Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2023 Mar 1.
Published in final edited form as: J Obstet Gynecol Neonatal Nurs. 2021 Nov 15;51(2):205–217. doi: 10.1016/j.jogn.2021.10.006

Early Postpartum Patterns of Breastfeeding Exclusivity and Perceived Insufficient Milk by Pre-Pregnancy Body Mass Index

Rachel Renee Dieterich 1, Susan Sereika 2, Jill Demirci 3
PMCID: PMC8901544  NIHMSID: NIHMS1753389  PMID: 34793724

Abstract

Objective:

To explore trajectories of breastfeeding exclusivity and perceived insufficient milk (PIM) over the first 8 weeks postpartum among primiparous women and the association of these trajectories with pre-pregnancy body mass index (BMI).

Design:

Secondary analysis of data from a randomized controlled trial.

Setting:

Recruitment for the primary study was conducted in Pittsburgh, Pennsylvania.

Participants:

One hundred twenty-two primiparous women with intention to exclusively breastfeed.

Methods:

We used group-based trajectory modeling to classify participants into breastfeeding exclusivity and PIM trajectory groups. We used logistic regression to explore the predictive relationship between pre-pregnancy BMI and breastfeeding exclusivity and PIM trajectory group memberships.

Results:

We identified two trajectories each for breastfeeding exclusivity and PIM over the first 8 weeks postpartum. For breastfeeding exclusivity, one group (n = 60, 49%) had low initial probability of exclusive breastfeeding with linear decline in likelihood of exclusivity over time. The other group (n = 62, 51%) had high initial probability of exclusive breastfeeding, which remained constant over time. For PIM, one group (n = 41, 34%) had consistently high probability of endorsing PIM at each time point, while the other group (n = 81, 66%) had consistently low probability of endorsing PIM over time. Pre-pregnancy BMI did not predict group membership in breastfeeding exclusivity, X2(1) = 2.8, p = 0.094 or PIM, X2(1) = 0.72, p = 0.397.

Conclusion:

Breastfeeding exclusivity and PIM appeared to be relatively stable phenomena in the postpartum period among a sample of predominately White primiparous women who intended to breastfeed. We did not find a clear association with pre-pregnancy BMI.

Keywords: Breastfeeding exclusivity, body mass index, insufficient milk, pregnancy, perception, parity

Precis

Breastfeeding exclusivity and perceived insufficient milk remained consistent among participants who intended to breastfeed; there was not a clear association with pre-pregnancy body mass index.

While breastfeeding is considered the gold standard infant feeding method with important health and economic implications for women and families (American Academy of Pediatrics Section on Breastfeeding, 2012), it is even more crucial for vulnerable groups susceptible to certain morbidities (World Health Organization, 2019). Among these groups are women with high body mass index (BMI) who are at a heightened risk to develop cardio-metabolic disease compared to those with normal range pre-pregnancy BMI (Lyall et al., 2017). Growing evidence supports a dose-dependent association between increased breastfeeding intensity (percentage of exclusive breast milk feeds) and reduced risk of cardio-metabolic dysfunction in breastfeeding dyads (Stuebe et al., 2011). Sustained breastfeeding has been linked with reduced risk of obesity and type II diabetes in offspring and reduced risk of maternal hypertension (Stuebe et al., 2011). Additionally, continued breastfeeding is associated with reduced risk of subclinical and clinical cardiovascular disease (Gunderson et al., 2015) and cardiovascular mortality (Natland Fagerhaug et al., 2013) in breastfeeding women. Results from a recent retrospective study on the relationship between breastfeeding and childhood cardiovascular disease risk factors suggested that any breastfeeding had a protective effect on childhood triglyceride levels among a sample of 11,980 West Virginia children (Umer et al., 2019). Similarly, Brazilian researchers who conducted a cross sectional investigation of the influence of breastfeeding duration on blood pressure of 213 school-aged children discovered that longer breastfeeding duration was associated with significantly lower mean values of systolic blood pressure (Amorim et al., 2014).

Flores et al. (2018) found that women who were overweight and obese were less likely to initiate and continue breastfeeding and more likely to introduce their infants to formula and complementary foods at a younger age than women with normal range BMI. Barriers to breastfeeding among women who are overweight and obese are multi-factorial and interrelated. Psychosocial barriers include lower likelihood of being offered breastfeeding support during postpartum hospitalization (Kair & Colaizy, 2016), body image concerns when breastfeeding in public (Zimmerman et al., 2019), failure of health care professionals to recognize and discuss potential weight-related breastfeeding challenges (Hawkins et al., 2019), and lower breastfeeding-self efficacy (Kronborg et al., 2013). Physiologically, women with increased body weight pre-pregnancy are also more likely than those with BMI less than 25 to have endocrine profiles associated with insufficient mammary gland development during puberty and pregnancy, delayed onset of lactogenesis II (mature milk production), and insufficient milk production (Nommsen-Rivers, 2016; Preusting et al., 2017).

Perceived insufficient milk (PIM) is one of the most frequently reported reasons for early formula introduction and cessation of breastfeeding among the general adult breastfeeding population (Odom et al., 2013) and among breastfeeding women who are overweight or obese (Massov, 2015). It is currently thought that more individuals perceive milk supply inadequacy versus those with true low milk supply (Whipps & Demirci, 2020). Researchers of one qualitative study found that compared to women with pre-pregnancy BMIs less than 25, women with BMIs greater than 25 were less likely to report producing sufficient milk to satisfy their infants at one and three months postpartum (Mok et al., 2008). First-time breastfeeding women may also be at risk for PIM due to lack of prior breastfeeding experience (Hackman et al., 2015; Whipps & Demirci, 2020).

Prospective research on how PIM and associated breastfeeding outcomes evolve over time is limited, and few researchers have focused specifically on primiparous women or BMI as a potential moderator. Therefore, the purpose of this secondary analysis was to explore trajectories of breastfeeding exclusivity and PIM over the first 8 weeks postpartum among primiparous women and the association of these trajectories with pre-pregnancy BMI.

Methods

Design

We completed a secondary data analysis of prospectively collected breastfeeding outcome data from participants enrolled in an attention control arm of a randomized controlled trial. In the parent study, researchers investigated the efficacy of a text message (i.e., short message service, SMS) breastfeeding support intervention on breastfeeding outcomes in a sample of first-time parents (trial registration: Clinicaltrials.gov NCT02724969; Demirci et al., 2020). At enrollment, control group participants were provided instructions for enrolling in the Text4Baby program, which is a free, publicly available service offering prenatal and postpartum text messages containing general perinatal education and support timed to a participant’s due date and infant’s date of birth (text4baby, 2017). At the time of the study, text4baby sent text messages approximately three times per week beginning at enrollment during pregnancy and continuing for up to one year postpartum on various aspects of infant care, including limited content on breastfeeding (seven prenatal breastfeeding-specific messages and three postpartum breastfeeding specific messages). The parent study and secondary data analysis were approved by the University of Pittsburgh Institutional Review Board.

