibilities. Approximately one-third of participants (80, or 36.4%) had switched medications. More than half of the patients with MDD (114, or 51.82%) experienced ADRs related to antidepressants. The prevalence of non-adherence to medication was 54.6%; 95% CI: 48.6, 60.9. Antidepressant treatment change was significantly associated with having a history of relapse (AOR = 2.52, 95% CI: 1.28, 5.42) and a history of hospital admission (AOR = 2.35, 95% CI: 1.25, 7.59).

Conclusion:

The management of MDD in Ethiopia faces challenges such as high non-adherence rates, significant ADRs, and limited access to alternative treatments. About 36.4% of patients underwent antidepressant treatment changes. A history of relapse and admission history were associated with antidepressant treatment changes. Future research should investigate longitudinal research to understand non-response and its impact on patient outcomes.

Plain language summary

Patterns and reasons for antidepressant treatment changes among patients with major depressive disorder in Northwest

This study investigates the reasons behind medication changes among patients with major depressive disorder in Ethiopia, focusing on a sample from Debre Tabor Comprehensive Specialized Hospital. Findings revealed that over half of the patients did not respond to their treatment and approximately one-third encountered antidepressant treatment changes, often due to side effects. It was noted that individuals with a history of relapse or prior hospitalizations were more correlated with changing their antidepressants. The study underscores the challenges in managing depression in Ethiopia and highlights the necessity for additional research to enhance treatment responses and patient care.

Keywords

adherence adverse drug reactions antidepressant treatment change major depressive disorder mental health interventions

Introduction

Major depressive disorder (MDD) is a complex psychiatric condition marked by persistent sadness, hopelessness, anhedonia, sleep disturbances, indecision, impaired concentration, and recurrent suicidal ideation.¹ Major depression continues to pose a significant public health challenge, even though there are now more potential first-line medications for its treatment than ever.² Depression treatment in real-world settings is complex, often requiring multiple pharmacotherapeutic adjustments.³ Unfortunately, however, these first-line treatments are often ineffective despite optimal treatment being prescribed and adherence ensured. This lack of effectiveness is because some patients do not respond to a particular agent. When this occurs (after ensuring that the dose has been adequate and the patient has been adherent), three options are suggested: dose escalation, augmentation with another agent, or switching antidepressants.⁴

Individuals suffering from depression face increased risks of suicide and suicide attempts. However, it remains uncertain how these risks vary among patients taking different types of antidepressants.⁵ In Africa, approximately 29.19 million people (9% of 322 million) suffer from depression, with over 7 million cases in Nigeria (3.9% of 322 million). The lifetime prevalence of depressive disorders is estimated to range from 3.3% to 9.8%.⁶

Antidepressant treatment change is commonly practiced in managing major depressive disorder due to inadequate responses and tolerability issues.⁷ Antidepressant treatment change may be necessary due to inadequate response, intolerable side effects, or safety concerns. It is essential to monitor for rare but serious events like antidepressant-emergent mania (AEM), especially in patients with undiagnosed bipolar spectrum illness. Recognizing these risks is crucial for effective treatment and harm reduction.⁸

Antidepressants are the primary treatment for depression, but many patients fail to achieve or maintain adequate response, with some experiencing a loss of efficacy, known as tachyphylaxis. This phenomenon leads to relapse, functional impairment, and potential changes in therapy. Understanding these dynamics is essential for optimizing depression management.⁹ Antidepressants are essential for treating depression, but many patients do not achieve sufficient clinical response, resulting in treatment-resistant depression (TRD). Defined as an inadequate response to at least two trials of antidepressants, TRD can lead to increased relapse rates and psychosocial issues. Management strategies for TRD include augmenting treatment with atypical antipsychotics, switching medications, or using combination therapy. New options like brexpiprazole show promise as adjunctive treatments, underscoring the need for personalized approaches in managing TRD.¹⁰

Switching antidepressants is often necessary due to intolerable side effects or significant drug–drug interactions with other prescribed medications.⁴ While switching antidepressants for lack of efficacy is common, clinically justified and seemingly obvious, there is limited research evidence to guide clinicians when a trial of a first-line antidepressant is no longer worth pursuing and, in particular, what agent(s) to switch to, as demonstrated by a recent meta-analysis.⁴ If there is minimal response to treatment (less than 20% improvement) after 3 weeks on the initial antidepressant, it is advisable to consider switching medications; for mild to moderate depression, a within-class switch may be appropriate, while for severe or melancholic depression, transitioning to a dual-acting antidepressant is recommended.¹¹

The high rates of non-adherence and adverse drug reactions (ADRs) among MDD patients in Ethiopia raise critical questions about the effectiveness and management of antidepressant treatment strategies. With approximately 32.9% of patients demonstrating non-adherence and a significant 69% experiencing ADRs, it becomes imperative to explore the phenomenon of antidepressant switching.^12
–14 Research in Ethiopia reveals a significant prevalence of treatment-resistant depression (TRD) among patients with MDD at Saint Amanuel Mental Specialized Hospital,¹⁵ and depression was prevalent and influenced by demographic and social factors.¹⁶ This highlights a necessity for alternative treatment approaches, such as antidepressant treatment changes. However, there is limited data in Ethiopia regarding patterns of antidepressant treatment changes and their associated factors in routine clinical settings, despite increasing evidence on their use, which this study aims to address.

Methods

Study design, study area and period

A cross-sectional study was conducted from June 01, 2025, to August 30, 2025. The study was conducted at Debre Tabor Comprehensive Specialized Hospital (DTCSH), which is found in Debre Tabor city, Amhara region, Northwest Ethiopia, which is 667 km from Addis Ababa.

