Participation in a Community-Based Women’s Health Education Program and At-Risk Child Development in Rural Kenya: Developmental Screening Questionnaire Results Analysis

Background: Over 43% of children living in low- and middle-income countries are at risk for developmental delays; however, access to protective interventions in these settings is limited. We evaluated the effect of maternal participation in Chamas for Change (Chamas)-a community-based women’s health education program during pregnancy and postpartum-and risk of developmental delay among their children in rural Kenya. Methods: We analyzed developmental screening questionnaire (DSQ) data from a cluster randomized controlled trial in Trans Nzoia County, Kenya (ClinicalTrials.gov, NCT03187873). Intervention clusters (Chamas) participated in community health volunteer-led, group-based health lessons twice a month during pregnancy and postpartum; controls had monthly home visits (standard of care). We screened all children born during the trial who were alive at 1-year follow-up. We labeled children with any positive item on the DSQ as “at-risk development.” We analyzed data using descriptive statistics and multilevel regression models (a=.05); analyses were intention-to-treat using individual-level data. Results: Between November 2017 and March 2018, we enrolled 1,920 pregnant women to participate in the parent trial. At 1-year follow-up, we screened 1,273 (689 intervention, 584 control) children born during the trial with the DSQ. Intervention mothers had lower education levels and higher poverty likelihood scores than controls (P<.001 and P=.007, respectively). The overall rate of at-risk development was 3.5%. Children in Chamas clusters demonstrated significantly lower rates of at-risk development than controls (2.5% vs. 4.8%, P=.025). Adjusted analyses revealed lower odds for at-risk development in the intervention arm (OR=0.50; 95% confidence interval=0.27, 0.94). Conclusions: Maternal participation in a community-based women's health education program was associated with lower rates of at-risk development compared to the standard of care. Overall, rates of at-risk development were lower than expected for this population, warranting further investigation. Chamas may help protect children from developmental delay in rural Kenya and other resource-limited settings.

