Midwifery & Emergency Obstetrics

Background: Stunting is a pervasive issue in low‑ and middle‑income countries, reflecting biological processes that adversely affect childhood cognitive development and increase the risk of chronic disease in adulthood. Nutritional intake is an important causative factor in stunting. Understanding the nutritional intake patterns of children with stunting can help inform nutrition program development. Objective: To characterize breastfeeding and dietary patterns from a clinical cohort of children with stunting in Guatemala and identify factors associated with linear growth. Methods: We included children with at least one diet record and one length/height‑for‑age z‑score below -2 from ages 0-5 years. We excluded children enrolled in complex care for severe non‑nutritional illness and records from prior to 6 months of age. We described adherence to World Health Organization infant and young child feeding indicators upon program enrollment. We longitudinally characterized breastfeeding and complementary feeding patterns of the cohort using generalized additive mixed modeling. We identified and quantified associations between various nutritional factors and linear growth using linear mixed effects models. Results: The final analytical dataset included 19,476 patient encounters from 2,352 children. Most children did not meet World Health Organization standards for dietary adequacy upon enrollment in the program. Dietary intakes were predominantly carbohydrate‑based. The factors most strongly associated with linear growth were food insecurity (negatively associated), portion size, and continued breastfeeding from 12 to 23 months. Adherence to the infant and young child feeding indicators was positively associated with linear growth. The intake of most food groups was also positively associated with linear growth. Conclusions: These findings suggest that the nutritional focus of interventions should be on adequate dietary diversity, earlier introduction and higher frequency of the less frequently consumed food groups, age‑appropriate portion sizes, and breastfeeding through 2 years of age. Public policy measures to address food insecurity are also necessary.

To test the feasibility of a smartphone app, BebeApp, that provides evidence-based early childhood development (ECD) guidance in rural Guatemala and pilot procedures for an adequately powered randomised controlled trial. We conducted an individually randomised pilot feasibility trial to compare BebeApp to printed ECD guidance. This trial was conducted in Tecpán, Chimaltenango, a semi-rural community that is 95% Kaqchikel Indigenous Maya. First-time primary caregiver-infant (0-28 days) dyads. BebeApp provides age-dependent, evidence-based caregiver guidance for breastfeeding and complementary feeding, sleep and developmental support. A mixed-methods evaluation of BebeApp's implementation and acceptability using the Reach, Effectiveness, Adoption, Implementation, Maintenance framework. App usability, usefulness and satisfaction were assessed via questionnaires, semi-structured interviews and app interaction data. 41 infant-caregiver dyads were enrolled with 40 completing the study. Engagement was high with caregivers opening BebeApp a median (IQR) 11 (6-21) times per month. Usability was found to be acceptable but no difference was found between pre-measurements and post-measurements. In interviews, caregivers expressed some initial difficulty using BebeApp but were able to gain confidence with a training session. App usefulness and satisfaction responses were positive. Caregivers often noted that BebeApp was their only source of ECD information outside of their family. We found that caregivers in rural Guatemala responded positively to a smartphone ECD app and engaged with it throughout the trial. Given the urgent need for ECD programmes in low- and middle-income settings, smartphone apps may be a method to deliver services directly to caregivers.

(1) To compare the ability of body mass index (BMI), waist-to-height ratio and visceral fat, as measured by bioelectrical impedance analysis (BIA), to predict hypertension and diabetes in men and women and (2) to determine whether the correlation between BMI and visceral fat varies by height quantile. We conducted a cross-sectional analysis of a representative survey that included data on anthropometrics, body composition, glycosylated haemoglobin and blood pressure. We used receiver operating characteristic analysis and DeLong CIs to compare the ability of each adiposity measure to predict diabetes and hypertension in each gender. Tecpán and San Antonio Suchitepéquez, Guatemala. 806 non-pregnant adults from 347 households, primarily of Indigenous ethnicity. Diabetes, defined as a haemoglobin A1c of greater than 6.5% or self-reported history and hypertension, defined as a systolic blood pressure over 140 or a diastolic blood pressure over 90. Among the three adiposity measures, visceral fat was the best predictor of diabetes (area under the curve; AUC 0.73 (95% CI 0.66 to 0.81) (men); AUC 0.75 (95% CI 0.7 to 0.8) (women)) and hypertension (AUC 0.7 (95% CI 0.61 to 0.79) (men); AUC 0.76 (95% CI 0.7 to 0.82) (women)), followed by waist-to-height ratio followed by BMI. All three measures better predicted hypertension in women than in men. In sensitivity analysis, visceral fat and waist-to-height ratio better predicted hypertension and diabetes when BMI was below 30 kg/m2. The correlation between BMI and visceral fat did not vary appreciably by height. Of the three adiposity measures studied, BIA-derived visceral fat best predicted cardiometabolic disease in the population. In clinical practice, alternative techniques beyond BMI need to be considered when assessing adiposity, screening for cardiometabolic disease and diagnosing clinical obesity.

This study presents a technical framework for real-time fetal Doppler data quality assessment using deep learning and edge-AI, designed to improve data collection and support future clinical studies in low-resource settings. Integrated into a low-cost, edge-computing system co-designed with Indigenous midwives in rural Guatemala, our solution utilizes an Android phone for data acquisition and decision support. Retrospective analysis demonstrates the potential to detect fetal growth restriction, hypertension, and other pregnancy-related conditions using Doppler-based fetal cardiac signals. To ensure accurate assessments and provide immediate feedback, a real-time signal quality metric is essential. We analyzed two fetal Doppler datasets: 191 recordings, captured in rural Guatemala, for training and validation, and five captured in a German hospital (in Leipzig) for testing. The data were segmented into 3.75 s intervals, and categorized into five quality levels: good, poor, radiofrequency interference, talking, and silent. A deep neural network was trained on these segments, achieving a micro F 1 score of 97.4% and a macro F 1 score of 94.2%, with 99.2% accuracy for 'Good' quality in the Guatemala dataset, based on five-fold cross-validation. For the Leipzig dataset, the F 1 score was 93.3% on 'Good' quality segments, demonstrating the model's ability to generalize across different datasets. By implementing the algorithm within an Android decision-support application in an mHealth framework, we have enabled real-time feedback during signal acquisition, improving data quality at the source. This scalable, edge mHealth solution offers significant potential to enhance maternal and fetal health monitoring in the Global South, contributing to global health efforts through the integration of mobile technology, AI, and healthcare.

