Comparing pregnancy and pregnancy outcome rates between adolescents with and without pre-existing mental disorders

Nakyung Jeon; Yasser Albogami; Sun-Young Jung; Regina Bussing; Almut G Winterstein

doi:10.1371/journal.pone.0296425

. 2024 Mar 14;19(3):e0296425. doi: 10.1371/journal.pone.0296425

Comparing pregnancy and pregnancy outcome rates between adolescents with and without pre-existing mental disorders

Nakyung Jeon ^1,^2,^*, Yasser Albogami ³, Sun-Young Jung ⁴, Regina Bussing ⁵, Almut G Winterstein ⁶

Editor: Giuseppe Marano⁷

PMCID: PMC10939254 PMID: 38483946

Abstract

Background

There are limited population-based data on the role of mental disorders in adolescent pregnancy, despite the presence of mental disorders that may affect adolescents’ desires and decisions to become pregnant.

Objective

This study aimed to examine the relationship between specific types of mental disorders and pregnancy rates and outcome types among adolescents aged 13–19 years, using single-year age groups.

Methods

We conducted a retrospective cohort study using data from the Merative™ MarketScan Research Databases. The study population consisted of females aged 13–19 years with continuous insurance enrollment for three consecutive calendar years between 2005 and 2015. Pregnancy incidence rates were calculated both overall and within the different categories of mental disorders. The presence of mental disorders, identified through diagnosis codes, was classified into 15 categories. Pregnancy and pregnancy outcome types were determined using diagnosis and procedure codes indicating the pregnancy status or outcome. To address potential over- or underestimations of mental disorder-specific pregnancy rates resulting from variations in age distribution across different mental disorder types, we applied age standardization using 2010 U.S. Census data. Finally, multivariable logistic regression models were used to examine the relationships between 15 specific types of mental disorders and pregnancy incidence rates, stratified by age.

Results

The age-standardized pregnancy rate among adolescents diagnosed with at least one mental disorder was 15.4 per 1,000 person-years, compared to 8.5 per 1,000 person-years among adolescents without a mental disorder diagnosis. Compared to pregnant adolescents without a mental disorder diagnosis, those with a mental disorder diagnosis had a slightly but significantly higher abortion rate (26.7% vs 23.8%, P-value < 0.001). Multivariable logistic regression models showed that substance use-related disorders had the highest odds ratios (ORs) for pregnancy incidence, ranging from 2.4 [95% confidence interval (CI): 2.1–2.7] to 4.5 [95% CI:2.1–9.5] across different age groups. Overall, bipolar disorders (OR range: 1.6 [95% CI:1.4–1.9]– 1.8 [95% CI: 1.7–2.0]), depressive disorders (OR range: 1.4 [95% CI: 1.3–1.5]– 2.7 [95% CI: 2.3–3.1]), alcohol-related disorders (OR range: 1.2 [95% CI: 1.1–1.4]– 14.5 [95% CI: 1.2–178.6]), and attention-deficit/conduct/disruptive behavior disorders (OR range: 1.1 [95% CI: 1.0–1.1]– 1.8 [95% CI: 1.1–3.0]) were also significantly associated with adolescent pregnancy, compared to adolescents without diagnosed mental disorders of the same age.

Conclusion

This study emphasizes the elevated rates of pregnancy and pregnancy ending in abortion among adolescents diagnosed with mental disorders, and identifies the particular mental disorders associated with higher pregnancy rates.

Introduction

Mental disorders are relatively common among adolescents, particularly females, and are a significant clinical and public health concern. In 2020, the prevalence of persistent depressive feelings among U.S. high school students was 42.0%, with a higher prevalence in females than in males (57% vs 29%) [1]. The percentage of female students who have? experienced persistent feelings of sadness or hopelessness has increased dramatically from 36% to 57% over the last decade, whereas there has only been an 8% increase in the rate among males during the same period [1]. Investigating the role of mental health problems in female-specific health outcomes is critical.

Adolescents with pre-existing mental health conditions may have inadequate knowledge, resources, and psychosocial skills to maintain healthy relationships and avoid unintended pregnancies [2]. Recent literature has demonstrated a significant relationship between mental health and sexual risky behaviors [3]. Ambivalent pregnancy desire, poor contraceptive behavior, lack of contraceptive knowledge, early ages of first intercourse, more sexual partners, and low self-efficacy have all been associated with several mental disorders [4–6]. Although limited, studies have demonstrated a lack of utilization of women’s health services and medical care-seeking behaviors among mentally ill women [7–9]. In 2011, the rate of unintended pregnancies in the United States was 48%, with a higher rate (75%) among adolescents.

Understanding the epidemiology of pregnancy in adolescents with mental disorders is a critical step toward understanding the psychological and behavioral effects of mental disorders on pregnancy desires, disparities, and health outcomes [10, 11]. However, there is limited epidemiological data available to estimate the pregnancy rate among adolescents with a pre-existing mental disorder. As an initial effort to improve women’s health in vulnerable populations, this study aimed to identify patterns of adolescent pregnancy rates across different types of mental disorders among privately insured females aged 13–19 years old.

Methods

Data source

This study used healthcare claims data obtained from the Merative™ MarketScan Research Databases [12]. This database provides details on reimbursed health services, including medical encounters and drugs dispensed in outpatient pharmacies for patients in approximately 150 employer-sponsored insurance plans. Because the database includes claims from many private insurers and has very wide geographic coverage, it has been frequently used in analyses of healthcare utilization as a data source representing privately insured individuals [12–14]. A retrospective cohort of females aged 13–19 years between 2006 and 2014 was established. The inclusion criteria for the cohort was as follows: continuous enrollment for at least three years between 2005 and 2015, allowing for eligibility periods such as 2005–2007, 2006–2008 and 2013–2015.

Pregnancy and pregnancy outcome type ascertainment

Pregnancy episodes were identified based on two or more inpatient or outpatient encounters, indicating pregnancy, antenatal care, or delivery with or without specified pregnancy outcomes. The identified pregnancies were categorized into pregnancy outcome types, namely abortion, ectopic pregnancy, stillbirth, and live birth, using the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM), the Current Procedural Terminology (CPT), and the Healthcare Common Procedure Coding System (HCPCS) codes assigned to inpatient or outpatient encounter (S1 Table). Pregnancies with an unspecified outcome were also considered, and were defined as pregnancies identified by diagnoses or procedure codes of prenatal visits without any diagnoses indicating the other four pregnancy outcome types. The conception date for each pregnancy episode was estimated based on previously validated algorithms, using five pregnancy outcome types [15–17]. Briefly, the algorithm assigned fixed gestational ages (GA) for each pregnancy endpoint: 273 days for live birth, 196 days for stillbirth, 70 days for spontaneous/induced abortions, and 56 days for ectopic pregnancy. For "pregnancy with unspecified outcome," a GA of 55 days was assigned based on the median time between the estimated last menstrual period (LMP) and the first pregnancy care claim for other episodes with a specified outcome [17]. Using the estimated conception date, pregnancy rates were calculated for all females aged 13–19 years in each year from 2006 to 2014. Of note, two consecutive years of data (2006–2007, 2007–2008, and 2014–2015) were assessed to determine pregnancies that began in the second year of the three years, using the aforementioned algorithm, which estimates the conception date of pregnancy episodes in claim-based healthcare data.

