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Low birth weight is one of the leading factors for infant morbidity and mortality. To a large extent affect, various maternal risk factors are associated with pregnancy outcomes by increasing odds of delivering an infant with low birth weight. Despite this association, understanding the maternal risk factors affecting term low birth weight has been a challenging task. To date, limited studies have been conducted in India that exert independent magnitude of these effects on term low birth weight. The aim of this review is to examine the current knowledge of maternal risk factors that contribute to term low birth weight in the Indian population. In order to identify the potentially relevant articles, an extensive literature search was conducted using PubMed, Goggle Scholar and IndMed databases (1993 – Dec 2020). Our results indi­cate that maternal age, educational status, socio-economic status, ethnicity, parity, pre-pregnancy weight, maternal stature, maternal body mass index, obstetric history, maternal anaemia, gestational weight gain, short pregnancy outcome, hypertension during pregnancy, infection, antepartum haemorrhage, tobacco consumption, maternal occupation, maternal psychological stress, alcohol consumption, antenatal care and mid-upper arm circumference have all independent effects on term low birth weight in the Indian pop­ulation. Further, we argue that exploration for various other dimensions of maternal factors and underlying pathways can be useful for a better understanding of how it exerts independent association on term low birth weight in the Indian sub-continent.

IntroductionPregnancy is characterised by a high rate of metabolic shifts from early to late phases of gestation in order to meet the raised physiological and metabolic needs. This change in levels of metabolites is influenced by gestational weight gain (GWG), which is an important characteristic of healthy pregnancy. Inadequate/excessive GWG has short-term and long-term implications on maternal and child health. Exploration of gestational metabolism is required for understanding the quantitative changes in metabolite levels during the course of pregnancy. Therefore, our aim is to study trimester-specific variation in levels of metabolites in relation to GWG and its influence on fetal growth and newborn anthropometric traits at birth.Methods and analysisA prospective longitudinal study is planned (start date: February 2018; end date: March 2023) on pregnant women that are being recruited in the first trimester and followed in subsequent trimesters and at the time of delivery (total 3 follow-ups). The study is being conducted in a hospital located in Bikaner district (66% rural population), Rajasthan, India. The estimated sample size is of 1000 mother-offspring pairs. Information on gynaecological and obstetric history, socioeconomic position, diet, physical activity, tobacco and alcohol consumption, depression, anthropometric measurements and blood samples is being collected for metabolic assays in each trimester using standardised methods. Mixed effects regression models will be used to assess the role of gestational weight in influencing metabolite levels in each trimester. The association of maternal levels of metabolites with fetal growth, offspring’s weight and body composition at birth will be investigated using regression modelling.Ethics and disseminationThe study has been approved by the ethics committees of the Department of Anthropology, University of Delhi and Sardar Patel Medical College, Rajasthan. We are taking written informed consent after discussing the various aspects of the study with the participants in the local language.

Machine learning approaches are utilized in healthcare for computational decision-making in cases where critical medical data analysis is required to identify hidden linkages or anomalies that are not evident to humans. Artificial intelligence (AI) tools can assess a wide range of health data; patient data from multi-omics methods; clinical, behavioural, environmental, pharmacological data; and data from the biomedical literature to respond to research issues that necessitate a big sample size on a difficult-to-reach population. In healthcare, digitising health data has eased the development of computational models and AI systems to extract insights from the data. This chapter initially addressed the prospectus of machine learning in public health with significant focus areas. The medical devices and equipment section contain device-based modelling approaches to various diseases. The chapter also includes brief details on chatbots, wearable technologies, drug distribution systems, vending machines, and text recognition from prescriptions and medicine boxes are addressed.