Published online February 1, 2008
PEDIATRICS Vol. 121 No. 2 February 2008, pp. e223-e232 (doi:10.1542/peds.2006-3629)
This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow P3Rs: Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when P3Rs are posted
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow E-mail this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My File Cabinet
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via CrossRef
Google Scholar
Right arrow Articles by Shapiro-Mendoza, C. K.
Right arrow Articles by Declercq, E.
PubMed
Right arrow PubMed Citation
Right arrow Articles by Shapiro-Mendoza, C. K.
Right arrow Articles by Declercq, E.
Related Collections
Right arrow Premature & Newborn

ARTICLE

Effect of Late-Preterm Birth and Maternal Medical Conditions on Newborn Morbidity Risk

Carrie K. Shapiro-Mendoza, PhD, MPHa, Kay M. Tomashek, MD, MPHa, Milton Kotelchuck, PhD, MPHb, Wanda Barfield, MD, MPHa, Angela Nannini, PhDc, Judith Weiss, ScDb and Eugene Declercq, PhDb

a Division of Reproductive Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia
b Department of Maternal and Child Health, Boston University School of Public Health, Boston, Massachusetts
c Massachusetts Department of Public Health, Bureau of Family and Community Health, and Northeastern University, Boston, Massachusetts


   ABSTRACT
TOP
 ABSTRACT
METHODS
 RESULTS
 DISCUSSION
 CONCLUSIONS
 REFERENCES
 
OBJECTIVES. Late-preterm infants (34–36 weeks’ gestation) account for nearly three quarters of all preterm births in the United States, yet little is known about their morbidity risk. We compared late-preterm and term (37–41 weeks’ gestation) infants with and without selected maternal medical conditions and assessed the independent and joint effects of these exposures on newborn morbidity risk.

METHODS. We used 1998–2003, population-based, Massachusetts birth and death certificates data linked to infant and maternal hospital discharge records from the Massachusetts Pregnancy to Early Life Longitudinal data system. Newborn morbidity risks that were associated with gestational age and selected maternal medical conditions, both independently and as joint exposures, were estimated by calculating adjusted risk ratios. A new measure of newborn morbidity that was based on hospital discharge diagnostic codes, hospitalization duration, and transfer status was created to define newborns with and without life-threatening conditions. Eight selected maternal medical conditions were assessed (hypertensive disorders of pregnancy, diabetes, antepartum hemorrhage, lung disease, infection, cardiac disease, renal disease, and genital herpes) in relation to newborn morbidity.

RESULTS. Our final study population included 26170 infants born late preterm and 377638 born at term. Late-preterm infants were 7 times more likely to have newborn morbidity than term infants (22% vs 3%). The newborn morbidity rate doubled in infants for each gestational week earlier than 38 weeks. Late-preterm infants who were born to mothers with any of the maternal conditions assessed were at higher risk for newborn morbidity compared with similarly exposed term infants. Late-preterm infants who were exposed to antepartum hemorrhage and hypertensive disorders of pregnancy were especially vulnerable.

CONCLUSIONS. Late-preterm birth and, to a lesser extent, maternal medical conditions are each independent risk factors for newborn morbidity. Combined, these 2 factors greatly increased the risk for newborn morbidity compared with term infants who were born without exposure to these risks.

Key Words: preterm birth • near-term infant • late-preterm infant • morbidity • maternal health

Abbreviations: HDP—hypertensive disorders of pregnancy • PELL—Pregnancy to Early Life Longitudinal • ICD-9-CM—International Classification of Diseases, Ninth Revision, Clinical Modification • GED—general equivalency diploma • cRR—crude risk ratio • aRR—adjusted risk ratio • CI—confidence interval

Preterm delivery is the most important determinant of neonatal morbidity and mortality in developed countries.1 Preterm delivery can occur spontaneously or by obstetric intervention and may or may not be a result of pregnancy complications or preexisting maternal medical conditions. In either case, infants who are born preterm are at an increased risk for newborn morbidity and mortality.2

Late-preterm births accounted for 74% of all preterm births in 2002.3 During the past decade, the proportion of late-preterm infants (34–36 weeks’ gestation) among US singleton live births has increased by 11.6%, from 6.9% in 1992 to 7.7% in 2002.3 This increase is thought to be attributable, in large part, to an increase in obstetric interventions, often resulting from maternal complications or preexisting medical conditions.4,5 A number of maternal medical conditions, including hypertensive disorders of pregnancy (HDP), diabetes, and asthma, are associated with an increased risk for indicated or spontaneous preterm birth.6 The decision to deliver an infant preterm is informed by balancing the morbidity and mortality risks that are associated with prematurity against the maternal and fetal consequences of continuing the pregnancy.6 By 34 weeks’ gestation, newborn infants have a lower risk for neonatal complications such as respiratory distress syndrome than infants who are born earlier; therefore, many of the obstetric management decisions use 34 weeks as an influential marker for assessing the potential for developing newborn complications.7

Although the risk for neonatal morbidity and mortality among preterm infants of 34 to 36 weeks’ gestation is much lower than among infants of <34 weeks’ gestation, past research on short- and long-term morbidity among late-preterm infants has been limited. Late-preterm infants, many of whom are of normal birth weight, are often treated as term infants in the normal newborn nursery, a practice that is not evidence based. Recently, the National Institutes of Health's National Institute of Child Health and Human Development7 and the Association of Women's Health, Obstetric and Neonatal Nurses8 set a research agenda to understand better short- and long-term medical complications that are associated with late-preterm births. Understanding morbidity risk among late-preterm infants is not only important for helping newborn care providers to anticipate and to manage potential morbidity during the birth hospitalization but also may possibly assist in guiding nonemergency obstetric intervention decisions and enhance knowledge of maternal care to decrease the risk for newborn morbidity.