Mental disorder ascertainment by mental disorder type

For each pregnancy, the year preceding the conception year was used to ascertain diagnoses of mental disorders. For this reason, three years of continuous eligibility were required. At least one inpatient or outpatient encounter with a mental disorder diagnosis as the primary diagnosis was required to identify a patient with a mental disorder. An overview of the study design and designation of the study population as having a pre-existing mental disorder and pregnancy episode (i.e., the outcome) is illustrated in Fig 1.

Fig 1 — The designation of the study population to having a pre-existing mental disorder and pregnancy episode to construct the binary variables (yes/no) is illustrated.

The Clinical Classification Software (CCS) provided by the Agency for Healthcare Research and Quality (CCS650–670) was modified to categorize each mental disorder-related diagnosis code into 15 disorder types: Adjustment Disorders, Alcohol-Related Disorders, Anxiety Disorders, Attention-Deficit/Conduct/Disruptive Behavior Disorders, Bipolar Disorders, Delirium/Dementia/Amnestic/Other Cognitive Disorders, Depressive Disorders, Developmental Disorders, Disorders Usually Diagnosed in Infancy, Childhood, or Adolescence Impulse Control Disorders, Miscellaneous Mental Disorders, Personality Disorders, Schizophrenia and Other Psychotic Disorders, Substance-Related Disorders, and Suicide Attempt and Intentional Self-inflicted Injury [18]. The codes used in the modified CCS are available in S2 Table. Patients were assigned to multiple categories if they had two or more visits for the primary diagnoses of different types of mental disorder.

Statistical analyses

Given the anticipated variations in mental disorder prevalence with age, it is important to address the potential underestimation or overestimation of pregnancy rates due to differences in age distribution among mental disorder groups. To mitigate the influence of age distribution on pregnancy rate estimation, age standardization was employed by aligning the estimated pregnancy rate in each mental disorder group with the 2010 U.S. Census age distribution [19]. To begin, the incidence rates of pregnancy were calculated for 15 different mental disorder groups, thereby determining the number of pregnancies within each mental disorder group. Next, the age distribution derived from the U.S. Census population was applied to each mental disorder group to account for the diverse age distributions among the mental disorder groups and the U.S. Census population. Through this process, age-adjusted pregnancy rates were estimated for each mental disorder group, enabling a meaningful comparison of pregnancy rates across different mental disorder types while assuming a standardized age distribution across the mental disorder groups.

The distributions of the five pregnancy outcome types among pregnant adolescents were presented as counts and percentages with and without at least one mental disorder diagnosis. Differences in the rates of each pregnancy outcome type between adolescents with and without at least one mental disorder diagnosis were assessed using the Chi-square test. Finally, multivariable logistic regressions were used to identify the relationships between the types of mental disorders and the incidence of adolescent pregnancy stratified by age. In the logistic regression model, a binary coding approach was used to create separate dummy variables for each mental disorder category. This was used to assess the individual effects of each disorder while accounting for potential overlapping effects. However, it is important to note that the model did not explicitly consider the interactions or combined effects between multiple concurrent mental disorders. Instead, the focus was solely on examining the main effects of mental disorder variables on pregnancy events.

This study involved de-identified, aggregate data and was not subject to Institutional Review Board approval at the University of Florida. All methods were performed in accordance with the relevant guidelines and regulations of Merative™ MarketScan Research Databases [12].

Alpha was set to 0.05 as the significance level for all statistical analyses. SAS software version 9.4. was used for all analyses (SAS institute Inc., Cary, NC, USA).

Results

The overall prevalence of mental disorders in female adolescents increased gradually from 9.7% in 2005 to 14.7% in 2013. Depressive Disorders, Anxiety Disorders and Attention-deficit/Conduct/Disruptive Behavior Disorders were the three most prevalent mental disorders (Table 1). Approximately 95% of Suicide Attempt and Intentional Self-inflicted Injury diagnoses were concurrent with a diagnosis of Depressive Disorder (∼90%), Anxiety Disorder (∼62%), or Attention-deficit/Conduct/Disruptive Behavior Disorder (∼31%) in the same calendar year. Approximately 80% of Personality Disorders, Schizophrenia and Other Psychotic Disorder and over 60% of Bipolar, Impulse Control or Substance-related Disorders were concurrent with at least one of the three most prevalent disorders.

Table 1. Crude and age–standardized pregnancy rates per 1,000 person-year of females aged 13–19 years, overall and according to mental disorder type.

	Numerator	Denominator	Crude rate	Age-standardized rate	95% Confidence intervals
Overall	69,580	7,591,716 (100%)	9.2	9.3	9.2–9.4
No mental disorder	54,423	6,729,529 (88.6%)	8.1	8.5	8.4–8.6
With any mental disorder	15,157	862,187 (11.4%)	17.6	15.4	15.1–15.6
Substance-Related Disorders	1,727	28,429 (0.4%)	60.7	42.2	39.9–44.5
Alcohol-Related Disorders	913	19,054 (0.3%)	47.9	34.5	30.5–38.6
Suicide Attempt and Intentional Self-inflicted Injury	755	19,103 (0.3%)	39.5	32.5	30.0–34.9
Bipolar Disorders	2,622	78,708 (1.0%)	33.3	26.5	25.5–27.6
Personality Disorders	216	6,795 (0.1%)	31.8	24.3	20.9–27.6
Schizophrenia and Other Psychotic Disorders	350	12,236 (0.2%)	28.6	23.0	20.6–25.5
Depressive Disorders	6434	259,321 (3.4%)	24.8	18.8	18.3–19.3
Impulse Control Disorders	145	7,620 (0.1%)	19.0	18.1	15.1–21.1
Anxiety Disorders	4313	239,985 (3.2%)	18.0	14.7	14.2–15.1
Miscellaneous Mental Disorders	1072	61,485 (0.8%)	17.4	13.8	12.9–14.6
Adjustment Disorders	3102	200,626 (2.6%)	15.5	14.2	13.7–14.7
Attention-Deficit/Conduct/Disruptive Behavior Disorders	3667	265,927 (3.5%)	13.8	14.4	13.9–14.9
Delirium/Dementia/Amnestic/Other Cognitive Disorders	198	15,251 (0.2%)	13.0	10.8	9.2–12.3
Developmental Disorders	127	20,790 (0.3%)	6.1	7.4	6.1–8.7
Disorders Usually Diagnosed in Infancy, Childhood, or Adolescence	144	27,656 (0.4%)	5.2	6.5	5.4–7.6

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As shown in Table 2, approximately nine out of a thousand female adolescents experienced pregnancy in a given year. The age-standardized pregnancy rates were 15.4 (95% CI: 15.1–15.6) and 8.5 per 1,000 person-years (95% CI: 8.4–8.6) for adolescents with and without mental disorders, respectively. Live births accounted for more than half of the pregnancies (68.1%), followed by abortions (24.4%) and unspecified pregnancy outcomes (6.6%). Ectopic pregnancies and stillbirths contributed to 1% of pregnancy incidence. Compared to pregnant adolescents without mental disorders, those with mental disorders had a slightly but significantly higher abortion rate (26.7% vs 23.8%, P-value < 0.001) and a lower live birth rate (65.6% vs 68.8%, P-value < 0.001), as shown in Table 2.

Table 2. Distribution of outcome types according to the presence of pre-existing mental disorder in females aged 13–19 years.