For more than three decades, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) has provided a framework to quantify health loss due to diseases, injuries, and associated risk factors. This paper presents GBD 2023 findings on disease and injury burden and risk-attributable health loss, offering a global audit of the state of world health to inform public health priorities. This work captures the evolving landscape of health metrics across age groups, sexes, and locations, while reflecting on the remaining post-COVID-19 challenges to achieving our collective global health ambitions. The GBD 2023 combined analysis estimated years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs) for 375 diseases and injuries, and risk-attributable burden associated with 88 modifiable risk factors. Of the more than 310 000 total data sources used for all GBD 2023 (about 30% of which were new to this estimation round), more than 120 000 sources were used for estimation of disease and injury burden and 59 000 for risk factor estimation, and included vital registration systems, surveys, disease registries, and published scientific literature. Data were analysed using previously established modelling approaches, such as disease modelling meta-regression version 2.1 (DisMod-MR 2.1) and comparative risk assessment methods. Diseases and injuries were categorised into four levels on the basis of the established GBD cause hierarchy, as were risk factors using the GBD risk hierarchy. Estimates stratified by age, sex, location, and year from 1990 to 2023 were focused on disease-specific time trends over the 2010-23 period and presented as counts (to three significant figures) and age-standardised rates per 100 000 person-years (to one decimal place). For each measure, 95% uncertainty intervals [UIs] were calculated with the 2·5th and 97·5th percentile ordered values from a 250-draw distribution. Total numbers of global DALYs grew 6·1% (95% UI 4·0-8·1), from 2·64 billion (2·46-2·86) in 2010 to 2·80 billion (2·57-3·08) in 2023, but age-standardised DALY rates, which account for population growth and ageing, decreased by 12·6% (11·0-14·1), revealing large long-term health improvements. Non-communicable diseases (NCDs) contributed 1·45 billion (1·31-1·61) global DALYs in 2010, increasing to 1·80 billion (1·63-2·03) in 2023, alongside a concurrent 4·1% (1·9-6·3) reduction in age-standardised rates. Based on DALY counts, the leading level 3 NCDs in 2023 were ischaemic heart disease (193 million [176-209] DALYs), stroke (157 million [141-172]), and diabetes (90·2 million [75·2-107]), with the largest increases in age-standardised rates since 2010 occurring for anxiety disorders (62·8% [34·0-107·5]), depressive disorders (26·3% [11·6-42·9]), and diabetes (14·9% [7·5-25·6]). Remarkable health gains were made for communicable, maternal, neonatal, and nutritional (CMNN) diseases, with DALYs falling from 874 million (837-917) in 2010 to 681 million (642-736) in 2023, and a 25·8% (22·6-28·7) reduction in age-standardised DALY rates. During the COVID-19 pandemic, DALYs due to CMNN diseases rose but returned to pre-pandemic levels by 2023. From 2010 to 2023, decreases in age-standardised rates for CMNN diseases were led by rate decreases of 49·1% (32·7-61·0) for diarrhoeal diseases, 42·9% (38·0-48·0) for HIV/AIDS, and 42·2% (23·6-56·6) for tuberculosis. Neonatal disorders and lower respiratory infections remained the leading level 3 CMNN causes globally in 2023, although both showed notable rate decreases from 2010, declining by 16·5% (10·6-22·0) and 24·8% (7·4-36·7), respectively. Injury-related age-standardised DALY rates decreased by 15·6% (10·7-19·8) over the same period. Differences in burden due to NCDs, CMNN diseases, and injuries persisted across age, sex, time, and location. Based on our risk analysis, nearly 50% (1·27 billion [1·18-1·38]) of the roughly 2·80 billion total global DALYs in 2023 were attributable to the 88 risk factors analysed in GBD. Globally, the five level 3 risk factors contributing the highest proportion of risk-attributable DALYs were high systolic blood pressure (SBP), particulate matter pollution, high fasting plasma glucose (FPG), smoking, and low birthweight and short gestation-with high SBP accounting for 8·4% (6·9-10·0) of total DALYs. Of the three overarching level 1 GBD risk factor categories-behavioural, metabolic, and environmental and occupational-risk-attributable DALYs rose between 2010 and 2023 only for metabolic risks, increasing by 30·7% (24·8-37·3); however, age-standardised DALY rates attributable to metabolic risks decreased by 6·7% (2·0-11·0) over the same period. For all but three of the 25 leading level 3 risk factors, age-standardised rates dropped between 2010 and 2023-eg, declining by 54·4% (38·7-65·3) for unsafe sanitation, 50·5% (33·3-63·1) for unsafe water source, and 45·2% (25·6-72·0) for no access to handwashing facility, and by 44·9% (37·3-53·5) for child growth failure. The three leading level 3 risk factors for which age-standardised attributable DALY rates rose were high BMI (10·5% [0·1 to 20·9]), drug use (8·4% [2·6 to 15·3]), and high FPG (6·2% [-2·7 to 15·6]; non-significant). Our findings underscore the complex and dynamic nature of global health challenges. Since 2010, there have been large decreases in burden due to CMNN diseases and many environmental and behavioural risk factors, juxtaposed with sizeable increases in DALYs attributable to metabolic risk factors and NCDs in growing and ageing populations. This long-observed consequence of the global epidemiological transition was only temporarily interrupted by the COVID-19 pandemic. The substantially decreasing CMNN disease burden, despite the 2008 global financial crisis and pandemic-related disruptions, is one of the greatest collective public health successes known. However, these achievements are at risk of being reversed due to major cuts to development assistance for health globally, the effects of which will hit low-income countries with high burden the hardest. Without sustained investment in evidence-based interventions and policies, progress could stall or reverse, leading to widespread human costs and geopolitical instability. Moreover, the rising NCD burden necessitates intensified efforts to mitigate exposure to leading risk factors-eg, air pollution, smoking, and metabolic risks, such as high SBP, BMI, and FPG-including policies that promote food security, healthier diets, physical activity, and equitable and expanded access to potential treatments, such as GLP-1 receptor agonists. Decisive, coordinated action is needed to address long-standing yet growing health challenges, including depressive and anxiety disorders. Yet this can be only part of the solution. Our response to the NCD syndemic-the complex interaction of multiple health risks, social determinants, and systemic challenges-will define the future landscape of global health. To ensure human wellbeing, economic stability, and social equity, global action to sustain and advance health gains must prioritise reducing disparities by addressing socioeconomic and demographic determinants, ensuring equitable health-care access, tackling malnutrition, strengthening health systems, and improving vaccination coverage. We live in times of great opportunity. Gates Foundation and Bloomberg Philanthropies.

Timely and comprehensive analyses of causes of death stratified by age, sex, and location are essential for shaping effective health policies aimed at reducing global mortality. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023 provides cause-specific mortality estimates measured in counts, rates, and years of life lost (YLLs). GBD 2023 aimed to enhance our understanding of the relationship between age and cause of death by quantifying the probability of dying before age 70 years (70q0) and the mean age at death by cause and sex. This study enables comparisons of the impact of causes of death over time, offering a deeper understanding of how these causes affect global populations. GBD 2023 produced estimates for 292 causes of death disaggregated by age-sex-location-year in 204 countries and territories and 660 subnational locations for each year from 1990 until 2023. We used a modelling tool developed for GBD, the Cause of Death Ensemble model (CODEm), to estimate cause-specific death rates for most causes. We computed YLLs as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. Probability of death was calculated as the chance of dying from a given cause in a specific age period, for a specific population. Mean age at death was calculated by first assigning the midpoint age of each age group for every death, followed by computing the mean of all midpoint ages across all deaths attributed to a given cause. We used GBD death estimates to calculate the observed mean age at death and to model the expected mean age across causes, sexes, years, and locations. The expected mean age reflects the expected mean age at death for individuals within a population, based on global mortality rates and the population's age structure. Comparatively, the observed mean age represents the actual mean age at death, influenced by all factors unique to a location-specific population, including its age structure. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 250-draw distribution for each metric. Findings are reported as counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2023 include a correction for the misclassification of deaths due to COVID-19, updates to the method used to estimate COVID-19, and updates to the CODEm modelling framework. This analysis used 55 761 data sources, including vital registration and verbal autopsy data as well as data from surveys, censuses, surveillance systems, and cancer registries, among others. For GBD 2023, there were 312 new country-years of vital registration cause-of-death data, 3 country-years of surveillance data, 51 country-years of verbal autopsy data, and 144 country-years of other data types that were added to those used in previous GBD rounds. The initial years of the COVID-19 pandemic caused shifts in long-standing rankings of the leading causes of global deaths: it ranked as the number one age-standardised cause of death at Level 3 of the GBD cause classification hierarchy in 2021. By 2023, COVID-19 dropped to the 20th place among the leading global causes, returning the rankings of the leading two causes to those typical across the time series (ie, ischaemic heart disease and stroke). While ischaemic heart disease and stroke persist as leading causes of death, there has been progress in reducing their age-standardised mortality rates globally. Four other leading causes have also shown large declines in global age-standardised mortality rates across the study period: diarrhoeal diseases, tuberculosis, stomach cancer, and measles. Other causes of death showed disparate patterns between sexes, notably for deaths from conflict and terrorism in some locations. A large reduction in age-standardised rates of YLLs occurred for neonatal disorders. Despite this, neonatal disorders remained the leading cause of global YLLs over the period studied, except in 2021, when COVID-19 was temporarily the leading cause. Compared to 1990, there has been a considerable reduction in total YLLs in many vaccine-preventable diseases, most notably diphtheria, pertussis, tetanus, and measles. In addition, this study quantified the mean age at death for all-cause mortality and cause-specific mortality and found noticeable variation by sex and location. The global all-cause mean age at death increased from 46·8 years (95% UI 46·6-47·0) in 1990 to 63·4 years (63·1-63·7) in 2023. For males, mean age increased from 45·4 years (45·1-45·7) to 61·2 years (60·7-61·6), and for females it increased from 48·5 years (48·1-48·8) to 65·9 years (65·5-66·3), from 1990 to 2023. The highest all-cause mean age at death in 2023 was found in the high-income super-region, where the mean age for females reached 80·9 years (80·9-81·0) and for males 74·8 years (74·8-74·9). By comparison, the lowest all-cause mean age at death occurred in sub-Saharan Africa, where it was 38·0 years (37·5-38·4) for females and 35·6 years (35·2-35·9) for males in 2023. Lastly, our study found that all-cause 70q0 decreased across each GBD super-region and region from 2000 to 2023, although with large variability between them. For females, we found that 70q0 notably increased from drug use disorders and conflict and terrorism. Leading causes that increased 70q0 for males also included drug use disorders, as well as diabetes. In sub-Saharan Africa, there was an increase in 70q0 for many non-communicable diseases (NCDs). Additionally, the mean age at death from NCDs was lower than the expected mean age at death for this super-region. By comparison, there was an increase in 70q0 for drug use disorders in the high-income super-region, which also had an observed mean age at death lower than the expected value. We examined global mortality patterns over the past three decades, highlighting-with enhanced estimation methods-the impacts of major events such as the COVID-19 pandemic, in addition to broader trends such as increasing NCDs in low-income regions that reflect ongoing shifts in the global epidemiological transition. This study also delves into premature mortality patterns, exploring the interplay between age and causes of death and deepening our understanding of where targeted resources could be applied to further reduce preventable sources of mortality. We provide essential insights into global and regional health disparities, identifying locations in need of targeted interventions to address both communicable and non-communicable diseases. There is an ever-present need for strengthened health-care systems that are resilient to future pandemics and the shifting burden of disease, particularly among ageing populations in regions with high mortality rates. Robust estimates of causes of death are increasingly essential to inform health priorities and guide efforts toward achieving global health equity. The need for global collaboration to reduce preventable mortality is more important than ever, as shifting burdens of disease are affecting all nations, albeit at different paces and scales. Gates Foundation.

