A Predictive Tobacco Control Mass Media Programming Model to Achieve Best Buys in Low –and Middle-Income Country Settings
Background Evidence based message design and efficient dissemination of messages are critical to the success of tobacco control mass media campaigns. Although evidence to measure effectiveness of messages is emerging within low -and middle-income country (LMIC) settings, evidence-based approaches for mass media message dissemination is currently lacking due to challenges in accurate assessment of gross rating points (GRPs) for efficient delivery of campaign messages. Approaches to more accurately predict optimal campaign impact are required to achieve best-buys in resource constrained settings Method A case study approach compared findings from two national tobacco control mass media campaigns implemented in Bangladesh. Stage one reviewed protocols to assess the efficacy of message designs. Second stage analysis involved a review of the mass media campaign recall findings from cross-sectional, post-intervention surveys. Last, a post assessment of GRPs for both campaigns was conducted to support the development of an algorithm to better predict campaign impact at the greatest cost-efficiencies. Results Message mean pre-test scores identified that the Baby Alive campaign scored approximately 20% lower than mean pre-test scores of messages for the Graphic Health Warning campaign. Media dissemination for the Baby Alive campaign was also relatively low at 165GRPs achieving 16.8% prompted recall while the Graphic Health Warning campaign delivered 292GRPs to achieve 47.0% prompted recall. The analytic-predictive model identified that for messages with high pre-test scores an increase of only 1.5GRPs was required to the existing media plan to potentially achieve an additional percentage point of recall. Discussion Given the weaknesses in GRP calculations in LMIC settings, analysis of multiple metrics should be considered to achieve best buys for tobacco control mass media campaigns. Based on optimal message mean pre-test scores of 90%+ and delivery of 292GRPs, which achieved 47% campaign recall, optimal recall of 70% could be predicted with a media plan delivering 342GRPs. More analytical-predictive mass media programming models need to be developed in other LMIC settings examining multiple campaign findings to confirm if this algorithm can provide better returns on investment with efforts directed toward delivering interventions that are supported by a strong evidence base.