To our knowledge, earlier studies have not comprehensively examined the association between preexisting maternal medical conditions and newborn morbidity among late-preterm infants. In this population-based study, we compared infants who were born at term (37–41 weeks’ gestation) with late-preterm infants and assessed the independent and joint effects of late-preterm birth and selected maternal medical conditions on the development of newborn morbidity during the birth hospitalization.


   METHODS
TOP
 ABSTRACT
 METHODS
RESULTS
 DISCUSSION
 CONCLUSIONS
 REFERENCES
 
We used a population-based cohort of singleton, late-preterm and term infants who were born in Massachusetts hospitals to Massachusetts residents. The data available for the analysis were from January 1, 1998, through November 30, 2003. Data were derived from the Pregnancy to Early Life Longitudinal (PELL) data system; PELL is a public-private partnership among the Boston University School of Public Health, the Massachusetts Department of Public Health, and the Centers for Disease Control and Prevention, the funding agency. The PELL data system is a longitudinally linked data system of mothers and their children from delivery and birth to early childhood. The PELL data system includes linked vital statistics records (birth and death certificates), hospital care use data, and public health program participation data. Deterministic and probabilistic methods are used to link records from the data sets using LinkPro software (InfoSoft, Inc; Winnipeg, Manitoba, Canada). For this analysis, we used birth certificate data that were linked to infant death certificates, maternal delivery, and infant birth hospital discharge data. An institutional review board at the Massachusetts Department of Public Health approved the study protocol and the use of the PELL data system for analysis.

There were 445917 singleton births to Massachusetts residents in Massachusetts hospitals between January 1, 1998, and November 30, 2003. More than 98% of the live birth certificates were linked successfully to the newborn and maternal hospital discharge records; 4657 infants were excluded because their birth certificate could not be linked to a birth hospital discharge record, and 2339 were excluded because their records could not be linked to their mother's hospital discharge record. Of the remaining 438921 births, 2007 were missing data on gestational age and 33006 were born at <34 or >41 weeks’ gestation. Our final study population was 26170 infants who were born at 34 to 36 weeks’ gestation and 377638 infants who were born at 37 to 41 weeks’ gestation.

Newborn Morbidity
Because the most common measures of newborn morbidity (temperature instability, hypoglycemia, respiratory distress, hyperbilirubinemia, prolonged hospitalization, and neonatal mortality) are limited in their ability to differentiate newborns with and without life-threatening conditions that likely require specialized care, closer monitoring, prolonged hospital stay, or future health care needs, we created a new, high-threshold measure of newborn morbidity. This measure of newborn morbidity used a combination of indicators that were available on each infant's hospital discharge record, including reported diagnoses, length of hospital stay, and transfer to another facility, as well as mortality available from the infant death certificate. Because we considered mortality as an end point on the morbidity continuum, mortality before birth hospital discharge was a criterion for classifying the presence of a morbid condition. A newborn was classified as having a morbid condition during his or her birth hospitalization when at least 1 of the following 3 criteria was met: (1) newborn hospital stay of >5 nights and any morbidity diagnostic code considered life-threatening (when non–life-threatening diagnostic codes were reported alone, newborns were not classified as having morbidity [Table 1]; n = 7322 for term infants and n = 4659 for late-preterm infants), (2) newborn hospital stay of ≤5 nights and transfer to a higher level medical facility (n = 3998 for term infants and n = 1138 for late-preterm infants), and (3) infant death before hospital discharge (n = 98 for term infants and n = 69 for late-preterm infants).


View this table:
[in this window]
[in a new window]

  		TABLE 1 Non–Life-Threatening ICD-9-CM Diagnostic Conditions and Codes From Infant Hospital Discharge Data

 
All 15 possible International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM)9 diagnosis codes that were available on each infant hospital discharge record were used to classify newborn morbidity, although no infant had >6 of these codes reported. Infants were classified as having a morbidity diagnosis when they had ≥1 ICD-9-CM diagnosis code reported on their hospital discharge record, except when all reported diagnosis codes were non–life-threatening. Infants with only non–life-threatening diagnostic codes were classified as not having morbidity (Table 1). A board-certified pediatrician (Dr Tomashek), who was blinded to the infant's gestational age to minimize the opportunity for bias, assessed every ICD-9-CM diagnostic code reported in the hospital discharge data to identify likely non–life-threatening conditions, such as benign skin conditions, polydactyly, phimosis, circumcision, and undescended testicles. Because we did not have access to original medical charts, we could not assess the validity of the codes reported on the hospital discharge data. None of the infants had morbidity codes pertaining to complications of pregnancy, childbirth, and the puerperium (ICD-9-CM codes 630–677); these codes are reserved for the maternal record only.

