Normalizing Community Mask-Wearing: A Cluster Randomized Trial in Bangladesh

Background: A growing body of scientific evidence suggests that face masks can slow the spread of COVID-19 and save lives, but mask usage remains low across many parts of the world, and strategies to increase mask usage remain untested and unclear. Methods: We conducted a cluster-randomized trial of community-level mask promotion in rural Bangladesh involving 341,830 adults in 600 villages. We employed a series of strategies to promote mask usage, including free household distribution of surgical or cloth masks, distribution and promotion at markets and mosques, mask advocacy by Imams during Friday prayers, role modeling by local leaders, promoters periodically monitoring passers-by and reminding people to put on masks, village police accompanying those mask promoters, providing monetary rewards or certificates to villages if mask-wearing rate improves, public signaling of mask-wearing via signage, text message reminders, messaging emphasizing either altruistic or self-protection motives for mask-wearing, and extracting verbal commitments from households. The primary objective was to assess which of these interventions would increase proper (covering nose and mouth) wearing of face masks, and secondarily, whether mask promotion unintentionally creates moral hazard and decreases social distancing. This analysis is part of larger study evaluating the effect of mask-wearing on transmission of SARS-CoV-2. Results: There were 64,937 households in the intervention group and 64,183 households in the control group; study recruitment has ended. In the control group, proper mask-wearing was practiced by 13% of those observed across the study period. Free distribution of masks along with role modeling by community leaders produced only small increases in mask usage during pilot interventions. Adding periodic monitoring by mask promoters to remind people in streets and public places to put on the masks we provided increased proper mask-wearing by 29.0 percentage points (95% CI: 26.7% - 31.3%). This tripling of mask usage was sustained over all 10 weeks of surveillance, which includes a period after intervention activities ended. Physical distancing, measured as the fraction of individuals at least one arm’s length apart, also increased by 5.2 percentage points (95% CI: 4.2%-6.3%). Beyond the core intervention package comprised of free distribution and promotion at households/mosques/markets, leader endorsements plus periodic monitoring and reminders, several elements had no additional effect on mask wearing, including: text reminders, public signage commitments, monetary or non-monetary incentives, altruistic messaging or verbal commitments, or village police accompanying the mask promoters (the last not cross-randomized, but assessed in panel data). No adverse events were reported during the study period. Conclusions: Our intervention demonstrates a scalable and cost-effective method to promote mask adoption and save lives, and identifies a precise combination of intervention activities that were necessary. Comparisons between pilots shows that free mask distribution alone is not sufficient to increase mask-wearing, but adding periodic monitoring in public places to remind people to wear the distributed masks had large effects on behavior. The absence of any further effect of the village police suggests that the operative mechanism is not any threat of formal legal sanctions, but shame and people’s aversion to a light informal social sanction. The persistence of effects for 10 weeks and after the end of the active intervention period, as well as increases in physical distancing, all point to changes in social norms as a key driver of behavior change. Our cross-randomizations suggest that improved mask-wearing norms can be achieved without incentives that require costly monitoring, that aesthetic design choices and colors can influence mask-wearing, and that surgical masks with a substantially higher filtration efficiency can be a cost-effective alternative to cloth masks (1/3 the cost) and are equally or more likely to be worn. Implementing these interventions – including distribution of free masks, and the information campaign, reminders, encouragement – cost $2.30-$3.75 per villager, or between $8 and$13 per person adopting a mask. Combined with existing estimates of the efficacy of masks in preventing COVID-19 deaths, this implies that the intervention cost $28,000-$66,000 per life saved. Beyond reducing the transmission of COVID-19, mask distribution is likely to be a cost-effective strategy to prevent future respiratory disease outbreaks

Subsidy policies and insurance demand

Using data from a two-year pricing experiment, we study the impact of subsidy policies on weather insurance take-up. Results show that subsidies increase future insurance take-up through their influence on payout experiences. Exploring mechanisms of the payout effect, we find that for households that randomly benefited from financial education, receiving a payout provides a one-time learning experience that improves take-up permanently. In contrast, households with poor insurance knowledge continuously update take-up decisions based on recent experiences with disasters and payouts. Combining subsidy policies with financial education can thus be effective in promoting long-run insurance adoption

When Celebrities Speak: A Nationwide Twitter Experiment Promoting Vaccination in Indonesia