Neonatal mortality remains a significant equity issue in rural Indigenous communities of Guatemala, where structural barriers and systemic discrimination impede access to quality newborn care. This field report describes a novel community-based initiative implemented by Maya Health Alliance, an Indigenous-lead NGO, to address high neonatal mortality in Maya Kaqchikel communities through a quality improvement (QI) framework. The intervention centers on home-based neonatal care delivered by trained Neonatal Technicians (NTs), supported by a co-designed smartphone application enabling early identification of neonatal danger signs, clinical decision-making, and data collection. The initiative also leverages a culturally responsive referral and patient navigation system to overcome humanistic barriers to care. Designed using QI methodology, the project applies iterative cycles to track key performance indicators such as perinatal and neonatal mortality rates, referral success rates, and the proportion of newborns receiving timely home evaluations. Since launching in 2024, the program has reached 85% of reported newborns, increased referral rates, and engaged local midwives and health staff through ongoing training and co-design efforts. However, challenges have emerged, including high prevalence of low birth weight, limitations in local hospital capacity, and discriminatory care at facilities that discourage families from accepting referrals. The intervention centers Indigenous practices by positioning TMMs at the frontline and adapting protocols to the communities' lived realities. This initiative demonstrates the potential for culturally embedded, digitally supported, and equity-focused QI interventions to improve neonatal outcomes in resource-limited Indigenous settings. Future efforts will focus on expanding staff capacity, deepening community trust, and strengthening health system partnerships. Main findings: A community-based neonatal health program that integrates traditional Maya practices, home visits, and digital decision support in rural Guatemala has improved detection of at-risk newborns and referral rates.Added knowledge: This study highlights the feasibility of combining culturally tailored care, mHealth tools, and accompaniment in Indigenous settings, while documenting significant barriers rooted in structural inequities and cultural distrust.Global health impact for policy and action: Scaling such integrated models may strengthen neonatal care in marginalized communities, but it requires systemic reforms, investment in local health systems, and respect for cultural autonomy.

Cancer is a leading cause of death globally. Accurate cancer burden information is crucial for policy planning, but many countries do not have up-to-date cancer surveillance data. To inform global cancer-control efforts, we used the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023 framework to generate and analyse estimates of cancer burden for 47 cancer types or groupings by age, sex, and 204 countries and territories from 1990 to 2023, cancer burden attributable to selected risk factors from 1990 to 2023, and forecasted cancer burden up to 2050. Cancer estimation in GBD 2023 used data from population-based cancer registration systems, vital registration systems, and verbal autopsies. Cancer mortality was estimated using ensemble models, with incidence informed by mortality estimates and mortality-to-incidence ratios (MIRs). Prevalence estimates were generated from modelled survival estimates, then multiplied by disability weights to estimate years lived with disability (YLDs). Years of life lost (YLLs) were estimated by multiplying age-specific cancer deaths by the GBD standard life expectancy at the age of death. Disability-adjusted life-years (DALYs) were calculated as the sum of YLLs and YLDs. We used the GBD 2023 comparative risk assessment framework to estimate cancer burden attributable to 44 behavioural, environmental and occupational, and metabolic risk factors. To forecast cancer burden from 2024 to 2050, we used the GBD 2023 forecasting framework, which included forecasts of relevant risk factor exposures and used Socio-demographic Index as a covariate for forecasting the proportion of each cancer not affected by these risk factors. Progress towards the UN Sustainable Development Goal (SDG) target 3.4 aim to reduce non-communicable disease mortality by a third between 2015 and 2030 was estimated for cancer. In 2023, excluding non-melanoma skin cancers, there were 18·5 million (95% uncertainty interval 16·4 to 20·7) incident cases of cancer and 10·4 million (9·65 to 10·9) deaths, contributing to 271 million (255 to 285) DALYs globally. Of these, 57·9% (56·1 to 59·8) of incident cases and 65·8% (64·3 to 67·6) of cancer deaths occurred in low-income to upper-middle-income countries based on World Bank income group classifications. Cancer was the second leading cause of deaths globally in 2023 after cardiovascular diseases. There were 4·33 million (3·85 to 4·78) risk-attributable cancer deaths globally in 2023, comprising 41·7% (37·8 to 45·4) of all cancer deaths. Risk-attributable cancer deaths increased by 72·3% (57·1 to 86·8) from 1990 to 2023, whereas overall global cancer deaths increased by 74·3% (62·2 to 86·2) over the same period. The reference forecasts (the most likely future) estimate that in 2050 there will be 30·5 million (22·9 to 38·9) cases and 18·6 million (15·6 to 21·5) deaths from cancer globally, 60·7% (41·9 to 80·6) and 74·5% (50·1 to 104·2) increases from 2024, respectively. These forecasted increases in deaths are greater in low-income and middle-income countries (90·6% [61·0 to 127·0]) compared with high-income countries (42·8% [28·3 to 58·6]). Most of these increases are likely due to demographic changes, as age-standardised death rates are forecast to change by -5·6% (-12·8 to 4·6) between 2024 and 2050 globally. Between 2015 and 2030, the probability of dying due to cancer between the ages of 30 years and 70 years was forecasted to have a relative decrease of 6·5% (3·2 to 10·3). Cancer is a major contributor to global disease burden, with increasing numbers of cases and deaths forecasted up to 2050 and a disproportionate growth in burden in countries with scarce resources. The decline in age-standardised mortality rates from cancer is encouraging but insufficient to meet the SDG target set for 2030. Effectively and sustainably addressing cancer burden globally will require comprehensive national and international efforts that consider health systems and context in the development and implementation of cancer-control strategies across the continuum of prevention, diagnosis, and treatment. Gates Foundation, St Jude Children's Research Hospital, and St Baldrick's Foundation.