We chose a hospital stay of ≥6 nights as a conservative measure of morbidity; the longer the stay, the more likely an infant may have a life-threatening or serious morbidity. Also, hospital stays of >96 hours (4 days) go beyond the guidelines for uncomplicated vaginal and cesarean section deliveries; an infant who is discharged earlier than that would be less likely to have a serious condition.10 Because the time of discharge in hours is not reported on the hospital discharge data in Massachusetts, we calculated the length of stay in days by subtracting the date of birth on the birth certificate from the date of discharge on the newborn hospital discharge record.

Maternal Medical Conditions
We examined 8 maternal medical conditions that were caused by the pregnancy itself, by the pregnancy's management, or by an underlying medical condition that may be exacerbated during pregnancy. The selected preexisting maternal medical conditions and complications of pregnancy included HDP, diabetes (gestational and established), antepartum hemorrhage, acute or chronic lung disease, maternal infection, cardiac disease, renal disease, and genital herpes. We used ICD-9-CM diagnosis codes from the maternal delivery hospital discharge data or from the maternal medical risk factors that are reported on the infant birth certificate to classify these maternal conditions (Table 2). When a woman had 1 of the selected medical conditions reported on either data source, she was classified as having the condition. Because these conditions were meant to represent antepartum conditions, we excluded ICD-9-CM codes that indicated conditions that occurred postpartum.


View this table:
[in this window]
[in a new window]

  		TABLE 2 Maternal Medical Conditions Classification Based on ICD-9-CM Diagnostic Codes on the Hospital Discharge Record or a Maternal Risk or Complication on the Infant Birth Certificate

 
Measurement of all of the conditions, except maternal infection, had been validated previously against medical charts in a study conducted in Washington state.11 That study found that combining the birth certificate and hospital discharge data more accurately detected these selected conditions than a single data source when compared with medical charts. Like other studies, we combined chronic hypertension, pregnancy-induced hypertension, and eclampsia into 1 condition called HDP, and we combined gestational and established diabetes into another. Many studies have concluded that these conditions should not be separated when using medical charts and/or vital statistics data because of inaccuracies and inconsistencies of reporting across centers and providers.

Births were classified as term or late preterm on the basis of the calculated estimate of gestational age in completed weeks as reported on the birth certificate. Implausible gestational ages for birth weight were identified using a method developed by Alexander et al.12,13 Plausible values for the clinical estimate of gestational age were used when the calculated gestational age was missing or implausible.

Potential covariates that were identified for inclusion in the final multivariable models were based on theoretical and known risk factors for newborn morbidity from the maternal and child health epidemiologic literature and the availability of variables in the PELL data system. Covariates were derived from the birth certificate and included infant gender (male/female), maternal education (no high school diploma or general equivalency diploma [GED], high school diploma or GED, or any post–high school education), maternal age in years (<20, 20–24, 25–29, 30–34, 35–39, 40–44, or ≥45 years), parity (1, 2, 3, or ≥4), race/ethnicity (non-Hispanic white, Hispanic, non-Hispanic black, Asian/Pacific Islander, or other), maternal smoking during pregnancy (yes/no), and payer at time of delivery (public insurance, private insurance, or self-pay).

We estimated the risk for newborn morbidity that was associated with gestational age and select maternal medical conditions, both independently and as joint exposures. We considered the method of delivery (vaginal or cesarean section) and obstetric interventions (induction and/or augmentation) as variables in the intervening (causal) pathway between maternal condition and newborn morbidity and, therefore, did not adjust or stratify for these variables in our analyses.

Joint effects were assessed on an additive scale by creating 4 indicator variables of different categories or combinations of joint exposure for each of the 8 selected maternal conditions: (1) term birth and no maternal condition (RR00, the reference category), (2) term birth and a maternal condition (RR01), (3) late-preterm birth and no maternal condition (RR10), and (4) late-preterm birth and a maternal condition (RR11).14–17 We calculated the crude and adjusted risk ratios (cRR and aRR, respectively) and the 95% confidence intervals (CIs) for each of the 8 maternal conditions, contrasting each of the 3 exposure categories to the reference category, using a modified Poisson regression model.18 On the additive scale, the joint effect of being born late preterm and having a mother with 1 of the selected medical conditions was quantified by determining whether the additive RR of having both conditions (RR11) was greater than the independent RR of having either condition alone (ie, RR10 or RR01)15: (RR11 – RR00) = (RR10 – RR00) + (RR01 – RR00).

Analyses were conducted by using Stata 9.0 statistical software (Stata Corp, College Station, TX). To take into account possible nonindependence among siblings in our data set who have the same mother, we used the cluster and robust options in Stata to adjust the ratio estimates and the SEs.19 We interpreted our results using SEs that yielded robust variance estimates even when the independent working correlation in the model was not specified correctly.