Setting

Recruitment for the parent study was conducted at University of Pittsburgh Magee-Women’s Hospital prenatal clinics in the greater Pittsburgh region. These high-volume clinics serve the city of Pittsburgh and seven surrounding suburban communities.

Participants

Eligible participants for the parent study were nulliparous, 18 years or older, English-speaking, 13 to 25 weeks pregnant with one fetus, owned a cell phone with internet access and an unlimited SMS plan, and intended to breastfeed exclusively or nearly exclusively (less than 2 ounces of formula per day) for at least 2 months. For the parent study, researchers enrolled 250 participants at UPMC Magee-Women’s Hospital prenatal clinics and via local advertisement in Pittsburgh, Pennsylvania. Recruitment was conducted over a 15-month period between February 2017 and May 2018. Intervention arm participants received targeted breastfeeding support text messages intended to promote exclusive breastfeeding behavior. Therefore, only control arm participants were included in this secondary data analysis. This analysis includes data from all 122 control group participants.

Participant gender was not assessed in the parent study. Thus, there may have been participants who identified as cis-gender, transgender, and gender non-conforming.

Measures

Demographic and pregnancy data were collected via participant self-report at enrollment and medical record abstraction, respectively.

Demographics

At the time of enrollment (13-25 weeks of pregnancy), participants provided demographic information via an online survey administered through REDCap, which is a secure, web-based software platform to support data capture for research studies (Harris et al., 2009). Demographic information collected included race, ethnicity, age, education level, marital status, and employment status.

Participant pre-pregnancy BMI

Participant BMI was calculated from self-reported pre-pregnancy height and weight assessed at the time of enrollment at 13 to 25 weeks of gestation.

Maternal/Neonate Complications

Data on complication for the participant and/or neonate were gathered via medical record abstraction from the birth and postpartum hospitalization. For the participant, these data included diabetes (prediabetes, gestational diabetes, type I diabetes mellitus, or type II diabetes mellitus), hypertension (chronic hypertension, gestational hypertension, or pre-eclampsia), breast augmentation or other surgery, placenta previa, intrauterine growth restriction (IUGR), preterm premature rupture of membranes (PPROM), preterm labor, suspected or confirmed chorioamnionitis, vacuum or forceps use, and fetal or infant demise. For the neonate, these data included gestational age, birth weight, and NICU admission.

Breastfeeding Self-Efficacy Scale Short Form (BSES- SF)

Breastfeeding self-efficacy was measured with the Breastfeeding Self-Efficacy Scale Short Form (BSES- SF) at 34 to 36 weeks gestation via an online survey administered through REDcap. The BSES-SF is a 14-item tool with a five-point Likert-type response scale that measures an individual’s confidence in their ability to breastfeed. Scores range from 14-70, with higher BSES scores indicative of greater breastfeeding self-efficacy (Dennis, 2003). Internal consistency reliability of the BSES-SF was supported with a Cronbach’s alpha coefficient of 0.94. Factor analysis indicated that all 14 items on the BSES-SF loaded at 0.66 or higher (Dennis, 2003), far exceeding the 0.32 recommended for item retention (Tabachnik & Fidell, 2011).

Perceived Insufficient Milk (PIM)

We assessed perceived insufficient milk at 1, 2, 5, and 8 weeks postpartum in two ways: a single investigator-created survey item (“Do you feel you make enough breast milk to satisfy your baby?” yes, no, unsure) and participants’ scores on the Perceived Infant Breastfeeding Satiety (PIBS) subscale (5 items) of the H & H Lactation Scale (20 items), which is used to measure perception of insufficient milk (Hill & Humenick, 1996). During initial psychometric testing, the H & H Lactation Scale had an overall Cronbach’s alpha coefficient of 0.91. Additionally, the PIBS subscale demonstrated acceptable predictive validity of (r = 0.51, p = 0.001) for infant satisfaction and satiety (Hill & Humenick, 1996). Both measures of PIM were assessed via REDCap online survey.

Breastfeeding Exclusivity

We assessed breastfeeding exclusivity with a single survey item at 1,2, 5, and 8 weeks postpartum: “How are you currently feeding your baby?” Response options included breast milk only, formula only, both formula and breast milk, breast milk and solids, formula and solids, breast milk, formula and solids, or other. Response options were collapsed into two categories: “breast milk only” and “no breast milk or non-exclusive breast milk.”

Procedures

After screening for eligibility, study team members who were trained in research ethics and informed consent best practices obtained written informed consent from each participant. Informed consent was obtained during in-person prenatal visits at 13-25 gestational weeks. After enrollment, participants were randomized using equal allocation to the intervention or control group. Baseline data, including demographics and health history, were obtained after informed consent procedures.

Electronic medical record reviews were completed by the senior author (J.D) with help of trained research assistants. Medical record reviews were completed using birth hospitalization data at or near time of delivery. Medical record data was initially entered into an Excel spreadsheet and then imported into REDcap.

Study participants completed online surveys administered through REDcap. The surveys assessed infant feeding plans and behaviors and were administered at time of enrollment (13-25 gestational weeks), 34-36 gestational weeks, and at one, two, five and eight weeks postpartum.

Statistical Analysis

We adjusted for numerous covariates known to be associated with breastfeeding outcomes (see Table 1). Maternal complications were controlled for and were regressed as one collective covariate. We used the 8-week postpartum assessment for employment status in this analysis due to the known association between breastfeeding exclusivity and return to employment (Lubold, 2016) and because most women in this analysis returned to employment 6 to 8 weeks after they gave birth. We also controlled for breastfeeding self-efficacy measured via the Breastfeeding Self-Efficacy Scale Short Form (BSES- SF) at 34-36 weeks gestation.

Table 1.

