Applying WHO COVID-19 workforce estimate tools remotely in an African context: a case report from Mali and Kenya

• Main Authors: Pamela A. McQuide (Author), Amy Finnegan (Author), Katherine M. Terry (Author), Andrew Brown (Author), Cheick Oumar Toure (Author), Jeanne Tessougue (Author), Ibrahim Cisse (Author), Mathew Kariuki Thuku (Author), Janet Muriuki (Author), Mary Ochola (Author), Julius Ogato (Author), Etienne Coulibaly (Author), Toure Djeneba Togora (Author)
• Format: Book
• Published: BMC, 2022-01-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 10.1186/s12960-021-00653-5; 1478-4491
• Keywords: COVID-19; Health workforce; Kenya; Mali; Workload modelling; WISN; Medicine (General); R5-920; Public aspects of medicine; RA1-1270
• Source: Human Resources for Health, Vol 19, Iss S1, Pp 1-9 (2022)

Abstract Background The COVID-19 pandemic has increased the burden on health systems, particularly in low- and middle-income countries, where health systems already struggle. To meet health workforce planning needs during the pandemic, IntraHealth International used two tools created by the World Health Organization (WHO) Regional Office for Europe. The Health Workforce Estimator (HWFE) allows the estimation of the quantity of health workers needed to treat patients during a surge, and the Adaptt Surge Planning Support Tool helps to predict the timing of a surge in cases and the number of health workers and beds needed for predicted caseload. These tools were adapted to fit the African context in a rapid implementation over 5 weeks in one region in Mali and one region in Kenya with the objective to test the feasibility of adapting these tools, which use a Workload Indicators of Staffing Need (WISN)-inspired human resources management methodology, to obtain daily and surge projections of COVID-19 human resources for health needs. Case presentation Using a remote team in the US and in-country teams in Mali and Kenya, IntraHealth enacted a phased plan to gather stakeholder support, collect data related to health systems and COVID-19 cases, populate data into the tools, verify modeled results with results on the ground, enact policy measures to meet projected needs, and conduct national training workshops for the ministries of health. Conclusions This phased implementation in Mali and Kenya demonstrated that the WISN approach applied to the Health Workforce Estimator and Adaptt tools can be readily adapted to the local context for African countries to rapidly estimate the number of health workers and beds needed to respond to the predicted COVID-19 pandemic caseload. The results may also be used to give a proxy estimate for needed health supplies-e.g., oxygen, medications, and ventilators. Challenges included accurate and timely data collection and updating data. The success of the pilot can be attributed to the adapted WHO tools, the team composition in both countries, access to human resources data, and early support of the ministries of health, with the expectation that this methodology can be applied to other country contexts.