Sage Journals: Discover world-class research

Abstract

Objective:

The use of attention-deficit/hyperactivity disorder (ADHD) medications, dexamphetamine (DEX) and methylphenidate (MPH), during pregnancy is increasing. While previous studies have investigated ADHD medication usage compared to no use, it remains unclear if the risks differ between these medications, as no studies have compared their safety in pregnancy. This study examined maternal and neonatal outcomes (health risk to pregnant person and infant) associated with DEX and MPH use during pregnancy.

Methods:

This retrospective cohort study used whole-population dispensing and perinatal records to identify mother-infant dyads exposed to DEX (n = 852) or MPH (n = 65) in Western Australia between 2003 and 2018. Maternal and neonatal outcomes were compared between the groups using generalised linear models.

Results:

Maternal outcomes, including preeclampsia, c-sections, and post-partum haemorrhage, were not different between people dispensed MPH or DEX during pregnancy. Neonates prenatally exposed to DEX compared to MPH had a higher length-for-gestational-age (coeff:1.59, 95% CI: 0.35, 2.84). However, there were no differences between the two groups in terms of other neonatal outcomes, including gestational age, birth weights and rates of pre-term births.

Conclusion:

Maternal and neonatal health outcomes were comparable between people treated with DEX or MPH during pregnancy. Further large-scale studies are needed to confirm findings.

Keywords

attention-deficit/hyperactivity disorder dexamphetamine maternal outcomes methylphenidate neonatal outcomes pregnancy

Introduction

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental condition characterised by attention difficulties, hyperactivity, and impulsiveness (Lang et al., 2024; Posner et al., 2020). It is predominantly diagnosed in childhood, however, approximately 67% of children with ADHD require ongoing treatment as adults (Agnew-Blais et al., 2016; Faraone et al., 2006). Additionally, some individuals are first diagnosed with ADHD as adults(Agnew-Blais et al., 2016). ADHD affects approximately 8% of children and adolescents globally (Ayano et al., 2023).

Stimulant medications, including dexamphetamine (DEX) and methylphenidate (MPH), are first-line treatments for ADHD. Medication is typically chosen when ADHD significantly impairs daily activities, academic performance, occupational functioning, or relationships, and non-pharmacological approaches are insufficient (Besag, 2014). Both medications increase neurotransmitters such as dopamine and norepinephrine but differ slightly in action; DEX enhances neurotransmitter release and inhibits reuptake, whereas MPH primarily blocks neurotransmitter reuptake without significantly promoting their release (Faraone et al., 2006). The choice between MPH and DEX can depend on individual response, side effect profiles, duration of action, or economic accessibility (Preen et al., 2007). Furthermore, the usage of DEX and MPH may vary by region (Kolar et al., 2008). For example, MPH is predominant in Europe and Nordic countries While amphetamines are more common in Australia (Preen et al., 2007).

The use of ADHD stimulant medication has been increasing among adults, including females of reproductive age (Huybrechts et al., 2023; Karlstad et al., 2016; Renoux et al., 2016). Consequently, there has been a corresponding increase in the use of ADHD medications during pregnancy (Cohen et al., 2023; Hærvig et al., 2014; Leong et al., 2017). A recent systematic review reported that ADHD medication dispensing during pregnancy occurs at rates ranging from 0.07 to 6.01 per 1,000 pregnancies (Russell et al., 2025). Although the reasons for these increases remain largely unexplored, some reasons may be: Social media, heightened recognition, clinicians better understanding ADHD, increased seeking of professional support, and recent post-COVID initiatives to shorten neurodevelopmental-diagnostic waiting lists (particularly in the UK) (Matheiken et al., 2024; McKechnie et al., 2023).

Despite such large increases in the dispensings of ADHD stimulants during pregnancy, research regarding the safety of ADHD stimulants during pregnancy is limited. Animal studies have shown adverse outcomes associated with the use of ADHD medications in pregnancy, including spina bifida in rabbits administered MPH (Beckman et al., 2008) and foetal malformations in mice that were given amphetamines (Mirjalili et al., 2013). However, these animal studies administered doses much higher than the equivalent recommended doses for humans (Beckman et al., 2008; Mirjalili et al., 2013). Although animal studies can provide preliminary insights, they may not directly apply to humans due to inherent biological differences and the complexity of human neonatal experiences. Human epidemiology studies have indicated that ADHD medications are associated with adverse outcomes, including an increased risk of preeclampsia, hypertension and preterm birth when compared to no treatment (Cohen et al., 2017; Poulton et al., 2018). Furthermore, evidence has shown an association between MPH use during pregnancy and a small increased risk of cardiac malformations in the neonate (Huybrechts et al., 2018). The current literature on the safety of ADHD medications during pregnancy typically either combine various ADHD medications together or focuses on a single stimulant, comparing it to a non-treatment group (Kittel-Schneider et al., 2021; Li et al., 2020). However, studies directly comparing DEX and MPH are absent in the literature.

Comparisons of the safety between medications are crucial as the use of ADHD medications in pregnancy is often unavoidable, either because of unplanned pregnancies or for some individuals the therapeutic benefits of medication (e.g., improved executive functioning, mood stability, and reduced risk of mental-health deterioration) outweigh the potential medication-related risks (Besag, 2014). Understanding the comparable risks and benefits of each medication will allow patients of reproductive age and their treating clinicians to be more informed on the management of ADHD, thus reducing potential harm if they were to become pregnant. Thus, this study aimed to compare maternal and neonatal outcomes associated with using DEX and MPH during pregnancy.

