Safer Births Bundle of Care (SBBC) consists of innovative clinical and training tools for improved labour care and newborn resuscitation, integrated with new strategies for continuous quality improvement. After implementation, we hypothesised a reduction in 24-h newborn deaths, fresh stillbirths, and maternal deaths by 50%, 20%, and 10%, respectively. This is a 3-year stepped-wedged cluster randomised implementation study, including 30 facilities within five regions in Tanzania. Data collectors at each facility enter labour and newborn care indicators, patient characteristics and outcomes. This halfway evaluation reports data from March 2021 through July 2022. In total, 138,357 deliveries were recorded; 67,690 pre- and 70,667 post-implementations of SBBC. There were steady trends of increased 24-h newborn and maternal survival in four regions after SBBC initiation. In the first region, with 13 months of implementation (n = 15,658 deliveries), an estimated additional 100 newborns and 20 women were saved. Reported fresh stillbirths seemed to fluctuate across time, and increased in three regions after the start of SBBC. Uptake of the bundle varied between regions. This SBBC halfway evaluation indicates steady reductions in 24-h newborn and maternal mortality, in line with our hypotheses, in four of five regions. Enhanced focus on uptake of the bundle and the quality improvement component is necessary to fully reach the SBBC impact potential as we move forward.

Background: Early childhood enteric infection with Shigella/EIEC, enteroaggregative E. coli (EAEC), Campylobacter, and Giardia has been associated with reduced child growth, yet a recent randomized trial of antimicrobial therapy to reduce these infections did not improve growth outcomes. To interrogate this discrepancy, we measured the enteric infections from this study. Methods: We leveraged the Early Life Interventions for Childhood Growth and Development in Tanzania (ELICIT) trial, a randomized double-blind placebo-controlled trial of antimicrobial therapy with azithromycin and nitazoxanide provided quarterly to infants from 6 to 15 months of age. We tested 5,479 stool samples at time points across the study for 34 enteropathogens using quantitative PCR. Results: There was substantial carriage of enteropathogens in stool. Azithromycin administration led to reductions in Campylobacter jejuni/coli, enteroaggregative E. coli, and Shigella/EIEC (absolute risk difference ranged from -0.06 to 0.24) 2 weeks after treatment however there was no effect after 3 months. There was no difference in Giardia after nitazoxanide administration (ARR 0.03 at the 12 month administration). When examining the effect of azithromycin versus placebo on the subset of children infected with specific pathogens at the time of treatment, a small increase in weight-for-age Z score was seen only in those infected with Campylobacter jejuni/coli (0.10 Z score, 95% CI -0.01–0.20; length-for-age Z score 0.07, 95% CI -0.06–0.20). Conclusion: The antimicrobial intervention of quarterly azithromycin plus or minus nitazoxanide led to only transient decreases in enteric infections with Shigella/EIEC, enteroaggregative E. coli (EAEC), Campylobacter, and Giardia. There was a trend towards improved growth in children infected with Campylobacter that received quarterly azithromycin.

Introduction: Enhancing the proficiency of healthcare workers (HCWs) in handling birth-related complications is crucial for reducing maternal and newborn morbidity and mortality. To achieve this, the Safer Births Bundle of Care offers a comprehensive set of innovative, simulation-based training interventions designed to strengthen the skills and competencies of HCWs working as skilled birth attendants. Objective: To describe the use of in-situ low-dose, high-frequency simulation-based training, and the experiences of this usage among HCWs and stakeholders at facilities in Tanzania. Methods: This mixed-methods study included quantitative and qualitative data collected between July 2021 and July 2022 across 30 health facilities in five regions of Tanzania. NeoNatalie Live (NNL) simulators were installed for independent skills and scenario training, and in-situ facilitator-led team simulations were introduced. The training frequency was analyzed using descriptive and analytical statistics, and mentorship and supervision reports were analyzed using qualitative content analysis. Results: A large and sustained number of in-situ NNL skill-training sessions ( n = 35,101) and facilitator-led team simulations ( n = 266) were conducted during the first year. Clinical burden per HCW did not affect the frequency of NNL skills training at the health facility level ( r = −0.16, p = .40) nor facilitator-led team simulations. There was a positive but weak correlation between the frequency of facilitator-led team simulations and NNL skills training ( r = 0.34, p = .05). Qualitative data showed a high degree of motivation and participation among all stakeholders, and active use of hospital data, both clinical indicators and training data, was perceived as a success factor. Conclusion: Facilitator-led in-situ simulation training was more likely to occur where individual skills-training sessions were recorded. Training sessions took place regardless of the increased workload.