Population and eligibility criteria

Patients who were on follow-up on the psychiatric ward of DTCSH. Patients with MDD are on follow-up in the psychiatric ward. Adult patients (⩾18 years) diagnosed with Major Depressive Disorder according to DSM-5 criteria, patients who have been on antidepressant treatment for at least 6 weeks, and patients who provide informed consent were included in the study. Whereas patients with comorbid severe psychiatric disorders (e.g., schizophrenia, bipolar disorder), patients unable to provide reliable information due to cognitive impairment or severe illness, and patients currently enrolled in other interventional studies were excluded from the study.

Sampling technique and sample size

Study participants were selected using a simple random sampling technique (lottery method), in which each eligible participant had an equal chance of being included.

Sample size determination

The sample size is determined by using the single population proportion formula:

n = \frac{[z^{2} (1 - p) p]}{d^{2}}

where n = sample size

Z = 95% confidence interval, whose Z score value is 1.96

P = is the proportion, and since there are no previous studies done on switching patterns of antidepressants in Ethiopia, p = 50% will be taken to have the maximum sample size.

q = 1 - p

d = margin of error taken as 5%

Therefore $n = \frac{{(1.96)}^{2} (0.5) (0.5)}{{(0.05)}^{2}} = 384$

However, since the total population in our study was less than 10,000 (430), we recalculated the sample size using the correction formula: Nf = n/(1 + n/N), where Nf was the actual sample size using the correction formula, n was the minimum sample size (384), and N was the actual population size (430). By substituting these values into the formula, we found that Nf was equal to 203. Additionally, considering a 10% contingency for non-response rate, the minimum sample size required for this study was 223. Out of the 223 participants approached, a total of three patients were withdrawn or excluded from the study due to unwillingness to participate. Overall, 220 participants were involved in this study.

Study variables

Dependent variable

The study’s primary outcome was the change in treatment (yes/no), which encompassed any modifications to the initial antidepressant regimen during the observation period, including switching, augmentation, and discontinuation of antidepressants.

Independent variables

Sociodemographic status (such as age, sex, marital status, occupation status, and educational level); history of hospital admission; socioeconomic status; social and substance use history; medication adherence; history of relapse; efficacy; and adverse drug reaction.

Data collection

Data collection instrument and data collectors

This study adhered to the STROBE guidelines,¹⁷ with the completed checklist available as Supplemental File 1. Data collection was the process of gathering and measuring information on the target variables, and it was established in a systematic fashion that enabled one to answer the stated research question. The primary data was gathered from the follow-up patients in the psychiatric ward by using the KoboToolbox and interviews. The researcher-used questionnaire, which provides a relatively cheap, quick, and efficient way of obtaining large amounts of information.

Data collection procedures and quality control

The data collection instrument was developed following an extensive review of relevant literature. Data were gathered through a structured and pretested questionnaire that assessed sociodemographic and clinical characteristics, and it is available as Supplemental File 2. Before commencing the main study, a pretest was conducted on 12 patients (5% of the total sample size) to assess the clarity and effectiveness of the tool. Data from the pretest were not included in the final analysis. Based on the pretest findings, appropriate adjustments were made to refine the final version of the questionnaire. To ensure the quality of the data, all data collectors underwent training. The questionnaire, initially written in English, was translated into Amharic and then back-translated into English to verify consistency. The final tool was also reviewed for face validity and approved by a senior psychiatrist.

Multicollinearity was assessed through variance inflation factor analysis, with results showing no issues (VIF < 3). The model fit has been evaluated using the Hosmer-Lemeshow test, yielding an acceptable fit (p = 0.65). Results were reported using adjusted odds ratios (AORs) along with their corresponding 95% confidence intervals (CIs), providing a measure of the strength and precision of associations between independent variables and the outcome.

Clinical and medication-related data, including reasons for medication changes, were gathered through document review using a structured data extraction format. Two trained psychiatric nurses collected the data under daily supervision. In this study, “antidepressant treatment change” encompasses treatment switching (replacing one antidepressant with another), augmentation (adding another psychotropic medication), and discontinuation (cessation of antidepressant therapy).

The Antidepressant Side-Effect Checklist (ASEC) was used to classify side effects as mild, moderate, or severe. The Naranjo ADR probability scale assessed adverse drug reactions, categorized as definite (⩾9), probable (5–8), possible (1–4), or doubtful (0). Medication non-adherence was evaluated using a self-reported tablet count tool cross-verified with pharmacy refill records. Patients were considered non-adherent if they took less than 80% of prescribed doses. Adherence percentage was calculated using the formula: Adherence (in percent) = ((Total prescribed doses per month − Missed doses per month)/Total prescribed doses per month) × 100.¹⁸

Data analysis and interpretation

Data were entered and cleaned using EpiData version 3.5.1, then exported to SPSS version 27 for further analysis. Descriptive statistics were used to summarize sociodemographic and behavioral characteristics of the participants, including measures such as frequency, percentage, mean, standard deviation, and cross-tabulations. Associations between categorical variables were assessed using the chi-square test. Variables with a p-value less than 0.25 in the bivariable logistic regression were included in the multivariable logistic regression model to assess factors associated with antidepressant treatment changes. In the final multivariable analysis, variables with a p-value of 0.05 or less at a 95% CI were considered statistically significant and associated with antidepressant treatment changes. This study used a cross-sectional design to analyze changes in antidepressant treatment among patients with depression. As a result, it could not determine causal relationships between the factors and the treatment changes, indicating that the identified factors should be viewed as associations.

Operational definitions

Antidepressant treatment change was defined as any adjustment in a patient’s antidepressant regimen during the study, including dose changes, switching medications, augmentation with an additional psychotropic medication, or stopping or discontinuation of the current antidepressant.

