Hypertension management in rural western Kenya: a needs-based health workforce estimation model

Elevated blood pressure is the leading risk for mortality in the world. Task redistribution has been shown to be efficacious for hypertension management in low- and middle-income countries. However, the workforce requirements for such a task redistribution strategy are largely unknown. Therefore, we developed a needs-based workforce estimation model for hypertension management in western Kenya, using need and capacity as inputs.

Key informant interviews, focus group discussions, a Delphi exercise, and time-motion studies were conducted among administrative leadership, clinicians, patients, community leaders, and experts in hypertension management. These results were triangulated to generate the best estimates for the inputs into the health workforce model. The local hypertension clinical protocol was used to derive a schedule of encounters with different levels of clinician and health facility staff. A Microsoft Excel-based spreadsheet was developed to enter the inputs and generate the full-time equivalent workforce requirement estimates over 3 years.

Two different scenarios were modeled: (1) "ramp-up" (increasing growth of patients each year) and (2) "steady state" (constant rate of patient enrollment each month). The ramp-up scenario estimated cumulative enrollment of 7000 patients by year 3, and an average clinical encounter time of 8.9 min, yielding nurse full-time equivalent requirements of 4.8, 13.5, and 30.2 in years 1, 2, and 3, respectively. In contrast, the steady-state scenario assumed a constant monthly enrollment of 100 patients and yielded nurse full-time equivalent requirements of 5.8, 10.5, and 14.3 over the same time period.

A needs-based workforce estimation model yielded health worker full-time equivalent estimates required for hypertension management in western Kenya. The model is able to provide workforce projections that are useful for program planning, human resource allocation, and policy formulation. This approach can serve as a benchmark for chronic disease management programs in low-resource settings worldwide.