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Exclusive breastfeeding (EBF) during the first 6 months of life is crucial for optimizing child growth, development and survival, as well as the mother’s wellbeing. Mother’s employment may hinder optimal breastfeeding, especially in the first 6 months. We assessed the effectiveness of a baby-friendly workplace support intervention on EBF in Kenya. This pre-post intervention study was conducted between 2016 and 2018 on an agricultural farm in Kericho County. The intervention targeted pregnant/breastfeeding women residing on the farm and consisted of workplace support policies and programme interventions including providing breastfeeding flexi-time and breaks for breastfeeding mothers; day-care centres (crèches) for babies near the workplace and lactation centres with facilities for breast milk expression and storage at the crèches; creating awareness on available workplace support for breastfeeding policies; and home-based nutritional counselling for pregnant and breastfeeding women. EBF was measured through 24-h recall. The effect of the intervention on EBF was estimated using propensity score weighting. The study included 270 and 146 mother–child dyads in the nontreated (preintervention) group and treated (intervention) group, respectively. The prevalence of EBF was higher in the treated group (80.8%) than in the nontreated group (20.2%); corresponding to a fourfold increased probability of EBF [risk ratio (RR) 3.90; 95% confidence interval (CI) 2.95–5.15]. The effect of the intervention was stronger among children aged 3–5 months (RR 8.13; 95% CI 4.23–15.64) than among those aged <3 months (RR 2.79; 95% CI 2.09–3.73). The baby-friendly workplace support intervention promoted EBF especially beyond 3 months in this setting.
The study was conducted in one of the large‐scale agricultural farms in Kericho County, in the highlands west of the Kenyan rift valley. The county, which is home to some of the largest agricultural estates in Kenya, covers an area of 2,111 km2 and a population of 739,872 of which 44% are aged 0–14 years. The study site covers over 8,700 ha and has a population of over 80,000 people in 112 villages, accounting for over 90% of the population living within the agricultural plantation. There are close to 16,000 employees, a third being women. The majority of the employees are seasonal workers, working on the farms, whereas the rest are permanent employees working within the factories, offices and as security personnel. There is an organized employer‐supported health care system, which includes a major (Level 4) hospital, four health centres (Level 3), and 23 dispensaries (Level 2), and a comprehensive HIV/AIDs programme. The agricultural estate also has other social facilities including staff houses, social halls, schools (20 primary schools, 8 secondary schools and 53 early childhood development centres), clean water supply and electricity. The plantation has peer educators who work as volunteers on health and social matters. The study employed an outcome evaluation as well as an implementation research study design in line with the WHO's Alliance for Health Policy and Systems Research (WHO/AHPSR) implementation research guide (Peters, Tran, & Adam, 2013) and the 2010 Quality Standards for Development of Evaluation by the Organization for Economic Co‐operation and Development/Development Assistance Committee [Organisation for Economic Co‐operation and Development (OECD), 2010]. The effectiveness‐implementation hybrid trials combined elements of implementation research and effectiveness to assess both the implementation strategy and the effectiveness of the initiative (Peters, Adam, et al., 2013). The participatory action research included innovative participatory methods such as photovoice and participatory videos, which encouraged the involvement of the beneficiaries/communities and co‐ownership of the initiatives to enhance transparency, accountability and capacity building of beneficiaries/community members. A community readiness assessment and other formative assessments were done at the beginning of the study. The information, collected through qualitative approaches and participatory methodologies, was used to tailor the intervention to the context at the intervention development stage and to adapt the intervention during the implementation. More details on this can be obtained from the published protocol paper (Kimani‐Murage et al., 2021). The current paper focuses on the evaluation of the effect of the intervention on EBF. The evaluation employed a quasi‐experimental design, involving a pre‐post intervention design. The postintervention assessment was conducted after about 12 months of implementing the intervention. This evaluation design was deemed to be the most feasible evaluation design given that the intervention was designed to cover the entire study setting. The study focused on female employees (permanent and seasonal), specifically mothers with infants (aged 0–12 months), and the infants themselves. Because this paper focuses on EBF for the first 6 months of life, we used data from mothers of children younger than 6 months. A preimplementation formative assessment to assess community readiness for the intervention (Center for Community Health and Development, 1994) and to engage the community and collect data necessary to tailor the