Prescribed use of anxiolytics, hypnotics, sedatives, and antidepressants in adolescents in Spain: a comprehensive exploratory analysis of gender inequalities

Prescribed use of anxiolytics, hypnotics, sedatives, and antidepressants in adolescents in Spain: a comprehensive exploratory analysis of gender inequalities | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prescribed use of anxiolytics, hypnotics, sedatives, and antidepressants in adolescents in Spain: a comprehensive exploratory analysis of gender inequalities Maite Campo, Amaia Bacigalupe, Unai Martin, Timm Riffe This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8708984/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Purpose This study aims to examine the magnitude and trends of gender inequalities in AHS and antidepressant use, the role of medicalization, and the intersection with other axes of inequality among the adolescent population in Spain. Methods A cross-sectional analysis of the Spanish Primary Care Clinical Database (BDCAP) was conducted in the population aged 10–18 years in 2023 (n = 1,197,508), along with a temporal trend analysis for the period 2018–2023. Prevalences were calculated for girls and boys, and gender differences were estimated using prevalence ratios (PR) from age-adjusted robust Poisson regression models. Analyses were stratified by age, income, and place of birth. To assess gender inequalities in medicalization, models were additionally adjusted for mental health diagnoses and healthcare visits. Results Girls were more likely to use prescribed AHS and antidepressants than boys, and this gender gap widened between 2018 and 2023. After additional adjustments, the probability of AHS and antidepressant use remained significantly higher among girls (PR_AHS = 1.45 [1.43–1.47] and PR_antidepressants = 1.93 [1.90–1.97]). Gender differences emerged around age 13 and peaked at ages 17–18. No consistent pattern was observed for income level and place of birth. Conclusion The findings highlight persistent gender inequalities in adolescent psychotropic drug use linked to processes of medicalization and broader structural conditions. Addressing these disparities requires coordinated intersectoral interventions and structural transformations beyond the healthcare system alone. Anxiolytic Antidepressants Adolescent Gender Social inequalities Medicalization Figures Figure 1 Figure 2 Figure 3 Introduction The rise in diagnosed mental health problems and psychotropic drug use among adolescents has emerged as a major concern in contemporary global public health [1]. An analysis of Global Burden of Disease estimates found that depressive and anxiety disorders in adolescent ages have increased over the past decade worldwide, particularly in middle- and high-income countries [2]. Additionally, the available literature points to a global rise in psychotropic drug use in this age group [3–5], although the rate and magnitude of the increase show considerable variation across countries [6]. A gender gap in mental health problems begins to emerge in adolescence [7], and studies conducted across different countries consistently find that girls receive more psychotropic drug prescriptions than boys, particularly for anxiolytics, hypnotics, and sedatives (AHS), as well as antidepressants [8,9] One potential explanation for this gender gap is that girls are treated with psychotropic drugs, as they are more likely to experience higher levels of psychological distress due to their increased vulnerability within a heteropatriarchal structure [10,11]. This gender socialization from adolescence onward leads girls to face more challenges, such as greater exposure to stressful life events [12], social expectations tied to gender norms, and increased academic and social pressures [13,14]. Moreover, medicalization of women's suffering emerges as an additional reason, with some studies pointing to the existence of a gender bias in clinical practice, where women are more likely than men to receive psychotropic medication, even when presenting with similar mental health needs [15,16]. Alternatively, it could also happen that boys are systematically getting less prescriptions than needed, as a consequence of the same gender bias. However, the evidence on gender differences of this kind is primarily based on adult populations, with limited research exploring this phenomenon in adolescents. Intersectionality theory underscores the need to examine gender inequality in relation to other intersecting axes of inequality, including social class, ethnicity, and age as they interact to produce unequal mental health outcomes [17]. Age-related patterns reveal that, during childhood, boys are more likely to be prescribed a psychotropic drug (particularly psychostimulants) whereas in adolescence, the use of AHS and antidepressants becomes more prevalent among girls, giving rise to the pattern observed in the adult population [9]. Furthermore, gender inequalities may also vary according to socioeconomic status, as some studies have shown that psychotropic drug use is more common among adolescents from socioeconomically disadvantaged families [18]. For example, in a study based on Spanish adolescents, it was observed that lower levels of maternal education were associated with higher psychotropic use, being this pattern stronger among girls than boys of the same age [19]. By contrast, most studies seem to agree that adolescents from ethnic and racial minorities are less likely to be prescribed psychotropic medications than natives [20,21], but there are few studies that analyze this phenomenon from a gender perspective. Although some research has addressed gender differences in adolescent psychotropic drug use, this phenomenon has rarely been examined from an intersectional perspective, nor has the medicalization of adolescents been explored. Against this background, in the present study we aimed to address these gaps by (a) assessing the magnitude and evolution of gender differences in the prescribed use of anxiolytics, hypnotics, sedatives, and antidepressants; (b) examining the role of medicalization by evaluating whether these gender patterns could be explained by differences in depression or anxiety diagnoses and in healthcare utilization between girls and boys; and (c) exploring how these gender differences intersect with other axes of social inequality. Methods We conducted a cross-sectional study for 2023, and we analyzed trends for the period 2018–2023 among adolescents aged 12 to 18 years in Spain. Data Source and Study Population We used data from the Base de Datos de Atención Primaria, BDCAP , administered by the Spanish Ministry of Health. The BDCAP gives an annual, nationally representative sample of clinical records from individuals assigned to the National Health System (NHS), based on primary care electronic medical records. Around 97.6% of the Spanish population is covered by NHS, and the 2023 sample includes 27% of the population covered by the NHS. A more detailed description of the database and its methodology can be found elsewhere [22]. Our study included a representative sample of the population aged 10 to 18 years assigned to the NHS for years 2018–2023 (n = 4,222,539 observations of 1,645,587 individuals) and year 2023 (n = 1,197,508). Study variables AHS and antidepressant use : medication dispensed through community pharmacies with official prescriptions issued by physicians within the Spanish National Health System. It is recorded whether the medicine has ever been dispensed in each year of the study. The drug categories included were Anxiolytics (ATC N05B), Hypnotics and Sedatives (ATC N05C), and Antidepressants (ATC N06A). Diagnosis of depression or anxiety : clinical diagnoses recorded during the study year. Includes the diagnoses P01-P04, P73-PS9, P82, and P86 from the International Classification of Primary Care, 2nd edition (ICPC-2) [23]. Year of birth : used to calculate age reached in each year of the study and then expressed as completed integer age. Sex : categorized as male or female. Since differences between male and female adolescents are influenced by social and cultural gender constructs, we will interpret the results as gender differences and use the terms boys and girls to refer to specific gender subpopulations. The discussion will be done from a gender perspective [24]. Income level : derived from pharmaceutical co-payment classification, according to individual annual income tax. Individuals under the age of 26 who are dependent on their parents are assigned the income of their reference parent in the NHS database. The categories are: High income (≥€100,000), Medium income (€18,000–99,999), Low income (<€18,000), and Exempt from pharmaceutical copayment. The Exempt from pharmaceutical co-payment includes: beneficiaries of integration minimum income (RMI), unemployed individuals without benefits, and other vulnerable groups. The data from the “Unclassified” category is not shown (n = 26,075; 2.2%). Place of birth : classified according to the World Health Organization's health subregions and adapted to ensure representativeness within the Spanish context: Spain, European Union, Africa, Latin America, and Eastern Mediterranean. Countries included in the 'Other' category were not included when Place of origin is analyzed (n = 78,589, 6.6%). Use of primary health services : Measured by the number of visits to primary care in the NHS by year, encompassing family medicine, pediatrics, and nursing consultations. Statistical Analysis We calculated the prevalences of AHS and antidepressant use by gender and assessed the statistical significance of differences between girls and boys using Pearson’s chi-square test (p < 0.05). We estimated the magnitude of differences between boys and girls (and their statistical significance) under different adjustments with adjusted Prevalence Ratios (PR), using Poisson regression models with robust error variance [25] and boys as the reference group. To measure trends, we calculated the prevalence of AHS and antidepressant use and the Prevalence Ratios by gender in each year. We also calculated incremental prevalence ratios in consecutive years for both boys and girls, along with its interaction between year and sex, to assess changes in gender differences over time. To examine gender differences in the medicalization of mental health, we first adjusted the regression models for diagnoses of depression and anxiety and subsequently for the number of primary care visits, in order to control for differences between boys and girls in healthcare utilization. Finally, we analyzed the intersectional perspective by calculating the prevalence and prevalence ratios stratified by age, household income level, and place of birth, using the methods described above. In all calculation, we used person-weights provided by BDCAP, which account for the characteristics of the residents’ health district, sex, age, and place of birth to ensure the representativeness of the sample in the Spanish population assigned to the NHS. All the analyses were conducted with R [26] and IBM SPSS Statistics 28 [27]. Results Most of the population is concentrated in the intermediate income brackets, with only 2% in the highest income level. 