Document Type : Original Article
Authors
1 Department of Biostatistics and Epidemiology, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran
2 Modeling of Noncomunicable Disease Research Center, Department of Biostatistics, Faculty of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran
3 Department of Biostatistics, Proteomics Research Center, School of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
4 4Research Center for Health Sciences, Department of Biostatistics, Faculty of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran
5 5Department of Epidemiology and Reproductive Health, Reproductive Epidemiology Research Center, Royan Institute for Reproductive Biomedicine, ACECR, Tehran, Iran
Abstract
Keywords
World Health Organization definitions on gestational
age state that a baby is preterm if born before
37 weeks of gestation, full term if born between 37
through 42 weeks, and late or postterm if born after 42
weeks from the first day of the women’s last menstrual
period (
Preterm birth may result from risk factors that include
multiple pregnancy, infection, advanced maternal age,
short interval between pregnancies, low maternal body
mass index (BMI), poor maternal nutrition, the use
of assisted reproductive technology (ART), maternal
psychological health, and lifestyle (
Currently, abnormal gestational age is more common
due to the increased rate of multiple births, use of
ART, and higher numbers of obstetric interventions (
Birth weight, or the first weight of a newborn baby,
can be categorized as normal (=2.5 to < 4.0 kg), low
(<2.5 kg), and macrosomia (=4.0 kg) (
Macrosomia is associated with an increased risk of
cesarean delivery, prolonged labor, perinatal trauma,
maternal diabetes and obesity, excessive weight gain,
male infant sex, prolonged gestation, high maternal
age, multiparity, and postpartum hemorrhage. Adverse
outcomes of macrosomia for the infant include dystocia,
birth injury, or death. In addition, these children
will be at risk for diabetes and obesity later in life (
Cluster data are widely recorded in medical and
clinical areas where the cases are nested in clusters.
For instance, students may be clustered in schools. In
contrast to the traditional statistical approaches, multilevel
models take the correlation among subjects in
the same cluster into account (
The detrimental outcomes after abnormalities in birth weight and gestational age are well discussed in the literature. However, regarding the association between these two variables, it is very important to predict the classes of birth weight and gestational age using their potential risk factors. Hence, the current study aims to model birth weight and gestational age jointly for the data from maternity clinic centers in Tehran province by applying a joint multilevel multiple logistic regression model.
We conducted this cross-sectional study on 4415 fertile women who referred to maternity clinic centers in Tehran Province, Iran, from July 6-21, 2015. These centers are supervised by the following universities located in Tehran, Iran: Tehran University of Medical Sciences, Shahid Beheshti University of Medical Sciences, Iran University of Medical Science, and Islamic Azad University School of Medicine.
The Ethical Committee of Royan Institute approved our study. Patients received a clear explanation of the study goals as well as assurances for data confidentiality. The participants were assured that their choice to participate in the study would not affect their treatment procedures. Voluntary completion of the questionnaire was considered to be written informed consent.
The instrument used in this survey was based on a checklist that consisted of the mothers’ demographics and information about midwifery and the infant. We completed the checklist by interviewing the mother; medical files in the delivery room were checked by a midwife and well-trained nurse. The checklist contained information about the maternal and paternal age (years), socioeconomic status (SES), mother’s BMI (kg/m2), baby’s head circumference (cm), parity (1 and ≥ 2), education of mother (undergraduate and graduate), mother’s occupation (housewife or employed), type of pregnancy (wanted, unwanted), history of abortion (yes or no), history of stillbirth (yes or no), preeclampsia (yes or no), multiple pregnancy (yes or no), and ART (yes or no).
i. Birth weight (g): LBW (<2500 g), normal birth weight (2500-4000 g) and macrosomia (≥4000 g) and ii. Gestational age (weeks) at birth: preterm birth (<37 weeks), term birth (37-42 weeks) and postterm birth (≥42 weeks of gestation).
We performed a joint multilevel multiple logistic regression model (
In the model, y1iJ and y2iJ are the gestational age and birth weight for subject j at hospital I, respectively. Both x and z are predictor variables of the response variables. â and á are the estimated coefficient vectors that correspond to the predictors. The terms u1i and u2i are the random intercepts (hospital specific effects). ù and è are the thresholds of each response variable category (c). The reference level for gestational age was “term” as well as “normal weight” for birth weight. According to the logit link function, eâ (eá) indicates the OR of LBW (preterm)/macrosomia (postterm) to normal weight (term) for X=x+1 compared to X=x. A 95% confidence interval (CI) that contained 1 indicated a P>0.05 and a non-significant effect size. Simple univariate multilevel nominal logistic regression models were separately applied for predictors. Those variables with P<0.20 were used in the joint multilevel multiple logistic regression model.
