Resource Requirement for Cancer Registration in Low and Middle Income Countries: A Case Study in Kenya
Background: With an estimated 14.1 million new cancer cases and 8.2 million deaths from cancer worldwide in 2012, cancer is a leading cause of morbidity and mortality globally, with more than half the global burden of cancer borne by low- and middle-income countries. High quality cancer registry data is critical for successful cancer control policies. Therefore data on the resources needed to support cancer registration critical.
Aim: (1) Provide a framework for systematically collecting activity-based resource and cost data from cancer registries; (2) Engage global stakeholders to identify and quantify the resources needed to strengthen and expand existing registries or establish new registries where none exist to support the successful collection of high quality cancer data; and (3) Share estimates of the costs of establishing and maintaining cancer registries with stakeholders so they can be included in the components of national cancer plans.
Methods: Working with an in-country consultant, we conducted site visits to understand the data collection infrastructure and types of activities performed by cancer registries in Kenya. We adapted a cost data collection tool developed for use in the U.S. and pilot-tested the tool with registries in Nairobi and Eldoret and analyzed the resources necessary to operate a cancer registry in Kenya.
Results: Preliminary analyses find the majority of resources (62% in Nairobi) devoted to cancer registration in the registries are provided in-kind. Cost per case reported in Nairobi is $7.68 ($20.39 with in-kind). More than 80% of registry resources are expended on core activities, with more than half on data collection activities.
Conclusions: In-kind support is crucial to registry operations; partnerships with universities and hospitals will support the establishment of new registries. Expansion of existing registries requires streamlining data collection activities (involving travel to hospitals and other data sources and time spent accessing medical records).