Formal maternal employment is associated with lower odds of exclusive breastfeeding by 14 weeks postpartum: A cross-sectional survey in Naivasha, Kenya

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Study Justification:
The study aimed to investigate the association between maternal employment and breastfeeding practices in Naivasha, Kenya. The justification for the study was based on the observation that improvements in exclusive breastfeeding (EBF) have stalled in many low- and middle-income countries, which has delayed reductions in child mortality. Maternal employment was identified as a potential barrier to EBF, particularly in areas like Naivasha where commercial floriculture and hospitality industries employ many women.
Highlights:
1. The study conducted a cross-sectional survey among 1,186 mothers at four postpartum time points: hospital discharge, 6 weeks, 14 weeks, and 36 weeks postpartum.
2. The study found that formally employed mothers were less likely to report EBF at 14 weeks and 24 weeks postpartum compared to non-formally employed mothers.
3. The prevalence of continued breastfeeding at 36 weeks did not differ between formally employed and non-formally employed women.
4. The primary reasons reported for early EBF cessation were returning to work, introducing other foods based on the child’s age, and perceived milk insufficiency.
5. The study suggests that additional supports are needed to help prolong the period of EBF for formally employed mothers and their children.
Recommendations:
Based on the study findings, the following recommendations can be made:
1. Implement workplace policies and programs that support breastfeeding, such as providing adequate maternity leave, lactation rooms, and childcare facilities.
2. Raise awareness among employers about the importance of supporting breastfeeding and the potential benefits for both mothers and their children.
3. Provide education and counseling to formally employed mothers about strategies to overcome barriers to exclusive breastfeeding, such as expressing and storing breast milk.
4. Strengthen community support networks for breastfeeding mothers, including peer support groups and access to lactation consultants.
5. Conduct further research to explore the specific challenges faced by formally employed mothers in maintaining exclusive breastfeeding and develop targeted interventions to address these challenges.
Key Role Players:
1. Government health departments and policymakers responsible for maternal and child health programs.
2. Employers and human resource departments in formal employment sectors.
3. Healthcare providers, including doctors, nurses, and lactation consultants.
4. Non-governmental organizations (NGOs) working in the field of maternal and child health.
5. Community leaders and organizations involved in promoting breastfeeding support.
Cost Items for Planning Recommendations:
1. Development and implementation of workplace policies and programs: This may include costs for policy development, training of employers and employees, setting up lactation rooms, and providing childcare facilities.
2. Education and counseling programs for formally employed mothers: This may involve costs for developing educational materials, conducting training sessions, and hiring lactation consultants.
3. Community support networks: Costs may include establishing and maintaining peer support groups, organizing community events, and providing access to lactation consultants.
4. Research and evaluation: Funding may be required for further research to explore the challenges faced by formally employed mothers and evaluate the effectiveness of interventions.
Please note that the above cost items are estimates and may vary depending on the specific context and resources available.

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is rated 7 because it provides a clear description of the study design, sample size, and statistical analysis. However, it does not provide information on potential limitations or biases in the study, such as selection bias or confounding variables. To improve the evidence, the abstract could include a discussion of potential limitations and suggestions for future research to address these limitations.

Background: In many low-and middle-income countries, improvements in exclusive breastfeeding (EBF) have stalled, delaying reductions in child mortality. Maternal employment is a potential barrier to EBF. Objectives: We evaluated associations between maternal employment and breastfeeding (BF) status. We compared formally and non-formally employed mothers in Naivasha, Kenya, where commercial floriculture and hospitality industries employ many women. Methods: We conducted a cross-sectional survey among mothers (n = 1186) from September 2018 to October 2019 at 4 postpartum time points: At hospital discharge (n = 296) and at 6 wk (n = 298), 14 wk (n = 295), and 36 wk (to estimate BF at 24 wk; n = 297) postpartum. Mothers reported their BF status and reasons for EBF cessation. We used multivariable logistic regression models to test the association between formal maternal employment and 3 outcomes: early BF initiation (within 1 h of birth), EBF at each time point, and continued BF at 9 mo. Models were informed by a directed acyclic graph: A causal diagram used to characterize the relationship among variables that influence the independent (employment) and dependent (BF status) variables. Results: EBF did not differ by employment status at hospital discharge or at 6 wk postpartum. However, formally employed mothers were less likely than those not formally employed to report EBF at 14 wk (59.0% compared with 95.4%, respectively; AOR: 0.19; 95% CI: 0.10, 0.34) and at 24 wk (19.0% compared with 49.6%, respectively; AOR: 0.25; 95% CI: 0.14, 0.44). The prevalence of continued BF at 36 wk did not differ by group (98.1% for formally employed compared with 98.5% for non-formally employed women; AOR: 0.80; 95% CI: 0.10, 6.08). The primary reasons reported for early EBF cessation were returning to work (46.5%), introducing other foods based on the child’s age (33.5%), or perceived milk insufficiency (13.7%). Conclusions: As more women engage in formal employment in low-and middle-income countries, additional supports to help prolong the period of EBF may be beneficial for formally employed mothers and their children.