Participant Characteristics by Breastfeeding Exclusivity and Perceived Insufficient Milk Trajectory Groups

Breastfeeding (BF) Exclusivity Perceived Insufficient Milk (PIM)
Total Sample (N = 122) Group 1 (Lower chance of BF exclusivity over time) (n = 60) Group 2 (Higher chance of BF exclusivity over time) (n = 62) p-value Group 1 (higher chance of PIM) (n = 41) Group 2 (lower chance of PIM) (n = 81) p-value
Pre-pregnancy BMI
   Mean ±SD 26.0±5.2 26.8 ± 5.2 25.2 ± 5.2 0.10 26.6 ± 5.7 28.3 ± 4.5 0.40
Age
   Mean ±SD 28.7±5.3 29.1 ± 6.0 28.2 ± 4.4 0.33 29.3 ± 6.5 28.3 ± 4.5 0.32
Ethnicity n (%)
   Hispanic 7 (6) 6 (10) 1 (2) 0.04* 5 (12) 2 (2)
   Non-Hispanic 115 (94) 54 (90) 61 (98) 36 (88) 79 (98) 0.03*
Race n (%)
   White 93 (75) 42 (70) 51 (82) 0.24 30 (73) 63 (78) 0.81
   Black/African American 22 (18) 13 (22) 9 (14) 8 (19) 14 (17)
   Other (Asian/Indian, Mixed-biracial) 7 (7) 5 (8) 2 (4) 3 (8) 4 (5)
Education n (%)
   Graduated high school/GED or less 13 (10.6) 10 (16) 3 (5) 0.19 8 (19) 5 (6) 0.16
   Associates degree, vocational program, or some college 31 (25.2) 15 (25) 16 (26) 10 (24) 21 (26)
   Bachelor’s degree 33 (26.8) 14 (23) 19 (31) 10 (24) 23 (28)
   Masters/Doctoral degree 45 (36.6) 21 (36) 24 (38) 13 (33) 32 (40)
Marital status n (%)
   Married 76 (61.8) 34 (57) 42 (68) 0.19 23 (56) 53 (65) 0.26
   Living with partner 18 (14.6) 8 (13) 10 (16) 5 (12) 13 (16)
   Single 28 (22.8) 18 (30) 10 (16) 13 (32) 15 (19)
WIC recipient n (%)
   Yes 28 (22.8) 20 (33) 8 (13) 0.01* 14 (34) 14 (17) 0.04*
   No 94 (76.4) 40 (67) 54 (87) 27 (66) 67 (83)
Maternal complicationsb n (%)
   Yes 35 (29) 21 (38) 14 (23) 0.08 13 (34) 22 (28) 0.46
   No 83 (71) 35 (62) 48 (77) 25 (66) 58 (72)
Total gestational weight gain n (%) 0.46 0.50
   <25 pounds 43 (35) 20 (31) 23 (37) 13 (32) 30 (36)
   25-35 pounds 34 (28) 16 (27) 18 (29) 11 (27) 23 (28)
   >35 pounds 46 (37) 25 (42) 21 (34) 17 (41) 29 (36)
Employment status at 8 weeks postpartum n (%) 0.04* 0.14
   Working same hours as before pregnancy 93 (75.6) 41 (68) 52 (84) 28 (68) 65 (80)
   Working fewer hours as before pregnancy 29 (23.6) 19 (32) 10 (16) 13 (32) 16 (20)
Breastfeeding self-efficacy scorea (34-36wks gestation) 0.17 0.13
   Mean ±SD 48.1±8.8 47.0 ± 9.7 49.2 ± 7.9 46.4 ± 9.3 49.0 ± 8.5
Gestational age at delivery n (%)
   39 weeks or less 47 (38) 26 (42) 21 (34) 0.32 15 (37) 32 (38) 0.79
   >39 weeks 76 (62) 35 (58) 41 (66) 26 (63) 50 (62)
Infant weight at delivery n (%)
   <2500 g 16 (13) 9 (13) 7 (12) 0.56 6 (15) 10 (12) 0.78
   2500-4000 g 98 (80) 45 (75) 53 (85) 32 (78) 66 (81)
   >4000 g 9 (7) 7 (12) 2 (3) 3 (7) 6 (7)
NICU admission n (%)
   Yes 25 (20) 14 (24) 11 (18) 0.34 14 (37) 11 (14) 0.01*
   No 93 (80) 42 (75) 51 (82) 24 (63) 69 (86)
Delivery type n (%)
   Vaginal 80 (65) 36 (64) 44 (71) 0.44 27 (71) 53 (66) 0.61
   Cesarean 38 (35) 20 (36) 18 (29) 11 (29) 27 (34)
Documented formula use in hospital n (%) 0.01* 0.14
   Yes 63 (51) 38 (68) 25 (40) 24 (63) 39 (49)
   No 55 (49) 18 (32) 37 (60) 14 (37) 41 (51)
Lactation consultant in hospital n (%) 0.41 0.01*
   Yes 108 (91) 50 (89) 58 (94) 31 (82) 77 (95)
   No 10 (9) 6 (11) 4 (6) 7 (18) 3 (5)
Total volume of formula in hospital n (%) 0.12 0.81
   <100 ml 25 (20) 14 (23) 11 (18) 6 (15) 19 (22)
   100-2,000 ml 31 (25) 19 (32) 12 (19) 14 (34) 17 (21)
   >2000 ml 67 (55) 28 (45) 39 (63) 21 (51) 46 (57)
Open in a new tab

Note. SD= Standard Deviation; BF= breastfeeding; wks = weeks; BMI= body mass index; WIC = Supplemental Nutrition Program For Women, Infants and Children

a

Breastfeeding Self-Efficacy Scale Short Form (BSES- SF) at 34-36 weeks gestation. The BSES-SF is a 14-item tool with a 5-point Likert-type scale that measures an individual’s confidence in their ability to breastfeed. Scores range from 14-70, and higher scores indicate greater breastfeeding self-efficacy (Dennis, 2003).

b

Maternal complications include prediabetes, gestational diabetes, type I DM, type II DM, chronic hypertension, gestational hypertension, pre-eclampsia, breast augmentation or other surgery, placenta previa, PPROM, IUGR, preterm labor, suspected or confirmed chorioamnionitis,, vacuum or forceps use, infant demise.

*

statistically significant based on alpha = .05;

We used SPSS v. 25 (IBM Corp., 2017) to screen data for accuracy, outliers, missing data, multicollinearity, and underlying assumptions. We applied a score adjustment to one univariate outlier in participant pre-pregnancy BMI. All other underlying assumptions, including normality of continuous variables (e.g., BSES scores, BMI) were met. A missing value analysis (MVA) on repeated measures variables (e.g., dichotomous and continuous measure of PIM and breastfeeding exclusivity) determined that the data was multivariately missing at random (MAR). We imputed missing values for both PIM (n = 20 of 115 for dichotomous measure; n = 31 of 117 for continuous measure) and breastfeeding exclusivity (n = 11 of 88) using stochastic regression.

We used group-based trajectory modeling (GBTM) to identify distinct trajectories of breastfeeding exclusivity and PIM (measured by investigator-created survey item) over the first eight weeks postpartum using the PROC TRAJ function in SAS v.9.4 (SAS Enterprise). While traditional methods of analyzing longitudinal data such as hierarchical modeling and latent curve analysis account for individual variability around a mean population trend (Nagin, 2014), GBTM permits identification of meaningful subgroups of individuals following a common trajectory relative to an outcome of interest. GBTM also allows an analysis of factors that contribute to group trajectory membership. The PROC TRAJ function uses a logistic (LOGIT) model to examine the distribution of dichotomous data (PIM and breastfeeding exclusivity), given group membership.

