Inconsistent evidence for the role played in bipolar depression or anxiety
SERT —serotonin transporter gene; COMT —catechol-O-methyltransferase gene; OXTR —oxytocin receptor gene; ESR1 —estrogen receptor 1 gene; MAOA —monoamine oxidase A gene; AKR1C2 —aldo-keto reductase family 1 gene; BDNF —brain-derived neurotrophic factor; HMNC1 —hemicentin-1 gene; 5-HTTLPR —serotonin-transporter-linked promoter region; Val 158 Met —catechol-O-methyltransferase gene functional polymorphism; rs53576_CG —oxytocin receptor gene polymorphism; TA repeat; rs2077647 —estrogen receptor 1 gene polymorphisms; SNP rs1937863 —aldo-keto reductase family 1 gene polymorphism; BDNF Met —brain-derived neurotrophic factor polymorphism.
According to the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5; 2013), PPD occurs when a person meets the criteria for a major depressive episode during the first four weeks postpartum; however, women are at risk of developing depression several months after birth [ 3 , 4 ]. Some patients show a recurrence of depression after delivery, while others develop the first symptoms in the postnatal period [ 3 ]. Risk factors for PPD include depression during pregnancy, anxiety during pregnancy, stressful life events during pregnancy or the early puerperium, low levels of social support, and a personal or family history of depression [ 4 ].
The diagnosis of major depressive syndrome requires the patient to meet at least five of the characteristic symptoms occurring on several days over a period of at least two weeks and producing significant interference or distress in daily life [ 4 , 48 ]. A general status altered by the use of substances (such as drugs, alcohol, or medication) or that is the result of a medical condition is not considered a criterion for the diagnosis of a major depressive episode.
The signs and symptoms of PPD are identical to those of non-puerperal depression, with the exception that the former is associated with a history of childbirth [ 49 ].
PPD is differentiated from two other entities of emotional impairment in the postnatal period mentioned in DSM-IV. The postpartum blues (often called “baby blues”) represents a transient and mild behavioral change that begins in the first week postpartum and lasts from a few hours to a few days. Characteristic symptoms include uncontrollable crying, psycho-emotional lability, anxiety, and insomnia. Between 50% and 80% of postnatal patients experience the blues, but the consequences are minimal. In comparison, patients suffering from postnatal psychosis experience symptoms such as confused thinking, disillusionment, hallucinations, and disorganized thinking. The prevalence of postpartum psychosis is between 1 in 500 cases and 1 in 1.000 patients. The duration of postpartum psychosis varies and requires rapid diagnosis and hospitalization. According to DSM-5, all mood and psychotic symptoms occurring during pregnancy or within the first four weeks following delivery are referred to as peripartum mood or psychotic episodes [ 3 , 48 ].
A meta-analysis conducted by Gavin et al., who diagnosed patients with PPD employing the structured interview, reported that the combined prevalence of major depressive episodes varies between 6.5% and 12.9% in the first 6 months postpartum, with a peak incidence between 2 and 6 months after delivery [ 50 ]. Another cohort study conducted in Denmark reported that the first three months after birth present an increased risk of the onset of psychiatric conditions in primiparous patients [ 51 ].
These findings emphasize the need for obstetricians to assess patients for psychiatric history correctly and, with the help of psychiatrists, optimize the treatment of mothers in the peripartum period [ 4 ].
Women who are at risk of postnatal depression should be identified as early as possible in pregnancy so that assessment and treatment can be initiated promptly [ 52 ].
Over recent years, there has been an increase in focus on the importance of the early detection and treatment of depression during pregnancy. Screening can be performed four to six weeks in the postpartum period. Several methods of detecting depression symptoms among women have been tested [ 52 ].
During the history taking of the patient by the obstetrician, special attention should be paid to the personal or family history of depression, postpartum psychosis, or bipolar disorder. Once depressive symptoms have been identified, a comprehensive evaluation of risks that influence the clinician’s treatment recommendations should be performed. Furthermore, patients should be assessed for social support, substance use, or partner abuse. The evaluation of patients should include a complete mental status examination and a physical examination to determine if the symptoms suggest a medical cause. In this context, laboratory investigations (including dosages of hemoglobin and thyroid hormones) are necessary [ 53 ].
Patients with risk factors should be screened with the Edinburgh Postnatal Depression Scale (EPDS) [ 52 ]. This validated instrument consists of 10 items and has a completion time of approximately 5 min. It also includes a question about suicidal ideation proposed by Cox et al. in 1987 [ 52 ]. Each question is scored on a scale of zero to three. Established cut-off values (a score greater than 10 is suggestive of a mild depressive episode and a score of 13 or more is suggestive of a moderate or severe depressive episode) allow clinicians to identify women at increased risk of PPD requiring additional clinical evaluation [ 54 ]. The best time to screen for PPD with this tool is in the first month after giving birth. A study conducted in Hungary between 2010 and 2011 that applied this tool to assess patients who gave birth in the last 6 to 8 weeks showed that the application of this scale demonstrated good reliability and internal consistency after applying statistical analysis [ 55 ].
The Postpartum Depression Screening Scale (PDSS) is also used as a screening tool for the detection of PPD, evolving from qualitative interviews to exploring the postpartum maternal experience. The first part comprises seven items; patients with a PDSS score ≥ 14 receive an extended questionnaire with another 28 items. A score ≥ 60 suggests a risk of a minor or major depressive episode; a score ≥ 80 is predictive of a major depressive episode. The PDSS scale has been used effectively in telephone assessment but presents an increased risk of false positive results that restrict its usefulness [ 52 , 56 ].
The Patient Health Questionnaire (PHQ-9) is a 9-item scale with two components used in the analysis of symptoms and functional imbalance in the diagnosis of depressive syndrome. It is also used to make a severity score to assess the effectiveness of treatment. The total score can range from 1 to 27 and can be classified from mild to severe forms of depression. The high sensitivity (88%) and specificity (88%) of the method ensure validity for the identification of risk factors [ 56 ].
The Edinburgh Postnatal Depression Scale (EPDS) and Postpartum Depression Screening Scale (PDSS) are used specifically for the diagnosis of postnatal depression, while the Patient Health Questionnaire (PHQ-9) is recommended for the diagnosis of depression in psychiatric medical institutions [ 54 ].
In a study conducted by Chadka-Hooks et al. in which the original Edinburgh Postnatal Depression Scale (EPDS) was used to assess screening practices in the healthcare system (including obstetricians, pediatricians, and family physicians) to assess familiarity with PPD screening methods, it was shown that healthcare providers are not familiar with screening methods [ 57 ].
Acceptability for the assessment of PPD is an important indicator of the likelihood that patients will respond to questionnaires to inform about their risk of depression [ 56 ]. In a study led by Matthey S. et al., the researchers showed that patients who readily agreed to complete the Edinburgh Postnatal Depression Scale (EPDS) had a lower risk of PPD. Thus, in the case of 87% of the studied group, no discomfort was felt in completing the questionnaire; these patients had EPDS scores < 13, compared to 64% of women at high risk and with EPDS scores ≥ 13 (chi-square = 31.9, df = 2, p < 0.0001) [ 58 ]. Therefore, the perception of discomfort with PPD screening is closely related to the risk of developing depression [ 56 ].
Although screening for the detection of perinatal depression is becoming more and more accepted both in the United States of America and in Europe, there are controversies regarding the possibility of overidentifying patients at risk or over-pathologizing behavioral symptoms in the postpartum period or the inability of the system to provide the necessary treatment and follow-up. These issues are currently unresolved. However, in recent years, much work has been conducted to develop new therapies or adapt existing ones used in PPD. Thus, current studies have demonstrated the importance of screening methods for the early detection of PPD [ 59 , 60 ].
