In northern Nigeria, interventions are urgently needed to narrow the large gap between international breastfeeding recommendations and actual breastfeeding practices. Studies of integrated microcredit and community health interventions documented success in modifying health behaviors but typically had uncontrolled designs. We conducted a cluster-randomized controlled trial in Bauchi State, Nigeria, with the aim of increasing early breastfeeding initiation and exclusive breastfeeding among female microcredit clients. The intervention had 3 components. Trained credit officers led monthly breastfeeding learning sessions during regularly scheduled microcredit meetings for 10 mo. Text and voice messages were sent out weekly to a cell phone provided to small groups of microcredit clients (5-7 women). The small groups prepared songs or dramas about the messages and presented them at the monthly microcredit meetings. The control arm continued with the regular microcredit program. Randomization occurred at the level of the monthly meeting groups. Pregnant clients were recruited at baseline and interviewed again when their infants were aged ≥6 mo. Logistic regression models accounting for clustering were used to estimate the odds of performing recommended behaviors. Among the clients who completed the final survey (n = 390), the odds of exclusive breastfeeding to 6 mo (OR: 2.4; 95% CI: 1.4, 4.0) and timely breastfeeding initiation (OR: 2.6; 95% CI: 1.6, 4.1) were increased in the intervention vs. control arm. Delayed introduction of water explained most of the increase in exclusive breastfeeding among clients receiving the intervention. In conclusion, a breastfeeding promotion intervention integrated into microcredit increased the likelihood that women adopted recommended breastfeeding practices. This intervention could be scaled up in Nigeria, where local organizations provide microcredit to >500,000 clients. Furthermore, the intervention could be adopted more widely given that >150 million women, many of childbearing age, are involved in microfinance globally.

Epidemiologists often wish to estimate quantities that are easy to communicate and correspond to the results of realistic public health interventions. Methods from causal inference can answer these questions. We adopt the language of potential outcomes under Rubin’s original Bayesian framework and show that the parametric g-formula is easily amenable to a Bayesian approach. We show that the frequentist properties of the Bayesian g-formula suggest it improves the accuracy of estimates of causal effects in small samples or when data are sparse. We demonstrate an approach to estimate the effect of environmental tobacco smoke on body mass index among children aged 4–9 years who were enrolled in a longitudinal birth cohort in New York, USA. We provide an algorithm and supply SAS and Stan code that can be adopted to implement this computational approach more generally.

Summary Background. Many of an individual’s historically recorded personal measurements vary over time, thereby forming a time series (e.g., wearable-device data, self-tracked fitness or nutrition measurements, regularly monitored clinical events or chronic conditions). Statistical analyses of such n-of-1 (i.e., single-subject) observational studies (N1OSs) can be used to discover possible cause-e ect relationships to then self-test in an n-of-1 randomized trial (N1RT). However, a principled way of determining how and when to interpret an N1OS association as a causal effect (e.g., as if randomization had occurred) is needed. Objectives. Our goal in this paper is to help bridge the methodological gap between risk-factor discovery and N1RT testing by introducing a basic counterfactual framework for N1OS design and personalized causal analysis. Methods and Results. We introduce and characterize what we call the average period treatment effect (APTE), i.e., the estimand of interest in an N1RT, and build an analytical framework around it that can accommodate autocorrelation and time trends in the outcome, effect carryover from previous treatment periods, and slow onset or decay of the effect. The APTE is loosely defined as a contrast (e.g., difference, ratio) of averages of potential outcomes the individual can theoretically experience under different treatment levels during a given treatment period. To illustrate the utility of our framework for APTE discovery and estimation, two common causal inference methods are specified within the N1OS context. We then apply the framework and methods to search for estimable and interpretable APTEs using six years of the author’s self-tracked weight and exercise data, and report both the preliminary findings and the challenges we faced in conducting N1OS causal discovery. Conclusions. Causal analysis of an individual’s time series data can be facilitated by an N1RT counterfactual framework. However, for inference to be valid, the veracity of certain key assumptions must be assessed critically, and the hypothesized causal models must be interpretable and meaningful.

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Introduction E-cigarettes are popular and unregulated. Patient–provider communications concerning e-cigarettes were characterized to identify patient concerns, provider advice and attitudes, and research needs. Methods An observational study of online patient–provider communications was conducted January 2011–June 2015 from a network providing free medical advice, and analyzed July 2014–May 2016. Patient and provider themes, and provider attitudes toward e-cigarettes (positive, negative, or neutral) were coded qualitatively. Provider attitudes were analyzed with cumulative logit modeling to account for clustering. Patient satisfaction with provider responses was expressed via a Thank function. Results An increase in e-cigarette–related questions was observed over time. Patient questions (N=512) primarily concerned specific side effects and harms (34%), general safety (27%), e-cigarettes as quit aids (19%), comparison of e-cigarette harms relative to combusted tobacco (18%), use with pre-existing medical conditions (18%), and nicotine-free e-cigarettes (14%). Half of provider responses discussed e-cigarettes as a harm reduction option (48%); 26% discussed them as quit aids. Overall, 47% of providers’ responses represented a negative attitude toward e-cigarettes, 33% were neutral (contradictory or non-committal), and 20% were positive. Attitudes did not differ statistically by medical specialty; provider responses positive toward e-cigarettes received significantly more Thanks. Conclusions Examination of online patient–provider communications provides insight into consumer health experience with emerging alternative tobacco products. Patient concerns largely related to harms and safety, and patients preferred provider responses positively inclined toward e-cigarettes. Lacking conclusive evidence of e-cigarette safety or efficacy, healthcare provider encouraged smoking cessation and recommended first-line cessation treatment approaches.