Three criteria were used to objectively determine the GBTM models that best fit our data: Bayesian information criteria (BIC) statistics, significance of parameter estimates, and stability of the models. After the best-fit models for PIM and breastfeeding exclusivity were identified, we applied four additional criteria to further assess model adequacy outlined by Nagin and Odgers (2010). To examine the relationship between pre-pregnancy BMI and trajectory group membership for breastfeeding exclusivity and PIM over eight weeks postpartum, we conducted two separate binomial logistic regression analyses using SPSS v. 25. After verifying that all underlying assumptions of binomial logistic regression were met, the analyses were conducted with and without covariates added to the model. We hierarchically added interaction terms into the models.

To assess the relationship between pre-pregnancy BMI and the continuous-type measure of PIM (PIBS scores) we used hierarchical linear regression. For this modeling, the postpartum Week 2 PIBS scores were used as the outcome variable, since PIM has been demonstrated to peak at this point in the postpartum period (Demirci & Bogen, 2017). Participant/infant characteristics (see Table 2) and BMI category were added to the model as independent variables. Participants with a BMI equal to or less than 25 were classified as normal weight, those with BMI between 25.1 and 29.9 were classified as overweight, and those with BMI equal to or greater than 30 were classified as obese. We first added participant/infant characteristics to the model (block 1) and then added BMI category (block 2). This process was repeated for each of the sample characteristics examined. We explored main effects and two-way interactions between characteristics and BMI category.

Table 2:

Participant Characteristics by Perceived Infant Breastfeeding Satiety (PIBS)a subscale and Pre-pregnancy Body Mass Index Category at 2 Weeks Postpartum

PIBS Scores
Characteristic Overall BMI <25 BMI 25-29.9 BMI ≥ 30 p-values (univariate characteristic, characteristic + BMI)b
Mean, SD (n) 28.2, 7.6 (122)   29.4, 6.5 (61)   26.6, 8.8 (37)   27.6, 7.9 (24)
Race
White   28.3, 7.6 (82) 29.4, 6.7 (41) 26.1, 9.2 (23) 28.3, 7.0 (18) 0.64, 0.23
Black 28.2, 7.9 (15) 29.2, 3.8 (5) 29.6, 7.9 (7) 23.2, 13.2 (3)
Other 26.7, 8.3 (6) 29.0, 8.8 (4) 22.0, 7.1 (2) N/A
Ethnicity
Hispanic 20.0, 10.2 (4) 25.5, 13.4 (2) 14.5, 3.5 (2) 27.6, 7.9 (21)
Non-Hispanic 28.5, 7.3 (99) 29.5, 6.3 (48) 27.4, 8.4 (30) N/A 0.02, 0.18
Age
<25 years old 31, 3.1 (16) 30.3, 3.9 (6) 32, 1.5 (6) 30.5, 3.9 (4)
25-30 years old 29.6, 6.9 (45) 31.7, 4.6 (19) 28.7, 7.8 (16) 27.1, 8.6 (10)
>30 years old 25.5, 8.6 (42) 27.3, 7.6 (25) 20.1, 9.3 (10) 26.7, 9.2 (7) 0.01, 0.09
Marital Status
Married 28.1, 7.3 (69) 29.2, 6.9 (38) 26.4, 8.3 (17) 27.4, 7.6 (14)
Living with partner 30.0, 7.0 (15) 28.4, 7.2 (5) 29.2, 9.0 (6) 33.2, 2.4 (4)
Single 26.7, 8.7 (19) 30.9, 3.9 (7) 25.3, 10.1 (9) 21.3, 10.8 (3) 0.75, 0.25
Education level
<Bachelor’s degree 29.2, 6.4 (30) 28.9, 5.2 (11) 30.4, 6.7 (10) 28.3, 7.8 (9)
Bachelor’s degree 28.3, 8.2 (31) 31.2, 6.9 (13) 26.0, 8.5 (14) 27.2, 10.5 (4)
Masters or doctorate degree 27.2, 7.9 (42) 28.6, 6.8 (26) 23.0, 10.5 (8) 27.0, 7.8 (8) 0.17, 0.15
WIC recipient
Yes 27.4, 8.8 (18) 29.9, 6.3 (7) 26.3, 10.1 (7) 25.0, 11.3 (4)
No 28.3, 7.3 (85) 29.3, 6.6 (43) 26.7, 8.6 (25) 28.2, 7.2 (17) 0.70, 0.25
Employed (8 weeks postpartum)
Yes 28.0, 7.5 (77) 30.2, 5.6 (39) 25.6, 8.8 (23) 25.8, 8.6 (15)
No 30.7, 6.9 (18) 28.1, 8.7 (7) 31.4, 6.5 (7) 33.7, 2.5 (4) 0.13, 0.11
Maternal complications c
Yes 27.5, 7.2 (28) 30.8, 3.3 (9) 25.4, 9.5 (10) 26.7, 6.6 (9)
No 28.4, 7.7 (75) 29.0, 7.0 (41) 27.2, 8.6 (22) 28.3, 9.0 (12) 0.80, 0.27
Total weight gain in pregnancy
<25 pounds 27.7, 7.6 (37) 30.2, 4.9 (14) 23.8, 10.6 (9) 27.7, 7.1 (14)
25-35 pounds 29.0, 6.8 (29) 29.5, 6.8 (20) 29.6, 4.4 (7) 22.5, 14.8 (2)
>35 pounds 27.92, 8.2 (37) 28.4, 7.5 (16) 26.94, 9.0 (16) 29.4, 8.8 (5) 0.94, 0.24
Gestational age at delivery
39 weeks or less 28.2, 7.4 (40) 28.4, 7.5 (20) 28.9, 8.0 (11) 27.0, 6.9 (9)
>39 weeks 28.1, 7.8 (63) 30.0, 5.7 (30) 25.4, 9.1 (21) 28.1, 8.9 (12) 0.91, 0.24
Infant weight at delivery
<2500 g 27.5, 4.4 (13) 26.6, 4.5 (7) 30.7, 3.5 (3) 26.3, 4.6 (3)
2500-4000 g 28.1, 8.0 (87) 29.7, 6.7 (42) 25.8, 9.3 (27) 27.83, 8.4 (18) 0.46, 0.24
≥4000 g 32.3, 2.5 (3) 35.0, n/a (1) 31.0, 1.4 (2) N/A
NICU admission
Yes 26.1, 6.9 (17) 29.7, 3.1 (7) 21.2, 7.8 (5) 25.8, 7.7 (5)
No 28.6, 7.7 (86) 29.3, 6.9 (43) 27.6, 8.7 (27) 28.2, 8.1 (16) 0.25, 0.28
Delivery type
Vaginal 27.7, 8.1 (70) 28.6, 7.2 (36) 25.4, 9.5, (22) 29.4, 7.7 (12)
Cesarean 29.0, 6.2 (33) 31.2, 3.5 (14) 29.4, 6.5 (10) 25.2, 7.9 (9) 0.35, 0.20
Documented formula use in Hospital
Yes 27.0, 8.0 (53) 28.5, 7.5 (25) 25.20, 9.4 (15) 26.2, 7.4 (13)
No 29.4, 6.9 (50) 30.2, 5.3 (25) 27.9, 8.3 (17) 29.9, 8.70 (8) 0.14, 0.28
Lactation consult in hospital
Yes 28.0, 7.6 (100) 29.3, 6.5 (49) 26.62, 8.8 (32) 27.1, 8.1 (19)
No 33.9, 2.6 (3) 34, n/a (1) N/A 32.5, 3.5 (2) 0.19, 0.18
Total volume of formula in hospital
<100 ml 26.9, 9.2 (25) 27.1, 9.9 (11) 25.7, 9.7 (8) 28.2, 8.7 (6)
100-2,000 ml 27.1, 7.3 (25) 30.0, 5.0 (13) 24.6, 9.7 (7) 22.8, 6.7 (6) 0.18, 0.26
>2,000 ml 29.3, 6.8 (53) 30.0, 5.3 (26) 27.9, 8.3 (17) 29.7, 7.7 (10)
Open in a new tab
a