The data analysis was carried out recruiting the PROC NLMIXED in SAS software version 9.2 (SAS Institute, Inc.). A P<0.05 was considered significant. Two-sided tests were run for the statistical hypothesis.
We observed the following prevalence rates for preterm (5.5%), term (94%), and postterm (0.5%). LBW had a prevalence rate of 4.8%. The prevalence rate for normal weight was 92.4 and 2.8% for macrosomia. The independent chi-square test exposed a strong association between gestational age and weight at birth (Pearson chi-square=940.308, df=4, P<0.001).
The distribution of cases’ characteristics in three
groups of gestational age and weight at birth are illustrated
(Tables
The joint multilevel multiple logistic regression
model analysis adjusted the association between
gestational age and birth weight as well as the interaction
among several predictors (
We assessed infant weight at birth, mother’s age, mother’s BMI, mother’s education, parity, history of stillbirth, multiple pregnancy, preeclampsia, and infant sex as the candidate affective predictors by the univariate models. According to 95% CI, parity, multiple pregnancy, and preeclampsia had a statistical association with LBW. Macrosomia showed a significant association with mother’s BMI and infant sex. Mothers with more than two pregnancies were less likely to deliver a child with LBW (OR=0.59, 95% CI: 0.42-0.82). The patients with multiple pregnancy were 17.35 (95% CI: 9.73-30.94) times more prone for LBW than normal weight. Children from mothers with preeclampsia were 3.36 (95% CI: 2.15- 5.24) times more likely to experience LBW compared to normal mothers. The OR of macrosomia to normal weight for a mother with a higher BMI was 1.02 (95% CI: 1.01-1.04). Macrosomia was more common among male infants compared to females (OR= 1.78, 95% CI: 1.21-2.60).
Distribution of patients’ characteristics in the preterm, term and postterm groups
Variable | Preterm n=244 | Term n=4149 | Postterm n=22 | P value | |
---|---|---|---|---|---|
Mother’s age (Y) | 30.51 ± 5.95 | 29.11 ± 5.29 | 26.86 ± 6.36 | <0.001 | |
Father’s age (Y) | 34.64 ± 6.29 | 33.43 ± 5.73 | 33.05 ± 7.11 | 0.006 | |
SES | 0.16 ± 2.11 | 0.03 ± 2.02 | -1.93 ± 1.57 | <0.001 | |
Mother’s BMI (kg/m2) | 25.01 ± 4.12 | 24.99 ± 5.60 | 24.10 ± 5.31 | 0.752 | |
Baby’s head circumference (cm) | 32.78 ± 2.81 | 34.98 ± 4.93 | 35.36 ± 1.18 | <0.001 | |
Parity | 0.816 | ||||
1 | 122 (5.6) | 2026 (93.8) | 12 (0.6) | ||
≥2 | 122 (5.4) | 2123 (94.1) | 10 (0.4) | ||
Mother’s education | 0.007 | ||||
Undergraduate | 149 (5) | 2800 (94.3) | 20 (0.7) | ||
Graduate | 95 (6.6) | 1349 (93.3) | 2 (0.1) | ||
Father’s education | 0.253 | ||||
Undergraduate | 156 (5.2) | 2827 (94.2) | 17 (0.6) | ||
Graduate | 88 (6.2) | 1322 (93.4) | 5 (0.4) | ||
Mother’s occupation | 0.324 | ||||
Housewife | 209 (5.4) | 3645 (94.1) | 21 (0.5) | ||
Employed | 35 (6.5) | 504 (93.3) | 1 (0.2) | ||
Type of pregnancy | 0.882 | ||||
Wanted | 194 (5.4) | 3351 (94) | 18 (0.5) | ||
Unwanted | 50 (5.9) | 798 (93.7) | 4 (0.5) | ||
