Background Globally most neonatal deaths occur within the first week of life and in low-income and middle-income countries. Strengthening health system linkages for frontline providers-such as lay midwives providing home-based obstetrical care-may improve neonatal outcomes in these settings. Here, we conducted a quality improvement study to increase the detection of neonatal complications by lay midwives in rural Guatemala, thereby increasing referrals to a higher level of care. Methods A quality improvement team in Guatemala reviewed drivers of neonatal health services provided by lay midwives. Improvement interventions included training on neonatal warning signs, optimised mobile health technology to standardise assessments and financial incentives for providers. The primary quality outcome was the rate of neonatal referral to a higher level of care. Results From September 2017 to September 2018, participating midwives attended 869 home deliveries and referred 80 neonates to a higher level of care. A proportion control chart, using the preintervention period from January to September 2017 as the baseline, showed an increase in the referral rate of all births from 1.5% to 9.9%. Special cause was obtained in January 2018 and sustained except for May 2018. The proportion of neonates receiving assessments by midwives in the first week of life increased to >90%. A trend toward an increasing number of days between neonatal deaths did not attain special cause. Conclusions Structured improvement interventions, including mobile health decision support and financial incentives, significantly increased the detection of neonatal complications and referral of neonates to higher levels of care by lay midwives operating in rural home-based settings in Guatemala. The results show the value of improving the integration of lay midwives and other first responders into neonatal systems of care in low-resource settings.

Despite successes on the Sustainable Development Goals for access to improved water sources and sanitation, many low and middle-income countries (LMICs) continue to struggle with high rates of diarrheal disease. In Guatemala, 98% of water sources are estimated to have E. coli contamination. This project moves toward a novel low-cost approach to bridge the gap between the microbiologic identification of E. coli and the vast impact that this pathogen has on human health within marginalized communities using co-designed community-based tools, low-cost technology, and AI. An agile co-design process was followed with water quality stakeholders, community staff, and local graphic design artists to develop a community water quality education mobile app. A series of alpha- and beta-testers completed interactive demonstration, feedback, and in-depth interview sessions. A microbiology lab in Guatemala developed and piloted field protocols with lay community workers to collect and process water samples. A preliminary artificial intelligence (AI) algorithm was developed to detect the presence of E. coli in images generated from community-derived water samples. The mobile app emerged as a pictorial and audio-driven community-facing tool. The field protocol for water sampling and testing was successfully implemented by lay community workers. Feedback from the community workers indicated both desire and ability to conduct the water sampling and testing protocol under field conditions. However, images derived from the low-cost $2 microscope in field conditions were not of a suitable quality for AI object detection of E. coli, and additional low-cost technologies are being considered. The preliminary AI object detection algorithm from lab-derived images performed at 94% accuracy in identifying E. coli in comparison to the Chromocult gold-standard.

ClinicalTrials.gov: NCT02348840.

Objective: Open research on fetal heart rate (FHR) estimation is relatively rare, and evidence for the utility of metrics derived from Doppler ultrasound devices has historically remained hidden in the proprietary documentation of commercial entities, thereby inhibiting its assessment and improvement. Nevertheless, recent studies have attempted to improve FHR estimation; however, these methods were developed and tested using datasets composed of few subjects and are therefore unlikely to be generalizable on a population level. The work presented here introduces a reproducible and generalizable autocorrelation (AC)-based method for FHR estimation from one-dimensional Doppler ultrasound (1D-DUS) signals. Approach: Simultaneous fetal electrocardiogram (fECG) and 1D-DUS signals generated by a hand-held Doppler transducer in a fixed position were captured by trained healthcare workers in a European hospital. The fECG QRS complexes were identified using a previously published fECG extraction algorithm and were then over-read to ensure accuracy. An AC-based method to estimate FHR was then developed on this data, using a total of 721 1D-DUS segments, each 3.75 s long, and parameters were tuned with Bayesian optimization. The trained FHR estimator was tested on two additional (independent) hand-annotated Doppler-only datasets recorded with the same device but on different populations: one composed of 3938 segments (from 99 fetuses) acquired in rural Guatemala, and another composed of 894 segments (from 17 fetuses) recorded in a hospital in the UK. Main results: The proposed AC-based method was able to estimate FHR within 10% of the reference FHR values 96% of the time, with an accuracy of 97% for manually identified good quality segments in both of the independent test sets. Significance: This is the first work to publish open source code for FHR estimation from 1D-DUS data. The method was shown to satisfy estimations within 10% of the reference FHR values and it therefore defines a minimum accuracy for the field to match or surpass. Our work establishes a basis from which future methods can be developed to more accurately estimate FHR variability for assessing fetal wellbeing from 1D-DUS signals.