Perceived Infant Breastfeeding Satiety (PIBS) subscale (5 items) within the H & H Lactation Scale (20 items), which is intended to measure perception of insufficient milk. Potential scores range from 5-35, with a higher score indicative of higher perceived infant satiety.

b

p-values are derived from hierarchical linear regression analysis modeling relationship between the select characteristic (± pre-pregnancy BMI) and PIBS score

c

Maternal complications include prediabetes, gestational diabetes, type I DM, type II DM, chronic hypertension, gestational hypertension, pre-eclampsia, breast augmentation or other surgery, placenta previa, PPROM, IUGR, preterm labor, suspected or confirmed chorioamnionitis, vacuum or forceps use, infant demise

SD= standard deviation; BMI= body mass index; WIC = Supplemental Nutrition Program for Women, Infants and Children

Results

Of the 122 participants, most were non-Hispanic White, employed, married, and possessed at least a Bachelor’s degree (see Table 1). Additionally, 61 (50%), 37 (30%), and 24 (20%) participants had BMIs classified as normal, overweight, or obese, respectively.

We identified two distinct trajectory paths for breastfeeding exclusivity and PIM over the first eight weeks postpartum using GBTM. When examining BIC statistics, parameter estimates, and model stability, the intercept trajectory shape best fit the data. Consistently narrow confidence intervals indicated high precision of the predicted trajectories for both breastfeeding exclusivity and PIM outcomes.

For the breastfeeding exclusivity GBTM model, both trajectory paths contained approximately half of the total sample. One group (n = 62) followed a trajectory defined by a consistently high probability of exclusive breastfeeding over time, while the other (n = 60) followed a trajectory defined by a low initial probability of exclusive breastfeeding with a linear decline in probability over time. For the PIM GBTM model, approximately two-thirds (n = 81) of the participants followed a trajectory defined by a consistently high probability of reporting no PIM (less likely to report PIM over time). The remainder of participants (n = 41) followed a trajectory defined by a consistently low probability of reporting no PIM.

Post-hoc analysis revealed good model fit for both exclusivity and PIM models in terms of average posterior probability (AvePP) values >0.7 and odds of correct classification (OCC) >5 for each outcome and trajectory group. We found pre-pregnancy BMI did not predict trajectory group membership for either PIM (OR: 0.969 [0.901, 1.042], p = 0.397) or breastfeeding exclusivity (OR: 0.943 [0.877, 1.01], p = 0.111) over the first eight weeks postpartum.

However, results from our univariate analysis (Table 1) demonstrated that non-Hispanic participants were more likely to be in the high breastfeeding exclusivity group compared to Hispanic participants (p = 0.046). Additionally, PIM group membership significantly differed based on ethnicity, with Hispanic participants more likely to be in the high PIM group (p = 0.029). Likewise, participants working the same number of hours as before pregnancy (measured eight weeks postpartum) were more likely to be assigned to the high breastfeeding exclusivity group compared to those working fewer hours (p = 0.04).

We did not find any main effect or interaction effects when examining the relationship between participant/infant characteristics (+/− pre-pregnancy BMI) and the continuous measure of PIM (PIBS score) at two weeks postpartum (Table 2).

Discussion

In our analysis of mostly White, non-Hispanic women, we found that pre-pregnancy BMI was not predictive of PIM or breastfeeding exclusivity trajectories over the first eight weeks postpartum. We also found that breastfeeding exclusivity and PIM were stable phenomena during this period in that participants exclusively breastfeeding with no PIM during the first postpartum week were likely to continue this pattern until at least 2 months postpartum. Conversely, participants beginning their breastfeeding course with any formula use and concerns about milk supply were unlikely to reverse these breastfeeding problems by two months.

Our results align with those of other researchers who found that mothers are increasingly less likely to exclusively breastfeed their infant over time if they begin with non-exclusive breastfeeding. In a prospective cohort study involving over 300 primiparous U.S. women with prenatal intention to exclusively breastfeed, Chantry et al., (2014) found that in-hospital formula supplementation was associated with an almost 2-fold greater risk of non-exclusive breastfeeding during days 30 to 60 after birth. In a population based GBTM analysis using breastfeeding data from 2005-2007 in the United States for primiparous and multiparous women, Whipps et al. (2019) identified four trajectories of breastfeeding exclusivity over 12 months postpartum. Two of these trajectories demonstrated steadily decreasing proportion of breast milk feeds over the first two months postpartum for those beginning with non-exclusive breastfeeding with breastfeeding cessation by six months postpartum. In the other two trajectories beginning with high proportions of breast milk feeds, participants maintained this pattern until two months after birth. At the two-month timepoint, one group began a gradual decline in proportion of breast milk feeds and the other group maintained exclusive breast milk feeds to an average of nine months. Similarly, breastfeeding exclusivity data collected in a Canadian-based cross-sectional study of 632 primiparous and multiparous women indicated that self-reported breastfeeding exclusivity rates decreased over six months postpartum, with increasingly steeper falls-offs for each progressive month (Haiek et al., 2007).