Switching was defined as stopping of one antidepressant and starting another within 30–60 days, including within-class and between-class changes, consistent with definitions of treatment substitution of pharmacoepidemiology.¹⁹

Augmentation was defined as the addition of a non-antidepressant psychotropic to an ongoing regimen, typically for partial response.²⁰

Response was defined as any documented improvement in symptoms by the treating clinician.

Remission was defined as the absence of clinically noted depressive symptoms at the last follow-up.

Drug interaction was defined as the interactions between drugs and food, other drugs, beverages, and supplements that affect drug action.

Efficacy was defined as the ability to produce the maximal response possible for a particular biological system and relates to the extent of functional change in the receptor by the drug.

Relapse was defined as the return or worsening of depressive symptoms.

“Intolerable side effect” was defined as an adverse reaction to a drug or treatment that is so severe that a patient cannot tolerate it.

Discontinuation was defined as cessation of antidepressant treatment for ⩾42 days without a new prescription.²¹

Results

Sociodemographic status of the study participants

The majority of the study participants were females, 175 (79.5%). More than half of the study participants were in the age range between 18 and 23 years, 117 (53.2%). The mean (±SD) age of the study participants was 27.8 (±16.22). Most of the study participants, 209 (90.9%), were orthodox Christians, and 156 (70.9%) participants were married. The educational status of the majority was college education, which accounts for 117 (53.2%) (Table 1).

Table 1.

Sociodemographic and behavioral characteristics of MDD patient follow-up in DTCSH, Northwest Ethiopia (n = 220).

Variable	Frequency	%
Sex
Female	175	79.5
Male	45	20.5
Age (in years)
18–23	117	53.2
24–29	46	20.9
30–35	30	13.6
⩾36	26	11.8
Religion
Orthodox	200	90.9
Muslim	16	7.3
Protestant	3	1.4
Other	1	0.5
Level of education
College	117	53.2
9–12 grade	52	23.6
1–8 grade	24	10.9
Read and write	18	8.2
Can’t read and write	9	4.1
Occupational
Student	113	51.4
Merchant	54	24.5
Government employee	25	11.4
House wife	18	8.2
Farmer	3	1.4
Other	7	3.2
Marital status
Married	156	70.9
Widow	25	11.4
Separated	21	9.5
Single	7	3.2
History of relapse
Yes	86	39.1
No	134	60.9
Admission history
Yes	182	82.7
No	38	17.3

DTCSH, Debre Tabor Comprehensive Specialized Hospital; MDD, major depressive disorder.

Amount of money spent on medications

By using Sturge’s formula K = 1 + 3.322 log (n)

where n is the number of households and K is the number of classes

K = 1 + 3.322 log (220) = 8.77, approximately 9.

W = (Max − min)/K, where

W = class width W = (1500–1050)/10 = 50. From 220 households, 206 (67.8%) spent money around 1500–1050 birr for medication on average per year. The least percentage of money spent was for medication found between 1050 and 1099 birr (Table 2).

Table 2.

Amount of money used for medication purpose among MDD patient follow-up in DTCSH, Northwest Ethiopia (n = 220).

Amount of money spent on medication	Frequency	%
1050–1099	3	1.36
1100–1149	4	1.81
1150–1199	6	2.72
1200–1249	9	4.09
1250–1299	18	8.18
1300–1349	15	6.81
1350–1399	20	9.09
1400–1449	75	34.09
1450–1500	70	31.81

DTCSH, Debre Tabor Comprehensive Specialized Hospital; MDD, major depressive disorder.

Clinical and medication-related characteristics of the study participants

Efficacy

Among 220 study participants, 127 (57.7%) respondents did not achieve remission (Table 3).

Table 3.

Treatment efficacy among patients with MDD attending follow-up at DTCSH, Northwest Ethiopia.

Efficacy indicator	Frequency	%
Achieved remission/managed responsibilities	93	42.3
Did not achieve remission/suboptimal treatment response	127	57.7

Suboptimal responsibility in treatment management highlights inadequate adherence to medication, follow-up appointments, and self-care activities essential for effective depression management.

DTCSH, Debre Tabor Comprehensive Specialized Hospital; MDD, major depressive disorder.

Adverse drug reactions and non-adherence level of the study participants

According to the Antidepressant Side-Effect Checklist (ASEC), 114 (51.8%) of patients with MDD experienced ADRs (Table 4). The overall prevalence of non-adherence to antidepressant medication was 120 (54.6%; 95% CI: 48.6, 60.9).

Table 4.

Classifications of ADRs among MDD patients receiving treatment at DTCSH (n = 114).

Variable	Frequency (%)
Severity of ADR (n = 114)
Mild	24 (21.1)
Moderate	47 (41.2)
Severe	43 (37.7)
Naranjo ADR probability scale
Probable	40 (35.1)
Possible	30 (26.3)
Doubtful	20 (17.5)
Definite	24 (21.1)

ADR, adverse drug reaction; DTCSH, Debre Tabor Comprehensive Specialized Hospital; MDD, major depressive disorder.

Social and substance

It was found that about 52 (23.6%) respondents were living alone, while the majority, 168 (76.4%), lived with their families. A total of 177 participants (80.5%) indicated that they did not feel safe or supported by their families or partners (Table 5).

Table 5.

Social and substance assessment among MDD follow-up patients in DTCSH, Northwest Ethiopia (n = 220).

Social and substance	Frequency	%
Where do you live
Alone	52	23.6
With my family	168	76.4
Feel safe & supported	43	19.5
Did not feel safe & supported	177	80.5
Substance use history
Substance use	91	41.4
No history of substance use	129	58.6

DTCSH, Debre Tabor Comprehensive Specialized Hospital; MDD, major depressive disorder.

Cost and preference

The majority of respondents, 201 (91.4%), reported having no specific medication preference. Among the 19 respondents who did express a preference, all favored fluoxetine. Out of the total 220 participants, 70 (31.8%) were receiving financial assistance, while the remaining 150 (68.2%) were not (Table 6).

