Abstract
Objective:
To examine the effects of socioeconomic and demographic conditions on the prevalence of preterm birth in a local community.
Methods:
Pregnant women (aged ≥16 years) willing to provide informed consent in one of the two languages of the community were recruited in South Africa between August 2007 and January 2015. Specifically designed case report forms collected information and measurements prospectively.
Results:
After reviewing the medical records of 5806 women, it was possible to identify those who had spontaneous preterm birth (SPTB), induced preterm birth (IPTB), or spontaneous term birth (STB). Women with IPTB (vs SPTB) were more obese and had higher education levels and household incomes; more had telephones and running water at home. They enrolled earlier and more developed hypertension and pre-eclampsia. Women with SPTB (vs STB) were less obese, shorter, had smaller arm circumferences and higher gravidities and Edinburgh Depression Scores, lower education, fewer telephones, and less running water at home. More women with SPTB used methamphetamine, cigarettes, and were heavier smokers.
Conclusion:
SPTB and IPTB should not be conflated. Programs to reduce the high prevalence of SPTB should include improving education, lifestyle, and socioeconomic conditions. Addressing hypertension should help reduce preterm inductions.
Keywords: Induced preterm birth, Pregnancy, Small for gestational age infants, Socioeconomic status, Spontaneous preterm birth, Spontaneous term birth
1. INTRODUCTION
It is a high priority to address the heavy burden of preterm birth (PTB) on a low- to middle-class community in the Western Cape Province of South Africa. For this community, a very high rate (13.8%) of PTB was recently found [1]. In 2010, over 60% of all PTBs occurred in Africa and South Asia with rates of 12.3% for sub-Saharan Africa [2]. PTB is one of the most common causes of perinatally related deaths at Tygerberg Academic Hospital (TBH), the tertiary referral hospital for this community [3]. Complications of PTB also account for most of the neonatal deaths and infant morbidities and therefore remains one of the greatest challenges in obstetrics [4].
Perinatal outcome is particularly susceptible to socioeconomic conditions as these conditions have profound effects on the health of individuals and communities. The principal pathways by which socioeconomic status (SES) affect perinatal health include lifestyle and behavioral factors [5].
South Africa has one of the highest levels of alcohol consumption and heavy binging episodes in certain communities [6]. Consequently, the rates of heavy drinking during pregnancy are high. In addition, cigarette smoking and the use of marijuana and the recreational drug methamphetamine, locally known as “tik,” often co-occur during pregnancy in South Africa [7, 8]. Individuals with low SES experience greater exposure to stress and are more likely to engage in risk-taking behaviors such as smoking and alcohol/drug abuse [9], with smoking being a well-known risk factor for adverse birth outcomes [10].
The measures of SES used in published studies are heterogeneous, making discerning findings and comparative data difficult to come by [11]. The aim of the present study, therefore, was to identify variable measures of SES and to investigate their association with PTB in a large, previously disadvantaged community for which TBH is responsible for tertiary care.
2. MATERIALS AND METHODS
The Safe Passage Study (SPS) of the Prenatal Alcohol in SIDS and Stillbirth Network was a prospective, multicenter cohort study with data collection conducted between August 2007 and October 2016, to test the hypothesis that prenatal exposure to alcohol is associated with increased risk of stillbirth or sudden infant death syndrome (SIDS) [12].
In South Africa, at the antenatal midwife obstetric clinics near TBH, 7060 unselected pregnant women who met the inclusion criteria during the study period were enrolled in the study, from 6 weeks of gestation up to, but not including, delivery. A woman (aged 16 years or older) was eligible if she was pregnant with one or two fetuses and was willing to provide informed consent in Afrikaans or English, the two languages spoken in this community. Participants also gave informed consent for specific study components (e.g. collection of placental tissue, use of specimens for future studies). These options were provided so that women could decline aspects of the study that conflicted with their personal needs or cultural beliefs. The participant was also able to withdraw at any time during the study. Depending on the gestational age at enrollment, women were invited to attend up to three antenatal visits for the study at TBH, at 20–24, 28–32, and at 34–38 weeks of gestation.
A trained midwife measured the weight, height, head circumference, and mid upper arm circumference (MUAC) of the women. For the MUAC, the midpoint between the acromion of the scapula and olecranon of the ulna was first determined and then the circumference measured. Two measurements were taken for each variable. If they differed by more than 1 kg or 2 mm, a third measurement was taken and the mean of the closest two measurements was used (effect of outlier therefore excluded). The body mass index (BMI, calculated as weight in kilograms divided by the square of height in meters) was calculated by the electronic data capturing system [12]. A BMI less than 18.5 or greater than 30 was regarded as underweight or obese, respectively.
Self-reported maternal characteristics (e.g. demographics, medical and obstetric history), psychosocial (e.g. depression) information, anthropometry, and self-reported exposure (e.g. alcohol, tobacco, marijuana, and methamphetamine) were collected at antenatal visits. Women were specifically advised against drinking and smoking during pregnancy and were given pamphlets about the dangers of these habits for the fetus and telephone numbers where they could obtain help for a smoking or drinking problem. Women with high Edinburgh Depression Scores were also referred to the social worker of the study.
Research midwives checked delivery admissions daily to determine whether a study participant had delivered and, if so, obtained information on the pregnancy outcome. After birth, maternal and infant medical charts were abstracted to obtain information regarding the progress of pregnancy, admissions before birth, medical complications, results of laboratory tests, birth outcome, and early neonatal and infant development. This information included PTB (any delivery before 259 days, either spontaneous or induced), hypertensive disease (chronic or gestational hypertension, pre-eclampsia, and eclampsia), maternal age, and fetal sex. Z-scores and centiles of birthweight for gestational age were calculated from the international standards of the INTERGROWTH- 21st study (available for gestational ages in the range of 168–299 days, excluding twins) [13].