	The Number of Pregnancy
Pregnancy outcome types	Without any Mental Disorder Diagnosis	With at least one mental disorder diagnosis	P-value	All
Live birth	37,432 (68.78%)	9,939 (65.57%)	< 0.001	47,371 (68.08%)
Abortion	12,935 (23.77%)	4,047 (26.70%)	< 0.001	16,982 (24.41%)
Unspecified pregnancy outcomes	3,530 (6.49%)	1,046 (6.90%)	0.07	4,576 (6.58%)
Ectopic pregnancy	252 (0.46%)	58 (0.38%)	0.18	310 (0.45%)
Stillbirth	274 (0.50%)	67 (0.44%)	0.34	341 (0.49%)
Total	54,423 (100%)	15,157 (100%)		69,580 (100%)

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Overall, age-standardized pregnancy rates in female adolescents with at least one mental disorder diagnosis were relatively high regardless of mental disorder type, with the exception of Developmental Disorders (7.4 events/1,000person-year, 95% CI: 6.1–8.7) and Disorders Usually Diagnosed in Infancy, Childhood, or Adolescence (DICA), such as autism spectrum disorders or tic disorders (6.5 events/1,000person-years, 95% CI: 5.4–7.6). Female adolescents with Substance-related Disorders, Alcohol-related Disorders, Suicide Attempt and Intentional Self-inflicted Injury, Personality Disorders, and Bipolar Disorders had the highest age-standardized pregnancy rates ranging from 24.3 (95% CI: 20.9–25.5) to 42.2 (95% CI: 39.9–44.5) per 1,000 person-years (Table 1).

Pregnancy rates increased with age from 0.2 to 25.0 per 1000 person-years. Fig 2 displays forest plots illustrating the Odds Ratios (OR) with 95% CIs for adolescent pregnancies in cohorts stratified by patient age from 14 to 19 years. Multivariable analyses in age-stratified cohorts indicated that patients with certain mental disorders had a significantly higher adolescent pregnancy incidence regardless of age, including Substance-related Disorders, Bipolar Disorders and Depressive Disorders.

Overall, Substance-related Disorders (OR: 2.44 [95% CI: 2.33–3.02]), Bipolar Disorders (OR: 1.77 [95% CI: 1.71–1.85]), Depressive Disorders (OR: 1.57 [95% CI: 1.53–1.61]), Alcohol-related Disorders (OR: 1.42 [95% CI: 1.34–1.53]), Attention-deficit/Conduct/Disruptive Behavior Disorders (OR: 1.23 [95% CI: 1.19–1.27]), Adjustment Disorders (OR: 1.21 [95% CI: 1.17–1.25]), Suicide Attempt and intentional Self-inflicted Injury (OR: 1.17 [95% CI: 1.09–1.26]), Miscellaneous Mental Disorders (OR: 1.07 [95% CI:1.01–1.13]), Anxiety Disorders (OR: 1.05 [95% CI: 1.02–1.09]) were associated with higher adolescent pregnancy incidence (S1 Fig). However, patients with Disorders Usually Diagnosed in Infancy, Childhood, or Adolescence (OR: 0.52 [95% CI: 0.46–0.60]) or Developmental Disorders (OR: 0.73 [95% CI: 0.63–0.83]) exhibited lower pregnancy rates when compared to adolescents without any diagnosed mental disorders (S1 Fig).

Discussion

This was the first and largest retrospective cohort study to estimate pregnancy rates for different pre-existing mental disorders among adolescents. Our analysis of this representative sample of private insurance enrollees demonstrated that adolescents with pre-existing mental disorders had disproportionately higher pregnancy rates than their peers without such disorders.

Adolescents with mental disorders characterized by risk-taking behaviors including Substance-related Disorders, Alcohol-related Disorders, Bipolar Disorder, and Attention-deficit/Conduct/Disruptive Behavior Disorders had relatively high pregnancy rates. The results suggest that a large number of pregnancies in adolescents may be influenced by the effects of underlying psychiatric conditions on mood, behavior, and decision-making or treatment.

While the reasons for high adolescent pregnancy rates in individuals with mental disorders were not examined in this study, our study confirmed that mental disorders and pregnancy frequently coexist in adolescents and the pregnancy rates were higher among adolescents with certain mental disorder types.

The coexistence of mental disorders and pregnancy among adolescents underscores the need for public health interventions that extend beyond the healthcare system. These include promoting mental health awareness, improving access to mental health services, enhancing sexual and reproductive health education, and providing comprehensive and effective support for pregnant adolescents with mental disorders.

Pre-existing mental disorders may continue or worsen during pregnancy period. The management of mental disorders during and after pregnancy is challenging and requires specialized healthcare and social support. Some adolescents diagnosed with mental disorders may already be taking psychotropic medications. Hence, the introduction of pregnancy adds complexity to their healthcare needs because of the need to balance between the effectiveness and safety of the psychotropic medications. In addition, some mental disorders or their pharmacotherapy can affect the hypothalamic–pituitary–gonadal axis, leading to anovulatory cycles and menstrual disturbances [20–23]. These conditions may also interact with hormonal contraceptives, reducing effectiveness of contraceptive methods or the treatment.

Some mental disorders are also associated with poor medication adherence. Children born to mothers with untreated or poorly managed mental disorders may be at higher risk of developmental and behavioral problems. Mental disorders may also lead to poor prenatal care attendance and poor maternal health. Our study indicated that pregnant adolescents diagnosed with at least one mental disorder diagnosis had a higher abortion rate than those without a mental disorder diagnosis, a finding that partially supports the possibility of an increased likelihood of pregnancy complications and negative outcomes.

Our results emphasize the importance of public health interventions that can reduce unintended pregnancies in the context of mental disorder management. This is particularly relevant for adolescents who may face unique challenges due to their young age, severity of mental health, or pharmacotherapy, which can potentially harm the fetus. Although validated programs and evidence to support interventions for pre-conception health exist, they are directed toward encouraging safer-sex practices in the general adolescent population, and the benefits of these interventions for adolescents with mental disorders await further evaluation [24, 25].

One of the main findings of our study was that adolescents diagnosed with substance use disorder had a pregnancy rate more than double than that of adolescents without any mental disorder diagnosis. In response to the opioid crisis in the United Sates, the understanding of the epidemiology and evidence-based treatment of opioid use disorder has evolved over the past several decades. However, research or programs designed to improve outcomes in opioid use disorders are almost entirely absent for adolescents [26]. Given the unique importance for women of childbearing age to avoid substance use and for women with substance use disorders to prevent pregnancy, screening (i.e., drug test and pregnancy test) or the provision of relevant resources should be accessible to women to reduce the risk of substance use during pregnancy.

Interestingly, we found Depressive Disorders played a larger role in adolescent pregnancy rate at younger age. The ORs for adolescent pregnancies at ages 14–16 years were significantly higher than those at ages 18–19. While there are limited data specifically documenting the demographics of pregnant women with pre-pregnancy depression, related findings regarding higher rates of sexual initiation at a younger age, misuse of contraception, unintended pregnancy, and induced abortion among women with depression may support our findings [10, 27, 28].

In contrast, Disorders Usually Diagnosed in Infancy, Childhood, or Adolescence and Developmental Disorders such as intellectual disability (ID) exhibited a decreased adolescent pregnancy rate in our analytic cohort. To our knowledge, no previous study has directly compared the incidence of pregnancy between adolescents with and without ID. Further evaluation of pregnancy intentions in adolescents with ID will be particularly meaningful given the known vulnerability of children with ID in becoming victims of sexual abuse [29].