Table 6.

Cost and preference assessment among MDD patient follow-up patients DTCSH, Northwest Ethiopia (n = 220).

Cost and preference	Frequency	%
Financial issue
Get Financial assistance	150	31.8
Did not get financial assistance	70	68.2
Medication preference
Preference	19	8.7
Have no preference	201	91.3

DTCSH, Debre Tabor Comprehensive Specialized Hospital; MDD, major depressive disorder.

Patterns of antidepressant treatment changes among MDD patients on follow-up

Out of the 220 participants reviewed, 80 (36.4%) had changed antidepressant treatment in the last 1 year. Among those, 58 participants (72.5%) experienced their first instance of antidepressant treatment change. Regarding types of antidepressant treatment changes, lateral switching (within-class switching) accounts for 75%, followed by cross-class switching (between-class switching; Table 7).

Table 7.

Assessment of patterns of antidepressant treatment changes among MDD follow-up patients in DTCSH, Northwest Ethiopia (n = 220).

Patterns of antidepressant treatment changes	Frequency	%
Antidepressant treatment change	80	36.4
SSRI to SSRI	60	75.0
SSRI to TCA	9	11.2
TCA to SSRI	7	8.8
Fluoxetine with olanzapine (augmentation)	4	5.0
Times of treatment change
First time	58	72.5
Second time	22	27.5
No antidepressant treatment change	140	63.6
Duration of treatment
<1 year	127	57.7
⩾1 year	93	42.3
Types of antidepressant treatment changes
Lateral switching (within-class switching)	60	75.0
Cross-class switching (between-class switching)	16	20.0
Augmentation of SSRI_S (fluoxetine) with olanzapine	4	5.0

DTCSH, Debre Tabor Comprehensive Specialized Hospital; MAOIs, monoamine oxidase inhibitors; MDD, major depressive disorder; SNRIs, serotonin and norepinephrine reuptake inhibitors; SSRIs, selective serotonin reuptake inhibitors; TCAs, tricyclic antidepressants

Reasons for antidepressant treatment changes among MDD patients on follow-up

In this study, 80 patients were identified as encountering antidepressant treatment changes. The primary reason for this switch was lack of response or efficacy, which affected 27.5% of the patients who encountered antidepressant treatment changes. Additionally, 22.5% reported poor tolerance or adverse effects as a reason, while 12.5% mentioned medication non-adherence (Table 8).

Table 8.

Antidepressant treatment change patterns assessment among MDD follow-up patients in DTCSH, Northwest Ethiopia (n = 80).

Reasons for antidepressant treatment changes	Frequency	%
Lack of response/lack of efficacy	22	27.5
Medication non-availability	6	7.5
Poor tolerance/adverse effects	18	22.5
Cost of medication	8	10.0
Relapse	4	5.0
Patient Preference	12	15.0
Medication non-adherence	10	12.5

DTCSH, Debre Tabor Comprehensive Specialized Hospital; MDD, major depressive disorder.

Factors associated with antidepressant treatment changes

The multivariable logistic regression analysis was fitted to identify factors associated with antidepressant treatment changes among MDD patients in follow-up. History of relapse and admission history were variables statistically associated with antidepressant treatment changes. Patients with a history of relapse had higher odds of switching compared to patients without a history of relapse (AOR = 2.52, 95% CI: 1.28, 5.42). Patients who had an admission history in a hospital had 2.35 times higher odds of antidepressant treatment changes (AOR = 2.35, 95% CI: 1.25, 7.59) than patients who didn’t have a history of hospital admission in the last year (Table 9).

Table 9.

Factors associated with antidepressant treatment changes in patients with MDD at DTCSH, Northwest Ethiopia (n = 220).

Variables	Category	COR (95% CI)	AOR (95% CI)	p-Value
Gender	Male	0.67 (0.53, 1.32)	0.55 (0.45, 1.81)	0.072
	Female (Ref)	1	1
Residence	Rural	1.13 (0.43, 3.24)	1.22 (0.15, 3.37)	0.283
	Urban (Ref)	1	1
History of admission	Yes	2.22 (1.78, 5.67)	2.35 (1.25, 7.59)	0.032*
	No (Ref)	1	1
Adherence	Adherent	0.82 (0.53, 1.61)	0.49 (0.34, 1.81)	0.943
	Non-adherent (Ref)	1	1
Adverse drug reaction	Yes	2.55 (0.88, 3.86)	2.37 (0.92, 4.83)	0.583
	No (Ref)	1	1
History of relapse	Yes	2.03 (0.89, 2.45)	2.52 (1.28, 5.42)	0.014*
	No (Ref)	1	1
Duration of treatment	⩾1 year	1.13 (0.41, 3.22)	1.12 (0.55, 3.17)	0.078
	<1 year (Ref)	1	1

Statistically significant at p < 0.05.

AOR, adjusted odds ratio; CI, confidence interval; COR, crude odds ratio; DTCSH, Debre Tabor Comprehensive Specialized Hospital; MDD, major depressive disorder; Ref, Reference category.

Discussion

Prevalence of antidepressant treatment changes

This study assessed the patterns and prevalence of antidepressant treatment changes among patients with MDD at Debre Tabor comprehensive specialized hospital, Ethiopia. Antidepressant switching is a common response to inadequate therapeutic effects or adverse drug reactions and serves as an important indicator of treatment challenges in clinical practice.⁹ Understanding how frequently patients switch medications and the factors influencing these decisions is essential for optimizing depression management and improving patient outcomes.²² This research provides critical insight into antidepressant use dynamics in this context, highlighting areas for improved clinical interventions.³

Out of 220 participants, 80 (36.4%) changed their antidepressant treatment in the past year, with 58 participants (72.5%) experiencing their first treatment change. Lateral switching (within-class) comprised 75% of these changes, while cross-class switching (between-class) followed. The study reveals that the rate of antidepressant treatment changes aligns with observations in low- and middle-income countries, where limited psychiatric resources and medication adherence issues are prevalent. High medication non-adherence supports findings from other resource-limited contexts, underscoring the challenges in managing long-term depression.²³ This indicates that socioeconomic and healthcare system factors significantly impact treatment continuity and clinical outcomes.