The diagnosis of PPD can be performed in multiple ways. The DSM-5 is used to guide an interview design, and self-report tools, such as questionnaires, have been extensively employed in the clinical evaluation of PPD. However, using only such arbitrary scales could result in subjective bias. In order to improve diagnostic accuracy, the most efficient method in clinical practice is to apply some objective markers in addition to these subjective assessments [ 61 ]. Consequently, the endocrine system profile is considered, together with a self-rating scale, clinic interviews, and hormones of the hypothalamic–pituitary–adrenal (HPA) axis [ 62 ]. In PPD, there are three important HPA axis hormones that have been investigated because of the psychological changes mothers experience: the corticotrophin-releasing hormone (CRH), the adrenocorticotropic hormone (ACTH), and cortisol [ 63 , 64 ].
During pregnancy, childbirth, and lactation, the HPA axis and level of hormones modify severely, especially in the last weeks of pregnancy when cortisol levels rise most steeply, reaching levels three times higher than in non-pregnant women [ 65 ]. Rather than the HPA axis, the placenta is principally responsible for the higher basal cortisol concentrations during pregnancy. The placenta progressively takes over the function of the endocrine gland throughout pregnancy. In addition to producing progesterone and estrogen, the placenta also produces pCRH, which shares structural and bioactive similarities with hypothalamic CRH [ 66 ]. Cortisol levels rise during parturition, more noticeably after vaginal delivery. The difficulty of labor may contribute to an increase in cortisol levels. After delivery, plasma CRH concentrations return to preconception levels within 15 h. [ 67 ]. Women with significant depression had reduced cortisol stress responses. A study conducted by O’Connor et al. demonstrated that a diagnosis of depression in mothers was significantly associated with cortisol levels. The results from this study indicate that the strongest effect was that women with a diagnosis of depression had a lower initial waking level (depression × wakeup) [ 68 ].
Progesterone level and PPD have a strong connection. It has been demonstrated that the level of allopregnanolone increased steadily during gestation and then sharply decreased following birth [ 69 ]. A study conducted by Osborne L.M. et al. showed an association between a low level of allopregnanolone during the second trimester and higher depressive symptoms at six weeks postpartum, although it did not reach statistical significance in the adjusted model [ 70 ]. Also, lower levels of oxytocin are linked to an increased risk of PPD development in both gestational and postpartum periods. Jobst A. et al. demonstrated in one study that oxytocin plasma levels decreased from late pregnancy to the time of delivery in the depressed group, whereas oxytocin plasma levels increased continuously from pregnancy to the postpartum period in the non-depressed group. This difference in the course of oxytocin levels was significant and predicted postpartum depressive symptoms [ 71 ].
There is evidence that the thyroid hormone, which has been associated with severe neurological impairments, may enhance the risk of PPD due to its aberrant expression in the early postpartum period [ 72 ]. There is a positive association between a high thyroid stimulating hormone (TSH) (defined as >4 mIU/L) with free thyroxine (fT4) within the normal pre-pregnancy range at delivery and the development of self-reported depressive symptoms six months postpartum [ 73 ]. The presence of thyroid peroxidase antibodies (TPO-ab) during early gestation is predictive of both autoimmune thyroid disorders and depression. A positive TPO-ab status was associated with an increased risk for first-onset depression at four months postpartum (adjusted OR: 3.8; 95% CI: 1.3–11.6), but not at other postpartum time points [ 74 ].
It has been demonstrated that in the third trimester of pregnancy, proinflammatory cytokine levels are significantly greater, and these women are also at risk of developing PPD. The presence of a proinflammatory state throughout late pregnancy and the early postpartum period is essential to increase the risk of PPD [ 75 ]. Elevated levels of the potent proinflammatory cytokine interleukin 1β (IL-1 β) very early in the postpartum period increase a woman’s risk of developing symptoms of depression [ 76 ]. Furthermore, the serum levels of IL-6 after delivery were significantly higher in women with PPD. The optimal cut-off value as an indicator for screening was estimated to be 24 pg/mL, which yielded a sensitivity of 83.1% and a specificity of 79.4% [ 77 ].
Identifying biochemical and dietary indicators for PPD diagnostics has recently gained more attention. A significant finding of one of the recent studies is that lowered zinc levels during pregnancy are associated with prenatal and PPD symptoms [ 78 ]. PDD was also related to lower 25-hydroxylated vitamin D (25OHD) levels (≤80 nmol/L) in one study [ 79 ].
The transition to motherhood, whether for the first time or subsequently, involves a series of anatomical and physiological changes during pregnancy and the postpartum period that occur to optimize fetal development, childbirth, and maternal behavior [ 80 ]. At a functional level, recent work suggests that resting-state neural activity may change from pre-pregnancy to early postpartum, with increased functional connectivity in the default mode network localized to the bilateral cuneus [ 81 ]. One reliable and valid measurement of resting-state brain activity is electroencephalogram (EEG) coherence, which provides information regarding the coordinated functioning or synchronization between brain regions [ 82 ]. A study conducted by Sandoval I.K. et al. that evaluated EEG coherence before and after giving birth demonstrated that depression scores in the postpartum period correlated with the EEG coherence values between left frontopolar and parietal areas in the alpha1 band ( r = 0.36, p < 0.04). Depression symptoms were positively correlated with the EEG interhemispheric Fp1-P3 coherence measured during the postpartum period. Nevertheless, the lower Fp1-P3 coherence from pregnancy to postpartum remained when depression symptoms were controlled for in the analyses. However, the study should be considered in light of its limitations and directions for future research to assess a specific correlation between a typical EEG pattern for PPD [ 83 ].
The endeavor to diagnose PPD using predictors from both biological and EEG evaluations has increased. However, biochemicals may also serve as appropriate signs that can be employed to detect and anticipate PPD.
Treatment options for PPD vary depending on the severity of symptoms, functional status, and ability to care for the newborn. Mild symptoms can be managed in primary care, while cases with psychotic symptoms, those initially unresponsive to treatment, or severe forms require emergency admission to psychiatric facilities. Thus, therapeutic strategies can be divided into two categories: non-pharmacological therapies in mild forms (psychological treatment) of PPD or pharmacological ones that can be associated with the first category (drug therapies) [ 84 ].
Clinical studies have demonstrated the slight effectiveness of psychotherapy in the treatment of PPD. Valverde N. et al. conducted a systematic review regarding the psychotherapy effect for PPD in January 2023 that included seven trials with a total of 521 women who met the inclusion criteria. For the assessment of depressive symptoms, the EPDS was the most frequently used self-report symptom measurement system, applied in five of seven trials. Two studies used an author-generated questionnaire to obtain ratings on the treatment’s effectiveness. The ad hoc questionnaire assessed interpersonal connection and relationships, general well-being, outlook on life and parenting confidence, mood, and level of anxiety. Brief dynamic psychotherapy in individual format was the most frequently used type of intervention, applied in five out of seven trials, with the number of sessions ranging from four to twelve. The studies’ methodological quality was assessed considering the presence/absence of variables of the JADAD scale. The methodological analysis highlights the lack of high-quality designs concerning psychodynamic psychotherapy and PPD. Only two studies were randomized adequately out of seven; of the five with a control group, only one received treatment. The psychodynamic approach still plays a minor role in the treatment of PPD and is probably an efficient intervention. Therefore, research should be continued to assess the effectiveness of psychodynamic interventions in PPD compared to other effective treatments [ 85 ].
In another randomized trial, the goal was to compare healthcare providers trained to apply interventional psychology methods and identify patients with mild postnatal depression symptoms versus healthcare providers who did not receive this training. Thus, patients with EPDS scores ≥ 12 at six weeks postpartum were followed for 18 months. At six months postpartum, a significant number of PPD patients in the control group had higher EPDS scores compared to the two groups that received psychological interventions [ 86 ].
Qualitative research suggests that some of the barriers to treating patients with PPD include difficulties in scheduling therapy sessions and fear and stigma of dealing with a psychiatric diagnosis. Yet, studies show that they tend to choose psychological interventions instead of pharmacological treatment, especially if they are breastfeeding [ 54 ].
Antidepressant therapy is recommended when symptoms do not remit with psychological therapies, when the symptomatology begins severely and requires prompt treatment, or when it is preferred by the patient [ 84 ]. Once PPD is diagnosed, prompt treatment is essential. In the absence of prompt treatment, patients are at risk of long-term disease that can lead to functional disability, worsening symptoms, resistance to treatment, and suicide [ 56 ].