   RESULTS
TOP
 ABSTRACT
 METHODS
 RESULTS
DISCUSSION
 CONCLUSIONS
 REFERENCES
 
Overall, 22.2% of the 26170 late-preterm infants and 3.0% of the 377638 term infants had morbidity during their birth hospitalization (Fig 1). The morbidity rates were ~3% (2.5%–3.3%) among the infants between 38 and 41 weeks’ gestation and then approximately doubled for each additional gestational week earlier than 38 weeks, from 5.9% morbidity at 37 weeks’ gestation to 51.7% morbidity at 34 weeks’ gestation. The proportion of morbidity was the lowest for the infants who were born at 39 and 40 weeks’ gestation, 2.6% and 2.5%, respectively. The infants who were born at 34 weeks’ gestation had 20 times (RR: 20.6; 95% CI: 19.7–21.6) the risk for morbidity compared with the infants who were born at 40 weeks’ gestation; the infants who were born at 35 and 36 weeks’ gestation had 10 times (RR: 10.2; 95% CI: 9.7–10.8) and 5 times (RR: 4.8; 95% CI: 4.6–5.1) the risk for morbidity, respectively (Fig 1).


Figure 1
View larger version (15K):
[in this window]
[in a new window]

  		FIGURE 1 Proportion with newborn morbidity during birth hospitalization according to gestational age.

 
Sociodemographic characteristics of term and late-preterm infants and their mothers are compared in Table 3. The majority of term and late-preterm infants had mothers who were 25 to 34 years of age, primiparous, non-Hispanic white, nonsmoker during pregnancy, and privately insured at the time of delivery and had more than a high school education. Late-preterm infants were slightly more likely to be male and racial/ethnic minorities and to have mothers who were less educated, younger, primiparae or grand multiparae, and smokers and received public insurance at the time of delivery.


View this table:
[in this window]
[in a new window]

  		TABLE 3 Select Characteristics of Term and Late-Preterm Newborns and Proportion With Morbidity During Birth Hospitalization: Massachusetts, 1998–2003

 
Table 3 also compares sociodemographic characteristics that are associated with morbidity risk among late-preterm and term infants. The proportion of morbidity among late-preterm infants was relatively high across the board, ranging from 18.1% to 27.8%, whereas term infants had morbidity proportions that were significantly lower in the 2% to 5% range. Among both term and late-preterm infants, the proportion with morbidity was higher among infants who were male or had mothers who were ≥40 years of age, were primiparae or grand multiparae, smoked during pregnancy, or were self-pay at delivery.

There were also some clinically meaningful differences between these 2 groups of infants. For example, among the late-preterm group, there were few newborn morbidity differences by educational level, young maternal age, or race/ethnicity, whereas the morbidity proportion was higher for term infants with less educated and younger mothers. The highest proportion of morbidity among late-preterm infants occurred in those who were non-Hispanic white and Hispanic but among term infants in those who were Hispanic and non-Hispanic black.

Table 4 shows the proportion of newborn morbidity for each of the 8 maternal medical conditions for both term and late-preterm infants. Most mothers had no reported maternal conditions, 79% and 65% among late-preterm and term infants, respectively (data not shown). At the same time, few mothers had >1 of the selected maternal conditions, 6.8% and 2.9% among late-preterm term infants, respectively. The 2 most frequently reported medical conditions among mothers of term and late-preterm infants were HDP and diabetes. The third leading condition was antepartum hemorrhage for late-preterm infants and maternal infection for the term infants. Overall, 22.2% of late-preterm infants and 3.0% of term infants were reported to have newborn morbidity during the birth hospitalization, a sevenfold difference (aRR: 6.9; 95% CI: 6.7–7.1), regardless of maternal medical condition exposure.


View this table:
[in this window]
[in a new window]

  		TABLE 4 Independent and Joint Risk for Infant Morbidity During Birth Hospitalization According to Gestational Age Group and Exposure to Selected Maternal Medical Conditions: Massachusetts, 1998–2003

 
As expected, the proportion of offspring with newborn morbidity increased with a greater number of maternal morbidities reported. Among late-preterm infants with newborn morbidity, 17.9% had no maternal conditions reported, 28.7% had at least 1 maternal condition reported, and 36.6% had ≥2 maternal conditions reported. For term infants with newborn morbidity, 2.5% had no maternal conditions reported, 4.6% had at least 1 maternal condition reported, and 7.6% had ≥2 maternal conditions reported (data not shown).

In assessing select maternal conditions as independent exposures, both the late-preterm and term infants had a significantly increased risk for morbidity for each maternal condition when it was present versus when it was not, except for genital herpes (Table 4). The magnitude of the differences between the proportions with newborn morbidity in late-preterm infants compared with term infants who were exposed to mothers who had similar maternal conditions ranged from 2 to 5 times smaller. For example, for HDP, the proportion of newborns with morbidity was 2.4 (ie, 5.2–2.8) for term infants and 12.1 (ie, 32.5–20.4) for late-preterm term infants. The independent effect of being a late-preterm birth on newborn morbidity was approximately sevenfold stronger than the independent effect of any of the selected maternal medical conditions. For each model, adjustment for potential confounders did not substantially affect the results; the cRRs and aRRs were nearly the same for all models.

