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Site Visit Frequency Policies for Mobile Family Planning Services
Author(s) -
De Vries Harwin,
Swinkels Lisa E.,
Van Wassenhove Luk N.
Publication year - 2021
Publication title -
production and operations management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.279
H-Index - 110
eISSN - 1937-5956
pISSN - 1059-1478
DOI - 10.1111/poms.13484
Subject(s) - outreach , key (lock) , scale (ratio) , computer science , yield (engineering) , business , family planning , economic growth , economics , geography , computer security , population , demography , materials science , cartography , metallurgy , sociology , research methodology
Improving access to family planning services is key to achieving many of the United Nations sustainable development goals. To scale up access in remote areas and urban slums, many developing countries deploy mobile family planning teams that visit “outreach sites” several times per year. Visit frequencies have a significant effect on the total number of clients served and hence the impact of the outreach program. Using a large dataset of visits in Madagascar, Uganda, and Zimbabwe, our study models the relationship between the number of clients seen during a visit and the time since the last visit and uses this model to analyze the characteristics of optimal frequencies. We use the latter to develop simple frequency policies for practical use, prove bounds on the worst‐case optimality gap, and test the impact of the policies with a simulation model. Our main finding is that despite the complexity of the frequency optimization problem, simple policies yield near‐optimal results. This holds even when few data are available and when the relationship between client volume and the time since the last visit is misspecified or substantially biased. The simulation for Uganda shows a potential increase in client numbers of between 7% and 10%, which corresponds to more than 12,000 additional families to whom family planning services could be provided. Our results can assist policymakers in determining when to start data‐driven frequency determination and which policies to implement.