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10.1371/journal.pone.0309562
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Research Article
Medicine and Health Sciences
Mental Health and Psychiatry
Mood Disorders
Depression
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Schizophrenia
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Patients
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Antidepressant Drug Therapy
Self-stigmatization and treatment preferences: Measuring the impact of treatment labels on choices for depression medications
Self-stigmatization and treatment preferences
https://orcid.org/0000-0002-5386-0907
Gonzalez Sepulveda Juan Marcos Conceptualization Data curation Formal analysis Funding acquisition Writing – original draft Writing – review & editing 1 *
Townsend Michael Conceptualization Formal analysis Writing – review & editing 2
Waters Heidi C. Conceptualization Formal analysis Writing – review & editing 3
Brubaker Maalak Conceptualization Formal analysis Writing – review & editing 3
Wallace Matthew Data curation Formal analysis Writing – original draft Writing – review & editing 1
Johnson Reed Conceptualization Data curation Formal analysis Writing – original draft Writing – review & editing 1
1 Duke Clinical Research Institute, Duke University School of Medicine Durham, North Carolina, United States of America
2 LCSW Gateway Counseling Center, Smithtown, New York, United States of America
3 Otsuka Pharmaceutical Development & Commercialization, Inc., Princeton, NJ, United States of America
Mitra Souparno Editor
NYU Grossman School of Medicine: New York University School of Medicine, UNITED STATES OF AMERICA
Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: HCW and MB are employees of Otsuka Pharmaceutical Development & Commercialization, Inc. JMGS, RJ, and MW are employees of Duke Clinical Research Institute, which received funding to conduct this study. MT received compensation from Otsuka Pharmaceutical Development & Commercialization, Inc. for his review of the protocol and interpretation of the data for this study. This does not alter our adherence to PLOS ONE policies on sharing data and materials

* E-mail: jm.gonzalez@duke.edu
3 9 2024
2024
19 9 e030956227 2 2024
13 8 2024
© 2024 Gonzalez Sepulveda et al
2024
Gonzalez Sepulveda et al
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Objective

To collect evidence on the possibility that patients with depression experience self-stigmatization based on label information for medications.

Methods

We developed a discrete-choice experiment (DCE) survey instrument that asked respondents to make choices between hypothetical treatments for major depressive disorder (MDD). We also included treatment type (antidepressants versus antipsychotics) and approved indications for the medication. The choice questions mimicked the information presented in product inserts and required systematic tradeoffs between treatment efficacy, treatment type, and indication. We calculated how many patients were willing to forgo efficacy to avoid treatments with information associated with self-stigmatization, and how much efficacy they were willing to forgo. We also evaluated the impact of contextualizing the treatment information to reduce self-stigmatization by randomizing respondents who received additional context.

Results

A total of 501 patients with MDD were recruited to complete the DCE survey. Respondents had well-defined preferences for treatment outcomes. Over 60% (63.4%) of respondents were found to be significantly affected by treatment indication. These respondents were willing to forgo about 2.5 percentage points in the chance of treatment efficacy to avoid treatments indicated for schizophrenia. We also find that some level of contextualization of the treatment details could help reduce the negative impact of treatment type and indications.

Conclusions

Product-label treatment indication can potentially lead to patient self-stigmatization as shown by patients’ avoidance of treatments that are also used to treat schizophrenia. While the effect appears to be relatively small, results suggests that the issue is likely pervasive.

Otsuka Pharmaceutical Development & Commercialization, Inc https://orcid.org/0000-0002-5386-0907
Gonzalez Sepulveda Juan Marcos Lundbeck, LLC https://orcid.org/0000-0002-5386-0907
Gonzalez Sepulveda Juan Marcos Funding/Support: This work was funded by Lundbeck, LLC and Otsuka Pharmaceutical Development & Commercialization, Inc. The funder supported design and conduct of the study. However, all decisions regarding the study design and conduct, as well as data analysis and manuscript preparation were made by researchers unaffiliated to the funder. Data AvailabilityData cannot be shared publicly because of ownership restrictions. Data are available from the authors and/or from Lundbeck, LLC upon request for researchers who meet the criteria for access to confidential data. All data requests must be made to Dewilka Saleem Senior Manager, Global Scientific Communications dewilka.saleem@otsuka-us.com.
Data Availability

Data cannot be shared publicly because of ownership restrictions. Data are available from the authors and/or from Lundbeck, LLC upon request for researchers who meet the criteria for access to confidential data. All data requests must be made to Dewilka Saleem Senior Manager, Global Scientific Communications dewilka.saleem@otsuka-us.com.
==== Body
pmcIntroduction

Decisions to pursue therapies for major depressive disorder (MDD) should correspond to the clinical characteristics of the patient, the evidence available on the expected treatment-related outcomes, and the relative importance of those outcomes. However, clinical decision-making can be the result of a more complex process that extends beyond clinical aspects and into the decision maker’s own biases. Patients with mental health conditions can be particularly vulnerable to the phenomenon of self-stigmatization, in which the patients experience harmful levels of negative self-image because of the social stigma associated with their disease [1,2].

While there is strong evidence that self-stigmatization among patients with mental health issues can lead to avoidance of medical attention by a professional [2,3] or to treatment nonadherence [4–7], it remains uncertain whether associations with conditions not directly experienced by the patients exacerbate self-stigmatization. Specifically, whether knowing that a treatment is commonly prescribed to patients with other stigmatized conditions leads to avoidance of the treatment. Self-stigmatization may occur, even when the patient knows he or she does not have the stigmatized health issues.

