Health Workforce Productivity Index: Insights from a national assessment in Zimbabwe to optimise resource allocation
1
Health Systems and Services, World Health Organization, Brazzaville, Congo
2
Education, Research and Management, University of Ghana, Accra, Ghana
3
Health Services commission, Harare, Zimbabwe
4
World Health Organization, Harare, Zimbabwe
Popul. Med. 2026;8(Supplement Supplement 1):
ABSTRACT
INTRODUCTION:
In many low- and middle-income countries, performance-based reward mechanisms remain underutilized due to the absence of reliable productivity measurement tools to guide policy and resource allocation. Zimbabwe faces persistent health sector challenges, including workforce shortages and fiscal constraints that limit recruitment and retention. Improving health workforce productivity and efficiency is therefore essential to strengthening service delivery. This study applied the Workforce Productivity Index (WPI) to assess aggregate health workforce productivity across Zimbabwean districts.
METHODS:
Using district-level data extracted from administrative databases and routine health information systems, a three-stage WPI framework was applied. First, a Composite Services Index (CSI) was computed by aggregating weighted health service outputs. Second, a Composite Human Resources for Health Index (CHRHI) was derived from weighted health worker inputs. Finally, WPI was calculated as the ratio of CSI to CHRHI. District-level productivity scores were analysed alongside service coverage levels to classify districts into performance quadrants.
RESULTS:
The national average WPI was 75.94, indicating that every US$100 spent on health worker salaries generated service outputs equivalent to 76 outpatient consultations. Productivity varied widely across districts, with a tenfold difference between the highest-performing district (Mt. Darwin, WPI = 208.13) and the lowest (Chitungwiza, WPI = 21.6). Approximately 19% of districts exhibited both high productivity and high coverage (“frontier districts”), while another 19% had high productivity but low coverage, implying understaffing. Conversely, 44% of districts recorded both low productivity and low coverage, signalling deeper systemic inefficiencies. About 17% of districts showed low productivity but high coverage, suggesting potential overstaffing.
CONCLUSIONS:
This study provides critical empirical evidence on the efficiency and productivity of Zimbabwe’s health workforce, revealing substantial spatial disparities. The WPI offers a practical framework to guide policy dialogue, performance improvement, and equitable resource allocation for optimized workforce utilization.
In many low- and middle-income countries, performance-based reward mechanisms remain underutilized due to the absence of reliable productivity measurement tools to guide policy and resource allocation. Zimbabwe faces persistent health sector challenges, including workforce shortages and fiscal constraints that limit recruitment and retention. Improving health workforce productivity and efficiency is therefore essential to strengthening service delivery. This study applied the Workforce Productivity Index (WPI) to assess aggregate health workforce productivity across Zimbabwean districts.
METHODS:
Using district-level data extracted from administrative databases and routine health information systems, a three-stage WPI framework was applied. First, a Composite Services Index (CSI) was computed by aggregating weighted health service outputs. Second, a Composite Human Resources for Health Index (CHRHI) was derived from weighted health worker inputs. Finally, WPI was calculated as the ratio of CSI to CHRHI. District-level productivity scores were analysed alongside service coverage levels to classify districts into performance quadrants.
RESULTS:
The national average WPI was 75.94, indicating that every US$100 spent on health worker salaries generated service outputs equivalent to 76 outpatient consultations. Productivity varied widely across districts, with a tenfold difference between the highest-performing district (Mt. Darwin, WPI = 208.13) and the lowest (Chitungwiza, WPI = 21.6). Approximately 19% of districts exhibited both high productivity and high coverage (“frontier districts”), while another 19% had high productivity but low coverage, implying understaffing. Conversely, 44% of districts recorded both low productivity and low coverage, signalling deeper systemic inefficiencies. About 17% of districts showed low productivity but high coverage, suggesting potential overstaffing.
CONCLUSIONS:
This study provides critical empirical evidence on the efficiency and productivity of Zimbabwe’s health workforce, revealing substantial spatial disparities. The WPI offers a practical framework to guide policy dialogue, performance improvement, and equitable resource allocation for optimized workforce utilization.
| eISSN: | 2654-1459 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