When we assessed the combined or joint effect of being a late-preterm infant and exposure to each of the selected maternal conditions, we observed a greater than additive effect (interaction) in the aRR for morbidity for each maternal condition, except for maternal infection (Table 4). This was especially true for antepartum hemorrhage and HDP. For example, late-preterm newborns who were exposed to maternal antepartum hemorrhage were 12 times more likely to have morbidity during birth hospitalization (aRR: 12.3; 95% CI: 11.5–13.1) than term infants with no exposure. On an additive scale, the expected joint effect of late-preterm birth and maternal antepartum hemorrhage was an aRR of 7.1 (ie, [(2.4–1.0) + (6.7–1.0)]), whereas the observed combined effect was an aRR of 11.3 (ie, [12.3–1.0]). Thus, the joint effect of late-preterm birth and maternal antepartum hemorrhage was greater than additive. Similarly, late-preterm newborns who were exposed to HDP were 11 times more likely to have morbidity during birth hospitalization (aRR: 10.9; 95% CI: 10.4–11.5) than term infants with no exposure, with an expected joint effect of an aRR of 6.5; again a greater than additive effect. Of interest, genital herpes exposure seemed to have no effect on the morbidity for term infants but a positive effect for late-preterm infants who were exposed; however, only 13 cases of late-preterm infants with genital herpes exposure were reported in our data.


   DISCUSSION
TOP
 ABSTRACT
 METHODS
 RESULTS
 DISCUSSION
CONCLUSIONS
 REFERENCES
 
Approximately 1 (22%) in 5 late-preterm and 1 (3%) in 33 term infants in Massachusetts from 1998 to 2003 experienced morbidity during their birth hospitalization. Our study found that late-preterm birth and maternal medical condition exposure are each independent risk factors for newborn morbidity during birth hospitalization; however, late-preterm birth is the stronger risk factor. The risk for newborn morbidity was greater than additive when both risk factors were present compared with when neither exposure was present. Late-preterm infants with mothers who had antepartum hemorrhage and HDP had the highest risks for newborn morbidity.

There are several plausible explanations for the increased risk for late-preterm birth among women with chronic medical conditions, such as chronic renal or lung disease, or maternal cardiac conditions. First, with increases in maternal age and life expectancy for individuals with chronic disease, more women with chronic medical disease are becoming pregnant.20 Maternal medical conditions often worsen during pregnancy, necessitating a medical intervention that results in an early delivery.21 Second, it is possible that chronic medical conditions physiologically alter a person's ability to support a term pregnancy. For example, a recent study found that endothelial progenitor cells were altered in patients with long-term kidney disease. These cells may have an important role in angiogenesis and human pregnancy.22 Third, medications taken by women with chronic medical conditions may affect birth outcomes. A recent study of pregnant women with asthma found that those who took oral steroids were more likely to have an infant born with low birth weight or preterm compared with those who did not take oral steroids,23 and infants of women with asthma were more likely to have transient tachypnea of the newborn compared with control subjects.24 Additional studies are needed to understand the underlying mechanisms associated with poor birth outcomes and infant morbidity among women with chronic disease.

Several previous studies found that late-preterm infants are at higher risk than term infants for developing medical complications that result in higher rates of mortality and morbidity during the birth hospitalization, such as temperature instability, hypoglycemia, respiratory distress, hyperbilirubinemia, prolonged hospitalization, and neonatal mortality.1,25–33 Ours is the first, however, to explore the independent and joint effect of late-preterm birth and selected maternal medical condition exposure on the development of newborn morbidity.

This study builds on our previous studies that assessed the role of early discharge of late-preterm infants using this same Massachusetts population-based cohort. In the first study,25 we assessed risk factors for neonatal morbidity and found that late-preterm infants with early hospital discharge (<2-night hospital stay after a vaginal delivery) were more likely to be first born, breastfeeding at discharge, and of Asian and Pacific Islander descent and to have mothers who experienced labor and delivery complications and were recipients of public health insurance. In our second study comparing rates of postdischarge neonatal morbidity between singleton late-preterm and term infants who were discharged early,26 we found that late-preterm infants who were discharged early had more neonatal morbidity reported than did term infants who were discharged early.

Unlike previous studies, we applied a new, more specific case definition of newborn morbidity that excluded non–life-threatening morbid conditions. Our definition of morbidity allows for the identification of late-preterm and term infants who are most likely to require specialized care and closer monitoring. Identifying infants who are at the highest risk for life-threatening morbidity using this improved definition should be important for informing clinical decisions. Evaluating the effect of maternal morbidity on specific newborn conditions such as respiratory distress syndrome, persistent pulmonary hypertension, sepsis, and jaundice among late-preterm infants is important and a critical issue, and it should be the subject of future research.

Our study has several other strengths. First, we used data derived from a large population-based linked data system that drew from birth certificate and hospital discharge data with nearly complete record linkage of data sources. Second, there were multiple years of data from multiple institutions. Third, we improved our ability to detect accurately maternal conditions by using data from both birth certificate and maternal hospital discharge records for several of the select maternal medical conditions.11 For example, when the condition was found in either data source, it was included.

Despite the improved method and use of population-based data, our study has some limitations. Like other studies that rely on vital statistics and administrative data, we were limited to routinely collected data and were unable to assess adequately other important covariates, such as severity and extent of neonatal illness,34 NICU admission, duration of mechanical ventilation, severity and management of maternal conditions, and neonatal developmental problems. More important, the data cannot adequately provide insight into decisions that are related to the timing of delivery and whether the preterm birth was the result of an obstetric intervention aimed at protecting the life of the mother, fetus, and/or the infant. Future studies that examine these issues in clinical settings will better be able to inform clinical decision-making, especially regarding the ideal time of delivery.