After exclusions, predominantly for multiple enrollments (981, 13.9%), withdrawals (136, 1.9%), twin pregnancies (50, 0.7%), and miscarriages (46, 0.7%), 5806 (82.2%) women of the original 7060 recruited women remained and PTB was compared with term birth.
After further selection for spontaneous PTB (SPTB), induced PTB (IPTB), and spontaneous term birth (STB), a cohort of 4833 participants was obtained for the present study. Cases were excluded if delivery information was lacking. It was determined whether there were differences between early PTB, gestational age at birth of 154 days or more (22 weeks 0 days) and less than 238 days (34 weeks 0 days), and late PTB, gestational age at birth of 238 days or more (34 weeks 0 days) and less than 259 days (37 weeks 0 days). Subsequently, SPTB and IPTB were compared to ascertain whether there was a significant difference between these two groups. Lastly, SPTB was compared to STB.
As the SPTB group also included placental abruption, hypertensive diseases, and diabetes, all known risk factors for PTB, this group was not strictly idiopathic. Therefore, SPTB was also compared with IPTB and STB in groups where maternal medical problems had been excluded.
Data were entered in Excel 365 (Microsoft, USA) and then coded and exported for analysis in Stata 14 (StataCorp, USA). Statistical analyses were performed using SAS/STAT® software, Version 9.3, Copyright© 2011 and Dell STATISTICA® version 13 (Dell Inc., 2016, software.dell.com). Descriptive statistics were used to describe continuous variables, which were compared between groups with analysis of variance (ANOVA). Bonferroni or least significant difference multiple comparisons identified significant differences between the means in the ANOVA. The Mann–Whitney U test compared differences between two groups where responses are not normally distributed. The χ2 test determined significance in categorical data. Spearman correlations measured correlations between repetitions of several response variables. A P value less than 0.05 was considered to be statistically significant. All variables that were significantly related to SPTB according to the Pearson or Spearman correlations were used in a multiple regression analysis to ascertain which variables were the most significant predictors for SPTB. A principal component factor analysis with a varimax rotation was done with five factors which explains 73.7% of the variation among the 12 predictor variables considered. Variables with high loadings (in bold) in each factor are highly correlated (co-linear). The best uncorrelated predictors were a selection of four (or five) variables selected from the bold ones in each factor. This was done with a best subsets regression with the best predictors selected such that their intercorrelations did not exceed 0.7.
Permission to conduct this study (ethical approval number: N06/10/210 and S19/07/119) was obtained from the Health Research Ethics Committee of Stellenbosch University, as well as from the Western Cape Government: Health.
3. RESULTS
In Table 1, 5806 women were divided into preterm (771, 13.3%) and term (5035, 86.7%) birth groups. Women who had PTBs did not differ significantly from those who had term births regarding maternal age, household income, and birth weight centile and Z-scores. Whereas women with PTBs had a significantly higher gravidity, lower BMI, and lower education levels, fewer women had running water in the home and more women used methamphetamine during pregnancy.
Table 1.
Descriptive variables in preterm and term births.a
| Descriptive variable | Preterm birth | Term birth | P value (ANOVA) |
|---|---|---|---|
| Maternal age (years) | 24.8±6.1, 24 (16–43) | 24.4±5.9, 23 (15–45) | 0.112 |
| BMI (kg/m2) | 24.5±5.5, 23.2 (14.9–50.1) | 25.7±5.7, 24.3 (13.7–55.9) | <0.001 |
| Gravidity | 2.3±1.4, 2.0 (1–9) | 2.1±1.2, 2 (1–10) | <0.001 |
| Household monthly income (Randsb) | 847.3±624.4, 666.7 (71.4–3000) | 871.9±598.3, 750 (45.5–6000) | 0.413 |
| Maternal education years | 9.7±1.7, 10 (3–13) | 10.1±1.7, 10 (2–13) | <0.001 |
| Birthweight (g) | 2148.8±699.5, 2260 (190–4630) | 3112.9±463.4, 3100 (1160–5740) | <0.001 |
| Birthweight centile | 39.0±27.2, 36.3 (0–100) | 39.7±28.5, 35 (0–100) | 0.511 |
| Birthweight Z-score | −0.4±1.1, −0.4 (–6.3 to 3.7) | −0.4±1.0, −0.4 (−5.7 to 4.1) | 0.174 |
| P value (χ2) | |||
| Running water at home | 501 (78.2) | 4073 (82.7) | 0.006 |
| Use of marijuana | 84 (12.1) | 519 (10.4) | 0.189 |
| Use of methamphetamine | 58 (8.4) | 257 (5.2) | 0.001 |
Abbreviations: ANOVA, analysis of variance; BMI, body mass index; SD, standard deviation.
Values are given as mean ± SD, median (range), or number (percentage).
1 USD = 15.22 Rands.
PTB (734/4833 cases) was divided into early PTB (216, 29.4%) and late PTB (518, 70.6%). Differences between the socioeconomic variables of early and late PTB were not significant, except for household income. Unfortunately, the numbers for household income were small due to missing data (Table 2). Delivery before 34 weeks of gestation occurred in 81 (36%) women (of which 48 [59.3%] had medical problems) and 135 (26.5%) women (of which 17 [12.8%] had medical problems) in the IPTB and SPTB groups, respectively.
Table 2.
A comparison of preterm birth before 34 weeks of gestation to preterm birth at 34 weeks of gestation and thereafter.a
| Variables | Preterm birth < 34 weeks n = 216 (29.4%) | Preterm birth ≥ 34 weeks n = 518 (70.6%) | Test and P values |
|---|---|---|---|
| BMI (kg/m²) | 24.4±5.4 | 24.5±5.6 | ANOVA 0.798 |
| Gravidity | 2.3±1.5 | 2.3±1.4 | ANOVA 0.890 |
| Maternal education years | 9.8±1.8 | 9.7±1.7 | ANOVA 0.931 |
| Running water at home | 128±82.1 | 349±76.2 | χ2 0.123 |
| Use of methamphetamine | 11±6.6 | 46±9.3 | χ2 0.283 |
| Maternal age (years) | 24.9±6.4 | 24.8±5.9 | ANOVA 0.807 |
| Household income (Randsb) | 1016.26±670.08 115 (53.2) | 784.41±596.15 320 (61.8) | ANOVA <0.001 |
Abbreviations: BMI, body mass index; SD, standard deviation.