This study had several limitations that should be acknowledged when interpreting our findings. First, this study could not differentiate between unintended and intended pregnancies among those identified pregnancies in adolescents. Over 70%– 80% of adolescent pregnancies are commonly recognized as unintended, and the presence of mental disorders may potentially affect the intention to become pregnant [30, 31]. Among the individuals who do not intend to become pregnant, having certain mental disorders may increase or decrease the likelihood of conception, which may be affected by impulsivity, risky behavior, or difficulties in decision making and in considering the consequences of their actions [32] (S2 Fig). Future studies evaluating pregnancy intention as a mediator on the association between a mental disorder and adolescent pregnancy are warranted.

Second, despite our efforts to identify all conceptions and their timing regardless of pregnancy outcome type, we may have underestimated pregnancy rates due to failure to capture miscarriages and non-reimbursed abortion, and misclassification of gestational age. However, when interpreting the results, the focus should be on the pattern of pregnancy rates due to mental disorders, as the extent of misclassification would not be expected to differ systematically across diagnoses. Third, the data comprised patients with three continuous years of private health insurance, implying that all included study populations had employed parents. Therefore, the study results and interpretations cannot be generalized to uninsured or government insured populations. Fourth, we included age as the only covariate in the multivariable logistic regression analysis after adjusting for the relationship between the type of mental disorder and adolescent pregnancy. However, differences in race, income, or geographic area among mental disorder types may exist, which can affect the estimation of adolescent pregnancy. Future studies that provide basis for adjusting for differences in age-specific adolescent pregnancy rates across groups defined by sex, race, ethnicity, geography, and other sociodemographic categories are warranted.

Lastly, while we do not discount a more specific designation of mental disorder diagnoses requiring more stringent criteria (e.g., at least two outpatient diagnoses of the same mental disorder), we emphasize the distinction between girls with some level of mental disorder and those who were never diagnosed with any mental disorder during our look-back period.

Conclusion

With significantly higher adolescent pregnancy rates than their counterparts without diagnosed mental disorders, adolescents with certain mental disorders should be prioritized for evidence-based pre-conception health care.

Supporting information

S1 Table. Pregnancy-related ICD-9-CM codes.

The list of ICD-9-CM codes to indicate prenatal visits, pregnancy outcomes, or delivery.

(DOCX)

pone.0296425.s001.docx^{(23.7KB, docx)}

S2 Table. The fifteen mental disorder categories.

The list of ICD-9-CM codes categorized by 15 mental disorders based on the Clinical Classifications Software (CCS) developed by the Agency for Healthcare Research and Quality (AHRQ).

(DOCX)

pone.0296425.s002.docx^{(14.9KB, docx)}

S1 Fig. Age-adjusted odds ratios of becoming pregnant among adolescents with a mental disorder diagnosis.

(TIF)

pone.0296425.s003.tif^{(69KB, tif)}

S2 Fig. A directed acyclic graph illustrating the role of pregnancy intention as a mediator on the association between mental disorder and adolescent pregnancy.

(TIF)

pone.0296425.s004.tif^{(118.1KB, tif)}

Data Availability

Data for these analyses were made available to the authors through third-party license from Merative MarketScan Research Database, a commercial data provider in the United States. As such, the authors cannot make these data publicly available due to data use agreement. Other researchers can access these data by purchasing a license through Merative MarketScan Research Database. Inclusion criteria specified in the Methods section would allow other researchers to identify the same cohort of patients we used for these analyses. Interested individuals may see "https://www.merative.com/real-world-evidence" for more information on accessing Merative MarketScan Research Database. The authors did not have any special access privileges other authors would not have.

Funding Statement

This work is supported by Chonnam National University Hospital Biomedical Research Institute (BCRI202109-85), awarded to NJ. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS One. doi: 10.1371/journal.pone.0296425.r001

Decision Letter 0

Giuseppe Marano

25 Apr 2023

PONE-D-23-05808Comparing pregnancy and pregnancy outcome rates between adolescents with and without a pre-existing mental disorderPLOS ONE

Dear Dr. Jeon,

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Reviewer #1: Partly

Reviewer #2: Yes

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Reviewer #1: I Don't Know

Reviewer #2: I Don't Know

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Reviewer #1: Yes

Reviewer #2: No

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Reviewer #2: Yes

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5. Review Comments to the Author

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Reviewer #1: Study Summary: Using a retrospective cohort study design, with data was derived from the 2005-2015 IBM Marketscan Commercial Claims Research Database, the authors of this manuscript sought to examine the association between a mental health diagnosis and rates of pregnancy among adolescents aged 13-19. Investigators performed age-standardized logistic regression models and determined that adolescents with a mental health disorder had nearly a two times increased rate of pregnancy compared to those of a similar age group without a mental health disorder. Those diagnosed with substance-use related disorders showed the most striking differences. Pregnancy outcome types were also observed among the two groups with results indicating that those with a mental health disorder have a greater rate of abortions than those without. Implications are geared towards interventions that reduce unplanned pregnancy. The scope of this work is highly beneficial for amplifying the role of preconception mental health research and clinical practice. The authors present a interesting analysis on a very important topic. Additional strengths include a strong data source, with results adding to a much-needed growing body of research. However, several areas of clarification require further description. Given the comments below regarding the statistical approaches used, I would also recommend that a statistician review this manuscript. Overall, the study findings are a bit hard to interpret because some of the data and methods have not been provided or explained. Additionally, there are grammatical errors and stigmatizing language that need attention. Please find detailed feedback below:

Abstract:

1. Major: The authors state they used a case cohort study design, however there is no description of how cases were identified. A case cohort is used when multiple case groups are defined from a cohort study, compared to a control series from the same cohort. These are often useful when the risk ratio is of greater interest than incidence rate ratio or when multiple outcomes are of interest. I think this study can simply be stated as a retrospective cohort study.

2. Minor: Please revise for grammatical errors.

3. Minor: Please note at what confidence level the ranges in the results were derived from.

Introduction:

1. Minor: Please revise for grammatical errors.

2. Major: Avoid the term mental health ‘issues’ as this can be stigmatizing.

3. Major: Please incorporate additional citations in statements derived from evidence-based sources

4. Major: The introduction is a bit confusing, and several points can either be elaborated upon or stated more clearly. As written. It is unclear if the exposure is mental health status or a mental health condition/diagnosis.

5. Major: The investigators state that this study was developed as a means to prevent unplanned pregnancy. However, unplanned pregnancy was not discussed in this population at any other time in the introduction. This should be removed, or the introduction should be framed around the prevalence of unplanned pregnancy among persons diagnosed with a mental health disorder.

Methods:

1. Minor: Please provide a brief description of the validated algorithms used to create the five pregnancy outcome types.

2. Minor: Please add sub-headers throughout this section to improve readability. It was a bit challenging to follow what the measure were, from the data source and the analytic section (ex: add a ‘Statistical Analyses” right before the discussion of how distributions were assessed).

3. Major: Please discuss how comorbidities were addressed in the selection of mental health disorder type. Many of these conditions co-occur with one another and the authors allude to this point later on in the manuscript.

4. Major: In your statistical analysis you describe use of stratification – were possible interactions assessed in these?