Another limitation relating to reliance on vital statistics and administrative data is misclassification. First, health care providers may have inconsistently and inaccurately reported maternal and newborn medical conditions in the medical charts, which then affects how the coders, who rely on medical chart documentation, assign ICD-9-CM diagnostic codes.35–40 It is also possible that coding practices could have been influenced by reimbursement policies (eg, the ICD-9-CM codes are used for reimbursement of health care services). This may lead to overreporting of morbidities41; however, because diagnostic classifications and coding practices likely vary by institution and over time, misclassification and inaccurate coding of medical conditions would result in a nondifferential misclassification bias. Nevertheless, if late-preterm infants (or mothers with preexisting medical conditions) have more diagnoses because they are more closely monitored as a result of their higher morbidity risk, then maternal and infant diagnoses may be misclassified differentially, thereby producing a bias that would exaggerate the strength of any association. Last, although we attempted to improve the validity of the gestational age from the birth certificate data by excluding implausible birth weight and gestational age combinations using established methods,42,43 inconsistent reporting of gestational age could bias our findings. If vital statistics data overestimates preterm births by misclassifying term infants as late-preterm infants, or, similarly, if preterm infants were misclassified as term infants, then our estimates may be biased toward the null; however, because our associations were so strong, it seems unlikely that these biases would have substantially altered our basic findings.

Ideally, we would have liked to have distinguished chronic medical conditions from pregnancy-related medical conditions (eg, chronic hypertension from pregnancy-induced hypertension or preeclampsia, established diabetes from gestational diabetes); however, like many other published studies that relied on medical charts data, we were not able to separate them with a high degree of accuracy. Several hospital medical chart validation studies have demonstrated inconsistent and inaccurate reporting practices of these conditions.35,39,40 In addition, even with improved medical chart data, it would be difficult to distinguish chronic from pregnancy-induced disorders because women who do not frequently access the health care system are not screened for chronic conditions such as hypertension and diabetes.


   CONCLUSIONS
TOP
 ABSTRACT
 METHODS
 RESULTS
 DISCUSSION
 CONCLUSIONS
REFERENCES
 
Although late-preterm infants account for nearly three quarters of all preterm births in the United States, until recently, most research has focused on extremely preterm infants.3 Our study findings indicate that the risk for newborn morbidity increases twofold with each earlier week of gestation, beginning at 38 weeks’ gestation until 34 weeks’ gestation. Late-preterm infants are at greater risk for newborn morbidity than term infants, especially when maternal morbidity also is present. This risk seems to be particularly intensified when an infant has had prenatal maternal exposure to HDP and antepartum hemorrhage. Decisions regarding the early delivery of late-preterm infants should be weighed against the risks for neonatal and maternal morbidity. Moreover, knowledge of this increased risk among late-preterm infants is especially important when considering earlier delivery among women with certain medical conditions.

Greater awareness of the increased risk for newborn morbidity associated with late-preterm birth combined with exposure to a maternal medical condition may help health care providers manage potential complications and anticipate staffing needs and bed space.6 Standards of care and protocols that are developed for term infants may not be appropriate for late-preterm infants; higher risk infants require closer monitoring to identify and treat life-threatening conditions as early as possible. Because some maternal medical conditions are potentially preventable and/or amenable to treatment, early recognition and better treatment of women with chronic and pregnancy-related health conditions may decrease the rates of newborn morbidity in all infants but especially in late-preterm infants.


   ACKNOWLEDGMENTS
 
We thank William M. Callaghan, a board-certified obstetrician-gynecologist and researcher at the Centers for Disease Control and Prevention, for consultation about the antenatal maternal medical conditions assessed in this study and for thoughtful comments and feedback. We also thank the following current and former PELL team members for long hours and dedication to the linkage of the PELL data sets and for ensuring the quality of the linkages: Mary Barger, Howard Cabral, Mark McLaughlin, Karen Clements, Hafsatou Diop, Stephen Evans, Penny Liu, Emily Lu, Jane Lazar, Jessica Taubner, and Nancy Wilber.


   FOOTNOTES
 
Accepted Jun 19, 2007.

Address correspondence to Carrie K. Shapiro-Mendoza, PhD, MPH, Maternal and Infant Health Branch, Division of Reproductive Health, Centers for Disease Control and Prevention, Mail Stop K-23, 4770 Buford Hwy NE, Atlanta, GA 30341-3717. E-mail: ayn9{at}cdc.gov

The authors have indicated they have no financial relationships relevant to this article to disclose.

The findings and conclusions in this report are those of the authors and do not necessarily represent the views of the funding agency.