83.8% of boys and 84.4% of girls were born in Spain. Additionally, the prevalence of diagnoses for depressive and/or anxiety disorders is higher among girls (6.6%) than among boys (4.3%), and they exhibit a higher average annual number of visits to Primary Care services (Table 1 ). Table 1 Distribution of sample (%) by age and according to demographic, social position and health variables in boys and girls Distribution of sample, 2023 (%) Boys (n = 618,623) Girls (n = 578,885) Age (years) 10 10.0 (n = 61,657) 10.0 (n = 57,961) 11 10.6 (n = 65,713) 10.6 (n = 61,393) 12 10.7 (n = 66,369) 10.9 (n = 62,968) 13 11.0 (n = 68,354) 11.1 (n = 64,084) 14 11.3 (n = 69,703) 11.3 (n = 65,624) 15 12.0 (n = 73,955) 11.9 (n = 69,011) 16 11.6 (n = 71,561) 11.5 (n = 66,363) 17 11.6 (n = 71,661) 11.4 (n = 66,254) 18 11.3 (n = 69,650) 11.3 (n = 65,227) Income level High income (≥ 100.000 €) 1.9 (n = 11,644) 1.9 (n = 11,269) Medium income (18.000-99.999 €) 37.2 (n = 230,429) 38.3 (n = 221,744) Low income (< 18.000 €) 36.6 (n = 226,535) 36.9 (n = 213,527) Exempt from pharmaceutical copayment 22.0 (n = 136,385) 20.7 (n = 119,890) Unclassified 2.2 (n = 13,630) 2.2 (n = 12,455) Place of birth Spain 83.5 (n = 516,364) 84.2 (n = 487,252) European Union 1.9 (n = 11,662) 1.9 (n = 11,184) Africa 1.1 (n = 6,968) 0.5 (n = 3,102) Latin America 4.6 (n = 28,250) 4.8 (n = 27,904) Eastern Mediterranean 2.5 (n = 15,232) 1.9 (n = 11,001) Other 6.5 (n = 40,147) 6.6 (n = 38,442) Diagnosis of depression or anxiety 4.3 (n = 26,500) 6.6 (n = 38,153) Visits to primary care (average. SD) 4.8 (5.9) 5.3 (6.1) AHS use 1.5 (n = 9,002) 2.3 (n = 13,488) Antidepressant use 0.8 (n = 5,100) 1.9 (n = 11,240) Distribution of sample by year (%) Boys (n = 2,175,500) Girls (n = 2,047,039) 2018 10.6 (n = 230643) 10.7 (n = 219128) 2019 10.7 (n = 231897) 10.8 (n = 220173) 2020 10.8 (n = 234643) 10.8 (n = 221702) 2021 11.3 (n = 245217) 11.3 (n = 230895) 2022 28.2 (n = 614477) 28.2 (n = 576256) 2023 28.4 (n = 618623) 28.3 (n = 578885) As shown in Fig. 1 , AHS and antidepressant use increased since 2018, with a more pronounced rise among girls (Figs. 1 a and 1 b), which widened the gender differences (Figs. 1 c and 1 d). Prevalence Ratios for AHS use increased from 1.47 [1.44–1.49] in 2018 to 1.61 [1.59–1.64] in 2023, and for antidepressant use the increase was higher, from 1.78 [1.74–1.83] in 2018 to 2.35 [2.32–2.39] in 2023. However, as can be observed in Table 2 , from 2020 to 2021 a statistically significant interaction between year and sex revealed a more marked increase among girls, particularly in antidepressant use (PR = 1.36 [1.33–1.40]). In the last year, the consumption trend appears to stabilize—and even decline—among girls, whereas it remains unchanged among boys. a and b: Prevalences in boys and girls, all the differences are statistically significant (Chi-square < 0,005). c and d: Prevalence Ratios for girls (reference: boys), adjusted for age Table 2 Incremental Prevalence Ratios (PR) for consecutive years in boys and girls, and interaction terms between sex and year in the PR by sex, adjusted by age Boys Girls Sex*Year interaction Anxiolytic, Hypnotics and Sedatives Year PR p PR p PR p 2018–2019 1.32 (1.3–1.35) < 0.001 1.31 (1.29–1.33) < 0.001 0.99 (0.97–1.02) 0.566 2019–2020 0.91 (0.9–0.93) 0.001 0.93 (0.93–0.95) 0.008 1.03 (1.00–1.05) 0.348 2020–2021 1.08 (1.07–1.10) 0.002 1.25 (1.23–1.26) < 0.001 1.16 (1.14–1.19) < 0.001 2021–2022 1.06 (1.04–1.08) < 0.001 1.03 (1.02–1.04) 0.019 0.98 (0.96–1.00) 0.074 2022–2023 0.99 (0.98–1.01) 0.993 0.94 (0.93–0.95) < 0.001 0.94 (0.93–0.96) 0.004 Antidepressants Year RP p RP p RP p 2018–2019 1.4 (1.36–1.44) < 0.001 1.36 (1.34–1.39) < 0.001 0.98 (0.94–1.01) 0.178 2019–2020 1.05 (1.03–1.08) 0.012 1.04 (1.03–1.06) < 0.001 0.99 (0.96–1.03) 0.71 2020–2021 1.18 (1.15–1.20) < 0.001 1.60 (1.57–1.62) < 0.001 1.36 (1.33–1.40) < 0.001 2021–2022 1.11 (1.09–1.14) < 0.001 1.18 (1.16–1.20) < 0.001 1.07 (1.04–1.09) < 0.001 2022–2023 1.05 (1.03–1.07) < 0.001 0.98 (0.97–0.99) 0.001 0.94 (0.91–0.96) < 0.001 Based on 2023 data (Fig. 2 ), after adjusting the regression models for diagnoses of depression and anxiety, the probability of prescribed use of anxiolytics, hypnotics, and sedatives remained higher among girls than among boys of the same age (PR = 1.45 [1.43–1.47]), as did the use of antidepressants (PR = 1.93 [1.90–1.97]). Moreover, after additional adjustment for the number of primary care visits, this higher risk among girls persisted. *Model 1: Adjusted by age; Model 2: Adjusted by age and diagnosis of depression and anxiety; Model 3: Adjusted for age, diagnosis of depression and anxiety, and primary care visits. Figure 3 shows the differences between boys and girls in consumption by age, household income, and place of birth. No gender differences in consumption are observed until the age of 12, but the differences between boys and girls become clear by age 13. After age 12, consumption increases among girls, while among boys the increase begins after the age of 15 and follows a more moderate trend (Fig. 3 a and 3 b). The largest difference between boys and girls was observed at age 18 in absolute terms (percentage points) and at age 17 in relative terms (PR), when the PR was 2.14 [2.07–2.22] for AHS and 2.92 [2.81–3.03] for antidepressants ( Supplementary material ). Gender differences in AHS and antidepressant use are evident across all income levels (Figs. 3 c and 3 d). After adjusting the model for mental health diagnoses and primary care visits, higher PR is observed in the higher income level for AHS consumption (PR = 1.65 (1.41–1.94]), and in middle- and lower-income levels for antidepressants (PR = 2.15 [2.09–2.22] and PR = 2.15 [2.09–2.21]) ( Supplementary material ). Girls also show higher prevalence than boys across different origins (Figs. 3 e and 3 f). Exceptions to this pattern include the use of AHS among adolescents of African and Eastern Mediterranean origin, where no significant gender differences are found. The highest likelihood of AHS use among girls is observed in those born in the European Union (PR = 1.53 [1.35–1.74]). In contrast, for antidepressants, the greatest gender difference is found among adolescents of African origin, where girls are twice as likely as boys to receive a prescription (PR = 2.39 [1.83–3.13]) ( Supplementary material) . Discussion This clinical record–based study examines the magnitude, temporal trends, the role of medicalization, and the intersectional dimensions of gender inequalities in the use of prescribed psychotropic drugs among adolescents in Spain. To our knowledge, no previous study has comprehensively analyzed all of these factors simultaneously in an adolescent population. Our findings reveal that adolescent girls are more likely to receive prescriptions for AHS or antidepressants than adolescent boys, and this gender gap has been widening in recent years. Moreover, our study shows that these differences are not explained by girls’ higher rates of mental health diagnoses or more frequent visits to healthcare. The gender gap in the prescribed use of psychotropic drugs begins around age 13, peaks at the age of 17–18, with no clear pattern identified regarding the influence of other axes of inequality. The higher prescription of anxiolytic-hypnotic-sedative (AHS) and antidepressant medication among adolescent girls have been observed in other clinical record-based studies conducted in France [28], Norway [9], and other countries [5,6]. Similarly, population-based surveys among Spanish adolescents indicate that girls are more likely than boys to consume psychotropic drugs, both through medical prescriptions and non-medical use [19,29]. The trends we report show that while psychotropic drug use rose similarly among boys and girls between 2018 and 2020, a sharper increase among girls was described from that year onward. In this regard, it is well-established that the COVID-19 pandemic and the measures implemented to manage it significantly affected the mental health of adolescents, especially girls and those from disadvantaged socioeconomic backgrounds [30,31]. This may offer an explanation for the trends observed in our study. The reasons underlying these gender differences in AHS and antidepressant use cannot be explained by a single factor, but rather by a set of interrelated phenomena. On the one hand, early pubertal onset and associated hormonal changes may lead to greater psychological distress in girls [HANKIN]; however, these factors alone are insufficient to explain such disparities. It is therefore necessary to consider the underlying social factors that contribute to poorer mental health outcomes among girls, and place them in a position of greater vulnerability to psychotropic drug use. Adult women’s material and symbolic subordination, rooted in a heteropatriarchal social structure, increases their risk of suffering from depression and anxiety [32]. This gender socialization begins in early adolescence and intensifies during this developmental stage, as adolescents increasingly internalize societal expectations and roles based on gender [33]. Recent qualitative research with adolescent girls in England further illustrates this dynamic: participants emphasized the negative impact of gendered norms on their well-being, and they described poor mental health as a defining feature of adolescence, particularly for girls [13]. Moreover, the social expectations associated with masculinity and femininity shape distinct ways of responding to psychological distress. Boys are more likely to engage in disruptive or violent behaviors and are less likely to express the typical symptoms of distress, which may lead to a lower likelihood of seeking help, resulting in fewer emotional problems being diagnosed in this group. In contrast, girls tend to express their emotions more openly and seek help [34], behaviors that are often more readily interpreted as symptoms of depression or anxiety, leading to diagnoses and subsequent prescriptions for AHS or antidepressants. Additionally, the use of psychotropic drugs as a coping strategy is more socially accepted for girls, reinforcing gendered patterns in the prescription of AHS and antidepressants [35]. Moreover, a pattern of medicalization of mental health among girls is observed: differences in the frequency of mental health diagnoses and medical visits between boys and girls do not account for the observed gender gap in psychotropic drug use. Even having the same anxiety and depression diagnosis and number of healthcare visits, girls are more likely to be prescribed AHS or antidepressants than boys. Similar studies in adult populations suggest a gender bias in clinical mental health care, leading to a potential over-treatment of emotional symptoms in women [16]. However, it could also be the case that men are underdiagnosed and undertreated, which might explain the observed gap. Nevertheless, when both potential biases were assessed [36], the over-treatment of women seemed to be more pronounced than the under-treatment of men. This phenomenon has been interpreted through the lens of the medicalization of women’s mental health, as emotional symptoms in women are more frequently pathologized than in men, often being classified as depression or anxiety, and therefore viewed as in need of treatment with psychotropic drugs [37]. Furthermore, according to hegemonic conceptions of femininity, women’s expression of suffering aligns more closely with the symptoms described in diagnostic tools used in mental health assessments [38], which makes it easier to label their distress as depression and treat it with a drug. Another key finding of our study is that the gender gap in the prescribed use of AHS and antidepressants begins around age 13, with the gap widening as age increases and peaking at 17–18, which aligns with other empirical studies and meta-analyses [8,39]. Some authors have pointed out that symptoms of distress in girls accelerate earlier than in boys [40], which may explain the more pronounced initial increase in mental health symptoms and diagnoses among girls. A study conducted by Hankin et al concludes that depression increases after puberty, and more girls than boys begin to experience depression after age 12.5 [41]. Recent studies indicate that the age of pubertal onset has decreased over the past decade, particularly among girls, leading to an earlier acceleration of the psychological distress associated with entering puberty, as well as increased exposure to