5. Minor: The procedures used for standardization can be more clearly described.

6. Major: I do not see other covariates listed (outside of the age-based standardization). This is very concerning as no other descriptions of this population are provided. Furthermore, there are no confounders listed and there are several factors that are related with both exposure and outcome and would need to be controlled along the casual pathway.

Results:

1. Major: Terminology about the study population can be strengthened. Use of the term, ‘girls’ vs ‘females’ vs ‘adolescents’ vs ‘women’ makes it hard to follow. Please pick one and use it consistently throughout. ‘Adolescence’ may be the most appropriate and gender-neutral descriptor. Alternatively, you can make a note in the introduction describing that you will use the terms interchangeably.

2. Minor: Please define a tic disorder?

3. Minor: Please include confidence intervals when presenting your data.

4. Minor: Which exposure group does the 7.4 per 1000 and 6.5 per 1,000 finding pertain to? (Page 5)

5. Major: Please save all language that is geared towards interpreting the results for the discussion section (ex: ‘Consistent with previous studies’, ‘as expected’).

6. Major: Comorbidities are presented in the analyses and therefore should be discussed in the methods.

7. Minor: A hypothesis was not presented originally in the introduction and therefore ‘as expected’ is not appropriate in this section nor throughout the manuscript.

8. Major: The organization of study findings can be revised dramatically to increase readability. Please present prevalence’s and then measures of effect. As written, the outcome is unclear.

9. Minor: Physician-diagnosed mental disorders is used for the first time here. Either make consistent throughout or remove from this section.

10. Table 2: The exposure group of ‘adolescents with at least one mental disorder diagnosis’ is clear. This is the language that should be used throughout.

11. Table 2: Please round to 2 decimal places.

Discussion:

1. Major: It is unclear what the intent of paragraph 2 is, and I am not certain that the implications described here are justified based on the findings.

2. Minor: Some of the articles chosen to compare study findings may need revision. For this manuscript, I do not think treatment seeking women with opioid use disorder is a good group to use to present results in light of other work.

3. Major: This section should be revised to avoid stigmatizing language and to more aligned with harm reduction approaches. Intervention approaches should also be mindful of this unique age group. Particularly on page 3 use of the term ‘healthy peers’ is very jarring and should not be used at all.

4. Minor: What does DICA stand for?

5. Major: Again, I think before the discussion can be framed around unplanned pregnancy the introduction needs to be clear about this as the goal of this investigator. Also, while pregnancy intent is not something that could possibly be obtained from the data source, it may be helpful to create a directed acyclic graph to make sense of some of these relationships. If anything, it may be that unplanned pregnancy is a mediator.

6. Minor: STI’s do not contribute to unplanned pregnancy (page 4).

7. Minor: Please be consistent with what is the outcome vs outcome type throughout.

Reviewer #2: Little is known about the association between mental health and adolescent pregnancy. In this manuscript, the authors report on the findings of a retrospective case-cohort study of females at age 13-19. This study, therefore, makes a valuable contribution to the literature. Below I give some suggestions to improve the paper.

• It is unclear how the retrospective cohort of females was established (first sentence under methods). Perhaps it would be better to describe the source of data, and then follow this up with the description of the retrospective cohort. In describing the cohort, the text in the parentheses (i.e., eligibility period…..) in the first sentence can be deleted.

• It is unclear if the database includes information on sociodemographic characteristics of privately insured people (e.g., race, income, etc.). Including these in the model would be useful in assessing whether these characteristics are also associated with the outcomes. These data would also be useful in understanding how the study sample may differ from the wider population. This could also be highlighted as a limitation if the data are not available.

• It would have been useful to show whether the likelihood of pregnancy was different for those with comorbid mental health disorders compared to those with only one mental disorder diagnosis. This could have implications for the targeting of prevention interventions.

Minor edits

• The second paragraph in the discussion does not seem to add much and can be deleted.

• The manuscript needs a careful read through for grammar and spelling errors. Below I highlight just a few from the first two paragraphs

- Para 1, First sentence: Mental health disorders in “adolescence”

- Para 1, Second sentence: “The 2020 national prevalence…. in high schools in the United States was 42%”

- Para 1, Third sentence: “hopelessness has increased dramatically”

- Para 2, First sentence: “adolescent mental”

- Para 2, Second sentence: “An existing mental health problem/disorder”

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Reviewer #1: Yes: Brian W Jack

Reviewer #2: No

**********

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PLoS One. 2024 Mar 14;19(3):e0296425. doi: 10.1371/journal.pone.0296425.r002

Author response to Decision Letter 0

17 Aug 2023

Thank you for taking the time to review our manuscript. We appreciate your feedback and are grateful for your recognition of the study's strengths, including the robust data source and the implications for pre-conception mental health research and clinical practice. In response to your comments, we have carefully revised the manuscript to provide additional clarification of our study methods and data presentation to ensure that the findings are communicated clearly. We also agree with your suggestion to involve a statistician in reviewing the manuscript to verify the appropriateness of our statistical approach. Regarding the grammatical errors and stigmatizing language, we have made necessary revisions to enhance the manuscript's clarity and accuracy. Thank you again for your constructive comments, which have helped us improve the manuscript's quality.

Abstract:

We acknowledge the discrepancy between our stated study design as a case-cohort study and the absence of a clear description of how the cases within the sub-cohort were identified. We agree with your suggestion that this study may be more accurately described as a retrospective cohort study. As such, we changed the study design statement to “retrospective cohort study”. (Line 8)

2. Minor: Please revise for grammatical errors.

We have carefully revised the abstract to correct all grammar errors.

3. Minor: Please note at what confidence level the ranges in the results were derived from.

We used a 95% confidence level to calculate the confidence intervals in our results and have indicated this information in the abstract. (Lines 27 – 32)

Introduction:

1. Minor: Please revise for grammatical errors.

We have carefully revised the introduction to correct all grammar errors.

2. Major: Avoid the term mental health ‘issues’ as this can be stigmatizing.

We replaced “mental health issues” with “mental disorders”, “pre-existing mental health conditions”, mental health problems”, and “mentally ill” as appropriate.

3. Major: Please incorporate additional citations in statements derived from evidence-based sources

We have added the following five citations as appropriate in the introduction.

2. Olmsted AE, Markham CM, Shegog R, Ugueto AM, Johnson EL, Peskin MF, Emery ST, Baker KA, Newlin EW. Feasibility and Acceptability of Technology-supported Sexual Health Education Among Adolescents Receiving Inpatient Psychiatric Care. J Child Fam Stud. 2022;31(7):2050-2064.

3. Ramrakha S, Caspi A, Dickson N, Moffitt TE, Paul C. Psychiatric disorders and risky sexual behaviour in young adulthood: cross sectional study in birth cohort. BMJ. 2000;29;321(7256):263-6.

7. Berry MS, Sweeney MM, Dolan SB, Johnson PS, Pennybaker SJ, Rosch KS, Johnson MW. Attention-Deficit/Hyperactivity Disorder Symptoms Are Associated with Greater Delay Discounting of Condom-Protected Sex and Money. Arch Sex Behav. 2021;50(1):191-204.

8. Verlenden JV, Bertolli J, Warner L. Contraceptive Practices and Reproductive Health Considerations for Adolescent and Adult Women with Intellectual and Developmental Disabilities: A Review of the Literature. Sex Disabil. 2019;37(4):541-557.

