Department of Pediatrics and Human Early Learning Partnership
Human Early Learning Partnership
Department of Psychiatry
Human Early Learning Partnership, University of British Columbia, Vancouver, British Columbia, Canada
Correspondence: Dr Tim F. Oberlander, Early Human Experience Unit, Centre for Community Child Health Research, Room L408, 4480 Oak Street, Vancouver, BC V6 3V4, Canada. E-mail: toberlander{at}cw.bc.ca
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Late-gestational serotonin reuptake inhibitor (SRI) exposure has been linked to adverse neonatal outcomes; however, the impact of timing and duration of exposure is unknown.
Aims
To determine whether late-gestational exposure to an SRI is associated with increased risk of adverse neonatal outcome relative to early exposure.
Method
Population-based maternal and neonatal health records were linked to prenatal maternal prescription records for an SRI medication (n=3500).
Results
After controlling for maternal illness and duration of exposure, using propensity score matching, neonatal outcomes did not differ between late and early exposure (P>0.05). After controlling for maternal illness, longer prenatal exposure increased the risks of lower birth weight, respiratory distress and reduced gestational age (P<0.05).
Conclusions
Using population health data, length of gestational SRI exposure, rather than timing, increased the risk for neonatal respiratory distress, lower birth weight and reduced gestational age, even when controlling for maternal illness and medication dose. These findings highlight the importance of distinguishing the specific impact of medication exposure from exposure to maternal illness itself.
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Data-set compilation
Data used in this study came from five administrative sources housed in the
British Columbia Linked Health Database (registry of births, hospital
separation records, physician billing records and the registry of Medical
Services Plan
subscribers),6
linked to PharmaNet, a province-wide network record of all prescriptions
dispensed by British Columbia pharmacists. Individuals on all files are
identified with an encrypted personal health number (PHN). The data were
processed and linked by the Centre for Health Services and Policy Research
(CHSPR) at the University of British Columbia, as described in an earlier
paper.5 The CHSPR
replaced the encrypted PHN with an anonymised study ID. Diagnosis of maternal
mood was obtained from Ministry of Health Medical Services Plan (MSP)
ICD–9 diagnostic codes that referred to
depression.7 A total
of 203 520 registered live births in British Columbia occurred between 1 April
1997 and 31 March 2002. Of these, 200 291 (98.4%) had a valid study ID that
was linked to the mothers study ID and 192 725 (96.2%) of these records
unambiguously matched hospital birth records. Of these records, 1259 were
dropped because they did not report a gestational age. Another 13 records
(less than 0.01%) were dropped because they reported estimated gestational
ages less than 22 weeks on the hospital separation record. Eight records
(again less than 0.01%) with reported gestational ages greater than 43 weeks
were top-coded at 43 weeks. Hospital separation records also contain up to 16
diagnostic/procedure codes that are provided by the physician attending during
the neonatal period. Physicians entered at least one ICD–9 diagnostic
code for 40 733 (34%) and at least two diagnostic codes for 27 192 (23%) of
the births.
To match maternal prescription records in the PharmaNet database, we restricted our analysis to records of neonates with an estimated date of conception between 1 January 1998 and 26 March 2001, reducing our sample to 120 702. To ensure that babies with long hospital stays were not underreported in our sample, we restricted our analysis to those with dates of conception before 26 March 2001, allowing 90 days between the last expected birth date and the last hospital separation date of 31 March 2002. After removing 87 records with data entry errors and 1068 records for multiple births, the study population comprised records related to 119 547 live births. To these records we linked information about maternal prescriptions for all records for SRI antidepressants, other antidepressants, benzodiazepines and antipsychotic medications dispensed between 1 January 1998 and 31 March 2002. This was derived from 363 641 records with 915 distinct drug identity numbers; 98% of these records had a unique combination of date, drug identification number and study ID, leaving 356 727 prescriptions. The file identified the drug by brand name and generic name, the date that the drug was dispensed and the number of days supplied, together with the unique study number for the mother. From the total 356 727 prescriptions we identified 75 456 prescriptions for SRIs, specifically citalopram (2.7%), fluoxetine (24.6%), fluvoxamine (4.5%), paroxetine (38.9%), sertraline (22.7%) and venlafaxine (6.6%). Prenatal exposure occurred if the period from the date the drug was dispensed until that date plus the number of days for which the drug was supplied overlapped with the pregnancy. We excluded the date of birth from the pregnancy to eliminate drugs dispensed after the infants birth.