In the context of MDD, patients who experience a partial or inadequate response to antidepressants may be recommended to use adjunctive atypical antipsychotics. These therapies can offer significant improvements in depressive symptoms [8]. However, per the United States (US) Food and Drug Administration (FDA) requirements, the product prescribing information for these therapies contains details about drug class (i.e., atypical antipsychotics) and highlights the fact that these therapies are also used for the treatment of patients with schizophrenia or other psychiatric disorders [4]. Of particular concern is that negative associations with psychosis and schizophrenia may lead to aversion to adjunctive therapies due, at least in part, to patients’ self-stigmatization and forgoing the benefits that antipsychotics can provide.

This study assessed whether preferences for treatments for MDD among patients with inadequately managed depression were consistent with self-stigmatization when considering atypical antipsychotic adjunctive medications. Specifically, we sought to quantify patients’ willingness to forgo treatment efficacy to avoid product that included an indication for schizophrenia, all else equal. Furthermore, this study evaluated how alternative terms to describe these therapies and contextualization of the label information could change patients’ reactions and decrease resistance towards possible treatment options, thereby providing evidence to inform prescribers’ counseling strategies.

Methods

We developed a discrete-choice experiment (DCE) survey instrument that asked respondents to make choices between hypothetical treatments for MDD to measure the relative importance of different aspects in the scenarios presented [9]. The treatments were described based on general attributes, and how the treatment performs under each attribute (attribute level). The attributes were identified by a literature review of treatments for MDD not responsive to antidepressants, product inserts for treatments for MDD not responsive to antidepressants, and discussions with clinical experts. Table 1 includes the full list of study attributes and the list of possible levels shown to respondents under each attribute.

10.1371/journal.pone.0309562.t001 Table 1 Items considered in the DCE questions.

Attribute	Attribute Levels	
Chance of the medicine working well to treat depression symptoms	10 out of 100 (10%)
15 out of 100 (15%)
18 out of 100 (18%)
• 20 out of 100 (20%)	
Medication Type	• Antidepressant
• Atypical antipsychotic
• Serotonin-dopamine activity modulator	
Indication	• Major depressive disorder
• Major depressive disorder and schizophrenia	
Dosage and administration	• Daily oral tablet at home
• Monthly injection at a clinic	
Weight gain	None
• Most patients experienced a 2% increase in weight
• Most patients experienced a 7% increase in weight	
Akathisia	• None
• Most patients have experienced akathisia	

We included two attributes potentially associated with self-stigmatization. First, medication type, which included antidepressants, atypical antipsychotics, and serotonin-dopamine activity modulator. The second attribute showed the approved indications for the medication. The levels for this attribute included only MDD, and MDD and schizophrenia.

To prevent eliciting a reaction from participants beyond those typically triggered by the product label, we mimicked the information available to patients through product inserts as we described the attributes. This approach resulted in definitions that were short and technical, not just for the attributes related to self-stigmatization, but for all attributes other than efficacy—which is not directly covered in the product insert. We also randomized additional information for the description of medication type. The additional information was intended to provide more context around the use of atypical antipsychotics to treat MDD mimicking the kind of information a patient could receive from physicians to alleviate problems with self-stigmatization. Respondents who were offered the additional information (context arm) were reminded explicitly that patients need not experience psychoses to benefit from antipsychotics. We also allowed respondents to click a link for additional information on akathisia (inability to remain still) crafted by the study team and recorded whether respondents clicked on the link.

Our focus on the product label extended to the presentation of the DCE questions (Fig 1). That meant presenting the levels within alternatives following the presentation of medication labels in product inserts. While we presented the information in the form of a product insert, our intention was not to address the impact of inserts per se, but the information about the product conveyed by regulatory approval in a particular indication or medication category. In that sense, the use of a layout that mimicked the product insert was meant to capture the way such information may be accessed by patients. As mentioned before, this question layout was chosen to avoid highlighting the treatment type and indication in a way that would lead patients to overly focus on this information. We also asked respondents to state what treatment they would choose first, allowing them to consider the possibility of using the rejected treatment in the future. This format of the choice question is also consistent with the decision context for these patients.

10.1371/journal.pone.0309562.g001 Fig 1 Example DCE question.

The survey instrument was tested during 10 individual interviews with a convenience sample of patients with MDD. The interviews were conducted individually through videoconferencing and followed a semi-structured think-aloud protocol. During the 1-hour individual interviews we asked participants to read the survey out loud and to comment as needed throughout the document on things that seemed unclear or did not match their own experience with the disease or treatment. In addition, the interviewers probed specifically about the attribute definitions and the understandability of the preference-elicitation exercise. Through these interviews, the study team corroborated patients’ ability to complete the DCE questions with the limited information provided for the study attributes. We also verified that the additional information on medication type, indication, and akathisia was considered valuable to some participants. Importantly, interview participants voluntarily shared concerns about the use of antipsychotics and a treatment indication for schizophrenia as they felt taking such medications would suggest their condition was severe. Finally, the choice question format was confirmed to be consistent with the way the patients thought about treatment choices for themselves. Upon completion of the interviews, the survey was updated based on feedback from the interviewed patients. The final version of the survey can be found in S1 Appendix.

A D-efficient fractional-factorial experimental design was generated in the software package SAS® to populate the attribute levels to be shown in each DCE question. [10] The experimental design provides a way to control the statistical properties of the stimuli in the profiles and minimize the correlations between attributes across DCE questions [11]. Given the number of attributes and attribute levels, we generated a design with 24 questions split into 4 blocks of 6 questions each. We randomly assigned two unique blocks to each respondent, for a total of 12 choice questions [12]. The final design was chosen after using simulations to evaluate the statistical properties of the randomized block assignment and various assumptions about prior preference weights. The order of the blocks of questions and the order of the DCE questions in each block were randomized to address sequence effects.

