Using responsive feedback from routine monitoring data to guide course corrections for a family planning intervention in Nigeria

Abstract

Background This paper aims to promote the use of simple interrupted time series (ITS) analyses of routine data as a responsive feedback tool to improve public health programs. Although advanced ITS techniques exist, their use is often not feasible due to limitations in funding or research capacity. We propose an Excel-based analysis that requires minimal resources or statistical expertise, and illustrate it by measuring the effect of a radio campaign to promote a family planning call center in Nigeria on the demand for family planning information. Methods We used a single group interrupted time series design (ITS) as a responsive feedback mechanism to determine whether the radio campaign influenced use of the Honey&Banana call center. ITS is ideal when there is no control group. ITS uses the pre-intervention trend to predict what would have happened if the intervention were absent. Results After conducting ITS analyses, the results show that the number of calls requesting family planning information increased throughout the campaign period, with a gain of about 500 additional calls per month, and then decreased after the campaign ended. However, the number of calls gained from the campaign was substantially lower than anticipated. Conclusions While end-of-project impact evaluations are necessary, there should be regular feedback system to provide program implementers with information about the status of the project, such as failures, successes, and areas of improvements. This would allow implementers to make necessary adjustments as needed throughout the intervention period. The finding that the radio campaign was not living up to expectations helped Honey&Banana program implementers to end the campaign prematurely and re-allocate resources to a more promising activity. Our research shows that basic Excel-based ITS analysis of routine data can be a useful tool for receiving regular feedback to guide programming improvements for organizations that have limited resources and/or research capacity.