Our findings that pre-pregnancy BMI was not associated with the continuous-type measure of PIM and did not predict trajectory group membership for breastfeeding exclusivity or PIM was somewhat unexpected. Researchers have linked higher BMI to delayed onset of lactogenesis II (onset of copious milk production past three days postpartum), diminished prolactin response to lactogenesis II (Brownell et al., 2012; Preusting et al., 2017), PIM (Chang et al., 2020), and lower rates of breastfeeding exclusivity (Flores et al., 2018; Hauff et al., 2014; Hawkins et al., 2019). It is plausible that our sample was underpowered to detect an association between BMI and exclusivity or PIM group membership if it existed. For example, it is possible that the association between BMI and exclusivity or PIM group membership was more robust for individuals with a higher BMI. However, only 20% of our participants had a BMI in the obese category. This low percentage might have limited our ability to detect a potential relationship between BMI and exclusivity and/or PIM group membership among obese participants. However, in a small, longitudinal cohort study of 38 U.S. women followed from birth to four months, while no differences in breastfeeding outcomes were observed by pre-pregnancy BMI, overweight and obese women were more likely to feed their infants on a schedule, which was predictive of reduced breastfeeding exclusivity over time (Young et al., 2016). Thus, other participant characteristics and behaviors moderately correlated with BMI (e.g., lower feeding responsiveness, body image concerns (Zimmerman et al., 2018)) may be more important in breastfeeding outcomes compared to BMI alone (Hawkins et al., 2019). In addition, BMI may be too crude of a proxy measure for metabolic dysfunction associated with impaired milk production. In fact, Stuebe (2015) suggested that the timing of onset of excess adiposity (i.e., puberty) and distribution of adiposity (i.e., visceral) may be more important biological determinants of mammary gland function affecting breastfeeding outcomes.

Our findings that Hispanic women were more likely to be classified into the high probability of PIM and low probability of exclusive breastfeeding groups is consistent with existing research and national breastfeeding data from the Centers for Disease Control and Prevention (CDC). The CDC data indicate that breastfed Hispanic infants are more likely than any other race/ethnicity group to be supplemented with formula during the first two days of life and less likely than all other races/ethnicities (except non-Hispanic Black infants) to be exclusively breastfed through three months (Centers for Disease Control and Prevention, 2021). In a study of over 1,000 mothers interviewed prospectively over their infant’s first year of life, Hispanic mothers were more likely than non-Hispanic White mothers to endorse the statement “breast milk alone did not satisfy my baby” as an important reason for breastfeeding cessation (Li et al., 2008). Acculturation among Hispanic-American individuals is a documented factor influencing breastfeeding practices among this population. Highly acculturated Hispanic women were less likely to breastfeed compared to less acculturated Hispanic women according to a recent systematic review (Bigman et al., 2018). Additionally, recent Hispanic immigrants to the U.S. may choose to combination feed due to cultural taboos in the U.S. around breastfeeding in public and economic pressure to quickly return to employment following birth within industries not typically supportive of breastfeeding (Hohl et al., 2016).

Our results also indicated that return to pre-pregnancy work hours by eight weeks postpartum was associated with membership in the high exclusive breastfeeding group. However, paid parental leave and longer duration of leave is associated with improved breastfeeding outcomes, regardless of maternal BMI in numerous U.S based and international studies (Bai et al., 2015; Hamad et al., 2019; Navarro-Rosenblatt & Garmendia, 2018). Maternal employment was the most frequently cited barrier for maintenance of exclusive breastfeeding according to authors of a systemic review that examined barriers and facilitators of exclusive breastfeeding among infants aged 0-6 months (Balogun et al., 2015). One potential explanation for our findings is that in our highly-educated sample (two-thirds with at least a bachelor’s degree), participants were returning to jobs that allowed more flexible arrangements to facilitate breastfeeding (e.g., work from home).

Implications

Our findings highlight the importance of early support around the time of birth to prevent formula supplementation among women who wish to exclusively breastfeed. Early breastfeeding support for primiparous women from nurses and other healthcare professionals may facilitate positive breastfeeding outcomes for all individuals, irrespective of PIM or BMI.

Limitations

We recognize several limitations of this analysis. Our total sample (N = 122) and trajectory group sizes were relatively small (less than 82 per group). Thus, our statistical power to detect significant differences between group membership based on BMI was limited due to a potentially underpowered sample. Additionally, our data did not extend past eight weeks postpartum, limiting our ability to predict longer-term breastfeeding patterns. A major limitation of this analysis was lack of representation from Hispanic and non-White individuals. Though sample demographics were representative of the region from which participants were recruited (Pittsburgh, Pennsylvania: 23% Black or African American, 3% Hispanic in 2018 (U.S. Census Bureau, 2018), our findings are not generalizable to members of minority groups and the larger U.S. population. Further, our study sample was obtained from a larger randomized controlled trial and therefore cannot be viewed as a sample specifically drawn to answer the questions of this secondary analysis. Additionally, we did not examine reasons why participants stopped breastfeeding or if PIM caused them to stop exclusively breastfeeding during the 8-week time period. Future investigations into the reasons why participants in our study stopped breastfeeding would provide additional insight into the specific breastfeeding barriers our participants experienced.

Conclusion

In our secondary analysis examining breastfeeding exclusivity and PIM over the first eight weeks after birth among mostly White primiparous participants, we identified two distinct, stable trajectory paths for both breastfeeding exclusivity and PIM. Participants who began exclusively breastfeeding and without milk supply concerns were likely to continue these patterns. Likewise, participants who began with a combination breast/formula feeding and concerns about milk supply were unlikely to reverse course. We did not find that BMI predicted trajectory group membership for either breastfeeding exclusivity or PIM. Future studies are warranted to determine whether these patterns hold for larger, nationally-representative samples and members of minority groups.

Callouts:

1. High pre-pregnancy body mass index has been associated with reduced breastfeeding exclusivity and increased risk of perceived insufficient milk volume.

2. We did not find that body mass index predicted the trajectory of breastfeeding exclusivity or perceived insufficient milk.

3. Early breastfeeding support for women intending to breastfeed is essential as formula supplementation and perceived insufficient milk are difficult to modify once they occur.

Acknowledgement

The authors thank Brian Suffoletto, Jack Doman, Judy C. Chang, Debra L. Bogen, Melissa Glasser, Nora Lee, and Blair Powell for assistance with study conceptualization.

Funding

Funded by National Institute of Nursing Research (grant R00NR015106, PI: Jill Demirci). Rachel Dieterich received funding for her PhD education from the Robert Wood Johnson Foundation Future of Nursing Scholars Program.

Biographies

Author Bios

Rachel Renee Dieterich, PhD, RN, is an assistant professor, Nursing Programs, Chatham University, Pittsburgh, PA.

Susan Sereika, PhD, is a professor and the Associate Dean for Research and Education Support Services, Health & Community Systems, University of Pittsburgh, Pittsburgh, PA

Jill Demirci, PhD, RN, IBCLC, is an assistant professor, Health Promotion and Development, University of Pittsburgh, Pittsburgh, PA.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Disclosure: The authors report no conflicts of interest or relevant financial relationships.

Contributor Information

Rachel Renee Dieterich, Nursing Programs, Chatham University, Pittsburgh, PA.

Susan Sereika, Associate Dean for Research and Education Support Services, Health & Community Systems, University of Pittsburgh, Pittsburgh, PA.

Jill Demirci, Health Promotion and Development, University of Pittsburgh, Pittsburgh, PA.