Cardiotocography and obstetric ultrasound imaging are the standard for fetal monitoring during pregnancy and labor. These technologies are often expensive and, with very few exceptions, can only be used by highly trained personnel. Medical care during gestation differs in low- to middle-income countries (LMIC) from high-income countries. Our research has previously demonstrated that a low-cost 1D Doppler ultrasound (DUS) can be used during pregnancy to assess maternal and fetal health. However, differentiation between DUS signals from the fetal heart (FH) and umbilical cord (UC) can be challenging for untrained users. We trained a random forest classifier to detect whether 1D DUS recordings originated from FH activity or UC blood flow. This classifier was trained using the relative energy in each 10 Hz interval of the power spectrum derived from a balanced set of recordings. We used leave-one-out cross-validation to test our results. We achieved an area under the curve of 0.82 and an accuracy of 75% for identifying FH activity, and 84% for UC blood flow. It is possible to differentiate the source of the 1D Doppler ultrasound signal. Depending on the source, different clinical parameters can be analyzed, enabling more targeted assessments of maternal and fetal health.

This paper introduces a novel approach to address the challenges associated with transferring blood pressure (BP) data obtained from oscillometric devices used in self-measured BP monitoring systems to integrate this data into medical health records or a proxy database accessible by clinicians, particularly in low literacy populations. To this end, we developed an automated image transcription technique to effectively transcribe readings from BP devices, ultimately enhancing the accessibility and usability of BP data for monitoring and managing BP during pregnancy and the postpartum period, particularly in low-resource settings and low-literate populations. In the designed study, the photos of the BP devices were captured as part of perinatal mobile health (mHealth) monitoring programs, conducted in four studies across two countries. The Guatemala Set 1 and Guatemala Set 2 datasets include the data captured by a cohort of 49 lay midwives from 1697 and 584 pregnant women carrying singletons in the second and third trimesters in rural Guatemala during routine screening. Additionally, we designed an mHealth system in Georgia for postpartum women to monitor and report their BP at home with 23 and 49 African American participants contributing to the Georgia I3 and Georgia IMPROVE projects, respectively. We developed a deep learning-based model which operates in two steps: LCD localization using the You Only Look Once (YOLO) object detection model and digit recognition using a convolutional neural network-based model capable of recognizing multiple digits. We applied color correction and thresholding techniques to minimize the impact of reflection and artifacts. Three experiments were conducted based on the devices used for training the digit recognition model. Overall, our results demonstrate that the device-specific model with transfer learning and the device independent model outperformed the device-specific model without transfer learning. The mean absolute error (MAE) of image transcription on held-out test datasets using the device-independent digit recognition were 1.2 and 0.8 mmHg for systolic and diastolic BP in the Georgia IMPROVE and 0.9 and 0.5 mmHg in Guatemala Set 2 datasets. The MAE, far below the FDA recommendation of 5 mmHg, makes the proposed automatic image transcription model suitable for general use when used with appropriate low-error BP devices.

Undetected high-risk conditions in pregnancy are a leading cause of perinatal mortality in low-income and middle-income countries. A key contributor to adverse perinatal outcomes in these settings is limited access to high-quality screening and timely referral to care. Recently, a low-cost one-dimensional Doppler ultrasound (1-D DUS) device was developed that front-line workers in rural Guatemala used to collect quality maternal and fetal data. Further, we demonstrated with retrospective preliminary data that 1-D DUS signal could be processed using artificial intelligence and deep-learning algorithms to accurately estimate fetal gestational age, intrauterine growth and maternal blood pressure. This protocol describes a prospective observational pregnancy cohort study designed to prospectively evaluate these preliminary findings. This is a prospective observational cohort study conducted in rural Guatemala. In this study, we will follow pregnant women (N =700) recruited prior to 18 6/7 weeks gestation until their delivery and early postpartum period. During pregnancy, trained nurses will collect data on prenatal risk factors and obstetrical care. Every 4 weeks, the research team will collect maternal weight, blood pressure and 1-D DUS recordings of fetal heart tones. Additionally, we will conduct three serial obstetric ultrasounds to evaluate for fetal growth restriction (FGR), and one postpartum visit to record maternal blood pressure and neonatal weight and length. We will compare the test characteristics (receiver operator curves) of 1-D DUS algorithms developed by deep-learning methods to two-dimensional fetal ultrasound survey and published clinical pre-eclampsia risk prediction algorithms for predicting FGR and pre-eclampsia, respectively. Results of this study will be disseminated at scientific conferences and through peer-reviewed articles. Deidentified data sets will be made available through public repositories. The study has been approved by the institutional ethics committees of Maya Health Alliance and Emory University.

Sensitive measures to predict neuromotor outcomes from data collected early in infancy are lacking. Measures derived from the recordings of infant movement using wearable sensors may be a useful new technique. We collected full-day leg movement of 41 infants in rural Guatemala across 3 visits between birth and 6 months of age using wearable sensors. Average leg movement rate and fuzzy entropy, a measure to describe the complexity of signals, of the leg movements' peak acceleration time series and the time series itself were derived. We tested the three measures for the predictability of infants' developmental outcome, Bayley Scales of Infant and Toddler Development III motor, language, or cognitive composite score assessed at 12 months of age. We performed quantile regressions with clustered standard errors, accounting for the multiple visits for each infant. Fuzzy entropy was associated with the motor composite score at the 0.5 quantiles; this association was not found for the other two measures. Also, no leg movement characteristic was associated with language or cognitive composite scores. We propose that the entropy of leg movement associated peak accelerations calculated from the wearable sensor data collected for a full-day can be considered as one predictor for infants' motor developmental outcome assessed with Bayley Scales of Infant and Toddler Development III at 12 months of age.

Neonatal mortality is a global public health challenge. Guatemala has the fifth highest neonatal mortality rate in Latin America, and Indigenous communities are particularly impacted. This study aims to understand factors driving neonatal mortality rates among Maya Kaqchikel communities. We used sequential explanatory mixed methods. The quantitative phase was a secondary analysis of 2014-2016 data from the Global Maternal and Newborn Health Registry from Chimaltenango, Guatemala. Multivariate logistic regression models identified factors associated with perinatal and late neonatal mortality. A number of 33 in-depth interviews were conducted with mothers, traditional Maya midwives and local healthcare professionals to explain quantitative findings. Of 33 759 observations, 351 were lost to follow-up. There were 32 559 live births, 670 stillbirths (20/1000 births), 1265 (38/1000 births) perinatal deaths and 409 (12/1000 live births) late neonatal deaths. Factors identified to have statistically significant associations with a higher risk of perinatal or late neonatal mortality include lack of maternal education, maternal height <140 cm, maternal age under 20 or above 35, attending less than four antenatal visits, delivering without a skilled attendant, delivering at a health facility, preterm birth, congenital anomalies and presence of other obstetrical complications. Qualitative participants linked severe mental and emotional distress and inadequate maternal nutrition to heightened neonatal vulnerability. They also highlighted that mistrust in the healthcare system-fueled by language barriers and healthcare workers' use of coercive authority-delayed hospital presentations. They provided examples of cooperative relationships between traditional midwives and healthcare staff that resulted in positive outcomes. Structural social forces influence neonatal vulnerability in rural Guatemala. When coupled with healthcare system shortcomings, these forces increase mistrust and mortality. Collaborative relationships among healthcare staff, traditional midwives and families may disrupt this cycle.

Tools to accurately assess infants' neurodevelopmental status very early in their lives are limited. Wearable sensors may provide a novel approach for very early assessment of infant neurodevelopmental status. This may be especially relevant in rural and low-resource global settings. We conducted a longitudinal observational study and used wearable sensors to repeatedly measure the kinematic leg movement characteristics of 41 infants in rural Guatemala three times across full days between birth and 6 months of age. In addition, we collected sociodemographic data, growth data, and caregiver estimates of swaddling behaviors. We used visual analysis and multivariable linear mixed models to evaluate the associations between two leg movement kinematic variables (awake movement rate, peak acceleration per movement) and infant age, swaddling behaviors, growth, and other covariates. Multivariable mixed models of sensor data showed age-dependent increases in leg movement rates (2.16 [95% CI 0.80,3.52] movements/awake hour/day of life) and movement acceleration (5.04e-3 m/s2 [95% CI 3.79e-3, 6.27e-3]/day of life). Swaddling time as well as growth status, poverty status and multiple other clinical and sociodemographic variables had no impact on either movement variable. Collecting wearable sensor data on young infants in a rural low-resource setting is feasible and can be used to monitor age-dependent changes in movement kinematics. Future work will evaluate associations between these kinematic variables from sensors and formal developmental measures, such as the Bayley Scales of Infant and Toddler Development.