The present study found that 54.6% of patients with MDD were non-adherent to their antidepressant medications, a rate higher than the 32.9% reported in a 2024 multicenter study across Ethiopia.¹² Both studies highlight a substantial gap in consistent treatment adherence. Poor adherence to medication can stem from factors such as side effects, financial issues, limited access to mental health services, social stigma, inadequate follow-up, and lack of illness insight.²⁴ This non-adherence can lead to suboptimal treatment outcomes, persistent symptoms, and increased changes in antidepressant therapy, particularly in resource-limited settings where medication availability and psychiatric support are further constrained.

In the present sample, more than half of the patients (51.8%) experienced antidepressant-related ADRs, with 26.3% classified as “possible” and 35.1% as “probable.” This aligns with findings from Amanuel Mental Specialized Hospital (2020–2021), where ADR prevalence reached 69%, particularly among younger patients and those on polypharmacy.¹³ ADRs are known to contribute to poor tolerance and are likely key drivers of both non-adherence and treatment switching.²⁵

Most studies report that females, especially those who are divorced or widowed, are at a higher risk of depression compared to males and women who are single or married. One commonly suggested explanation for the higher prevalence in females is the influence of sex hormones, which are believed to play a role in the development of depression.^26,27 In this study, the majority of patients, 175 (79.5%), were females, which aligns with findings that females generally have a higher risk of depression compared to widowed or divorced individuals. In this study, patients aged 36–41 were the most prevalent. Those starting antidepressant treatment were more likely to have a college education and be employed or self-employed compared to those switching medications. Supporting this, research conducted across five European countries found statistically significant demographic differences between two patient groups: those switching antidepressant treatment were notably older than patients who were initiating treatment (p = 0.012).²⁸ Our findings align with recent evidence that illustrates the complexities of second-line treatment strategies for depressive disorders, which emphasizes the significance of alternative antidepressant strategies for patients not responding adequately to initial SSRI therapy, highlighting treatment switching as a means to improve outcomes. Factors such as poor symptom control, adverse reactions, and non-adherence are noted as contributors to treatment changes, which can adversely affect long-term results. Moreover, high levels of non-adherence observed in our study likely lead to increased switching and instability of treatment, especially in settings with limited access to continuous psychiatric care.²⁹

In the present study, the main antidepressant treatment change was switching. Lateral switching (within-class switching) was the most common strategy, employed in 75% of the cases. Within-class switching is often the first-line strategy after SSRI failure, primarily due to tolerability considerations.^30
–33 The augmentation of SSRIs with atypical antipsychotics, specifically olanzapine, accounted for 5% of the cases. This low rate is comparable to findings from augmentation studies,^34
–36 which demonstrate that while augmentation (especially with agents like olanzapine or aripiprazole) can be effective, it is typically reserved for more resistant forms of depression or partial responders. The limited use in our sample may also be influenced by concerns about side effects associated with atypical antipsychotics, including weight gain and metabolic issues.^37
–39 SSRIs, especially fluoxetine, are the most prescribed initial antidepressants. A common treatment change is switching from fluoxetine to amitriptyline, along with other transitions between SSRIs and tricyclic antidepressants, primarily due to inadequate response, adverse effects, or poor adherence.

Reasons for antidepressant treatment changes among MDD patients on follow-up

The most frequent reason for antidepressant treatment changes in this study was inadequate response or lack of efficacy, reported by 27.5% of patients. Poor tolerance or adverse effects accounted for 22.5%, while 12.5% switched due to non-adherence. Patient education on treatment duration and potential withdrawal symptoms is crucial for safe discontinuation, ultimately optimizing antidepressant use and reducing unnecessary long-term exposure.⁴⁰ The study observed treatment patterns consistent with real-world evidence of frequent discontinuation, switching, and augmentation of antidepressant therapy, highlighting the complexity of depression management. This shows the need for individualized strategies and better adherence to evidence-based recommendations for optimal patient outcomes.⁴¹ Switching antidepressants in patients with major depressive disorder is a common strategy to enhance treatment effectiveness.¹¹ Additionally, involving patients in shared decision-making and addressing psychosocial barriers can enhance adherence.⁴²

A study found that 57.7% of patients did not achieve adequate responses to their antidepressants, highlighting issues of TRD. TRD is linked to higher risks of relapse, hospitalization, and functional impairment. Strategies for addressing TRD include switching antidepressants, combination therapy, or augmentation with agents like brexpiprazole, which may enhance outcomes for patients not responding sufficiently to standard treatments.¹⁰

A significant percentage of participants did not achieve adequate response to antidepressants, attributed to true non-response, poor adherence, or unrecognized clinical complexities. One such complexity is antidepressant-emergent mania (AEM) in patients with latent bipolarity, which occurs in some patients undergoing antidepressant therapy and is linked to factors such as prior depressive history and age of onset. Clinicians should consider AEM when assessing treatment resistance or contemplating changes to therapy and should also closely monitor symptom trajectories to differentiate between adherence problems, tolerability issues, and true loss of efficacy.^8,9

Different studies reported the prevalence of antidepressant treatment changes across the world.^43
–47 In comparison, our study showed that about 36.4% of patients encountered antidepressant treatment changes. The difference may be due to the smaller sample size and sampling methods used in our research. One of the main reasons for treatment changes is the lack of response to antidepressants. Supporting this, the STAR*D trial reported that only up to 37% of patients achieved remission with their first antidepressant treatment in clinical practice.⁴⁸ Change of treatment is thus a frequent therapeutic action in patients with depression who do not respond to their initial treatment.⁴⁹ Antidepressant treatment is widely used in UK primary care; however, about 55% of patients do not respond to medication even when prescribed at an adequate dose and for an appropriate duration.⁵⁰ In our study, 127 respondents (57.7%) failed to achieve remission despite adhering to their prescribed antidepressant therapy, and many also demonstrated poor engagement in managing their treatment. These findings support the UK study, which highlights poor treatment response as a common reason for switching antidepressant medications.