Methods

Study Design

This retrospective cohort study used routinely collected administrative health data in Western Australia (WA) between 2003 and 2018. Linked datasets from WA included records on medication dispensing, hospital, perinatal and mortality, which were used to investigate maternal and neonatal outcomes associated with using DEX or MPH during pregnancy. DEX and MPH were specifically used, as they are the most commonly prescribed medications for ADHD in WA (Health, 2017; Preen et al., 2007).

Study Cohort

The study cohort included individuals aged 18–45 years who were dispensed MPH or DEX during pregnancy and delivered either a live or stillborn neonate between 1 January 2003 and 31 December 2018 in WA. Only singleton pregnancies were included. The latest pregnancy was included in cases with multiple eligible pregnancies per person since only one pregnancy was included for each person.

Medication exposure was defined as receiving a minimum of two prescriptions for either DEX or MPH during pregnancy (Grzeskowiak et al., 2012). Those who used both medications during pregnancy were excluded. The cohort comprised those who had dispensed any formulations for DEX or MPH treatment. DEX prescriptions encompassed both DEX sulphate and lisdexamfetamine dimesylate scripts. MPH prescriptions included both immediate and modified-release scripts.

Data Sources

The WA Monitoring of Drugs of Dependence System (MODDS) and the Midwives Notification System (MNS) were linked and extracted by the Data Linkage Services (DLSs). As outlined above, these datasets were used to identify individuals eligible for the study. MODDS data provided dispensing DEX and MPH information, such as dispensing dates, prescribed dose, and number of tablets per script. The MNS contained records of both live and stillborn neonates who were at least 20 or more weeks of gestational age or weighed more than 400 g at birth when gestational age was unknown. The timing of DEX or MPH dispensing obtained from the MODDS was cross-referenced with pregnancy timings from the MNS to identify likely exposure to DEX (n = 852) or MPH (n = 65) during pregnancy, with the estimation of conception date based on calculations of the last menstrual period.

The DLS also linked data for eligible people and their neonates from the Hospital Morbidity Data Collection (HMDC), the WA Registry of Developmental Anomalies, and the WA Register of Births, Deaths, and Marriages.

Cohort Characteristics

In assessing potential differences between the two exposure groups (DEX vs MPH), maternal characteristics, including maternal age, parity, self-reported smoking status during pregnancy, and comorbidities (e.g., gestational diabetes and history of mental health conditions) were extracted from the MNS and the HMDC. Given the increased susceptibility of ADHD patients to comorbid psychiatric conditions, mental health was compared (Freeman, 2014). A history of a mental health condition was defined as hospitalisation for a mental health diagnosis (ICD-10-AM: F00-F99) in the five years leading up to childbirth. Area-based socioeconomic status and residential remoteness information were also linked to the address provided at birth. Socioeconomic status was obtained through the Socioeconomic Indexes for Areas (SEIFA) for national Census data. The assessment of remoteness and socioeconomic status was conducted using the DLS spectrum software, which employed geocoding techniques to allocate SEIFA and remoteness areas (RAs) based on the relevant census year data (Bowes, 2015).

Outcomes

This study explored various maternal outcomes, encompassing complications during pregnancy and labour. Pregnancy complications investigated included threatened abortion (vaginal bleeding prior to 20 weeks gestation), preeclampsia, hypertension, antepartum haemorrhage, and premature rupture of membrane (PROM) (Cohen et al., 2017; Newport et al., 2016; Poulton et al., 2018). Labour and delivery characteristics included the onset of labour (spontaneous, induced, or planned caesarean section/delivery), caesarean section (both planned and emergency), postpartum haemorrhage, precipitated delivery, and failure to progress. The data on these outcomes were sourced from the MNS.

Neonatal outcomes investigated in the study included estimated gestation age, perinatal mortality, preterm birth (<37 weeks), very preterm birth (<32 weeks), birth weight, body length, head circumference, perinatal mortality, low Apgar score (defined as below seven at 5 min), (Poulton et al., 2018) congenital anomalies, cardiac anomalies, resuscitation requirements, admission to the special care unit, and infant respiratory distress syndrome (IRDS). Additionally, birth weight, length, and head circumference were presented as percentages of their optimal values, considering the neonates’ gestational age (as calculated by Blair and colleagues) (Blair et al., 2005).

Data on neonatal outcomes came from various sources. The MNS provided information on birth weight, length and head circumference, Apgar scores, gestational age and number of admissions to the special care unit. The MNS and the WA Death Registry provided information regarding perinatal mortality (stillborn neonates and those who passed away within 28 days after birth). Further, the WA Registry of Developmental Anomalies was used to provide diagnoses of congenital and cardiac anomalies as per the ICD-9 British Paediatric Association.

Statistical Methods

To minimise confounders and potential differences between the groups (DEX and MPH), the characteristics of the DEX and MPH groups were presented as descriptive statistics and compared using univariable generalised linear models. Smoking status was controlled for in the analysis of maternal and neonatal outcomes. To investigate maternal and neonatal health outcomes, multivariable linear regression (birthweight and gestational age), logistic regression (congenital anomalies and preterm birth) and multinominal logistic regression (labour classification) models were used. Cell counts below 5 were concealed in statistical output to protect the confidentiality of individuals. Stata was used to perform these statistical analyses (StataCorp, 2018).

Ethics

This project was approved by the WA Department of Health Human Research Ethics Committee (RGS0000003029) and the University of Western Australia Human Research Ethics Committee (RA/4/20/5530).