gendered social norms and expectations concerning adult-like behavior [42]. This premature adultification of girls could also set the stage for sooner medicalized interventions to address their distress. When analyzing gender disparities across various socioeconomic groups, the results vary depending on the type of medication. We observed that, even when individuals have the same mental health diagnoses, gender differences in the prescribed use of AHS are more pronounced in higher-income groups, while the use of antidepressants is more prevalent among those in medium and low-income groups. In a study conducted in Spanish adult population, gender differences in antidepressant use were higher among individuals of lower social class [16]. In contrast, a study of the Swedish older population revealed that gender differences in antidepressant use were greater in socioeconomically advantaged groups [36]. The variability of observed results indicate the need to further explore how gender intersects with socioeconomic conditions, even more in adolescents, where no studies exist until now. Regarding our findings by place of birth, we observed more pronounced gender disparities in the consumption of AHS among adolescents born in the European Union and Spain and in antidepressant use among those born in Africa and the EU. Nevertheless, psychotropic drug consumption was higher among native adolescents compared to those born abroad, in line with findings from other international studies [21,43]. In Spain, despite having universal public healthcare coverage, racialized individuals still face barriers to accessing the healthcare system, resulting in probable reduced opportunities for diagnosis and medical treatment [44]. All this evidence underscores the need for further research to explore the effect of gender alongside other axes of inequality on the consumption of AHS and antidepressants in the adolescent population. Strengths and limitations While the findings of this study provide valuable insights, it is important to acknowledge several limitations. First, in the clinical record data we use, sociodemographic variables inferred from clinical histories or social security records. The income level is categorized based on the criteria established for pharmaceutical copayment in the Spanish Health Service, meaning that the 'medium income' category is very broad, while the lowest income category includes individuals exempt from copayment for various reasons, resulting in a heterogeneous group. Further, and especially relevant with our focus on dependent boys and girls, income level refers only to a single reference parent and not to the household total, which we expect to add further noise to this variable. Nevertheless, this classification has been used in several clinical record-based studies in Spain [45] and seems to be a reliable indicator of the socioeconomic level of the Spanish population. Second, the BDCAP only includes clinical records from the Spanish Health Service, meaning that prescriptions and diagnoses made in private healthcare settings are excluded from the sample. Although NHS coverage in Spain is universal, with 98.3% of the population enrolled, 17,63% of the Spanish population also has private insurance [46], typically held by socioeconomically advantaged families, which could introduce a bias in analyses of income level. Note that the sample size triples from 2018 to 2023, as the database expands and the number of individuals included in the sample increases. However, the representativeness of the Spanish population is consistently ensured throughout all the years. Third, since consumption data is based on prescriptions dispensed at pharmacies, there may be situations where medication is dispensed but not actually consumed, or even consumed irregularly by other household members. Nonetheless, dispensing data represents the closest approximation to actual consumption in studies based on clinical records and is widely used in register-based research [47,48]. Finally, as a prevalence-based analysis, our measures are unable to disentangle the effects of gender differences in “starting” and “stopping” prescribed drug use, nor do we document longitudinal patterns or other incidence-based drivers of observed prevalence. Implications for public health The results of this study provide new insights into the growing public health issue of adolescent psychotropic drug use. By offering a comprehensive and in-depth analysis of gender inequalities in psychotropic drug consumption, this research presents critical evidence that emphasizes the urgent need for targeted and immediate public health interventions. First, it is essential to train healthcare professionals not only in sex differences but also in the gender inequalities in mental health [49], in order to prevent unequal diagnoses and treatments in adolescents, while also providing alternative resources beyond pharmacological treatment. In the educational sphere, schools also contribute to the normalization and medicalization of adolescent mental health by labelling non-normative behaviors and forms of distress according to gender norms that are interpreted through a biomedical lens and therefore referred to medical consultations [50]. Furthermore, school-based interventions such as socio-emotional learning, aimed at developing coping strategies that do not involve the use of psychotropic medication—proven to be effective— [51] should incorporate a gender perspective to ensure that the specific needs of both girls and boys are addressed. Second, there is a broader cultural paradigm that encourages the use of the healthcare system as a manager of everyday distress, particularly among girls. Specifically, girls tend to express their suffering through biomedical language, as femininity is socially associated with emotional expressiveness and vulnerability [52]. Therefore, it is essential to reverse the tendency to use biomedical discourse as the dominant way of understanding and addressing distress. At the same time, the growing tendency to treat everyday life through a therapeutic lens leads to a depoliticization and individualization of everyday adolescent issues, resulting in the search for individual solutions to these problems, often framed and addressed through the healthcare system as a health issue [53]. Finally, all these measures must be accompanied by a structural perspective, aimed at deconstructing the systems that perpetuate girls’ material and symbolic subordination, which begin to take shape during adolescence [33]. This would help mitigate several factors that contribute to higher levels of distress among girls, such as increased academic pressure, harmful romantic and sexual relationships, the hypersexualization of their bodies, symbolic and digital violence, aesthetic and emotional self-demand, the lack of diverse and empowering female role models, the lower social legitimacy of their suffering, and the pressure to embody ideals of success, beauty, and compliance [11]. Taken as a whole, the study’s findings suggest that addressing gender inequalities in the adolescent population, while considering other social dimensions, requires well-coordinated intersectoral and interdisciplinary interventions that imply the involvement of institutions that play a central role in the management of adolescent mental health, as well as the development of broader strategies aimed at transforming the structural conditions that produce suffering and perpetuate gender inequalities within this population [54]. Conclusions Gender inequalities in the prescribed use of anxiolytics, hypnotics, and sedatives (AHS) and antidepressants among the adolescent population constitute a complex phenomenon that requires a comprehensive understanding. It is particularly important to address the medicalization of girls’ mental health from early adolescence and to examine how other axes of inequality intersect with these processes. Future research should be oriented toward a deeper understanding of these dynamics in order to inform public policies aimed at reversing this trend. Declarations Competing interests The authors declare no competing interests. Study funding This work is carried out within the framework of the project “Analysis of the use of psychotropic drugs among adolescents: a gender and intersectional perspective” (PID2022-136340OB-100) funded by MICIU/AEI/ 10.13039/501100011033 and by “ERDF/EU”, and “European Union NextGenerationEU/PRTR; as well as by the Predoctoral research training fellowships associated with the Generación de Conocimiento research projects (Grant PREP2022-000855 funded by MICIU/AEI/10.13039/501100011033 and by “ESF+”). TR acknowledges funding from the Spanish Ministry of Science and Innovation (project PID2022-142762OA-I00). Data availability Data, analytic methods, and materials are available to other researchers for replication purposes. The study reported in the manuscript was not preregistered. Data are from the BDCAP. Access to these original data is available to the research community upon request to the Spanish Ministry of Health: [email protected] Ethical considerations The study is based on secondary data extracted from the BDCAP clinical database, which was accessed following a formal data request to the Ministry of Health. All data were fully anonymized prior to being made available to the research team, ensuring that no individuals could be identified. Conflicts of interest The authors declare no conflicts of interest. Contributorships MC: Conception and design of the study, Analysis and interpretation of data, Drafting the article. AmB: Conception and design of the study, Supervision, writing-review . UM: Conception and design of the study, Supervision, writing-review. TR: Analysis and interpretation of data, writing-review. All authors critically read and approved the final manuscript. Acknowledgements The authors would like to thank the OPIK Research Group for the support provided in the development of this study. The authors also acknowledge the Spanish Ministry of Health for granting access to the data and for the technical support provided in relation to these data. References Saunders DC, Knapp F.M, Veenstra-VanderWeele J (2024) Age-Not Just a Number in Youth Mental Health. JAMA psychiatry, 81(4), 327–328. https://doi.org/10.1001/jamapsychiatry.2023.4993 Dongjun Z, Mingyue W, Xinqi L, Lina W, Jiali W, Mengyao J (2025) Trends in depressive and anxiety disorders among adolescents and young adults (aged 10–24) from 1990 to 2021: a Global Burden of Disease study analysis. 