Between September 2018 and October 2019, we repeatedly conducted cross-sectional surveys at 4 postpartum time points to investigate the associations between maternal employment and BF practices in Naivasha, Kenya. We hypothesized that formal employment would be associated with a lower prevalence and reduced odds of BF at each time point and across each indicator (early initiation of BF at 0 wk; EBF at 0, 6, 14, and 24 wk; predominant BF at 0, 6, 14, and 24 wk; and continued BF at 36 wk). Naivasha is a peri-urban city in Nakuru County, located 100 km north of Nairobi, with a population of ∼355,000 (24). This area contains the largest concentration of commercial flower farms in the country, which are the primary sources of employment in this region. The majority of flower farm employees reside in several densely populated peri-urban informal settlements, with varying access to electricity and sanitation services. Employment within the floriculture industry is characterized by long commuting distances and separation from children, compared to employment in the informal sector (e.g., tailoring, subsistence farming, self-employment, trading), where women have more flexible schedules and BF opportunities (25). Mothers were recruited 1–4 d postpartum and at routine infant immunization visits at 6, 14, and 36 wk postpartum at 2 public facilities (the Naivasha Sub-County Referral Hospital and the Karagita Dispensary) and a private facility subsidized by a local floriculture company that serves farmworkers (the South Lake Medical Center). All postpartum women with a live birth admitted to the maternity wards or presenting for immunizations to the health facilities on recruitment days were screened for eligibility. Mothers who were 1–4 d, 5–7 wk, 13–15 wk, or 9 mo (± 1 wk) postpartum were eligible, regardless of past or present child morbidity. Screening did not include evaluating mental health status. Health center staff introduced mothers to the research team. The research team explained the study purpose to all mothers present for immunizations and within the maternity wards through group announcements. Upon recruitment, we obtained written, informed consent from all participants. Surveys were administered verbally by a team of 5 trained research staff in Swahili, English, or another preferred language of the mother, using paper questionnaires. Mothers were eligible to participate once in the cross-sectional survey. The survey collected information on 5 domains: 1) household assets and demographics; 2) employment status and benefits; 3) IYCF practices; 4) access to reproductive and other health services; and 5) health status of the child and the mother. We queried participants about their household assets (house material composition, vehicle, television, mobile phone) and demographics (educational attainment, household income, marital status, household size, parity, religion, and tribe) using questions from the Demographic and Health Survey (7). We asked mothers about whether they were employed, the type of employment and responsibilities, the name of the employer, the availability of a formal contract, the hours worked, and the availability of maternity leave and other policies to support BF. IYCF practices were assessed using standardized questions from the Demographic Health Survey and WHO Indicators (7, 26). Using a 24-h list-based recall method, we recorded the types of liquid, semisolid, and solid foods given during the previous day and the number of BF and other feeding episodes. Demographic Health Survey questions were used to assess antenatal care utilization, the delivery setting, the type of delivery, and the presence of a skilled attendant at childbirth (7). We assessed child morbidity in the prior 2 wk according to 5 common illnesses and symptoms: diarrhea, pneumonia, fever, malaria, and cough. The HIV status of the mother was self-reported. The primary anticipated reason for EBF cessation was assessed using a single question with multiple response options (going to work, child refusal, uncomfortable/did not want to, perceived milk insufficiency, pregnant again, baby cries after being breastfed, child age, other). Upon cessation of EBF, we also queried mothers regarding actual reasons why they introduced mixed feeding, defined as the initiation of feeding other liquids and foods along with breastmilk (26). The same multiple response options were provided. The survey also assessed the availability of workplace supports for BF through questions on maternity leave benefits and the availability and use of employer-supported lactation rooms, childcare, and housing. The full survey is available in Supplementary Methods 1. We examined missing data and replaced missing values by recontacting mothers by phone. To identify a 20% difference in EBF rates at each time point (1–4 d, 6 wk, 14 wk, and 24 wk postpartum), with 80% power and an alpha <0.05, we aimed to recruit a minimum of 124 mothers at each point. We successfully enrolled 296 women at 1–4 d postpartum, 298 women at 6 wk postpartum, 295 women at 14 wk postpartum, and 297 women at 36 wk postpartum who reported retrospectively to 24 wk postpartum. This higher sample size increased power to 96.4% for EBF, 96.4% for early initiation, and 99.9% for detecting a 20% difference in these outcomes between employment groups. Our initial recruitment approach did not use employment as a screening factor, which resulted in a higher proportion of non–formally employed mothers. Thus, we focused later recruitment on formally employed mothers at each time point, resulting in higher recruitment numbers than initially planned. Maternal employment status was ascertained by self-report. We first asked women about their current employment or, if mothers were not working and were within 6 wk postpartum, about their recent employment status. Mothers were then asked about the type of occupation, the number of hours worked per week, and the existence of a contract to be further classified as formally, informally, or self-employed. We classified employment as formal if women worked for a registered employer (e.g., a commercial farm, business, company, school, health-care facility), worked ≥20 h/wk, and received regular compensation. For mothers currently on maternity leave (recruited at 0 and 6 wk postpartum), employment