RESEARCH ARTICLE Open Access Socioeconomic disparities in
Transcript Of RESEARCH ARTICLE Open Access Socioeconomic disparities in
Ng et al. BMC Pregnancy and Childbirth 2014, 14:314 http://www.biomedcentral.com/1471-2393/14/314
RESEARCH ARTICLE
Open Access
Socioeconomic disparities in prepregnancy BMI and impact on maternal and neonatal outcomes and postpartum weight retention: the EFHL longitudinal birth cohort study
Shu-Kay Ng1*, Cate M Cameron2, Andrew P Hills3, Roderick J McClure4 and Paul A Scuffham1
Abstract
Background: Long-term obesity after pregnancy is associated with obesity prior to pregnancy and retention of weight postpartum. This study aims to identify socioeconomic differences in prepregnancy body mass index, quantify the impact of prepregnancy obesity on birth outcomes, and identify determinants of postpartum weight retention.
Methods: A total of 2231 pregnant women, recruited from three public hospitals in Southeast Queensland in Australia during antenatal clinic visits, completed a questionnaire to elicit information on demographics, socioeconomic and behavioural characteristics. Perinatal information was extracted from hospital records. A follow-up questionnaire was completed by each participant at 12 months after the birth to obtain the mother’s postpartum weight, breastfeeding pattern, dietary and physical activity characteristics, and the child’s health and development information. Multivariate logistic regression method was used to model the association between prepregnancy obesity and outcomes.
Results: Being overweight or obese prepregnancy was strongly associated with socioeconomic status and adverse behavioural factors. Obese women (18% of the cohort) were more likely to experience gestational diabetes, preeclampsia, cesarean delivery, and their children were more likely to experience intensive- or special-care nursery admission, fetal distress, resuscitation, and macrosomia. Women were more likely to retain weight postpartum if they consumed three or fewer serves of fruit/vegetables per day, did not engage in recreational activity with their baby, spent less than once a week on walking for 30 minutes or more or spent time with friends less than once per week. Mothers who breastfed for more than 3 months had reduced likelihood of high postpartum weight retention.
Conclusions: Findings provide additional specificity to the increasing evidence of the predisposition of obesity prepregnancy on adverse maternal and perinatal outcomes. They may be used to target effective behavioural change interventions to address obesity in women.
Keywords: Birth cohort, Obesity, Postpartum weight retention, Body mass index, Obstetric-neonatal outcome
* Correspondence: [email protected] 1School of Medicine, Griffith Health Institute, Griffith University, Brisbane, QLD 4131, Australia Full list of author information is available at the end of the article
© 2014 Ng et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
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Background In Australia, approximately one-third of all pregnant women are overweight or obese [1]. A similar prevalence of overweight and obesity among pregnant women has been observed in the United States [2]. In both countries the proportion of pregnant women who are overweight or obese is increasing [3,4].
Pregnant women who are overweight or obese have a disproportionate risk of induced preterm delivery [5] and maternal, intrapartum, peripartum, neonatal [6,7], and postpartum complications including gestational diabetes mellitus (GDM), type 2 diabetes, high blood pressure, dyslipidaemia, cardiovascular disease and several major cancers [7,8]. The offspring of these women also have a significantly elevated risk of adverse short- and long-term health issues [9-11]. For example, children of women with GDM are more likely to be obese and have impaired glucose tolerance and diabetes in childhood and adulthood [12,13].
Increased risk of being overweight after the first and subsequent pregnancies is associated with the level of obesity prior to pregnancy [14], gestational weight gain above the recommended guidelines [15,16], and failure to lose gestational weight in a reasonable timeframe (excessive postpartum weight retention) [17]. Substantial evidence also links net weight gain after pregnancy to obesity in later life [18] and shows that women who fail to lose weight postpartum have a higher risk of subsequent long-term obesity [19].
It has been shown that adverse factors such as lack of nutrition knowledge [20,21], poor dietary habits and physical inactivity [22,23] could contribute to being overweight or obese during pregnancy as well as having high postpartum weight gain and/or retention. Findings from a recent retrospective cohort study [4] confirmed the commonly described association between maternal obesity, lower socioeconomic status [24,25] and indicated the role of adverse health behaviours in explaining this socioeconomic status differential [26]. The importance of this finding relates to the potential for addressing the high prevalence of overweight and obesity among pregnant women through screening and targeted behaviour change interventions in high-risk groups. Interventions based on specific knowledge of the subgroups at greatest risk and the modifiable behavioural determinants would lead to substantial population benefit by interrupting the transgenerational repeating cycle of risk [25].
There is currently insufficient knowledge to generate and refine targeted public health interventions to reduce transgenerational obesity because population-based studies on the impact of obesity on birth outcomes are relatively scant, especially country-specific studies such as for Australia. The present study is a prospective and multi-year longitudinal birth cohort study, and collects a spectrum of eco-
epidemiological factors [27]. It thus offers a unique opportunity to understand the various exposures that have impact on birth and postpartum outcomes. The aims of this study were to identify any socioeconomic differential in prepregnancy body mass index (BMI), to quantify the impact of prepregnancy obesity on maternal and neonatal outcomes, and to identify determinants that are associated with postpartum weight retention. The identification of the socioeconomic differential in prepregnancy obesity and the modifiable risk factors in excessive weight retention postpartum will be useful for targeting future behavioural change interventions, identifying population groups who would benefit from public health interventions, and promoting research in women’s health to address the problem of obesity.
Methods This prospective cohort study was conducted and reported in accordance with the STROBE guidelines (http://www. strobe-statement.org/).
Study design and subjects The birth cohort “Environments for Healthy Living” (EFHL) is a population-based longitudinal study which commenced the pilot phase of recruitment in November 2006 and open recruitment in August 2007 to investigate the relationship between social, environmental and behavioural factors and the health and development of children in Southeast Queensland, Australia [27]. The study area contains an estimated population of over 1 300 000 people or approximately 30% of Queensland’s population. The study region is markedly heterogeneous with respect to socioeconomic distribution; in particular, the Health Districts of the study region are known to have higher proportions of socio-economic disadvantage than the national average [28,29].
Women who planned to give birth at one of three participating hospitals were eligible to participate and enrol their baby in this study. Pregnant women aged less than 16 years or unable to provide informed consent were excluded [27]. Written informed consent was obtained for release of hospital perinatal data related to the birth of each child, completion of a participant baseline survey and for individual follow-up. During the first four open recruitment phases of the study (2007 to 2010), the total number of mothers approached was 5149, of whom 2254 women (43.8%) agreed to participate and 2277 babies have been registered with the study (including 23 sets of twins).
Following recruitment, a questionnaire was completed by each mother to elicit baseline information on demographics, socioeconomic status, family structure, behavioural and pregnancy characteristics. Perinatal information was extracted from hospital birth records. Follow-up routinely
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occurs when each child reaches 1, 3, and 5 years of age [27]. Information on maternal physical activity, dietary intake and breastfeeding duration, familial and social exposures as well as child health was collected via self-report questionnaires. In this study, multiple births were excluded (n = 46), leaving 2231 mothers and babies at baseline. Of 2231 mothers, 2009 (90%) have complete information on prepregnancy BMI. At 1-year follow-up, 1426 mothers (63.9% of 2231) returned questionnaires. Of 1426 mothers, 1316 (92%) have complete information on prepregnancy BMI and maternal weight at 1-year follow up. Figure 1 presents a flow diagram of recruitment and loss to follow-up for the present study.
Measurements A wide variety of health-related exposures and outcomes variables were measured at baseline (self-report questionnaire and hospital birth record) and during the 1-year follow-up (self-report questionnaire) with variables classified under the following domains: (a) Maternal; (b) Nutrition and physical activity factors; (c) Household and family; (d) Pregnancy; and (e) Child factors.
Maternal characteristics measured at baseline included self-reported prepregnancy weight and height, place of birth, maternal age, education level, employment status, marital status, smoking, alcohol and ‘over the counter’ medications intake patterns for non-medical purposes
Figure 1 Flow diagram of recruitment and loss to follow-up for the EFHL cohorts 2007 – 2010.