All activities related to the research were evaluated by the Institutional Review Board (IRB) at a major US research institution (IRB #Pro00107673). Electronic consent was collected from all participants prior to completing the survey instrument.

A total of 501 respondents were recruited through an online consumer panel between October and November, 2021. Potential participants were invited to complete a screener that masked the necessary diagnosis to qualify for the study. Participants were required to self-report a physician diagnosis of MDD and must have failed at least one treatment for depression in the past. Answers to questions about their treatment history were used to verify their report was consistent with diagnosis. Participants who reported having psychotic events were excluded.

Statistical analysis

Choice data were first evaluated using the internal-consistency measures commonly applied with DCEs [13]. These consistency measures included answers to attribute-comprehension questions and attribute dominance. While we report the incidence of attribute dominance in the sample, we acknowledged that attribute dominance could be a valid expression of preferences, so respondents exhibiting this pattern of choices were retained for the analyses.

We modeled the choice data using logit-based regression models relating the patterns of choices between treatments to the differences in attribute levels [14]. Estimates from logit-based regression models approximate the percentage change in the probability of choice given the attribute levels in the alternatives. For this reason, they also are called preference weights. First, conditional logit models were estimated to evaluate the model specification that would most appropriately characterize the average choice patterns observed in the data. Through these conditional-logit models, we tested the sensitivity of estimates to internal-consistency results and respondents’ performance on comprehension questions.

Upon defining the final model specification, we obtained population-level preference estimates that explicitly accounted for preference heterogeneity across respondents by estimating two separate random-parameters logit (RPL) models, one for each of the two information arms in the study (i.e., context vs. no context) [14,15]. Finally, a latent-class (LC) logit model was used to better condition preference heterogeneity on observed characteristics [16], and to identify respondents who were more likely to care about medication indication. We accomplished the latter by running a 3-class model. Preferences in class 1 were required to favor treatments without an indication for schizophrenia by constraining the marginal effect of having an indication for schizophrenia to be non-positive. Preferences in class 2 were required to favor treatments with an indication for schizophrenia by constraining the marginal effect of treating MDD and schizophrenia to be non-negative. Preference estimates in class 3 were obtained without any parameter constraints. Table 2 summarizes the model specification by class.

10.1371/journal.pone.0309562.t002 Table 2 Model specification by class.

Class	Assumed model specification	
Class 1 –Favors treatments without schizophrenia indication	Preference weight for schizophrenia indications was constrained to be non-positive	
Class 2 –Favors treatments with schizophrenia indication	Preference weight for schizophrenia indications was constrained to be non-negative	
Class 3 –Unconstrained class	All parameters were estimated freely	

Membership probabilities to class 1 and class 2 reflect the expected number of patients who would find treatment indication to be important. We identified associations between respondent characteristics and class membership using a set of individual-specific covariates to help explain class-membership probabilities. We also used the preference weights from the latent classes to calculate minimum-acceptable treatment effectiveness (MATE) estimates. These values indicate how much chance of efficacy people were willing to give up to avoid treatments presented as antipsychotics or indicated for schizophrenia [17,18].

Results

Table 3 summarizes characteristics of respondents for the overall sample, as well as separately for those who received additional context information and those who did not. We found no statistically significant differences between respondents who received the additional context information and those who did not. Overall, over three-quarters of the sample was female (76.8%). On average, respondents were 55.7 years of age (SD = 13.1). Close to a third of respondents had a bachelor’s degree or higher level of education (31.2%). Also, over half the sample was either retired (31.7%) or unable to work or on disability (21.6%). Finally, a vast majority of the respondents reported feeling at least moderate depression symptoms over the past two weeks (89.4%).

10.1371/journal.pone.0309562.t003 Table 3 Survey responses by randomization of contextualization of treatment type.

