Risk factors for labour induction and augmentation: a multicentre prospective cohort study in India.

Background: Guidelines for labour induction/augmentation involve evaluating maternal and fetal complications, and allowing informed decisions from pregnant women. This study aimed to comprehensively explore clinical and non-clinical factors influencing labour induction and augmentation in an Indian population. Methods: A prospective cohort study included 9305 pregnant women from 13 hospitals across India. Self-reported maternal socio-demographic and lifestyle factors, and maternal medical and obstetric histories from medical records were obtained at recruitment (≥28 weeks of gestation), and women were followed up within 48 h after childbirth. Maternal and fetal clinical information were classified based on guidelines into four groups of clinical factors: (i) ≥2 indications, (ii) one indication, (iii) no indication and (iv) contraindication. Associations of clinical and non-clinical factors (socio-demographic, healthcare utilisation and lifestyle related) with labour induction and augmentation were investigated using multivariable logistic regression analyses. Findings: Over two-fifths (n = 3936, 42.3%, 95% confidence interval [CI] 41.3-43.3%) of the study population experienced labour induction and more than a quarter (n = 2537, 27.3%, 95% CI 26.4-28.2%) experienced augmentation. Compared with women with ≥2 indications, those with one (adjusted odds ratio [aOR] 0.50, 95% CI 0.42-0.58) or no indication (aOR 0.24, 95% CI 0.20-0.28) or with contraindications (aOR 0.12, 95% CI 0.07-0.20) were less likely to be induced, adjusting for non-clinical characteristics. These associations were similar for labour augmentation. Notably, 34% of women who were induced or augmented did not have any clinical indication. Several maternal demographic (age at labour, parity and body mass index in early pregnancy), healthcare utilization (number of antenatal check-ups, duration of iron-folic acid supplementation and individuals managing childbirth) and socio-economic factors (religion, living below poverty line, maternal education and partner's occupation) were independently associated with labour induction and augmentation. Interpretation: Although decisions about induction and augmentation of labour in our study population in India were largely guided by clinical recommendations, we cannot ignore that more than a third of the women did not have an indication. Decisions could also be influenced by non-clinical factors which need further research. Funding: The MaatHRI platform is funded by a Medical Research Council Career Development Award (Grant Ref: MR/P022030/1) and a Transition Support Award (Grant Ref: MR/W029294/1).

Maternal and fetal factors associated with stillbirth in singleton pregnancies in 13 hospitals across six states in India: A prospective cohort study.

OBJECTIVE: This study aimed to investigate the incidence of and risk factors for stillbirth in an Indian population. METHODS: We conducted a secondary data analysis of a hospital-based cohort from the Maternal and Perinatal Health Research collaboration, India (MaatHRI), including pregnant women who gave birth between October 2018-September 2023. Data from 9823 singleton pregnancies recruited from 13 hospitals across six Indian states were included. Univariable and multivariable Poisson regression analysis were performed to examine the relationship between stillbirth and potential risk factors. Model prediction was assessed using the area under the receiver-operating characteristic (AUROC) curve. RESULTS: There were 216 stillbirths (48 antepartum and 168 intrapartum) in the study population, representing an overall stillbirth rate of 22.0 per 1000 total births (95% confidence interval [CI]: 19.2-25.1). Modifiable risk factors for stillbirth were: receiving less than four antenatal check-ups (adjusted relative risk [aRR]: 1.75, 95% CI: 1.25-2.47), not taking any iron and folic acid supplementation during pregnancy (aRR: 7.23, 95% CI: 2.12-45.33) and having severe anemia in the third trimester (aRR: 3.37, 95% CI: 1.97-6.11). Having pregnancy/fetal complications such as hypertensive disorders of pregnancy (aRR: 1.59, 95% CI: 1.03-2.36), preterm birth (aRR: 4.41, 95% CI: 3.21-6.08) and birth weight below the 10th percentile for gestational age (aRR: 1.35, 95% CI: 1.02-1.79) were also associated with an increased risk of stillbirth. Identified risk factors explained 78.2% (95% CI: 75.0%-81.4%) of the risk of stillbirth in the population. CONCLUSION: Addressing potentially modifiable antenatal factors could reduce the risk of stillbirths in India.

Maternal and perinatal Health Research Collaboration, India (MaatHRI): methodology for establishing a hospital-based research platform in a low and middle income country setting.

Background: Maternal and perinatal Health Research collaboration, India (MaatHRI) is a research platform that aims to improve evidence-based pregnancy care and outcomes for mothers and babies in India, a country with the second highest burden of maternal and perinatal deaths. The objective of this paper is to describe the methods used to establish and standardise the platform and the results of the process. Methods: MaatHRI is a hospital-based collaborative research platform. It is adapted from the UK Obstetric Surveillance System (UKOSS) and built on a pilot model (IndOSS-Assam), which has been extensively standardised using the following methods: (i) establishing a network of hospitals; (ii) setting up a secure system for data collection, storage and transfer; (iii) developing a standardised laboratory infrastructure; and (iv) developing and implementing regulatory systems. Results: MaatHRI was established in September 2018. Fourteen hospitals participate across four states in India - Assam, Meghalaya, Uttar Pradesh and Maharashtra. The research team includes 20 nurses, a project manager, 16 obstetricians, two pathologists, a public health specialist, a general physician and a paediatrician. MaatHRI has advanced standardisation of data and laboratory parameters, real-time monitoring of data and participant safety, and secure transfer of data. Four observational epidemiological studies are presently being undertaken through the platform. MaatHRI has enabled bi-directional capacity building. It is overseen by a steering committee and a data safety and monitoring board, a process that is not normally used, but was found to be highly effective in ensuring data safety and equitable partnerships in the context of low and middle income countries (LMICs). Conclusion: MaatHRI is the first prototype of UKOSS and other similar platforms in a LMIC setting. The model is built on existing methods but applies new standardisation processes to develop a collaborative research platform that can be replicated in other LMICs.