To facilitate the shift from risk-factor management to primordial prevention of cardiovascular disease, the American Heart Association developed guidelines to score and track cardiovascular health (CVH). How the prevalence and trajectories of a high level of CVH across the life course compare among high- and lower-income countries is unknown. Nationally representative survey data with CVH variables (physical activity, cigarette smoking, body mass index, blood pressure, blood glucose, and total cholesterol levels) were identified in Ethiopia, Bangladesh, Brazil, England, and the US for adults (aged 18-69 years and not pregnant). Data were harmonized, and CVH metrics were scored using the American Heart Association guidelines, as high (2), moderate (1), or low (0), with the prevalence of high scores (better CVH) across the life course compared across countries. Among 28,092 adults (Ethiopia n = 7686, 55.2% male; Bangladesh n = 6731, 48.4% male; Brazil n = 7241, 47.9% male; England n = 2691, 49.5% male, and the US n = 3743, 50.3% male), the prevalence of high CVH scores decreased as country income level increased. Declining CVH with age was universal across countries, but differences were already observable in those aged 18 years. Excess body weight appeared to be the main driver of poor CVH in higher-income countries, and the prevalence of current smoking was highest in Bangladesh. Our findings suggest that CVH decline with age may be universal. Interventions to promote and preserve CVH throughout the life course are needed in all populations, tailored to country-specific time courses of the decline. In countries where the level of CVH remains relatively high, protection of whole societies from risk-factor epidemics may still be feasible. Afin de faciliter la transition de la prise en charge des facteurs de risque vers la prévention primordiale des maladies cardiovasculaires, l’American Heart Association a élaboré des lignes directrices en vue de mesurer la santé cardiovasculaire (SCV) et d’en faire le suivi. On ignore dans quelle mesure la prévalence et la trajectoire d’un niveau élevé de SCV au cours d’une vie se comparent entre les pays à revenu élevé et les pays à plus faible revenu. Des résultats de sondages représentatifs des pays concernant les variables de la SCV (activité physique, tabagisme, indice de masse corporelle, pression artérielle, glycémie et taux de cholestérol total) ont été obtenus de l’Éthiopie, du Bangladesh, du Brésil, de l’Angleterre et des États-Unis, pour des adultes âgés de 18 à 69 ans, excluant les femmes enceintes. Les données ont été harmonisées, et la SCV a été mesurée conformément aux lignes directrices de l’American Heart Association, et notée en fonction des scores suivants : élevée (2), modérée (1) ou faible (0). La prévalence de scores élevés, soit une meilleure SCV tout au long de la vie, a été comparée entre les pays. Parmi 28 092 adultes (Éthiopie, n = 7 686, 55,2 % de sexe masculin; Bangladesh, n = 6 731, 48,4 % de sexe masculin; Brésil, n = 7 241, 47,9 % de sexe masculin; Angleterre, n = 2 691, 49,5 % de sexe masculin, et États-Unis, n = 3 743, 50,3 % de sexe masculin), la prévalence de scores correspondant à une SCV élevée diminuait à mesure que le niveau de revenu du pays augmentait. La diminution de la SCV avec l’âge était universelle dans tous les pays, mais des différences étaient déjà observables chez les personnes âgées de 18 ans. Un surplus de poids corporel semblait être le principal facteur d’une faible SCV dans les pays à revenu plus élevé; la prévalence d’un tabagisme actuel était la plus élevée au Bangladesh. Nos observations laissent croire que le déclin de la SCV avec l’âge pourrait être universel. Il est nécessaire de mener des interventions adaptées à la progression du déclin dans chacun des pays en vue de favoriser et de préserver la SCV tout au long de la vie, et ce, dans toutes les populations. Dans les pays où le niveau de SCV demeure relativement élevé, il pourrait être encore possible de protéger des sociétés entières contre des épidémies liées aux facteurs de risque.

Multiple studies have highlighted the inequities minority and Indigenous children face when accessing health care. Health and wellbeing are positively impacted when Indigenous children are educated and receive care in their maternal language. However, less is known about the association between minority or Indigenous language use and child development risks and outcomes. In this study, we provide global estimates of development risks and assess the associations between minority or Indigenous language status and early child development using the ten-item Early Child Development Index (ECDI), a tool widely used for global population assessments in children aged 3-4 years. We did a secondary analysis of cross-sectional data from 65 UNICEF Multiple Indicator Cluster Surveys (MICS) containing the ECDI from 2009-19 (waves 4-6). We included individual-level data for children aged 2-4 years (23-60 months) from datasets with ECDI modules, for surveys that captured the language of the respondent, interview, or head of household. The Expanded Graded Intergenerational Disruption Scale was used to classify household languages as dominant versus minority or Indigenous at the country level. Our primary outcome was on-track overall development, defined per UNICEF's guidelines as development being on track for at least three of the four ECDI domains (literacy-numeracy, learning, physical, and socioemotional). We performed logistic regression of pooled, weighted ECDI scores, aggregated by language status and adjusting for the covariables of child sex, child nutritional status (stunting), household wealth, maternal education, developmental support by an adult caregiver, and country-level early child education proportion. Regression analyses were done for all children aged 3-4 years with ECDI results, and separately for children with functional disabilities and ECDI results. 65 MICS datasets were included. 186 393 children aged 3-4 years had ECDI and language data, corresponding to an estimated represented population of 34 714 992 individuals. Estimated prevalence of on-track overall development as measured by ECDI scores was 65·7% (95% CI 64·2-67·2) for children from a minority or Indigenous language-speaking household, and 76·6% (75·7-77·4) for those from a dominant language-speaking household. After adjustment, dominant language status was associated with increased odds of on-track overall development (adjusted OR 1·54, 95% CI 1·40-1·71), which appeared to be largely driven by significantly increased odds of on-track development in the literacy-numeracy and socioemotional domains. For the represented population aged 2-4 years (n=11 465 601), the estimated prevalence of family-reported functional disability was 3·6% (95% CI 3·0-4·4). For the represented population aged 3-4 years with a functional disability (n=292 691), language status was not associated with on-track overall development (adjusted OR 1·02, 95% CI 0·43-2·45). In a global dataset, children speaking a minority or Indigenous language were less likely to have on-track ECDI scores than those speaking a dominant language. Given the strong positive benefits of speaking an Indigenous language on the health and development of Indigenous children, this disparity is likely to reflect the sociolinguistic marginalisation faced by speakers of minority or Indigenous languages as well as differences in the performance of ECDI in these languages. Global efforts should consider performance of measures and monitor developmental data disaggregated by language status to stimulate efforts to address this disparity. None. For the Spanish, Kaqchikel and K'iche' translations of the abstract see Supplementary Materials section.

Aspirin is an effective and low-cost option for reducing atherosclerotic cardiovascular disease (CVD) events and improving mortality rates among individuals with established CVD. To guide efforts to mitigate the global CVD burden, there is a need to understand current levels of aspirin use for secondary prevention of CVD. To report and evaluate aspirin use for secondary prevention of CVD across low-, middle-, and high-income countries. Cross-sectional analysis using pooled, individual participant data from nationally representative health surveys conducted between 2013 and 2020 in 51 low-, middle-, and high-income countries. Included surveys contained data on self-reported history of CVD and aspirin use. The sample of participants included nonpregnant adults aged 40 to 69 years. Countries' per capita income levels and world region; individuals' socioeconomic demographics. Self-reported use of aspirin for secondary prevention of CVD. The overall pooled sample included 124 505 individuals. The median age was 52 (IQR, 45-59) years, and 50.5% (95% CI, 49.9%-51.1%) were women. A total of 10 589 individuals had a self-reported history of CVD (8.1% [95% CI, 7.6%-8.6%]). Among individuals with a history of CVD, aspirin use for secondary prevention in the overall pooled sample was 40.3% (95% CI, 37.6%-43.0%). By income group, estimates were 16.6% (95% CI, 12.4%-21.9%) in low-income countries, 24.5% (95% CI, 20.8%-28.6%) in lower-middle-income countries, 51.1% (95% CI, 48.2%-54.0%) in upper-middle-income countries, and 65.0% (95% CI, 59.1%-70.4%) in high-income countries. Worldwide, aspirin is underused in secondary prevention, particularly in low-income countries. National health policies and health systems must develop, implement, and evaluate strategies to promote aspirin therapy.