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during pregnancy, and psychological distress – measured using the Kessler-6 (K6) psychological distress scale. The K6 has been widely used and has demonstrated excellent internal consistency and reliability [30,31]. Three levels of risk of psychological distress were considered on the basis of the overall score of the 6 items: Low-risk (0–7); Medium-risk (8–12); and High-risk (13+) [32]. Using the self-reported prepregnancy weight and height, prepregnancy BMI was classified on the basis of the World Health Organisation (WHO) criteria (underweight: <18.5 kg/m2; normal-weight: 18.5– 24.9 kg/m2; overweight: 25.0–29.9 kg/m2; obese: ≥ 30 kg/ m2) [33]. At 1-year follow-up maternal measurements included self-reported postpartum weight, psychological distress, frequency of 5 or more (5+) alcoholic drinks on one occasion (once per week or more often compared to less than once per week), having a paid job after birth, and frequency of spending time with family and friends. Postpartum weight retention was calculated by subtracting the prepregnancy weight from the current weight at 1-year follow-up.
Nutrition and physical activity factors measured at baseline are mineral and vitamin supplements taken prior to and during pregnancy (Iron, Zinc, Calcium, Folic-acid, Multivitamins, Vitamin-C, Vitamin-E, Pregnancy/Breastfeeding supplement). Factors measured at 1-year follow up included breastfeeding duration, fruit and vegetable intake, frequency of takeaways, attendance at recreational activities with baby (such as, play groups, mothers’ groups, or swimming classes), and the frequencies of moderate physical activities and walking (30 minutes or more).
Household and family factors were obtained at baseline (including partner’s education level and employment status, household ownership and number of children under 16 years of age) and during the follow-up period at 1-year (including the frequency of being a single parent for 1 month or longer, whether the mother is living with the biological father of the child, spouse or partner, whether their relationship status changed, environment with child exposure to passive smoking, and frequency of eating takeaway food).
Variables in the pregnancy domain were obtained from hospital birth record, which included parity, pre-existing hypertension, obstetric complications (hyperglycaemia, gestational diabetes, preeclampsia, cesarean delivery), and neonatal complications (fetal distress, admission to an intensive or special care nursery, jaundice, respiratory distress, congenital anomaly, resuscitation procedure).
Child characteristics included gender, birthweight, and at 1-year mother-reported weight gain, injury, and indication of health-related conditions (asthma, chest infection) diagnosed by a medical doctor. Macrosomia was defined as birthweight > 4 kg, irrespective of gestational age [34].
Ethical approval Approval for the study was obtained from the Griffith University Ethics Committee and the Human Research Ethics Review Committees of the three participating public maternity hospitals (Logan, Gold Coast and Tweed Hospitals) in the study area.
Statistical analysis Data were analysed using IBM SPSS-22 (IBM, Chicago, IL). Baseline characteristics of the EFHL cohort were compared with all births with gestation of 28 weeks or more in the study region between 2007 and 2010, in an attempt to address potential selection bias. Missing data including loss to follow-up were handled using a complete-case approach. Measures of association and ordinal association between prepregnancy BMI groups and categorical variables were obtained using Pearson’s chi-square and Gamma statistics, respectively. Comparisons of continuous variables between the four prepregnancy BMI groups were tested using one-way ANOVA (with Tukey’s post-hoc multiple test). Multiple logistic regression was performed to identify pregnancy and neonatal problems that associate with the prepregnancy underweight, overweight and obese mothers. Adjusted odds ratios (ORs) with 95% confidence intervals (CIs) were calculated after accounting for potential confounding factors that are significantly associated with the prepregnancy BMI groups. These include maternal age, employment and education, alcohol intake and smoking during pregnancy, non-medical drug use, primiparity, pre-existing hypertension, marital status, paternal employment and education, household ownership, and the number of children living in the household aged under 16 years. The year of recruitment was also considered as a potential confounder, given temporal differences were identified for some antenatal exposures [29]. Polynomial contrast within multiple logistic regression models were used to test the linear trend of adjusted ORs.
Cut-off points for high postpartum weight retention were determined separately for three prepregnancy BMI groups (normal, overweight, and obese) by the highest weight retention quintile. This group of women with high postpartum weight retention (weight retention > top quintile) thus contains about 20% of women in each of the three pregnancy BMI groups. Multiple logistic regression was conducted to identify risk factors at 12-month follow-up that are associated with high postpartum weight retention. Adjusted ORs with 95% CIs were calculated, after accounting for potential confounding factors including the year of recruitment, the prepregnancy BMI grouping, and those factors defined above.
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Results
Sample characteristics Baseline characteristics of the EFHL cohort are displayed in Table 1 along with corresponding details of births with gestation of 28 weeks or more in the study region between 2007–2010. The birth cohort sample did not differ significantly from the general population for gender, plurality, or birth outcome. However, our sample had a smaller proportion of mothers who were younger than 20 years of age and infants who were born between
28 and 36 weeks gestation. Moreover, the percentage of low birthweight infants (<2500 g) was approximately half that of babies born in the general population.
Prepregnancy BMI The baseline descriptive characteristics of participants stratified by the prepregnancy BMI is shown in Table 2. Underweight women were younger compared to women with a normal prepregnancy BMI (reference group). On the other hand, compared to women with a normal
Table 1 Baseline characteristics of the cohort and comparisons with all births in the study region (with gestational
28 weeks or more)
Frequency (percentagea)
Characteristics
Birth cohort sample (Years 2007 to 2010) 2254 women 2277 babies
Deliveries in regionb (Years 2007 to 2010) 36620 women 37061 babies
p-valuec (two-sided)
Maternal age
< 20 years
107 (4.8%)
2559 (7.0%)
< 0.001
20-24 years
438 (19.9%)
6407 (17.6%)
25-29 years
632 (28.6%)
10376 (28.5%)
30-34 years
560 (25.4%)
10088 (27.7%)
≥ 35 years
469 (21.3%)
6980 (19.2%)
Missing data
48
0
Gender of infant
Male
1127 (50.3%)
18914 (51.3%)
0.324
Female
1115 (49.7%)
17926 (48.7%)
Missing data
35
0
Birthweight
< 2500 g
55 (2.5%)
1800 (4.9%)
< 0.001
2500-3999 g
1809 (81.5%)
30207 (82.0%)
≥ 4000 g
355 (16.0%)
4831 (13.1%)
Missing data
58
2
Gestational age at birth
28-36 weeks
79 (3.5%)
2376 (6.4%)
< 0.001
37-41 weeks
2131 (95.9%)
34264 (93.0%)
42 weeks
13 (0.6%)
200 (0.5%)
Missing data
54
0
Plurality
Singleton
2207 (98.0%)
35980 (97.7%)
Multiple
46 (2.0%)
860 (2.3%)
0.370
Missing data
24
0
Outcome
Live birth
2273 (99.8%)
36728 (99.7%)
Stillbirth
4 (0.2%)
112 (0.3%)
0.274
Missing data
0
0
aPercentages are calculated based on the available (non-missing) data. bData include only deliveries with gestational 28 weeks or more; data are provided by Queensland Health and New South Wales Health for the study region
(Logan, Beaudesert, Gold Coast and Tweed). cChi-square test for comparing proportions between birth cohort sample and the general population.