Values are given as mean ± SD or number (percentage).
1 USD = 15.22 Rands.
In Table 3, PTB (734 cases consisting of 509 [69.4%] SPTB and 225 [30.6%] IPTB cases) was examined. The women with IPTB (compared to women with SPTB) differed significantly regarding the following variables: BMI; MUAC; head circumference; maternal weight; obesity; education; household income; telephone; running water; gestational age at enrollment; hypertension; pre-eclampsia; placental abruption; gestational age at delivery; birth weight; birth weight centile; Z-score, low-birth-weight (LBW) infants; small-for-gestational-age (SGA) infants; infants admitted to the neonatal intensive care unit (NICU); and days kept in hospital. They had a larger mean BMI (25.8 vs 23.9), MUAC, head circumference, weighed more, and more women were obese. They also had higher levels of education and household incomes; more had telephones and running water in the home. These women enrolled earlier and significantly more women developed hypertension, pre-eclampsia, and placental abruption than the women in the SPTB group. They had a lower gestational age at delivery, lower birth weight (and birth weight centile and Z-score), more LBW and SGA infants, more infants admitted to the NICU, and their infants were kept in hospital nearly twice as long (Table 3).
Table 3.
Descriptive variables in spontaneous and induced preterm births.a
| Descriptive variable | SPTB | IPTB | P value (ANOVA) |
|---|---|---|---|
| Gestational age at enrollment (days) | 144.8±51, 143 (46–254) | 130.7±45.3, 130 (44–247) | <0.001 |
| Maternal age (years) | 24.6±6, 24 (16–43) | 25.3±6.2, 25 (16–43) | 0.142 |
| Maternal weight (kg) | 59.5±14, 56.6 (34–129.8) | 64±15.2, 61.2 (37.6–125.2) | <0.001 |
| Maternal length (cm) | 158±6, 158 (141–177) | 158±6, 158 (141–179) | 0.993 |
| Maternal head circumference (mm) | 542±19, 540 (485–602) | 545±19, 545 (500–600) | 0.040b |
| Maternal mid upper arm circumference (mm) | 262.5±43.4, 251.3 (185–535) | 277.1±47.6, 270 (189.5–475) | <0.001 |
| BMI (kg/m2) | 23.9±5.3, 22.5 (14.9–50.1) | 25.8±5.9, 24.9 (15.4–48.9) | <0.001 |
| Gravidity | 2.3±1.4, 2 (1–9) | 2.2±1.4, 2 (1–7) | 0.437 |
| Maternal education years | 9.6±1.7, 10 (3–13) | 10±1.7, 10 (5–13) | 0.005 |
| Household income (Rands c) | 802.4±609.8, 666.7 (71.4–3000) | 925.6±644.4, 750 (71.4–3000) | 0.049 |
| Edinburgh Depression Score d | 13.6±6.1, 14 (0–30) | 12.9±5.8, 13 (0–28) | 0.141 |
| Birth weight (g) | 2244.1±626.1, 2330 (460–4440) | 1954.6±791.9, 2075 (190–4630) | <0.001 |
| Birth weight Z-score | −0.3±1, −0.2 (−6.3 to 3.2) | −0.7±1.2, −0.6 (−5.7 to 3.7) | <0.001 |
| Birth weight centile | 42.8±26.8, 41.7 (0–99.9) | 30.8±25.8, 26.7 (0–100) | <0.001 |
| Days kept in hospital | 5.5±12.9, 1 (0–97) | 10.6±19.1, 3 (0–110) | <0.001 |
| P value (χ2) | |||
| Marital status: married | 101 (20.0) | 58 (25.9) | 0.205 |
| Poor housing e | 140 (33.5) | 56 (28.4) | 0.206 |
| Maternal underweight | 40 (8.1) | 12 (5.5) | 0.211 |
| Maternal obesity | 61 (12.3) | 49 (22.4) | <0.001 |
| Telephone | 336 (80.8) | 179 (90.9) | <0.001 |
| Water at home | 310 (74.3) | 167 (84.8) | 0.003 |
| Drinking of alcohol during pregnancy | 336 (68.7) | 136 (65.1) | 0.347 |
| Smoking of cigarettes during pregnancy | 376 (76.9) | 152 (72.7) | 0.240 |
| Use of marijuana | 59 (12.7) | 21 (10.4) | 0.392 |
| Use of methamphetamine | 43 (9.3) | 14 (7.0) | 0.314 |
| Primigravida | 186 (41.0) | 95 (44.8) | 0.350 |
| Previous stillbirth | 17 (6.3) | 7 (6.0) | 0.893 |
| Previous preterm birth | 81 (30.2) | 31 (26.5) | 0.456 |
| Placental abruption f | 15 (3.0) | 21 (9.4) | <0.001 |
| Pre-eclampsia f | 13 (2.6) | 53 (23.9) | <0.001 |
| Eclampsiaf | 3 (0.6) | 4 (1.8) | 0.149 |
| Diabetes f | 7 (1.4) | 5 (2.4) | 0.407 |
| Gestational hypertension f | 34 (6.8) | 98 (44.0) | <0.001 |
| LBW infants | 310 (61.3) | 163 (73.4) | 0.001 |
| SGA infants | 64 (12.9) | 60 (27.8) | <0.001 |
| Infant in NICU | 125 (27.3) | 87 (47.0) | <0.001 |
Abbreviations: ANOVA, analysis of variance; BMI, body mass index; IPTB, induced preterm birth; LBW, low birth weight; NICU, neonatal intensive care unit; SD, standard deviation; SGA, small for gestational age; SPTB, spontaneous preterm birth.