With the greatest burden of cardiovascular disease morbidity and mortality increasingly observed in lower-income countries least prepared for this epidemic, focus is widening from risk factor management alone to primordial prevention to maintain high levels of cardiovascular health (CVH) across the life course. To facilitate this, the American Heart Association (AHA) developed CVH scoring guidelines to evaluate and track CVH. We aimed to compare the prevalence and trajectories of high CVH across the life course using nationally representative adult CVH data from five diverse high- to low-income countries. Surveys with CVH variables (physical activity, cigarette smoking, body mass, blood pressure, blood glucose, and total cholesterol levels) were identified in Ethiopia, Bangladesh, Brazil, England, and the United States (US). Participants were included if they were 18-69y, not pregnant, and had data for these CVH metrics. Comparable data were harmonized and each of the CVH metrics was scored using AHA guidelines as high (2), moderate (1), or low (0) to create total CVH scores with higher scores representing better CVH. High CVH prevalence by age was compared creating country CVH trajectories. The analysis included 28,092 adults (Ethiopia n=7686, 55.2% male; Bangladesh n=6731, 48.4% male; Brazil n=7241, 47.9 % male; England n=2691, 49.5% male, and the US n=3743, 50.3% male). As country income level increased, prevalence of high CVH decreased (>90% in Ethiopia, >68% in Bangladesh and under 65% in the remaining countries). This pattern remained using either five or all six CVH metrics and following exclusion of underweight participants. While a decline in CVH with age was observed for all countries, higher income countries showed lower prevalence of high CVH already by age 18y. Excess body weight appeared the main driver of poor CVH in higher income countries, while current smoking was highest in Bangladesh. Harmonization of nationally representative survey data on CVH trajectories with age in 5 highly diverse countries supports our hypothesis that CVH decline with age may be universal. Interventions to promote and preserve high CVH throughout the life course are needed in all populations, tailored to country-specific time courses of the decline. In countries where CVH remains relatively high, protection of whole societies from risk factor epidemics may still be feasible.

In the prevention of cardiovascular disease, a WHO target is that at least 50% of eligible people use statins. Robust evidence is needed to monitor progress towards this target in low-income and middle-income countries (LMICs), where most cardiovascular disease deaths occur. The objectives of this study were to benchmark statin use in LMICs and to investigate country-level and individual-level characteristics associated with statin use. We did a cross-sectional analysis of pooled, individual-level data from nationally representative health surveys done in 41 LMICs between 2013 and 2019. Our sample consisted of non-pregnant adults aged 40-69 years. We prioritised WHO Stepwise Approach to Surveillance (STEPS) surveys because these are WHO's recommended method for population monitoring of non-communicable disease targets. For countries in which no STEPS survey was available, a systematic search was done to identify other surveys. We included surveys that were done in an LMIC as classified by the World Bank in the survey year; were done in 2013 or later; were nationally representative; had individual-level data available; and asked questions on statin use and previous history of cardiovascular disease. Primary outcomes were the proportion of eligible individuals self-reporting use of statins for the primary and secondary prevention of cardiovascular disease. Eligibility for statin therapy for primary prevention was defined among individuals with a history of diagnosed diabetes or a 10-year cardiovascular disease risk of at least 20%. Eligibility for statin therapy for secondary prevention was defined among individuals with a history of self-reported cardiovascular disease. At the country level, we estimated statin use by per-capita health spending, per-capita income, burden of cardiovascular diseases, and commitment to non-communicable disease policy. At the individual level, we used modified Poisson regression models to assess statin use alongside individual-level characteristics of age, sex, education, and rural versus urban residence. Countries were weighted in proportion to their population size in pooled analyses. The final pooled sample included 116 449 non-pregnant individuals. 9229 individuals reported a previous history of cardiovascular disease (7·9% [95% CI 7·4-8·3] of the population-weighted sample). Among those without a previous history of cardiovascular disease, 8453 were eligible for a statin for primary prevention of cardiovascular disease (9·7% [95% CI 9·3-10·1] of the population-weighted sample). For primary prevention of cardiovascular disease, statin use was 8·0% (95% CI 6·9-9·3) and for secondary prevention statin use was 21·9% (20·0-24·0). The WHO target that at least 50% of eligible individuals receive statin therapy to prevent cardiovascular disease was achieved by no region or income group. Statin use was less common in countries with lower health spending. At the individual level, there was generally higher statin use among women (primary prevention only, risk ratio [RR] 1·83 [95% CI 1·22-2·76), and individuals who were older (primary prevention, 60-69 years, RR 1·86 [1·04-3·33]; secondary prevention, 50-59 years RR 1·71 [1·35-2·18]; and 60-69 years RR 2·09 [1·65-2·65]), more educated (primary prevention, RR 1·61 [1·09-2·37]; secondary prevention, RR 1·28 [0·97-1·69]), and lived in urban areas (secondary prevention only, RR 0·82 [0·66-1·00]). In a diverse sample of LMICs, statins are used by about one in ten eligible people for the primary prevention of cardiovascular diseases and one in five eligible people for secondary prevention. There is an urgent need to scale up statin use in LMICs to achieve WHO targets. Policies and programmes that facilitate implementation of statins into primary health systems in these settings should be investigated for the future. National Clinician Scholars Program at the University of Michigan Institute for Healthcare Policy and Innovation, and National Institute of Diabetes and Digestive and Kidney Diseases. For the Spanish translation of the abstract see Supplementary Materials section.

More than 40% of children under 5 years of age in low-income and middle-income countries are at risk of not reaching their developmental potential. The international Guide for Monitoring Child Development (GMCD) early intervention package is a comprehensive programme to address developmental difficulties using an individualised intervention plan for young children and their families. We will conduct a hybrid type 1 effectiveness-implementation evaluation of the GMCD intervention in rural India and Guatemala. Using a cluster-randomised design, 624 children aged 0-24 months in 52 clusters (26 in India, 26 in Guatemala) will be assigned to usual care or the GMCD intervention plus usual care delivered by frontline workers for 12 months. After 12 months, the usual care arm will cross over to the intervention, which will continue for 12 additional months (24 total). The intervention will be delivered using a digital mobile device interface. Effectiveness will be assessed for developmental functioning (Bayley Scales of Infant Development, 3rd edition) and nurturing care (Home Observation for Measurement of the Environment Scale) outcomes. Implementation will be assessed using the Reach, Effectiveness, Adoption, Implementation, Maintenance framework. Explanatory qualitative analysis guided by the Consolidated Framework for Implementation Research will explore determinants between clusters with high versus low implementation effectiveness. The study has been approved by the Institutional Review Boards of Brigham and Women's Hospital, Mahatma Gandhi Institute of Medical Sciences and Maya Health Alliance; and by the Indian Council of Medical Research/Health Ministry Screening Committee. Key study findings will be published in international open-access journals. NCT04665297, CTRI/2020/12/029748. 1.0 (12 November 2020).