	Statistic or Category	Overall (N = 501)	Context (n = 250)	No Context (n = 251)	P-value	
All respondents						
Age in years	n	501	250	251		
	Mean (SD)	55.7 (13.1)	55.6 (13.2)	55.9 (12.9)	0.787	
Which of these descriptions most closely describes the worst depression symptoms you have ever experienced?	n	501	250	251		
	Very Severe	119 (23.8%)	56 (22.4%)	63 (25.1%)	0.777	
	Severe	211 (42.1%)	107 (42.8%)	104 (41.4%)		
	Moderate	171 (34.1%)	87 (34.8%)	84 (33.5%)		
Which description above most closely describes your depression symptoms over the past 2 weeks?	n	501	250	251		
	Very Severe	40 (8.0%)	18 (7.2%)	22 (8.8%)	0.220	
	Severe	138 (27.5%)	60 (24.0%)	78 (31.1%)		
	Moderate	265 (52.9%)	143 (57.2%)	122 (48.6%)		
	None of the above	58 (11.6%)	29 (11.6%)	29 (11.6%)		
What is your gender?	n	501	250	251		
	Male	115 (23.0%)	57 (22.8%)	58 (23.1%)	0.604	
	Female	385 (76.8%)	192 (76.8%)	193 (76.9%)		
	Other or prefer not to say	1 (0.2%)	1 (0.4%)	0		
Which of the following describes your ethnicity? (Check only one answer.)	n	501	250	251		
	Hispanic, Latino or Spanish	22 (4.4%)	12 (4.8%)	10 (4.0%)	0.656	
	Not Hispanic, Latino or Spanish	479 (95.6%)	238 (95.2%)	241 (96.0%)		
What is the highest level of education you have completed? (Check only one answer.)	n	501	250	251		
	Less than high school	2 (0.4%)	2 (0.8%)	0	0.521	
	Some high school	6 (1.2%)	3 (1.2%)	3 (1.2%)		
	High school or equivalent (such as GED)	98 (19.6%)	52 (20.8%)	46 (18.3%)		
	Some college but no degree	121 (24.2%)	56 (22.4%)	65 (25.9%)		
	Technical school	48 (9.6%)	30 (12.0%)	18 (7.2%)		
	Associate’s degree or 2-year college degree	70 (14.0%)	32 (12.8%)	38 (15.1%)		
	4-year college degree (such as BA, BS)	82 (16.4%)	38 (15.2%)	44 (17.5%)		
	Some graduate school but no degree	7 (1.4%)	4 (1.6%)	3 (1.2%)		
	Graduate or professional degree (such as MBA, MS, MA, MD, PhD)	67 (13.4%)	33 (13.2%)	34 (13.5%)		
Please indicate whether you are currently: (Check all that apply)*	n	501	250	251		
	Employed with hourly pay full time	83 (16.6%)	42 (16.8%)	41 (16.3%)	0.889	
	Employed with salary full time	19 (3.8%)	8 (3.2%)	11 (4.4%)	0.488	
	Employed with hourly pay part time	49 (9.8%)	25 (10.0%)	24 (9.6%)	0.869	
	Employed with salary part time	8 (1.6%)	5 (2.0%)	3 (1.2%)	0.504b	
	Self-employed	20 (4.0%)	10 (4.0%)	10 (4.0%)	0.993	
	Homemaker	38 (7.6%)	23 (9.2%)	15 (6.0%)	0.173	
	Student	9 (1.8%)	2 (0.8%)	7 (2.8%)	0.176b	
	Retired	159 (31.7%)	79 (31.6%)	80 (31.9%)	0.948	
	Volunteer work	3 (0.6%)	1 (0.4%)	2 (0.8%)	1.000b	
	Other	3 (0.6%)	1 (0.4%)	2 (0.8%)	1.000b	
	Not working but looking for a job	25 (5.0%)	12 (4.8%)	13 (5.2%)	0.845	
	Not working and NOT looking for a job	20 (4.0%)	10 (4.0%)	10 (4.0%)	0.993	
	Unable to work or on disability	108 (21.6%)	54 (21.6%)	54 (21.5%)	0.981	
Cm = Centimeters; Lbs = Pounds; Kg/m2 = Kilograms per square meters

a T-test computed using methods for unequal variance

b P-value calculated using Fisher’s exact test. Note 1: Percentages do not include missing responses in the denominator. Note 2: P-values are computed using the Pearson chi-square test for categorical variables or the Student’s t-test for continuous variables unless otherwise noted.

*Totals for this question may add to more than 100% given that respondents were allowed to select more than one answer.

Internal-consistency results suggest data are of good quality with less than 30 respondents (n = 27, 5.4% of the overall sample) exhibiting a choice pattern consistent with response non-variation or taking less than 5 minutes to complete the survey. These 27 respondents were dropped from further analyses. Full internal-consistency results, including conditional-logit sensitivity tests evaluating the impact of these consistency failures, can be found in S2 Appendix.

DCE results

Fig 2 presents the preference weights derived from the RPL models for the two information arm groups (i.e., context vs no context), normalized relative to the importance of treatment efficacy [19]. This fixes the highest and lowest preference weights for treatment efficacy to be the same across groups. Preference weights represent log-odds indicating whether a specific attribute level increases or decreases the probability of choice relative to another level in the same attribute [14]. As expected, better clinical outcomes were associated with higher preference weights, indicating that respondents were more likely to prefer treatments with those outcomes, all else equal.

10.1371/journal.pone.0309562.g002 Fig 2 Rescaled preference weights by information treatment (context vs no context).

MDD = Major Depressive Disorder; SDAM = Serotonin-Dopamine Activity Modulator.

The normalized preference weights indicate that respondents in the two groups had different preferences for weight gain and akathisia (marginally) relative to efficacy. We noted that the additional context information changed the acceptance rate of atypical antipsychotics in a small positive way. The context information was also associated with a greater chance of indifference about the treatment indications.

For the LC logit model, we pooled respondents from the two context arms and allow classification of respondents based on consistency with each of the three preference phenotypes described before (i.e., class 1—preference to avoid schizophrenia indication, class 2—preference to avoid MDD only indication, and class 3 –open to all preference patterns. Figs 3–5 present the estimated preference weights for each of these classes, normalized so the most important attribute—the one showing the greatest change in preference weights—had an absolute effect of 10 units. The expected membership probability for the classes were 63.43% (58.5% - 68.4%), 20.97% (17.1% - 24.9%), and 15.59% (11.7% - 19.5%) for classes 1, 2, and 3, respectively.

10.1371/journal.pone.0309562.g003 Fig 3 Rescaled preference weights for latent class 1.

MDD = Major Depressive Disorder; SDAM = Serotonin-Dopamine Activity Modulator.

10.1371/journal.pone.0309562.g004 Fig 4 Rescaled preference weights for latent class 2.

MDD = Major Depressive Disorder; SDAM = Serotonin-Dopamine Activity Modulator.

10.1371/journal.pone.0309562.g005 Fig 5 Rescaled preference weights for latent class 3.

MDD = Major Depressive Disorder; SDAM = Serotonin-Dopamine Activity Modulator.

Class 1 showed a significant preference for medications indicated only for the treatment of MDD versus a medication indicated for the treatment of MDD and schizophrenia. Meanwhile, class 2 and class 3 had flat preferences for treatment indication, suggesting respondents did not consistently prefer treatments that were indicated for both MDD and schizophrenia, but were indifferent between treatment indications. We also found that respondents who were likely to be in class 2 registered strong preferences for daily oral tablets taken at home versus monthly injections at a clinic. Members of class 3 preferred medications that were more effective in reducing depression symptoms. This can be seen by the relatively large increases in preference weights for that attribute as efficacy expectations grew.