Values are given as mean ± SD, median (range), or number (percentage).
Mann–Whitney U test.
1 USD = 15.22 Rands.
Validated.
Small model toy house (Wendy), informal shack, shelter, trailer.
Current pregnancy.
Spontaneous birth (4608 cases) was divided into SPTB (509, 11.1%) and STB (4099, 88.9%) in Table 4. The women who had a SPTB (compared to STB) differed significantly regarding the following variables: BMI; maternal length; MUAC; head circumference; maternal weight; obesity; underweight; education; household income; telephone; running water; placental abruption; previous PTBs; previous STBs; diabetes; methamphetamine; cigarettes; gestational age at delivery; birth weight; birth weight centile; Z-score; LBW infants; SGA infants; infants admitted to the NICU; and days kept in hospital. They had a lower mean BMI (23.9 vs 25.3), were shorter, had a smaller arm and head circumference, and weighed less. Significantly more women were underweight and fewer women were obese. They had higher gravidity and Edinburgh Depression Scores and lower levels of education. They had fewer telephones and less running water at home. They also had more previous PTBs, previous STBs, and suffered from diabetes and placental abruption more frequently. More women in this group used methamphetamine, cigarettes, and were heavier smokers. Their infants had lower birth weights, but higher birth weight centiles and birth weight Z-scores. They had fewer SGA infants, more infants admitted to the NICU, and their infants were kept in hospital for longer (Table 4).
Table 4.
Descriptive variables in spontaneous, preterm, and term births.a
| Descriptive variable | SPTB | STB | P value (ANOVA) |
|---|---|---|---|
| Gestational age at enrollment (days) | 144.8±51, 143 (46–254) | 144.0±48.9, 142 (38–279) | 0.736 |
| Maternal age (years) | 24.6±6, 24 (16–43) | 24.3±5.9, 23 (16–42) | 0.307 |
| Maternal weight (kg) | 59.5±14, 56.6 (34–129.8) | 63.8±14.4, 60.7 (30.1–136) | <0.001 |
| Maternal length (cm) | 158±6, 158 (141–177) | 159±6, 159 (138–184) | 0.002 |
| Maternal head circumference (mm) | 542±19, 540 (485–602) | 545±19, 545 (215–696) | 0.002 |
| Maternal mid upper arm circumference (mm) | 262.5±43.4, 251.3 (185–535) | 273.4±44.1, 265 (175–480) | <0.001 |
| BMI (kg/m2) | 23.9±5.3, 22.5 (14.9–50.1) | 25.3±5.5, 24.1 (13.7–52.3) | <0.001 |
| Gravidity | 2.3±1.4, 2 (1–9) | 2.1±1.2, 2 (1–10) | <0.001 |
| Maternal education years | 9.6±1.7, 10 (3–13) | 10±1.7, 10 (3–13) | <0.001 |
| Household income (Rands b) | 802.4±609.8, 666.7 (71.4–3000) | 853.1±595.1, 750 (45.5–6000) | 0.020c |
| Edinburgh Depression Score d | 13.6±6.1, 14 (0–30) | 12.9±5.9, 13 (0–30) | 0.014 |
| Daily cigarettes | 4.7±3.7, 4 (0–20.5) | 4.3±3.7, 3.6 (0–53) | 0.040 c |
| Birth weight (g) | 2244.1±626.1, 2330 (460–4440) | 3097.2±451, 3080 (1160–5740) | <0.001 |
| Birth weight Z-score | −0.3±1, −0.2 (−6.3 to 3.2) | −0.4±1, −0.4 (–5.7 to 4.1) | 0.007 |
| Birth weight centile | 42.8±26.8, 41.7 (0–99.9) | 38.9±28.1, 34.2 (0–100) | 0.003 |
| Days kept in hospital | 5.5±12.9, 1 (0–97) | 0.9±1.5, 0 (0–23) | <0.001 |
| P value (χ2) | |||
| Marital status: married | 101 (20.0) | 968 (23.7) | 0.078 |
| Poor housing e | 140 (33.5) | 1208 (30.2) | 0.164 |
| Maternal underweight | 40 (8.1) | 201 (5.0) | 0.008 |
| Maternal obesity | 61 (12.3) | 745 (18.7) | <0.001 |
| Telephone | 336 (80.8) | 3592 (89.6) | <0.001 |
| Water at home | 310 (74.3) | 3302 (82.4) | <0.001 |
| Drinking of alcohol during pregnancy | 336 (68.7) | 2738 (67.3) | 0.536 |
| Smoking of cigarettes during pregnancy | 376 (76.9) | 2782 (68.4) | <0.001 |
| Use of marijuana | 59 (12.7) | 450 (11.1) | 0.302 |
| Use of methamphetamine | 43 (9.3) | 231 (5.7) | 0.004 |
| Primigravida | 186 (41.0) | 1718 (42.5) | 0.533 |
| Previous stillbirth | 17 (6.3) | 37 (1.5) | <0.001 |
| Previous preterm birth | 81 (30.2) | 278 (12.0) | <0.001 |
| Placenta abruption f | 15 (3.0) | 4 (0.1) | <0.001 |
| Pre-eclampsia f | 13 (2.6) | 80 (2.0) | 0.384 |
| Eclampsia f | 3 (0.6) | 6 (0.2) | 0.075 |
| Diabetes f | 7 (1.4) | 8 (0.2) | <0.001 |
| Gestational hypertension f | 34 (6.8) | 332 (8.3) | 0.247 |
| LBW infants | 310 (61.3) | 307 (7.5) | <0.001 |
| SGA infants | 64 (12.9) | 776 (19.0) | <0.001 |
| Infant in NICU | 125 (27.3) | 122 (3.1) | <0.001 |
Abbreviations: ANOVA, analysis of variance; BMI, body mass index; LBW, low birth weight; NICU, neonatal intensive care unit; SD, standard deviation; SGA, small for gestational age; SPTB, spontaneous preterm birth; STB, spontaneous term birth.
