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Background: In Belgium, the Planning Commission for Medical Supply is responsible for monitoring human resources for health (HRH) and ultimately proposing workforce quotas. It is supported by the Planning Unit for the Supply of the Health Professions. This Unit quantifies and forecasts the workforce in the healthcare professions on the basis of a stock and flow model, based on trends observed in the past. In 2019, the Planning Unit asked the KCE (Belgian Health Care Knowledge Centre) to develop additional forecasting scenarios for the midwifery workforce, to complement the standard historical trend approach. The aim of this paper is to present the development of such forecasting scenarios. Methods: The Robust Workforce Planning Framework, developed by the Centre for Workforce Intelligence in the UK was used to develop alternative midwifery workforce scenarios. The framework consists of four steps (Horizon scanning, Scenario generation, Workforce modelling, and Policy analysis), the first two of which were undertaken by KCE, using two online surveys and five workshops with stakeholders. Results: Three alternative scenarios are proposed. The first scenario (close to the current situation) envisages pregnancy and maternity care centred on gynaecologists working either in a hospital or in private practice. The second scenario describes an organisation of midwife-led care in hospitals. In the third scenario, care is primarily organised by primary care practitioners (midwives and general practitioners) in outpatient settings. Conclusions: The Robust Workforce Planning Framework provides an opportunity to adjust the modelling of the health workforce and inform decision-makers about the impact of their future decisions on the health workforce. [ABSTRACT FROM AUTHOR]

Background: A competent, enabled and efficiently deployed health workforce is crucial to the achievement of the health-related sustainable development goals (SDGs). Methods for workforce planning have tended to focus on 'one size fits all' benchmarks, but because populations vary in terms of their demography (e.g. fertility rates) and epidemiology (e.g. HIV prevalence), the level of need for sexual, reproductive, maternal, newborn and adolescent health (SRMNAH) workers also varies, as does the ideal composition of the workforce. In this paper, we aim to provide proof of concept for a new method of workforce planning which takes into account these variations, and allocates tasks to SRMNAH workers according to their competencies, so countries can assess not only the needed size of the SRMNAH workforce, but also its ideal composition (the 'Dream Team').Methods: An adjusted service target model was developed, to estimate (i) the amount of health worker time needed to deliver essential SRMNAH care, and (ii) how many workers from different cadres would be required to meet this need if tasks were allocated according to competencies. The model was applied to six low- and middle-income countries, which varied in terms of current levels of need for health workers, geographical location and stage of economic development: Azerbaijan, Malawi, Myanmar, Peru, Uzbekistan and Zambia.Results: Countries with high rates of fertility and/or HIV need more SRMNAH workers (e.g. Malawi and Zambia each need 44 per 10,000 women of reproductive age, compared with 20-27 in the other four countries). All six countries need between 1.7 and 1.9 midwives per 175 births, i.e. more than the established 1 per 175 births benchmark.Conclusions: There is a need to move beyond universal benchmarks for SRMNAH workforce planning, by taking into account demography and epidemiology. The number and range of workers needed varies according to context. Allocation of tasks according to health worker competencies represents an efficient way to allocate resources and maximise quality of care, and therefore will be useful for countries working towards SDG targets. Midwives/nurse-midwives who are educated according to established global standards can meet 90% or more of the need, if they are part of a wider team operating within an enabled environment. [ABSTRACT FROM AUTHOR]