Information on medical histories, including diagnosis of maternal mood both during pregnancy and in the 12 months before conception, was obtained from the Ministry of Health Medical Services Plan billing records. Infant birth date and length of gestation from the hospital discharge record enabled us to calculate the dates of pregnancy. In the construction of the variable for the number of days on which an SRI was taken during pregnancy, double counting was eliminated by first identifying the days covered by any prescription for an SRI and then summing the days. The implicit assumption was that when prescriptions overlapped it was because physicians had prescribed a different SRI to replace one that had not been suitable for that patient.
Study group identification
The date of conception was estimated using the date of birth and
gestational age provided in the hospital record. The first trimester was from
the estimated date of conception to day 92 of the pregnancy. The second
trimester was from day 93 to day 185 (or birth, whichever came first) and the
third trimester was from day 186 to birth. Using Chambers definition of
exposure,2 we
assigned to the early exposure group infants of mothers who discontinued the
drug in the first and/or second trimester and never resumed taking it. A late
exposure (i.e. prolonged late-gestational exposure) group included infants of
mothers who had received a prescription for an SRI during the first and/or
second trimester and continued to take the drug into the third trimester (at
least 185 days of gestation).
Neonatal outcome
On the basis of previous
work,2 four key
neonatal outcomes were identified: birth weight (in grams, and incidence of
birth weight less than the 10th percentile for gestational age); percentage
born with a gestational age below 37 weeks; length of stay in hospital greater
than 3 days; and incidence of adverse neonatal symptoms (respiratory distress,
jaundice, convulsions, feeding difficulties).
Data analysis
Two approaches to data analysis were used. Propensity score matching was
used to control for potential confounding variables such as duration of
exposure and maternal characteristics that might reflect the severity of
mental illness in our analysis of the effects of early and late exposure on
these outcomes. Logistic regression was used to estimate the relationship
between duration of exposure and the same outcomes, again controlling for
possible confounding variables. For both techniques the following maternal
characteristics (pre-pregnancy and prenatal) were controlled for:
Propensity score matching
To account for the potential confounding influences of these differences in
maternal characteristics (see Data analysis), propensity score
matching8,9
was undertaken to identify a subgroup of women in the early exposure group who
were matched for characteristics with women in the late exposure group.
Propensity score matching was carried out in three stages. First, the
parameters of a model predicting SRI exposure were estimated using maximum
likelihood probit analysis. Second, these parameters were used to calculate
the propensity score for each individual in our sample. Third, for each
late-exposed mother, an early-exposed mother with a similar propensity score
was selected for comparison purposes, without replacement. We used Stata SE
version 9 for Windows for the first two steps and FoxPro version 9 for the
third step. In this way each infant with late exposure was compared with an
infant with early exposure with a mother with similar characteristics.
Probit regression
Multivariate regression models were used to study relationships between
risk of adverse neonatal outcome and intensity of prenatal exposure in terms
of days of exposure and dosage. To account for the possible impact of maternal
illness severity a number of key maternal variables reflecting illness
severity were added to the model. A major challenge in studying the
relationship between risk of adverse neonatal outcome and length of prenatal
exposure arises because each are highly interrelated – the length of
gestation can affect the duration of exposure, and duration of exposure can
affect the length of gestation. To isolate the effect of exposure on length of
gestation we restricted our sample to the 97% of mothers with gestation
lasting more than 244 days and limited our analysis to exposure that occurred
at some point during the first 244 days of gestation. Although this method
could introduce bias (i.e. if babies with longer exposure had shorter
gestations, resulting in neonates with longer exposures being
disproportionately withdrawn from the sample) we believe that the bias would
be small because few babies in our sample were born at less than 35 weeks
gestation.
We also used regression analysis to explore the effect of maternal dosage. We developed indicators of whether the dosage was low, medium or high in two steps. First, because of differences in dose range for each SRI medication, for each medication we converted dosage to a z score based on the distribution of dispensed doses. Second, we classified the dosage as low if the z score was less than –0.5, medium if the z score was between –0.5 and 0.5 and high if the z score was greater than 0.5. For example, paroxetine was prescribed in doses of 10 mg, 20 mg and 30 mg, so the 10 mg dose was classified as low, the 20 mg dose as medium and the 30 mg dose as high. We then summed the number of days of exposure for low, medium and high doses and entered these separately into the regression equations. We performed Wald tests for equality of the three coefficients.