History of abortion | 0.251 | ||||
No | 190 (5.3) | 3353 (94.1) | 20 (0.6) | ||
Yes | 54 (6.3) | 796 (93.4) | 2 (0.2) | ||
History of stillbirth | 0.354 | ||||
No | 237 (5.5) | 4079 (94) | 22 (0.5) | ||
Yes | 7 (9.1) | 70 (90.9) | 0 (0) | ||
Preeclampsia | <0.001 | ||||
No | 198 (4.7) | 3961 (94.8) | 21 (0.5) | ||
Yes | 46 (19.6) | 188 (80) | 1 (0.4) | ||
ART | <0.001 | ||||
No | 197 (4.8) | 3867 (94.7) | 19 (0.5) | ||
Yes | 47 (14.2) | 282 (84.9) | 3 (0.9) | ||
Infant sex | 0.113 | ||||
Female | 108 (5) | 2054 (94.4) | 14 (0.6) | ||
Male | 136 (6.1) | 2095 (93.6) | 8 (0.4) | ||
Multiple pregnancy | <0.001 | ||||
No | 210 (4.8) | 4121 (94.7) | 22 (0.5) | ||
Yes | 34 (54.8) | 28 (45.2) | 0 (0) | ||
Birth weight | <0.001 | ||||
LBW | 111 (52.1) | 102 (47.9) | 0 (0) | ||
Normal | 131 (3.2) | 3928 (96.3) | 19 (0.5) | ||
Macrosomia | 2 (1.6) | 119 (96) | 3 (2.4) | ||
Values are given as mean ± SD or number (%).
SES; Socioeconomic status, BMI; Body mass index, ART; Assisted reproductive technology, and LBW; Low birth weight.
Distribution of patients’ characteristics in the LBW, normal, and macrosomia groups
Variable | LBW n=213 | Normal n=4078 | Macrosomia n=124 | P value | |
---|---|---|---|---|---|
Mother’s age (Y) | 29.40 ± 5.61 | 29.15 ± 5.35 | 29.90 ± 4.73 | 0.247 | |
Father’s age (Y) | 33.75 ± 6.41 | 33.44 ± 5.74 | 34.96 ± 5.61 | 0.013 | |
SES | 0.13 ± 2.01 | 0.02 ± 2.03 | 0.12 ± 1.93 | 0.625 | |
Mother’s BMI (kg/m2) | 24.98 ± 12.71 | 24.94 ± 4.92 | 26.49 ± 3.81 | 0.009 | |
Baby’s head circumference (cm) | 32.01 ± 2.39 | 34.96 ± 4.96 | 36.59 ± 1.43 | <0.001 | |
Parity | |||||
1 | 123 (5.7) | 1983 (91.8) | 54 (2.5) | 0.016 | |
≥2 | 90 (4) | 2095 (92.9) | 70 (3.1) | ||
Mother’s education | 0.176 | ||||
Undergraduate | 13 (4.4) | 2756 (92.8) | 82 (2.8) | ||
Graduate | 82 (5.7) | 1322 (91.4) | 42 (2.9) | ||
Father’s education | 0.155 | ||||
Undergraduate | 142 (4.7) | 2764 (92.1) | 94 (3.1) | ||
Graduate | 71 (5) | 1314 (92.9) | 30 (2.1) | ||
Mother’s occupation | 0.487 | ||||
Housewife | 182 (4.7) | 3582 (92.4) | 111 (2.9) | ||
Employed | 31 (5.7) | 496 (91.9) | 13 (2.4) | ||
Type of pregnancy | 0.449 | ||||
Wanted | 179 (5) | 3284 (92.2) | 100 (2.8) | ||
Unwanted | 34 (4) | 794 (93.2) | 24 (2.8) | ||
History of abortion | 0.957 | ||||
No | 173 (4.9) | 3238 (92.3) | 101 (2.8) | ||
Yes | 40 (4.7) | 789 (92.6) | 23 (2.7) | ||
History of stillbirth | 0.061 | ||||
No | 207 (4.8) | 4012 (92.5) | 119 (2.7) | ||
Yes | 6 (7.8) | 66 (85.7) | 5 (6.5) | ||
Preeclampsia | <0.001 | ||||
No | 178 (4.3) | 3887 (93) | 115 (2.8) | ||
Yes | 35 (14.9) | 191 (81.3) | 9 (3.8) | ||
ART | 0.281 | ||||
No | 191 (4.7) | 3777 (92.5) | 115 (2.8) | ||
Yes | 22 (6.6) | 301 (90.7) | 9 (2.7) | ||
Infant sex | 0.009 | ||||
Female | 112 (5.1) | 2019 (92.8) | 45 (2.1) | ||
Male | 101 (4.5) | 2059 (92) | 79 (3.5) | ||
Multiple pregnancy | <0.001 | ||||
No | 185 (4.2) | 4045 (92.9) | 123 (2.8) | ||
Yes | 28 (45.2) | 33 (53.2) | 1 (1.6) | ||
Values are given as mean ± SD or number (%).