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Table 2 Baseline characteristics of participants stratified by the prepregnancy BMI (n = 2009)
Prepregnancy BMI
Underweight Normal weight Overweight Obese (BMI ≥ 30) (BMI < 18.5) (BMI 18.5-24.99) (BMI 25-29.99)
Characteristics
(N=167)
(N= 1052)
(N= 430)
(N= 360)
Maternal Characteristics:
Maternal age (y)
< 20
12 (7.3%)
48 (4.7%)
14 (3.3%)
11 (3.1%)
20-24
49 (29.7%)
198 (19.2%)
69 (16.3%)
74 (21.0%)
25-29
38 (23.0%)
293 (28.4%)
135 (31.8%)
99 (28.0%)
30-34
33 (20.0%)
280 (27.2%)
119 (28.1%)
89 (25.2%)
≥ 35
33 (20.0%)
212 (20.6%)
87 (20.5%)
80 (22.7%)
Missing data
2
21
6
7
Born in Australia
114 (68.3%)
733 (69.7%)
312 (72.6%)
259 (71.9%)
Missing data
0
1
0
0
Maternal employment
Employed
75 (46.3%)
564 (54.5%)
209 (49.2%)
169 (48.3%)
Unemployed
26 (16.0%)
113 (10.9%)
42 (9.9%)
36 (10.3%)
Not in-labour force
61 (37.7%)
358 (34.6%)
174 (40.9%)
145 (41.4%)
Missing data
5
17
5
10
Marital status
Single
15 (9.0%)
65 (6.2%)
20 (4.7%)
31 (8.6%)
Dating relationship
14 (8.4%)
32 (3.1%)
17 (4.0%)
22 (6.1%)
De facto
76 (45.5%)
382 (36.4%)
158 (37.0%)
126 (35.0%)
Married
62 (37.1%)
557 (53.1%)
225 (52.7%)
175 (48.6%)
Separate/Divorce/Widow
0 (0%)
13 (1.2%)
7 (1.6%)
6 (1.7%)
Missing data
0
3
3
0
Maternal education level
Primary school
2 (1.2%)
9 (0.9%)
4 (0.9%)
4 (1.1%)
Not complete secondary
37 (22.3%)
176 (16.8%)
85 (20.0%)
77 (21.5%)
Secondary school
50 (30.1%)
301 (28.7%)
137 (32.2%)
129 (36.0%)
TAFE/Trade
46 (27.7%)
306 (29.2%)
129 (30.3%)
107 (29.9%)
University
31 (18.7%)
257 (24.5%)
71 (16.7%)
41 (11.5%)
Missing data
1
3
4
2
Nutrition Factors:
Supplements Intake
Iron
51 (30.5%)
301 (28.6%)
118 (27.4%)
67 (18.6%)
Zinc
37 (23.3%)
196 (19.8%)
80 (19.2%)
53 (15.4%)
Calcium
27 (16.2%)
139 (13.2%)
40 (9.3%)
32 (8.9%)
Folic acid
84 (52.5%)
511 (51.5%)
222 (53.0%)
166 (47.7%)
Multivitamins
93 (55.7%)
548 (52.1%)
214 (49.8%)
140 (38.9%)
Vitamin C
55 (34.6%)
337 (34.1%)
133 (32.0%)
80 (23.2%)
Vitamin E
113 (67.7%)
735 (69.9%)
280 (65.1%)
191 (53.1%)
Pregnancy/breastfeeding sup.
38 (23.9%)
193 (19.6%)
91 (21.9%)
40 (11.5%)
Household and Family Structure:
Partner’s employment
p-value (two-sided) 0.017*b
0.589 0.038*b
0.001*b
<0.001*b
0.001*a0.039*a0.001*a0.499 <0.001*a0.001*a<0.001*a0.004*a-
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Table 2 Baseline characteristics of participants stratified by the prepregnancy BMI (n = 2009) (Continued)
Employed
142 (89.9%)
931 (93.7%)
392 (96.8%)
301 (90.7%)
Unemployed
10 (6.3%)
37 (3.7%)
7 (1.7%)
17 (5.1%)
Not in-labour force
6 (3.8%)
26 (2.6%)
6 (1.5%)
14 (4.2%)
Missing data
9
58
25
28
Partner’s education level
Primary school
4 (2.6%)
9 (0.9%)
5 (1.2%)
0 (0%)
Not complete secondary
33 (21.2%)
182 (18.2%)
89 (21.9%)
94 (28.6%)
Secondary school
41 (26.3%)
243 (24.3%)
114 (28.0%)
89 (27.1%)
TAFE/Trade
52 (33.3%)
421 (42.2%)
150 (36.9%)
120 (36.5%)
University
26 (16.7%)
143 (14.3%)
49 (12.0%)
26 (7.9%)
Missing data
11
54
23
31
Household ownership
Own outright
6 (3.7%)
33 (3.2%)
3 (0.7%)
3 (0.8%)
Own with mortgage
52 (31.9%)
457 (44.3%)
189 (44.9%)
121 (34.1%)
Private rent
72 (44.2%)
446 (43.2%)
193 (45.8%)
178 (50.1%)
Public housing
5 (3.1%)
23 (2.2%)
7 (1.7%)
13 (3.7%)
Board with parents
28 (17.2%)
73 (7.1%)
29 (6.9%)
40 (11.3%)
Missing data
4
20
9
5
Number of children under 16 y (household)
Nil
78 (47.3%)
413 (39.7%)
134 (31.6%)
115 (31.9%)
1-3
83 (50.3%)
595 (57.2%)
279 (65.8%)
217 (60.3%)
More than 3
4 (2.4%)
33 (3.2%)
11 (2.6%)
28 (7.8%)
Missing data
2
11
6
0
Maternal Health and Behaviours During Pregnancy:
Maternal psychological distress
Low risk
128 (77.6%)
853 (81.6%)
352 (83.0%)
280 (78.4%)
Medium risk
30 (18.2%)
158 (15.1%)
64 (15.1%)
61 (17.1%)
High risk
7 (4.2%)
34 (3.3%)
8 (1.9%)
16 (4.5%)
Missing data
2
7
6
3
Smoke during pregnancy
59 (35.3%)
233 (22.2%)
109 (25.5%)
108 (30.3%)
Missing data
0
1
3
3
Alcohol intake
69 (41.3%)
536 (51.0%)
202 (47.4%)
137 (38.3%)
Missing data Non-medical drug usea
0 29 (17.6%)
0 98 (9.5%)
4 35 (8.3%)
2 42 (11.8%)
Missing data
2
21
8
3
Birth-related Factors:
Primiparity
97 (58.8%)
674 (64.8%)
313 (73.5%)
267 (75.2%)
Missing data
2
12
4
5
Baby gender (Male)
80 (48.5%)
527 (50.9%)
201 (47.2%)
182 (51.3%)
Missing data
2
16
4
5
Pre-existing hypertension
0 (0%)
6 (0.7%)
3 (0.8%)
8 (2.4%)
Missing data
23
144
48
26
*Significant at the 0.05 level. aTest for ordinal association using Gamma coefficient (+indicates a positive association; −indicates a negative association).
bTest for association using chi-square test.
0.017*b
<0.001*b
<0.001*b
<0.001*a+
0.343
<0.001*b 0.001*b 0.005*b <0.001*a+ 0.564 0.021*a+
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prepregnancy BMI, overweight and obese women had lower education level. Their partners were more likely to be unemployed or not in the labour force, and had lower education level. Obese and underweight women were less likely to be married, own their own home, and consume alcohol during pregnancy. However, they were more likely to smoke during pregnancy and be engaged in non-medical drug use. Supplement intake prior to and during pregnancy was different between the four prepregnancy BMI groups. There were moderate negative ordinal relationships between intake of major supplements and prepregnancy BMI with obese women less likely to take supplements of all types considered in the study, especially iron, multivitamins, vitamins-C and E. There was a moderate positive ordinal relationship between number of children under 16 years of age in the household and prepregnancy BMI (0.170, p < 0.001), indicating that the proportion of prepregnancy obesity increases when the number of children under 16 years old increases. A moderate positive ordinal relationship between parity and prepregnancy BMI was also observed (0.201, p < 0.001), indicating that the proportion of prepregnancy obesity increases for mothers who have had previous pregnancies. The strong relationship between pre-existing hypertension and prepregnancy BMI (0.495, p = 0.021) confirms that obese women are more likely to have the pre-existing condition.
Pregnancy and neonatal outcomes The impact of prepregnancy obesity on pregnancy and neonatal outcomes after adjusting for confounders is presented in Table 3. With an increasing level of obesity prior to pregnancy, a linear trend of increasing risk of adverse outcomes was observed for hyperglycaemia (p for trend = 0.024), cesarean delivery (p for trend < 0.001), intensive or special care nursery admission (p for trend < 0.001), resuscitation (p for trend = 0.001), and macrosomia (p for trend < 0.001). In addition to these adverse outcomes, obese women were also more likely to have GDM (adjusted OR = 2.327) and preeclampsia (adjusted OR = 3.143). Further, their newborns had also a higher chance of having fetal distress (adjusted OR = 1.870). Babies of overweight women were also more likely to have jaundice (adjusted OR = 1.762).
Mothers who completed and returned the 12-month follow-up questionnaire (63.9% of 2231) were more likely to be born in Australia (p = 0.013), married (p < 0.001), older (p < 0.001), employed (p < 0.001), had lower likelihood of psychological distress based on K6 scale (p < 0.001), consumed alcohol during pregnancy (p < 0.001), had higher education level (p < 0.001) and household income (p < 0.001), than those who did not return questionnaires. Further, they were less likely to have underweight or obese prepregnancy BMI (p = 0.009), or smoke during
pregnancy (p < 0.001). There were no significant differences in gender of the babies.