Results

Demographics

The study included 917 eligible individuals prescribed DEX or MPH during pregnancy. Of these, 852 individuals (92.9%) were dispensed DEX, while 65 individuals (7.1%) were dispensed MPH. Individuals who were dispensed DEX during pregnancy were more than twice as likely to smoke than those who were dispensed MPH (OR: 2.42; 95% CI: 1.21, 4.81; p = .006) (Table 1). The two groups are comparable with no differences seen regarding maternal age, parity, diabetes diagnosis, mental health hospitalisations, socioeconomic status (measured as SEIFA) or rurality (Table 1).

Table 1.

Characteristics of Pregnant Individuals who were Dispensed Dexamphetamine During Pregnancy Compared with Individuals who were Dispensed Methylphenidate During Pregnancy.

	Dexamphetamine	Methylphenidate	Effect Size (95% CI)	p-value
Number	852	65	-	-
Maternal age, mean ± sd	29.0 ± 5.7	29.7 ± 6.6	coeff: −0.71 (−2.16, 0.75)	0.339
Previous pregnancies, n (%)	542 (63.6%)	40 (61.5%)	OR: 1.09 (0.65, 1.84)	0.738
Smoking, n (%)	260 (30.5%)	10 (15.4%)	OR: 2.42 (1.21, 4.81)	0.012
Diabetes, n (%)	60 (7.0%)	8 (12.3%)	OR: 0.54 (0.25, 1.18)	0.124
Mental health^a, n (%)	132 (15.5%)	14 (21.5%)	OR: 0.67 (0.36, 1.24)	0.202
Remoteness, n (%)			OR: 1.07 (0.58, 1.98)	0.818
- Major city	643 (75.5%)	51 (78.5%)
- Inner regional	147 (17.3%)	5 (7.7%)
- Outer regional	41 (4.8%)	6 (9.2%)
- Remote and very remote	21 (2.5%)	<5 (<5%)
SEIFA advantage			OR: 1.06 (0.67, 1.67)	0.819
- 1 (highest disadvantage)	78 (9.2%)	11 (16.9%)
- 2	116 (13.6%)	6 (9.2%)
- 3	265 (31.1%)	15 (23.1)
- 4	206 (24.2%)	20 (30.8%)
- 5 (lowest disadvantage)	187 (22.0%)	13 (20.0%)

n: number, sd: standard deviation; SEIFA: socio-economic indexes for areas. Bold font where p < 0.05.

Hospital admissions for mental health conditions in the five years prior to birth.

Maternal Health Outcomes

There was no difference between individuals dispensed DEX or MPH during pregnancy in terms of labour and delivery characteristics, including onset of labour, caesarean section delivery, postpartum haemorrhage, precipitated delivery, and failure to progress. Furthermore, individuals dispensed DEX during pregnancy were not different to those dispensed MPH in terms of pregnancy complications, which included threatened abortion and threatened early labour, preeclampsia, hypertension, antepartum haemorrhage, and PROM (Table 2).

Table 2.

Maternal Outcomes of the Individuals Dispensed Dexamphetamine Treatment During Pregnancy Compared with Individuals Dispensed Methylphenidate Treatment During Pregnancy.

	Dexamphetamine	Methylphenidate	Effect size (95% CI)	P-value	Adjusted measure of effect (95%CI)^a	P-value^a
Number	852	65
Onsets of labour, n (%)
- Spontaneous	392 (46.0%)	33 (50.8%)
- Induced	283 (33.2%)	18 (27.7%)	coeff: 0.28 (−0.31, 0.87)	0.355	coeff: 0.34 (−0.25, 0.94)	0.258
- No labour (planned caesarian)	177 (20.8%)	14 (21.5%)	coeff: 0.06 (−0.59, 0.71)	0.851	coeff: 0.09 (−0.56, 0.74)	0.786
Caesarian section delivers (emergency or planned), n (%)	321 (37.7%)	25 (38.5%)	OR: 0.97 (0.58, 1.62)	0.900	OR: 0.97 (0.58, 1.62)	0.908
Threatened abortion^b, n (%)	24 (2.8%)	0 (0%)	-^c	-	-	-
Threatened pre-term labour <37 weeks, n (%)	33 (3.9%)	<5 (<5%)	-^c	NS	-^c	NS
Preeclampsia, n (%)	48 (5.6%)	6 (9.2%)	OR: 0.59 (0.24. 1.43)	0.240	OR: 0.60 (0.25, 1.47)	0.263
Hypertension, n (%)	13 (1.5%)	<5 (<5%)	OR: 0.99 (0.13, 7.70)	0.994	OR: 1.16 (0.15, 9.03)	0.889
Antepartum haemorrhage, n (%)	39 (4.6%)	< 5 (<5%)	OR: 0.99 (0.30, 3.30)	0.989	OR: 0.10 (0.30, 3.34)	0.997
Postpartum haemorrhage, n (%)	118 (13.9%)	< 5 (-^c)	-^c	NS	-^c	NS
Premature rupture of membranes, n (%)	47 (5.5%)	< 5 (<5%)	-^c	NS	-^c	NS
Precipitated delivery, n (%)^d	78 (9.2%)	6 (9.2%)	OR: 0.99 (0.41, 2.37)	0.984	OR: 0.93 (0.39, 2.24)	0.874
Failure to progress, n (%)	66 (7.8%)	6 (9.2%)	OR: 0.83(0.34, 1.98)	0.668	OR: 0.89 (0.37, 2.15)	0.979

n: number. Bold font where P < 0.05.

Adjusted for smoking.

Vaginal bleeding occurs before the 20th gestational week, with the uterus identified as the source of bleeding.

Data not reported to maintain anonymity.

Combination of 1st less than 3 h or a second stage of less than 10 min.