type was classified based on the type of work before delivery. Mothers were classified as not employed if they indicated that they reported no current employment. If mothers were employed during their pregnancies, but indicated that they did not intend to return to work and were not receiving maternity leave benefits, they were classified as not employed. In the study context, women usually work through 36 wk of pregnancy. Our dependent variables encompassed 3 BF indicators: 1) early initiation of BF; 2) EBF or predominant BF at 6, 14, and 24 wk postpartum (coded as separate variables in the analysis); and 3) continued BF at 9 mo postpartum. Early initiation of BF was defined as BF within 1 h of childbirth (26). EBF was defined as feeding breastmilk only with no other liquids or solids (26) from childbirth through the time point of data collection. EBF duration, defined as the number of weeks of reported EBF, was measured retrospectively by mothers who were recruited at 36 wk. These mothers were considered to have been EBF at 6 mo if only breastmilk was fed through 24 wk of age. To establish conservative estimates of BF status, we applied a modified approach to the WHO method to determine BF status. BF status was determined by the IYCF practices during the previous 24 h, as well as by assessing the last week when mothers gave breastmilk exclusively. Thus, a mother who was EBF during the previous day, but who fed infant formula in the previous week, was classified as not EBF. We classified children as predominantly breastfed if they were given breastmilk along with juice, water, or other liquids, including medicines and vitamins/minerals, but not milk or semisolid or other foods through the specified time (26). Continued BF at 9 mo was defined as any BF among children in this age group (26). We attempted to minimize social desirability or recall bias in the assessment of BF outcomes by including multiple questions about BF duration and exclusivity. We assessed EBF duration, the week at which other foods and drinks were first consumed, and the child's diet in the 24 h preceding the survey, using the validated IYCF feeding practices tool (26). Vitamin or mineral supplements were not assessed. We sought to minimize temporal changes in BF practices by completing study recruitment over 13 mo. To reduce the likelihood of selection bias, we restricted our recruitment to 3 health facilities that represented 3 levels of the health-care system and we recruited women without prior knowledge of their socio-demographic factors. The research team received training on responsible conduct in research, survey procedures, and anthropometric assessments. Reporting bias related to maternal employment was minimized by describing the purpose of the study as seeking to identify opportunities to better support mothers by understanding the challenges they encounter in feeding their infants. The study was not promoted as an employment study. For questions related to reasons for cessation of EBF, self-reported answers were coded into categories. “Returning to work” was indicated if a participant responded that this was a reason for EBF cessation; it was not provided as an option before the respondent offered a response. To identify confounding variables, we constructed a directed acyclic graph (Supplemental Figure 1): a causal diagram used to characterize the relationships among variables that influence the primary independent variable (employment) and the dependent variable (BF status) and are not on the causal pathway (27). Informed by existing literature on the employment–BF relationship and influences of BF in the study context, the same set of confounders were used for all models (13, 16, 28). These include maternal age (years), marital status (married or not), maternal education (some secondary or higher versus less than secondary), tribal affiliation (Kikuyu, Luhya, Kiisi, or other), child morbidity (presence of diarrhea, fever, malaria, cough, or fever in the previous 2 wk), cesarean delivery (versus vaginal birth), and HIV status (positive or negative). We assessed the linearity of continuous variables (maternal age, maternal education) with outcomes by specifying disjointed indicator variables. Household income was hypothesized as a mediator of the association a priori. Maternal employment would likely result in increased individual-level income, thus increasing household wealth and, subsequently, influencing BF practices. However, wealth prior to workforce entry could be a plausible confounder of the employment–EBF association (i.e., household wealth could influence whether a woman works or not). In these cross-sectional data, we cannot establish the temporality of wealth and workforce entry. Therefore, wealth was considered a mediator and was not included as a covariate in our models (29). We employed separate multivariable logistic regression models to test the association between maternal employment (formally employed versus informally employed, self-employed, and not employed) and each BF variable (early initiation of BF and EBF, predominant BF, and continued BF at 9 mo). In sensitivity analyses, we compared BF outcomes by employment status when restricting formal employment to only mothers employed at flower farms or in other commercial agricultural farms (n = 398). In addition, we adjusted for multiple comparisons using the Holm-Bonferroni sequential correction, given that we assessed associations for multiple outcomes across 4 time points (30). First, results for both chi-square tests and linear regressions were ordered from the smallest P value to the largest P value. Second, the second-smallest P value was corrected with a Bonferroni approach [(number of tests − order of test + 1) × P value]. The correction procedure stops when the first nonsignificant test is obtained (30). In our main analyses, the alpha was set to 0.05. STATA version 14.1 (StataCorp LP) was used to conduct all analyses. The study adhered to the Strengthening Research for Observational Studies in Epidemiology (STROBE) guidance (Supplementary Methods 2) (31). All study procedures were approved by the Kenya Medical Research Institute Scientific Ethical Review Unit (study number KEMRI/SERU/CCR/0112/3712) and the Wheaton College Institutional Review Board (study number 3712).

