Caregiver parenting practices, dietary diversity knowledge, and association with early childhood development outcomes among children aged 18-29 months in Zanzibar, Tanzania: a cross-sectional survey.

BACKGROUND: Many children in low- and middle-income countries fail to reach their cognitive potential, with experiences before age 3 critical in shaping long-term development. Zanzibar's Jamii ni Afya program is the first national, digitally enabled community health volunteer (CHV) program promoting early childhood development (ECD) following the Nurturing Care Framework within an integrated maternal and child healthcare package. Using program baseline data, we explored home environment, caregivers' parenting, health and nutrition knowledge and practices, and ECD outcomes in Zanzibar. METHODS: We conducted a national household survey among 499 children aged 18-29 months using two-stage cluster sampling in February 2019. The primary outcome was child development score measured using the Caregiver Reported Early Developmental Index (CREDI), with higher scores representing higher levels of child development. We analyzed CREDI scores, along with MICS questions on parenting knowledge, practices, and characteristics of the home environment. We developed multivariate regression models to assess associations between caregiver-child interactions, knowledge of dietary diversity, and ECD. RESULTS: Ten percent of children had overall CREDI z-scores 2 standard deviations [SD] or more below the global reference population mean, with 28% of children at risk of developmental delay with z-scores 1 SD or more below the mean. Cognitive and language domains were of highest concern (10.2 and 12.7% with z-score < - 2 SD). In 3-day recall, 75% of children engaged in ≥4 early stimulating activities with all caregivers averaging 3 total hours of play. CREDI scores were positively associated with greater frequency of caregivers' engagement (ß = 0.036, p = 0.002, 95%CI = [0.014, 0.058]), and dietary diversity knowledge (ß = 0.564, p < 0.001, 95%CI = [0.281, 0.846]). CONCLUSIONS: Our findings demonstrate a positive association between both the frequency of caregiver child interactions and knowledge of adequate dietary diversity, and ECD outcomes. This aligns with global evidence that promoting early stimulation, play and learning opportunities, and dietary diversity can improve developmental outcomes. Further study is needed to establish causal relationships and assess the impact of ECD programming in Zanzibar.

Identifying Programmatic Factors that Increase Likelihood of Health Facility Delivery: Results from a Community Health Worker Program in Zanzibar.

INTRODUCTION: Community health worker (CHW) interventions have been utilized to address barriers that prevent pregnant women from delivering in health facilities in low- and middle-income countries (LMICs). The objective of this research was to assess the programmatic factors that increase the likelihood of health facility delivery within a large digital health-supported CHW program in Zanzibar, Tanzania. METHODS: This study included 36,693 women who were enrolled in the Safer Deliveries program with a live birth between January 1, 2017 and July 31, 2019. We assessed whether long-term enrollment, recency of CHW pregnancy visit prior to delivery, and number of routine home pregnancy visits were associated with an increased likelihood of health facility delivery compared to home delivery. We used Chi-squared tests to assess bivariate relationships and performed logistic regression analyses to assess the association between each programmatic variable and health facility delivery, adjusting for relevant confounders. RESULTS: We found that long-term enrollment was significantly associated with increased likelihood of health facility delivery, with the strongest relationship among women with a previous home delivery (OR = 1.4, 95%CI [1.0,1.7]). Among first-time mothers, two or more pregnancy visits by a CHW was positively associated with health facility delivery (OR = 1.8, 95%CI [1.2, 2.7]). Recent pregnancy visit by a CHW was positively associated with health facility delivery, but was not significant at the α = 0.05 level. DISCUSSION: In this program, we found evidence that at least two routine home pregnancy visits, longer length of enrollment in the program, and recency of home visit to the delivery date were strategies to increase health facility delivery rates among enrolled mothers. Maternal and child health programs should undertake similar evaluations to improve program delivery.

Progress in the Typhoid Conjugate Vaccine Program Rollout Supported by Gavi During the COVID-19 Pandemic and the Path Forward.

Gavi supports countries to introduce typhoid conjugate vaccine (TCV) with catch-up campaigns. Available TCVs are highly efficacious, equity-focused, and critical to curbing the expansion of antimicrobial resistance. Four Gavi-supported countries have introduced TCVs since 2018. In the wake of the COVID-19 emergency, momentum is building to scale up TCV introduction worldwide, supported by global partners and Gavi's funding for improved typhoid diagnostics.

Machine learning for maternal health: Predicting delivery location in a community health worker program in Zanzibar.

Background: Maternal and neonatal health outcomes in low- and middle-income countries (LMICs) have improved over the last two decades. However, many pregnant women still deliver at home, which increases the health risks for both the mother and the child. Community health worker programs have been broadly employed in LMICs to connect women to antenatal care and delivery locations. More recently, employment of digital tools in maternal health programs have resulted in better care delivery and served as a routine mode of data collection. Despite the availability of rich, patient-level data within these digital tools, there has been limited utilization of this type of data to inform program delivery in LMICs. Methods: We use program data from 38,787 women enrolled in Safer Deliveries, a community health worker program in Zanzibar, to build a generalizable prediction model that accurately predicts whether a newly enrolled pregnant woman will deliver in a health facility. We use information collected during the enrollment visit, including demographic data, health characteristics and current pregnancy information. We apply four machine learning methods: logistic regression, LASSO regularized logistic regression, random forest and an artificial neural network; and three sampling techniques to address the imbalanced data: undersampling of facility deliveries, oversampling of home deliveries and addition of synthetic home deliveries using SMOTE. Results: Our models correctly predicted the delivery location for 68%-77% of the women in the test set, with slightly higher accuracy when predicting facility delivery versus home delivery. A random forest model with a balanced training set created using undersampling of existing facility deliveries accurately identified 74.4% of women delivering at home. Conclusions: This model can provide a "real-time" prediction of the delivery location for new maternal health program enrollees and may enable early provision of extra support for individuals at risk of not delivering in a health facility, which has potential to improve health outcomes for both mothers and their newborns. The framework presented here is applicable in other contexts and the selection of input features can easily be adapted to match data availability and other outcomes, both within and beyond maternal health.