Values are given as mean ± SD, median (range), or number (percentage).
1 USD = 15.22 Rands.
Mann–Whitney U test.
Validated.
Small model toy house (Wendy), informal shack, shelter, trailer.
Current pregnancy.
In Tables 5 and 6, 4833 cases were divided into three groups: SPTB, 509 (10.5%); IPTB, 225 (4.7%); and STB, 4099 (84.8%). As pathological conditions such as pre-eclampsia, eclampsia, and placental abruption are indications for induced delivery, their presence in the “spontaneous” labor groups was counter-intuitive. Each of the three groups were therefore sub-divided into all (including medical disease) and all (excluding medical disease) for socioeconomic variables. Exclusions for medical disease were 59 (11.6%), 114 (50.7%), and 421 (10.3%) for SPTB, IPTB, and STB, respectively (Tables 5 and 6).
Table 5.
Descriptive variable means in preterm/term births with/without maternal medical problems.a
| Descriptive variable | SPTB n=509 (10.5%) | IPTB n=225 (4.7%) | STB n=4099 (84.8%) | |||
|---|---|---|---|---|---|---|
| All, including medical problems | All, excluding medical problems (n=450, 88.4%) | All, including medical problems | All, excluding medical problems (n=111, 49.3%) | All, including medical problems | All, excluding medical problems (n=3678, 89.7%) | |
| Gestational age at enrollment (days) | 144.8 | 146.6 | 130.7 | 133.4 | 144.0 | 144.5 |
| Maternal age (years) | 24.6 | 24.5 | 25.3 | 25.2 | 24.3 | 24.4 |
| Maternal weight (kg) | 59.5 | 58.7 | 64 | 61.3 | 63.8 | 63.6 |
| Maternal length (cm) | 158 | 158 | 158 | 158 | 159 | 159 |
| Maternal head circumference (mm) | 542 | 542 | 545 | 543 | 545 | 544 |
| Maternal mid upper arm circumference (mm) | 262.5 | 259.7 | 277.1 | 268.7 | 273.4 | 272.6 |
| BMI (kg/m2) | 23.9 | 23.6 | 25.8 | 24.5 | 25.3 | 25.2 |
| Gravidity | 2.3 | 2.3 | 2.2 | 2.4 | 2.1 | 2.1 |
| Maternal education years | 9.6 | 9.6 | 10.0 | 9.8 | 10.0 | 10.0 |
| Household income (Randsb) | 802.4 | 796.5 | 925.6 | 909.1 | 853.1 | 850.0 |
| Edinburgh Depression Scorec | 13.6 | 13.6 | 12.9 | 12.7 | 12.9 | 12.9 |
| Daily cigarettes | 4.7 | 4.6 | 4.7 | 4.7 | 4.3 | 4.3 |
| Birth weight (g) | 2244.1 | 2249.1 | 1954.6 | 2015.3 | 3097.2 | 3097.3 |
| Birth weight Z-score | −0.3 | −0.3 | −0.7 | −0.65 | −0.4 | −0.4 |
| Birth weight centile | 42.8 | 42.9 | 30.8 | 34.47 | 38.9 | 38.8 |
| Days kept in hospital | 5.5 | 5.5 | 10.6 | 6.7 | 0.9 | 0.8 |
Abbreviations: BMI, body mass index; IPTB, induced preterm birth; SPTB, spontaneous preterm birth; STB, spontaneous term birth.
Values are given as the mean.
1 USD = 15.22 Rands.
Validated.
Table 6.
Descriptive variable percentages in preterm/term births with/without maternal medical problems.a
| Descriptive variable | SPTB n=509 (10.5%) | IPTB n=225 (4.7%) | STB n=4099 (84.8%) | |||
|---|---|---|---|---|---|---|
| All, including medical problems | All, excluding medical problems (n=450, 88.4%) | All, including medical problems | All, excluding medical problems (n=111, 49.3%) | All, including medical problems | All, excluding medical problems (n=3678, 89.7%) | |
| Marital status: married | 20.0 | b | 25.9 | b | 23.7 | b |
| Poor housingc | 33.5 | b | 28.4 | b | 30.2 | b |
| Maternal underweight | 8.1 | 8 | 5.5 | 7 | 5.0 | 5 |
| Maternal obesity | 12.3 | 10 | 22.4 | 17 | 18.7 | 18 |
| Telephone | 80.8 | 80 | 90.9 | 93 | 89.6 | 89 |
| Water at home | 74.3 | 75 | 84.8 | 82 | 82.4 | 82 |
| Drinking of alcohol during pregnancy | 68.7 | b | 65.1 | b | 67.3 | b |
| Smoking of cigarettes during pregnancy | 76.9 | b | 72.7 | b | 68.4 | b |
| Use of marijuana | 12.7 | 13 | 10.4 | 12 | 11.1 | 11 |
| Use of methamphetamine | 9.3 | 9 | 7.0 | 6 | 5.7 | 6 |
| Previous stillbirth | 6.3 | 6 | 6.0 | 7 | 1.5 | 1 |
| Previous preterm birth | 30.2 | 29 | 26.5 | 25 | 12.0 | 12 |
| Placenta abruptiond | 3.0 | 0 | 9.4 | 0 | 0.1 | 0 |
| Pre-eclampsiad | 2.6 | 0 | 23.9 | 0 | 2.0 | 0 |
| Eclampsiad | 0.6 | 0 | 1.8 | 0 | 0.2 | 0 |
| Diabetesd | 1.4 | 0 | 2.4 | 0 | 0.2 | 0 |
| Gestational hypertensiond | 6.8 | 0 | 44.0 | 0 | 8.3 | 0 |
| LBW infants | 61.3 | 61 | 73.4 | 72 | 7.5 | 7 |
| SGA infants | 12.9 | 13 | 27.8 | 21 | 19.0 | 19 |
| Infants in NICU | 27.3 | 25 | 47.0 | 37 | 3.1 | 3 |
Abbreviations: BMI, body mass index; IPTB, induced preterm birth; LBW, low birth weight; NICU, neonatal intensive care unit; SGA, small for gestational age; SPTB, spontaneous preterm birth; STB, spontaneous term birth.