We analyzed DSQ data from a 2-arm cluster randomized controlled trial in 74 communities across 4 subcounties (Cherangany, Saboti, Kwanza, and Kiminini) in Trans Nzoia County, Kenya. We chose a cluster-randomized design to minimize contamination due to intervention exposure between neighboring villages. We defined clusters as community health units—geographically defined health service delivery areas for populations of 5,000 people overseen by community health volunteers (CHVs).16 We randomized community health units 1:1 (non-stratified, non-matched) using a simple random allocation sequence to participate in Chamas (intervention) or receive recommended monthly home visits from CHVs (standard of care) for 1 year. Data collectors, analysts, and investigators were masked to cluster allocation throughout the study; however, trial arms were identifiable to participants and CHVs by design. We selected Trans Nzoia due to its geographic and socioeconomic diversity, as well as the presence of longstanding collaborations between the Government of Kenya, Ministry of Health, and Academic Model Providing Access to Healthcare. Trans Nzoia has nearly 1 million residents who largely subside on agricultural businesses and raising livestock. Moreover, health indicators for mothers and infants are consistently poorer than national estimates, reflecting a need for increased attention to MNCH policy and programming.17 Pregnant women presenting to the local health facility for their first antenatal care visit by 32 weeks' gestation were eligible to participate in the parent trial. Participants were allocated to each arm by their randomized community of residence. At approximately 1-year follow-up (i.e., 12 months of Chamas participation, initiating prenatally), we screened all children born during the trial with the DSQ, with no additional exclusion criteria. Data collectors traveled to participant homes to collect in-person data using electronic tablet-based, structured questionnaires. We synced data at the end of each collection day to a central, encrypted server. Research assistants made 3 attempts to contact participants over a 2-week period before declaring them lost to follow-up. Intervention details are described in our protocol (ClinicalTrials.gov {"type":"clinical-trial","attrs":{"text":"NCT03187873","term_id":"NCT03187873"}}NCT03187873)18 and previous publications. A detailed summary of the intervention is noted within Supplement 1. Briefly, Chamas clusters convened twice per month for 12 months for group-based health lessons led by CHVs. Each group typically included 15–20 women, their infants, 2 CHV facilitators, and 2 postmenopausal mentor mothers. Health lessons during the first year of the program promoted positive MNCH behaviors during pregnancy (e.g., attending adequate antenatal care) and infancy (e.g., exclusively breastfeeding and immunizing infants). Women were also introduced to topics contributing to risk factors and social determinants associated with developmental delays such as infant growth monitoring and nutrition,1,19 disease prevention,20,21 childhood harsh punishment,22 and parental stress.14,23 These and other developmentally focused lessons are largely addressed during the second and third years of the curriculum. After each lesson, women were also invited to participate in an optional microfinance program called Group Integrated Savings for Health and Empowerment (GISHE). Participation in GISHE was completely optional so as not to deter women without financial means to contribute to group savings from joining Chamas. Women who chose to participate were encouraged to use savings and loans generated by GISHE to enroll in health insurance, pay for school fees and educational materials, and/or start small businesses. Control clusters had monthly CHV home visits during the antenatal and postpartum period as recommended by the current standard of care.25 During monthly visits, CHVs aimed to collect basic health information, recognize antenatal and early postpartum danger signs, aid in infant growth monitoring, and refer individuals requiring services to health facilities. Further, CHVs were expected to encourage women to adopt the same positive health behaviors emphasized in Chamas, namely: attending antenatal care, delivering in health facilities, exclusively breastfeeding, adopting modern methods of contraception, fully immunizing infants, and ensuring adequate infant nutrition. Our primary outcome of interest was the rate of “at-risk development” across study arms. We defined “at-risk development” as any child who screened positive in 1 or more functional domains on the DSQ. We selected the DSQ as it has been validated for use among children less than aged 2 years in an LMIC.26 Of note, this tool is a screening questionnaire and not a diagnostic neurodevelopmental assessment. This validated questionnaire asks parents to report responses to 8 dichotomous “yes/no” questions in each of the following functional domains, which are specific to age (in months): gross motor, fine motor, vision, hearing, cognition, socialization, behavior, and speech. A list of the DSQ testing items can be viewed within Supplement 2. We considered any “yes” response a positive screen. Data collectors used age-appropriate DSQs for each infant by imposing an electronic checkpoint in RedCap (coded by birthdate). Lastly, if participants carried a multiple gestation pregnancy, we conducted independent questionnaires for each child. Our primary outcome of interest was the rate of “at-risk development” across study arms. To assess modifying effects of covariates, we collected baseline sociodemographic and reproductive health data for all participants. We used the validated Kenya 2015 Poverty Probability Index (PPI) tool to calculate individual poverty likelihood scores using the National Poverty Line Look-Up Table.27 This tool comprises 10 questions that assess sociodemographic factors such as county of residence, household education level, housing materials, and recent household purchases. Answers are coded using a numeric scoring system and summarized in a composite PPI score, which can be converted to a percentage value for poverty probability. We additionally collected end-line data on participant attitudes toward harsh punishment, infant birth weight, and age of first mixed-feeding as these variables have demonstrated significant associations with developmental delay outcomes in previous studies.22,23,29 We used a single item from the validated ISPCAN child abuse screening tool, parent version, to assess harsh punishment.30 Responses were collected using a 5-point Likert scale, ranging from “strongly disagree” to “strongly agree.” The sample size calculation was based on the study's primary outcome, which was facility-based births.14 This calculation used methods described by Rutterford et al. for a proposed mixed-effects regression analysis31 using derived baseline estimates.14,32 We assumed a mean cluster size of 20 individuals, with 77 clusters (equally allocated between arms) and intracluster correlation coefficient of 0.44 (based on pilot data14), and 20% attrition. With these assumptions, a total of 1,280 individuals would be needed to detect a 4.7% difference on the risk difference scale with 80% power at a (2-tailed) significance level of .05. A total of 1,273 (66.3%) of participants completed the study at 12-month follow-up with DSQ data: we included 689 individuals from 37 clusters in the intervention (69.2%) and 584 individuals from 37 clusters in the control (63.2%) arms for analysis. We assessed individual-level outcomes on all participants with complete DSQ data at 12-months follow-up. We used an intention-to-treat approach by evaluating all intervention participants, regardless of Chamas attendance. We summarized participant characteristics in a tabular form. We calculated frequencies and percentages for categorical variables, as well as means and standard deviations for continuous variables. We compared proportions using chi-squared tests and means using independent t-tests. We compared proportions of at-risk development among children within each study arm using chi-square tests. We used simple descriptive statistics to determine DSQ testing items and domains for which children most commonly screened positive. We reviewed available sociodemographic as well as endline variables and identified potential confounders using clinical judgment and evidence from the literature. We performed a univariate analysis with each identified variable and included those demonstrating statistical significance (P<.05) or clinical meaning in our adjusted multivariate logistic regression model. We reported overall tests for each variable as well as estimated odds ratios with their 95% confidence intervals (CI). For households with twin children, we included DSQ data from the twin screened first in our regression model to limit bias introduced from duplicating covariate data from the same household and mother. We decided a priori to restrict analyses solely to participants with both complete DSQ data and complete PPI data. We disaggregated items within the PPI to appropriately adjust for specific confounding variables known to impact child development (e.g., primary caregiver education level, head of household education level, housing materials).32,33 We conducted all statistical analyses in SAS 9.4 (SAS Institute, Cary, NC) statistical software and with α set to .05. We prospectively registered the parent trial with ClinicalTrials.gov ({"type":"clinical-trial","attrs":{"text":"NCT03187873","term_id":"NCT03187873"}}NCT03187873). Our study received ethics approval from the Institutional Research Ethics Committee at Moi Teaching and Referral Hospital and Moi University (IREC/2018/269) and the Office of Research Administration at Indiana University (1905296355). We obtained written informed consent from participants before data collection. The funders had no role in the research design, collection, analysis, or interpretation of data, writing this report, or the decision to submit this manuscript for publication. The corresponding author had full access to all data in the study as well as final responsibility for the decision to submit this manuscript for publication.

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