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OECD Publishing, Paris. https://doi.org/10.1787/507433b0-en Reiss F, Cosma A, Bersia M, Erhart M, Dalmasso P, Devine J, Hulbert S, Catunda C, Gobina I, Giladi A, Jeriček Klanšček H, Ravens-Sieberer U (2024) Adolescents’ perception of the COVID-19 pandemic restrictions and associated mental health and well-being: gender, age and socioeconomic differences in 22 countries. Child Adolesc Psychiatry Ment Health 18:86. https://doi.org/10.1186/s13034-024-00779-z Chesler P (2005) Women and madness. Palgrave Macmillan, New York. Hill JP, Lynch ME (1983) The intensification of gender-related role expectations during early adolescence. In: Brooks-Gunn J, Petersen AC (eds) Girls at puberty . Springer, New York, pp 201–228. https://doi.org/10.1007/978-1-4899-0354-9_10 Cela-Bertran X, Peguero G, Serral G, Sánchez-Ledesma E, Martínez-Hernáez A, Pié-Balaguer A (2024) Understanding the relationship between gender and mental health in adolescence: the Gender Adherence Index (GAI). Eur Child Adolesc Psychiatry 33:229–240. https://doi.org/10.1007/s00787-023-02150-7 Doblytė S (2020) ‘Women are tired and men are in pain’: gendered habitus and mental healthcare utilization in Spain. J Gend Stud 29:694–705. https://doi.org/10.1080/09589236.2020.1780420 Bacigalupe A, Martín U, Triolo F, Sjöberg L, Sterner TR, Dekhtyar S, Fratiglioni L, Calderón-Larrañaga A (2024) Is the diagnosis and treatment of depression gender-biased? Evidence from a population-based aging cohort in Sweden. Int J Equity Health 23:252. https://doi.org/10.1186/s12939-024-02320-2 Ussher JM (2010) Are we medicalizing women’s misery? A critical review of women’s higher rates of reported depression. Fem Psychol 20:9–35. https://doi.org/10.1177/0959353509350213 Baños RM, Miragall M (2024) Gender matters: a critical piece in mental health. Span J Psychol 27:e28. https://doi.org/10.1017/SJP.2024.29 Salk RH, Hyde JS, Abramson LY (2017) Gender differences in depression in representative national samples: meta-analyses of diagnoses and symptoms. Psychol Bull 143:783–822. https://doi.org/10.1037/bul0000102 Salk RH, Petersen JL, Abramson LY, Hyde JS (2016) The contemporary face of gender differences and similarities in depression throughout adolescence: development and chronicity. J Affect Disord 205:28–35. https://doi.org/10.1016/j.jad.2016.03.071 Hankin BL, Young JF, Abela JR, Smolen A, Jenness JL, Gulley LD, Technow JR, Gottlieb AB, Cohen JR, Oppenheimer CW (2015) Depression from childhood into late adolescence: influence of gender, development, genetic susceptibility, and peer stress. J Abnorm Psychol 124:803–816. https://doi.org/10.1037/abn0000089 Keyes KM, Platt JM (2024) Annual Research Review: Sex, gender, and internalizing conditions among adolescents in the 21st century - trends, causes, consequences. J. Child Psychol. Psychiatry, 65:384–407. https://doi.org/10.1111/jcpp.13864 Zhang S, Cain DS, Liao M (2019) Racial/ethnic disparities in the decision points of mental health service use and psychotropic medication receipt among depressed youth. Youth Soc 53:610–635. https://doi.org/10.1177/0044118X19871853 González Rábago Y, Lanborena N, Rodríguez Álvarez E (2025) Barriers to healthcare for racialised populations in Europe: a scoping review of reviews. Int J Equity Health 24:212. https://doi.org/10.1186/s12939-025-02577-1 Aguilar-Palacio I, Maldonado L, Malo S, Sánchez-Recio R, Marcos-Campos I, Magallón-Botaya R, Rabanaque MJ (2021) COVID-19 inequalities: individual and area socioeconomic factors (Aragón, Spain). Int J Environ Res Public Health 18:6607. https://doi.org/10.3390/ijerph18126607 Instituto Nacional de Estadística (2025) Modalidad de la cobertura sanitaria (multirrespuesta) según sexo. Encuesta de Salud de España 2023. INE, Madrid. https://www.ine.es/jaxi/Datos.htm?tpx=72288#_tabs-tabla Pesiou S, Barcelo R, Fradera M, Torres F, Pontes C (2023) Utilisation of drugs for the treatment of psychiatric diseases in the pediatric population: focus on off-label use. Front Pharmacol 14:1157135. https://doi.org/10.3389/fphar.2023.1157135 Rasmussen L, Wettermark B, Steinke D, Pottegård A (2022) Core concepts in pharmacoepidemiology: measures of drug utilization based on individual level drug dispensing data. Pharmacoepidemiol Drug Saf 31:1015–1026. https://doi.org/10.1002/pds.5490 Plessen KJ, Kelly-Irving M (2025) Sex- and gender-specific aspects in child and adolescent psychiatry: a blind spot requiring our attention. Eur Child Adolesc Psychiatry 34:1687–1689. https://doi.org/10.1007/s00787-025-02749-y Chaves Lima ML, de Almeida Cruz B, Norat de Lima L, da Silva Brandão DA (2021) Debating medicalization with teachers in public and private schools. Psicol Esc Educ 25:e222921 https://doi.org/10.1590/2175-35392021222921 Clarke A, Sorgenfrei M, Mulcahy J, Davie P, Friedrich C, McBride T (2021) Adolescent mental health: a systematic review on the effectiveness of school-based interventions. Early Intervention Foundation, London. https://www.eif.org.uk/report/adolescent-mental-health-a-systematic-review-on-the-effectiveness-of-school-based-interventions Rosenfield S, Mouzon D (2013) Gender and mental health. In: Aneshensel CS, Phelan JC, Bierman A (eds) Handbook of the sociology of mental health. Springer, Dordrecht, pp 277–296. https://doi.org/10.1007/978-94-007-4276-5_14 McLeod J, Wright K (2009) The talking cure in everyday life: gender, generations and friendship. Sociology 43:122–139. https://doi.org/10.1177/0038038508099101 Ani C, Ola B, Hodes M, Eapen V (2024) Equity, diversity and inclusion in child and adolescent mental health: equality of opportunities should be every child’s right and every society’s obligation. Child Adolesc Ment Health 29:123–125. https://doi.org/10.1111/camh.12698 Additional Declarations No competing interests reported. Supplementary Files SupplementarymaterialSPPE.docx Supporting information Additional Supporting Information may be found in the online version of this article: Table S1: Prevalence Ratios (CI 95%) of AHS and antidepressant consumption in women (reference: men) by age, household income and place of birth according to different adjustments. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 10 Apr, 2026 Reviewers agreed at journal 25 Mar, 2026 Reviewers invited by journal 24 Mar, 2026 Editor assigned by journal 05 Mar, 2026 Submission checks completed at journal 29 Jan, 2026 First submitted to journal 27 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8708984","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":601211044,"identity":"ecd6a435-e697-4419-bffb-86acbc43ef52","order_by":0,"name":"Maite Campo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYFACxgY46wGCjV9LI0wdswGRWhDWsEkQpUW3/XD7gw8MdvK6s5uPVXzccZiBXyL9AV4tZmcSGxtnMCQbbrtzLO3mzDOHGSRn5Bjg13IgsbGZh+EA47YbOWa3edsOMxjcyMHvMLPzDxub/zAcsAdpKf4L1GJ/g5DDbgBtYWA4kAjSwswIskUigYDDbjxsnNljkJwM9EuyZG9bOo/EmTcEtJxPf/DhR4Wd7bbbzQc//GyzluNvJ+AwCAAZKwFh8hChHAYkSFA7CkbBKBgFIwsAABzmTukOfKz0AAAAAElFTkSuQmCC","orcid":"","institution":"University of the Basque Country","correspondingAuthor":true,"prefix":"","firstName":"Maite","middleName":"","lastName":"Campo","suffix":""},{"id":601211045,"identity":"7b06f65e-83bc-4c04-9be8-c40b20491408","order_by":1,"name":"Amaia Bacigalupe","email":"","orcid":"","institution":"University of the Basque Country","correspondingAuthor":false,"prefix":"","firstName":"Amaia","middleName":"","lastName":"Bacigalupe","suffix":""},{"id":601211046,"identity":"09828608-66dd-4598-a409-1fdebae5c383","order_by":2,"name":"Unai Martin","email":"","orcid":"","institution":"University of the Basque Country","correspondingAuthor":false,"prefix":"","firstName":"Unai","middleName":"","lastName":"Martin","suffix":""},{"id":601211059,"identity":"cd84df39-de39-45ec-893d-eb26455832e5","order_by":3,"name":"Timm Riffe","email":"","orcid":"","institution":"University of the Basque Country","correspondingAuthor":false,"prefix":"","firstName":"Timm","middleName":"","lastName":"Riffe","suffix":""}],"badges":[],"createdAt":"2026-01-27 10:22:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8708984/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8708984/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104820089,"identity":"33a0a488-5993-4639-a717-57925213592a","added_by":"auto","created_at":"2026-03-17 14:21:56","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":38368,"visible":true,"origin":"","legend":"\u003cp\u003ePrevalence (%) and age-adjusted Prevalence Ratios (CI 95%) of AHS and antidepressant use in 10-18-year-old boys and girls in Spain in 2018-2023\u003c/p\u003e\n\u003cp\u003ea and b: Prevalences in boys and girls, all the differences are statistically significant (Chi-square \u0026lt;0,005). c and d: Prevalence Ratios for girls (reference: boys), adjusted for age\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8708984/v1/da7dd396eacc2ed1c87cdbc0.jpg"},{"id":104820092,"identity":"f77f5e0b-dbad-4e87-b39b-756f50f0e938","added_by":"auto","created_at":"2026-03-17 14:21:56","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":15379,"visible":true,"origin":"","legend":"\u003cp\u003ePR (CI 95%) of anxiolytic, hypnotic and sedative, and antidepressant consumption (reference category: boys) according to different adjustments*\u003c/p\u003e\n\u003cp\u003e*Model 1: Adjusted by age; Model 2: Adjusted by age and diagnosis of depression and anxiety; Model 3: Adjusted for age, diagnosis of depression and anxiety, and primary care visits.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8708984/v1/cf135b738bd9061a910a6586.jpg"},{"id":104835498,"identity":"f6fa4f32-b500-4946-ae39-c5f731d5271c","added_by":"auto","created_at":"2026-03-17 17:45:29","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":74941,"visible":true,"origin":"","legend":"\u003cp\u003ePrevalence of AHS and antidepressant use in boys and girls, according to age, household income, and place of birth\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8708984/v1/3d85d62594d83c2241ee5860.jpg"},{"id":104836134,"identity":"b7b48a8c-2414-440b-b238-cdfc233ac4fd","added_by":"auto","created_at":"2026-03-17 17:51:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1149089,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8708984/v1/5183c2e0-d296-44bd-b2a7-1b74f6cf5de1.pdf"},{"id":104820091,"identity":"5585b607-2b32-408a-8032-18335cc8daa8","added_by":"auto","created_at":"2026-03-17 14:21:56","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":22730,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupporting information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdditional Supporting Information may be found in the online version of this article:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S1\u003c/strong\u003e: Prevalence Ratios (CI 95%) of AHS and antidepressant consumption in women (reference: men) by age, household income and place of birth according to different adjustments.\u003c/p\u003e","description":"","filename":"SupplementarymaterialSPPE.docx","url":"https://assets-eu.researchsquare.com/files/rs-8708984/v1/daf536a25771df73d4d9e531.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prescribed use of anxiolytics, hypnotics, sedatives, and antidepressants in adolescents in Spain: a comprehensive exploratory analysis of gender inequalities","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe rise in diagnosed mental health problems and psychotropic drug use among adolescents has emerged as a major concern in contemporary global public health [1]. An analysis of Global Burden of Disease estimates found that depressive and anxiety disorders in adolescent ages have increased over the past decade worldwide, particularly in middle- and high-income countries [2]. Additionally, the available literature points to a global rise in psychotropic drug use in this age group [3\u0026ndash;5], although the rate and magnitude of the increase show considerable variation across countries [6].\u003c/p\u003e \u003cp\u003eA gender gap in mental health problems begins to emerge in adolescence [7], and studies conducted across different countries consistently find that girls receive more psychotropic drug prescriptions than boys, particularly for anxiolytics, hypnotics, and sedatives (AHS), as well as antidepressants [8,9] One potential explanation for this gender gap is that girls are treated with psychotropic drugs, as they are more likely to experience higher levels of psychological distress due to their increased vulnerability within a heteropatriarchal structure [10,11]. This gender socialization from adolescence onward leads girls to face more challenges, such as greater exposure to stressful life events [12], social expectations tied to gender norms, and increased academic and social pressures [13,14]. Moreover, medicalization of women's suffering emerges as an additional reason, with some studies pointing to the existence of a gender bias in clinical practice, where women are more likely than men to receive psychotropic medication, even when presenting with similar mental health needs [15,16]. Alternatively, it could also happen that boys are systematically getting less prescriptions than needed, as a consequence of the same gender bias. However, the evidence on gender differences of this kind is primarily based on adult populations, with limited research exploring this phenomenon in adolescents.