The first-line antidepressants used in the treatment of PPD are selective serotonin reuptake inhibitors (SSRIs) due to their easy administration and low toxicity [ 54 ]. In a meta-analysis conducted by Zhang Q. et al. that compared the efficacy and acceptability of different pharmacotherapies for PPD, they included 11 studies with 944 participants that met the inclusion criteria. They concluded that among the most effective antidepressants was an SSRI: paroxetine (64.3%) [ 87 ]. A meta-analysis evaluating the response and remission rates of patients in the selective serotonin reuptake inhibitor (SSRI) group versus the control group demonstrated higher response and remission rates in the antidepressant group (response rate: 52.2% vs. 36.5% and remission rate: 46% vs. 25.7%) [ 88 ]. Another systematic review of randomized clinical trials demonstrates the superior efficacy of treatment with selective serotonin reuptake inhibitors compared to a placebo and (or other performed treatments) for PPD. All studies demonstrated higher response and remission rates among those treated with SSRIs and more significant mean changes on depression scales, although findings were not always statistically significant [ 89 ].
Regarding mothers with PPD who are breastfeeding, although most antidepressants pass into breast milk, most infants exposed to antidepressants while breastfeeding do not develop adverse effects. A meta-analysis of 57 studies regarding antidepressants used during breastfeeding showed reduced plasma concentrations in infants exposed to paroxetine, sertraline, or nortriptyline. However, infants exposed to fluoxetine were at a greater risk of adverse events [ 90 ]. Among the most common adverse reactions in infants of fluoxetine-treated mothers, studies have described increased crying, decreased sleep hours, gastrointestinal sensitivity, and irritability [ 90 , 91 ]. The maximum concentration of sertraline and fluoxetine in breast milk is reached approximately 8 h after administration. In infants of mothers on antidepressants, serum levels were below the level of quantification for each type of drug [ 90 ]. Paroxetine and sertraline are the safest selective serotonin reuptake inhibitors during breastfeeding [ 92 ].
Serotonin–norepinephrine reuptake inhibitors are used as a second-line therapy if selective serotonin reuptake inhibitors (SSRIs) are ineffective or if the patient has a history of a positive response to this type of therapeutic agent. Data regarding their use in the treatment of PPD are limited, but their efficacy has nevertheless been demonstrated in nursing mothers due to minimal passage into breast milk [ 84 ].
For patients with severe symptoms of PPD who are breastfeeding and prioritize relatively rapid improvement, the recommended antidepressant is brexanolone. Brexanolone is a neuroactive steroid synthesized by the progesterone metabolite allopregnanolone [ 93 ]. The effect of brexanolone on depressive symptoms, anxiety, and insomnia in women with PPD was analyzed in the HUMMINGBIRD clinical program, and it was demonstrated that patients receiving BRX90 (n = 102) versus a placebo (n = 107) achieved a more rapid resolution of symptoms (response rate was 81.4% vs. 67.3%) [ 94 ]. However, for patients who are breastfeeding, it is considered that they should temporarily cease nursing during treatment with brexanolone and wait until four days after the end of infusion [ 95 ]. Based on low-quality evidence, brexanolone quickly disappears from breast milk [ 96 ].
The US Food and Drug Administration (FDA) has approved zuranolone as the first oral agent indicated for PPD [ 97 ]. Zuranolone is a neuroactive steroid that functions as a GABA-A receptor-positive allosteric modulator, with a mechanism of action compatible with brexanolone, which is intravenously administered. In a phase 3 double-blind, randomized, placebo-controlled clinical trial conducted between January 2017 and December 2018, participants with diagnosed PPD were tested for the efficacity of zuranolone. Starting at day 3, remission occurred in more patients who received zuranolone than a placebo (19% vs. 5%), and remission remained greater with active medication throughout treatment [ 98 ].
In severe forms of depression, additional drug treatments are indicated. Benzodiazepines may be used temporarily for severe forms of anxiety, insomnia, or both until antidepressant medication becomes effective. Antipsychotic therapy may become necessary for forms of depression with psychotic symptoms. In severe conditions, in cases that do not respond to treatment with antidepressant agents, or in states of psychosis with suicidal ideation, hospitalization of the patient may be necessary [ 84 ].
Since hormonal fluctuations are considered to be a trigger for the onset of PPD in some women, hormonal interventions have been studied in the treatment of PPD. In a study published by Dowlati et al. in 2020 regarding hormone interventions to prevent PPD, it was shown that, presently, the development of hormonal products for the prevention of PPD is at an early stage, with most trials showing preliminary, not definitive, results. Given the number of trend-level findings and the multifactorial etiology of PPD, it may be more prudent to investigate combined interventions rather than monotherapies [ 99 ].
In a similar manner, other complementary therapies used in the approach to antidepressant treatment in the postpartum period are mentioned, such as electroconvulsive therapy used in severe forms unresponsive to antidepressant medication or states with psychotic symptoms. A single more recent study conducted by Forray et al. demonstrates a 100% response rate, but data are nevertheless limited [ 100 ].
Women are at higher risk of developing a major depressive episode than men, and this risk is particularly accentuated by reproductive periods: adolescence, pregnancy, the postpartum period, or menopause. Female adolescents are at a two-to-threefold higher risk of major depressive disorder than males and a nearly fourfold higher risk of severe major depressive disorder. Obstetrician–gynecologists are usually the providers of medical services that women consult during these periods of vulnerability, usually with symptoms or conditions other than depression or anxiety [ 101 ].
Several depression screening methods have been validated over time, such as the Edinburgh Postnatal Depression Scale (EPDS) and the Patient Health Questionnaire (PHQ-9) [ 102 ]. The American College of Obstetricians and Gynecologists (ACOG) recommends screening by one of these scales at least once during the perinatal period [ 103 ].
Obstetricians play a unique role in identifying patients who require psychiatric evaluation in the postpartum period and could incorporate these screening methods into their practices. An ACOG’s effort should be focused on supporting by creating a more transparent environment regarding PPD screening [ 104 ]. One study conducted in the United States of America showed that the incidence of PPD symptoms was over 23% when applying screening in an obstetrical facility, higher than the overall incidence between 10% and 15% in the general population [ 105 ].
However, at the European level, there are no concrete recommendations for the integration of the assessment of a mother’s emotional status by the obstetrician and this is the main reason that women with PPD remain underdiagnosed or undertreated. Although a study conducted in Spain in 2014–2015 evaluated screening for prenatal depression during the first trimester in an obstetrics setting in a hospital in Madrid using PHQ-9, the rate of depressive symptoms was low (87.9% scored in the none-to-mild range). One study conducted in the United States of America showed that the incidence of PPD symptoms was over 23% when applying screening in an obstetrical facility, higher than the overall incidence between 10% and 15% in the general population [ 105 ]. Based on a needs assessment, researchers determined that women could be screened for PPD in the waiting room, which enabled women more privacy. The timing of the first ultrasound also allowed enough time in the prenatal period for women to participate in the 8-week group preventive intervention if they met high-risk criteria. Of the 445 women screened, 87.9% were scored in the none-to-mild risk range (PHQ-9: <9), while 9.4% were considered at high risk and recruited for the prevention study (PHQ-9: 10–14). The study had several limitations, such as the absence of incorporating screening in the third trimester. Still, the study suggests that it is feasible to integrate prenatal depression screening into obstetrics settings. There is certainly a need for more work to prevent the negative consequences associated with perinatal depression for women and their families internationally [ 106 ].
PPD is an increasingly common health problem among women who have recently given birth. Because of the potentially severe consequences that can affect both the patient and the family, it is crucial that healthcare providers, especially obstetricians with whom the woman comes into contact most frequently during the perinatal period, facilitate the early identification and treatment of PPD.
In addition, postnatal depression negatively affects the mother’s relationship with the infant and its development, but also the relationship with the partner and other family members. The association between molecular genetics and PPD is a highly current issue that has developed in recent years. However, there is still a small amount of research in this area, and it deserves further study due to its importance in terms of public health.
