Methods and Lessons From Costing a Large mHealth Intervention at Scale in India

The use of mobile devices to deliver public health interventions is rapidly increasing, particularly in low resource settings. Despite their proliferation, several mHealth interventions in developing countries fail to reach geographical scale, and long-term sustainability for most remains uncertain. There is a need to cost for such programs, to enable better planning and budgeting and tailor programs as required. Cost estimates can contribute to a more informed debate on resource allocation priorities and help make choices clearer for policymakers. This paper has two main objectives: (1) present a detailed protocol on determining the costs of a large national mHealth job aid and behavior change communication tool known as Integrated Child Development Services - Common Application Software (ICDS-CAS) in India, and (2) to present lessons for policymakers on how to ensure financial planning for scaling mHealth interventions. The study uses the Activity Based Costing-Ingredients (ABC-I) method. The major advantage of the ABC-I method is the clarity it brings to costs for each input and activity, across levels and geographies. It also accounts for indirect costs. There are five key lessons while costing for mHealth programs. First, that there are many activities and ingredients that must be budgeted for and discussed while planning and implementing mHealth programs. Second, the ABC-I method described in this paper provides great clarity on costs, yet its major limitation is the availability of data, which must be mitigated with the careful use of assumptions. Third, mHealth technology life cycles have financial implications which must be accounted for. Fourth, determining cost locations and all sources of funding including non-government sources is crucial. Fifth, since costing estimates are subject to a set of assumptions, a disaggregation of costs allows for scenario-building, which is useful while planning ahead and accounting for program changes. The evidence generated can be used for more informed debate on resource allocation priorities, given competing priorities in low- and middle-income countries.