SES; Socioeconomic status, BMI; Body mass index, ART; Assisted reproductive technology, and LBW; Low birth weight.
The results of joint multilevel multiple logistic regression model determining gestational age and birth weight categories
Predictor | Preterm to termOR (95% CI) | Postterm to termOR (95% CI) | |
---|---|---|---|
Mother’s age (Y) | 1.04 (1.02-1.07) | 0.92 (0.85-1.01) | |
SES | 0.97 (0.89-1.07) | 0.53 (0.37-0.74) | |
Mother’s education | 1.29 (0.90-1.86) | 0.82 (0.17-3.91) | |
Preeclampsia | |||
Yes | 4.14 (2.71-6.31) | 109 (0.14-8.39) | |
No | Reference category | ||
ART | |||
Yes | 2.47 (1.64-33.73) | 109 (0.14-8.39) | |
No | Reference category | ||
Multiple pregnancy | |||
Yes | 18.04 (9.75-33.38) | 0.61 (0.001-4.68) | |
No | Reference category | ||
Mother’s age (Y) | 1.01 (0.98-1.04) | 1.01 (0.98-1.05) | |
Mother’s BMI (kg/m2) | 1.01 (0.97-1.03) | 1.02 (1.01-1.04) | |
Mother’s education | 1.10 (0.72-1.69) | ||
Graduate | 1.15 (0.82-1.61) | ||
Underggraduate | Reference category | ||
History of stillbirth | |||
Yes | 2.17 (0.89-5.28) | 2.47 (0.94-6.52) | |
No | Reference category | ||
Multiple pregnancy | |||
Yes | 17.35 (9.73-30.94) | 0.72 (0.08-6.84) | |
No | Reference category | ||
Preeclampsia | |||
Yes | 3.36 (2.15-5.24) | 1.4 (0.68-2.87) | |
No | Reference category | ||
Infant sex | |||
Male | 0.87 (0.65-1.17) | 1.78 (1.21-2.60) | |
Female | Reference category | ||
SES; Socioeconomic status, BMI; Body mass index, ART; Assisted reproductive technology, OR; Odds ratio, and CI; Confidence interval.
Numerous studies reported a direct, positive correlation
between weight at birth and gestational age (
A systematic review Flenady et al. (
Kistka et al. (
We showed that the presence of preeclampsia increased
the odds for preterm and LBW. However, our study showed
that the odds for postterm delivery and macrosomia were
higher among those with preeclampsia. These ratios were
not significant. Davies et al. (
Dunietz et al. (
Based on the results from our study, macrosomia was
affected by mother’s BMI. Rockhill et al. (
The strength of this study was the association between
two ordinal outcomes of pregnancy and determining their
potential risk factors by using an advanced statistical
joint modeling approach. Ignoring the strong association
between the two response variables, weight at birth and
gestational age, reduced the statistical power to find their
significant risk factors. In contrast to univariate models
and traditional approaches, jointly modeling several response
variables increases the statistical power of data
analysis. In a study by Santos et al. (
Kassahun et al. used a joint model for hierarchical continuous
and zero-inflated overdispersed count data to assess
the diarrhoeal disease burden. To do so, the combined
infant body weight and number of days of diarrhoeal illness
using a longitudinal design (
These types of data are widely used in medical and
clinical areas; ignoring its’ natural variance causes misleading
estimations (
The study results showed an association between preterm and postterm births to maternal age and SES. In contrast to postterm births, preeclampsia, multiple pregnancy, and ART affected preterm births. Macrosomia was caused by higher maternal BMI. Macrosomia was more common among male infants. We observed an association between LBW and parity, preeclampsia, and multiple pregnancy. We have determined that the joint multilevel multiple logistic regression model is a proper statistical tool for these types of data.