Objective: The COVID-19 pandemic continues to place an inordinate burden on U.S. population health, and vaccination is the most powerful tool for curbing SARS-CoV-2 transmission, saving lives, and promoting economic recovery. However, much of the U.S. population remains hesitant to get vaccinated against COVID-19, despite having access to these life-saving vaccines. This study's objective was to examine the demographic characteristics, experiences, and disease- and vaccine-related risk perceptions that influence an individual's decision to adhere to vaccine recommendations for COVID-19. Study design: A telephone survey was performed with a convenience sample of 57 participants. Methods: This mixed-methods study collected quantitative and qualitative responses about seasonal influenza and COVID-19 vaccine intentions to compare vaccine hesitancies between a novel and routine vaccine. Results: The primary facilitators of uptake for the COVID-19 vaccine were personal protection, protecting others, preserving public health, and general vaccine confidence. Concerns about vaccine side effects, concerns about the COVID-19 vaccine trials, misinformation about vaccination, personal aversions to the vaccine, general distrust in vaccination, complacency, and distrust in government were the primary barriers to vaccine uptake. Race was also associated with COVID-19 vaccine intentions. Conclusions: The results of this research have been condensed into four recommendations designed to optimize public health messaging around the COVID-19 vaccine and maximize future vaccine uptake.

In-utero progress of fetal development is normally assessed through manual measurements taken from ultrasound images, requiring relatively expensive equipment and well-trained personnel. Such monitoring is therefore unavailable in low- and middle-income countries (LMICs), where most of the perinatal mortality and morbidity exists. The work presented here attempts to identify a proxy for IUGR, which is a significant contributor to perinatal death in LMICs, by determining gestational age (GA) from data derived from simple-to-use, low-cost one-dimensional Doppler ultrasound (1D-DUS) and blood pressure devices. A total of 114 paired 1D-DUS recordings and maternal blood pressure recordings were selected, based on previously described signal quality measures. The average length of 1D-DUS recording was 10.43 ± 1.41 min. The min/median/max systolic and diastolic maternal blood pressures were 79/102/121 and 50.5/63.5/78.5 mmHg, respectively. GA was estimated using features derived from the 1D-DUS and maternal blood pressure using a support vector regression (SVR) approach and GA based on the last menstrual period as a reference target. A total of 50 trials of 5-fold cross-validation were performed for feature selection. The final SVR model was retrained on the training data and then tested on a held-out set comprising 28 normal weight and 25 low birth weight (LBW) newborns. The mean absolute GA error with respect to the last menstrual period was found to be 0.72 and 1.01 months for the normal and LBW newborns, respectively. The mean error in the GA estimate was shown to be negatively correlated with the birth weight. Thus, if the estimated GA is lower than the (remembered) GA calculated from last menstruation, then this could be interpreted as a potential sign of IUGR associated with LBW, and referral and intervention may be necessary. The assessment system may, therefore, have an immediate impact if coupled with suitable intervention, such as nutritional supplementation. However, a prospective clinical trial is required to show the efficacy of such a metric in the detection of IUGR and the impact of the intervention.

Objective: Low birth weight is one of the leading contributors to global perinatal deaths. Detecting this problem close to birth enables the initiation of early intervention, thus reducing the long-term impact on the fetus. However, in low-and middle-income countries, sometimes newborns are weighted days or months after birth, thus challenging the identification of low birth weight. This study aims to estimate birth weight from observed postnatal weights recorded in a Guatemalan highland community. Approach: With 918 newborns recorded in postpartum visits at a Guatemalan highland community, we fitted traditional infant weight models (Count's and Reeds models). The model that fitted the observed data best was selected based on typical newborn weight patterns reported in the medical literature and previous longitudinal studies. Then, estimated birth weights were determined using the weight gain percentage derived from the fitted weight curve. Main results: The best model for both genders was the Reeds2 model, with a mean square error of 0.30 kg2 and 0.23 kg2 for male and female newborns, respectively. The fitted weight curves exhibited similar behavior to those reported in the literature, with a maximum weight loss around three to five days after birth, and birth weight recovery, on average, by day ten. Moreover, the estimated birth weight was consistent with the 2015 Guatemalan National Survey, no having a statistically significant difference between the estimated birth weight and the reported survey birth weights (two-sided Wilcoxon rank-sum test;). Significance: By estimating birth weight at an opportune time, several days after birth, it may be possible to identify low birth weight more accurately, thus providing timely treatment when is required.

At the very heart of global health fieldwork, relationships—real-world connections among people and across institutions—give meaning to the goals and projects of this multidisciplinary field. Those relationships inspire us and compel us to act to reduce health inequalities and promote health and social justice. Yet, in working toward these goals, we must more fully consider the asymmetries embedded in global health practice—imbalances of power, access to resources, and decision making—many of which come to a head in the context of fieldwork.