Table 4 presents the covariate probabilities characterizing the distribution of each covariate level across the three classes. Recent history of depression and whether the respondents clicked to obtain more information about akathisia were significantly correlated with class assignment. Respondents who reported having more serious depression symptoms in the last 2 weeks were more likely to be found in class 1, the same was true of respondents who clicked for additional information on akathisia. Finally, we found that respondents with higher educational attainment were positively associated with being in class 2, and negatively associated with being in class 1.

10.1371/journal.pone.0309562.t004 Table 4 Class membership probability by covariate.

Covariates	Class1	Class2	Class3	p-value	
Overall class membership	0.634	0.210	0.156		
Depression in the past two weeks	 	 	 	0.041	
Very severe	0.714	0.192	0.094	 	
Severe	0.679	0.178	0.143	 	
Moderate	0.575	0.258	0.167	 	
None of the above	0.761	0.071	0.168	 	
Clicked to learn more about akathisia	 	 	 	0.007	
Did not click	0.591	0.234	0.175	 	
Clicked	0.790	0.121	0.089	 	
Age ranges	 	 	 	0.190	
21–43	0.685	0.144	0.171	 	
44–54	0.636	0.201	0.163	 	
55–60	0.579	0.242	0.178	 	
61–68	0.631	0.247	0.122	 	
69–80	0.635	0.217	0.148	 	
Education	 	 	 	0.051	
More than high school	0.667	0.184	0.150	 	
High school or less	0.514	0.306	0.180	 	
Years since diagnosis				0.430	
Less than 2 years ago	0.460	0.353	0.187		
Between 2 and 5 years ago	0.614	0.159	0.227		
Between 5 and 10 years ago	0.585	0.233	0.182		
Between 10 and 15 years ago	0.710	0.219	0.071		
More than 15 years ago	0.632	0.202	0.166		
Treatment history				0.220	
Had a treatment work well in the past	0.542	0.284	0.174		
Did not have a treatment work well	0.657	0.192	0.151		
Medication history				0.340	
Had a doctor suggest AA	0.662	0.202	0.137		
Have not had a doctor suggest AA	0.597	0.221	0.182		
Employment				0.230	
Employed part-time or full-time	0.617	0.237	0.146		
Other	0.670	0.153	0.177		

Minimum-acceptable treatment efficacy (MATE)

Table 5 reports MATE estimates for select attribute changes for each latent class. Respondents with a high probability of membership in the first latent class would accept a reduction in the chance of efficacy from 20% to 17.6% if they could have a medication not indicated for the treatment of schizophrenia as well as MDD. The same respondents would have accepted a reduction from 20% to 19.3% to take an antidepressant in lieu of a treatment labeled as a serotonin-dopamine activity modulator. Taking an antidepressant in lieu of a treatment labeled as an atypical antipsychotic had a negative value, indicating they would require an increase in efficacy to offset the negative perception of that treatment.

10.1371/journal.pone.0309562.t005 Table 5 MATEs for treatment indication and type.

	Improvement	MATE (95% CI)*	
Class 1	From MDD and schizophrenia to MDD	17.6% (16.1%, 18.9%)	
From SDAM to antidepressant	19.3% (17.0%, 21.4%)	
From atypical antipsychotic to antidepressant**	-	
Class 2	From MDD and schizophrenia to MDD***	-	
From SDAM to antidepressant**	-	
From atypical antipsychotic to antidepressant	17.3% (8.3%, 22.4%)	
Class 3	From MDD and schizophrenia to MDD	19.8% (19.3%, 20.3%)	
From SDAM to antidepressant	19.6% (18.8%, 20.3%)	
From atypical antipsychotic to antidepressant**	-	
MDD = Major Depressive Disorder; SDAM = Serotonin-Dopamine Activity Modulator.

*From 20% efficacy

**These changes had negative MATEs and were not calculated for the table

*** Class-2 was indifferent to indications.

Discussion

Our study objective was to evaluate whether self-stigmatization was present among patients with MDD when the treatments they are offered are also approved for stigmatized mental-health conditions. We measured the degree to which this avoidance lead to acceptance of reduced treatment efficacy. Additionally, we assessed the impact of adding minimal contextualization of the self-stigmatizing aspects of treatments. Our intention was not to evaluate the right language to discuss treatments with patients, but to evaluate the potential of communication as a tool to counteract self-stigmatization.

We found that a product indication can offer important information about treatments and can influence patients’ perceptions and their preferences between treatment options. Specifically, we observed that product-label information such as treatment indication can impact patients’ preferences for medications to treat MDD. This effect is potentially related to the issue of self-stigmatization. While the effect is relatively small, we found evidence that it is likely pervasive.

A majority of patients were willing to accept statistically significant reductions in potential treatment efficacy to avoid a medication indicated for the treatment of schizophrenia. While the acceptable reductions in efficacy are small, they point to the possibility that people who experience self-stigmatization may be limiting their ability to experience relief from MDD symptoms. Thus, results provide supportive evidence to the idea that self-stigmatization is a real problem among patients with MDD.