Background: The leading causes of fever requiring hospital admission in rural sub-Saharan Africa remain understudied, particularly using modern molecular diagnostics. Methods: We enrolled 258 patients with acute fever (cases) and 48 controls admitted to 1 hospital in Tanzania for 1 year starting in February 2023. Blood was tested by blood culture, polymerase chain reaction (PCR) for 35 pathogens, and serology for 6 pathogens. Results: The leading pathogen detected in cases was measles, with 70 of 258 cases detected by PCR, 74 of 238 by immunoglobulin M serology, and 91 allowing either method. Other pathogens were detected in <5% of cases including, in descending order, Plasmodium, Schistosoma, and Bartonella, among others. Measles was the only pathogen clearly associated with cases versus controls. Of 91 measles cases, only 58 (64%) were clinically suspected and only 56 (62%) met the World Health Organization definition of a clinical measles case, largely because 27 (30%) measles cases did not have recorded rash. Outcomes were poorer for measles without rash versus with rash, with a longer hospital stay (mean, 5.5 vs 3.3 days; P = .010), less full recovery (48% vs 75%; P = .016), and high mortality (7% vs 2%; P = .20). Increased laboratory testing would have detected measles 2 months prior to the first clinical suspicion. Conclusions: Measles was found to be the leading cause of fever during 1 year of acute febrile illness surveillance in rural Tanzania. Measles can present without notable rash in these settings and has poor outcomes. Increasing laboratory test availability and clinician suspicion for measles is a priority.

Background: Growth trajectories are highly variable between children, making epidemiological analyses challenging both to the identification of malnutrition interventions at the population level and also risk assessment at individual level. We introduce stochastic differential equation (SDE) models into child growth research. SDEs describe flexible dynamic processes comprising: drift - gradual smooth changes – such as physiology or gut microbiome, and diffusion - sudden perturbations, such as illness or infection. Methods: We present a case study applying SDE models to child growth trajectory data from the Haydom, Tanzania and Venda, South Africa sites within the MAL-ED cohort. These data comprise n=460 children aged 0-24 months. A comparison with classical curve fitting (linear mixed models) is also presented. Results: The SDE models offered a wide range of new flexible shapes and parameterizations compared to classical additive models, with performance as good or better than standard approaches. The predictions from the SDE models suggest distinct longitudinal clusters that form distinct ‘streams’ hidden by the large between-child variability. Conclusions: Using SDE models to predict future growth trajectories revealed new insights in the observed data, where trajectories appear to cluster together in bands, which may have a future risk assessment application. SDEs offer an attractive approach for child growth modelling and potentially offer new insights.

Background: Growth trajectories are highly variable between children, making epidemiological analyses challenging both to the identification of malnutrition interventions at the population level and also risk assessment at individual level. We introduce stochastic differential equation (SDE) models into child growth research. SDEs describe flexible dynamic processes comprising: drift - gradual smooth changes – such as physiology or gut microbiome, and diffusion - sudden perturbations, such as illness or infection. Methods: We present a case study applying SDE models to child growth trajectory data from the Haydom, Tanzania and Venda, South Africa sites within the MAL-ED cohort. These data comprise n=460 children aged 0-24 months. A comparison with classical curve fitting (linear mixed models) is also presented. Results: The SDE models offered a wide range of new flexible shapes and parameterizations compared to classical additive models, with performance as good or better than standard approaches. The predictions from the SDE models suggest distinct longitudinal clusters that form distinct ‘streams’ hidden by the large between-child variability. Conclusions: Using SDE models to predict future growth trajectories revealed new insights in the observed data, where trajectories appear to cluster together in bands, which may have a future risk assessment application. SDEs offer an attractive approach for child growth modelling and potentially offer new insights.