References

  1. American Academy of Pediatrics Section on Breastfeeding. (2012). Breastfeeding and the use of human milk. Pediatrics, 129(3), e827–841.22371471 [Google Scholar]
  2. Amorim RM, Coelho AC, de Lira PC, & Lima MC (2014). Is breastfeeding protective for blood pressure in schoolchildren? A cohort study in northeast Brazil. Breastfeeding Medicine,9(3), 149–156. 10.1089/bfm.2013.0118 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Bai DL, Fong DY, & Tarrant M (2015). Factors associated with breastfeeding duration and exclusivity in mothers returning to paid employment postpartum. Maternal and Child Health Journal, 19(5), 990–999. 10.1007/s10995-014-1596-7 [DOI] [PubMed] [Google Scholar]
  4. Balogun OO, Dagvadorj A, Anigo KM, Ota E, & Sasaki S (2015). Factors influencing breastfeeding exclusivity during the first 6 months of life in developing countries: A quantitative and qualitative systematic review. Maternal and Child Nutrition, 11(4), 433–451. 10.1111/mcn.12180 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Bigman G, Wilkinson AV, Pérez A, & Homedes N (2018). Acculturation and breastfeeding among Hispanic American women: A systematic review. Maternal and Child Health Journal, 22(9), 1260–1277. 10.1007/s10995-018-2584-0 [DOI] [PubMed] [Google Scholar]
  6. Brownell E, Howard CR, Lawrence RA, & Dozier AM (2012). Delayed onset lactogenesis II predicts the cessation of any or exclusive breastfeeding. Journal of Pediatrics, 161(4), 608–614. 10.1016/j.jpeds.2012.03.035 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Centers for Disease Control and Prevention. (2021). National center for chronic disease prevention and health promotion, division of nutrition, physical activity, and obesity data, trend and maps. https://www.cdc.gov/nccdphp/dnpao/data-trends-maps/index.html
  8. Chang YS, Glaria AA, Davie P, Beake S, & Bick D (2020). Breastfeeding experiences and support for women who are overweight or obese: A mixed-methods systematic review. Maternal and Child Nutrition, 16(1), e12865. 10.1111/mcn.12865 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Chantry CJ, Dewey KG, Peerson JM, Wagner EA, & Nommsen-Rivers LA (2014). In-hospital formula use increases early breastfeeding cessation among first-time mothers intending to exclusively breastfeed. Journal of Pediatrics, 164(6), 1339–1345. 10.1016/j.jpeds.2013.12.035 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Demirci JR, & Bogen DL (2017). An ecological momentary assessment of primiparous women’s breastfeeding behavior and problems from birth to 8 weeks. Journal of Human Lactation, 33(2), 285–295. 10.1177/0890334417695206 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Demirci JR, Suffoletto B, Doman J, Glasser M, Chang JC, Sereika SM, & Bogen DL (2020). The development and evaluation of a text message program to prevent perceived insufficient milk among first-time mothers: Retrospective analysis of a randomized controlled trial. JMIR Mhealth and Uhealth, 8(4), e17328. 10.2196/17328 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Dennis CL (2003). The breastfeeding self-efficacy scale: Psychometric assessment of the short form. Journal of Obstetric, Gynecologic, & Neonatal Nursing, 32(6), 734–744. 10.1177/0884217503258459 [DOI] [PubMed] [Google Scholar]
  13. Flores TR, Mielke GI, Wendt A, Nunes BP, & Bertoldi AD (2018). Prepregnancy weight excess and cessation of exclusive breastfeeding: A systematic review and meta-analysis. European Journal of Clinical Nutrition, 72(4), 480–488. 10.1038/s41430-017-0073-y [DOI] [PubMed] [Google Scholar]
  14. Gunderson EP, Quesenberry CP Jr., Ning X, Jacobs DR Jr., Gross M, Goff DC, Pletcher MJ, & Lewis CE (2015). Lactation duration and midlife atherosclerosis. Obstetrics & Gynecology, 126(2), 381–390. 10.1097/aog.0000000000000919 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Hackman NM, Schaefer EW, Beiler JS, Rose CM, & Paul IM (2015). Breastfeeding outcome comparison by parity. Breastfeed Medicine, 10(3), 156–162. 10.1089/bfm.2014.0119 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Haiek LN, Gauthier DL, Brosseau D, & Rocheleau L (2007). Understanding breastfeeding behavior: Rates and shifts in patterns in Quebec. Journal of Human Lactation, 23(1), 24–31. 10.1177/0890334406297278 [DOI] [PubMed] [Google Scholar]
  17. Hamad R, Modrek S, & White JS (2019). Paid family leave effects on breastfeeding: A quasi-experimental study of US policies. American Journal of Public Health, 109(1), 164–166. 10.2105/ajph.2018.304693 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Harris P, Taylor R, Thielke JP, Gonzalez N, & Conde JG (2009). Research electronic data capture (REDCap) - A metadata-driven methodology and workflow process for providing translational research informatics support. Journal of Biomedical Informatics, 42(2), 377–81. 10.1016/j.jbi.2008.08.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Hauff LE, Leonard SA, & Rasmussen KM (2014). Associations of maternal obesity and psychosocial factors with breastfeeding intention, initiation, and duration. American Journal of Clinical Nutrition, 99(3), 524–534. 10.3945/ajcn.113.071191 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Hawkins MW, Colaizzi J, Rhoades-Kerswill S, Fry ED, Keirns NG, & Smith CE (2019). Earlier onset of maternal excess adiposity associated with shorter exclusive breastfeeding duration. Journal of Human Lactation, 35(2), 292–300. 10.1177/0890334418799057 [DOI] [PubMed] [Google Scholar]
  21. Hill PD, & Humenick SS (1996). development of the H & H lactation scale. Nursing Research, 45(3), 136–140. 10.1097/00006199-199605000-00003 [DOI] [PubMed] [Google Scholar]
  22. Hohl S, Thompson B, Escareño M, & Duggan C (2016). Cultural norms in conflict: Breastfeeding among Hispanic immigrants in rural Washington State. Maternal and Child Health Journal, 20(7), 1549–1557. 10.1007/s10995-016-1954-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. IBM Corp. (2017). IBM SPSS Statistics for Windows (Version 25.0) [Computer Software]. https://www.ibm.com/analytics/spss-statistics-software?pl=Search&p4=43700050436265474&p5=b&gclid=CjwKCAjwy7CKBhBMEiwA0Eb7agefqn_tXER2IXYCpPazOpjnggxKklD4_4G2-W2Xd2otpMhvxReKaxoC5qMQAvD_BwE&gclsrc=aw.ds