Neonatal Health Outcomes

Neonates prenatally exposed to DEX were, on average, 1.59% longer at birth (based on percentage optimal birth length) compared to those prenatally exposed to MPH (coeff: 1.59; 95% CI: 0.35, 2.84; p = 0.012). Other neonatal outcomes related to size, such as mean birth weight, length, head circumference and percentages of optimal birth weight and head circumference were not different between neonates exposed to DEX compared to MPH (Table 3).

Table 3.

Neonatal Outcomes of the Neonates Prenatally Exposed to Dexamphetamine Compared with Neonates Prenatally Exposed to Methylphenidate.

	Dexamphetamine	Methylphenidate		P-value	Adjusted measure of effect (95%CI)^a	P-value
Number of neonates	852	65
Sex (male), n (%)	465 (54.6%)	31 (47.7%)	Coeff: −0.07 (−0.19, 0.06)	0.283	coeff: −0.06 (−0.19, 0.06)	0.340
Estimated gestation (weeks), mean ± sd	38.3 ± 2.0	38.5 ± 1.70	Coeff: −0.24 (−0.74, 0.25)	0.330	coeff: −0.19 (−0.69, 0.30)	0.440
Perinatal mortality, n (%)	<5 (0.6%)	0 (0.0%)	-	-	-	-
Preterm, n (%)	100 (11.7%)	7 (10.8%)	OR: 1.10 (0.49, 2.48)	0.815	OR: 1.01 (0.44, 2.28)	0.989
Very preterm, n (%)	9 (1.1%)	0 (0.0%)	-	-	-	-
Birth weight (g), mean ± sd	3322 ± 562	3296 ± 544	Coeff: 26.3 (−115.5, 168)	0.716	coeff: 48.06 (−93.27, 189.4)	0.505
% Optimal birth weight, mean ± sd	100.2 ± 13.4	97.7 ± 12.1	Coeff: 2.49 (−0.95, 5.94)	0.156	coeff: 2.99 (−0.44, 6.43)	0.08
Body length (cm), mean ± sd	49.9 ± 3.1	49.4 ± 3.2	Coeff: 0.47 (0.32, 1.26)	0.244	coeff: 0.60 (−0.18, 1.39)	0.133
% Optimal length, mean ± sd	100.2 ± 4.9	98.8 ± 5.4	Coeff: 1.39 (0.14, 2.64)	0.030	coeff: 1.59 (0.35, 2.84)	0.012
Head circumference (cm), mean ± sd	34.2 ± 2.0	34.2 ± 1.70	Coeff: −0.04 (−0.54, 0.45)	0.862	coeff: 0.03 (−0.46, 0.52)	0.911
% Optimal head circumference, mean ± sd	99.3 ± 4.5	99.1 ± 3.9	Coeff: 0.22 (−0.91, 1.34)	0.707	coeff: 0.37 (−0.75, 1.50)	0.516
Apgar score <7 at 5 min, n (%)	20 (2.4%)	<5 (-^b)	-^b	NS	-^b	<0.05
Congenital anomalies, n (%)	36 (4.2%)	<5 (<7.7%)	-^b	NS	-^b	NS
Major congenital anomalies, n (%)	32 (3.8%)	<5 (<7.7%)	-^b	NS	-^b	NS
Cardiac anomalies, n (%)	6 (0.7%)	<5 (<7.7%)	^−b	NS	-^b	NS
Resuscitation required^{c, d}, n (%)	249 (30.5%)	20 (34.5%)	OR: 0.83 (0.48, 1.46)	0.527	OR: 0.81 (0.46, 1.43)	0.467
Special care unit^c, n (%)	131 (15.4%)	15 (23.1%)	OR: 0.61 (0.33, 1.11)	0.107	OR: 0.58 (0.31, 1.07)	0.079
IRDS diagnosis^c, n (%)	28 (3.3%)	<5 (<7.7%)	-^b	NS	^−b	NS

n: number; sd: standard deviation; NS: not significant (value is less than 0.05); IRDS: infant respiratory distress syndrome. Bold font where P < 0.05.

Adjusted for smoking status.

Data not reported to maintain anonymity.

Excluding stillborn babies.

Resuscitation methods included suction, oxygen therapy, bag and mask, endotracheal intubation, external cardiac massage, ventilation, and drugs.

Neonates prenatally exposed to DEX had lower odds of having an Apgar score below seven at 5 min than neonates prenatally exposed to MPH (odds ratio and confidence intervals are omitted due to low numbers of outcomes). Other neonatal health indicators, including required resuscitation, special care unit admissions, and IRDS diagnosis, were not different between neonates prenatally exposed to DEX compared to MPH (Table 3). The portion of neonates diagnosed with congenital anomalies did not differ between neonates prenatally exposed to MPH and DEX. These included all congenital anomalies, major congenital anomalies, and cardiac anomalies (Table 3).

Discussion

DEX or MPH treatment during pregnancy did not largely influence overall health outcomes for pregnancy people or neonates. Maternal health outcomes were not significantly different between those treated with DEX and MPH during pregnancy. Neonates prenatally exposed to MPH were more likely to have a low Apgar score (<7 at 5 min) and were slightly shorter at birth than neonates prenatally exposed to DEX. However, apart from these differences, all other neonatal health outcomes were comparable between study groups.

A substantial numerical imbalance in prescriptions was evident with DEX accounting for 92.9% of the cohort, presenting a contrast to cohorts in studies conducted internationally. For example, prevalence studies conducted in Canada, the United Kingdom and Nordic countries revealed a preference for MPH among pregnant people in their cohorts (Cohen et al., 2023; Lemelin et al., 2021; Renoux et al., 2016). These regional differences in stimulant preferences during pregnancy likely contribute to the limited information about DEX compared to MPH in the literature. A potential reason behind the difference in the number dispensed DEX (n = 852) compared to MPH (n = 65) in WA may be due to MPH becoming a part of government-subsidised medications in 2009 in Australia, whereas DEX had been included much earlier (Health, 2017; Preen et al., 2007). This might have made DEX a more economically accessible option prior to 2009 in the cohort (Preen et al., 2007). This economic consideration is particularly relevant given the increased likelihood that individuals with ADHD, who form the study cohort, come from low socioeconomic backgrounds (Table 1) (Russell et al., 2016).