Outcomes at 12-month follow-up The 12-month follow-up descriptive characteristics of participants and their babies are given in Table 4. There was a moderate negative ordinal relationship (−0.219, p < 0.001) between breastfeeding duration and prepregnancy BMI, indicating that the proportion of mothers who breastfed longer than or equal to 3 months decreased when prepregnancy BMI increased. Further, compared to women with a normal prepregnancy BMI, overweight, obese and underweight women were less likely to attend a play group or other recreational activity with their babies (p = 0.002) but are more likely to have medium or high risks of psychological distress (p = 0.032) and more frequently a lone parent (p = 0.030). A significant difference in weight retention at 12 months was observed between the four prepregnancy BMI groups (p < 0.001). There was a moderate positive ordinal relationship (0.221, p = 0.004) between asthma and prepregnancy BMI, indicating that the proportion of babies with asthma increases when prepregnancy BMI increases. The difference in weight gain of infants between normal and overweight mothers prepregnancy was also significant (p = 0.019).
Postpartum weight retention In Table 5, the determinants of high postpartum weight retention (within the top quintile) for the normal, overweight, and obese prepregnancy BMI groups, with adjustments for potential confounding factors that are significantly associated with the prepregnancy BMI groups, are presented. Women were more likely to have high postpartum weight retention if they consumed three or less serves of fruit/vegetables (adjusted OR = 2.005, p = 0.001), did not engage in recreational activity with their babies (adjusted OR = 1.916, p < 0.001), spent less than once a week on walking for 30 minutes or more (adjusted OR = 1.691, p = 0.029) or spent time with friends less than once per week (adjusted OR = 1.695, p = 0.040). However, mothers who breastfed for more than 3 months had reduced the chance of high postpartum weight retention (adjusted OR = 0.673, p = 0.030).
Discussion An important contributing factor to the development of long-term obesity in women is excessive weight retention after pregnancy [17,19]. There is marked variability in weight changes that are associated with pregnancy and pregnancy is a critical period for prevention of obesity in women [35,36]. This large cohort study has contributed to the growing evidence that helps to target interventions during pregnancy by the identification of
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Table 3 Impact of prepregnancy obesity on pregnancy and neonatal outcomes (n = 2009)
Outcome
Frequency (%)
Adjusted ORa (95% CI)
Obstetric Complication:
Hyperglycaemia
Normal weight
65 (8.0%)
Reference
Underweight
5 (3.7%)
0.532 (0.207 – 1.371)
Overweight
35 (9.7%)
1.210 (0.754 – 1.940)
Obesity Missingb
38 (11.8%) 382
1.464 (0.921 – 2.328)
Gestational diabetes
Normal weight
21 (2.4%)
Reference
Underweight
0 (0%)
—
Overweight
7 (2.0%)
1.040 (0.396 – 2.732)
Obesity
11 (3.6%)
2.327 (1.037 – 5.223)
Missing
337
Preeclampsia
Normal weight
25 (3.1%)
Reference
Underweight
0 (0%)
—
Overweight
11 (3.3%)
1.184 (0.559 – 2.505)
Obesity
29 (9.8%)
3.143 (1.694 – 5.830)
Missing
430
Cesarean delivery
Normal weight
238 (23.0%)
Reference
Underweight
38 (23.0%)
0.950 (0.588 – 1.536)
Overweight
146 (34.4%)
1.802 (1.349 – 2.409)
Obesity
127 (35.9%)
2.229 (1.646 – 3.018)
Missing
32
Neonatal Complication:
Intensive/Special care admission
Normal weight
117 (11.3%)
Reference
Underweight
15 (9.1%)
0.590 (0.296 – 1.176)
Overweight
67 (15.9%)
1.339 (0.921 – 1.947)
Obesity
73 (20.6%)
1.839 (1.269 – 2.665)
Missing
36
Fetal distress
Normal weight
62 (6.7%)
Reference
Underweight
17 (11.3%)
2.178 (1.147 – 4.133)
Overweight
24 (6.0%)
1.222 (0.706 – 2.113)
Obesity
33 (9.6%)
1.870 (1.103 – 3.169)
Missing
191
Jaundice
Normal weight
39 (4.8%)
Reference
Underweight
11 (8.2%)
1.782 (0.821 – 3.866)
Overweight
30 (8.4%)
1.762 (1.017 – 3.053)
Obesity
22 (6.8%)
1.592 (0.882 – 2.872)
Missing
382
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p-value (2-sided)
0.192 0.430 0.107 Trendc (0.024*)
— 0.937 0.041*
— 0.660 < 0.001*
0.834 <0.001* <0.001* Trend (< 0.001*)
0.134 0.126 0.001* Trend (<0.001*)
0.017* 0.474 0.020* Trend (0.143)
0.144 0.044* 0.123 Trend (0.163)
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Table 3 Impact of prepregnancy obesity on pregnancy and neonatal outcomes (n = 2009) (Continued)
Respiratory distress
Normal weight
38 (4.7%)
Reference
Underweight
6 (4.5%)
0.879 (0.332 – 2.330)
0.796
Overweight
20 (5.6%)
1.275 (0.700 – 2.322)
0.427
Obesity
22 (6.8%)
1.294 (0.700 – 2.394)
0.411
Missing
382
Trend (0.288)
Congenital anomaly
Normal weight
62 (7.3%)
Reference
Underweight
10 (7.3%)
1.362 (0.662 – 2.804)
0.401
Overweight
26 (7.1%)
0.966 (0.559 – 1.668)
0.901
Obesity
25 (7.9%)
1.179 (0.692 – 2.011)
0.544
Missing
343
Trend (0.868)
Macrosomia (birthweight >4kg)
Normal weight
140 (13.6%)
Reference
Underweight
10 (6.2%)
0.389 (0.176 – 0.859)
0.019*
Overweight
70 (16.5%)
1.334 (0.945 – 1.884)
0.101
Obesity
93 (26.3%)
2.262 (1.619 – 3.160)
< 0.001*
Missing
43
Trend (< 0.001*)
Resuscitation
Normal weight
253 (26.1%)
Reference
Underweight
32 (20.9%)
0.675 (0.417 – 1.091)
0.108
Overweight
112 (27.8%)
1.186 (0.887 – 1.587)
0.248
Obesity
118 (34.5%)
1.499 (1.109 – 2.024)
0.009*
Missing
143
Trend (0.001*)
*Significant at the 0.05 level. aAdjusted for maternal age, employment (categorical) and education, marital status (categorical), paternal employment (categorical) and education, household
ownership, number of children under 16, alcohol intake and smoking status during pregnancy, maternal non-medical drug use, primiparity, pre-existing hypertension,
and year of recruitment. bDue to differences in hospital perinatal data collection among participating hospitals, some maternal morbidities have more missing data. cp-value for linear trend of adjusted ORs.
modifiable risk factors that are associated with high postpartum weight retention. Women who consumed three or fewer serves of fruit/vegetables per day, did not engaged in recreational activity with their baby, and spending less time on leisure walking or with friends were at higher risk. Mothers who breastfed for more than 3 months had a lower likelihood of sustained weight retention after pregnancy. These findings help to clarify the inconsistent results obtained in previous studies [37,38] on the role of dietary intake and breastfeeding duration as predictors of excessive weight retention postpartum, suggesting that the promotion of healthy eating, breastfeeding, modest physical activity and recreational activity with their baby and friends may help to improve weight retention outcomes. In this context, a systematic review of interventions designed to prevent excessive weight gain during pregnancy and postpartum weight retention found that there are only a few studies that evaluated the effect of interventions on postpartum
weight retention and these interventions had limited success [39]. More recent randomized controlled trial interventions have been conducted aiming to reduce postpartum weight retention through nutritional counselling, exercise sessions, and psychological counselling [39,40].
The prevalence of overweight and obesity prepregnancy in the present cohort of pregnant women in Southeast Queensland was 39%. Specifically, women with lower socioeconomic status, and adverse dietary habits appear to be at greater risk of being overweight or obese prepregnancy. For example, obese women were more likely to use non-medical drugs, smoke during pregnancy, have lower education level and their partners being unemployed or not in the labour force. They were less likely own their own home. More than half of them did not take major supplements prior to and during pregnancy. These findings have contributed to the evidence-base obtained by studies conducted in various
RESEARCH ARTICLE
Open Access
Socioeconomic disparities in prepregnancy BMI and impact on maternal and neonatal outcomes and postpartum weight retention: the EFHL longitudinal birth cohort study
Shu-Kay Ng1*, Cate M Cameron2, Andrew P Hills3, Roderick J McClure4 and Paul A Scuffham1
Abstract
Background: Long-term obesity after pregnancy is associated with obesity prior to pregnancy and retention of weight postpartum. This study aims to identify socioeconomic differences in prepregnancy body mass index, quantify the impact of prepregnancy obesity on birth outcomes, and identify determinants of postpartum weight retention.