Importantly, our results show that psychoeducation can play a key role in preventing stigma in most patients with MDD. We found evidence that additional information on the use of atypical antipsychotics to treat MDD may increase patients’ willingness to accept a medication classified as an atypical antipsychotic and reduce the impact of an indication for schizophrenia. Specifically, information regarding treatment efficacy was found to influence willingness to take a medication. Interestingly, the additional context also reduced the negative impact of treatment side effects, like weight gain and akathisia. This highlights the importance of discussions between patients and physicians to address self-stigmatization when treating MDD. Shared decision-making (SDM), in which all treatment options are explored and discussed, is recommended for difficult-to-treat depression [20]. SDM is a systematic process in which providers share information about different treatments, patients are encouraged to discuss their experiences, history, preferences, values, and cultural beliefs, and patients and providers evaluate the pros and cons of each option based on preferences, values, and cultural beliefs to arrive at the best treatment choice. Using SDM may help a patient understand the benefits and risks of treatment choices, and may assist the provider in understanding barriers to use, including self-stigmatization [21].

In our application, simply highlighting the fact that taking antipsychotics does not require experiencing psychotic episodes reduced avoidance of these treatments. Future research should consider measuring how SDM affect actual patient treatment-taking behavior. This information could help quantify real-world implications of self-stigmatization among MDD patients and support the development of strategies that reduce the avoidance of potentially effective treatments.

Some important limitations of our work are also worth highlighting. One limitation is that the elicited choices did not carry the same consequences as those made in real-world scenarios. However, we setup the choice questions in a way that preference-revealing answers were encouraged. Also, to some degree, our DCE question design included more information than the limited details given on product inserts (i.e., patient-specific treatment efficacy) in order to generate meaningful preference weights. Nevertheless, we focused on limiting the information to the extent possible based on what patients would be expected to find in a product insert. Also, the information treatment provided through the context arm was very basic. Additional details or different communication strategies could have yielded a greater impact for the context information. The nature of our study, also did not explicitly allow us to capture any effects associated with the patient-provider relationship that could moderate the effects we identified in our study. Future studies should measure the impact of these relationships on patients’ behaviors and the potential stigma generated by the use of antipsychotics to treat MDD. Finally, while we consider the avoidance of adjuvant antipsychotics because of self-stigmatization, there are other potential reasons for this choice behavior. Our interviews with patients during survey development suggest self-stigmatization was indeed a major driver for this avoidance, but qualitative information on this was not directly collected from the participants who completed the online survey.

Conclusions

The systematic aversion to adjuvant antipsychotics suggests people who experience self-stigmatization may be limiting their ability to experience relief from MDD symptoms. Discussions between patients and physicians to address self-stigmatization when treating MDD can potentially increase patients’ willingness to accept a medication classified as an atypical antipsychotic and reduce the impact of an indication for schizophrenia.

Supporting information

S1 Appendix Survey_submitted.

(DOCX)

S2 Appendix Validity checks.

(DOCX)

The study team would like to acknowledge the support of Jui-Chen Yang during the analysis of the study data.

10.1371/journal.pone.0309562.r001
Decision Letter 0
Mitra Souparno Academic Editor
© 2024 Souparno Mitra
2024
Souparno Mitra
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version0
29 Mar 2024

PONE-D-24-07744Self-stigmatization and treatment preferences: Measuring the impact of treatment labels on choices for depression medicationsPLOS ONE

Dear Dr. Gonzalez Sepulveda,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

==============================

ACADEMIC EDITOR: Please review edits as recommended by the reviewer and respond to your comments. Depending on your responses the paper will be further reviewed for acceptance. 

==============================

Please submit your revised manuscript by May 13 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

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If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Souparno Mitra, M.D.

Academic Editor

PLOS ONE

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[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: It is very practical and well thought out manuscript. All parts of the manuscript are easily understood and supported by the statistical and clinically relevant design. Table and figures are simple to comprehend.

Reviewer #2: Dear Editor,

I have had the honor of reviewing the article titled “Self-stigmatization and treatment preferences: Measuring the impact of treatment labels on choices for depression medications” submitted to PLOS One for peer review.

Below is the review:

Under Introduction:

“it is unknown whether self-stigmatization is exacerbated by association with conditions no experienced by the patients” Recommend reconstructing the sentence for clarity: Like “it remains uncertain whether associations with conditions not directly experienced by the patients exacerbate self-stigmatization.”

Under Methods:

“To avoid inducing a reaction from participants in response to these two attributes beyond what would be triggered by the product label” Recommend reconstructing the sentence for clarity: Like “To prevent eliciting a reaction from participants beyond those typically triggered by the product label”

Recommend only using attributes or characteristics and not interchanging them.

Define: akathisia and acronyms: SAS

Recommend using voluntarily instead of spontaneously and taking out the extra they in “Importantly, interview participants spontaneously shared concerns about the use of antipsychotics and a treatment indication for schizophrenia as they felt taking such medications would suggest their condition was severe”

In “The interviews were conducted individually through videoconferencing and followed a

semi-structured think-aloud protocol” Please describe how a semi-structured think-aloud protocol was followed.

“We accomplished the latter by running a 3-class model, where preferences in class 1 were required to favor treatments without an indication for schizophrenia (i.e., constraining the marginal effect of avoiding schizophrenia to be positive), preferences in class 2 were required to favor treatments with an indication for schizophrenia (i.e., constraining the marginal effect of treating MDD and schizophrenia to be positive). Preference estimates in class 3 were obtained without any parameter constraints.” Combine the sentences with a “while”.

Recommend making this a table for clarity as they are referenced frequently under results. In general, the statistical analysis was difficult to follow and understand. Recommend using more description and rewording sentences.

Under Results:

The first paragraph describing the number of participants and description of encounters can go in under

methods.

Use another phrase for “preference weights” or define what is being referenced by preference weights.

Reword for clarity: “We also noted a small, marginally statistically significant positive effect of additional context information on the acceptance of atypical antipsychotics and greater chance of indifference about the treatment indications.”