Maternal Health Outcomes

Overall, maternal health outcomes investigated in the study were not different from those dispensed DEX or MPH during pregnancy, including the risk of preeclampsia or maternal hypertension. Prior research has found associations between maternal use of ADHD medications during pregnancy and an increased risk of preeclampsia and maternal hypertension when compared to individuals who did not use ADHD medications during pregnancy (Cohen et al., 2017; Newport et al., 2016; Poulton et al., 2018). However, these previous studies typically grouped ADHD medications together (Cohen et al., 2017; Newport et al., 2016). Therefore, it was difficult to determine if one or all medications were contributing to the reported maternal hypertension and preeclampsia in these studies (Cohen et al., 2017; Newport et al., 2016; Poulton et al., 2018). Our findings suggest that the risk of maternal hypertension and preeclampsia occurring remains the same between the individuals dispensed DEX or MPH. However, it is essential to consider that the group dispensed MPH had a relatively small size (n = 65), which resulted in a low number of cases of maternal hypertension (n < 5) within this group. Consequently, the findings on maternal hypertension lack statistical robustness, and further research with larger cohorts is necessary to validate these findings. The incidence of pre-eclampsia was higher than expected, with 5.6% for DEX and 9.2% for MPH, compared to the estimated 3% incidence in Australia and 5–10% globally (Khedagi & Bello, 2021; Thornton et al., 2016). These slightly elevated figures compared to the Australian population emphasise the importance of closely monitoring pregnant people with ADHD for potential preeclampsia regardless of the prescribed stimulant.

Maternal outcomes, including antepartum haemorrhage, postpartum haemorrhage and PROM, were not different between individuals dispensed DEX or MPH during pregnancy. Previous studies have found associations between maternal amphetamine usage, haemorrhage, and PROM (Ornoy & Koren, 2021). However, prior studies focused on contexts of misuse and abuse, which may not accurately represent the therapeutic use of amphetamines for ADHD treatment (Ornoy & Koren, 2021). This study differed from previous studies in that it comprised individuals who have been dispensed amphetamines for therapeutic purposes to treat ADHD. However, the analysis of these outcomes lacks statistical robustness as the number of outcomes was small (n < 5). Therefore, further studies are warranted to confirm antepartum and postpartum haemorrhage outcomes and PROM findings.

Neonatal Health Outcomes

The odds of having a low Apgar score (<7) at five minutes were lower for neonates prenatally exposed to DEX compared with MPH. However, the disclosure of the odds ratio and confidence intervals was not possible due to confidentiality, as the number for the outcome was below 5. In the discussion of neonatal health outcomes, it should be noted that the significant p-value observed could be highly due to chance, reflecting the low precision inherent in analyses with a limited number of outcomes. Previous studies have found an association between maternal use of ADHD medications and low Apgar scores at 1 min but not at 5 min compared to non-users (Poulton et al., 2018). However, Poulton and colleagues (2018) proposed that the low Apgar scores may be due to the ADHD condition in the pregnant person and not the medications (Poulton et al., 2018). Additionally, other studies found that maternal use of MPH and atomoxetine (a non-stimulant medication) was associated with lower Apgar scores compared to those who ceased ADHD medications during pregnancy (Bro et al., 2015; Casey et al., 2001). However, a low Apgar score in the study conducted by Bro and colleagues (2015) was defined as a score below ten (Bro et al., 2015). Furthermore, previous studies that employed a similar definition of a “low” Apgar score found that neonates exposed to MPH were not at a greater risk of low Apgar scores when compared to non-exposure (Damer et al., 2021; Nörby et al., 2017). While our study found that the odds of low Apgar scores were elevated for MPH-exposed neonates compared to DEX, other neonatal mortality and morbidity measures remained comparable across both DEX and MPH groups.

Percentages of optimal birth length were significantly lower in neonates prenatally exposed to MPH than in DEX, with neonates prenatally exposed to DEX being 1.6% closer to their optimal length than neonates exposed to MPH. Such results may not be clinically relevant, especially because other outcomes related to size, such as birth weight and length, were comparable between the groups.

Previous studies have found lower birth lengths in neonates prenatally exposed to methamphetamine compared to non-exposed neonates (Kalaitzopoulos et al., 2018; LaGasse et al., 2012). Comparing our study with previous studies is challenging due to differences in context. This study specifically focuses on the therapeutic use of stimulants for ADHD, setting it apart from prior studies focusing on the abuse of methamphetamine (Kalaitzopoulos et al., 2018; LaGasse et al., 2012). Overall, the outcomes related to size indicate a minimal difference in risk between DEX and MPH.

Limitations

The study relied on administrative health data sources with inherent limitations. One notable limitation is the assumption that medications were taken following prescribed guidelines. The criterion used (≥2 scripts during pregnancy) was deemed the most suitable indicator of an individual's use of the medications, given that there was an absence of specific usage instructions and adherence in the data provided. Further, the small sample size in the MPH group is primarily due to the infrequent use of MPH before 2009, which significantly affected the overall data availability for this period. This limited sample size for MPH impacted the examination of some health outcomes (e.g., cardiac malformations and maternal hypertension) due to a small number of events. It also prevented the investigation of less common outcomes like perinatal mortality and various anomalies because the number of cases was zero.