Values are given as percentage.
Not done.
Small model toy house (Wendy), informal shack, shelter, trailer.
Current pregnancy.
Table 7 shows the results of the multiple regression analyses, carried out on 4608 spontaneous birth cases. Of the 12 predictor variables, five groups of co-linear variables were found with BMI and the co-linear variables maternal weight and MUAC were the best predictors for SPTB.
Table 7.
Principal component factor analysis (part A) followed by multiple regression analysis (part B).
| Part A: Correlated predictor variables for each factor | |||||
|---|---|---|---|---|---|
| Variable | Factor 1 | Factor 2 | Factor 3 | Factor 4 | Factor 5 |
| Maternal weight | 0.930364 | 0.002417 | 0.089508 | 0.256579 | 0.070118 |
| Maternal length | −0.024548 | −0.022467 | 0.049679 | 0.882309 | 0.088056 |
| Maternal head circumference | 0.399983 | 0.085028 | −0.058328 | 0.595652 | −0.077126 |
| Maternal MUAC | 0.948964 | 0.003450 | 0.058614 | 0.053263 | 0.070048 |
| BMI | 0.977222 | 0.012898 | 0.071442 | −0.057009 | 0.037778 |
| Gravidity | 0.278960 | 0.077188 | −0.304497 | −0.050412 | 0.635918 |
| Maternal education years | −0.005942 | −0.043566 | 0.637171 | 0.226012 | −0.267856 |
| Household income | 0.064088 | −0.016186 | 0.750879 | 0.033422 | −0.044503 |
| Edinburgh Depression Score | −0.081356 | −0.031239 | −0.558879 | 0.156191 | −0.060168 |
| Daily cigarettes | −0.055668 | 0.041612 | 0.042263 | 0.073941 | 0.873811 |
| Total binge episodes | 0.005574 | 0.970597 | −0.013111 | 0.014805 | 0.066043 |
| Total standard drinks | 0.019028 | 0.971908 | −0.002525 | 0.020178 | 0.041917 |
| Part B: Regression summary for best four predictor variables for SPTB | |||||||
|---|---|---|---|---|---|---|---|
| Position (Factor) | Variable (co-linear variables) | b* | SE of b* | b | SE of b | t(4468) | P value |
| 1st (Factor 1) | BMI (Maternal weight) (Maternal MUAC) | 0.155879 | 0.015346 | 0.4322 | 0.042546 | 10.15794 | 0.000000 |
| 2nd (Factor 5) | Gravidity (Daily cigarettes) | −0.098828 | 0.015665 | −1.1850 | 0.187827 | −6.30884 | 0.000000 |
| 3rd (Factor 3) |
Maternal education years (Edinburgh depression score) (Household income) |
0.071159 | 0.015133 | 0.6296 | 0.133894 | 4.70211 | 0.000003 |
| 4th (Factor 4) | Maternal length (cm) (Maternal head circumference) | 0.064871 | 0.014725 | 0.1600 | 0.036328 | 4.40559 | 0.000011 |
Abbreviations: BMI, body mass index; MUAC, middle upper arm circumference; SE, standard error; SPTB, spontaneous preterm birth.
Part A: Highly correlated (co-linear) predictor variables for each factor indicated in bold.
Part B: BMI (with co-linear variables maternal weight and MUAC) was found to be the best predictor variable for SPTB, followed by gravidity (with co-linear variable daily cigarettes), education (with co-linear variables Edinburgh Depression Score and household income), and maternal length (with co-linear variable maternal head circumference).
4. DISCUSSION
PTB is a syndrome with a variety of causes. It can be classified into two broad subtypes of SPTB and IPTB for maternal and/or fetal indications, presentation for delivery, and placental findings [14]. When comparing the variables of SPTB and IPTB, it became apparent that there were significant differences between the two groups and that it would be wrong to regard them as a single, homogeneous PTB group. Obesity, hypertensive disease, and specifically pre-eclampsia dominated the IPTB group.
The results of a large Finnish study showed that SES played a substantial role in PTB, even after adjustment for risk factors [15]. However, almost all cases in the present index study were located within low socioeconomic circumstances. It was found that several variables, such as maternal age (whether young or advanced), marital status, primigravity, poor housing, use of marijuana, eclampsia, and drinking alcohol, showed no significant association with PTB and some of the findings of the present study differed from the literature. Räisänen et al. [15] reported that women who delivered preterm were significantly more often primiparous and of an advanced age, while Muglia and Katz [16] mention a young or advanced maternal age, consumption of alcohol, and unmarried status as risk factors associated with an increased risk of spontaneous PTB.
Women who had had induction of labor before term (IPTB group) had several significant variables that distinguished them from the other groups. They had more medical problems such as hypertension and pre-eclampsia, both of which are obvious indications for induction. This might also be the reason why these women enrolled into the study significantly earlier as early booking for antenatal care is encouraged for women with medical disorders or previous complications in pregnancy. The high occurrence of pre-eclampsia in this group (53, 24%) was not associated with primigravity [17], as 95 (45%) primigravidae were found in the IPTB group compared to 186 (41%) and 1718 (43%) in the SPTB and STB groups, respectively. It has long been shown that pre-eclampsia is more common in multiparous women in the Western Cape [18] and the results of the present study confirm this. The higher obesity in the IPTB group can also be associated with obstetric complications such as gestational diabetes, hypertension (found in 98 [44%]), and pre-eclampsia [19].