A study conducted in the United States reported that the total economic burden of major depressive disorder increased significantly over the past decade, reaching approximately $210 billion in 2010.⁵¹ Workplace productivity losses (48%) and healthcare expenses (47%) were nearly equal contributors to costs associated with medication discontinuation, primarily due to a lack of financial assistance for 68% of participants. Switching antidepressants is often required because of intolerable side effects or significant interactions with other medications.⁴

A study in five European countries indicated that adverse events prompted treatment changes in 20 patients (9.3%). In a related study, 20 out of 220 patients (9.0%) switched due to adverse drug reactions, reflecting a slight difference. Non-compliance was also a factor; the European study reported a 3.2% non-compliance rate (7 patients), whereas this research identified a significantly higher rate of 26.4% (58 patients).

Factors influencing antidepressant treatment changes among patients on follow-up

The findings from our study revealed the factors associated with antidepressant treatment changes in this patient population. Notably, the analysis demonstrated that a history of relapse was associated with treatment change, with patients exhibiting a 2.52-fold increase in odds compared to those without such a history (AOR = 2.52, 95% CI: 1.28, 5.42). This underscores the importance of relapse as a potent associated factor of treatment modifications, suggesting that recurrent depressive episodes may lead clinicians to reconsider the efficacy of current pharmacotherapy. The implications of these findings align with previous literature, which has shown that patients with recurrent MDD are often more challenging to treat and may require tailored pharmacological strategies to achieve optimal outcomes.²

Additionally, our study identified hospital admission history as another factor associated with antidepressant treatment changes. Patients with a recent history of hospitalization exhibited 2.35 times higher odds of changing medications (AOR = 2.35, 95% CI: 1.25, 7.59). This association highlights the severity of depressive episodes that necessitate inpatient care, potentially signaling a need for more aggressive or alternative treatment approaches post-discharge. The correlation between hospitalization and medication adjustments reflects broader trends in psychiatric care, where acute episodes often prompt re-evaluation of treatment regimens.⁵²

Strengths and limitations of the study

This study highlights several limitations: the cross-sectional design restricts causal inference regarding antidepressant treatment changes and its associated factors. Additionally, the single-center approach may limit generalizability to other settings in Ethiopia. Medication adherence assessments were based on self-reported tablet counts, vulnerable to biases that may inflate adherence rates. The evaluation of adverse drug reactions was partially dependent on self-reports and may not encompass all effects. The absence of standardized severity scales for depression restricted exploration of symptom severity and treatment response. The potential for recall bias due to self-reported adherence, as well as the possibility of residual confounding despite multivariable adjustment, is also a limitation of this study.

Conclusion

The findings of this study highlighted significant challenges in the management of major depressive disorder among patients at the comprehensive specialized hospital in Ethiopia. A substantial proportion of respondents were female and within the younger age group, reflecting a demographic that may require targeted mental health interventions. More than half of patients (57.7%) did not achieve remission from their antidepressant therapy. The majority of patients (72.5%) had their antidepressant treatment changed for the first time, while 27.5% underwent a second treatment change, emphasizing the necessity for clinicians to closely monitor patient responses and adjust treatment plans accordingly.

The findings of this study point to the urgent need for enhanced mental health resources, education, and support systems to improve treatment outcomes for individuals with major depressive disorder in Ethiopia. Further research, a longitudinal study is essential to identify the underlying causes of non-response and non-adherence, enabling the development of more effective, personalized treatment approaches.

Recommendations

Prescribers should exercise caution in dosing antidepressants and carefully select appropriate medications and antidepressant treatment change strategies. Clinicians need to investigate potential associated factors for antidepressant treatment changes to identify early changes in patients’ responses, thereby assessing the efficacy, tolerability, and safety of treatments more effectively. It is essential for prescribers to establish a routine for documenting the reasons for antidepressant treatment changes to enhance understanding of treatment patterns. Physicians should consider the severity of depression and other factors affecting the patient’s condition, such as daily functioning, anxiety, substance use, and treatment duration, when selecting optimal second-line treatments. Efforts must be made to address financial barriers to treatment by creating financial support systems. Counseling for patients and caregivers should be prioritized to improve adherence to treatment regimens.