This study did not investigate child health outcomes beyond the neonatal stage, potentially missing anomalies or conditions that may be diagnosed as children develop. For instance, it did not explore long-term issues such as long-term heart defects, previously linked to MPH use during pregnancy, which often manifests later in childhood (Huybrechts et al., 2023).

Conclusion

This study contributes to the understanding of the safety profiles of ADHD stimulants during pregnancy, particularly highlighting distinctions between DEX and MPH. The choice between DEX or MPH prescriptions did not influence the risk of adverse maternal outcomes. However, some evidence suggests that DEX is more favourable regarding some neonatal health outcomes, like Apgar scores at 5 min and birth length. These results should be interpreted with caution due to the exploratory nature of this study, and further large-scale studies are needed to confirm these findings and to better understand the comparative risks associated with ADHD stimulant medications.

Lay Summary

(a) What is Already Known About the Topic?

Rising use: Prescriptions for the two leading ADHD drugs – DEX and MPH – have increased among people who become pregnant. Observational studies comparing people who continue ADHD medication with those who stop have not shown major increases in adverse pregnancy or infant outcomes.

Evidence gap: No study has yet compared the safety of DEX versus MPH head-to-head in pregnancy, so their relative risk profiles remain unknown.

(b) What This Paper Adds?

This exploratory study highlights this question by providing the first head-to-head comparison of DEX and MPH in pregnancy. Our findings show no meaningful differences in maternal or newborn health between the two medicines, offering early and preliminary evidence that their safety profiles during pregnancy appear broadly comparable. Preliminary evidence means that larger studies must be conducted to confirm these results.

(c) Implications for Practice, Research or Policy

Although pharmacological studies are less commonly featured in neurodiversity research, examining medication use among neurodivergent populations – especially during pregnancy – is essential to improving informed decision-making, autonomy, and health outcomes for neurodivergent individuals and their families. These results also highlight the need for larger follow-up studies to confirm these preliminary findings.

Footnotes

Acknowledgments

The research team would like to acknowledge the support and assistance provided by the Linkage, Data Outputs and Client Services Teams at the Western Australian Data Linkage Services. The authors also wish to thank the Western Australian Department of Health data custodians for providing the Western Australian datasets. These datasets included the Western Australian Registry of Births, Deaths and Marriages, Emergency Department Data Collection, Hospital Morbidity Data Collection, Midwives Notification System, WA Register of Developmental Anomalies Birth Defects and the Monitoring of Drugs of Dependence System.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethical Approval

This project was approved by the WA Department of Health Human Research Ethics Committee (RGS0000003029) and the University of Western Australia Human Research Ethics Committee (RA/4/20/5530).

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: EK is supported by a National and Health Medical Research Council Fellowship. A Department of Health Western Australia Merit Award partially funded the research.

ORCID iD

Amina Rhaman

Caitlin Wyrwoll

Danielle J Russell

Erin Kelty

References

Agnew-Blais

J. C.

Polanczyk

G. V.

Danese

Wertz

Moffitt

T. E.

Arseneault

(2016). Evaluation of the persistence, remission, and emergence of attention-deficit/hyperactivity disorder in young adulthood. JAMA Psychiatry, 73(7), 713–720. https://doi.org/10.1001/jamapsychiatry.2016.0465

Ayano

Demelash

Gizachew

Tsegay

Alati

(2023). The global prevalence of attention deficit hyperactivity disorder in children and adolescents: An umbrella review of meta-analyses. Journal of Affective Disorders, 339, 860–866. https://doi.org/S0165-0327(23)00923-0. [pii]

Beckman

D. A.

Schneider

Youreneff

Tse

F. L.

(2008). Developmental toxicity assessment of d,l-methylphenidate and d-methylphenidate in rats and rabbits. Birth Defects Research Part B Developmental and Reproductive Toxicology, 83(5), 489–501. https://doi.org/10.1002/bdrb.20168

Besag

F. M.

(2014). ADHD Treatment and pregnancy. Drug Safety, 37(6), 397–408. https://doi.org/10.1007/s40264-014-0168-5

Blair

E. M.

Liu

de Klerk

N. H.

Lawrence

D. M.

(2005). Optimal fetal growth for the Caucasian singleton and assessment of appropriateness of fetal growth: An analysis of a total population perinatal database. BMC Pediatrics, 5(1), 13. https://doi.org/10.1186/1471-2431-5-13

Bowes

(2015). MapMarker 24.1 release notes.

Bro

S. P.

Kjaersgaard

M. I. S.

Parner

E. T.

Sørensen

M. J.

Olsen

Bech

B. H.

Pedersen

L. H.

Christensen

Vestergaard

(2015). Adverse pregnancy outcomes after exposure to methylphenidate or atomoxetine during pregnancy. Clinical Epidemiology, 7, 139–147. https://doi.org/10.2147/CLEP.S72906

Casey

B. M.

McIntire

D. D.

Leveno

K. J.

(2001). The continuing value of the apgar score for the assessment of newborn infants. New England Journal of Medicine, 344(7), 467–471. https://doi.org/10.1056/NEJM200102153440701

Cohen

J. M.

Hernández-Díaz

Bateman

B. T.

Park

Desai

R. J.

Gray

K. J.

Patorno

Mogun

Huybrechts

K. F.

(2017). Placental complications associated with psychostimulant use in pregnancy. Obstetrics and Gynecology, 130(6), 1192–1201. https://doi.org/10.1097/AOG.0000000000002362

10.

