Closing the gap on institutional delivery in northern India: a case study of how integrated machine learning approaches can enable precision public health.

INTRODUCTION: Meeting ambitious global health goals with limited resources requires a precision public health (PxPH) approach. Here we describe how integrating data collection optimisation, traditional analytics and causal artificial intelligence/machine learning (ML) can be used in a use case for increasing hospital deliveries of newborns in Uttar Pradesh, India. METHODS: Using a systematic behavioural framework we designed a large-scale survey on perceptual, interpersonal and structural drivers of women's behaviour around childbirth (n=5613). Multivariate logistic regression identified factors associated with institutional delivery (ID). Causal ML determined the cause-and-effect ordering of these factors. Variance decomposition was used to parse sources of variation in delivery location, and a supervised learning algorithm was used to distinguish population subgroups. RESULTS: Among the factors found associated with ID, the causal model showed that having a delivery plan (OR=6.1, 95% CI 6.0 to 6.3), believing the hospital is safer than home (OR=5.4, 95% CI 5.1 to 5.6) and awareness of financial incentives were direct causes of ID (OR=3.4, 95% CI 3.3 to 3.5). Distance to the hospital, borrowing delivery money and the primary decision-maker were not causal. Individual-level factors contributed 69% of variance in delivery location. The segmentation analysis showed four distinct subgroups differentiated by ID risk perception, parity and planning. CONCLUSION: These findings generate a holistic picture of the drivers and barriers to ID in Uttar Pradesh and suggest distinct intervention points for different women. This demonstrates data optimised to identify key behavioural drivers, coupled with traditional and ML analytics, can help design a PxPH approach that maximise the impact of limited resources.

Effectiveness of a brief community outreach tobacco cessation intervention in India: a cluster-randomised controlled trial (the BABEX Trial).

BACKGROUND: Tobacco use kills half a million people every month, most in low-middle income countries (LMICs). There is an urgent need to identify potentially low-cost, scalable tobacco cessation interventions for these countries. OBJECTIVE: To evaluate a brief community outreach intervention delivered by health workers to promote tobacco cessation in India. DESIGN: Cluster-randomised controlled trial. SETTING: 32 low-income administrative blocks in Delhi, half government authorised ('resettlement colony') and half unauthorised ('J.J. cluster') communities. PARTICIPANTS: 1213 adult tobacco users. INTERVENTIONS: Administrative blocks were computer randomised in a 1:1 ratio, to the intervention (16 clusters; n=611) or control treatment (16 clusters; n=602), delivered and assessed at individual level between 07/2012 and 11/2013. The intervention was single session quit advice (15 min) plus a single training session in yogic breathing exercises; the control condition comprised very brief quit advice (1 min) alone. Both were delivered via outreach, with contact made though household visits. MEASUREMENTS: The primary outcome was 6-month sustained abstinence from all tobacco, assessed 7 months post intervention delivery, biochemically verified with salivary cotinine. RESULTS: The smoking cessation rate was higher in the intervention group (2.6% (16/611)) than in the control group (0.5% (3/602)) (relative risk=5.32, 95% CI 1.43 to 19.74, p=0.013). There was no interaction with type of tobacco use (smoked vs smokeless). Results did not change materially in adjusted analyses, controlling for participant characteristics. CONCLUSIONS: A single session community outreach intervention can increase tobacco cessation in LMIC. The effect size, while small, could impact public health if scaled up with high coverage. TRIAL REGISTRATION NUMBER: ISRCTCN23362894.

A cluster randomized controlled trial of a brief tobacco cessation intervention for low-income communities in India: study protocol.

BACKGROUND: India has 275 million adult tobacco users and tobacco use is estimated to contribute to more than a million deaths in the country each year. There is an urgent need to develop and evaluate affordable, practicable and scalable interventions to promote cessation of tobacco use. Because tobacco use is so harmful, an increase of as little as 1 percentage point in long-term quit success rates can have an important public health impact. This protocol paper describes the rationale and methods of a large randomized controlled trial which aims to evaluate the effectiveness of a brief scalable smoking cessation intervention delivered by trained health professionals as an outreach programme in poor urban communities in India. METHODS/DESIGN: This is a pragmatic, two-arm, community-based cluster randomized controlled trial focused on tobacco users in low-income communities. The treatment arm is a brief intervention comprising brief advice including training in craving control using simple yogic breathing exercises (BA-YBA) and the control arm is very brief advice (VBA). Of a total of 32 clusters, 16 will be allocated to the intervention arm and 16 to the control arm. Each cluster will have 31 participants, making a total of 992 participants. The primary outcome measure will follow the Russell Standard: self-report of sustained abstinence for at least 6 months following the intervention confirmed at the final follow-up by salivary cotinine. DISCUSSION: This trial will inform national and international policy on delivery of scalable and affordable brief outreach interventions to promote tobacco use cessation in low resource settings where tobacco users have limited access to physicians and medications.

The acute effects of yogic breathing exercises on craving and withdrawal symptoms in abstaining smokers.

RATIONALE: Breathing exercises have been proposed as a way of combating cigarette cravings, potentially presenting a low-cost, easily scalable smoking cessation aid. OBJECTIVE: The aim of this study is to evaluate the acute impact of breathing exercises based on yogic pranayama on cravings in abstaining smokers. METHODS: Participants visited the laboratory on two occasions 24 h apart and were asked to abstain from smoking 12 h prior to the first visit until the end of the second visit. Smokers (N = 96) were randomly allocated to a yogic breathing exercise (YBG) or video control (VCG) group. The former was instructed on breathing exercises, practised these for 10 min and asked to use these when experiencing cravings until the next visit. The latter was shown a breathing exercise video for 10 min and asked to concentrate on their breathing. Strength of urges to smoke, other craving measures and mood and physical symptoms associated with cigarette withdrawal were assessed at the beginning and end of the first visit, and again at the second visit. RESULTS: At immediate follow-up, in the laboratory, all craving measures were reduced in YBG compared with VCG (strength of urges: F(1, 96) = 16.1, p < 0.001; cigarette craving: F(1, 96) = 11.3, p = 0.001; desire to smoke: F(1, 96) = 6.6, p = 0.012). There was no effect on mood or physical symptoms. Adherence to the breathing exercise regimen in the following 24 h was low, and at 24 h follow-up, there was no evidence of reduced cravings in YBG compared with VCG. CONCLUSIONS: Simple yogic-style breathing exercises can reduce cigarette craving acutely in the laboratory. Further research is needed to determine how far this translates into field settings.