How multiple episodes of exclusive breastfeeding impact estimates of exclusive breastfeeding duration: report from the eight-site MAL-ED birth cohort study

The duration of exclusive breastfeeding (EBF) is often defined as the time from birth to the first non-breast milk food/liquid fed (EBFLONG), or it is estimated by calculating the proportion of women at a given infant age who EBF in the previous 24 h (EBFDHS). Others have measured the total days or personal prevalence of EBF (EBFPREV), recognizing that although non-EBF days may occur, EBF can be re-initiated for extended periods. We compared breastfeeding metrics in the MAL-ED study; infants’ breastfeeding trajectories were characterized from enrollment (median 7 days, IQR: 4, 12) to 180 days at eight sites. During twice-weekly surveillance, caretakers were queried about infant feeding the prior day. Overall, 101 833 visits and 356 764 child days of data were collected from 1957 infants. Median duration of EBFLONG was 33 days (95% CI: 32–36), compared to 49 days based on the EBFDHS. Median EBFPREV was 66 days (95% CI: 62–70). Differences were because of the return to EBF after a non-EBF period. The median number of returns to EBF was 2 (IQR: 1, 3). When mothers re-initiated EBF (second episode), infants gained an additional 18.8 days (SD: 25.1) of EBF, and gained 13.7 days (SD: 18.1) (third episode). In settings where women report short gaps in EBF, programmes should work with women to return to EBF. Interventions could positively influence the duration of these additional periods of EBF and their quantification should be considered in impact evaluation studies. © 2016 John Wiley & Sons Ltd. The eight sites in the MAL‐ED birth cohort study followed a harmonized protocol to collect information on nutrition, morbidity, gut function, growth, vaccine response and cognitive development for the first two years of the child’s life (MAL‐ED Network Investigators 2014). Each site participating in the MAL‐ED network obtained the ethical approval for the study from their respective institutions and written informed consent was obtained from the participants. Throughout the report, the MAL‐ED study sites are referred to by abbreviations for their location: Dhaka, Bangladesh (BGD); Fortaleza, Brazil (BRF); Vellore, India (INV); Bhaktapur, Nepal (NEB); Naushahro Feroze, Pakistan (PKN); Loreto, Peru (PEL); Venda, South Africa (SAV); Haydom, Tanzania (TZH). (Turab et al. 2014; Shrestha et al. 2014; Lima et al. 2014; John et al. 2014; Mduma et al. 2014; Bessong et al. 2014; Yori et al. 2014; Ahmed et al. 2014). Enrollment and baseline information has been described elsewhere (MAL‐ED Network Investigators 2014). Briefly, mother–infant dyads were eligible for enrollment in the MAL‐ED study if the infant was less than 17 days of age, a singleton weighing at least 1500 g at birth, without congenital defects or serious illness, born to a mother at least 16 years of age, whose mother was willing to participate in the study and had no plans to move out of the study area for six months (at the time of enrollment). Of the 2145 infants enrolled in the MAL‐ED cohort, analyses were conducted on 1957 infants with complete data to 180 days of age. A total of 188 infants were excluded from analyses here, for the following reasons: 44 were lost to follow‐up, 113 moved out of the study area, 15 died prior to reaching six months of age, 15 had more than 25% missing data from home visits and one infant was enrolled after 17 days of birth. At enrollment, baseline data on household demographics were collected, which included information on head of household (mother, father, grandparent, other) and maternal characteristics (age, education, parity, pregnancy age). Biweekly nutritional and morbidity surveillance were initiated at the time of enrollment as further explained below. At one and sixth months, maternal depressive symptoms were measured using a Self‐Report Questionnaire (SRQ), and at eight months; maternal reasoning capacities were measured using the Ravens Combined Matrices (RCM) instrument (Murray‐Kolb et al. 2014; Pendergast et al. 2014). As described elsewhere (Caulfield et al. 2014), nutritional surveillance was conducted through home visits twice weekly, during which the caregiver was queried about the child’s consumption in the previous 24 h of breast milk, animal milk, formula, other liquids, water, tea, fruit juice, semi solids and specific solid foods. A second questionnaire, administered monthly, collected more detailed information on non‐breastmilk foods consumed the previous day. Based on the definitions of Labbok and Krasovec, breastfeeding status at each visit was characterized as: exclusive, predominant, partial or none (Labbok & Krasovec 1990). Breastfeeding status was defined as EBF in the previous 24 h if the child received only breast milk with the exception of vitamins or medicine. Predominant breastfeeding was identified when a child received water or water‐based liquids such as juice or tea in addition to breast milk. If the child received milk‐based liquids, semi‐solid or solid food in addition to breast milk, it was considered partial breastfeeding. Finally, a child’s breastfeeding status would be categorized as none, if there were no consumption of breast milk the day prior to the study visit. For analyses, we further separated partial breastfeeding into partial breastfeeding with liquids only or partial breastfeeding with both liquids and solids to examine the nature of the transition between exclusive and partial breastfeeding status. Days between visits were assumed to have the same status as the preceding visit for the calculation of duration of each breastfeeding practice in days (Henkle et al. 2013). Based on our knowledge and site observations, there is very little feeding of expressed breastmilk, and we assume if a child consumed only expressed breast milk that the mother would report this as breastfeeding. Early in our analyses, we observed gaps in EBF, which is the re‐initiation of EBF after at least one visit reported as non‐EBF. Therefore, we quantified the number of these episodes of EBF for each infant over the 6‐month period, the number of days of EBF for each episode of EBF, and considered the number of days of EBF beyond cessation of the first episode as a being the gain in EBF. During analyses, we