Cohen

J. M.

Srinivas

Furu

Cesta

C. E.

Reutfors

Karlstad

(2023). Prevalence trends and individual patterns of ADHD medication use in pregnancy in Norway and Sweden, 2010–2019. European Journal of Clinical Pharmacology, 79(1), 173–180. https://doi.org/10.1007/s00228-022-03428-6

11.

Damer

E. A.

Edens

M. A.

van der Loos

M. L. M.

van Esenkbrink

Bunkers

van Roon

E. N.

Ter Horst

P. G. J.

(2021). Fifteen years’ experience with methylphenidate for attention-deficit disorder during pregnancy: Effects on birth weight, apgar score and congenital malformation rates. General Hospital Psychiatry, 73, 9–15. https://doi.org/10.1016/j.genhosppsych.2021.09.003

12.

Faraone

S. V.

Biederman

Mick

(2006). The age-dependent decline of attention deficit hyperactivity disorder: A meta-analysis of follow-up studies. Psychological Medicine, 36(2), 159–165. https://doi.org/10.1017/S003329170500471X

13.

Freeman

M. P.

(2014). ADHD And pregnancy. American Journal of Psychiatry, 171(7), 723–728. https://doi.org/10.1176/appi.ajp.2013.13050680

14.

Grzeskowiak

L. E.

Gilbert

A. L.

Morrison

J. L.

(2012). Exposed or not exposed? Exploring exposure classification in studies using administrative data to investigate outcomes following medication use during pregnancy. European Journal of Clinical Pharmacology, 68(5), 459–467. https://doi.org/10.1007/s00228-011-1154-9

15.

Health, A. G. D. o. (2017). Western Australian Stimulant Regulatory Scheme Annual Report.

16.

Hærvig

K. B.

Mortensen

L. H.

Hansen

A. V.

Strandberg-Larsen

(2014). Use of ADHD medication during pregnancy from 1999 to 2010: A danish register-based study. Pharmacoepidemiology and Drug Safety, 23(5), 526–533. https://doi.org/10.1002/pds.3600

17.

Huybrechts

K. F.

Bröms

Christensen

L. B.

Einarsdóttir

Engeland

Furu

Gissler

Hernandez-Diaz

Karlsson

Karlstad

Kieler

Lahesmaa-Korpinen

A.-M.

Mogun

Nørgaard

Reutfors

Sørensen

H. T.

Zoega

Bateman

B. T.

(2018). Association between methylphenidate and amphetamine use in pregnancy and risk of congenital malformations: A cohort study from the international pregnancy safety study consortium. JAMA Psychiatry, 75(2), 167–175. https://doi.org/10.1001/jamapsychiatry.2017.3644

18.

Huybrechts

K. F.

Straub

Karlsson

Pazzagli

Furu

Gissler

Hernandez-Diaz

Nørgaard

Zoega

Bateman

B. T.

Cesta

C. E.

Cohen

J. M.

Leinonen

M. K.

Reutfors

Selmer

R. M.

Suarez

E. A.

Ulrichsen

S. P.

Kieler

(2023). Association of in utero antipsychotic medication exposure with risk of congenital malformations in nordic countries and the US. JAMA Psychiatry, 80(2), 156–166. https://doi.org/10.1001/jamapsychiatry.2022.4109

19.

Kalaitzopoulos

D.-R.

Chatzistergiou

Amylidi

A.-L.

Kokkinidis

D. G.

Goulis

D. G.

(2018). Effect of methamphetamine hydrochloride on pregnancy outcome: A systematic review and meta-analysis. Journal of Addiction Medicine, 12(3), 220–226. https://doi.org/10.1097/ADM.0000000000000391

20.

Karlstad

Zoëga

Furu

Bahmanyar

Martikainen

J. E.

Kieler

Pottegård

(2016). Use of drugs for ADHD among adults-a multinational study among 15.8 million adults in the Nordic countries. European Journal of Clinical Pharmacology, 72(12), 1507–1514. https://doi.org/10.1007/s00228-016-2125-y

21.

Khedagi

A. M.

Bello

N. A.

(2021). Hypertensive disorders of pregnancy. Cardiology Clinical, 39(1), 77–90.. [pii]. https://doi.org/10.1016/j.ccl.2020.09.005

22.

Kittel-Schneider

Quednow

B. B.

Leutritz

A. L.

McNeill

R. V.

Reif

(2021). Parental ADHD in pregnancy and the postpartum period - A systematic review. Neuroscience Biobehavioral Reviews, 124, 63–77. https://doi.org/10.1016/j.neubiorev.2021.01.002

23.

Kolar

Keller

Golfinopoulos

Cumyn

Syer

Hechtman

(2008). Treatment of adults with attention-deficit/hyperactivity disorder. Neuropsychiatric Disease and Treatment, 4(2), 389–403. https://doi.org/10.2147/ndt.s6985

24.

LaGasse

L. L.

Derauf

Smith

L. M.

Newman

Shah

Neal

Arria

Huestis

M. A.

DellaGrotta

Lin

Dansereau

L. M.

Lester

B. M.

(2012). Prenatal methamphetamine exposure and childhood behavior problems at 3 and 5 years of age. Pediatrics, 129(4), 681–688. https://doi.org/10.1542/peds.2011-2209

25.

Lang

Wylie

Haig

Gillberg

Minnis

(2024). Towards system redesign: An exploratory analysis of neurodivergent traits in a childhood population referred for autism assessment. PloS one, 19(1), e0296077. https://doi.org/10.1371/journal.pone.0296077

26.

Lemelin

Boukhris

Zhao

J. P.

Sheehy

Bérard

(2021). Prevalence and determinants of attention deficit/hyperactivity disorder (ADHD) medication use during pregnancy: Results from the Quebec Pregnancy/Children Cohort. Pharmacology Research and Perspectives, 9(3), e00781. https://doi.org/10.1002/prp2.781

27.