Based on the provided description, here are some potential innovations that could be used to improve access to maternal health:

1. Telemedicine: Implementing telemedicine services can provide remote access to healthcare professionals, allowing pregnant women to receive prenatal care and consultations without the need for physical visits to healthcare facilities.

2. Mobile health applications: Developing mobile applications that provide information and resources on maternal health, including prenatal care guidelines, breastfeeding support, and postpartum care. These apps can also offer reminders for important appointments and medication schedules.

3. Community health workers: Training and deploying community health workers who can provide education, support, and basic healthcare services to pregnant women in their communities. These workers can help bridge the gap between healthcare facilities and remote areas, ensuring that women have access to necessary care.

4. Maternal health clinics: Establishing dedicated maternal health clinics that provide comprehensive care for pregnant women, including prenatal check-ups, nutritional support, breastfeeding counseling, and postpartum care. These clinics can be equipped with necessary medical equipment and staffed by trained healthcare professionals.

5. Maternity waiting homes: Building maternity waiting homes near healthcare facilities to accommodate pregnant women who live far away and need to travel for delivery. These homes can provide a safe and comfortable environment for women to stay during the final weeks of pregnancy, ensuring timely access to healthcare services.

6. Mobile clinics: Setting up mobile clinics that travel to remote areas, providing prenatal care, vaccinations, and other essential maternal health services. This approach can help reach women who have limited access to healthcare facilities due to geographical barriers.

7. Maternal health education programs: Implementing educational programs that focus on maternal health, including prenatal care, breastfeeding, nutrition, and postpartum care. These programs can be conducted in community centers, schools, and workplaces to reach a wider audience.

8. Public-private partnerships: Collaborating with private sector organizations, such as flower farms and hospitality industries, to provide maternal health services and support for their employees. This can include on-site healthcare facilities, flexible work arrangements, and breastfeeding-friendly policies.