Obstetricians should include brief screening methods for PPD in the evaluation of pregnant women during their visits. Thus, if PPD is diagnosed, patients should be informed about all therapeutic techniques.
Therapeutic considerations include symptom severity, breastfeeding, and therapeutic preferences. Combined therapeutic approaches, including psychotherapy and pharmacological treatment, are recommended for treating moderate and severe forms of PPD. Complementary and alternative therapeutic methods are promising but require studies to demonstrate their effectiveness.
Until prenatal screening for PPD becomes standard practice, the healthcare system will fail to detect mothers at risk of developing depression and provide early assessment and effective treatment.
This survey is part of the PhD degree study of the article’s first author.
This research received no external funding. The processing charge for this article was partially supported by the PhD School, Carol Davila University and Pharmacy, Bucharest, Romania.
Conceptualization, D.A.-M.D. and A.P.; methodology, R.-C.P.; resources, M.C.D., F.Ș., and C.M.; data curation, D.A.-M.D. and R.-C.P.; writing—original draft preparation, D.A.-M.D., M.C.D., F.Ș., and C.M.; writing—review and editing, A.P. and R.-C.P.; visualization, D.A.-M.D.; supervision, A.P. All authors have read and agreed to the published version of the manuscript.
Informed consent statement, data availability statement, conflicts of interest.
The authors declare no conflicts of interest.
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Scientific Reports volume 14 , Article number: 19363 ( 2024 ) Cite this article
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Air pollution stands as an environmental risk to child mental health, with proven relationships hitherto observed only in urban areas. Understanding the impact of pollution in rural settings is equally crucial. The novelty of this article lies in the study of the relationship between air pollution and behavioural and developmental disorders, attention deficit hyperactivity disorder (ADHD), anxiety, and eating disorders in children below 15 living in a rural area. The methodology combines spatio-temporal models, Bayesian inference and Compositional Data (CoDa), that make it possible to study areas with few pollution monitoring stations. Exposure to nitrogen dioxide (NO 2 ), ozone (O 3 ), and sulphur dioxide (SO 2 ) is related to behavioural and development disorders, anxiety is related to particulate matter (PM 10 ), O 3 and SO 2 , and overall pollution is associated to ADHD and eating disorders. To sum up, like their urban counterparts, rural children are also subject to mental health risks related to air pollution, and the combination of spatio-temporal models, Bayesian inference and CoDa make it possible to relate mental health problems to pollutant concentrations in rural settings with few monitoring stations. Certain limitations persist related to misclassification of exposure to air pollutants and to the covariables available in the data sources used.
Child mental health and air pollution.
According to the 2019 Child and Adolescent Mental Health report published by the Statistics National Institute of the United Kingdom, mental disorders between the ages of 5 and 15 have increased from 9.7 to 11.2% in the last decade 1 . The World Health Organization (WHO) states that more than 20% of adolescents worldwide (10 to 19 years old) suffer from mental disorders, and that 50% of these begin before the age of 14. In most cases, however, they are neither detected nor treated. According to the WHO, depression is one of the leading causes of illness and disability among adolescents and suicide is the third leading cause of death between the ages of 15 and 19 2 . Failure to address adolescent mental disorders has consequences that extend into adulthood, affecting both physical and mental health and limiting opportunities for a satisfying adult life 3 .
Extant research on mental health of children related to pollution is based on subjects living mainly or only in urban areas. To the best of our knowledge, there are no studies focusing on rural areas. It is thus important to conduct a study in a rural area to discern whether pollution and child mental health have a similar relationship as in urban areas. It is well known that pollution behaves differently in rural areas 4 , and it is an open question whether rural children exhibit similar or different relationships from their urban counterparts.
Regarding the association between exposure to air pollution and the occurrence of mental health problems in urban children, we carried out a bibliographic search in the PubMed database at the end of July 2023, using the key words “mental health”, “child” and “air pollution”. Specifically, we found three reviews: Volk et al . 5 ; Karrari et al. 6 and Zhao et al . 7 . The last two are systematic reviews and follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 8 reporting guidelines, whereas Volk et al . 5 , is a review within the guidelines of the Environmental Influences on Child Health Outcomes (ECHO) Program.
Karrari et al . , in their systematic review of articles published between 1990 and 2011, showed that lead exposure can have adverse effects on health throughout life such as hearing loss, anaemia, renal failure, and weakened immune system. In the case of children, it is more detrimental to mental development. At low doses, this element is related to motor and cognitive dysfunctions 6 .
Zhao et al . conducted a review of the literature between 1960 and 2017, to identify the associations between air pollution and mental health and behavioural disorders. Specifically, they studied ozone exposure and the immune and nervous systems. The results from their research did not relate ozone to autism spectrum disorders, impaired cognitive function, dementia, depression or suicide, the immune and nervous systems, mental health, or behavioural disorders 7 .
Volk et al . provide a review of the literature from 2006 to 2020 on prenatal exposure to pollution and neurodevelopment including autism spectrum disorder, attention deficit hyperactivity disorder (ADHD), general measures of cognition and intelligence, state of mood and image. They concluded that there is a relationship between prenatal exposure and neurodevelopment 5 .
In exploring the literature not included in the reviews, we looked for problems other than neurodevelopment, as well as exposure to air pollutants other than lead and ozone. Five groups of mental health problems were identified: behavioural and development disorders 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ; ADHD 12 , 16 , 19 , 20 , 21 , 22 , 23 , 24 ; anxiety 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 ; eating disorders 33 , 34 , 35 , 36 , and other mental health problems 37 , 38 , 39 , 40 , 41 .
Long scientific evidence highlights the relationship between environmental pollution and cognitive development, understood as the processes through which human beings acquire skills that allow them to interpret reality and interact with it. Maternal exposure to air pollution is related to child neurological development during the first 24 months of life through the onset of a neurological delay 10 . There is an association between traffic pollution and the neurodevelopment of children aged 7 to 12 years, with a lower cognitive development as well as behavioural impairments 11 , 12 , 17 , 25 . There are sex-dependent differences in children aged 4 to 6 years, with increased vulnerability in male children 14 . Air pollution is also connected to memory, attention, and verbal cognition 15 . Some studies on traffic pollution show a relationship with a cognitive deficit in children, despite the lack of significance 9 while others do not identify any relationship between short-term exposure to traffic-induced pollution and neuropsychological problems 13 , 17 .
Air pollution is related to ADHD and behavioural disruptions and development problems in children aged 7 to 11 years 12 , 19 . Environmental pollutants such as O 3 , NO 2 , PM 1 , PM 2.5 and PM 10 are associated with children’s and adolescents’ mental health and the development of ADHD 20 , 21 , 22 , 23 , 24 .
When exposure to air pollution is prolonged over time, the risk of anxiety in children and adolescents increases 25 , 26 , 27 , 29 , 30 . Ma et al. highlight that the hospital admissions for anxiety increase with short-term exposure to NO 2 and sulphur dioxide (SO 2 ) 32 . Hao et al . find a positive association between PM 2.5 and anxiety 31 , however, other studies relating PM 2.5 and anxiety are unconclusive 28 .
To the best of our knowledge, evidence on pollution and eating disorders focuses on obesity and overweight, but not other mental health problems. Limaye and Salvi suggest that air pollution is a risk factor for the development of obesity 33 . In particular, prenatal exposure to PM 2.5 and NO 2 is related to an increased risk of being overweight or obese 34 , 35 . Shi et al . find that the increased risk not only develops in a prenatal stage, but also later in life 36 .
Exposure to certain fine particles such as PM 2.5 is associated with other psychiatric problems in children and adolescents like bipolar disorder, impulse control disorder, or suicidality 37 , 38 , 40 although other studies do not obtain significant results 39 , 41 .
The studies above focus on mental health of children living mainly or only in urban areas. We have identified no studies focusing on rural areas devoted to children.