Rural Indigenous Maya communities in Guatemala have some of the worst obstetrical health outcomes in Latin America, due to widespread discrimination in healthcare and an underfunded public sector. Multiple systems-level efforts to improve facility birth outcomes have been implemented, primarily focusing on early community-based detection of obstetrical complications and on reducing discrimination and improving the quality of facility-level care. However, another important feature of public facility-level care are the out-of-pocket payments that patients are often required to make for care. To estimate the burden of out-of-pocket costs for public obstetrical care in Indigenous Maya communities in Guatemala. We conducted a retrospective review of electronic medical record data on obstetrical referrals collected as part of an obstetrical care navigation intervention, which included documentation of out-of-pocket costs by care navigators accompanying patients within public facilities. We compared the median costs for both emergency and routine obstetrical facility care. Cost data on 709 obstetric referrals from 479 patients were analyzed (65% emergency and 35% routine referrals). The median OOP costs were Q100 (IQR 75-150) [$13 USD] and Q50 (IQR 16-120) [$6.50 USD] for emergency and routine referrals. Costs for transport were most common (95% and 55%, respectively). Costs for medication, supply, laboratory, and imaging costs occurred less frequently. Food and lodging costs were minimal. Out-of-pocket payments for theoretically free public care are a common and important barrier to care for this rural Guatemalan setting. These data add to the literature in Latin American on the barriers to obstetrical care faced by Indigenous and rural women.

The majority of indigenous Guatemalan women give birth at home with traditional birth attendants (TBAs), and maternal mortality rates are high (Ministerio de Salud, 2017). Our objective was to better understand decision-making around whether to remain in the home or to seek facility-level care for obstetric complications. This study was a qualitative analysis using semi-structured interviews in a Maya population in the Western Highlands of Guatemala who received prenatal care between April 2017 and December 2018. We used qualitative interviews with women who were identified as medically high-risk and needing facility-level care, offered assistance with acquiring such care, and yet declined this option. Women interviewed were connected to a primary care organization called Maya Health Alliance, through care with TBAs involved in a program utilizing a smartphone-based decision support application to identify maternal and neonatal complications of pregnancy. Interviews were analyzed using Dedoose (www.dedoose.com). Deductive and inductive analysis was performed. Barriers to care included a disagreement between the respondent and TBA about indications for facility care, fear of hospital care, concerns about the quality of hospital care, logistical obstacles, and lack of control; and they were more often described by respondents who had previous healthcare experiences. Therapeutic misalignment occurred more with conditions perceived to be less severe. Participants described a balancing of fears and apprehensions against concerns of low quality and disrespectful maternity care, and in the setting of emergent conditions, disregarded barriers that were often described as inhibiting non-urgent obstetric care. The decision to engage in medical care in this population of Maya women involves a weighing of the perception of seriousness of the medical complication against fears of facility level care and concerns of a poor quality of care.

Global disparities in maternal mortality could be reduced by universal facility delivery. Yet, deficiencies in the quality of care prevent some mothers from seeking facility-based obstetric care. Obstetric care navigators (OCNs) are a new form of lay health workers that combine elements of continuous labor support and care navigation to promote obstetric referrals. Here we report qualitative results from the pilot OCN project implemented in Indigenous villages in the Guatemalan central highlands. We conducted semi-structured interviews with 17 mothers who received OCN accompaniment and 13 staff-namely physicians, nurses, and social workers-of the main public hospital in the pilot's catchment area (Chimaltenango). Interviews queried OCN's impact on patient and hospital staff experience and understanding of intended OCN roles. Audiorecorded interviews were transcribed, coded, and underwent content analysis. Maternal fear of surgical intervention, disrespectful and abusive treatment, and linguistic barriers were principal deterrents of care seeking. Physicians and nurses reported cultural barriers, opposition from family, and inadequate hospital resources as challenges to providing care to Indigenous mothers. Patient and hospital staff identified four valuable services offered by OCNs: emotional support, patient advocacy, facilitation of patient-provider communication, and care coordination. While patients and most physicians felt that OCNs had an overwhelmingly positive impact, nurses felt their effort would be better directed toward traditional nursing tasks. Many barriers to maternity care exist for Indigenous mothers in Guatemala. OCNs can improve mothers' experiences in public hospitals and reduce limitations faced by providers. However, broader buy-in from hospital staff-especially nurses-appears critical to program success. Future research should focus on measuring the impact of obstetric care navigation on key clinical outcomes (cesarean delivery) and mothers' future care seeking behavior.

Routine blood pressure (BP) measurement in pregnancy is commonly performed using automated oscillometric devices. Since no wireless oscillometric BP device has been validated in preeclamptic populations, a simple approach for capturing readings from such devices is needed, especially in low-resource settings where transmission of BP data from the field to central locations is an important mechanism for triage. To this end, a total of 8192 BP readings were captured from the Liquid Crystal Display (LCD) screen of a standard Omron M7 self-inflating BP cuff using a cellphone camera. A cohort of 49 lay midwives captured these data from 1697 pregnant women carrying singletons between 6 weeks and 40 weeks gestational age in rural Guatemala during routine screening. Images exhibited a wide variability in their appearance due to variations in orientation and parallax; environmental factors such as lighting, shadows; and image acquisition factors such as motion blur and problems with focus. Images were independently labeled for readability and quality by three annotators (BP range: 34-203 mm Hg) and disagreements were resolved. Methods to preprocess and automatically segment the LCD images into diastolic BP, systolic BP and heart rate using a contour-based technique were developed. A deep convolutional neural network was then trained to convert the LCD images into numerical values using a multi-digit recognition approach. On readable low- and high-quality images, this proposed approach achieved a 91% classification accuracy and mean absolute error of 3.19 mm Hg for systolic BP and 91% accuracy and mean absolute error of 0.94 mm Hg for diastolic BP. These error values are within the FDA guidelines for BP monitoring when poor quality images are excluded. The performance of the proposed approach was shown to be greatly superior to state-of-the-art open-source tools (Tesseract and the Google Vision API). The algorithm was developed such that it could be deployed on a phone and work without connectivity to a network.

We propose that the collection of infant experiential and environmental data using smartphone surveys has the potential to fill a gap in foundational and clinical knowledge. To achieve this, these data need to be collected in a systematic way that is translatable globally. We can then begin to understand differences in child development and physical therapy from a variety of cultures and traditions. An infant's development is shaped by experiences in everyday life, and everyday experiences vary around the world. Hence, it is important to quantify these experiences to better understand variability in developmental trajectories. Recent increase in smartphone access has made the capability of collecting infant experiential data more feasible around the world. We provide examples and suggestions for ways in which experiential and environmental data can be collected for future practice.

There is limited evidence regarding the utility of fetal monitoring during pregnancy, particularly during labor and delivery. Developed countries rely on consensus 'best practices' of obstetrics and gynecology professional societies to guide their protocols and policies. Protocols are often driven by the desire to be as safe as possible and avoid litigation, regardless of the cost of downstream treatment. In high-resource settings, there may be a justification for this approach. In low-resource settings, in particular, interventions can be costly and lead to adverse outcomes in subsequent pregnancies. Therefore, it is essential to consider the evidence and cost of different fetal monitoring approaches, particularly in the context of treatment and care in low-to-middle income countries. This article reviews the standard methods used for fetal monitoring, with particular emphasis on fetal cardiac assessment, which is a reliable indicator of fetal well-being. An overview of fetal monitoring practices in low-to-middle income counties, including perinatal care access challenges, is also presented. Finally, an overview of how mobile technology may help reduce barriers to perinatal care access in low-resource settings is provided.

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.

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. 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. 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; [Formula: see text]). 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.

Many maternal and perinatal deaths in low-resource settings are preventable. Inadequate access to timely, quality care in maternity facilities drives poor outcomes, especially where women deliver at home with traditional birth attendants (TBA). Yet few solutions exist to support TBA-initiated referrals or address reasons patients frequently refuse facility care, such as disrespectful and abusive treatment. We hypothesised that deploying accompaniers-obstetric care navigators (OCN)-trained to provide integrated patient support would facilitate referrals from TBAs to public hospitals. This project built on an existing collaboration with 41 TBAs who serve indigenous Maya villages in Guatemala's Western Highlands, which provided baseline data for comparison. When TBAs detected pregnancy complications, families were offered OCN referral support. Implementation was guided by bimonthly meetings of the interdisciplinary quality improvement team where the OCN role was iteratively tailored. The primary process outcomes were referral volume, proportion of births receiving facility referral, and referral success rate, which were analysed using statistical process control methods. Over the 12-month pilot, TBAs attended 847 births. The median referral volume rose from 14 to 27.5, meeting criteria for special cause variation, without a decline in success rate. The proportion of births receiving facility-level care increased from 24±6% to 62±20% after OCN implementation. Hypertensive disorders of pregnancy and prolonged labour were the most common referral indications. The OCN role evolved to include a number of tasks, such as expediting emergency transportation and providing doula-like labour support. OCN accompaniment increased the proportion of births under TBA care that received facility-level obstetric care. Results from this of obstetric care navigation suggest it is a feasible, patient-centred intervention to improve maternity care.