Under Discussion:

This sentence is not necessary: “While the effect is relatively small, we found evidence that it is likely pervasive”

Under “limitations” section: Other limitations identified are including and limited to not addressing other communication strategies, qualitative information on avoidance of adjuvant antipsychotics because of self-stigmatization was not taken from the participants who completed the online survey, and participants answered on the online survey may not reflect in their real-world decision making.

A suggestion to include is giving specific points that doctors can talk about to their patients that subside the patient’s aversion to adjuvant antipsychotics for MDD symptoms. Consider taking about directions for future research.

Limitations accounted for: consistency check was done where only data of good quality was taken into account and statistical lengths were taken to account for different variances in data.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Rajesh Mehta

Reviewer #2: No

**********

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While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

10.1371/journal.pone.0309562.r002
Author response to Decision Letter 0
Submission Version1
9 May 2024

COMMENT:

Under Introduction:

“it is unknown whether self-stigmatization is exacerbated by association with conditions no experienced by the patients” Recommend reconstructing the sentence for clarity: Like “it remains uncertain whether associations with conditions not directly experienced by the patients exacerbate self-stigmatization.”

RESPONSE:

We have updated the cited text as suggested by the reviewer.

COMMENT:

Under Methods:

“To avoid inducing a reaction from participants in response to these two attributes beyond what would be triggered by the product label” Recommend reconstructing the sentence for clarity: Like “To prevent eliciting a reaction from participants beyond those typically triggered by the product label”

Recommend only using attributes or characteristics and not interchanging them.

RESPONSE:

We have updated the cited text as suggested by the reviewer.

COMMENT:

Define: akathisia and acronyms: SAS

RESPONSE:

We have added the definition for akathisia immediately after it is first mentioned.

COMMENT:

Recommend using voluntarily instead of spontaneously and taking out the extra they in “Importantly, interview participants spontaneously shared concerns about the use of antipsychotics and a treatment indication for schizophrenia as they felt taking such medications would suggest their condition was severe”

RESPONSE:

We have updated the cited text as suggested by the reviewer.

COMMENT:

In “The interviews were conducted individually through videoconferencing and followed a

semi-structured think-aloud protocol” Please describe how a semi-structured think-aloud protocol was followed.

RESPONSE:

We have added additional information explaining the process followed as part of the semi-structured think-aloud protocol. The following text has been added to the methods section.

During the 1-hour individual interviews we asked participants to read the survey out loud and to comment as needed throughout the document on things that seemed unclear or did not match their own experience with the disease or treatment. In addition, the interviewers probed specifically about the attribute definitions and the understandability of the preference-elicitation exercise.

COMMENT:

“We accomplished the latter by running a 3-class model, where preferences in class 1 were required to favor treatments without an indication for schizophrenia (i.e., constraining the marginal effect of avoiding schizophrenia to be positive), preferences in class 2 were required to favor treatments with an indication for schizophrenia (i.e., constraining the marginal effect of treating MDD and schizophrenia to be positive). Preference estimates in class 3 were obtained without any parameter constraints.” Combine the sentences with a “while”.

Recommend making this a table for clarity as they are referenced frequently under results. In general, the statistical analysis was difficult to follow and understand. Recommend using more description and rewording sentences.

RESPONSE:

Per the reviewer’s suggestion, we updated the text describing the 3 respondent classes in the analysis and added a new table (Table 2) summarizing the information.

The added text included the following:

We accomplished the latter by running a 3-class model. Preferences in class 1 were required to favor treatments without an indication for schizophrenia by constraining the marginal effect of having an indication for schizophrenia to be non-positive. Preferences in class 2 were required to favor treatments with an indication for schizophrenia by constraining the marginal effect of treating MDD and schizophrenia to be non-negative. Preference estimates in class 3 were obtained without any parameter constraints. Table 2 summarizes the model specification by class.

Table 2. Model specification by class

Class Assumed model specification

Class 1 – Favors treatments without schizophrenia indication Preference weight for schizophrenia indications was constrained to be non-positive

Class 2 – Favors treatments with schizophrenia indication Preference weight for schizophrenia indications was constrained to be non-negative

Class 3 – Unconstrained class All parameters were estimated freely

COMMENT:

Under Results:

The first paragraph describing the number of participants and description of encounters can go in under methods.

RESPONSE:

We have moved the cited paragraph to the end of the method’s section.

COMMENT:

Use another phrase for “preference weights” or define what is being referenced by preference weights.

RESPONSE:

We now have defined the results from a logit-based model of preferences as preference weights. This is because the estimates represent the rate of change in the probability of choices with particular attribute levels. The new text in the analysis section reads as follows.

Estimates from logit-based regression models represent the percentage change in the probability of choice given the attribute levels in the alternatives. For this reason, they also are called preference weights.

COMMENT:

Reword for clarity: “We also noted a small, marginally statistically significant positive effect of additional context information on the acceptance of atypical antipsychotics and greater chance of indifference about the treatment indications.”

RESPONSE:

We have updated the cited text for clarity. The new text reads as follows:

We noted that the additional context information changed the acceptance rate of atypical antipsychotics in a small positive way. The context information was also associated with a greater chance of indifference about the treatment indications.

COMMENT:

Under Discussion:

This sentence is not necessary: “While the effect is relatively small, we found evidence that it is likely pervasive”

RESPONSE:

We would like to maintain this statement as we think it is important to note how widespread the issue of self-stigmatization appears to be in this population based on our results.

COMMENT:

Under “limitations” section: Other limitations identified are including and limited to not addressing other communication strategies, qualitative information on avoidance of adjuvant antipsychotics because of self-stigmatization was not taken from the participants who completed the online survey, and participants answered on the online survey may not reflect in their real-world decision making.

A suggestion to include is giving specific points that doctors can talk about to their patients that subside the patient’s aversion to adjuvant antipsychotics for MDD symptoms. Consider taking about directions for future research.