However, some studies in rural areas do analyse the relationship between air pollution and mental health of the general population. Exposure to wildfire smoke in rural communities, particularly among vulnerable groups like working-age adults, non-Hispanic whites, and those without a university education, increases the risk of suicide 42 . In China, concerns about physical and mental health are growing, with air pollution, income inequality, food contamination, and limited green spaces relating to health 43 . Air pollution is also connected to the mental health of China's aging population, with urban elderly individuals having better psychological well-being compared to their rural counterparts 44 . These studies emphasize the need to address air pollution and health in various populations, with a focus on rural communities and vulnerable groups.
The reviewed studies have certain limitations at the spatial and temporal level. Pollutant monitoring stations are mostly located in urban areas, thus offering limited spatial coverage 5 . This makes it very difficult to estimate the relationship between pollutants and health in rural settings.
The aim of the article is to provide first-time scientific evidence on the association between air pollution and mental health of children and adolescents under 15 in a rural setting, including behavioural and developmental disorders, ADHD, anxiety, and eating disorders beyond obesity and overweight (anorexia nervosa, bulimia nervosa, binge eating disorder, and avoidant and restrictive disorder). The methodological value of this research lies in combining spatio-temporal models, Bayesian inference, and the Compositional Data (CoDa) analysis method, thus providing a new perspective on the relationship between the concentrations of air pollutants and mental health problems. This innovative methodology allows for a better understanding of the relationship between air pollution and mental disorders in children and adolescents in rural areas where pollution monitoring stations are few and far between.
By utilizing spatio-temporal models, both the geographical location and the temporal variability of air pollutants are taken into account, providing better information about exposure. Bayesian inference makes a rigorous assessment of the associations between air pollution and mental disorders, considering the inherent uncertainty in the data and the sparse monitoring network.
The use of the CoDa analysis method is particularly relevant in this study, as it addresses the compositional nature of air pollutants. By considering the relative proportions of different components in the pollutant composition, CoDa analysis relates air pollution and mental health taking into account possible trade-offs between pollutants and not only overall pollution levels. This improves on studies focusing only on absolute pollution levels or focusing only on a single pollutant.
All the variables, except age, number of chronic diseases and pollutant concentrations, were expressed in proportions. Of the population studied in the Alt Empordà region in Catalonia, Spain (24,674 children under 15 years old), 8.82% had behavioural and developmental disorders, 1.74% anxiety, 0.76% ADHD, 0.37% eating disorders, 47.9% were girls and 52.1% boys, 1.1% had glucose intolerance, 7.4% were obese, 1.6% smoked, and 0.2% consumed alcohol. In relation to the pharmaceutical co-payment, which is an indicator of individual socioeconomic level, 10.3% made an economic contribution of up to 10%, 68.8% contributed economically with 40% and the remaining 20.9% with 50% or more. Regarding the average income in the census tract, 79.0% were in the first quartile of lowest income census tracts (reference category), 8.8% in Q 2 , 5.8% in Q 3, and 6.4% in Q 4 . The average age was 11 years, and, on average, the subjects suffered from 0.15 chronic diseases. The average concentrations of each pollutant were 22.52 μg/m 3 of PM 10 , 34.69 μg/m 3 of NO 2 , 44.10 μg/m 3 of O 3 , 0.29 mg/m 3 of CO, and 2.20 μg/m 3 of SO 2 (Table 1 ).
The results for the relationship between air pollutants and the behavioural and development disorders, anxiety, ADHD, and eating disorders can be seen in Table 2 . The \(\upbeta\) coeficients, show the compositional behaviour indicating trade-offs between pollutants: \({\upbeta }_{1}\) refers to PM 10 , \({\upbeta }_{2}\) to NO 2 , \({\upbeta }_{3}\) to O 3 , \({\upbeta }_{4 }\) to CO, and \({\upbeta }_{5}\) to SO 2 . \(\upgamma\) indicates the geometric mean of all pollutants standing for overall pollution, hereinafter the total. The total pollution level (γ 95% credibility interval (CI) -Bayesian equivalent of a 95% confidence interval—from 17.9264 to 20.2053) related to the incidence of behavioural and development disorders. In relative terms, when NO 2 (β 2 CI from 20.1750 to 25.9906) and O 3 (β 3 CI from 18.0670 to 23.8689) increased, and PM 10 (β 1 CI from − 28.5884 to − 20.8427) and CO (β 4 CI from − 23.6106 to − 16.3512) decreased, the incidence also increased. When adding up the β and γ coefficients, behavioural and development disorders appeared to be related to NO 2 , O 3 and SO 2 .
In the case of anxiety, total pollution (γ CI from 8.2395 to 11.2685) was also related to incidence. When O 3 (β 3 CI from 7.8187 to 15.4243) and SO 2 (β 5 CI from 1.4973 to 9.4917) increased and CO (β 4 CI from − 21.3632 to − 9.4764) decreased, the incidence also increased. When adding up the β and γ coefficients, anxiety appeared to be related to PM 10 , O 3 and SO 2 . Total pollution (γ) was also related to the incidence of ADHD (CI from 3.2102 to 4.7697) and eating disorders (CI from 3.2065 to 5.1064).
Gender, age, number of chronic diseases, glucose intolerance, obesity, alcohol consumption, tobacco consumption and individual socioeconomic level had a significant relationship with the risk of having behavioural and development disorders. Being a girl (CI from 0.5867 to 0.8874) and having a medium (CI from 0.4690 to 0.8499) or high (CI from 0.2229 to 0.4895) individual socioeconomic level acted as protective factors. Age (CI from 1.6661 to 2.2696), number of chronic diseases (CI from 1.4647 to 2.1420), glucose intolerance (CI from 1.5983 to 5.0600), obesity (CI from 1.4125 to 2.5303), and alcohol (CI from 1.6891 to 17.8359) and tobacco (CI from 3.8947 to 10.1989) consumption indicated increased risks of behavioural and development disorders given that their odds-ratio was greater than 1 (Table 3 ).
The covariables which were significant with respect to anxiety were gender, age, glucose intolerance, obesity, alcohol and tobacco consumption, individual socioeconomic level, and average income in the census tract. Protective factors were being a girl (CI from 0.0928 to 0.3004), having a high individual socioeconomic level (CI from 6.6988 × 10 −4 to 5.4451 × 10 –3 ), and living in a census tract where the average level of income was medium to high (Q 2 (CI from 6.1181 × 10 –3 to 1.1462 × 10 –1 ) and Q 3 (CI from 2.9175 × 10 –3 to 5.3412 × 10 −2 )). Age (CI from 4.8610 to 14.0504), glucose intolerance (CI from 1.7828 to 391.4102), obesity (CI from 1.0087 to 6.2730), alcohol consumption (CI from 1.2828 to 9.3783 × 10 3 ), smoking (CI from 3.6832 to 113.2430) and living in a census tract where the average level of income was very high (Q 4 (CI from 2.0168 × 10 4 to 1.5426 × 10 7 )), were related to an increased risk of anxiety (Table 4 ).
Regarding ADHD (Table 5 ), gender, age, individual socioeconomic level, and average income in the census tract were significant. Age (CI from 2.9929 to 9.7336) and living in a census tract where the average level of income was very high (Q 4 (CI from 2.8765 to 77.3954)) were related to an increased risk. Protective factors were being a girl (CI from 5.7924 × 10 −3 to 5.3888 × 10 −2 ), having a high individual socioeconomic level (CI from 0.0134 to 0.1935), and living in a census tract where the average level of income was high (Q 3 (CI from 0.0215 to 0.3252)).
The significant variables for eating disorders were being a girl (CI from 5.7278 to 45.1946), age (CI from 3.6859 to 12.6260), obesity (CI from 1.2639 to 9.2735), and tobacco consumption (CI from 4.2096 to 350.7618). All of them acted as risk factors (Table 6 ).
Our results for the first time reveal a significant association between air pollution and child mental health in a rural setting. Exposure to PM 10 , NO 2 , O 3 , and SO 2 is linked to increased risks of behavioural and developmental disorders, anxiety, ADHD, and eating disorders. Different pollutants exhibit distinct relationships. NO 2 , O 3 , and SO 2 are linked to behavioural and developmental disorders, while PM 10 , O 3 , and SO 2 correlate with anxiety. Overall pollution levels also contribute to higher rates of the disorders under study.