Supplemental Material

sj-pdf-1-tpp-10.1177_20451253261463711 – Supplemental material for Patterns of antidepressant treatment changes among adults with major depressive disorder in Northwest Ethiopia: a hospital-based cross-sectional study

Supplemental material, sj-pdf-1-tpp-10.1177_20451253261463711 for Patterns of antidepressant treatment changes among adults with major depressive disorder in Northwest Ethiopia: a hospital-based cross-sectional study by Tilaye Arega Moges, Fasil Bayafers Tamene, Woretaw Sisay Zewdu, Getachew Yitayew Tarekegn, Asanti Dejen, Tigabu Eskeziya Zerihun, Abel Temeche Kassaw, Desalegn Addis Mussie, Samuel Agegnew Wondm, Abaynesh Fentahun Bekalu and Samuel Berihun Dagnew in Therapeutic Advances in Psychopharmacology

Supplemental Material

sj-pdf-2-tpp-10.1177_20451253261463711 – Supplemental material for Patterns of antidepressant treatment changes among adults with major depressive disorder in Northwest Ethiopia: a hospital-based cross-sectional study

Supplemental material, sj-pdf-2-tpp-10.1177_20451253261463711 for Patterns of antidepressant treatment changes among adults with major depressive disorder in Northwest Ethiopia: a hospital-based cross-sectional study by Tilaye Arega Moges, Fasil Bayafers Tamene, Woretaw Sisay Zewdu, Getachew Yitayew Tarekegn, Asanti Dejen, Tigabu Eskeziya Zerihun, Abel Temeche Kassaw, Desalegn Addis Mussie, Samuel Agegnew Wondm, Abaynesh Fentahun Bekalu and Samuel Berihun Dagnew in Therapeutic Advances in Psychopharmacology

Footnotes

Acknowledgements

The authors express their sincere gratitude to the data collectors and the staff of the Psychiatric Department at Debre Tabor Comprehensive Specialized Hospital for their valuable support.

Declarations

ORCID iDs

Tilaye Arega Moges

Fasil Bayafers Tamene

Woretaw Sisay Zewdu

Tigabu Eskeziya Zerihun

Abel Temeche Kassaw

Samuel Agegnew Wondm

Abaynesh Fentahun Bekalu

Samuel Berihun Dagnew

Supplemental material

Supplemental material for this article is available online.

References

Wang

Major depressive disorder: a general overview. In: SHS web of conferences, 2024. EDP Sciences.

Karrouri

Hammani

Benjelloun

, et al. Major depressive disorder: Validated treatments and future challenges. World J Clinical Cases 2021; 9(31): 9350.

Zanardi

Manfredi

Montrasio

, et al. Pharmacogenetic-guided treatment of depression: real-world clinical applications, challenges, and perspectives. Clin Pharmacol Therap 2021; 110(3): 573–581.

Seo

MacPherson

Young

AH.

Atypical antipsychotics and other therapeutic options for treatment of resistant major depressive disorder. Pharmaceuticals 2010; 3(12): 3522–3542.

Coupland

Hill

Morriss

, et al. Antidepressant use and risk of suicide and attempted suicide or self harm in people aged 20 to 64: cohort study using a primary care database. BMJ 2015; 350: h517.

Gbadamosi

Henneh

Aluko

, et al. Depression in sub-Saharan Africa. IBRO Neurosci Reports 2022; 12: 309–322.

Soreide

Ward

Bostwick

JR.

Strategies and solutions for switching antidepressant medications. Psychiatr Times 2017; 34(12).

Fornaro

Anastasia

Novello

, et al. Incidence, prevalence and clinical correlates of antidepressant-emergent mania in bipolar depression: a systematic review and meta-analysis. Bipolar Disord 2018; 20(3): 195–227.

Fornaro

Anastasia

Novello

, et al. The emergence of loss of efficacy during antidepressant drug treatment for major depressive disorder: an integrative review of evidence, mechanisms, and clinical implications. Pharmacol Res 2019; 139: 494–502.

10.

Fornaro

Fusco

Anastasia

, et al. Brexpiprazole for treatment-resistant major depressive disorder. Expert Opin Pharmacother 2019; 20(16): 1925–1933.

11.

Boyce

Hopwood

Morris

, et al. Switching antidepressants in the treatment of major depression: when, how and what to switch to? J Affect Disord 2020; 261: 160–163.

12.

Chanie

Belachew

Limenh

, et al. Prevalence and determinants of antidepressant non-adherence among patients with major depressive disorder in Ethiopia: a multi-center cross sectional study. Sci Reports 2025; 15(1): 29775.

13.

Sisay

Wami

Adverse drug reactions among major depressive disorders: patterns by age and gender. Heliyon 2021; 7(12): e08655.

14.

Zewdu

Dagnew

Tarekegn

, et al. Non-adherence level of pharmacotherapy and its predictors among mental disorders in a resource-limited life trajectories: a systematic review and meta-analysis. BMC Psychiatry 2025; 25(1): 512.

15.

Siyoum

Kibrom

Fanta

, et al. Prevalence of treatment-resistant depression and associated factors among major depressive disorder follow-up patients at Saint Amanuel Mental Specialised Hospital in Ethiopia: a cross-sectional study. BMJ Open 2024; 14(11): e087006.

16.

Bitew

Prevalence and risk factors of depression in Ethiopia: a review. Ethiopian J Health Sci 2014; 24(2): 161.

17.

Von Elm

Altman

Egger

, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet 2007; 370(9596): 1453–1457.

18.

Ernawati

Lubada

Lusiyani

, et al. Association of adherence measured by self-reported pill count with achieved blood pressure level in hypertension patients: a cross-sectional study. Clin Hypertension 2022; 28(1): 12.

19.

Gauthier

Abdel-Ahad

Gaillard

Switching and stopping antidepressants. L'encephale 2018; 44(4): 379–386.

20.

Kern

Cepeda

Castilla-Puentes

, et al. Characteristics of patients with major depressive disorder switching SSRI/SNRI therapy compared with those augmenting with an atypical antipsychotic in a real-world setting. Curr Med Res Opinion. 2021; 37(8): 1377–1384.

21.

Kato

Hori

Inoue

, et al. Discontinuation of antidepressants after remission with antidepressant medication in major depressive disorder: a systematic review and meta-analysis. Mol Psychiatry 2021; 26(1): 118–133.

22.

Saltiel

Silvershein

DI.

Major depressive disorder: mechanism-based prescribing for personalized medicine. Neuropsychiatr Dis Treat 2015; 11: 875–888.

23.

Agoye

FO.