Celebrity endorsements are often sought to influence public opinion. We ask whether celebrity endorsement per se has an effect beyond the fact that their statements are seen by many, and whether on net their statements actually lead people to change their beliefs. To do so, we conducted a nationwide Twitter experiment in Indonesia with 46 high-profile celebrities and organizations, with a total of 7.8 million followers, who agreed to let us randomly tweet or retweet content promoting immunization from their accounts. Our design exploits the structure of what information is passed on along a retweet chain on Twitter to parse reach versus endorsement effects. Endorsements matter: tweets that users can identify as being originated by a celebrity are far more likely to be liked or retweeted by users than similar tweets seen by the same users but without the celebrities' imprimatur. By contrast, explicitly citing sources in the tweets actually reduces diffusion. By randomizing which celebrities tweeted when, we find suggestive evidence that overall exposure to the campaign may influence beliefs about vaccination and knowledge of immunization-seeking behavior by one's network. Taken together, the findings suggest an important role for celebrity endorsement.Comment: 55 pages, 13 tables, 6 figure

Improving chronic disease prevention and screening in primary care: results of the BETTER pragmatic cluster randomized controlled trial.

BackgroundPrimary care provides most of the evidence-based chronic disease prevention and screening services offered by the healthcare system. However, there remains a gap between recommended preventive services and actual practice. This trial (the BETTER Trial) aimed to improve preventive care of heart disease, diabetes, colorectal, breast and cervical cancers, and relevant lifestyle factors through a practice facilitation intervention set in primary care.MethodsPragmatic two-way factorial cluster RCT with Primary Care Physicians' practices as the unit of allocation and individual patients as the unit of analysis. The setting was urban Primary Care Team practices in two Canadian provinces. Eight Primary Care Team practices were randomly assigned to receive the practice-level intervention or wait-list control; 4 physicians in each team (32 physicians) were randomly assigned to receive the patient-level intervention or wait-list control. Patients randomly selected from physicians' rosters were stratified into two groups: 1) general and 2) moderate mental illness. The interventions involved a multifaceted, evidence-based, tailored practice-level intervention with a Practice Facilitator, and a patient-level intervention involving a one-hour visit with a Prevention Practitioner where patients received a tailored 'prevention prescription'. The primary outcome was a composite Summary Quality Index of 28 evidence-based chronic disease prevention and screening actions with pre-defined targets, expressed as the ratio of eligible actions at baseline that were met at follow-up. A cost-effectiveness analysis was conducted.Results789 of 1,260 (63%) eligible patients participated. On average, patients were eligible for 8.96 (SD 3.2) actions at baseline. In the adjusted analysis, control patients met 23.1% (95% CI: 19.2% to 27.1%) of target actions, compared to 28.5% (95% CI: 20.9% to 36.0%) receiving the practice-level intervention, 55.6% (95% CI: 49.0% to 62.1%) receiving the patient-level intervention, and 58.9% (95% CI: 54.7% to 63.1%) receiving both practice- and patient-level interventions (patient-level intervention versus control, P < 0.001). The benefit of the patient-level intervention was seen in both strata. The extra cost of the intervention was $26.43CAN (95$16 to $44) per additional action met.ConclusionsA Prevention Practitioner can improve the implementation of clinically important prevention and screening for chronic diseases in a cost-effective manner

Inefficiencies in Digital Advertising Markets

Digital advertising markets are growing and attracting increased scrutiny. This article explores four market inefficiencies that remain poorly understood: ad effect measurement, frictions between and within advertising channel members, ad blocking, and ad fraud. Although these topics are not unique to digital advertising, each manifests in unique ways in markets for digital ads. The authors identify relevant findings in the academic literature, recent developments in practice, and promising topics for future research

Analyzing two-stage experiments in the presence of interference

Two-stage randomization is a powerful design for estimating treatment effects in the presence of interference; that is, when one individual's treatment assignment affects another individual's outcomes. Our motivating example is a two-stage randomized trial evaluating an intervention to reduce student absenteeism in the School District of Philadelphia. In that experiment, households with multiple students were first assigned to treatment or control; then, in treated households, one student was randomly assigned to treatment. Using this example, we highlight key considerations for analyzing two-stage experiments in practice. Our first contribution is to address additional complexities that arise when household sizes vary; in this case, researchers must decide between assigning equal weight to households or equal weight to individuals. We propose unbiased estimators for a broad class of individual- and household-weighted estimands, with corresponding theoretical and estimated variances. Our second contribution is to connect two common approaches for analyzing two-stage designs: linear regression and randomization inference. We show that, with suitably chosen standard errors, these two approaches yield identical point and variance estimates, which is somewhat surprising given the complex randomization scheme. Finally, we explore options for incorporating covariates to improve precision. We confirm our analytic results via simulation studies and apply these methods to the attendance study, finding substantively meaningful spillover effects.Comment: Accepted for publication in the Journal of the American Statistical Associatio