\u003c/p\u003e \u003cp\u003eIntersectionality theory underscores the need to examine gender inequality in relation to other intersecting axes of inequality, including social class, ethnicity, and age as they interact to produce unequal mental health outcomes [17]. Age-related patterns reveal that, during childhood, boys are more likely to be prescribed a psychotropic drug (particularly psychostimulants) whereas in adolescence, the use of AHS and antidepressants becomes more prevalent among girls, giving rise to the pattern observed in the adult population [9]. Furthermore, gender inequalities may also vary according to socioeconomic status, as some studies have shown that psychotropic drug use is more common among adolescents from socioeconomically disadvantaged families [18]. For example, in a study based on Spanish adolescents, it was observed that lower levels of maternal education were associated with higher psychotropic use, being this pattern stronger among girls than boys of the same age [19]. By contrast, most studies seem to agree that adolescents from ethnic and racial minorities are less likely to be prescribed psychotropic medications than natives [20,21], but there are few studies that analyze this phenomenon from a gender perspective.\u003c/p\u003e \u003cp\u003eAlthough some research has addressed gender differences in adolescent psychotropic drug use, this phenomenon has rarely been examined from an intersectional perspective, nor has the medicalization of adolescents been explored. Against this background, in the present study we aimed to address these gaps by (a) assessing the magnitude and evolution of gender differences in the prescribed use of anxiolytics, hypnotics, sedatives, and antidepressants; (b) examining the role of medicalization by evaluating whether these gender patterns could be explained by differences in depression or anxiety diagnoses and in healthcare utilization between girls and boys; and (c) exploring how these gender differences intersect with other axes of social inequality.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eWe conducted a cross-sectional study for 2023, and we analyzed trends for the period 2018\u0026ndash;2023 among adolescents aged 12 to 18 years in Spain.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Source and Study Population\u003c/h2\u003e \u003cp\u003eWe used data from the \u003cem\u003eBase de Datos de Atenci\u0026oacute;n Primaria, BDCAP\u003c/em\u003e, administered by the Spanish Ministry of Health. The BDCAP gives an annual, nationally representative sample of clinical records from individuals assigned to the National Health System (NHS), based on primary care electronic medical records. Around 97.6% of the Spanish population is covered by NHS, and the 2023 sample includes 27% of the population covered by the NHS. A more detailed description of the database and its methodology can be found elsewhere [22]. Our study included a representative sample of the population aged 10 to 18 years assigned to the NHS for years 2018\u0026ndash;2023 (n\u0026thinsp;=\u0026thinsp;4,222,539 observations of 1,645,587 individuals) and year 2023 (n\u0026thinsp;=\u0026thinsp;1,197,508).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy variables\u003c/h3\u003e\n\u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eAHS and antidepressant use\u003c/em\u003e: medication dispensed through community pharmacies with official prescriptions issued by physicians within the Spanish National Health System. It is recorded whether the medicine has ever been dispensed in each year of the study. The drug categories included were Anxiolytics (ATC N05B), Hypnotics and Sedatives (ATC N05C), and Antidepressants (ATC N06A).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eDiagnosis of depression or anxiety\u003c/em\u003e: clinical diagnoses recorded during the study year. Includes the diagnoses P01-P04, P73-PS9, P82, and P86 from the International Classification of Primary Care, 2nd edition (ICPC-2) [23].\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eYear of birth\u003c/em\u003e: used to calculate age reached in each year of the study and then expressed as completed integer age.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eSex\u003c/em\u003e: categorized as male or female. Since differences between male and female adolescents are influenced by social and cultural gender constructs, we will interpret the results as gender differences and use the terms \u003cem\u003eboys\u003c/em\u003e and \u003cem\u003egirls\u003c/em\u003e to refer to specific gender subpopulations. The discussion will be done from a gender perspective [24].\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eIncome level\u003c/em\u003e: derived from pharmaceutical co-payment classification, according to individual annual income tax. Individuals under the age of 26 who are dependent on their parents are assigned the income of their reference parent in the NHS database. The categories are: High income (\u0026ge;\u0026euro;100,000), Medium income (\u0026euro;18,000\u0026ndash;99,999), Low income (\u0026lt;\u0026euro;18,000), and Exempt from pharmaceutical copayment. The Exempt from pharmaceutical co-payment includes: beneficiaries of integration minimum income (RMI), unemployed individuals without benefits, and other vulnerable groups. The data from the \u0026ldquo;Unclassified\u0026rdquo; category is not shown (n\u0026thinsp;=\u0026thinsp;26,075; 2.2%).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003ePlace of birth\u003c/em\u003e: classified according to the World Health Organization's health subregions and adapted to ensure representativeness within the Spanish context: Spain, European Union, Africa, Latin America, and Eastern Mediterranean. Countries included in the 'Other' category were not included when Place of origin is analyzed (n\u0026thinsp;=\u0026thinsp;78,589, 6.6%).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eUse of primary health services\u003c/em\u003e: Measured by the number of visits to primary care in the NHS by year, encompassing family medicine, pediatrics, and nursing consultations.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eWe calculated the prevalences of AHS and antidepressant use by gender and assessed the statistical significance of differences between girls and boys using Pearson\u0026rsquo;s chi-square test (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). We estimated the magnitude of differences between boys and girls (and their statistical significance) under different adjustments with adjusted Prevalence Ratios (PR), using Poisson regression models with robust error variance [25] and boys as the reference group. To measure trends, we calculated the prevalence of AHS and antidepressant use and the Prevalence Ratios by gender in each year. We also calculated incremental prevalence ratios in consecutive years for both boys and girls, along with its interaction between year and sex, to assess changes in gender differences over time.\u003c/p\u003e \u003cp\u003eTo examine gender differences in the medicalization of mental health, we first adjusted the regression models for diagnoses of depression and anxiety and subsequently for the number of primary care visits, in order to control for differences between boys and girls in healthcare utilization. Finally, we analyzed the intersectional perspective by calculating the prevalence and prevalence ratios stratified by age, household income level, and place of birth, using the methods described above.\u003c/p\u003e \u003cp\u003eIn all calculation, we used person-weights provided by BDCAP, which account for the characteristics of the residents\u0026rsquo; health district, sex, age, and place of birth to ensure the representativeness of the sample in the Spanish population assigned to the NHS. All the analyses were conducted with R [26] and IBM SPSS Statistics 28 [27].\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eMost of the population is concentrated in the intermediate income brackets, with only 2% in the highest income level. 83.8% of boys and 84.4% of girls were born in Spain. Additionally, the prevalence of diagnoses for depressive and/or anxiety disorders is higher among girls (6.6%) than among boys (4.3%), and they exhibit a higher average annual number of visits to Primary Care services (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of sample (%) by age and according to demographic, social position and health variables in boys and girls\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eDistribution of sample, 2023\u0026nbsp;(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eBoys (n\u0026thinsp;=\u0026thinsp;618,623)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eGirls (n\u0026thinsp;=\u0026thinsp;578,885)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.0 (n\u0026thinsp;=\u0026thinsp;61,657)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.0 (n\u0026thinsp;=\u0026thinsp;57,961)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.6 (n\u0026thinsp;=\u0026thinsp;65,713)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.6 (n\u0026thinsp;=\u0026thinsp;61,393)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.7 (n\u0026thinsp;=\u0026thinsp;66,369)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.9 (n\u0026thinsp;=\u0026thinsp;62,968)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.0 (n\u0026thinsp;=\u0026thinsp;68,354)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.1 (n\u0026thinsp;=\u0026thinsp;64,084)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.3 (n\u0026thinsp;=\u0026thinsp;69,703)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.3 (n\u0026thinsp;=\u0026thinsp;65,624)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.0 (n\u0026thinsp;=\u0026thinsp;73,955)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.9 (n\u0026thinsp;=\u0026thinsp;69,011)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.6 (n\u0026thinsp;=\u0026thinsp;71,561)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.5 (n\u0026thinsp;=\u0026thinsp;66,363)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.6 (n\u0026thinsp;=\u0026thinsp;71,661)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.4 (n\u0026thinsp;=\u0026thinsp;66,254)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.3 (n\u0026thinsp;=\u0026thinsp;69,650)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.3 (n\u0026thinsp;=\u0026thinsp;65,227)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIncome level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh income (\u0026ge;\u0026thinsp;100.000 \u0026euro;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.9 (n\u0026thinsp;=\u0026thinsp;11,644)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9 (n\u0026thinsp;=\u0026thinsp;11,269)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium income (18.000-99.999 \u0026euro;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.2 (n\u0026thinsp;=\u0026thinsp;230,429)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.3 (n\u0026thinsp;=\u0026thinsp;221,744)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow income (\u0026lt;\u0026thinsp;18.000 \u0026euro;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.6 (n\u0026thinsp;=\u0026thinsp;226,535)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.9 (n\u0026thinsp;=\u0026thinsp;213,527)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExempt from pharmaceutical copayment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.0 (n\u0026thinsp;=\u0026thinsp;136,385)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.7 (n\u0026thinsp;=\u0026thinsp;119,890)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnclassified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.2 (n\u0026thinsp;=\u0026thinsp;13,630)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.2 (n\u0026thinsp;=\u0026thinsp;12,455)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlace of birth\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83.5 (n\u0026thinsp;=\u0026thinsp;516,364)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84.2 (n\u0026thinsp;=\u0026thinsp;487,252)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEuropean Union\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.9 (n\u0026thinsp;=\u0026thinsp;11,662)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9 (n\u0026thinsp;=\u0026thinsp;11,184)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAfrica\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1 (n\u0026thinsp;=\u0026thinsp;6,968)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5 (n\u0026thinsp;=\u0026thinsp;3,102)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLatin America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.6 (n\u0026thinsp;=\u0026thinsp;28,250)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.8 (n\u0026thinsp;=\u0026thinsp;27,904)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEastern Mediterranean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.5 (n\u0026thinsp;=\u0026thinsp;15,232)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9 (n\u0026thinsp;=\u0026thinsp;11,001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.5 (n\u0026thinsp;=\u0026thinsp;40,147)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.6 (n\u0026thinsp;=\u0026thinsp;38,442)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiagnosis of depression or anxiety\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.3 (n\u0026thinsp;=\u0026thinsp;26,500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.6 (n\u0026thinsp;=\u0026thinsp;38,153)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVisits to primary care (average. SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.8 (5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.3 (6.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAHS use\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5 (n\u0026thinsp;=\u0026thinsp;9,002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.3 (n\u0026thinsp;=\u0026thinsp;13,488)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAntidepressant use\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8 (n\u0026thinsp;=\u0026thinsp;5,100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9 (n\u0026thinsp;=\u0026thinsp;11,240)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDistribution of sample by year (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eBoys (n\u0026thinsp;=\u0026thinsp;2,175,500)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eGirls (n\u0026thinsp;=\u0026thinsp;2,047,039)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.6 (n\u0026thinsp;=\u0026thinsp;230643)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.7 (n\u0026thinsp;=\u0026thinsp;219128)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.7 (n\u0026thinsp;=\u0026thinsp;231897)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.8 (n\u0026thinsp;=\u0026thinsp;220173)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.8 (n\u0026thinsp;=\u0026thinsp;234643)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.8 (n\u0026thinsp;=\u0026thinsp;221702)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.3 (n\u0026thinsp;=\u0026thinsp;245217)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.3 (n\u0026thinsp;=\u0026thinsp;230895)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.2 (n\u0026thinsp;=\u0026thinsp;614477)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.2 (n\u0026thinsp;=\u0026thinsp;576256)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.4 (n\u0026thinsp;=\u0026thinsp;618623)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.3 (n\u0026thinsp;=\u0026thinsp;578885)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003e\u003c/h3\u003e\n\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, AHS and antidepressant use increased since 2018, with a more pronounced rise among girls (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb), which widened the gender differences (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). Prevalence Ratios for AHS use increased from 1.47 [1.44\u0026ndash;1.49] in 2018 to 1.61 [1.59\u0026ndash;1.64] in 2023, and for antidepressant use the increase was higher, from 1.78 [1.74\u0026ndash;1.83] in 2018 to 2.35 [2.32\u0026ndash;2.39] in 2023. However, as can be observed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, from 2020 to 2021 a statistically significant interaction between year and sex revealed a more marked increase among girls, particularly in antidepressant use (PR\u0026thinsp;=\u0026thinsp;1.36 [1.33\u0026ndash;1.40]). In the last year, the consumption trend appears to stabilize\u0026mdash;and even decline\u0026mdash;among girls, whereas it remains unchanged among boys.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ea and b: Prevalences in boys and girls, all the differences are statistically significant (Chi-square\u0026thinsp;\u0026lt;\u0026thinsp;0,005). c and d: Prevalence Ratios for girls (reference: boys), adjusted for age\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIncremental Prevalence Ratios (PR) for consecutive years in boys and girls, and interaction terms between sex and year in the PR by sex, adjusted by age\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eBoys\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eGirls\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eSex*Year interaction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAnxiolytic, Hypnotics and Sedatives\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.32 (1.3\u0026ndash;1.35)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.31 (1.29\u0026ndash;1.33)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.99 (0.97\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.566\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.91 (0.9\u0026ndash;0.93)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.93 (0.93\u0026ndash;0.95)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.03 (1.00\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.348\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u0026ndash;2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.08 (1.07\u0026ndash;1.10)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.25 (1.23\u0026ndash;1.26)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.16 (1.14\u0026ndash;1.19)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u0026ndash;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.06 (1.04\u0026ndash;1.08)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.03 (1.02\u0026ndash;1.04)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.019\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.98 (0.96\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022\u0026ndash;2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.98\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.94 (0.93\u0026ndash;0.95)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.94 (0.93\u0026ndash;0.96)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAntidepressants\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.4 (1.36\u0026ndash;1.44)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.36 (1.34\u0026ndash;1.39)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.98 (0.94\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.05 (1.03\u0026ndash;1.08)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.04 (1.03\u0026ndash;1.06)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.99 (0.96\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u0026ndash;2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.18 (1.15\u0026ndash;1.20)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.60 (1.57\u0026ndash;1.62)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.36 (1.33\u0026ndash;1.40)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u0026ndash;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.11 (1.09\u0026ndash;1.14)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.18 (1.16\u0026ndash;1.20)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.07 (1.04\u0026ndash;1.09)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022\u0026ndash;2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.05 (1.03\u0026ndash;1.07)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.98 (0.97\u0026ndash;0.99)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.94 (0.91\u0026ndash;0.96)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBased on 2023 data (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), after adjusting the regression models for diagnoses of depression and anxiety, the probability of prescribed use of anxiolytics, hypnotics, and sedatives remained higher among girls than among boys of the same age (PR\u0026thinsp;=\u0026thinsp;1.45 [1.43\u0026ndash;1.47]), as did the use of antidepressants (PR\u0026thinsp;=\u0026thinsp;1.93 [1.90\u0026ndash;1.97]). Moreover, after additional adjustment for the number of primary care visits, this higher risk among girls persisted.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e*Model 1: Adjusted by age; Model 2: Adjusted by age and diagnosis of depression and anxiety; Model 3: Adjusted for age, diagnosis of depression and anxiety, and primary care visits.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the differences between boys and girls in consumption by age, household income, and place of birth. No gender differences in consumption are observed until the age of 12, but the differences between boys and girls become clear by age 13. After age 12, consumption increases among girls, while among boys the increase begins after the age of 15 and follows a more moderate trend (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). The largest difference between boys and girls was observed at age 18 in absolute terms (percentage points) and at age 17 in relative terms (PR), when the PR was 2.14 [2.07\u0026ndash;2.22] for AHS and 2.92 [2.81\u0026ndash;3.03] for antidepressants (\u003cb\u003eSupplementary material\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eGender differences in AHS and antidepressant use are evident across all income levels (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). After adjusting the model for mental health diagnoses and primary care visits, higher PR is observed in the higher income level for AHS consumption (PR\u0026thinsp;=\u0026thinsp;1.65 (1.41\u0026ndash;1.94]), and in middle- and lower-income levels for antidepressants (PR\u0026thinsp;=\u0026thinsp;2.15 [2.09\u0026ndash;2.22] and PR\u0026thinsp;=\u0026thinsp;2.15 [2.09\u0026ndash;2.21]) (\u003cb\u003eSupplementary material\u003c/b\u003e). Girls also show higher prevalence than boys across different origins (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef). Exceptions to this pattern include the use of AHS among adolescents of African and Eastern Mediterranean origin, where no significant gender differences are found. The highest likelihood of AHS use among girls is observed in those born in the European Union (PR\u0026thinsp;=\u0026thinsp;1.53 [1.35\u0026ndash;1.74]). In contrast, for antidepressants, the greatest gender difference is found among adolescents of African origin, where girls are twice as likely as boys to receive a prescription (PR\u0026thinsp;=\u0026thinsp;2.39 [1.83\u0026ndash;3.13]) (\u003cb\u003eSupplementary material)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis clinical record\u0026ndash;based study examines the magnitude, temporal trends, the role of medicalization, and the intersectional dimensions of gender inequalities in the use of prescribed psychotropic drugs among adolescents in Spain. To our knowledge, no previous study has comprehensively analyzed all of these factors simultaneously in an adolescent population. Our findings reveal that adolescent girls are more likely to receive prescriptions for AHS or antidepressants than adolescent boys, and this gender gap has been widening in recent years. Moreover, our study shows that these differences are not explained by girls\u0026rsquo; higher rates of mental health diagnoses or more frequent visits to healthcare. The gender gap in the prescribed use of psychotropic drugs begins around age 13, peaks at the age of 17\u0026ndash;18, with no clear pattern identified regarding the influence of other axes of inequality.