   REFERENCES
TOP
 ABSTRACT
 METHODS
 RESULTS
 DISCUSSION
 CONCLUSIONS
 REFERENCES
 

  1. Kramer MS, Demissie K, Yang H, Platt RW, Sauvé R, Liston R. The contribution of mild and moderate preterm birth to infant mortality. Fetal and Infant Health Study Group of the Canadian Perinatal Surveillance System. JAMA. 2000;284 (7):843 –849[Abstract/Free Full Text]
  2. Klebanoff MA. Conceptualizing categories of preterm birth. Prenat Neonatal Med. 1998;3 (1):13 –15[ISI]
  3. Davidoff MJ, Dias T, Damus K, et al. Changes in the gestational age distribution among U.S. singleton births: impact on rates of late preterm birth, 1992 to 2002 [published correction appears in Semin Perinatol. 2006;30(1):313]. Semin Perinatol. 2006;30 :8 –15[CrossRef][ISI][Medline]
  4. Villar J, Abalos E, Carroli G, et al. Heterogeneity of perinatal outcomes in the preterm delivery syndrome. Obstet Gynecol. 2004;104 (1):78 –87[Abstract/Free Full Text]
  5. Joseph KS. Obstetric intervention, stillbirth, and preterm birth. Semin Perinatol. 2002;26 (4):250 –259[CrossRef][ISI][Medline]
  6. Institute of Medicine, Committee on Understanding Premature Birth and Assuring Healthy Outcomes, Board on Health Sciences Policy, Behrman RE, Butler AS, eds. Preterm Birth: Causes, Consequences, and Prevention. Washington, DC: National Academies Press; 2007
  7. Raju TN, Higgins RD, Stark AR, Leveno KJ. Optimizing care and outcome for late-preterm (near-term) infants: a summary of the workshop sponsored by the National Institute of Child Health and Human Development. Pediatrics. 2006;118 (3):1207 –1214[Abstract/Free Full Text]
  8. Medoff-Cooper B, Bakewell-Sachs S, Buus-Frank ME, et al. Near-Term Infant Advisory Panel. The AWHONN near-term infant initiative: a conceptual framework for optimizing health for near-term infants. J Obstet Gynecol Neonatal Nurs. 2005;34 (6):666 –671[CrossRef][ISI][Medline]
  9. World Health Organization, Collaborating Centres for Classification of Diseases. International Statistical Classification of Diseases and Related Health Problems. 10th revised ed. Geneva, Switzerland: World Health Organization; 1992
  10. American Academy of Pediatrics, Committee on Fetus and Newborn. Hospital stay for healthy term newborns. Pediatrics. 2004;113 (5):1434 –1436[Abstract/Free Full Text]
  11. Lydon-Rochelle MT, Cardenas V, Nelson JL, Tomashek KM, Mueller BA, Easterling TR. Validity of maternal and perinatal risk factors reported on fetal death certificates. Am J Public Health. 2005;95 (11):1948 –1951[Abstract/Free Full Text]
  12. Alexander GR, Himes JH, Kaufman RB, Mor J, Kogan M. A United States national reference for fetal growth. Obstet Gynecol. 1996;87 (2):163 –168[Abstract]
  13. Alexander GR, Allen MC. Conceptualization, measurement, and use of gestational age: I—clinical and public health practice. J Perinatol. 1996;16 (1):53 –59[Medline]
  14. Rothman KJ, Greenland S. Modern Epidemiology. 2nd ed. Philadelphia, PA: Lippincott, Williams & Wilkins; 1998
  15. Rothman KJ. Epidemiology: An Introduction. New York, NY: Oxford University Press; 2002
  16. Rothman KJ. Concepts of interaction. Am J Epidemiol. 1980;112 (4):467 –470[Free Full Text]
  17. Pearce N. Analytical implications of epidemiological concepts of interaction. Int J Epidemiol. 1989;18 (4):976 –980[Abstract/Free Full Text]
  18. Zou G. A modified poisson regression approach to prospective studies with binary data. Am J Epidemiol. 2004;159 (7):702 –706[Abstract/Free Full Text]
  19. Zeger SL, Liang KY. Longitudinal data analysis for discrete and continuous outcomes. Biometrics. 1986;42 (1):121 –130[CrossRef][ISI][Medline]
  20. Gyi KM, Hodson ME, Yacoub MY. Pregnancy in cystic fibrosis lung transplant recipients: case series and review. J Cyst Fibros. 2006;5 (3):171 –175[CrossRef][ISI][Medline]
  21. Ananth CV, Vintzileos AM. Maternal-fetal conditions necessitating a medical intervention resulting in preterm birth. Am J Obstet Gynecol. 2006;195 (6):1557 –1563[CrossRef][ISI][Medline]
  22. Robb AO, Mills NL, Newby DE, Denison FC. Endothelial progenitor cells in pregnancy. Reproduction. 2007;133 (1):1 –9[Abstract/Free Full Text]
  23. Schatz M, Dombrowski MP, Wise R, et al. The relationship of asthma medication use to perinatal outcomes. J Allergy Clin Immunol. 2004;113 (6):1040 –1045[CrossRef][ISI][Medline]
  24. Demissie K, Marcella SW, Breckenridge MB, Rhoads GG. Maternal asthma and transient tachypnea of the newborn. Pediatrics. 1998;102 (1 pt 1):84 –90[Abstract/Free Full Text]
