Using modeling and scenario analysis to support evidence-based health workforce strategic planning in Malawi

The health workforce is an essential building block for a functioning health system and achievement of universal health coverage [1]. The Government of Malawi has made significant investments in its health workforce since 2006 with the introduction of the Emergency Human Resources Programme (EHRP), which led to the scale up of health workers in 11 priority cadres by 53% from 5453 at baseline, to 8369 by 2009 [2]. Government commitment has been complemented by significant donor investment in the health workforce. Government and donors invested USD 257 357 000 in health worker salaries and USD 58 487 000 in pre-service education of health workers between 2012 and 2016 [3].

Despite these investments, review of achievements during Malawi’s first Human Resources for Health Strategic Plan (HRH SP) 2012–2016 highlighted widespread and persistent workforce challenges [4]. At the close of the first HRH strategic planning period, the country continued to face significant health worker shortages, over-concentration of health workers in urban areas, insufficient and misaligned training of new health workers to fill large staffing gaps, and limited capacity to hire newly trained health workers due to budgetary constraints [4‐6].

Health workforce interventions can be costly, take many years to show results, and require cooperation of numerous stakeholders [1, 7]. Investing appropriately in strengthening the health workforce requires an evidence-based approach to HRH planning to ensure limited resources are efficiently utilized towards targeted efforts to increase the number of health workers, deploy health workers where they are most needed, and optimize the use of existing health workers.

Demand-based approaches to estimating workforce requirements offer low- and middle-income countries (LMICs) a practical methodology for medium-term target setting. Demand-based target setting considers variation in the use of services across geographies and complexities of service delivery by different cadres of health workers, which are missed by target setting methods using population ratios or facility-based standards [8]. While needs-based estimation can offer idealized targets, results often suggest health worker volumes that are unrealistic for setting national targets in LMICs [8‐10].

The World Health Organization introduced the Workload Indicators of Staffing Need (WISN) model in 1998, which uses health worker workload data to determine workforce requirements to deliver a specific package of services [8]. The model has since been widely used to determine volume and cadre of staff required in selected health facilities [11‐13]. In 2009, the Clinton Health Access Initiative (CHAI) and the Zambian Ministry of Health (MoH) first developed the Workforce Optimization Model (WFOM), which has since been used in Malawi, Lesotho, Liberia, and Eswatini [14]. The model uses health worker time-motion observations alongside service delivery data to generate national workforce targets by cadre, facility type, and region. The WFOM and WISN are functionally similar, although the WFOM can be conducted in Stata using a simplified approach to inputting data, and has functionality to allow comparison of various policy scenarios.

The pre-service education system in LMICs is largely responsible for the overall supply of health workers. Pre-service education planning is critical to determining overall vacancy rates, distribution of health workers, and types of care available in-country [15]. Nonetheless, pre-service education planning is frequently overlooked by health system planners [15, 16]. Efforts to better align health worker supply with health system demand to ensure future availability of the health workforce requires an analysis of the pre-service training pipeline. This paper describes a pre-service training model developed by CHAI to forecast training production and projected workforce volumes for prioritized cadres under current and simulated scenarios.

While HRH modelling is a critical tool for evidence-based HRH decision-making [1, 17], these approaches are rarely used explicitly for the purposes of informing national HRH strategic planning in LMICs [18, 19]. A recent review of health workforce analytics in 20 LMICs did not identify any examples of countries that had institutionalized routine use of evidence-based methods for workforce planning [20]. Against this background, the Malawi MoH and collaborators decided to undertake a practical and evidence-based approach to developing its successor HRH Strategic Plan (HRH SP) 2018–2022. This paper describes the process of developing a data-driven HRH SP, including identifying information gaps, designing a targeted data collection and analysis plan, developing practical and flexible HRH models to conduct workforce scenario analyses, and ultimately utilizing model results to develop interventions in Malawi’s HRH SP 2018–2022.

This section explains the approach to data collection and to executing the three key analyses presented in Malawi’s HRH SP 2018–2022: a vacancy analysis, pipeline model, and Workforce Optimization Model.

In May 2018, the Government of Malawi successfully launched the HRH SP 2018–2022, which includes the analyses described here [26].

Evidence must be carefully considered in designing a national HRH strategic plan to prioritize finite resources and develop actionable strategies reflective of the country context. The Malawi MoH and collaborators undertook an evidence-based approach to its HRH strategic planning process, developing a targeted data collection and analysis plan, and using vacancy analysis, workforce modelling, and scenario analysis to inform strategies recommended in the HRH SP 2018–2022. The results of these analyses are contained in the HRH SP, and the outputs were used to inform prioritization of objectives and interventions recommended in the strategic plan [26]. By highlighting data gaps and incorporating evidence throughout the HRH SP, the plan makes a strong case for strengthening HRH data systems and routinely using HRH data for planning and management of the health workforce [27].

The non-intervention scenario in the pipeline model highlights that if current trends in training, recruitment and attrition continue, Malawi will not meet its establishment within the strategic planning period. The intervention-based scenario analysis shows the most impactful intervention to close workforce gaps is doubling training enrollments. Malawi has already made significant investment in decentralizing education programs and scaling up rural health training, which are highly effective strategies to increase the rural health workforce [28]. Further increasing training enrollments would require significant financial investment in infrastructure, student scholarships, and educators. Otherwise, quality of education may be compromised, a challenge which was previously observed in the rapid scale up of training during the EHRP period [2]. Furthermore, as the main employer of health workers in Malawi, MoH would need to prepare to recruit larger cohorts upon graduation, which would require advanced multi-sectoral and multi-year fiscal planning. Recognizing these are significant constraints in Malawi, where the country is already under pressure to reduce its wage bill [29], it would be unlikely that the government could sponsor large new enrollments and recruitments. Therefore, in the short term, the HRH SP recommends aligning training plans with recruitment needs to prioritize training to fill immediate workforce gaps, and strengthening data systems to better monitor and plan for new graduates to ensure they are hired in a timely manner.