\u003c/p\u003e \u003cp\u003eThe higher prescription of anxiolytic-hypnotic-sedative (AHS) and antidepressant medication among adolescent girls have been observed in other clinical record-based studies conducted in France [28], Norway [9], and other countries [5,6]. Similarly, population-based surveys among Spanish adolescents indicate that girls are more likely than boys to consume psychotropic drugs, both through medical prescriptions and non-medical use [19,29]. The trends we report show that while psychotropic drug use rose similarly among boys and girls between 2018 and 2020, a sharper increase among girls was described from that year onward. In this regard, it is well-established that the COVID-19 pandemic and the measures implemented to manage it significantly affected the mental health of adolescents, especially girls and those from disadvantaged socioeconomic backgrounds [30,31]. This may offer an explanation for the trends observed in our study.\u003c/p\u003e \u003cp\u003eThe reasons underlying these gender differences in AHS and antidepressant use cannot be explained by a single factor, but rather by a set of interrelated phenomena. On the one hand, early pubertal onset and associated hormonal changes may lead to greater psychological distress in girls [HANKIN]; however, these factors alone are insufficient to explain such disparities. It is therefore necessary to consider the underlying social factors that contribute to poorer mental health outcomes among girls, and place them in a position of greater vulnerability to psychotropic drug use. Adult women\u0026rsquo;s material and symbolic subordination, rooted in a heteropatriarchal social structure, increases their risk of suffering from depression and anxiety [32]. This gender socialization begins in early adolescence and intensifies during this developmental stage, as adolescents increasingly internalize societal expectations and roles based on gender [33]. Recent qualitative research with adolescent girls in England further illustrates this dynamic: participants emphasized the negative impact of gendered norms on their well-being, and they described poor mental health as a defining feature of adolescence, particularly for girls [13]. Moreover, the social expectations associated with masculinity and femininity shape distinct ways of responding to psychological distress. Boys are more likely to engage in disruptive or violent behaviors and are less likely to express the typical symptoms of distress, which may lead to a lower likelihood of seeking help, resulting in fewer emotional problems being diagnosed in this group. In contrast, girls tend to express their emotions more openly and seek help [34], behaviors that are often more readily interpreted as symptoms of depression or anxiety, leading to diagnoses and subsequent prescriptions for AHS or antidepressants. Additionally, the use of psychotropic drugs as a coping strategy is more socially accepted for girls, reinforcing gendered patterns in the prescription of AHS and antidepressants [35].\u003c/p\u003e \u003cp\u003eMoreover, a pattern of medicalization of mental health among girls is observed: differences in the frequency of mental health diagnoses and medical visits between boys and girls do not account for the observed gender gap in psychotropic drug use. Even having the same anxiety and depression diagnosis and number of healthcare visits, girls are more likely to be prescribed AHS or antidepressants than boys. Similar studies in adult populations suggest a gender bias in clinical mental health care, leading to a potential over-treatment of emotional symptoms in women [16]. However, it could also be the case that men are underdiagnosed and undertreated, which might explain the observed gap. Nevertheless, when both potential biases were assessed [36], the over-treatment of women seemed to be more pronounced than the under-treatment of men. This phenomenon has been interpreted through the lens of the medicalization of women\u0026rsquo;s mental health, as emotional symptoms in women are more frequently pathologized than in men, often being classified as depression or anxiety, and therefore viewed as in need of treatment with psychotropic drugs [37]. Furthermore, according to hegemonic conceptions of femininity, women\u0026rsquo;s expression of suffering aligns more closely with the symptoms described in diagnostic tools used in mental health assessments [38], which makes it easier to label their distress as depression and treat it with a drug.\u003c/p\u003e \u003cp\u003eAnother key finding of our study is that the gender gap in the prescribed use of AHS and antidepressants begins around age 13, with the gap widening as age increases and peaking at 17\u0026ndash;18, which aligns with other empirical studies and meta-analyses [8,39]. Some authors have pointed out that symptoms of distress in girls accelerate earlier than in boys [40], which may explain the more pronounced initial increase in mental health symptoms and diagnoses among girls. A study conducted by Hankin et al concludes that depression increases after puberty, and more girls than boys begin to experience depression after age 12.5 [41]. Recent studies indicate that the age of pubertal onset has decreased over the past decade, particularly among girls, leading to an earlier acceleration of the psychological distress associated with entering puberty, as well as increased exposure to gendered social norms and expectations concerning adult-like behavior [42]. This premature adultification of girls could also set the stage for sooner medicalized interventions to address their distress.\u003c/p\u003e \u003cp\u003eWhen analyzing gender disparities across various socioeconomic groups, the results vary depending on the type of medication. We observed that, even when individuals have the same mental health diagnoses, gender differences in the prescribed use of AHS are more pronounced in higher-income groups, while the use of antidepressants is more prevalent among those in medium and low-income groups. In a study conducted in Spanish adult population, gender differences in antidepressant use were higher among individuals of lower social class [16]. In contrast, a study of the Swedish older population revealed that gender differences in antidepressant use were greater in socioeconomically advantaged groups [36]. The variability of observed results indicate the need to further explore how gender intersects with socioeconomic conditions, even more in adolescents, where no studies exist until now. Regarding our findings by place of birth, we observed more pronounced gender disparities in the consumption of AHS among adolescents born in the European Union and Spain and in antidepressant use among those born in Africa and the EU. Nevertheless, psychotropic drug consumption was higher among native adolescents compared to those born abroad, in line with findings from other international studies [21,43]. In Spain, despite having universal public healthcare coverage, racialized individuals still face barriers to accessing the healthcare system, resulting in probable reduced opportunities for diagnosis and medical treatment [44]. All this evidence underscores the need for further research to explore the effect of gender alongside other axes of inequality on the consumption of AHS and antidepressants in the adolescent population.\u003c/p\u003e\n\u003ch3\u003eStrengths and limitations\u003c/h3\u003e\n\u003cp\u003eWhile the findings of this study provide valuable insights, it is important to acknowledge several limitations. First, in the clinical record data we use, sociodemographic variables inferred from clinical histories or social security records. The income level is categorized based on the criteria established for pharmaceutical copayment in the Spanish Health Service, meaning that the 'medium income' category is very broad, while the lowest income category includes individuals exempt from copayment for various reasons, resulting in a heterogeneous group. Further, and especially relevant with our focus on dependent boys and girls, income level refers only to a single reference parent and not to the household total, which we expect to add further noise to this variable. Nevertheless, this classification has been used in several clinical record-based studies in Spain [45] and seems to be a reliable indicator of the socioeconomic level of the Spanish population.\u003c/p\u003e \u003cp\u003eSecond, the BDCAP only includes clinical records from the Spanish Health Service, meaning that prescriptions and diagnoses made in private healthcare settings are excluded from the sample. Although NHS coverage in Spain is universal, with 98.3% of the population enrolled, 17,63% of the Spanish population also has private insurance [46], typically held by socioeconomically advantaged families, which could introduce a bias in analyses of income level. Note that the sample size triples from 2018 to 2023, as the database expands and the number of individuals included in the sample increases. However, the representativeness of the Spanish population is consistently ensured throughout all the years.\u003c/p\u003e \u003cp\u003eThird, since consumption data is based on prescriptions dispensed at pharmacies, there may be situations where medication is dispensed but not actually consumed, or even consumed irregularly by other household members. Nonetheless, dispensing data represents the closest approximation to actual consumption in studies based on clinical records and is widely used in register-based research [47,48]. Finally, as a prevalence-based analysis, our measures are unable to disentangle the effects of gender differences in \u0026ldquo;starting\u0026rdquo; and \u0026ldquo;stopping\u0026rdquo; prescribed drug use, nor do we document longitudinal patterns or other incidence-based drivers of observed prevalence.\u003c/p\u003e\n\u003ch3\u003eImplications for public health\u003c/h3\u003e\n\u003cp\u003eThe results of this study provide new insights into the growing public health issue of adolescent psychotropic drug use. By offering a comprehensive and in-depth analysis of gender inequalities in psychotropic drug consumption, this research presents critical evidence that emphasizes the urgent need for targeted and immediate public health interventions.\u003c/p\u003e \u003cp\u003eFirst, it is essential to train healthcare professionals not only in sex differences but also in the gender inequalities in mental health [49], in order to prevent unequal diagnoses and treatments in adolescents, while also providing alternative resources beyond pharmacological treatment. In the educational sphere, schools also contribute to the normalization and medicalization of adolescent mental health by labelling non-normative behaviors and forms of distress according to gender norms that are interpreted through a biomedical lens and therefore referred to medical consultations [50]. Furthermore, school-based interventions such as socio-emotional learning, aimed at developing coping strategies that do not involve the use of psychotropic medication\u0026mdash;proven to be effective\u0026mdash; [51] should incorporate a gender perspective to ensure that the specific needs of both girls and boys are addressed.