Demographic factors are also linked to disorder risks. Protective factors include being female and having higher socioeconomic status, while risks rise with age, chronic diseases, glucose intolerance, obesity, and alcohol and tobacco consumption.
We compared the findings of our study to other research addressing the associations between air pollution and child mental health in urban populations, as the relationship has not yet been established in rural settings. This comparison revealed relevant similarities but also differences. We found the review by Volk et al . 5 particularly useful, as well as some individual studies in topics not covered by these authors.
Regarding behavioural and development disorders, the level of total air pollution (γ) is related to a higher incidence, a fact that also occurs when NO 2 (β 2 ) and O 3 (β 3 ) increase and PM 10 (β 1 ) and CO (β 4 ) decrease. By adding the β and γ coefficients, behavioural and developmental disorders seem to be related to NO 2 , O 3 and SO 2 ; as shown by the results of the research carried out by Sunyer et al . in children aged from 7 to 11 in the city of Barcelona, with a reduction in cognitive development when traffic-related pollutants increased 11 . Pérez-Crespo et al . 18 reached the same conclusion in their study of 9- to 12-year-olds in Rotterdam.
Regarding anxiety, our results show that incidence is also related to the total (γ) and, when adding the β and γ coefficients, to PM 10 , O 3, and SO 2 . This coincides with the research by Yolton et al . 27 in the city of Cincinnati, with a significant association between pollutants present in road traffic and anxiety in 12-year-olds. Another significant relationship between PM 10 , NO 2 and anxiety is reported by Jorcano et al . 26 in boys and girls aged 7 to 11 who had been exposed in pre- and post-natal phases.
Total air pollution (γ) is also connected to the incidence of ADHD in this investigation. Volk et al . , carried out a systematic review determining that exposure to air pollutants both pre- and post-natal is associated with an increased risk of ADHD. Some of the reviewed studies analysed similar pollutants to ours, such as PM 2.5 , NO 2 , O 3 , and PM 10 5 . Similar results were also observed by Forns et al . 12 in boys and girls aged 7 to 11 living in Barcelona. These children presented an increase in ADHD cases at higher levels of exposure. Maitre et al . 16 through data from the "Human Early Life Exposome" (HELIX) project, based on 6 longitudinal birth cohorts in Europe between the ages of 6 and 11, also found a positive relationship. Likewise, Markevych et al . 20 identified the increased risk of ADHD in relation to PM 10 and NO 2 in children under 10. Shim et al . 23 in their study with children aged 7 to 12, found a relationship between particulate matter exposure and the prevalence of ADHD. Zhou et al . 24 found a significant association between long-term ozone exposure and ADHD in children aged 3 to 12 years residing in seven cities of Liaoning, China.
With regard to eating disorders, our study relates total air pollution with anorexia nervosa, bulimia nervosa, binge eating disorder, and avoidant and restrictive disorder, all of which are absent from the literature relating pollution and eating disorders.
The relation between pollutant concentrations and behavioural and developmental disorders, ADHD, anxiety, and eating disorders has been assessed through the combination of spatio-temporal models, Bayesian inference, and CoDa. This method combination is new in air pollution and health research, but Bayesian inference has been applied by Saez et al . 21 to identify the association between environmental factors and ADHD, and gains relevance in a rural setting.
The combination of CoDa with a total pollution level is also new in studies related to air pollution and health and makes it possible to disentangle overall pollution from the relative importance of each pollutant with respect to one another. These relationships have hitherto been presented together, thus making it impossible to assess trade-offs between sets of pollutants.
There are several limitations in our study. First and foremost was the misclassification of exposure to air pollutants. In fact, this is an ecological (or average) exposure in the area in which the subject lives and does not necessarily coincide with the exposure of each subject. In addition, the subjects are not immobile, and so are exposed to air pollution not only in the place where they live, but also at the school they attend, the place where they do extracurricular activities (for example, languages or sports), etc. Fortunately, on the one hand, for the spatial prediction of exposure we considered the census tract where each subject lives 45 . While this did not eliminate the problem entirely, it did greatly minimize it. On the other hand, the subjects we analysed, boys and girls, do not move very far from their homes, so exposure would not vary very much. Furthermore, it is a non-differential misclassification. That is, all subjects have the same probability of being misclassified.
Second, the covariables used were limited to what was available in the data base. In particular, the proxy variable for individual socioeconomic level based on the grades of pharmaceutical co-payment had to be grouped into only three categories because very few cases were present in some of the original nine. Thus, this variable may have been measured in error. A less coarse measure of socioeconomic level would be desirable in future research.
Finally, there could be residual confounding bias. However, we have tried to control for bias including both a large number of observed confounders as covariables, and random effects that captured unobserved confounders.
We used a sub-cohort of a population-based cohort composed of 124,112 individuals from the Alt Empordà, a rural region in Catalonia, Spain, from 2009 to 2019. Specifically, this retrospective population-based cohort study was conducted including data from all residents in the region. Data were obtained from the Public Data Analysis for Health Research and Innovation Program (PADRIS) database, which includes admission data from primary care centres, hospitals (inpatient and outpatient care), extended care facilities, and mental health centres, as well as sociodemographic (sex and age) and socioeconomic (pharmaceutical co-payment) variables.
Alt Empordà, located in the province of Girona, Catalonia stands out as a predominantly rural area. With a population density of 104 inhabitants per square kilometre, Alt Empordà starkly contrasts with Catalonia, which boasts a density of 248 inhabitants per square kilometre. This underscores the significance of the primary sector in this region, whose value-added contribution amounts to 4%, as opposed to Catalonia's 1.2%. These figures highlight the reliance of Alt Empordà on agriculture, winery, fishery, and livestock farming. According to data from the Institute of Statistics of Catalonia in 2022, Alt Empordà had 144,926 inhabitants, of which 15.2% were 14 years old or younger, compared with 7,899,056 inhabitants in Catalonia, and 14.2% respectively 45 . We selected children and adolescents who were under the age of 15 at some point between 2009 and 2019. The number of subjects was 24,674 children and adolescents, of whom 2,382 had at least one of the following mental disorders: behavioural and development disorders, ADHD, anxiety, and eating disorders.
The mental disorders listed below were considered as dependent variables. The codes of each accord to the International Classification of Diseases, 10th Revision (ICD-10):
Behavioural and development disorders (F94)
Anxiety (F41.9)
Eating disorders (F50)
As explanatory environmental variables we included the pollutants PM 10 (µg/m 3 ), NO 2 (µg/m 3 ), O 3 (µg/m 3 ), carbon monoxide (CO) (mg/m 3 ), and SO 2 (µg/m 3 ). We obtained their hourly levels for 2009–2019 from the 143 automatic monitoring stations from the Catalan Network for Pollution Control and Prevention (XVPCA) (open data) 46 . Instead of the short-term effects of these pollutants on children's health, we were interested in the effects of long-term exposure. For this reason, we used the monthly averages, from January 2009 to December 2019 (further details can be found in Saez and Barceló 47 and in Bertran et al . 48 ).
While it is essential to obtain the pollution data in each census tract of the studied population, there may not always be a monitoring station, and this is even more so in a rural setting. We used a Bayesian hierarchical spatiotemporal model 47 to predict exposure to air pollutants in locations or time periods without pollution monitoring sites. The estimation of the (second-order stationary) Gaussian field suffers from the so-called 'big n problem', that is, there are large computational costs of the linear algebra operations required for model fitting and prediction. The costs are larger for large datasets in space and time, as in our case, in which a frequentist approach proved unfeasible.
Even using a Bayesian approach, these computational problems subsist when the MCMC algorithm is used, since dense matrices must be computed for each iteration. Among the solutions that have been proposed to Bayesian inferences under the 'big n problem', we select the Integrated Nested Laplace Approximation (INLA) by Rue et al . 49 , 50 , in the specification suggested by Saez and Barceló 47 and used by Mota-Bertran et al . 4 , 48 . The INLA approximation provides a fast and yet quite exact approach to fitting complex latent Gaussian models which comprise many statistical models in a Bayesian context 49 , 50 .
