The influence of maternal agency on severe child undernutrition in conflict-ridden Nigeria: Modeling heterogeneous treatment effects with machine learning

Experiences and Perceptions of Maternal Autonomy and Racism Among BIPOC Veterans Receiving Cesarean Sections

BACKGROUND

Previous studies of pregnant veterans enrolled in Department of Veterans Affairs (VA) care reveal high rates of cesarean sections among racial/ethnic minoritized groups, particularly in southern states. The purpose of this study was to better understand contributors to and veteran perceptions of maternal autonomy and racism among veterans receiving cesarean sections.

METHODS

We conducted semi-structured interviews to understand perceptions of maternal autonomy and racism among 27 Black, Indigenous, People of Color (BIPOC) veterans who gave birth via cesarean section using VA maternity care benefits.

RESULTS

Our study found that a substantial proportion (67%) of veterans had previous cesarean sections, ultimately placing them at risk for subsequent cesarean sections. More than 60% of veterans with a previous cesarean section requested a labor after cesarean (LAC) but were either refused by their provider or experienced complications that led to another cesarean section. Qualitative findings revealed the following: (1) differences in treatment by veterans' race/ethnicity may reduce maternal agency, (2) many veterans felt unheard and uninformed regarding birthing decisions, (3) access to VA-paid doula care may improve maternal agency for BIPOC veterans during labor and birth, and (4) BIPOC veterans face substantial challenges related to social determinants of health.

CONCLUSION

Further research should examine veterans' perceptions of racism in obstetrical care, and the possibility of VA-financed doula care to provide additional labor support to BIPOC veterans.

On AI Approaches for Promoting Maternal and Neonatal Health in Low Resource Settings: A Review

A significant challenge for hospitals and medical practitioners in low- and middle-income nations is the lack of sufficient health care facilities for timely medical diagnosis of chronic and deadly diseases. Particularly, maternal and neonatal morbidity due to various non-communicable and nutrition related diseases is a serious public health issue that leads to several deaths every year. These diseases affecting either mother or child can be hospital-acquired, contracted during pregnancy or delivery, postpartum and even during child growth and development. Many of these conditions are challenging to detect at their early stages, which puts the patient at risk of developing severe conditions over time. Therefore, there is a need for early screening, detection and diagnosis, which could reduce maternal and neonatal mortality. With the advent of Artificial Intelligence (AI), digital technologies have emerged as practical assistive tools in different healthcare sectors but are still in their nascent stages when applied to maternal and neonatal health. This review article presents an in-depth examination of digital solutions proposed for maternal and neonatal healthcare in low resource settings and discusses the open problems as well as future research directions.

The future of human malnutrition: rebalancing agency for better nutritional health

The major threat to human societies posed by undernutrition has been recognised for millennia. Despite substantial economic development and scientific innovation, however, progress in addressing this global challenge has been inadequate. Paradoxically, the last half-century also saw the rapid emergence of obesity, first in high-income countries but now also in low- and middle-income countries. Traditionally, these problems were approached separately, but there is increasing recognition that they have common drivers and need integrated responses. The new nutrition reality comprises a global ‘double burden’ of malnutrition, where the challenges of food insecurity, nutritional deficiencies and undernutrition coexist and interact with obesity, sedentary behaviour, unhealthy diets and environments that foster unhealthy behaviour. Beyond immediate efforts to prevent and treat malnutrition, what must change in order to reduce the future burden? Here, we present a conceptual framework that focuses on the deeper structural drivers of malnutrition embedded in society, and their interaction with biological mechanisms of appetite regulation and physiological homeostasis. Building on a review of malnutrition in past societies, our framework brings to the fore the power dynamics that characterise contemporary human food systems at many levels. We focus on the concept of agency, the ability of individuals or organisations to pursue their goals. In globalized food systems, the agency of individuals is directly confronted by the agency of several other types of actor, including corporations, governments and supranational institutions. The intakes of energy and nutrients by individuals are powerfully shaped by this ‘competition of agency’, and we therefore argue that the greatest opportunities to reduce malnutrition lie in rebalancing agency across the competing actors. The effect of the COVID-19 pandemic on food systems and individuals illustrates our conceptual framework. Efforts to improve agency must both drive and respond to complementary efforts to promote and maintain equitable societies and planetary health.

Machine learning algorithms for predicting undernutrition among under-five children in Ethiopia

OBJECTIVE

Child undernutrition is a global public health problem with serious implications. In this study, we estimate predictive algorithms for the determinants of childhood stunting by using various machine learning (ML) algorithms.

DESIGN

This study draws on data from the Ethiopian Demographic and Health Survey of 2016. Five ML algorithms including eXtreme gradient boosting, k-nearest neighbours (k-NN), random forest, neural network and the generalised linear models were considered to predict the socio-demographic risk factors for undernutrition in Ethiopia.

SETTING

Households in Ethiopia.

PARTICIPANTS

A total of 9471 children below 5 years of age participated in this study.

RESULTS

The descriptive results show substantial regional variations in child stunting, wasting and underweight in Ethiopia. Also, among the five ML algorithms, xgbTree algorithm shows a better prediction ability than the generalised linear mixed algorithm. The best predicting algorithm (xgbTree) shows diverse important predictors of undernutrition across the three outcomes which include time to water source, anaemia history, child age greater than 30 months, small birth size and maternal underweight, among others.