sought to identify maternal and child factors associated with this infant feeding pattern. In addition to the nutritional surveillance, twice weekly morbidity surveillance was also conducted at the same time, which queried the mother on child’s illness symptoms and treatment since the previous visit (number of loose stools, fever, cough, appetite, vomiting, diarrhea, etc.). Detailed information on morbidity definitions and visit frequency have been presented elsewhere (Richard et al. 2014). Supervisors at each of the sites performed repeat visits on 5% of households for quality control checks. For the first six months of life, a total of 1731 (average 216 per site) repeat visits were conducted across all of the eight sites. Overall, the percent agreement in reported practices was 90% for EBF, 87% for predominant breastfeeding, 90% for partial breastfeeding and essentially 100% for no breastfeeding. Among mothers who re‐initiated EBF, average agreement among sites was 87% and ranged from 78% in PEL to 100% in BRF for EBF. For predominant feeding, agreement was 85% and ranged from 55% in PKN to 98% in TZH. For partial breastfeeding, agreement was also 90% (51% in PKN to 100% in BRF) and for no breastfeeding, the agreement was 99% across all sites. For diarrhea, agreement was above 93% for all sites (Richard et al. 2014). For the first aim, we compared three different metrics for estimating the median duration of EBF in the first six months of life, and two summary measures. First, we defined EBF duration for each child as the number of days from birth to the first study visit at which breastfeeding status was not designated EBF; from this, the median duration of EBF collected longitudinally was identified (EBFLONG). Second, we estimated the proportion of children at each age who were EBF the day before; we randomly chose one visit per infant to make this metric comparable to the other two, and from this estimate, EBFDHS was identified as the time when 50% of mothers report EBF the day before. We utilized the DHS interpolation method in which age‐stratified proportions of EBF are linearized. The proportion before and after 0.5 (rounded to one decimal) is weighted to estimate the median duration (EBFDHS) (Rutstein & Rojas 2006; Pullum 2014). Third, we estimated the personal prevalence of EBF, or the proportion of time each infant was EBF during the first 6 months, and estimated the median % days EBF during the first 6 months (EBFPREV). To estimate the 95% confidence interval (CI) for EBFLONG, we used survival analysis to first default, and to estimate the 95% CI for EBFPREV we used a binomial method. Using the data populated from EBFDHS we also constructed the WHO core indicator (EBFWHO) which is the proportion of children 0–5.9 months reported as exclusively breastfed (World Health Organization (WHO) 2010), and for comparison, the proportion of children exclusively breastfed using the full longitudinal data (EBFTRUE). To illustrate individual patterns of feeding over time, breastfeeding trajectory plots were created for each site using 50 randomly selected infants (Fig. Fig. 1 and supplementary Figs 1–7), sorted based on the EBFLONG metric (Kohler & Brzinsky‐Fay 2005). Each colour in the figure corresponds to their breastfeeding status: blue, EBF; orange, predominant feeding; yellow, partial breastfeeding with liquids only; brown, partial breastfeeding with both liquids and solid; red, no breastfeeding. Breastfeeding trajectory plot of 50 children from Loreto, PEL. Each number/row on the y‐axis indicates the pattern of feeding for a child with age in days on x axis. Blue represents exclusive breastfeeding (EBF); orange represents predominant feeding (Predominant BF); yellow represents partial breastfeeding with liquids only (Part BF:liq); brown represents partial breastfeeding with solids (Part BF: sol) and red represents no breastfeeding (No BF). ‘|’ in the sequence indicates when the visit was made. The preceding visit feeding is assumed in the days in between for illustrative purposes. For example, child 25 starts out with exclusive bf, shifts to predominant bf ~day 40, shifts back to exclusive at day 60, which stops at ~day 90. The total gain of EBF days in the first episode of EBF is 40 days and in the second episode, gain is 30 days. To evaluate whether illness episodes and/or low appetite were associated with changes in infant feeding, we evaluated the temporal relations of these factors with the report of non‐EBF, EBF re‐initiation, and with gap length using Chi‐square and t‐tests. We also constructed a logistic regression model to identify maternal, child and household characteristics associated with initiating EBF at least three times (i.e. three or more EBF episodes) in the first 180 days. We choose three or more episodes based on frequencies across the sites that would allow comparison, and we posited that this frequency indicated a behaviour. We examined associations with parity (1, 2–4, >5 children), maternal age (years), maternal age at first pregnancy (years), maternal education (0–5, 6–10, 11+ years), maternal reasoning ability and maternal depressive symptoms (average of the two SRQ surveys). When evaluating maternal depressive symptoms, the BRF site was excluded from the model because of measurement concerns (Pendergast et al. 2014). Sex was the only child characteristic considered in the logit model. Models were also adjusted for the length of first EBF episode (categorized into 1, 2, 3+ months of EBF), because the longer the duration of the first EBF episode, the lower the likelihood that three or more re‐initiations in the first 180 days would be observed. For household characteristics, we included components of a socio‐economic status scale within the MAL‐ED study called the WAMI index, which includes a composite score based on maternal education, access to improved water and sanitation facilities, assets and monthly income (Psaki et al. 2014). For covariates that were collinear, we kept whichever of the covariates provided a more meaningful interpretation. Factors were considered statistically significant at P < 0.05 but for these analyses we also included factors marginally significant at P < 0.10. Sites were treated as fixed effects in the overall model. Data analyses were conducted using STATA Version 13.1 (StataCorp 2013, College Station, TX).