Predictions of exposure to pollutants were made at the level of the census tract where the subject lives, for the period between the beginning of the follow-up (January 1, 2009) and the day before the diagnosis of the mental illness.
In the context of pollution studies, the CoDa methodology 51 , 52 , 53 , 54 , 55 identifies patterns of varying relative importance of pollutants 48 , 56 , 57 , 58 which can later be associated to potential health risks 59 . The CoDA methodology makes it possible to study the relative importance of pollutant concentrations, that is, how pollutants behave relative to one another, beyond the information provided by total pollution levels. This is crucial for understanding air pollution. The commonest tool within the CoDa methodology is the log-ratio transformation, the simplest of which is the log-ratio between pairs of pollutant concentrations 53 .
For instance, Mota-Bertran et al . 48 identified three substantial trade-offs among the pollutants defined at the beginning of this section: NO 2 versus O 3 , and SO 2 versus NO 2 and O 3 . In other words, the log-ratios with the highest variance were ln(O 3 /NO 2 ), ln(NO 2 /SO 2 ), and ln(O 3 /SO 2 ). The CoDa approach is tailored to studying these forms of joint variability, rather than considering one pollutant individually and rather than considering only overall pollution levels.
The specification of a composition as an explanatory variable was first developed by Aitchison and Bacon-Shone as the so-called log-contrast model, and indicates the increase of which pollutants, at the expense of the decrease of which other, is related to the incidence of a disease 60 . For this purpose, the right-hand side of the model equation must include:
The constraint \({\upbeta }_{1}+{\upbeta }_{2}+{\upbeta }_{3}{+\upbeta }_{4}+{\upbeta }_{5}=0\) ensures that total pollution is constant, as required for the interpretation of compositional effects as trade-offs. In other words, the increases of some pollutant concentrations in relative terms are only possible when decreasing some others. For ease of estimation, the so-called additive log-ratio transformation 60 can be applied to introduce said constraint by setting log-ratios with a common denominator. These log-ratios can be used as variables in any statistical model:
where \({\upbeta }_{5}\) is obtained either as \({\upbeta }_{5}={-\upbeta }_{1}-{\upbeta }_{2}-{\upbeta }_{3}-{\upbeta }_{4}\) or by rerunning the model with a different pollutant in the denominator 61 .
The CoDa approach does not imply disregarding the absolute pollution levels. Apart from considering the relative importance of air pollutants, some form of total air pollution can be added to the compositional information by means of a \(\mathcal{T}\) -space 62 , 63 , 64 , 65 , also known as CoDa with a total. The composition extracts the relative importance of air pollutants to each other, while the total T speaks of global levels of air pollution. This total is best defined as the so-called multiplicative total, in other words, the product of air pollutant concentrations 48 , 65 , 66 .
The total refers to increasing all pollutant concentrations in absolute terms by a common factor and can be specified by adding the γ coefficient to the right-hand side of the model equation:
For interpretation, the effects of pollutant concentrations can be decomposed into the log-contrast part and the total part as:
with \({\upbeta }_{1}+{\upbeta }_{2}+{\upbeta }_{3}{+\upbeta }_{4}+{\upbeta }_{5}=0\) . This decomposition provides complementary views on pollution and mental health which are made possible by the \(\mathcal{T}\) -space. Firstly, the \(\beta\) coefficients are interpreted as trade-offs: positive \(\beta\) \(\text{s}\) indicate pollutants whose relative increase is related to a greater incidence of mental disorders when coupled with relative decreases in pollutants with negative \(\beta {\text{s}}\) , the total remaining constant. Secondly, the \(\upgamma\) coefficient relates the incidence of mental disorders to increases in the overall pollution level while keeping its composition constant, in other words, when multiplying all pollutant concentrations by a common factor. Thirdly, the sums \(\beta + {\upgamma }\) relate mental disorders to the increase of the absolute concentration of each pollutant while leaving the absolute concentrations of all other pollutants constant, which implies both an increase in the relative importance of the pollutant and an increase in the total pollution. This interpretation is the only one available in standard pollution modelling. The CoDa approach with a total adds to this the decomposition of the sums \(\beta + {\upgamma }\) into \(\beta\) and \(\upgamma\) .
Because the numbers of cases of ADHD and eating disorders are small, in these two disorders we simplified Eq. 4 , by dropping the log-ratios and taking only the total pollution \(\gamma (\text{ln}\left({\text{PM}}_{10}\right)+\text{ln}\left({\text{NO}}_{2}\right)+\text{ln}\left({\text{O}}_{3}\right)+\text{ln}\left(\text{CO}\right)+\text{ln}\left({\text{SO}}_{2}\right) )\) into account. This simplification improved the stability of the estimates.
We used the following covariables.
Gender: binary variable indicating girls (1) or boys (0).
Age: standardized to zero mean and unit standard deviation.
Number of chronic diseases: including bronchitis (7.5%), asthma (6.4%), neoplasms (2.3%) and others (chronic obstructive pulmonary disease, hypertension, and ischemic cardiomyopathy).
Glucose intolerance: binary variable indicating if the subject has glucose intolerance (1) or not (0).
Obesity: binary variable indicating if the subject is obese (BMI ≥ 30 kg/m 2 ) (1) or not (0).
Alcohol consumption: binary variable indicating if the subject consumes alcohol (1) or not (0).
Tobacco consumption: binary variable indicating if the subject is a smoker or was a former smoker (1) or not (0).
A proxy variable for individual socioeconomic level: categorical variable that shows the different levels of pharmaceutical co-payment present in Catalonia 67 . The following categories were defined for the economic contribution: up to 10% (Lowest socioeconomic level, reference category); 40% (medium socioeconomic level); and 50% or more (Highest socioeconomic level). The first category contains incomes below 18,000 EUR/year, the second between 18,000 and 100,000 EUR/year and the third above 100,000 EUR/year.
Average income (EUR). Average from 2015 to 2019 observed at census-tract level 68 and transformed into quartiles (lowest quartile as reference category).
We specified a generalized linear mixed model (GLMM) for each health outcome, with binary response from the binomial family:
where the subindexes \(i\) and \(t\) indicate the subject and the year, respectively; Y it indicates the mental health problem of the subject i at year t (0 absence, 1 presence); pollutant k,it denotes the exposure to PM 10 , NO 2 , O 3 , CO, and SO 2 in relative terms; T it denotes the exposure to overall pollution as in Eq. 3 \(; {\eta }_{i} \text{and} S\left({census\_tract}_{i}\right)\) denote random effects (explained below); and \(\beta s\) and \(\gamma\) are the coefficients of the explanatory variables and covariables ( \({e}^{\beta }\) is the relative risk associated with each covariable).
We considered two random effects in the models. First, \({\eta }_{i}\) is a random effect indexed on the subject. This random effect is unstructured (independent and identically distributed), and captures individual heterogeneity, i.e., unobserved confounders specific to the subject and invariant in time.
Second, we included the structured random effect S(census_tract i ) to control spatial dependency: census tracts that are close in space show more similar incidence than those that are not.
The spatially structured random effect S(census_tract i ) is normally distributed with zero mean and a Matérn covariance function:
where \({\text{K}}_{\nu }\) is the modified Bessel function of the second type and order. \(\nu >0\) is a smoother parameter, \({\sigma }^{2}\) is the variance and \(\kappa >0\) is related to the range ( \(\rho =\sqrt{8 \nu }/\kappa\) ), the distance to which the spatial correlation is close to 0.1 69 .
As can be seen, we introduced many unobserved variations into the GLMMs for each of the observations. This prevents using a frequentist approach. For this reason, inferences were made with a Bayesian perspective and the INLA approach 49 , 50 . We used priors that penalize complexity (called PC priors). These priors are robust in the sense that they do not have an impact on the results 70 . All analyses were carried out using the free software R (version 4.2.2) 71 , through the INLA package 49 , 50 , 72 .