CONCLUSIONS

The xgbTree algorithm was a reasonably superior ML algorithm for predicting childhood undernutrition in Ethiopia compared to other ML algorithms considered in this study. The findings support improvement in access to water supply, food security and fertility regulation, among others, in the quest to considerably improve childhood nutrition in Ethiopia.

A scoping review on the use of machine learning in research on social determinants of health: Trends and research prospects

Background

Machine learning (ML) has spread rapidly from computer science to several disciplines. Given the predictive capacity of ML, it offers new opportunities for health, behavioral, and social scientists. However, it remains unclear how and to what extent ML is being used in studies of social determinants of health (SDH).

Methods

Using four search engines, we conducted a scoping review of studies that used ML to study SDH (published before May 1, 2020). Two independent reviewers analyzed the relevant studies. For each study, we identified the research questions, Results, data, and algorithms. We synthesized our findings in a narrative report.

Results

Of the initial 8097 hits, we identified 82 relevant studies. The number of publications has risen during the past decade. More than half of the studies (n = 46) used US data. About 80% (n = 66) utilized surveys, and 70% (n = 57) employed ML for common prediction tasks. Although the number of studies in ML and SDH is growing rapidly, only a few studies used ML to improve causal inference, curate data, or identify social bias in predictions (i.e., algorithmic fairness).

Conclusions

While ML equips researchers with new ways to measure health outcomes and their determinants from non-conventional sources such as text, audio, and image data, most studies still rely on traditional surveys. Although there are no guarantees that ML will lead to better social epidemiological research, the potential for innovation in SDH research is evident as a result of harnessing the predictive power of ML for causality, data curation, or algorithmic fairness.

Identifying Factors Associated with Neonatal Mortality in Sub-Saharan Africa using Machine Learning

This study aimed at identifying the factors associated with neonatal mortality. We analyzed the Demographic and Health Survey (DHS) datasets from 10 Sub-Saharan countries. For each survey, we trained machine learning models to identify women who had experienced a neonatal death within the 5 years prior to the survey being administered. We then inspected the models by visualizing the features that were important for each model, and how, on average, changing the values of the features affected the risk of neonatal mortality. We confirmed the known positive correlation between birth frequency and neonatal mortality and identified an unexpected negative correlation between household size and neonatal mortality. We further established that mothers living in smaller households have a higher risk of neonatal mortality compared to mothers living in larger households; and that factors such as the age and gender of the head of the household may influence the association between household size and neonatal mortality.

The role of evaluation in iterative learning and implementation of quality of care interventions

Background: The Lancet Global Health Commission (LGHC) has argued that quality of care (QoC) is an emergent property that requires an iterative process to learn and implement. Such iterations are required given that health systems are complex adaptive systems.Objective: This paper explores the multiple roles that evaluations need to play in order to help with iterative learning and implementation. We argue evaluation needs to shift from a summative focus toward an approach that promotes learning in complex systems. A framework is presented to help guide the iterative learning, and includes the dimensions of clinical care, person-centered care, continuum of care, and 'more than medicine. Multiple roles of evaluation corresponding to each of the dimensions are discussed.Methods: This paper is informed by reviews of the literature on QoC and the roles of evaluation in complex systems. The proposed framework synthesizes the multiple views of QoC. The recommendations of the roles of evaluation are informed both by review and experience in evaluating multiple QoC initiatives.Results: The specific roles of different evaluation approaches, including summative, realist, developmental, and participatory, are identified in relationship to the dimensions in our proposed framework. In order to achieve the potential of LGHC, there is a need to discuss how different evaluation approaches can be combined in a coherent way to promote iterative learning and implementation of QoC initiatives.Conclusion: One of the implications of the QoC framework discussed in the paper is that time needs to be spent upfront in recognizing areas in which knowledge of a specific intervention is not complete at the outset. This, of course, implies taking stock of areas of incompleteness in knowledge of context, theory of change, support structures needed in order for the program to succeed in specific settings. The role of evaluation should not be limited to only providing an external assessment, but an important goal in building evaluation capacity should be to promote adaptive management among planners and practitioners. Such iterative learning and adaptive management are needed to achieve the goals of sustainable development goals.

Mother's education level is associated with anthropometric failure among 3- to 12-year-old rural children in Purba Medinipur, West Bengal, India

Maternal education plays a central role in children's health and nutrition. Living conditions and socioeconomic status are linked with mother's education, which in turn determines the health and development of a child. The Composite Index of Anthropometric Failure (CIAF) is a single indicator that reflects overall rate of three conventional indices of undernutrition: underweight, stunting and wasting. The study was undertaken among 621 rural Bengalee children (308 boys and 313 girls) aged 3-12 years from the Purba Medinipur district of West Bengal, India. Height (cm) and weight (kg) were recorded and NCHS standard values used to calculate z-scores (<-2SD). The same data were used to calculate CIAF as an indicator of 'anthropometric failure' (AF) or undernutrition. The prevalence of AF among the children was 59.40%. Chi-squared analysis was employed to evaluate the significance of differences in the prevalence of CIAF between the sexes and the association between nutritional indicators and socioeconomic parameters in the two sexes. Multiple binary logistic regression (MBLR) analyses (including the forward stepwise method) were also performed. Odds ratios with 95% confidence intervals were used to assess the risk of having AF. Results showed that mother's education was significantly associated with undernutrition (AF) controlling for the other factors considered. A very high prevalence of undernutrition is persisting in this region of India despite national nutritional supplementation programmes being operational. More attention to the improvement of living conditions and hygiene, and more particularly the education of women, in this population might be effective in attaining improved child growth and health.