9. Maternal health awareness campaigns: Launching public awareness campaigns to educate the community about the importance of maternal health and the available resources. These campaigns can use various media channels, including radio, television, and social media, to reach a wide audience.

10. Maternal health financing schemes: Developing innovative financing schemes, such as health insurance programs or microfinance initiatives, to ensure that pregnant women have access to affordable and quality maternal healthcare services.

It is important to note that the implementation of these innovations should be context-specific and tailored to the needs and resources of the community.
AI Innovations Description
The study titled “Formal maternal employment is associated with lower odds of exclusive breastfeeding by 14 weeks postpartum: A cross-sectional survey in Naivasha, Kenya” explores the relationship between maternal employment and breastfeeding practices in Naivasha, Kenya. The study aims to identify potential barriers to exclusive breastfeeding (EBF) and evaluate the association between formal maternal employment and breastfeeding status.

The study was conducted between September 2018 and October 2019 in Naivasha, Kenya, where commercial floriculture and hospitality industries employ many women. The researchers conducted a cross-sectional survey among 1,186 mothers at four postpartum time points: hospital discharge, 6 weeks, 14 weeks, and 36 weeks postpartum. The mothers reported their breastfeeding status and reasons for EBF cessation.

The findings of the study indicate that formal employment is associated with lower odds of EBF at 14 weeks and 24 weeks postpartum. However, there was no significant difference in EBF rates between formally and non-formally employed mothers at hospital discharge and 6 weeks postpartum. The primary reasons reported for early EBF cessation were returning to work, introducing other foods based on the child’s age, and perceived milk insufficiency.

Based on the study’s findings, the researchers suggest that additional supports are needed to help prolong the period of exclusive breastfeeding for formally employed mothers and their children. These supports could include workplace policies that support breastfeeding, such as providing maternity leave, lactation rooms, and childcare facilities. By implementing these recommendations, access to maternal health can be improved, leading to better breastfeeding practices and potentially reducing child mortality rates.
AI Innovations Methodology
Based on the provided information, here are some potential recommendations to improve access to maternal health:

1. Implement workplace policies: Introduce policies that support breastfeeding mothers in the workplace, such as providing adequate maternity leave, flexible work hours, and designated lactation rooms. This would allow mothers to continue breastfeeding while being employed.

2. Expand access to childcare: Improve access to affordable and quality childcare services near workplaces, allowing mothers to have their infants nearby and breastfeed during breaks or lunchtime.

3. Increase awareness and education: Conduct awareness campaigns to educate both employers and employees about the benefits of breastfeeding and the importance of supporting breastfeeding mothers in the workplace. This can help reduce stigma and create a supportive environment.

4. Strengthen healthcare services: Enhance access to maternal healthcare services, including antenatal care, postnatal care, and lactation support. This can be achieved by improving the availability and quality of healthcare facilities, training healthcare providers, and ensuring adequate resources for breastfeeding support.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could include the following steps:

1. Define the indicators: Identify specific indicators that measure access to maternal health, such as the percentage of mothers who receive adequate antenatal care, the percentage of mothers who exclusively breastfeed for the recommended duration, or the percentage of mothers who have access to lactation support.

2. Collect baseline data: Gather data on the current status of the selected indicators in the target population. This can be done through surveys, interviews, or existing data sources.

3. Define the intervention scenarios: Develop different scenarios that represent the implementation of the recommendations. For example, one scenario could assume the implementation of workplace policies, while another scenario could include both workplace policies and expanded access to childcare.

4. Simulate the impact: Use statistical modeling or simulation techniques to estimate the potential impact of each scenario on the selected indicators. This can involve analyzing the data collected in step 2 and applying appropriate statistical methods to estimate the changes that would occur with the implementation of the recommendations.

5. Compare the scenarios: Compare the results of each scenario to assess the relative effectiveness of the different recommendations in improving access to maternal health. This can help identify the most promising interventions and inform decision-making.

6. Refine and iterate: Based on the simulation results, refine the recommendations and iterate the process to further optimize the interventions and their potential impact.

It is important to note that the methodology described above is a general framework and the specific details may vary depending on the context and available data.

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