Medication nonadherence among African American male psychiatric patients: healthcare providers’ perspectives. Dissertation. Walden University, 2020.

24.

Deng

Zhai

Ouyang

, et al. Factors influencing medication adherence among patients with severe mental disorders from the perspective of mental health professionals. BMC Psychiatry 2022; 22(1): 22.

25.

Parmar

Puradkar

Kadam

, et al. An observational study to evaluate the awareness of drug treatment, prescription pattern, adverse drug reactions, and adherence in patients of major depressive disorder. Perspectives Clin Res 2024; 15(2): 59–65.

26.

Lei

Sun

Liao

, et al. Sex hormone levels in females of different ages suffering from depression. BMC Women's Health. 2021; 21(1): 215.

27.

Kuehner

Why is depression more common among women than among men?

Lancet Psychiatry 2017; 4(2): 146–158.

28.

Haro

Lamy

Jönsson

, et al. Characteristics of patients with depression initiating or switching antidepressant treatment: baseline analyses of the PERFORM cohort study. BMC Psychiatry 2018; 18(1): 80.

29.

Ahmadifar

Pour

Dini

, et al. Evaluation of a second-line treatment in female adolescents with SSRI-nonresponsive major depression: a real-world data analysis on bupropion versus duloxetine. J Psychiatr Pract 2025; 31(6): 302–311.

30.

Connolly

Thase

ME.

If at first you don’t succeed: a review of the evidence for antidepressant augmentation, combination and switching strategies. Drugs 2011; 71(1): 43–64.

31.

McGrath

Miller

JM.

Pharmacological management of treatment-resistant unipolar depression. In: Tasman

Kay

Lieberman

, et al. (eds) Psychiatry, 4th ed. est Sussex, UK: John Wiley & Sons, Ltd, 2015, pp.2311–2330.

32.

Souery

Pitchot

Switching antidepressants in patients with treatment-resistant depression. In: de Quevedo

Riva-Posse

Bobo

(eds) Managing treatment-resistant depression. Academic Press, 2022, pp.167–174.

33.

Dold

Kasper

Evidence-based pharmacotherapy of treatment-resistant unipolar depression. Int J Psychiatry Clin Practice 2017; 21(1): 13–23.

34.

Wright

Eiland

III Lorenz

Augmentation with atypical antipsychotics for depression: a review of evidence-based support from the medical literature. Pharmacotherapy 2013; 33(3): 344–359.

35.

Cantù

Ciappolino

Enrico

, et al. Augmentation with atypical antipsychotics for treatment-resistant depression. J Affective Disorders 2021; 280: 45–53.

36.

Shelton

Papakostas

GI.

Augmentation of antidepressants with atypical antipsychotics for treatment-resistant major depressive disorder. Acta Psychiatrica Scandinavica 2008; 117(4): 253–259.

37.

Strawbridge

Carter

Marwood

, et al. Augmentation therapies for treatment-resistant depression: systematic review and meta-analysis. Br J Psychiatry 2019; 214(1): 42–51.

38.

Miller

Benjamin

Shapero

, et al. The Massachusetts General Hospital guide to depression. Curr Clin Psychiatry 2019; 147: 151.

39.

Caldiroli

Capuzzi

Tagliabue

, et al. Augmentative pharmacological strategies in treatment-resistant major depression: a comprehensive review. Int J Mol Sci 2021; 22(23): 13070.

40.

Kendrick

Strategies to reduce use of antidepressants. Br J Clin Pharmacol 2021; 87(1): 23–33.

41.

Kern

Cepeda

Defalco

, et al. Treatment patterns and sequences of pharmacotherapy for patients diagnosed with depression in the United States: 2014 through 2019. BMC Psychiatry 2020; 20(1): 4.

42.

Solmi

Miola

Croatto

, et al. How can we improve antidepressant adherence in the management of depression? a targeted review and 10 clinical recommendations. Brazilian J Psychiatry 2020; 43: 189–202.

43.

Marcus

Hassan

Olfson

Antidepressant switching among adherent patients treated for depression. Psychiatric Serv 2009; 60(5): 617–623.

44.

Haro

Lamy

F-X

Jönsson

, et al. Characteristics of patients with depression initiating or switching antidepressant treatment: baseline analyses of the PERFORM cohort study. BMC Psychiatry 2018; 18(1): 80.

45.

Lampela

Tanskanen

Lähteenvuo

, et al. Switches and early discontinuations of antidepressant medication in young adults with depression. J Affective Disorders 2021; 295: 1474–1481.

46.

Mars

Heron

Gunnell

, Prevalence and patterns of antidepressant switching amongst primary care patients in the UK. J Psychopharmacol 2017; 31(5): 553–560.

47.

Keks

Hope

Keogh

Switching and stopping antidepressants. Australian Prescriber 2016; 39(3): 76.

48.

Trivedi

Rush

Wisniewski

, et al. Evaluation of outcomes with citalopram for depression using measurement-based care in STAR*D: implications for clinical practice. Am J Psychiatry 2006; 163(1): 28–40.

49.

Fava

Diagnosis and definition of treatment-resistant depression. Biol Psychiatry 2003; 53(8): 649–659.

50.

Papakostas

GI.

Managing partial response or nonresponse: switching, augmentation, and combination strategies for major depressive disorder. J Clin Psychiatry 2009; 70(Suppl 6): 16–25.

51.

Cutler

Keyloun

Higa

, et al. Annual costs among patients with major depressive disorder and the impact of key clinical events. J Manage Care Specialty Pharm 2022; 28(12): 1335–1343.

52.

Barnes

Drake

Paton

, et al. Evidence-based guidelines for the pharmacological treatment of schizophrenia: updated recommendations from the British Association for Psychopharmacology. J Psychopharmacol 2020; 34(1): 3–78.

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