Limitations accounted for: consistency check was done where only data of good quality was taken into account and statistical lengths were taken to account for different variances in data.

RESPONSE:

In response to the reviewer’s comments we have added the following paragraph to the discussion section.

In our application, simply highlighting the fact that taking antipsychotics does not require experiencing psychotic episodes reduced avoidance of these treatments. Future research should consider measuring how SDM affect actual patient treatment-taking behavior. This information could help quantify real-world implications of self-stigmatization among MDD patients and support the development of strategies that reduce the avoidance of potentially effective treatments.

Attachment Submitted filename: Response to reviewers 5-9-24.docx

10.1371/journal.pone.0309562.r003
Decision Letter 1
Mitra Souparno Academic Editor
© 2024 Souparno Mitra
2024
Souparno Mitra
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version1
19 Jun 2024

PONE-D-24-07744R1Self-stigmatization and treatment preferences: Measuring the impact of treatment labels on choices for depression medicationsPLOS ONE

Dear Dr. Gonzalez Sepulveda,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

==============================

ACADEMIC EDITOR: Please see review comments including the ones alluding to including inclusion and exclusion criteria and resubmit for further consideration

==============================

Please submit your revised manuscript by Aug 03 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Souparno Mitra, M.D.

Academic Editor

PLOS ONE

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: All comments have been addressed

Reviewer #3: (No Response)

Reviewer #4: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #2: Yes

Reviewer #3: Partly

Reviewer #4: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

Reviewer #3: I Don't Know

Reviewer #4: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: Yes

Reviewer #3: No

Reviewer #4: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #2: Thank you for the opportunity to rereview "Self-stigmatization and treatment preferences: Measuring the impact of treatment labels on choices for depression medications". All the requested review comments were adequately addressed.

Reviewer #3: Self-stigmatization and treatment preferences: Measuring the impact of treatment labels on choices for depression medications.

Summary of research

The paper by et al. analyzed the influence of medication product inserts on patients’ perception and their preference between treatment options. 501 Patients with MDD were made to complete DCE survey. Based on the result author suggest, Product-label treatment indication can potentially lead to patient self-stigmatization as shown by patients’ avoidance of treatments that are also used to treat schizophrenia.

Weaknesses:

1) In the study participants were made to read the label and make recommendations. It is important to consider the percentage of patients who read the labels of the medications in detail in real life scenario.

2) Psychoeducation plays a key role in preventing stigma in most patients with MDD. It is usual practice for psychiatrists to explain the risks, benefits, FDA black box warning, other indications of medications prescribed. Psychiatrists usually explain these to the patient before starting new meds and make them understand why specific medication was chosen for them and how it could help them. These discussions usually help in preventing stigma in most cases. I am curious to know in the study sample if a provider explained them the above and answered all questions.

3) Study states following: The additional information was intended to provide more context around the use of atypical antipsychotics to treat MDD mimicking the kind of information a patient could receive from physicians to alleviate problems with self-stigmatization. Respondents who were offered the additional information (context arm) were reminded explicitly that patients need not experience psychoses to benefit from antipsychotics.

---- It is important to note: The therapeutic alliance made between patients and psychiatrist usually makes a great difference in stigma, as patients trust on provider is greater to a researcher explaining them.

4) In discussion it states: Also, to some degree, our DCE question design included more information than the limited details given on product inserts to generate meaningful preference weights.

� It is important to know what additional information was added as it changes the validity of the study, and its application to real life scenarios.

Major issues:

1) Inclusion criteria: Specification regarding inclusion criteria is missing. Recommended to write clear inclusion criteria.

Pl refer to PLOS ONE submission guideline: https://journals.plos.org/plosone/s/submission-guidelines#:~:text=In%20the%20text%2C%20cite%20the,not%20include%20citations%20in%20abstracts

5) In methods pl include the sample size.

6) Exclusion criteria needed to be added.

Minor issues:

1) We included two attributes potentially associated with self-stigmatization. First, medication type, which included antidepressants and two different names that could be used to describe the same family of antipsychotics to treat MDD. It is difficult to follow this sentence. Recommend rephrasing

General comments:

While the data from this article is informative. Study methodology is unclear with missing information to reproduce the study. Also, study could be more valid if questionnaire used included psychiatrist/ physician involvement in recommending the medication and discussing with patient

Reviewer #4: This article offers valuable insights into self-stigmatization and how it interacts with treatment and medication compliance. Although some of the conclusions were difficult to grasp, overall, the article successfully identifies the impact of self-stigmatization on the treatment of specific mental health issues using a decent sample size.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #2: No

Reviewer #3: Yes: Mallikarjuna Bagewadi Ellur

Reviewer #4: No

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

10.1371/journal.pone.0309562.r004
Author response to Decision Letter 1
Submission Version2
17 Jul 2024

We have edited the manuscript to address the comments from the reviewer. We hope the changes made satisfy the reviewer. Thanks for the opportunity to respond to that feedback.

Attachment Submitted filename: Response to reviewers 6-20-24.docx

10.1371/journal.pone.0309562.r005
Decision Letter 2
Mitra Souparno Academic Editor
© 2024 Souparno Mitra
2024
Souparno Mitra
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version2
14 Aug 2024

Self-stigmatization and treatment preferences: Measuring the impact of treatment labels on choices for depression medications

PONE-D-24-07744R2

Dear Dr. Gonzalez Sepulveda,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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10.1371/journal.pone.0309562.r006
Acceptance letter
Mitra Souparno Academic Editor
© 2024 Souparno Mitra
2024
Souparno Mitra
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
23 Aug 2024

PONE-D-24-07744R2

PLOS ONE

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PLOS ONE
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