  24. Kair LR, & Colaizy TT (2016). Obese mothers have lower odds of experiencing pro-breastfeeding hospital practices than mothers of normal weight: CDC Pregnancy Risk Assessment Monitoring System (PRAMS), 2004-2008. Maternal and Child Health Journal, 20(3), 593–601. 10.1007/s10995-015-1858-z [DOI] [PubMed] [Google Scholar]
  25. Kronborg H, Vaeth M, & Rasmussen KM (2013). Obesity and early cessation of breastfeeding in Denmark. European Journal of Public Health, 23(2), 316–322. 10.1093/eurpub/cksl35 [DOI] [PubMed] [Google Scholar]
  26. Li R, Fein SB, Chen J, & Grummer-Strawn LM (2008). Why mothers stop breastfeeding: Mothers’ self-reported reasons for stopping during the first year. Pediatrics, 122 Suppl 2, S69–76. 10.1542/peds.2008-1315i [DOI] [PubMed] [Google Scholar]
  27. Lubold AM (2016). Breastfeeding and employment: A propensity score matching approach. Sociological Spectrum, 36(6), 391–405. 10.1080/02732173.2016.1227286 [DOI] [Google Scholar]
  28. Lyall DM, Celis-Morales C, Ward J, Iliodromiti S, Anderson JJ, Gill JMR, Smith DJ, Uduakobong EN, Mackay DF, Holmes MV, Sattar N, & Pell JP (2017). Association of body mass index with cardiometabolic disease in the UK Biobank: A mendelian randomization study. JAMA Cardiology, 2(8), 882–889. 10.1001/jamacardio.2016.5804 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Massov L (2015). Clinically overweight and obese mothers and low rates of breastfeeding: Exploring women’s perspectives. New Zealand College of Midwives Journal, (51), 23–29. 10.12784/nzcomjnl51.2015.4.23-29 [DOI] [Google Scholar]
  30. Mok E, Multon C, Piguel L, Barroso E, Goua V, Christin P, Perez MJ, & Hankard R (2008). Decreased full breastfeeding, altered practices, perceptions, and infant weight change of prepregnant obese women: A need for extra support. Pediatrics, 121(5), e1319–1324. 10.1542/peds.2007-2747 [DOI] [PubMed] [Google Scholar]
  31. Nagin DS (2014). Group-based trajectory modeling: An overview. Annals of Nutrition and Metabolism, 65(2-3), 205–210. 10.1159/000360229 [DOI] [PubMed] [Google Scholar]
  32. Nagin DS, & Odgers CL (2010). Group-based trajectory modeling in clinical research. Annual Review of Clinical Pyschology, 6, 109–138. 10.1146/annurev.clinpsy.121208.131413 [DOI] [PubMed] [Google Scholar]
  33. Natland Fagerhaug T, Forsmo S, Jacobsen GW, Midthjell K, Andersen LF, & Ivar Lund Nilsen T (2013). A prospective population-based cohort study of lactation and cardiovascular disease mortality: The HUNT study. BMC Public Health, 13, 1070. 10.1186/1471-2458-13-1070 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Navarro-Rosenblatt D, & Garmendia ML (2018). Maternity leave and its impact on breastfeeding: A review of the literature. Breastfeed Medicine, 13(9), 589–597. 10.1089/bfm.2018.0132 [DOI] [PubMed] [Google Scholar]
  35. Nommsen-Rivers LA (2016). Does insulin explain the relation between maternal obesity and poor lactation outcomes? An overview of the literature. Advances in Nutrition, 7(2), 407–414. 10.3945/an.115.011007 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Odom EC, Li R, Scanlon KS, Perrine CG, & Grummer-Strawn L (2013). Reasons for earlier than desired cessation of breastfeeding. Pediatrics, 131(3), e726–732. 10.1542/peds.2012-1295 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Preusting I, Brumley J, Odibo L, Spatz DL, & Louis JM (2017). Obesity as a predictor of delayed lactogenesis II. Journal of Human Lactation, 33(A), 684–691. 10.1177/0890334417727716 [DOI] [PubMed] [Google Scholar]
  38. SAS Enterprise. (2017). SAS Analytics Software (Version 9.4) [Computer Software]. https://www.sas.com/en_us/curiosity.html?utm_source=other&utm_medium=cpm&utm_campaign=non-cbo-us&dclid=&gclid=CjwKCAjwy7CKBhBMEiwA0Eb7ajZvOoPNj1ZgYzks4b_GQPcpyf2hGI0lOEHUH4HbfX_JWrhlR05MjhoCj9YQAvD_BwE
  39. Stuebe AM (2015). Does breastfeeding prevent the metabolic syndrome, or does the metabolic syndrome prevent breastfeeding? Seminars in Perinatology, 39(4), 290–295. 10.1053/j.semperi.2015.05.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Stuebe AM, Schwarz EB, Grewen K, Rich-Edwards JW, Michels KB, Foster EM, Curhan G, & Forman J (2011). Duration of lactation and incidence of maternal hypertension: A longitudinal cohort study. American Journal of Epidemiology, 174(10), 1147–1158. 10.1093/aje/kwr227 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Tabachnick BG, & Fidell LS (2001). Using multivariate statistics (4th ed.). Allyn & Bacon. [Google Scholar]
  42. text4baby. (2017). text4baby. https://www.text4baby.org/
  43. U.S. Census Bureau. (2018). American Community Survey 1-year estimates. Retrieved July 21, 2019, from http://censusreporter.org/profiles/16000US4261000-pittsburgh-pa/
  44. Umer A, Hamilton C, Edwards RA, Cottrell L, Giacobbi P Jr., Innes K, John C, Kelley GA, Neal W, & Lilly C (2019). Association between breastfeeding and childhood cardiovascular disease risk factors. Maternal and Child Health Journal, 23(2), 228–239. 10.1007/s10995-018-2641-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Whipps MD, & Demirci JR (2020). The sleeper effect of perceived insufficient milk supply in US mothers. Public Health Nutrition, 1–7. 10.1017/s1368980020001482 [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Whipps MDM, Yoshikawa H, & Demirci JR (2019). Latent trajectories of infant breast milk consumption in the United States. Maternal & Child Nutrition, 15(1), e12655. 10.1111/mcn.12655 [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. World Health Organization. (2019). Breastfeeding. World Health Organization. Retrieved July 6, 2019, from https://www.who.int/topics/breastfeeding/en/ [Google Scholar]
  48. Young BE, Farazandeh S, Westra K, & Krebs N (2016). Maternal beliefs surrounding infant feeding, but not maternal BMI or hospital experience, predict breastfeeding exclusivity and behavior. Austin Journal of Pediatrics, 3(4), 1041. https://pubmed.ncbi.nlm.nih.gov/28553661 [PMC free article] [PubMed] [Google Scholar]
  49. Zimmerman E, Rodgers RF, O’Flynn J, & Bourdeau A (2019). Weight-related concerns as barriers to exclusive breastfeeding at 6 months. Journal of Human Lactation, 35(2), 284–291. 10.1177/0890334418797312 [DOI] [PubMed] [Google Scholar]

RESOURCES