  25. Shapiro-Mendoza CK, Tomashek KM, Kotelchuck M, Barfield W, Weiss J, Evans S. Risk factors for neonatal morbidity and mortality among "healthy," late preterm newborns. Semin Perinatol. 2006;30 (2):54 –60[CrossRef][ISI][Medline]
  26. Tomashek KM, Shapiro-Mendoza CK, Weiss J, et al. Early discharge among late preterm and term newborns and risk of neonatal morbidity. Semin Perinatol. 2006;30 (2):61 –68[CrossRef][ISI][Medline]
  27. Wang ML, Dorer DJ, Fleming MP, Catlin EA. Clinical outcomes of near-term infants. Pediatrics. 2004;114 (2):372 –376[Abstract/Free Full Text]
  28. Escobar GJ, Joffe S, Gardner MN, et al. Rehospitalization in the first two weeks after discharge from the neonatal intensive care unit. Pediatrics. 1999;104 (1). Available at: www.pediatrics.org/cgi/content/full/104/1/e2
  29. Escobar GJ, Gonzales VM, Armstrong MA, Folck BF, Xiong B, Newman TB. Rehospitalization for neonatal dehydration: a nested case-control study. Arch Pediatr Adolesc Med. 2002;156 (2):155 –161[Abstract/Free Full Text]
  30. Escobar GJ, Greene JD, Hulac P, et al. Rehospitalisation after birth hospitalisation: patterns among infants of all gestations. Arch Dis Child. 2005;90 (2):125 –131[Abstract/Free Full Text]
  31. Escobar GJ, Clark RH, Greene JD. Short-term outcomes of infants born at 35 and 36 weeks gestation: we need to ask more questions. Semin Perinatol. 2006;30 (1):28 –33[CrossRef][ISI][Medline]
  32. Escobar GJ, McCormick MC, Zupancic JA, et al. Unstudied infants: outcomes of moderately premature infants in the neonatal intensive care unit. Arch Dis Child Fetal Neonatal Ed. 2006;91 (4):F238 –F244[Abstract/Free Full Text]
  33. Gilbert WM, Nesbitt TS, Danielsen B. The cost of prematurity: quantification by gestational age and birth weight. Obstet Gynecol. 2003;102 (3):488 –492[Abstract/Free Full Text]
  34. Richardson DK, Gray JE, McCormick MC, Workman K, Goldmann DA. Score for neonatal acute physiology: a physiologic severity index for neonatal intensive care. Pediatrics. 1993;91 (3):617 –623[Abstract/Free Full Text]
  35. Geller SE, Ahmed S, Brown ML, Cox SM, Rosenberg D, Kilpatrick SJ. International Classification of Diseases-9th Revision coding for preeclampsia: how accurate is it? Am J Obstet Gynecol. 2004;190 :1629 –1633; discussion 1633–1634[CrossRef][ISI][Medline]
  36. Parrish KM, Holt VL, Connell FA, Williams B, LoGerfo JP. Variations in the accuracy of obstetric procedures and diagnoses on birth records in Washington State, 1989. Am J Epidemiol. 1993;138 (2):119 –127[Abstract/Free Full Text]
  37. Centers for Disease Control and Prevention. Use of hospital discharge data to monitor uterine rupture: Massachusetts, 1990–1997. MMWR Morb Mortal Wkly Rep. 2000;49 (12):245 –248[Medline]
  38. Strom BL. Data validity issues in using claims data. Pharmacoepidemiol Drug Saf. 2001;10 (5):389 –392[CrossRef][ISI][Medline]
  39. Higgins JR, de Swiet M. Blood-pressure measurement and classification in pregnancy. Lancet. 2001;357 (9250):131 –135[CrossRef][ISI][Medline]
  40. Brown MA, Buddle ML. What's in a name? Problems with the classification of hypertension in pregnancy. J Hypertens. 1997;15 (10):1049 –1054[CrossRef][ISI][Medline]
  41. Public Health Service and Health Care Financing Administration. International Classification of Diseases, 9th Revision, Clinical Modification. Washington, DC: Public Health Service; 1997
  42. Alexander GR, Tompkins ME, Petersen DJ, Hulsey TC, Mor J. Discordance between LMP-based and clinically estimated gestational age: implications for research, programs, and policy. Public Health Rep. 1995;110 (4):395 –402[ISI][Medline]
  43. Mustafa G, David RJ. Comparative accuracy of clinical estimate versus menstrual gestational age in computerized birth certificates. Public Health Rep. 2001;116 (1):15 –21[CrossRef][ISI][Medline]
PEDIATRICS (ISSN 1098-4275). ©2008 by the American Academy of Pediatrics



This article has been cited by other articles:


Home page
 PediatricsHome page
T. N.K. Raju
Late-Preterm Births: Challenges and Opportunities
Pediatrics, February 1, 2008; 121(2): 402 - 403.
[Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow P3Rs: Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when P3Rs are posted
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow E-mail this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My File Cabinet
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via CrossRef
Google Scholar
Right arrow Articles by Shapiro-Mendoza, C. K.
Right arrow Articles by Declercq, E.
PubMed
Right arrow PubMed Citation
Right arrow Articles by Shapiro-Mendoza, C. K.
Right arrow Articles by Declercq, E.
Related Collections
Right arrow Premature & Newborn