Methods: A total of 2231 pregnant women, recruited from three public hospitals in Southeast Queensland in Australia during antenatal clinic visits, completed a questionnaire to elicit information on demographics, socioeconomic and behavioural characteristics. Perinatal information was extracted from hospital records. A follow-up questionnaire was completed by each participant at 12 months after the birth to obtain the mother’s postpartum weight, breastfeeding pattern, dietary and physical activity characteristics, and the child’s health and development information. Multivariate logistic regression method was used to model the association between prepregnancy obesity and outcomes.
Results: Being overweight or obese prepregnancy was strongly associated with socioeconomic status and adverse behavioural factors. Obese women (18% of the cohort) were more likely to experience gestational diabetes, preeclampsia, cesarean delivery, and their children were more likely to experience intensive- or special-care nursery admission, fetal distress, resuscitation, and macrosomia. Women were more likely to retain weight postpartum if they consumed three or fewer serves of fruit/vegetables per day, did not engage in recreational activity with their baby, spent less than once a week on walking for 30 minutes or more or spent time with friends less than once per week. Mothers who breastfed for more than 3 months had reduced likelihood of high postpartum weight retention.
Conclusions: Findings provide additional specificity to the increasing evidence of the predisposition of obesity prepregnancy on adverse maternal and perinatal outcomes. They may be used to target effective behavioural change interventions to address obesity in women.
Keywords: Birth cohort, Obesity, Postpartum weight retention, Body mass index, Obstetric-neonatal outcome
* Correspondence: [email protected] 1School of Medicine, Griffith Health Institute, Griffith University, Brisbane, QLD 4131, Australia Full list of author information is available at the end of the article
© 2014 Ng et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
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Background In Australia, approximately one-third of all pregnant women are overweight or obese [1]. A similar prevalence of overweight and obesity among pregnant women has been observed in the United States [2]. In both countries the proportion of pregnant women who are overweight or obese is increasing [3,4].
Pregnant women who are overweight or obese have a disproportionate risk of induced preterm delivery [5] and maternal, intrapartum, peripartum, neonatal [6,7], and postpartum complications including gestational diabetes mellitus (GDM), type 2 diabetes, high blood pressure, dyslipidaemia, cardiovascular disease and several major cancers [7,8]. The offspring of these women also have a significantly elevated risk of adverse short- and long-term health issues [9-11]. For example, children of women with GDM are more likely to be obese and have impaired glucose tolerance and diabetes in childhood and adulthood [12,13].
Increased risk of being overweight after the first and subsequent pregnancies is associated with the level of obesity prior to pregnancy [14], gestational weight gain above the recommended guidelines [15,16], and failure to lose gestational weight in a reasonable timeframe (excessive postpartum weight retention) [17]. Substantial evidence also links net weight gain after pregnancy to obesity in later life [18] and shows that women who fail to lose weight postpartum have a higher risk of subsequent long-term obesity [19].
It has been shown that adverse factors such as lack of nutrition knowledge [20,21], poor dietary habits and physical inactivity [22,23] could contribute to being overweight or obese during pregnancy as well as having high postpartum weight gain and/or retention. Findings from a recent retrospective cohort study [4] confirmed the commonly described association between maternal obesity, lower socioeconomic status [24,25] and indicated the role of adverse health behaviours in explaining this socioeconomic status differential [26]. The importance of this finding relates to the potential for addressing the high prevalence of overweight and obesity among pregnant women through screening and targeted behaviour change interventions in high-risk groups. Interventions based on specific knowledge of the subgroups at greatest risk and the modifiable behavioural determinants would lead to substantial population benefit by interrupting the transgenerational repeating cycle of risk [25].
There is currently insufficient knowledge to generate and refine targeted public health interventions to reduce transgenerational obesity because population-based studies on the impact of obesity on birth outcomes are relatively scant, especially country-specific studies such as for Australia. The present study is a prospective and multi-year longitudinal birth cohort study, and collects a spectrum of eco-
epidemiological factors [27]. It thus offers a unique opportunity to understand the various exposures that have impact on birth and postpartum outcomes. The aims of this study were to identify any socioeconomic differential in prepregnancy body mass index (BMI), to quantify the impact of prepregnancy obesity on maternal and neonatal outcomes, and to identify determinants that are associated with postpartum weight retention. The identification of the socioeconomic differential in prepregnancy obesity and the modifiable risk factors in excessive weight retention postpartum will be useful for targeting future behavioural change interventions, identifying population groups who would benefit from public health interventions, and promoting research in women’s health to address the problem of obesity.
Methods This prospective cohort study was conducted and reported in accordance with the STROBE guidelines (http://www. strobe-statement.org/).
Study design and subjects The birth cohort “Environments for Healthy Living” (EFHL) is a population-based longitudinal study which commenced the pilot phase of recruitment in November 2006 and open recruitment in August 2007 to investigate the relationship between social, environmental and behavioural factors and the health and development of children in Southeast Queensland, Australia [27]. The study area contains an estimated population of over 1 300 000 people or approximately 30% of Queensland’s population. The study region is markedly heterogeneous with respect to socioeconomic distribution; in particular, the Health Districts of the study region are known to have higher proportions of socio-economic disadvantage than the national average [28,29].
Women who planned to give birth at one of three participating hospitals were eligible to participate and enrol their baby in this study. Pregnant women aged less than 16 years or unable to provide informed consent were excluded [27]. Written informed consent was obtained for release of hospital perinatal data related to the birth of each child, completion of a participant baseline survey and for individual follow-up. During the first four open recruitment phases of the study (2007 to 2010), the total number of mothers approached was 5149, of whom 2254 women (43.8%) agreed to participate and 2277 babies have been registered with the study (including 23 sets of twins).
Following recruitment, a questionnaire was completed by each mother to elicit baseline information on demographics, socioeconomic status, family structure, behavioural and pregnancy characteristics. Perinatal information was extracted from hospital birth records. Follow-up routinely
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occurs when each child reaches 1, 3, and 5 years of age [27]. Information on maternal physical activity, dietary intake and breastfeeding duration, familial and social exposures as well as child health was collected via self-report questionnaires. In this study, multiple births were excluded (n = 46), leaving 2231 mothers and babies at baseline. Of 2231 mothers, 2009 (90%) have complete information on prepregnancy BMI. At 1-year follow-up, 1426 mothers (63.9% of 2231) returned questionnaires. Of 1426 mothers, 1316 (92%) have complete information on prepregnancy BMI and maternal weight at 1-year follow up. Figure 1 presents a flow diagram of recruitment and loss to follow-up for the present study.
Measurements A wide variety of health-related exposures and outcomes variables were measured at baseline (self-report questionnaire and hospital birth record) and during the 1-year follow-up (self-report questionnaire) with variables classified under the following domains: (a) Maternal; (b) Nutrition and physical activity factors; (c) Household and family; (d) Pregnancy; and (e) Child factors.
Maternal characteristics measured at baseline included self-reported prepregnancy weight and height, place of birth, maternal age, education level, employment status, marital status, smoking, alcohol and ‘over the counter’ medications intake patterns for non-medical purposes
Figure 1 Flow diagram of recruitment and loss to follow-up for the EFHL cohorts 2007 – 2010.
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during pregnancy, and psychological distress – measured using the Kessler-6 (K6) psychological distress scale. The K6 has been widely used and has demonstrated excellent internal consistency and reliability [30,31]. Three levels of risk of psychological distress were considered on the basis of the overall score of the 6 items: Low-risk (0–7); Medium-risk (8–12); and High-risk (13+) [32]. Using the self-reported prepregnancy weight and height, prepregnancy BMI was classified on the basis of the World Health Organisation (WHO) criteria (underweight: <18.5 kg/m2; normal-weight: 18.5– 24.9 kg/m2; overweight: 25.0–29.9 kg/m2; obese: ≥ 30 kg/ m2) [33]. At 1-year follow-up maternal measurements included self-reported postpartum weight, psychological distress, frequency of 5 or more (5+) alcoholic drinks on one occasion (once per week or more often compared to less than once per week), having a paid job after birth, and frequency of spending time with family and friends. Postpartum weight retention was calculated by subtracting the prepregnancy weight from the current weight at 1-year follow-up.