With certain variables, the opposite effect was seen in IPTB women compared to SPTB women. By combining these two groups under the umbrella of PTB, the true effect of the variable may be cancelled out. For example: obesity: SPTB least, IPTB most; BMI: SPTB smaller, IPTB larger; maternal education: SPTB lower, IPTB higher; household income: SPTB lowest, IPTB highest; telephone and water at home: SPTB less, IPTB more; BW Z-score/centile: SPTB larger, IPTB smaller; and SGA infant: SPTB least, IPTB most.
As more women in the IPTB group were obese, had hypertension or pre-eclampsia, or SGA infants, it was decided to focus on SPTB compared to STB.
Several reproductive risk factors of PTB have previously been identified, such as previous SPTB, multiple pregnancies, primiparity, advanced maternal age, smoking, obesity, and level of education [15].
In the present study, maternal history of PTB was found to be a strong risk factor, with women in the SPTB group having the highest gravidity and more previous PTBs and previous stillbirths, thus supporting literature. This is most likely driven by the interaction of genetic, epigenetic, and environmental risk factors [20].
Women in the SPTB group (compared to the STB group) were less obese and more underweight, had a smaller head and especially arm circumference, were shorter, weighed less, and had the lowest BMI. In a study in South Africa, household income was the only socioeconomic variable that was significantly associated with BMI, and women who had a lower income were at risk of having a lower BMI during pregnancy [21]. According to our results, SPTB women had the lowest household income, owned fewer telephones, and had running water at home less often. These findings further corroborate the risk factors described in the abovementioned literature. Poverty / low levels of income also seems to have a large influence on depression and is a strong predictor of PTB [22]. Antenatal depression may be significantly associated with increased risks of PTB and fetal growth restriction (FGR) [23]. Of the SPTB women, 213 (51%) were depressed or anxious (median Edinburgh Score = 14) and were referred to the study’s social worker.
Maternal level of education was significantly lower in the SPTB women, with 231 (45%) women not having completed grade 10. Bivariate analyses performed in a Canadian study revealed that maternal level of education was significantly associated with smoking, BMI, use of drugs, and anxiety [24]. Education is critical to social and economic development and has a profound impact on population health [25]. Gravidity, also implicating shorter inter-pregnancy intervals, was higher for the women in the SPTB group. They smoked more cigarettes per day and used more methamphetamine during pregnancy. These risks are supported by the literature, as smoking and use of recreational drugs both contribute to a suboptimal intrauterine environment [10].
The mean gestational age at enrollment was the same in the SPTB and STB groups. However, at enrollment, maternal level of education, weight, length, MUAC, head circumference, and BMI already differed significantly. Thus, it can be postulated that the trigger for PTB takes place long before PTB occurs and therefore makes determination (and treatment) thereof even more difficult.
SGA newborns were found in 60 (27.8%), 776 (19.0%), and 64 (12.9%) of the IPTB, STB, and SPTB groups, respectively. It might be that women with very SGA infants were induced before SPTB could occur (IPTB group) or that women had late onset FGR (STB group). Another explanation is that FGR developed later and the SGA infants now increased in the STB group. The mean birth weight Z-scores were higher for SPTB infants compared to STB and IPTB infants. It was suspected that the lower prevalence of FGR in the SPTB group played a role here. The significantly higher frequency of diabetes in the SPTB group may also contribute to potential fetal macrosomia and hence higher Z-scores. Conditions that affect fetal growth might differ from conditions that trigger PTB with two different operating mechanisms.
BMI (with co-linear variables maternal weight and MUAC) was found to be the best predictor variable for SPTB, followed by gravidity (with co-linear variable daily cigarettes), education (with co-linear variables Edinburgh Depression Score and household income), and maternal length (with co-linear variable maternal head circumference). It is difficult to identify the role of poor SES (seen here as education, household income, and partly contributing to BMI) more specifically.
As PTB is a multifactorial complex condition, attention must be focused on the underlying factors and conditions. SPTB appears to be different from IPTB and should not be placed in the same category. Programs to reduce the high prevalence rate of SPTB should include improving education and poor socioeconomic conditions, as these will help to improve unhealthy lifestyle choices. Preventing or addressing underlying medical problems such as hypertension should help reduce the need for preterm delivery.
Synopsis.
Spontaneous and induced preterm birth differ. Addressing education, lifestyle, poor socioeconomic conditions, and medical problems such as hypertension are important.
Acknowledgments
The study was funded by the National Institute on Alcohol Abuse and Alcoholism, Eunice Kennedy Shriver National Institute of Child Health and Human Development, and National Institute on Deafness and Other Communication Disorders: U01 HD055154, U01 HD045935, U01 HD055155, U01 HD045991, and U01 AA016501.
Footnotes
Conflicts of interest
The authors have no conflicts of interest.