9. Goyal S, Monsour M, Ko JY, Curtis KM, Whiteman MK, Coy KC, Cox S, Romero L. Contraception claims by medication for opioid use disorder prescription status among insured women with opioid use disorder, United States, 2018. Contraception. 2023;117:67-72.

We have revised the introduction entirely to provide more clarity on the exposure (=a mental disorder)

We have revised the introduction as follows to frame the introduction around the unintended pregnancy among individuals with a mental disorder. (Lines 55-66)

“In 2011, the rate of unintended pregnancy in the United States was 48%, with a higher rate (75%) in adolescents. At present, studies on unintended pregnancy among adolescents with mental disorders are sparse. Alternatively, a meta-analysis of 11 studies reported that the rate of unintended pregnancy among mentally ill women (regardless of age) concluded that women diagnosed with mood, anxiety, psychotic, substance use, conduct or eating disorders have a 34% higher risk of unintended pregnancy than women without such a diagnosis. A Canadian study that examined the prevalence and characteristics of adolescent women who intended to become pregnant found that approximately 27% of the pregnancies were intended. Furthermore, these women were less likely to have experienced violence within the last two years or had used alcohol prior to their pregnancy. However, adolescent women who had intended to become pregnant were more likely to have used drugs before pregnancy.”

Methods:

1. Minor: Please provide a brief description of the validated algorithms used to create the five pregnancy outcome types.

The following statement was added to the methods. (Lines 98 – 103)

“Briefly, the algorithm assigned fixed gestational ages (GA) for each pregnancy endpoint: 273 days for live birth, 196 days for stillbirth, 70 days for spontaneous/induced abortions, and 56 days for ectopic pregnancy. For "pregnancy with unspecified outcome," a GA of 55 days was assigned based on the median time between the estimated last menstrual period (LMP) and the first pregnancy care claim for other episodes with a specified outcome.”

We have added the following subheadings to the method section: Data source, Pregnancy and pregnancy outcome ascertainment, Mental disorder ascertainment by mental disorder type, and Statistical Analyses

In our logistic regression model, we did not specifically account for patients with multiple concurrent mental disorders. Rather, we treated each mental disorder as a separate independent variable. Therefore, the interpretation of the model for a patient with multiple concurrent mental disorders would involve examining the individual effects of each disorder independently, without explicitly considering potential interactions or combined effects. To clarify, we have added the following paragraph to the manuscript. (Lines 152 – 158)

“In the logistic regression model, a binary coding approach was used to create separate dummy variables for each mental disorder category. This was used to assess the individual effects of each disorder while accounting for potential overlapping effects. However, it is important to note that the model did not explicitly consider the interactions or combined effects between multiple concurrent mental disorders. Instead, the focus was solely on examining the main effects of the mental disorder variables on pregnancy events.”

4. Major: In your statistical analysis you describe use of stratification – were possible interactions assessed in these?

Our analysis did not involve a formal assessment of the interaction between age and mental disorder type on the occurrence of pregnancy. Instead, we repeated the aforementioned logistic regression analyses within individual cohorts stratified by age to investigate how mental disorder type might exert different impacts on pregnancy occurrence across various age-groups. By utilizing this approach, we aimed to explore the potential variations in the association between mental disorders and pregnancy events within distinct age strata.

5. Minor: The procedures used for standardization can be more clearly described.

We have provided an explanation of the age-standardization procedure as follows (Lines 134 – 146);

“Given the anticipated variations in mental disorder prevalence with age, it is crucial to address the potential underestimation or overestimation of pregnancy rates due to differences in age distributions among mental disorder groups. To mitigate the influence of age distribution on pregnancy rate estimation, age-standardization was employed by aligning the estimated pregnancy rate in each mental disorder group with the 2010 U.S. Census age-distribution. To begin, the incidence rates of pregnancy were calculated for 15 different mental disorders, thereby determining the number of pregnancies within each mental disorder group. Next, the age distribution derived from the U.S. Census population was applied to each mental disorder group to account for the diverse age distributions among the mental disorder groups and the U.S. Census population. Through this process, the age-adjusted pregnancy rates were estimated for each mental disorder group, enabling a meaningful comparison of pregnancy rates across different mental disorder types while assuming a standardized age distribution across the mental disorder groups.”

The objective of this study did not involve examining the causal pathway between mental disorder type and pregnancy incidence. Thus, the application or utilization of epidemiological methods for potential confounding or selection bias was not necessary, as they are not relevant to the research focus.

However, we do acknowledge that it is a study limitation that we only included “age” in our analysis as opposed to other sociodemographic information available in the data source. We added the following paragraph to address the limitation in the discussion section. (Lines 305 – 311)

“…, we included age as the only covariate in the multivariable logistic regression after adjusting for the relationship between the type of mental disorder and adolescent pregnancy. However, differences in race, income, or geographic areas among mental disorder types may exist, which can affect the estimation of adolescent pregnancy. Future study that provide a basis for adjusting for differences in the age-specific adolescent pregnancy rates across groups defined by sex, race, ethnicity, geography, and other sociodemographic categories are warranted.”

Results:

Thank you for your suggestion. We have carefully revised the manuscript to use adolescent consistently as the term to describe the study population.

2. Minor: Please define a tic disorder?

Below are the ICD-9-CM codes that this study used to define a tic disorder.

307.20: Tic disorder, unspecified

307.21: Transient tic disorder

307.22: Chronic motor or vocal tic disorder

The above information is available in eTable 2. Based on CCS classification, all of the three ICD-9-codes are classified as Disorders Usually Diagnosed In Infancy, Childhood, or Adolescence.

3. Minor: Please include confidence intervals when presenting your data.

We included 95% confidence intervals throughout the manuscript whenever we saw fit.

4. Minor: Which exposure group does the 7.4 per 1000 and 6.5 per 1,000 finding pertain to? (Page 5)

The answers are as follow: Developmental Disorders (7.4 events/ per 1,000 person-years, 95% CI: 6.1 – 8.7) and Disorders Usually Diagnosed in Infancy, Childhood, or Adolescence (DICA), such as autism spectrum disorders or tic disorders (6.5 events/ per 1,000person-years, 95% CI: 5.4 – 7.6).

5. Major: Please save all language that is geared towards interpreting the results for the discussion section (ex: ‘Consistent with previous studies’, ‘as expected’).

We removed ‘Consistent with previous studies’ and ‘as expected’ from the manuscript.

6. Major: Comorbidities are presented in the analyses and therefore should be discussed in the methods.

We have added the following paragraph in the methods section to explain how we addressed the concurrent mental disorders in the statistical analyses. (Lines 150 – 158)

“Finally, multivariable logistic regressions were used to identify the relationships between the types of mental disorders and the incidence of adolescent pregnancy stratified by age. In the logistic regression model, a binary coding approach was used to create separate dummy variables for each mental disorder category. This was used to assess the individual effects of each disorder while accounting for potential overlapping effects. However, it is important to note that the model did not explicitly consider the interactions or combined effects between multiple concurrent mental disorders. Instead, the focus was solely on examining the main effects of mental disorder variables on pregnancy events.”

7. Minor: A hypothesis was not presented originally in the introduction and therefore ‘as expected’ is not appropriate in this section nor throughout the manuscript.

We removed the phrase ‘as expected’ from the manuscript.

8. Major: The organization of study findings can be revised dramatically to increase readability. Please present prevalence’s and then measures of effect. As written, the outcome is unclear.