The pipeline model illustrates that reducing voluntary attrition to 1% would allow Malawi to meet its establishment target by 2028 and forms the foundation for recommendations in the HRH SP to develop a retention strategy and strengthen health workforce management interventions. Significant research in Malawi has highlighted financial and non-financial strategies to improve retention which could be considered by government in developing a national retention strategy [30‐33]. Given financial constraints in Malawi’s health sector, non-financial retention incentives such as housing provision, improved facility quality, and supervision would be more feasible to implement, as compared to financial incentives, such as increased salary or hazard pay. Importantly, in none of the intervention scenarios would Malawi meet the WHO recommendation of 4.45 health workers per 1000 population by 2030. This highlights the need for greater investment in the health workforce, in addition to efforts to optimize the existing workforce and align education plans with employment needs to more efficiently utilize existing resources.

The vacancy analysis highlights a workforce gap of 45% against national targets, with significant variation across districts. While this gap may seem intractable in Malawi’s fiscally constrained landscape [34], the WFOM provides a prioritized immediate term scenario that is actionable within the strategic planning period. For example, while NMTs form the backbone of Malawi’s primary care delivery system and, therefore, have the highest number of established posts and the greatest vacancy rate, the WFOM results prioritize the immediate recruitment of 1529 NMTs to meet current service utilization levels. This is a more realistic goal for the strategic plan period as compared to the gap of 8250 NMTs needed to meet the establishment. Using a demand-based approach to modeling human resource requirements, the WFOM results provide government with a tool to develop annual recruitment plans that prioritize districts and cadres with greatest need, and consider more efficient deployment of existing health workers. The model also provides government with a method to consider for reviewing national establishment targets based on current and projected service demand at facilities.

While the WFOM results generally recommend targets lower than the establishment for immediate term recruitment, for certain higher skilled frontline workers, such as medical and nursing officers, pharmacists, and lab officers, the WFOM results recommend recruitment of more health workers than the establishment. These results emphasize the importance of striving for a comprehensive and balanced mix of primary health care cadres within a national HRH strategy. Ensuring sufficient primary care workers with focus in medicine, nursing, pharmacy, and lab is critical to providing effective prevention and health promotion alongside continuous and coordinated care at the primary care level [28].

Data incompleteness and non-availability, and potential underreporting of service delivery data, impacted the scope of our analyses and required us to make assumptions, where data were not available (see Additional file 1). For example, duty station was not recorded in most staff return data, and therefore, we were unable to conduct vacancy and WFOM analyses at facility level. Furthermore, incomplete service delivery data led to exclusion of certain facilities from the analyses, and lack of data for services such as nutrition, mental health, and radiography led to the exclusion of certain cadres which provide these services from the WFOM. Recognizing these data gaps, the HRH SP makes a strong call to strengthen HRH and sector-wide data systems.

The judgment of clinical experts and observations of patient–provider interactions was used to determine the time it takes to deliver specific health services in the WFOM. While this is a widely accepted methodology for demand-based workforce modelling [8], clinical management of cases can vary significantly between providers, and clinical experts may have differing opinions on the ideal amount of time a service should take. To limit potential observation bias in the collection of activity times, multiple observations of the same activity were captured within and across facilities, and clinical experts were consulted to further validate the time estimates that were used as normative inputs for the WFOM model (see Additional file 1).

The models do not consider the service needs or production targets for cadres with advanced training required to deliver specialty services at the secondary and tertiary-level, as many of these training programs do not yet exist in Malawi. While the HRH SP makes recommendations regarding specialist training, additional work would be needed to validate these targets and to design models that could effectively project specialist needs. DCSAs are also excluded from the WFOM given their work takes place primarily outside the facility—at the community level. The DCSA targets in the HRH SP are drawn from the 2018 National Community Health Strategy [35], highlighting the importance of integrated sub-sectoral planning. Finally, as service utilization levels fall below national targets for service coverage articulated in the Health Sector Strategic Plan II 2017–2022 [4], the WFOM outputs should be considered as intermediate targets to provide high-quality services at current levels of utilization. However, these targets should be re-evaluated as utilization of services increases overtime.

The WFOM does not model scenarios that the government has yet to consider, such as productivity improvement in service delivery due to use of technology, task-shifting, or improved skill levels of health workers overtime. This is a common limitation in the use of workforce modeling for long-term planning [36]. However, as the WFOM is a flexible tool, it can be updated to incorporate any new scenarios overtime.

Malawi’s HRH SP 2018–2022 presents a detailed vacancy analysis, HRH modeling, and scenario analysis to inform the strategies and interventions recommended in the plan, and makes a call to strengthen HRH data systems for improved workforce decision-making. The models and results presented in the HRH SP have been subsequently used to prioritize and cost HRH investments in national funding requests, to harmonize HRH projections across sub-sectoral plans, to catalyze momentum around HRH policy change, and have been incorporated into national and subnational annual planning tools and processes [37‐40]. By following a practical methodology to identify information gaps, design data collection and analysis plans around these gaps, and utilize the evidence generated to build strategic priorities, Malawi’s HRH SP offers a replicable model that can be used in-country and beyond to promote evidence-informed strategic planning.