Leong

Raymond

Château

Dahl

Alessi-Severini

Falk

Bugden

Katz

(2017). Psychotropic drug use before, during, and after pregnancy: A population-based study in a Canadian cohort (2001-2013). Canadian Journal of Psychiatry, 62(8), 543–550. https://doi.org/10.1177/0706743717711168

28.

Taylor

M. J.

Bälter

Kuja-Halkola

Chen

Hegvik

T. A.

Tate

A. E.

Chang

Arias-Vásquez

Hartman

C. A.

Larsson

(2020). Attention-deficit/hyperactivity disorder symptoms and dietary habits in adulthood: A large population-based twin study in Sweden. American Journal of Medical Genetics Part B Neuropsychiatric Genetics, 183(8), 475–485. https://doi.org/10.1002/ajmg.b.32825

29.

Matheiken

Erden

Krishnadas

Pinto da Costa

(2024). Adult attention-deficit hyperactivity disorder: Time for a rethink? BJPsych Advances, 30(5), 298–302. https://doi.org/10.1192/bja.2023.54

30.

McKechnie

D. G. J.

O'Nions

Dunsmuir

Petersen

(2023). Attention-deficit hyperactivity disorder diagnoses and prescriptions in UK primary care, 2000–2018: Population-based cohort study. BJPsych Open, 9(4), e121. Article e121. https://doi.org/10.1192/bjo.2023.512

31.

Mirjalili

Kalantar

S. M.

Shams Lahijani

Sheikhha

M. H.

Talebi

(2013). Congenital abnormality effect of methamphetamine on histological, cellular and chromosomal defects in fetal mice. Iran Journal of Reproductive Medicine, 11(1), 39–46.

32.

Newport

D. J.

Hostetter

A. L.

Juul

S. H.

Porterfield

S. M.

Knight

B. T.

Stowe

Z. N.

(2016). Prenatal psychostimulant and antidepressant exposure and risk of hypertensive disorders of pregnancy. Journal of Clinical Psychiatry, 77(11), 1538–1545. https://doi.org/10.4088/JCP.15m10506

33.

Nörby

Winbladh

Källén

(2017). Perinatal outcomes after treatment with ADHD medication during pregnancy. Pediatrics, 140(6). https://doi.org/10.1542/peds.2017-0747

34.

Ornoy

Koren

(2021). The effects of drugs used for the treatment of attention deficit hyperactivity disorder (ADHD) on pregnancy outcome and breast-feeding: A critical review. Current Neuropharmacology, 19(11), 1794–1804. https://doi.org/10.2174/1570159X18666201127164000

35.

Posner

Polanczyk

G. V.

Sonuga-Barke

(2020). Attention-deficit hyperactivity disorder. Lancet, 395(10222), 450–462. https://doi.org/10.1016/S0140-6736(19)33004-1

36.

Poulton

A. S.

Armstrong

Nanan

R. K.

(2018). Perinatal outcomes of women diagnosed with attention-deficit/hyperactivity disorder: An Australian population-based cohort study. CNS Drugs, 32(4), 377–386. https://doi.org/10.1007/s40263-018-0505-9

37.

Preen

D. B.

Calver

Sanfilippo

F. M.

Bulsara

Holman

C. A. J.

(2007). Patterns of psychostimulant prescribing to children with ADHD in Western Australia: Variations in age, gender, medication type and dose prescribed. Australian and New Zealand Journal of Public Health, 31(2), 120–126. https://doi.org/10.1111/j.1753-6405.2007.00028.x

38.

Renoux

Shin

J. Y.

Dell'Aniello

Fergusson

Suissa

(2016). Prescribing trends of attention-deficit hyperactivity disorder (ADHD) medications in UK primary care, 1995-2015. British Journal of Clinical Pharmacology, 82(3), 858–868. https://doi.org/10.1111/bcp.13000

39.

Russell

A. E.

Ford

Williams

Russell

(2016). The association between socioeconomic disadvantage and attention deficit/hyperactivity disorder (ADHD): A systematic review. Child Psychiatry & Human Development, 47(3), 440–458. https://doi.org/10.1007/s10578-015-0578-3

40.

Russell

D. J.

Quintrell

Wyrwoll

C. S.

Kelty

(2025). Prevalence of the prescribing of attention-deficit hyperactivity disorder medication during pregnancy: A systematic review. Journal Psychoactive Drugs, 1–11. https://doi.org/10.1080/02791072.2025.2478085

41.

Thornton

Tooher

Ogle

von Dadelszen

Makris

Hennessy

(2016). Benchmarking the hypertensive disorders of pregnancy. Pregnancy Hypertension, 6(4), 279–284. https://doi.org/10.1016/j.preghy.2016.04.009

Maternal and Neonatal Outcomes Associated with the use of Dexamphetamine or Methylphenidate in Pregnancy: A Retrospective Cohort Study

Abstract

Objective:

Methods:

Results:

Conclusion:

Keywords

Introduction

Methods

Study Design

Study Cohort

Data Sources

Cohort Characteristics

Outcomes

Statistical Methods

Ethics

Results

Demographics

Maternal Health Outcomes

Neonatal Health Outcomes

Discussion

Maternal Health Outcomes

Neonatal Health Outcomes

Limitations

Conclusion

Lay Summary

(a) What is Already Known About the Topic?

(b) What This Paper Adds?

(c) Implications for Practice, Research or Policy

Footnotes

Acknowledgments

Declaration of Conflicting Interests

Ethical Approval

Funding

ORCID iD

References