\u003c/p\u003e \u003cp\u003eSecond, there is a broader cultural paradigm that encourages the use of the healthcare system as a manager of everyday distress, particularly among girls. Specifically, girls tend to express their suffering through biomedical language, as femininity is socially associated with emotional expressiveness and vulnerability [52]. Therefore, it is essential to reverse the tendency to use biomedical discourse as the dominant way of understanding and addressing distress. At the same time, the growing tendency to treat everyday life through a therapeutic lens leads to a depoliticization and individualization of everyday adolescent issues, resulting in the search for individual solutions to these problems, often framed and addressed through the healthcare system as a health issue [53].\u003c/p\u003e \u003cp\u003eFinally, all these measures must be accompanied by a structural perspective, aimed at deconstructing the systems that perpetuate girls\u0026rsquo; material and symbolic subordination, which begin to take shape during adolescence [33]. This would help mitigate several factors that contribute to higher levels of distress among girls, such as increased academic pressure, harmful romantic and sexual relationships, the hypersexualization of their bodies, symbolic and digital violence, aesthetic and emotional self-demand, the lack of diverse and empowering female role models, the lower social legitimacy of their suffering, and the pressure to embody ideals of success, beauty, and compliance [11].\u003c/p\u003e \u003cp\u003eTaken as a whole, the study\u0026rsquo;s findings suggest that addressing gender inequalities in the adolescent population, while considering other social dimensions, requires well-coordinated intersectoral and interdisciplinary interventions that imply the involvement of institutions that play a central role in the management of adolescent mental health, as well as the development of broader strategies aimed at transforming the structural conditions that produce suffering and perpetuate gender inequalities within this population [54].\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eGender inequalities in the prescribed use of anxiolytics, hypnotics, and sedatives (AHS) and antidepressants among the adolescent population constitute a complex phenomenon that requires a comprehensive understanding. It is particularly important to address the medicalization of girls\u0026rsquo; mental health from early adolescence and to examine how other axes of inequality intersect with these processes. Future research should be oriented toward a deeper understanding of these dynamics in order to inform public policies aimed at reversing this trend.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work is carried out within the framework of the project \u0026ldquo;Analysis of the use of psychotropic drugs among adolescents: a gender and intersectional perspective\u0026rdquo; (PID2022-136340OB-100) funded by MICIU/AEI/ 10.13039/501100011033 and by \u0026ldquo;ERDF/EU\u0026rdquo;, and \u0026ldquo;European Union NextGenerationEU/PRTR; as well as by the Predoctoral research training fellowships associated with the Generaci\u0026oacute;n de Conocimiento research projects (Grant PREP2022-000855 funded by MICIU/AEI/10.13039/501100011033 and by \u0026ldquo;ESF+\u0026rdquo;).\u003c/p\u003e\n\u003cp\u003eTR acknowledges funding from the Spanish Ministry of Science and Innovation (project PID2022-142762OA-I00).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData, analytic methods, and materials are available to other researchers for replication purposes. The study reported in the manuscript was not preregistered. Data are from the BDCAP. \u0026nbsp;Access to these original data is available to the research community upon request to the Spanish Ministry of Health: [email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study is based on secondary data extracted from the BDCAP clinical database, which was accessed following a formal data request to the Ministry of Health. All data were fully anonymized prior to being made available to the research team, ensuring that no individuals could be identified.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributorships\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMC: Conception and design of the study, Analysis and interpretation of data, Drafting the article. AmB: Conception and design of the study, Supervision, writing-review . UM: Conception and design of the study, Supervision, writing-review. TR: Analysis and interpretation of data, writing-review. All authors critically read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the OPIK Research Group for the support provided in the development of this study. The authors also acknowledge the Spanish Ministry of Health for granting access to the data and for the technical support provided in relation to these data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSaunders DC, Knapp F.M, Veenstra-VanderWeele J (2024) Age-Not Just a Number in Youth Mental Health. JAMA psychiatry, 81(4), 327–328. https://doi.org/10.1001/jamapsychiatry.2023.4993\u003c/li\u003e\n\u003cli\u003eDongjun Z, Mingyue W, Xinqi L, Lina W, Jiali W, Mengyao J (2025) Trends in depressive and anxiety disorders among adolescents and young adults (aged 10–24) from 1990 to 2021: a Global Burden of Disease study analysis. 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INE, Madrid. https://www.ine.es/jaxi/Datos.htm?tpx=72288#_tabs-tabla\u003c/li\u003e\n\u003cli\u003ePesiou S, Barcelo R, Fradera M, Torres F, Pontes C (2023) Utilisation of drugs for the treatment of psychiatric diseases in the pediatric population: focus on off-label use. Front Pharmacol 14:1157135. https://doi.org/10.3389/fphar.2023.1157135\u003c/li\u003e\n\u003cli\u003eRasmussen L, Wettermark B, Steinke D, Pottegård A (2022) Core concepts in pharmacoepidemiology: measures of drug utilization based on individual level drug dispensing data. Pharmacoepidemiol Drug Saf 31:1015–1026. https://doi.org/10.1002/pds.5490\u003c/li\u003e\n\u003cli\u003ePlessen KJ, Kelly-Irving M (2025) Sex- and gender-specific aspects in child and adolescent psychiatry: a blind spot requiring our attention. Eur Child Adolesc Psychiatry 34:1687–1689. https://doi.org/10.1007/s00787-025-02749-y\u003c/li\u003e\n\u003cli\u003eChaves Lima ML, de Almeida Cruz B, Norat de Lima L, da Silva Brandão DA (2021) Debating medicalization with teachers in public and private schools. Psicol Esc Educ 25:e222921 https://doi.org/10.1590/2175-35392021222921\u003c/li\u003e\n\u003cli\u003eClarke A, Sorgenfrei M, Mulcahy J, Davie P, Friedrich C, McBride T (2021) Adolescent mental health: a systematic review on the effectiveness of school-based interventions. Early Intervention Foundation, London. https://www.eif.org.uk/report/adolescent-mental-health-a-systematic-review-on-the-effectiveness-of-school-based-interventions\u003c/li\u003e\n\u003cli\u003eRosenfield S, Mouzon D (2013) Gender and mental health. In: Aneshensel CS, Phelan JC, Bierman A (eds) Handbook of the sociology of mental health. Springer, Dordrecht, pp 277–296. https://doi.org/10.1007/978-94-007-4276-5_14\u003c/li\u003e\n\u003cli\u003eMcLeod J, Wright K (2009) The talking cure in everyday life: gender, generations and friendship. Sociology 43:122–139. https://doi.org/10.1177/0038038508099101\u003c/li\u003e\n\u003cli\u003eAni C, Ola B, Hodes M, Eapen V (2024) Equity, diversity and inclusion in child and adolescent mental health: equality of opportunities should be every child’s right and every society’s obligation. Child Adolesc Ment Health 29:123–125. https://doi.org/10.1111/camh.12698\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"social-psychiatry-and-psychiatric-epidemiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sppe","sideBox":"Learn more about [Social Psychiatry and Psychiatric Epidemiology](http://link.springer.com/journal/127)","snPcode":"127","submissionUrl":"https://submission.nature.com/new-submission/127/3","title":"Social Psychiatry and Psychiatric Epidemiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Anxiolytic, Antidepressants, Adolescent, Gender, Social inequalities, Medicalization","lastPublishedDoi":"10.21203/rs.3.rs-8708984/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8708984/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eThis study aims to examine the magnitude and trends of gender inequalities in AHS and antidepressant use, the role of medicalization, and the intersection with other axes of inequality among the adolescent population in Spain.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional analysis of the Spanish Primary Care Clinical Database (BDCAP) was conducted in the population aged 10\u0026ndash;18 years in 2023 (n\u0026thinsp;=\u0026thinsp;1,197,508), along with a temporal trend analysis for the period 2018\u0026ndash;2023. Prevalences were calculated for girls and boys, and gender differences were estimated using prevalence ratios (PR) from age-adjusted robust Poisson regression models. Analyses were stratified by age, income, and place of birth. To assess gender inequalities in medicalization, models were additionally adjusted for mental health diagnoses and healthcare visits.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eGirls were more likely to use prescribed AHS and antidepressants than boys, and this gender gap widened between 2018 and 2023. After additional adjustments, the probability of AHS and antidepressant use remained significantly higher among girls (PR_AHS\u0026thinsp;=\u0026thinsp;1.45 [1.43\u0026ndash;1.47] and PR_antidepressants\u0026thinsp;=\u0026thinsp;1.93 [1.90\u0026ndash;1.97]). Gender differences emerged around age 13 and peaked at ages 17\u0026ndash;18. No consistent pattern was observed for income level and place of birth.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe findings highlight persistent gender inequalities in adolescent psychotropic drug use linked to processes of medicalization and broader structural conditions. Addressing these disparities requires coordinated intersectoral interventions and structural transformations beyond the healthcare system alone.\u003c/p\u003e","manuscriptTitle":"Prescribed use of anxiolytics, hypnotics, sedatives, and antidepressants in adolescents in Spain: a comprehensive exploratory analysis of gender inequalities","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-17 14:21:51","doi":"10.21203/rs.3.rs-8708984/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-10T14:36:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"61723147167030350886126332493368214472","date":"2026-03-25T08:27:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-24T09:28:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-05T11:50:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-29T08:54:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"Social Psychiatry and Psychiatric Epidemiology","date":"2026-01-27T09:32:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"social-psychiatry-and-psychiatric-epidemiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sppe","sideBox":"Learn more about [Social Psychiatry and Psychiatric Epidemiology](http://link.springer.com/journal/127)","snPcode":"127","submissionUrl":"https://submission.nature.com/new-submission/127/3","title":"Social Psychiatry and Psychiatric Epidemiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"569e8fc9-4e7e-4f73-952e-9ee0220a7873","owner":[],"postedDate":"March 17th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-24T09:38:23+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-17 14:21:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8708984","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8708984","identity":"rs-8708984","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}