The use of the data included is authorized by the Catalan Health Institute (ICS) and the Data Analysis Program for Health Research and Innovation (PADRIS) which ensure the pseudo-anonymization of the information. When linkage with other public data sources is required, ICS or PADRIS act as a Trusted Third Party (TTP) to execute the linkage and provide the new data set already pseudoanonymized; otherwise, informed consent of patients is needed to access their personal data, using the same TTP. The data are managed in a secure server following all the present legal requirements of the General Data Protection Regulation (European Union) 2016/679 and of the Council of 27 April 2016 and the Spanish Organic Law 3/2018 of 5 December on the protection of personal data and guarantee of digital rights.
Due to the ethical (in accordance with the protocol approved by the Research Ethics Committee CEI Girona—October 10, 2019-) and legal (the provisions of the General Data Protection Regulation -European Union- 2016/679; the Council of 27 April 2016; the Spanish Organic Law 3/2018 of 5 December on the protection of personal data and guarantee of digital rights; and the Agreement for the transfer of anonymized health data between the Department of Health, the Catalan Health Service, the Catalan Institute of Health, the Blood and Tissue Bank, the University of Girona and the Fundació Salut Empordà) reasons, there are restrictions on the transfer of data to third parties and they are not publicly available. However, the data (appropriately anonymized) will be available, after approval of the research proposal plan and with a signed data access agreement, upon reasonable request to the corresponding author. Code will be available at www.researchprojects.es .
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This study was carried out within the ‘Cohort-Real World Data’ subprogram of CIBER of Epidemiology and Public Health (CIBERESP). We appreciate the comments of two anonymous reviewers of a previous version of this work who, without doubt, helped us to improve our work. The usual disclaimer applies.
This work was supported by the AGAUR of Generalitat de Catalunya (grant numbers 2020 FISDU 00238, and 2023 CLIMA 00037); MCIN/AEI and the ERDF A way of making Europe (grant number PID2021-123833013); and the Department of Research and Universities of Generalitat de Catalunya (grant number 2021 SGR 01197). The funding sources did not participate in the design or conduct of the study, the collection, management, analysis, or interpretation of the data, or the preparation, review, and approval of the manuscript.
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Anna Mota-Bertran, Germà Coenders, Marc Saez & Maria Antònia Barceló
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Mota-Bertran, A., Coenders, G., Plaja, P. et al. Air pollution and children’s mental health in rural areas: compositional spatio-temporal model. Sci Rep 14 , 19363 (2024). https://doi.org/10.1038/s41598-024-70024-2
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COMMENTS
This case study provides encouraging data for the preliminary evidence of the acceptability, feasibility, and efficacy of the Be a Mom Coping with Depression intervention in the reduction of depressive symptoms during the postpartum period. Advantages of this treatment format and implications for clinical practice and future research are discussed.
Case-based learning: postnatal depression Appropriate early recognition and timely treatment of postnatal depression is essential if patients are to make a full recovery.
Postpartum depression is common and affects the woman, infant, and family. Treatment depends on the severity of symptoms and the level of functional impairment and can include social support, psych...
The following keywords were applied in the databases during the literature search: "postpartum depression" OR "postnatal depression" OR "puerperal depression." The research was limited to human studies published in the English language. The search strategy and search terms used for this research are detailed in Appendix 1.
Abstract. The high prevalence and negative consequences of postnatal depression (PND) are well-reported in the literature. Although there are effective psychological interventions for reducing depressive symptoms in the perinatal period, such as cognitive behavioral therapy, there are still some barriers to care reported both by women and ...
Background Women can experience a range of psychological problems after birth, including anxiety, depression and adjustment disorders. However, research has predominantly focused on depression. Qualitative work on women's experiences of postnatal mental health problems has sampled women within particular diagnostic categories so not looked at the range of potential psychological problems ...
Background Post-natal Depression (PND) is a depressive disorder that causes significant distress or impairment on different levels in the individual's life and their families. There is already evidence of the efficacy of psychological treatments for PND. We conducted a narrative review and researched the literature for identifying systematic reviews and studies for the best psychological ...
This case study outlines a comprehensive approach to postnatal depression treatment, which includes maternal mood management strategies, inclusion of partner, mother-infant dyadic work, an opportunity to learn from other women (not only for 'experts') as well as individualised treatment of one's early relationships.
Request PDF | On Jan 1, 2023, Mariana Branquinho and others published Case study: Cognitive behavioral therapy for postnatal depression | Find, read and cite all the research you need on ResearchGate
One of the most common psychological effects following childbirth is postpartum depression. Postpartum depression (PPD) has a significant negative impact on the child's emotional, mental as well as intellectual development if left untreated, which can ...
Postnatal depression case study - what is it really like to give birth and find you have PND? Can you get over it? And how? What should you do if this is you?
This article reviews the literature and recommendations for the treatment of postpartum depression, with a focus on the range of pharmacological, psychotherapeutic, and other nonpharmacologic interventions. Keywords: postpartum depression, postnatal depression, lactation, antidepressant, hormone therapy, psychotherapy, bright light therapy, omega-3
Methods: We used data from 1406 mothers of the intervention arm of the Post-Up study. Risk factors were collected at 3 weeks and 12 months postpartum. Depression and anxiety symptoms were measured in the first month postpartum by the Edinburgh Postnatal Depression Scale (EPDS) and 6-item State-Trait Anxiety Inventory (STAI-6), respectively.
Postpartum depression occurs in at least one in seven new mothers, usually within the first 6 months after delivery. By the time of onset of postpartum depression, the mother has usually long since been discharged from the maternity hospital. Early identification and treatment of these mothers reduces both maternal and infant suffering.
Background Postnatal depression is a public health problem requiring intervention. To provide effective care, information is needed on the experiences of those with high levels of depressive symptoms who are offered and accept, or decline, psychological intervention postnatally. Aim To provide the first integrated in-depth exploration of postnatal women's experiences of the identification and ...
There remains limited evidence regarding the effectiveness and safety of antidepressants in the management of postnatal depression, particularly for those with more severe depression. We found low-certainty evidence that SSRI antidepressants may be more effective in treating postnatal depression tha …
What is postpartum depression and anxiety? It's common for women to experience the "baby blues"—feeling stressed, sad, anxious, lonely, tired or weepy—following their baby's birth. But some women, up to 1 in 7, experience a much more serious mood disorder—postpartum depression (PPD). (Postpartum psychosis, a condition that may involve psychotic symptoms like delusions or ...
Postpartum depression is a commonly occurring psychiatric disorder. Prevalence of postpartum depression varies across cultures and countries. Many factors affect, trigger, or aggravate post-partum depression, including sociocultural factor. Bedapu is ...
Background: Postpartum depression (PPD) is a mild to severe mood disorder, starting at 6 weeks after birth and with an incidence of approximately 25% in Brazilian puerperae. Its occurrence induce significant aggravations to maternal and child health, however, its risk factors, although known, are little explored for the appropriate diagnosis.
Conclusions Our results evidenced a high percentage of puerperae with PPD related to maternal anxiety, demonstrating the importance and the necessity of increasing care for women's mental health in the gestational and puerperal periods. Keywords: Anxiety case-control study Edinburgh Postnatal Depression Scale mental health postpartum depression
This case provides information on the use of a BBA technique in the treatment of postnatal depression and is the first case report to investigate this in a new multiparous mother.
Current research separately analyzed the connection between postpartum depression, fatigue, sleep and infant development. However, depression, fatigue and sleep quality often coexisted as adverse symptoms in postpartum women and influenced infant development together. This study explored the maternal postpartum symptoms on infant growth. Methods
The review includes empirical papers using observational study designs, including cohort studies, case-control studies, case-crossover studies, and cross-sectional studies. ... One study found that the odds of postnatal depression is 82% higher for mothers living in a mold-affected home compared with mothers living in a home that was not ...
Moreover, the results of the studies that analyzed the prevalence of postnatal depression during the COVID-19 pandemic indicate a two-fold higher incidence of postnatal depression cases compared to the non-pandemic period [ 12 ]. Untreated PPD has a negative impact on both mother and infant.
According to the WHO, depression is one of the leading causes of illness and ... pollutants both pre- and post-natal is associated ... and fine particulate matter: a case-crossover study.