We have re-organized the manuscript to present the pregnancy incidence findings, followed by the measure of effects as the signals of association between mental disorder type and adolescent pregnancy.

9. Minor: Physician-diagnosed mental disorders is used for the first time here. Either make consistent throughout or remove from this section.

We noticed that we used mental disorders, diagnosed mental disorders, and physician-diagnosed mental disorders interchangeably throughout the manuscript, which may lead to confusion. As the reviewer suggested, we have revised the manuscript and use “diagnosed mental disorder” consistently throughout the text.

10. Table 2: The exposure group of ‘adolescents with at least one mental disorder diagnosis’ is clear. This is the language that should be used throughout.

We agree that “adolescents with at least one mental disorder diagnosis” is the operational definition used in our study to define adolescents with a pre-existing mental disorder diagnosis. Thus, we revised the text and used the operational definition as we saw fit.

11. Table 2: Please round to 2 decimal places.

We modified table 2 as suggested.

Discussion:

1. Major: It is unclear what the intent of paragraph 2 is, and I am not certain that the implications described here are justified based on the findings.

We have removed the following paragraph from the manuscript.

“Pregnancy can result in both live births and non-live births (e.g., abortions). The trends in the adolescent pregnancy rate between 1973 and 2017 are available and were reported by the Guttmacher Institute. The decline in the adolescent pregnancy rate over the past two and a half decades was reflected by declines in both birth and abortion rates; however, we noted that the pregnancy rate did not change significantly among the age group under 15 years. Together with our study findings, additional efforts to decrease pregnancy rates among sub-populations in adolescents who may be at a higher risk for unplanned pregnancy, individuals who are considered too young to have children and/or who have mental disorders are required.”

We removed the citation (the study on women with OUD seeking treatment).

We revised the sentence as below. (Lines 284 – 286)

“Further evaluation of the pregnancy intentions of adolescents with ID will be particularly meaningful given the known vulnerability of children with ID in becoming victims of sexual abuse.”

4. Minor: What does DICA stand for?

DICA stands for “Disorders usually diagnosed in Infancy, Childhood, or Adolescence (DICA)” and was first introduced in the Results section.

We have edited the introduction to inform readers that adolescent pregnancy can be intended and unintended. With supporting information on the potential effect of mental disorders on pregnancy intention and unintended pregnancy, we tried to frame the manuscript around unintended pregnancy. As suggested by the reviewer, we have included a directed acyclic graph in the discussion to illustrate the relationship between mental disorder, pregnancy intention and adolescent pregnancy.

6. Minor: STI’s do not contribute to unplanned pregnancy (page 4).

The following sentence has been removed from the manuscript to address one of your previous comments. “For example, mental disorders have been associated with the increased likelihood of being sexually active at a younger age, higher rates of sexually transmitted diseases, and becoming victims of sexual abuse during childhood, indicating that adolescents with mental disorders are at a higher risk of unplanned pregnancy. (27, 32, 33)”

7. Minor: Please be consistent with what is the outcome vs outcome type throughout.

We chose to use outcome type and made the appropriate changes as in the manuscript.

Thank you for your suggestion. We have made changes to first describe the data source and then describe the retrospective cohort. (Lines 75 – 85)

“Data source

This study used healthcare claims data obtained from the Merative™ MarketScan Commercial Claims Research Database. This database provides details on reimbursed health services, including medical encounters and drugs dispensed in outpatient pharmacies for patients in approximately 150 employer-sponsored insurance plans. Because the database includes claims from many private insurers and has very wide geographic coverage, it has been frequently used in the analysis of healthcare utilization as a data source representing privately insured individuals. (12-14) A retrospective cohort of females aged 13 - 19 years was established between 2006 and 2014. The inclusion criteria for the cohort was as follows: continuous enrollment for at least three years between 2005 and 2015, allowing for eligibility periods such as 2005-2007, 2006-2008 and 2013-2015.”

We acknowledge the limitation that we only included “age” in our analysis as opposed to other sociodemographic information available in the data source. We added the following paragraph to address the limitation in the discussion section. (Lines 305 – 311)

“Fourth, we included age as the only covariate in the multivariable logistic regression after adjusting for the relationship between the type of mental disorder and adolescent pregnancy. However, differences in race, income, or geographic areas among mental disorder types may exist, which can affect the estimation of adolescent pregnancy. Future study that provide a basis for adjusting for differences in the age-specific adolescent pregnancy rates across groups defined by sex, race, ethnicity, geography, and other sociodemographic categories are warranted.”

We agree that 1) mental disorders often co-occur, and 2) the likelihood of pregnancy could be different for those with comorbid mental disorders compared to those with only one mental disorder. In our logistic regression model, we did not specifically account for patients with multiple concurrent mental disorders. Rather, we treated each mental disorder as a separate independent variable. Therefore, the interpretation of the model for a patient with multiple concurrent mental disorders would involve examining the individual effects of each disorder independently, without explicitly considering potential interactions or combined effects. To clarify, we have added the following paragraph. (Lines 152 – 158)

Minor edits

• The second paragraph in the discussion does not seem to add much and can be deleted.

The second paragraph in the discussion section was deleted.

• The manuscript needs a careful read through for grammar and spelling errors. Below I highlight just a few from the first two paragraphs

- Para 1, First sentence: Mental health disorders in “adolescence”

- Para 1, Second sentence: “The 2020 national prevalence…. in high schools in the United States was 42%”

- Para 1, Third sentence: “hopelessness has increased dramatically”

- Para 2, First sentence: “adolescent mental”

- Para 2, Second sentence: “An existing mental health problem/disorder”

Thank you so much for identifying the grammar and spelling errors listed above. We have carefully proofread the manuscript to correct not only the errors listed above, but also additional errors throughout the revised manuscript.

Attachment

Submitted filename: Response to Reviewers.docx

pone.0296425.s005.docx^{(36.5KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0296425.r003

Decision Letter 1

Giuseppe Marano

19 Sep 2023

PONE-D-23-05808R1Comparing pregnancy and pregnancy outcome rates between adolescents with and without pre-existing mental disordersPLOS ONE

Dear Dr. Jeon,

Thank you for submitting your manuscript to October 9. 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.

Please submit your revised manuscript by Nov 03 2023 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.

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Giuseppe Marano

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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Comments to the Author

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Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

**********

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

Reviewer #1: Yes

Reviewer #2: Yes

**********

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

Reviewer #1: Yes

Reviewer #2: I Don't Know

**********

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

Reviewer #1: Yes

Reviewer #2: Yes

**********

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

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: Thank you for being responsive to our review of your manuscript. This version has been carefully edited and therefore strongly enhanced both in terms of readability and in scientific reproducibility.

Reviewer #2: The authors have done a great job responding to earlier comments.

In response to earlier comments, the authors included a paragraph in the introduction (line 57 - 66) that I feel is unnecessary given that the data used do not provide details on whether pregnancies were intended or unintended. The authors already clearly articulate the lack of data on pregnancy as a limitation. What they have written in the discussion is adequate.

There are some minor grammatical errors that I have highlighted in the attached document in tracked changes

**********

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Reviewer #1: Yes: Brian W. Jack

Reviewer #2: No

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Attachment

Submitted filename: PONE-D-23-05808_R1-Review.docx

pone.0296425.s006.docx^{(55.1KB, docx)}

PLoS One. 2024 Mar 14;19(3):e0296425. doi: 10.1371/journal.pone.0296425.r004

Author response to Decision Letter 1

26 Sep 2023

We reviewed the reference list to ensure it is complete and correct. Also, there were no references that have been retracted.