Nutrition and physical activity factors measured at baseline are mineral and vitamin supplements taken prior to and during pregnancy (Iron, Zinc, Calcium, Folic-acid, Multivitamins, Vitamin-C, Vitamin-E, Pregnancy/Breastfeeding supplement). Factors measured at 1-year follow up included breastfeeding duration, fruit and vegetable intake, frequency of takeaways, attendance at recreational activities with baby (such as, play groups, mothers’ groups, or swimming classes), and the frequencies of moderate physical activities and walking (30 minutes or more).
Household and family factors were obtained at baseline (including partner’s education level and employment status, household ownership and number of children under 16 years of age) and during the follow-up period at 1-year (including the frequency of being a single parent for 1 month or longer, whether the mother is living with the biological father of the child, spouse or partner, whether their relationship status changed, environment with child exposure to passive smoking, and frequency of eating takeaway food).
Variables in the pregnancy domain were obtained from hospital birth record, which included parity, pre-existing hypertension, obstetric complications (hyperglycaemia, gestational diabetes, preeclampsia, cesarean delivery), and neonatal complications (fetal distress, admission to an intensive or special care nursery, jaundice, respiratory distress, congenital anomaly, resuscitation procedure).
Child characteristics included gender, birthweight, and at 1-year mother-reported weight gain, injury, and indication of health-related conditions (asthma, chest infection) diagnosed by a medical doctor. Macrosomia was defined as birthweight > 4 kg, irrespective of gestational age [34].
Ethical approval Approval for the study was obtained from the Griffith University Ethics Committee and the Human Research Ethics Review Committees of the three participating public maternity hospitals (Logan, Gold Coast and Tweed Hospitals) in the study area.
Statistical analysis Data were analysed using IBM SPSS-22 (IBM, Chicago, IL). Baseline characteristics of the EFHL cohort were compared with all births with gestation of 28 weeks or more in the study region between 2007 and 2010, in an attempt to address potential selection bias. Missing data including loss to follow-up were handled using a complete-case approach. Measures of association and ordinal association between prepregnancy BMI groups and categorical variables were obtained using Pearson’s chi-square and Gamma statistics, respectively. Comparisons of continuous variables between the four prepregnancy BMI groups were tested using one-way ANOVA (with Tukey’s post-hoc multiple test). Multiple logistic regression was performed to identify pregnancy and neonatal problems that associate with the prepregnancy underweight, overweight and obese mothers. Adjusted odds ratios (ORs) with 95% confidence intervals (CIs) were calculated after accounting for potential confounding factors that are significantly associated with the prepregnancy BMI groups. These include maternal age, employment and education, alcohol intake and smoking during pregnancy, non-medical drug use, primiparity, pre-existing hypertension, marital status, paternal employment and education, household ownership, and the number of children living in the household aged under 16 years. The year of recruitment was also considered as a potential confounder, given temporal differences were identified for some antenatal exposures [29]. Polynomial contrast within multiple logistic regression models were used to test the linear trend of adjusted ORs.
Cut-off points for high postpartum weight retention were determined separately for three prepregnancy BMI groups (normal, overweight, and obese) by the highest weight retention quintile. This group of women with high postpartum weight retention (weight retention > top quintile) thus contains about 20% of women in each of the three pregnancy BMI groups. Multiple logistic regression was conducted to identify risk factors at 12-month follow-up that are associated with high postpartum weight retention. Adjusted ORs with 95% CIs were calculated, after accounting for potential confounding factors including the year of recruitment, the prepregnancy BMI grouping, and those factors defined above.
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Results
Sample characteristics Baseline characteristics of the EFHL cohort are displayed in Table 1 along with corresponding details of births with gestation of 28 weeks or more in the study region between 2007–2010. The birth cohort sample did not differ significantly from the general population for gender, plurality, or birth outcome. However, our sample had a smaller proportion of mothers who were younger than 20 years of age and infants who were born between
28 and 36 weeks gestation. Moreover, the percentage of low birthweight infants (<2500 g) was approximately half that of babies born in the general population.
Prepregnancy BMI The baseline descriptive characteristics of participants stratified by the prepregnancy BMI is shown in Table 2. Underweight women were younger compared to women with a normal prepregnancy BMI (reference group). On the other hand, compared to women with a normal
Table 1 Baseline characteristics of the cohort and comparisons with all births in the study region (with gestational
28 weeks or more)
Frequency (percentagea)
Characteristics
Birth cohort sample (Years 2007 to 2010) 2254 women 2277 babies
Deliveries in regionb (Years 2007 to 2010) 36620 women 37061 babies
p-valuec (two-sided)
Maternal age
< 20 years
107 (4.8%)
2559 (7.0%)
< 0.001
20-24 years
438 (19.9%)
6407 (17.6%)
25-29 years
632 (28.6%)
10376 (28.5%)
30-34 years
560 (25.4%)
10088 (27.7%)
≥ 35 years
469 (21.3%)
6980 (19.2%)
Missing data
48
0
Gender of infant
Male
1127 (50.3%)
18914 (51.3%)
0.324
Female
1115 (49.7%)
17926 (48.7%)
Missing data
35
0
Birthweight
< 2500 g
55 (2.5%)
1800 (4.9%)
< 0.001
2500-3999 g
1809 (81.5%)
30207 (82.0%)
≥ 4000 g
355 (16.0%)
4831 (13.1%)
Missing data
58
2
Gestational age at birth
28-36 weeks
79 (3.5%)
2376 (6.4%)
< 0.001
37-41 weeks
2131 (95.9%)
34264 (93.0%)
42 weeks
13 (0.6%)
200 (0.5%)
Missing data
54
0
Plurality
Singleton
2207 (98.0%)
35980 (97.7%)
Multiple
46 (2.0%)
860 (2.3%)
0.370
Missing data
24
0
Outcome
Live birth
2273 (99.8%)
36728 (99.7%)
Stillbirth
4 (0.2%)
112 (0.3%)
0.274
Missing data
0
0
aPercentages are calculated based on the available (non-missing) data. bData include only deliveries with gestational 28 weeks or more; data are provided by Queensland Health and New South Wales Health for the study region
(Logan, Beaudesert, Gold Coast and Tweed). cChi-square test for comparing proportions between birth cohort sample and the general population.
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Table 2 Baseline characteristics of participants stratified by the prepregnancy BMI (n = 2009)
Prepregnancy BMI
Underweight Normal weight Overweight Obese (BMI ≥ 30) (BMI < 18.5) (BMI 18.5-24.99) (BMI 25-29.99)
Characteristics
(N=167)
(N= 1052)
(N= 430)
(N= 360)
Maternal Characteristics:
Maternal age (y)
< 20
12 (7.3%)
48 (4.7%)
14 (3.3%)
11 (3.1%)
20-24
49 (29.7%)
198 (19.2%)
69 (16.3%)
74 (21.0%)
25-29
38 (23.0%)
293 (28.4%)
135 (31.8%)
99 (28.0%)
30-34
33 (20.0%)
280 (27.2%)
119 (28.1%)
89 (25.2%)
≥ 35
33 (20.0%)
212 (20.6%)
87 (20.5%)
80 (22.7%)
Missing data
2
21
6
7
Born in Australia
114 (68.3%)
733 (69.7%)
312 (72.6%)
259 (71.9%)
Missing data
0
1
0
0
Maternal employment
Employed
75 (46.3%)
564 (54.5%)
209 (49.2%)
169 (48.3%)
Unemployed
26 (16.0%)
113 (10.9%)
42 (9.9%)
36 (10.3%)
Not in-labour force
61 (37.7%)
358 (34.6%)
174 (40.9%)
145 (41.4%)
Missing data
5
17
5
10
Marital status
Single
15 (9.0%)
65 (6.2%)
20 (4.7%)
31 (8.6%)
Dating relationship
14 (8.4%)
32 (3.1%)
17 (4.0%)
22 (6.1%)
De facto
76 (45.5%)
382 (36.4%)
158 (37.0%)
126 (35.0%)
Married
62 (37.1%)
557 (53.1%)
225 (52.7%)
175 (48.6%)
Separate/Divorce/Widow
0 (0%)
13 (1.2%)
7 (1.6%)
6 (1.7%)
Missing data
0
3
3
0
Maternal education level
Primary school
2 (1.2%)
9 (0.9%)
4 (0.9%)
4 (1.1%)
Not complete secondary
37 (22.3%)
176 (16.8%)
85 (20.0%)
77 (21.5%)
Secondary school
50 (30.1%)
301 (28.7%)
137 (32.2%)
129 (36.0%)
TAFE/Trade
46 (27.7%)
306 (29.2%)
129 (30.3%)
107 (29.9%)
University
31 (18.7%)
257 (24.5%)
71 (16.7%)
41 (11.5%)
Missing data
1
3
4
2
Nutrition Factors:
Supplements Intake
Iron
51 (30.5%)
301 (28.6%)
118 (27.4%)
67 (18.6%)
Zinc
37 (23.3%)
196 (19.8%)
80 (19.2%)
53 (15.4%)
Calcium
27 (16.2%)
139 (13.2%)
40 (9.3%)
32 (8.9%)
Folic acid
84 (52.5%)
511 (51.5%)
222 (53.0%)
166 (47.7%)
Multivitamins
93 (55.7%)
548 (52.1%)
214 (49.8%)
140 (38.9%)
Vitamin C
55 (34.6%)
337 (34.1%)
133 (32.0%)
80 (23.2%)
Vitamin E
113 (67.7%)
735 (69.9%)
280 (65.1%)
191 (53.1%)
Pregnancy/breastfeeding sup.