Regression analyses were undertaken using probit, Stata SE version 9. We estimated parameters of eight models, one for each of the outcomes reported and another eight models were used to study risk and maternal dosage.
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Propensity score matching
Importantly, characteristics of the mothers during pregnancy differed
substantially between the late exposure and early exposure groups
(Table 1). Although the women
in the two groups were similar in the year before pregnancy, during pregnancy
women in the late exposure group were diagnosed as depressed about 1.7 times
more frequently, visited a psychiatrist about 2.5 times more frequently and
had 3.6 times more days of SRI exposure than those in the early group, clearly
suggesting that women who took SRIs during their late pregnancy substantially
differed from the early exposure group in duration of exposure and in ways
that might have reflected differences in depression severity. Propensity score
matching as described above was used to ensure that a subgroup of infants with
late exposure (n=429) were comparable with those in the early
exposure group in terms of maternal
characteristics.10
In contrast to the increased risk of lower birth weight, reduced gestational
age and increased incidence of respiratory distress observed using unmatched
comparison between the late and early exposure groups
(Table 2), using
propensity-matched subgroups neonatal outcomes were similar between the late
and early exposure groups, with the exception of lower birth weights
persisting in the late exposure group. However, this difference did not remain
statistically significant after a Bonferroni correction for multiple
comparisons (P>0.05).
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View this table: [in a new window] | Table 1 Maternal characteristics: early v. late exposure groups |
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View this table: [in a new window] | Table 2 Neonatal outcomes: early v. late gestational exposure |
Effects of duration of gestational exposure
After controlling for characteristics reflecting maternal illness severity,
increased duration of exposure (days) was significantly associated with
reduced gestational age (z=4.59), decreased birth weight
(z=2.61), increased risk of respiratory distress (z=4.24),
and low birth weight for gestational age (z=2.34); P<0.01
respectively (Table 3).
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View this table: [in a new window] | Table 3 Length of gestational exposure and neonatal outcome |
Effect of maternal dosage
Regression analysis was used to examine the impact of maternal SRI dosage.
No adverse neonatal outcome was associated with either high or low dosage
(P>0.05 for each outcome). Incidentally, high dosage was
associated with an increased incidence of caesarean birth
(P=0.02).
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Impact of maternal illness
Increased risk of neonatal behavioural disturbances following late exposure
has been widely reported using cohort
studies;1 however,
none of these studies controlled for the impact of maternal depression during
pregnancy. Population-based studies using birth registries have also reported
a variety of similar
effects,11–13
but outcomes have been inconsistent. Using linked population health data,
comparing first and third trimester exposure, accounting for some maternal
characteristics (e.g. smoking), Malm reported an increased rate of admission
to special care nurseries with third-trimester
exposure,11 but
failed to demonstrate a difference in gestational age or birth weight between
trimesters. Similarly, Simon, using linked health data reported an association
between SRI exposure and lower gestational age and birth weight that was not
limited to late-gestational
exposure.12
Comparison between studies has been challenged by a number of methodological
limitations, including the lack of comparable control groups, cohort selection
bias, failure to account for concurrent exposure to other psychotropic
medication (e.g. benzodiazepines) and limited knowledge of the exact timing of
gestational exposure. Importantly, failure to account for the impact of
maternal illness has been recently cited as a critical methodological
limitation in studies examining the impact of early v. late SRI
exposure.1 Exposure
to maternal depression itself is associated with increased irritability,
decreased motor tone and poor feeding in the
neonate.14 For
logistical, ethical and medical reasons, however, it is not possible to
undertake masked randomised controlled studies of the effects of SRI exposure
limited to specific trimesters.