intervention to the context in which it was applied was conducted before the implementation of the intervention (Kimani‐Murage et al., 2021). The formative assessment was conducted between September and November 2016. This was followed by a period of development of the intervention between December 2016 and April 2017. The intervention was then implemented for 12 months (from May 2017 to April 2018) (Kimani‐Murage et al., 2021). The formative assessment revealed that the employing company had policies to support breastfeeding mothers. These included a 3‐month paid maternity leave for full‐time female workers, breastfeeding breaks, peer counsellors and flexible working hours. Eighty percent of the mothers were aware of these policies; however, several factors hampered their implementation. These included poor adherence to the policies by either the line managers or the mothers, long distances between the place of work and home (where the infants were) which hindered the utilization of the breastfeeding breaks, a lack of understanding of the importance of EBF by the managers, the mother and other employees, competing priorities—although some mothers desired to adhere to the policies they were forced to forgo breastfeeding to meet their minimum daily targets, and the volunteer peer‐educators were not empowered to educate the mothers on breastfeeding and combine it with work. The intervention, which targeted all women living on the agricultural farm regardless of their employment status, consisted of advocacy, technical and collaborative financial support to the agricultural farm management to update and implement workplace support policies and programme interventions including providing paid breastfeeding breaks for breastfeeding mothers; establishing day‐care centres (crèches) for babies near the workplace where working mothers could access their babies for breastfeeding easily and lactation centres with facilities for breast milk expression and storage at the crèches; and creating awareness on both the value and the availability of workplace support for breastfeeding policies. There was also home‐based nutritional counselling through monthly visits for pregnant and breastfeeding women residing within the agricultural farm and nutrition education to other farmworkers to support breastfeeding. A detailed communication strategy was developed to provide a road map for behaviour change. The communication strategy was based on the socioecological model (Golden & Earp, 2012), which classified different spheres and key influencers to be targeted for behavioural interventions in maternal, newborn, and child health and nutrition (Figure S1). The employing company refurbished available buildings into two daycare centres with dedicated rooms for expressing breastmilk, hired experienced nurses as caretakers to work in the centres, and provided equipment and supplies—including bottles for expressing breast milk, breast milk freezing and storage containers and fridges—for storing expressed breast milk. UNICEF provided early childhood development kits, television screens, digital versatile discs and videos on breastfeeding for training the mothers and other key influencers on good positioning, expression and storage of expressed milk. An existing workplace breastfeeding‐friendly policy by the employing company was revised based on the technical support provided and included implementation of the 3‐month paid maternity leave policy, allowing lactating mothers to take paid breastfeeding work breaks and flexibility in the time to report to work, and regular sensitization of women; their influencers (team members and supervisors) and management staff on the policy. Incentivized counsellors who were residents of the agricultural estate conducted home‐based nutritional counselling. The counsellors had received a 1‐week training, continuous support supervision, and mentorship and conducted house‐to‐house visits educating pregnant and lactating women and their partners on issues related to maternal, infant and young child nutrition (MIYCN) and childcare. To enhance their breastfeeding support, health workers, the supervisors of the counsellors and key welfare staff of the agricultural farm received an 8‐h/day, 6‐day training on MIYCN based on a standard curriculum developed by the Ministry of Health (2020). Health workers and incentivized counsellors created awareness about daycare centres, mobilized women to use the facilities and helped to form mother‐to‐mother support groups. A community‐based management structure called the community mother support group oversaw the day‐to‐day work of the incentivized counsellors, their supervisors, the health facility staff and the overall management of the programme. The employing company and UNICEF co‐financed the intervention. A preintervention survey was conducted between September and November 2016, whereas a postintervention survey was conducted between May and July 2018. The two study groups (i.e., for preintervention and postintervention survey) were independent of each other. All women with children aged less than 1 year and living in the plantation were recruited to assess their breastfeeding practices based on 24‐h recall using a questionnaire. The women were invited to participate in the study by filling a screening form administered by community health volunteers/peer educators or by health care