38 (23.9%)
193 (19.6%)
91 (21.9%)
40 (11.5%)
Household and Family Structure:
Partner’s employment
p-value (two-sided) 0.017*b
0.589 0.038*b
0.001*b
<0.001*b
0.001*a0.039*a0.001*a0.499 <0.001*a0.001*a<0.001*a0.004*a-
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Table 2 Baseline characteristics of participants stratified by the prepregnancy BMI (n = 2009) (Continued)
Employed
142 (89.9%)
931 (93.7%)
392 (96.8%)
301 (90.7%)
Unemployed
10 (6.3%)
37 (3.7%)
7 (1.7%)
17 (5.1%)
Not in-labour force
6 (3.8%)
26 (2.6%)
6 (1.5%)
14 (4.2%)
Missing data
9
58
25
28
Partner’s education level
Primary school
4 (2.6%)
9 (0.9%)
5 (1.2%)
0 (0%)
Not complete secondary
33 (21.2%)
182 (18.2%)
89 (21.9%)
94 (28.6%)
Secondary school
41 (26.3%)
243 (24.3%)
114 (28.0%)
89 (27.1%)
TAFE/Trade
52 (33.3%)
421 (42.2%)
150 (36.9%)
120 (36.5%)
University
26 (16.7%)
143 (14.3%)
49 (12.0%)
26 (7.9%)
Missing data
11
54
23
31
Household ownership
Own outright
6 (3.7%)
33 (3.2%)
3 (0.7%)
3 (0.8%)
Own with mortgage
52 (31.9%)
457 (44.3%)
189 (44.9%)
121 (34.1%)
Private rent
72 (44.2%)
446 (43.2%)
193 (45.8%)
178 (50.1%)
Public housing
5 (3.1%)
23 (2.2%)
7 (1.7%)
13 (3.7%)
Board with parents
28 (17.2%)
73 (7.1%)
29 (6.9%)
40 (11.3%)
Missing data
4
20
9
5
Number of children under 16 y (household)
Nil
78 (47.3%)
413 (39.7%)
134 (31.6%)
115 (31.9%)
1-3
83 (50.3%)
595 (57.2%)
279 (65.8%)
217 (60.3%)
More than 3
4 (2.4%)
33 (3.2%)
11 (2.6%)
28 (7.8%)
Missing data
2
11
6
0
Maternal Health and Behaviours During Pregnancy:
Maternal psychological distress
Low risk
128 (77.6%)
853 (81.6%)
352 (83.0%)
280 (78.4%)
Medium risk
30 (18.2%)
158 (15.1%)
64 (15.1%)
61 (17.1%)
High risk
7 (4.2%)
34 (3.3%)
8 (1.9%)
16 (4.5%)
Missing data
2
7
6
3
Smoke during pregnancy
59 (35.3%)
233 (22.2%)
109 (25.5%)
108 (30.3%)
Missing data
0
1
3
3
Alcohol intake
69 (41.3%)
536 (51.0%)
202 (47.4%)
137 (38.3%)
Missing data Non-medical drug usea
0 29 (17.6%)
0 98 (9.5%)
4 35 (8.3%)
2 42 (11.8%)
Missing data
2
21
8
3
Birth-related Factors:
Primiparity
97 (58.8%)
674 (64.8%)
313 (73.5%)
267 (75.2%)
Missing data
2
12
4
5
Baby gender (Male)
80 (48.5%)
527 (50.9%)
201 (47.2%)
182 (51.3%)
Missing data
2
16
4
5
Pre-existing hypertension
0 (0%)
6 (0.7%)
3 (0.8%)
8 (2.4%)
Missing data
23
144
48
26
*Significant at the 0.05 level. aTest for ordinal association using Gamma coefficient (+indicates a positive association; −indicates a negative association).
bTest for association using chi-square test.
0.017*b
<0.001*b
<0.001*b
<0.001*a+
0.343
<0.001*b 0.001*b 0.005*b <0.001*a+ 0.564 0.021*a+
Ng et al. BMC Pregnancy and Childbirth 2014, 14:314 http://www.biomedcentral.com/1471-2393/14/314
Page 8 of 15
prepregnancy BMI, overweight and obese women had lower education level. Their partners were more likely to be unemployed or not in the labour force, and had lower education level. Obese and underweight women were less likely to be married, own their own home, and consume alcohol during pregnancy. However, they were more likely to smoke during pregnancy and be engaged in non-medical drug use. Supplement intake prior to and during pregnancy was different between the four prepregnancy BMI groups. There were moderate negative ordinal relationships between intake of major supplements and prepregnancy BMI with obese women less likely to take supplements of all types considered in the study, especially iron, multivitamins, vitamins-C and E. There was a moderate positive ordinal relationship between number of children under 16 years of age in the household and prepregnancy BMI (0.170, p < 0.001), indicating that the proportion of prepregnancy obesity increases when the number of children under 16 years old increases. A moderate positive ordinal relationship between parity and prepregnancy BMI was also observed (0.201, p < 0.001), indicating that the proportion of prepregnancy obesity increases for mothers who have had previous pregnancies. The strong relationship between pre-existing hypertension and prepregnancy BMI (0.495, p = 0.021) confirms that obese women are more likely to have the pre-existing condition.
Pregnancy and neonatal outcomes The impact of prepregnancy obesity on pregnancy and neonatal outcomes after adjusting for confounders is presented in Table 3. With an increasing level of obesity prior to pregnancy, a linear trend of increasing risk of adverse outcomes was observed for hyperglycaemia (p for trend = 0.024), cesarean delivery (p for trend < 0.001), intensive or special care nursery admission (p for trend < 0.001), resuscitation (p for trend = 0.001), and macrosomia (p for trend < 0.001). In addition to these adverse outcomes, obese women were also more likely to have GDM (adjusted OR = 2.327) and preeclampsia (adjusted OR = 3.143). Further, their newborns had also a higher chance of having fetal distress (adjusted OR = 1.870). Babies of overweight women were also more likely to have jaundice (adjusted OR = 1.762).
Mothers who completed and returned the 12-month follow-up questionnaire (63.9% of 2231) were more likely to be born in Australia (p = 0.013), married (p < 0.001), older (p < 0.001), employed (p < 0.001), had lower likelihood of psychological distress based on K6 scale (p < 0.001), consumed alcohol during pregnancy (p < 0.001), had higher education level (p < 0.001) and household income (p < 0.001), than those who did not return questionnaires. Further, they were less likely to have underweight or obese prepregnancy BMI (p = 0.009), or smoke during
pregnancy (p < 0.001). There were no significant differences in gender of the babies.