[Abstract not available]

[Abstract not available]

Guatemala's indigenous Maya population has one of the highest perinatal and maternal mortality rates in Latin America. In this population most births are delivered at home by traditional birth attendants (TBAs), who have limited support and linkages to public hospitals. The goal of this study was to characterize the detection of maternal and perinatal complications and rates of facility-level referral by TBAs, and to evaluate the impact of a mHealth decision support system on these rates. A pragmatic one-year feasibility trial of an mHealth decisions support system was conducted in rural Maya communities in collaboration with TBAs. TBAs were individually randomized in an unblinded fashion to either early-access or later-access to the mHealth system. TBAs in the early-access arm used the mHealth system throughout the study. TBAs in the later-access arm provided usual care until crossing over uni-directionally to the mHealth system at the study midpoint. The primary study outcome was the monthly rate of referral to facility-level care, adjusted for birth volume. Forty-four TBAs were randomized, 23 to the early-access arm and 21 to the later-access arm. Outcomes were analyzed for 799 pregnancies (early-access 425, later-access 374). Monthly referral rates to facility-level care were significantly higher among the early-access arm (median 33 referrals per 100 births, IQR 22-58) compared to the later-access arm (median 20 per 100, IQR 0-30) (p = 0.03). At the study midpoint, the later-access arm began using the mHealth platform and its referral rates increased (median 34 referrals per 100 births, IQR 5-50) with no significant difference from the early-access arm (p = 0.58). Rates of complications were similar in both arms, except for hypertensive disorders of pregnancy, which were significantly higher among TBAs in the early-access arm (RR 3.3, 95% CI 1.10-9.86). Referral rates were higher when TBAs had access to the mHealth platform. The introduction of mHealth supportive technologies for TBAs is feasible and can improve detection of complications and timely referral to facility-care within challenging healthcare delivery contexts. Clinicaltrials.gov NCT02348840 .

[Abstract not available]

Disrespectful and abusive maternity care is a common and pervasive problem that disproportionately impacts marginalized women. By making mothers less likely to agree to facility-based delivery, it contributes to the unacceptably high rates of maternal mortality in low- and middle-income countries. Few programmatic approaches have been proposed to address disrespectful and abusive maternity care. Care navigation was pioneered by the field of oncology to improve health outcomes of vulnerable populations and promote patient autonomy by providing linkages across a fragmented care continuum. Here we describe the novel application of the care navigation model to emergency obstetric referrals to hospitals for complicated home births in rural Guatemala. Care navigators offer women accompaniment and labor support intended to improve the care experience-for both patients and providers-and to decrease opposition to hospital-level obstetric care. Specific roles include deflecting mistreatment from hospital staff, improving provider communication through language and cultural interpretation, advocating for patients' right to informed consent, and protecting patients' dignity during the birthing process. Care navigators are specifically chosen and trained to gain the trust and respect of patients, traditional midwives, and biomedical providers. We describe an ongoing obstetric care navigator pilot program employing rapid-cycle quality improvement methods to quickly identify implementation successes and failures. This approach empowers frontline health workers to problem solve in real time and ensures the program is highly adaptable to local needs. Care navigation is a promising strategy to overcome the "humanistic barrier" to hospital delivery by mitigating disrespectful and abusive care. It offers a demand-side approach to undignified obstetric care that empowers the communities most impacted by the problem to lead the response. Results from an ongoing pilot program of obstetric care navigation will provide valuable feedback from patients on the impact of this approach and implementation lessons to facilitate replication in other settings.

Technology provides the potential to empower frontline healthcare workers with low levels of training and literacy, particularly in low- and middle-income countries. An obvious platform for achieving this aim is the smartphone, a low cost, almost ubiquitous device with good supply chain infrastructure and a general cultural acceptance for its use. In particular, the smartphone offers the opportunity to provide augmented or procedural information through active audiovisual aids to illiterate or untrained users, as described in this article. In this article, the process of refinement and iterative design of a smartphone application prototype to support perinatal surveillance in rural Guatemala for indigenous Maya lay midwives with low levels of literacy and technology exposure is described. Following on from a pilot to investigate the feasibility of this system, a two-year project to develop a robust in-field system was initiated, culminating in a randomized controlled trial of the system, which is ongoing. The development required an agile approach, with the development team working both remotely and in country to identify and solve key technical and cultural issues in close collaboration with the midwife end-users. This article describes this process and intermediate results. The application prototype was refined in two phases, with expanding numbers of end-users. Some of the key weaknesses identified in the system during the development cycles were user error when inserting and assembling cables and interacting with the 1-D ultrasound-recording interface, as well as unexpectedly poor bandwidth for data uploads in the central healthcare facility. Safety nets for these issues were developed and the resultant system was well accepted and highly utilized by the end-users. To evaluate the effectiveness of the system after full field deployment, data quality, and corruption over time, as well as general usage of the system and the volume of application support for end-users required by the in-country team was analyzed. Through iterative review of data quality and consistent use of user feedback, the volume and percentage of high quality recordings was increased monthly. Final analysis of the impact of the system on obstetrical referral volume and maternal and neonatal clinical outcomes is pending conclusion of the ongoing clinical trial.

Limited funding for medical technology, low levels of education and poor infrastructure for delivering and maintaining technology severely limit medical decision support in low- and middle-income countries. Perinatal and maternal mortality is of particular concern with millions dying every year from potentially treatable conditions. Guatemala has one of the worst maternal mortality ratios, the highest incidence of intra-uterine growth restriction (IUGR), and one of the lowest gross national incomes per capita within Latin America. To address the lack of decision support in rural Guatemala, a smartphone-based system is proposed including peripheral sensors, such as a handheld Doppler for the identification of foetal compromise. Designed for use by illiterate birth attendants, the system uses pictograms, audio guidance, local and cloud processing, SMS alerts and voice calling. The initial prototype was evaluated on 22 women in highland Guatemala. Results were fed back into the refinement of the system, currently undergoing RCT evaluation.

A recent article in GHSP calls for classifying fertility awareness methods as “modern contraceptives” despite their inferiority. We believe in a rights-based approach, which considers the real-world conditions that many women face, including constrained sexual agency and low baseline reproductive health literacy. We must demonstrate true commitment to increasing access to the most effective and reliable contraceptive methods. A recent article in GHSP calls for classifying fertility awareness methods as “modern contraceptives” despite their inferiority. We believe in a rights-based approach, which considers the real-world conditions that many women face, including constrained sexual agency and low baseline reproductive health literacy. We must demonstrate true commitment to increasing access to the most effective and reliable contraceptive methods.

to examine the present-day knowledge formation and practice of indigenous Kaqchikel-speaking midwives, with special attention to their interactions with the Guatemalan medical community, training models, and allopathic knowledge in general. a qualitative study consisting of participant-observation in lay midwife training programs; in-depth interviews with 44 practicing indigenous midwives; and three focus groups with midwives of a local non-governmental organization. Kaqchikel Maya-speaking communities in the Guatemalan highlands. the cumulative undermining effects of marginalization, cultural and linguistic barriers, and poorly designed training programs contribute to the failure of lay midwife-focused initiatives in Guatemala to improve maternal-child health outcomes. Furthermore, in contrast to prevailing assumptions, Kaqchikel Maya midwives integrate allopathic obstetrical knowledge into their practice at a high level. as indigenous midwives in Guatemala will continue to provide a large fraction of the obstetrical services among rural populations for many years to come, maternal-child policy initiatives must take into account that: (1)Guatemalan midwife training programs can be significantly improved when instruction occurs in local languages, such as Kaqchikel, and (2)indigenous midwives' increasing allopathic repertoire may serve as a productive ground for synergistic collaborations between lay midwives and the allopathic medical community.