Previously, controlling for the concurrent impact of maternal depression in cohort studies has been challenging because of ethical, medical and logistical factors. In this study propensity score matching was specifically used to account for measured maternal characteristics that could have influenced neonatal outcomes, but could not be directly controlled for using administrative population health data. Additionally, these factors had not been accounted for in previous population health data research in this field.11–13 One of the strengths of propensity score matching is that it identifies the part of the treated group for which there is an appropriate comparison. It is possible that the women taking SRIs in our sample who used these medications for more than 90 days were likely to have been treated for considerably longer times, and this too could have influenced neonatal outcomes. We controlled for this possibility by matching for length of exposure in our propensity-matched groups. The advantages of the use of propensity score matching include avoidance of functional form assumptions that underlie regression methods and the ability to identify the part of the untreated population that can be compared with the treated population without extrapolation. In their analysis of selection bias, Heckman et al concluded that the largest part of selection bias arises from differences in the support and differences in densities over the region of common support.10 Moreover, inaccurate functional form and violations of the support condition – separately and in combination – introduce bias in regression methods, as illustrated in recent empirical work.15 Propensity score matching is a transparent method for eliminating bias due to measured confounders because the reader can verify that matching has produced a comparison group with similar characteristics to the treatment group.
Our findings suggest that when accounting for factors reflecting the character of maternal mental illness in multiple comparisons, differences in neonatal outcomes between late and early exposure no longer remained significant. These findings raise the question of whether there is empiric evidence to support the suggestion to taper the dosage of antidepressants during the last trimester to ensure that there is no drug exposure in the last 7–10 days of gestation.16 Further, our failure to detect neonatal differences between early- and late-gestational exposure when accounting for maternal illness characteristics also questions whether neonatal behavioural disturbances, such as respiratory distress, reflect a genuine pharmacological withdrawal phenomenon secondary to a sudden cessation of prenatal exposure or altered serotonin-related neurobehaviours. Instead, these findings might reflect the effects of prolonged prenatal exposure which could lead to a number of mechanisms, including neurotransmitter suppression,17 pharmacological toxicity,18 altered pulmonary vasculature,4 changes in serotonergic-related neurodevelopment19,20 or effects of maternal illness itself.5 The mechanisms that underlie our findings remain to be determined; however, there is emerging evidence from human studies suggesting that prenatal SRI exposure may be associated with changes in neonatal behaviours in ways that might reflect altered serotonin-dependent processes.19 Serotonin reuptake inhibitors block the reuptake of 5-hydroxytryptamine (5-HT), a monoamine neurotransmitter that has a key role in regulating neural growth and arousal regulation. Before it takes on its role as a neurotransmitter in the mature brain,21 5-HT also acts as a trophic signal in the developing brain by directing neural ontogeny of the serotonergic and other systems.22 As a developmental signal, 5-HT regulates prenatal neural growth and physiological processes,23 including foetal lung development. Using animal models, SRI exposure reduced central 5-HT levels and changes in serotonin transporter densities.24 In human studies direct evidence of such alterations remain to be determined; however, changes in neonatal pulmonary blood flow,4 behavioural state regulation and pain reactivity19,20 have also been observed, possibly reflecting altered 5-HT-mediated processes.
Methodological limitations
The use of administrative health data to study the effects of prenatal SRI
exposure poses a number of challenges. The concurrent use of tobacco and
alcohol during pregnancy, maternal weight gain and parity could not be
directly studied using such data. Although our sample was restricted to women
with a diagnosis of depression treated with an SRI medication, dispensed
during pregnancy, comorbid maternal conditions, which went unreported, could
also not be accounted for in our study. The level, severity or course of
prenatal depression as assessed by the womans clinician could not be
directly determined, nor could the accuracy or way in which the physician made
the mental health diagnosis be ascertained. Although we believed that because
the medication was paid for and dispensed it was actually taken, this
consumption could not be verified. Because SRI use occurred in the context of
maternal depression, we were not able to study the effects of SRI exposure
independent of exposure to depression alone or fluctuations in maternal mood
that would not have been tabulated in administrative health data.
This study was undertaken to determine the effects of timing and duration of gestational medication exposure taking into account maternal illness severity and was not directed at assessing the safety of prenatal SRI use. Although the benefits of SRI treatment during pregnancy remain to be determined, it is important to emphasise that none of these findings should diminish the urgency of recognising and treating maternal depression during pregnancy in a timely fashion which may require pharmacological and non-pharmacological strategies. The decision to start or stop SRI treatment during pregnancy should be made by an informed patient with her physician on an individual basis.25 Given that the neonatal risks associated with both early and late gestational exposure do not appear to be substantially different, the need to taper or stop the use of an antidepressant during late gestation must be weighed against the risks of undertreated maternal illness and potential relapse.
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