Outcomes at 12-month follow-up The 12-month follow-up descriptive characteristics of participants and their babies are given in Table 4. There was a moderate negative ordinal relationship (−0.219, p < 0.001) between breastfeeding duration and prepregnancy BMI, indicating that the proportion of mothers who breastfed longer than or equal to 3 months decreased when prepregnancy BMI increased. Further, compared to women with a normal prepregnancy BMI, overweight, obese and underweight women were less likely to attend a play group or other recreational activity with their babies (p = 0.002) but are more likely to have medium or high risks of psychological distress (p = 0.032) and more frequently a lone parent (p = 0.030). A significant difference in weight retention at 12 months was observed between the four prepregnancy BMI groups (p < 0.001). There was a moderate positive ordinal relationship (0.221, p = 0.004) between asthma and prepregnancy BMI, indicating that the proportion of babies with asthma increases when prepregnancy BMI increases. The difference in weight gain of infants between normal and overweight mothers prepregnancy was also significant (p = 0.019).
Postpartum weight retention In Table 5, the determinants of high postpartum weight retention (within the top quintile) for the normal, overweight, and obese prepregnancy BMI groups, with adjustments for potential confounding factors that are significantly associated with the prepregnancy BMI groups, are presented. Women were more likely to have high postpartum weight retention if they consumed three or less serves of fruit/vegetables (adjusted OR = 2.005, p = 0.001), did not engage in recreational activity with their babies (adjusted OR = 1.916, p < 0.001), spent less than once a week on walking for 30 minutes or more (adjusted OR = 1.691, p = 0.029) or spent time with friends less than once per week (adjusted OR = 1.695, p = 0.040). However, mothers who breastfed for more than 3 months had reduced the chance of high postpartum weight retention (adjusted OR = 0.673, p = 0.030).
Discussion An important contributing factor to the development of long-term obesity in women is excessive weight retention after pregnancy [17,19]. There is marked variability in weight changes that are associated with pregnancy and pregnancy is a critical period for prevention of obesity in women [35,36]. This large cohort study has contributed to the growing evidence that helps to target interventions during pregnancy by the identification of
Ng et al. BMC Pregnancy and Childbirth 2014, 14:314 http://www.biomedcentral.com/1471-2393/14/314
Table 3 Impact of prepregnancy obesity on pregnancy and neonatal outcomes (n = 2009)
Outcome
Frequency (%)
Adjusted ORa (95% CI)
Obstetric Complication:
Hyperglycaemia
Normal weight
65 (8.0%)
Reference
Underweight
5 (3.7%)
0.532 (0.207 – 1.371)
Overweight
35 (9.7%)
1.210 (0.754 – 1.940)
Obesity Missingb
38 (11.8%) 382
1.464 (0.921 – 2.328)
Gestational diabetes
Normal weight
21 (2.4%)
Reference
Underweight
0 (0%)
—
Overweight
7 (2.0%)
1.040 (0.396 – 2.732)
Obesity
11 (3.6%)
2.327 (1.037 – 5.223)
Missing
337
Preeclampsia
Normal weight
25 (3.1%)
Reference
Underweight
0 (0%)
—
Overweight
11 (3.3%)
1.184 (0.559 – 2.505)
Obesity
29 (9.8%)
3.143 (1.694 – 5.830)
Missing
430
Cesarean delivery
Normal weight
238 (23.0%)
Reference
Underweight
38 (23.0%)
0.950 (0.588 – 1.536)
Overweight
146 (34.4%)
1.802 (1.349 – 2.409)
Obesity
127 (35.9%)
2.229 (1.646 – 3.018)
Missing
32
Neonatal Complication:
Intensive/Special care admission
Normal weight
117 (11.3%)
Reference
Underweight
15 (9.1%)
0.590 (0.296 – 1.176)
Overweight
67 (15.9%)
1.339 (0.921 – 1.947)
Obesity
73 (20.6%)
1.839 (1.269 – 2.665)
Missing
36
Fetal distress
Normal weight
62 (6.7%)
Reference
Underweight
17 (11.3%)
2.178 (1.147 – 4.133)
Overweight
24 (6.0%)
1.222 (0.706 – 2.113)
Obesity
33 (9.6%)
1.870 (1.103 – 3.169)
Missing
191
Jaundice
Normal weight
39 (4.8%)
Reference
Underweight
11 (8.2%)
1.782 (0.821 – 3.866)
Overweight
30 (8.4%)
1.762 (1.017 – 3.053)
Obesity
22 (6.8%)
1.592 (0.882 – 2.872)
Missing
382
Page 9 of 15
p-value (2-sided)
0.192 0.430 0.107 Trendc (0.024*)
— 0.937 0.041*
— 0.660 < 0.001*
0.834 <0.001* <0.001* Trend (< 0.001*)
0.134 0.126 0.001* Trend (<0.001*)
0.017* 0.474 0.020* Trend (0.143)
0.144 0.044* 0.123 Trend (0.163)
Ng et al. BMC Pregnancy and Childbirth 2014, 14:314 http://www.biomedcentral.com/1471-2393/14/314
Page 10 of 15
Table 3 Impact of prepregnancy obesity on pregnancy and neonatal outcomes (n = 2009) (Continued)
Respiratory distress
Normal weight
38 (4.7%)
Reference
Underweight
6 (4.5%)
0.879 (0.332 – 2.330)
0.796
Overweight
20 (5.6%)
1.275 (0.700 – 2.322)
0.427
Obesity
22 (6.8%)
1.294 (0.700 – 2.394)
0.411
Missing
382
Trend (0.288)
Congenital anomaly
Normal weight
62 (7.3%)
Reference
Underweight
10 (7.3%)
1.362 (0.662 – 2.804)
0.401
Overweight
26 (7.1%)
0.966 (0.559 – 1.668)
0.901
Obesity
25 (7.9%)
1.179 (0.692 – 2.011)
0.544
Missing
343
Trend (0.868)
Macrosomia (birthweight >4kg)
Normal weight
140 (13.6%)
Reference
Underweight
10 (6.2%)
0.389 (0.176 – 0.859)
0.019*
Overweight
70 (16.5%)
1.334 (0.945 – 1.884)
0.101
Obesity
93 (26.3%)
2.262 (1.619 – 3.160)
< 0.001*
Missing
43
Trend (< 0.001*)
Resuscitation
Normal weight
253 (26.1%)
Reference
Underweight
32 (20.9%)
0.675 (0.417 – 1.091)
0.108
Overweight
112 (27.8%)
1.186 (0.887 – 1.587)
0.248
Obesity
118 (34.5%)
1.499 (1.109 – 2.024)
0.009*
Missing
143
Trend (0.001*)
*Significant at the 0.05 level. aAdjusted for maternal age, employment (categorical) and education, marital status (categorical), paternal employment (categorical) and education, household
ownership, number of children under 16, alcohol intake and smoking status during pregnancy, maternal non-medical drug use, primiparity, pre-existing hypertension,
and year of recruitment. bDue to differences in hospital perinatal data collection among participating hospitals, some maternal morbidities have more missing data. cp-value for linear trend of adjusted ORs.
modifiable risk factors that are associated with high postpartum weight retention. Women who consumed three or fewer serves of fruit/vegetables per day, did not engaged in recreational activity with their baby, and spending less time on leisure walking or with friends were at higher risk. Mothers who breastfed for more than 3 months had a lower likelihood of sustained weight retention after pregnancy. These findings help to clarify the inconsistent results obtained in previous studies [37,38] on the role of dietary intake and breastfeeding duration as predictors of excessive weight retention postpartum, suggesting that the promotion of healthy eating, breastfeeding, modest physical activity and recreational activity with their baby and friends may help to improve weight retention outcomes. In this context, a systematic review of interventions designed to prevent excessive weight gain during pregnancy and postpartum weight retention found that there are only a few studies that evaluated the effect of interventions on postpartum
weight retention and these interventions had limited success [39]. More recent randomized controlled trial interventions have been conducted aiming to reduce postpartum weight retention through nutritional counselling, exercise sessions, and psychological counselling [39,40].
The prevalence of overweight and obesity prepregnancy in the present cohort of pregnant women in Southeast Queensland was 39%. Specifically, women with lower socioeconomic status, and adverse dietary habits appear to be at greater risk of being overweight or obese prepregnancy. For example, obese women were more likely to use non-medical drugs, smoke during pregnancy, have lower education level and their partners being unemployed or not in the labour force. They were less likely own their own home. More than half of them did not take major supplements prior to and during pregnancy. These findings have contributed to the evidence-base obtained by studies conducted in various