workers during postnatal care. All eligible women agreed to participate. The questionnaire, which also collected socio‐demographic and economic data, was developed in English, translated to Swahili, programmed in Survey CTO software and uploaded in mobile phones for data collection. Trained research assistants collected data through face‐to‐face interviews. Supervision of the research assistants by field supervisors and members of the research team and regular review of the data were performed to ensure data quality. A minimum sample of 600 women (i.e., 300 women in the preintervention group and 300 in the intervention group) was calculated assuming an increase in EBF from 17% in the preintervention group (hereafter referred to as the nontreated group) to 27% in the intervention group (hereafter referred to as the treated group), a two‐sided hypothesis test with a 5% significance level, a power of 80% and a non‐response rate of 10%. However, a comprehensive sampling was carried out by recruiting all consenting mothers with children under the age of 1 year. Accordingly, both at baseline and endline, consecutive mass recruitment of all mothers (employed permanently, casually or not working) who had children younger than 1 year and living in the agricultural plantation was followed. The outcome variable was EBF, defined by WHO as consumption of only breastmilk and nothing else except oral rehydration fluids, drops or syrups in the past 24 h (Wold Health Organization, 2008). We considered, a priori, the following socio‐demographic variables as covariates based on their theoretical association with the intervention and/or breastfeeding: child's age in months (continuous) and sex, mother's age (1‐year interval); parity (1, 2, 3 and 4+); ethnicity (Kalenjin, Kisii and others); education (primary or less, secondary, and tertiary); religion (Christian and others); marital status (in a union and not in a union); and employment status (employed in the agricultural estate, employed elsewhere and unemployed). Characteristics of the participants in the treated and nontreated groups were summarized using descriptive statistics. A propensity score, defined as the probability of being assigned to a treatment group given an individual's observed covariates (D'Agostino, 1998), was used to weight the sample and to ensure the covariates balanced across treatment groups. This approach is akin to applying survey weights in a sample survey. First, we generated propensity scores (using the ‘pscore’ command in Stata) by including the treatment variable and all the above covariates in the model. There was no evidence of covariate imbalance between the treated and nontreated groups within blocks of the propensity score. Next, we weighted the treatment groups by the propensity score based on the inverse probability of treatment weighting method using doubly robust estimation (Funk et al., 2011). Doubly robust estimation combines outcome regression and propensity score modelling to obtain an unbiased effect estimator (Funk et al., 2011). Each child in the intervention group received a weight equal to the inverse of the propensity score, whereas each comparison child received a weight equal to the inverse of one minus the propensity score (Garrido et al., 2014). The weighting variable was then included in a generalized linear model (Poisson regression with robust error variance) to assess the effect of the intervention on the outcome; expressed as risk ratio (RR) with 95% confidence interval (CI). We also assessed the effect of the intervention on the outcome in the usual way by using Poisson regression with robust error variance and adjusting for variables that showed some imbalance (at a conservative P < 0.2) between the treated and nontreated groups. Because this study included all women residing in the agricultural estate regardless of their employment status, we stratified the results by mother's employment status (i.e., employed in the estate or unemployed). Moreover, because the probability of EBF reduces with the child's age, we stratified the results by child's age (<3 months or 3–5 months). Because 47 participants in the nontreated group had missing outcome data, we compared the socio‐demographic characteristics of the participants with complete data and those with missing data and found no significant differences, apart from marital status (Table S1). We then performed sensitivity analysis to account for the missing data through multiple imputation using chained equations with 20 iterations. The imputation model included all the variables in Table 1 together with the treatment group. We then repeated the above analyses based on the imputed datasets and combined the estimates using Rubin's rules (Rubin, 1987). All analyses were conducted using Stata 15.1 and a two‐tailed α of 0.05. Characteristics of mothers and children in the non‐treated and treated groups Note. Data are presented as n (%) except for mother's age, which is presented as mean ± SD. All P values are from Pearson's χ 2 tests, except for mother's age, which is from an independent samples t‐test. Ethical approval for this study was granted by Amref Health Africa's Ethics and Scientific Review Committee (study protocol number: P231/2016). Written informed consent was obtained from all eligible participants. Participation in the study was voluntary and without any financial incentive.