References #11, #14, #20, #21, and #32 were re-formatted to meet the submission guideline. (See below). As for the reference #21, we have updated the URL as the address has been changed since we checked it last time.

11. Sekharan VS, Kim TH, Oulman E, Tamim H. Prevalence and characteristics of intended adolescent pregnancy: an analysis of the Canadian maternity experiences survey. Reprod Health. 2015;12:101.

14.Merative™ MarketScan Research Databases. [Cited 2023 Sep 26]. Available from: https://www.merative.com/real-world-evidence.

20.Files H. Clinical Classifications Software (CCS) for ICD-9-CM: Agency for healthcare research and quality. [Cited 2023 Sep 26]. Available from: https://www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp.

21.Centers for Disease Control and Prevention. Annual Estimates of the resident population by single year of age and sex for the United States. U.S. Department of Health & Human Services. [Cited 2023 Sep 26]. Available from: https://catalog.data.gov/dataset/us-census-annual-estimates-of-the-resident-population-for-selected-age-groups-by-sex-for-t.

32.Kost K M-ZI, Arpaia A. Pregnancies, births and abortions among adolescents and young women in the United States: Guttmacher Institute; 2013. [Cited 2023 Sep 26]. Available from: https://www.guttmacher.org/sites/default/files/report_pdf/us-adolescent-pregnancy-trends-2013.pdf.

Attachment

Submitted filename: Response to Reviewers.docx

pone.0296425.s007.docx^{(18.4KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0296425.r005

Decision Letter 2

Giuseppe Marano

18 Oct 2023

PONE-D-23-05808R2Comparing pregnancy and pregnancy outcome rates between adolescents with and without pre-existing mental disordersPLOS ONE

Dear Dr. Jeon,

Please submit your revised manuscript by Dec 02 2023 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:

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We look forward to receiving your revised manuscript.

Kind regards,

Giuseppe Marano

Academic Editor

PLOS ONE

Journal Requirements:

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

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: (No Response)

Reviewer #2: (No Response)

**********

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

Reviewer #1: Yes

Reviewer #2: Yes

**********

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

Reviewer #1: Yes

Reviewer #2: I Don't Know

**********

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

Reviewer #1: Yes

Reviewer #2: Yes

**********

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

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: The authors have responded to suggestions

Reviewer #2: The comment in the introduction is still unanswered.

In the introduction, line 44: confirm if the inserted word, "have", is the right one and delete the question mark

**********

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.

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Reviewer #1: No

Reviewer #2: No

**********

PLoS One. 2024 Mar 14;19(3):e0296425. doi: 10.1371/journal.pone.0296425.r006

Author response to Decision Letter 2

3 Dec 2023

Reviewer #2 Comment: In response to earlier comments, the authors included a paragraph in the introduction (line 57 - 66) that I feel is unnecessary given that the data used do not provide details on whether pregnancies were intended or unintended. The authors already clearly articulate the lack of data on pregnancy as a limitation. What they have written in the discussion is adequate.

The lines 57-66 are now removed and along with the removal, the ref 10 is relocated to ref 30 and ref 11 is deleted. In addition, we updated the reference numbering as follow;

- References 12-31 are renumbered to ref 10-29

- References 32&33 are now ref 31&32

Attachment

Submitted filename: Response to Reviewers.docx

pone.0296425.s008.docx^{(16.8KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0296425.r007

Decision Letter 3

Giuseppe Marano

14 Dec 2023

Comparing pregnancy and pregnancy outcome rates between adolescents with and without pre-existing mental disorders

PONE-D-23-05808R3

Dear Dr. Jeon,

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.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Giuseppe Marano

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

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Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

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

Reviewer #1: Yes

Reviewer #2: Yes

**********

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

Reviewer #1: Yes

Reviewer #2: I Don't Know

**********

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

Reviewer #1: Yes

Reviewer #2: Yes

**********

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

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: Thank you for your responsiveness to reviewer comments and for this important contribution to the field.

Reviewer #2: (No Response)

**********

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Reviewer #1: No

Reviewer #2: No

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PLoS One. doi: 10.1371/journal.pone.0296425.r008

Acceptance letter

Giuseppe Marano

4 Mar 2024

PONE-D-23-05808R3

PLOS ONE

Dear Dr. Jeon,

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team.

At this stage, our production department will prepare your paper for publication. This includes ensuring the following:

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on behalf of

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Table. Pregnancy-related ICD-9-CM codes.

The list of ICD-9-CM codes to indicate prenatal visits, pregnancy outcomes, or delivery.

(DOCX)

pone.0296425.s001.docx^{(23.7KB, docx)}

S2 Table. The fifteen mental disorder categories.

The list of ICD-9-CM codes categorized by 15 mental disorders based on the Clinical Classifications Software (CCS) developed by the Agency for Healthcare Research and Quality (AHRQ).

(DOCX)

pone.0296425.s002.docx^{(14.9KB, docx)}

S1 Fig. Age-adjusted odds ratios of becoming pregnant among adolescents with a mental disorder diagnosis.

(TIF)

pone.0296425.s003.tif^{(69KB, tif)}

S2 Fig. A directed acyclic graph illustrating the role of pregnancy intention as a mediator on the association between mental disorder and adolescent pregnancy.

(TIF)

pone.0296425.s004.tif^{(118.1KB, tif)}

Attachment

Submitted filename: Response to Reviewers.docx

pone.0296425.s005.docx^{(36.5KB, docx)}

Attachment

Submitted filename: PONE-D-23-05808_R1-Review.docx

pone.0296425.s006.docx^{(55.1KB, docx)}

Attachment

Submitted filename: Response to Reviewers.docx

pone.0296425.s007.docx^{(18.4KB, docx)}

Attachment

Submitted filename: Response to Reviewers.docx

pone.0296425.s008.docx^{(16.8KB, docx)}

Data Availability Statement

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PERMALINK

Comparing pregnancy and pregnancy outcome rates between adolescents with and without pre-existing mental disorders

Nakyung Jeon

Yasser Albogami

Sun-Young Jung

Regina Bussing

Almut G Winterstein

Roles

Abstract

Background

Objective

Methods

Results

Conclusion

Introduction

Methods

Data source

Pregnancy and pregnancy outcome type ascertainment

Mental disorder ascertainment by mental disorder type

Fig 1. Overview of study design.

Statistical analyses

Results

Table 1. Crude and age–standardized pregnancy rates per 1,000 person-year of females aged 13–19 years, overall and according to mental disorder type.

Table 2. Distribution of outcome types according to the presence of pre-existing mental disorder in females aged 13–19 years.

Fig 2. Age-stratified odds ratios of becoming pregnancy among adolescents with a mental disorder diagnosis.

Discussion

Conclusion

Supporting information

Data Availability

Funding Statement

References

Decision Letter 0

Giuseppe Marano

Roles

Author response to Decision Letter 0

Decision Letter 1

Giuseppe Marano

Roles

Author response to Decision Letter 1

Decision Letter 2

Giuseppe Marano

Roles

Author response to Decision Letter 2

Decision Letter 3

Giuseppe Marano

Roles

Acceptance letter

Giuseppe Marano

Roles

Associated Data

Supplementary Materials

Data Availability Statement

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