References
- 1.Brink LT, Gebhardt GS, Mason D, Groenewald CA, Odendaal HJ. The association between preterm labour, perinatal mortality and infant death (during the first year) in Bishop Lavis, Cape Town, South Africa. South African Med J. 2019;109(2):102. [DOI] [PubMed] [Google Scholar]
- 2.Howson Eds CP, Kinney JLWHOG MV. Born too soon. Born Too Soon, Glob Action Rep Preterm Birth Eds CP Howson, MV Kinney, JE Lawn World Heal Organ Geneva [Internet]. 2012;13(5):1–126. Available from: http://www.ncbi.nlm.nih.gov/pubmed/23911366 [Google Scholar]
- 3.Odendaal HJ, Dukes K, Elliott A. Stillbirth and Prenatal Exposure to Alcohol and Cigarettes GLASGOW ABSTRACT, 2018 International Conference on Stillbirth, SIDS, and Baby Survival, Glasgow, Scotland; 7–9 June 2018. [Google Scholar]
- 4.Rhoda N, Velaphi S, Gebhardt GS, Kauchali S, Barron P. Reducing neonatal deaths in South Africa: Progress and challenges. South African Med J. 2018;108(3a):9. [Google Scholar]
- 5.Joseph KS, Mb RML, Dodds L, Dahlgren L, Allen AC. R esearch Socioeconomic status and perinatal outcomes in a setting. C Can Med Assoc J = J l’Association medicale Can. 2007;177(6):3–10. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Myers B, Koen N, Donald KA, et al. Effect of Hazardous Alcohol Use During Pregnancy on Growth Outcomes at Birth: Findings from a South African Cohort Study. Alcohol Clin Exp Res. 2018;42(2):369–77. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.May PA, Gossage JP, Marais AS, et al. The epidemiology of fetal alcohol syndrome and partial FAS in a South African community. Drug Alcohol Depend. 2007;88(2–3):259–71. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Jones HE, Browne FA, Myers BJ, et al. Pregnant and Nonpregnant Women in Cape Town, South Africa: Drug Use, Sexual Behavior, and the Need for Comprehensive Services. Int J Pediatr [Internet]. 2011;2011:1–8. Available from: http://www.hindawi.com/journals/ijpedi/2011/353410/ [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Seabrook JA, Avison WR. Socioeconomic Status and Cumulative Disadvantage for Health Outcomes. Can Rev Sociol. 2012;49:50–68. [DOI] [PubMed] [Google Scholar]
- 10.Baba S, Wikström AK, Stephansson O, Cnattingius S. Influence of smoking and snuff cessation on risk of preterm birth. Eur J Epidemiol. 2012;27(4):297–304. [DOI] [PubMed] [Google Scholar]
- 11.Solaski M, Majnemer A, Oskoui M. Contribution of socio-economic status on the prevalence of cerebral palsy: A systematic search and review. Dev Med Child Neurol. 2014;56(11):1043–51. [DOI] [PubMed] [Google Scholar]
- 12.Dukes KA, Burd L, Elliott AJ, et al. The safe passage study: Design, methods, recruitment, and follow-up approach. Paediatr Perinat Epidemiol. 2014;28(5):455–65. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Villar J, Papageorghiou AT, Pang R, et al. The likeness of fetal growth and newborn size across non-isolated populations in the INTERGROWTH-21st project: The fetal growth longitudinal study and newborn cross-sectional study. Lancet Diabetes Endocrinol. 2014;2(10):781–92. [DOI] [PubMed] [Google Scholar]
- 14.Goldenberg RL, Gravett MG, Iams J, et al. The preterm birth syndrome: Issues to consider in creating a classification system. Am J Obstet Gynecol [Internet]. 2012;206(2):113–8. Available from: 10.1016/j.ajog.2011.10.865 [DOI] [PubMed] [Google Scholar]
- 15.Räisänen S, Gissler M, Saari J, Kramer M, Heinonen S. Contribution of Risk Factors to Extremely, Very and Moderately Preterm Births - Register-Based Analysis of 1,390,742 Singleton Births. PLoS One. 2013;8(4):1–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Muglia LJ, Katz M. The enigma of spontaneous preterm birth. New Engl J Med. 2010;362:529–35. [DOI] [PubMed] [Google Scholar]
- 17.Sibai BM. Preeclampsia as a cause of preterm and late preterm (near-term) births. Semin Perinatol. 2006;30(1):16–9. [DOI] [PubMed] [Google Scholar]
- 18.Hall DR, Grové D, Carstens E. Early pre-eclampsia: What proportion of women qualify for expectant management and if not, why not? Eur J Obstet Gynecol Reprod Biol. 2006;128(1–2):169–74. [DOI] [PubMed] [Google Scholar]
- 19.Triunfo S, Lanzone A. Impact of overweight and obesity on obstetric outcomes. J Endocrinol Invest. 2014;37(4):323–9. [DOI] [PubMed] [Google Scholar]
- 20.Plunkett J, Muglia LJ. Genetic contributions to preterm birth : Implications from epidemiological and genetic association studies. Annals of Medicine 2008; 40 (3):167–179. [DOI] [PubMed] [Google Scholar]
- 21.Davies H, Visser J, Tomlinson M, Rotherham-Borus M, LeRoux I, Gissane C. An Investigation into the Influence of Socioeconomic Variables on Gestational Body Mass Index in Pregnant Women Living in a Peri-Urban Settlement, South Africa. Matern Child Heal J. 2012;16(8):1732–41. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Behrman RE, Butler AS, Editors. Preterm birth: causes, consequences, and prevention Preterm birth: causes, consequences and prevention. Washington DC: National Academies Press; 2007; 2007. 772 p. [PubMed] [Google Scholar]
- 23.Grote NK, Bridge JA, Gavin AR, Melville JL, Iyengar S, Katon WJ. A meta-analysis of depression during pregnancy and the risk of preterm birth, low birth weight, and intrauterine growth restriction. Arch Gen Psychiatry [Internet]. 2010;67(10):1012–24. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-77957708524&partnerID=40&md5=b9765d1e7499f4fea80e72f83441f226 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Campbell EE, Gilliland J, Dworatzek PDN, De Vrijer B, Penava D, Seabrook JA. Socioeconomic status and adverse birth outcomes: A population-based Canadian sample. J Biosoc Sci. 2018;50(1):102–13. [DOI] [PubMed] [Google Scholar]
- 25.Kaplan R, Spittel M, David D (Eds). Population Health: Behavioral and Social Science Insights / Understanding the Relationship Between Education and Health [Internet]. AHRQ Publication; 2015. Available from: https://www.ahrq.gov/professionals/education/curriculum-tools/population-health/satterfield.html%0Ahttps://www.ahrq.gov/cpi/about/index.html [Google Scholar]