One size does not fit all: an application of stochastic modeling to estimating primary healthcare needs in Ethiopia at the sub-national level

BMC Health Services Research · 3 authors, 1 centre

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Researchers used a stochastic simulation model to project primary healthcare clinical workload in Ethiopian regions through 2035. They found that workload varies by region and is driven by local demographics and disease trends, not population size alone.

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This study used the PACE-HRH stochastic Monte Carlo simulation model to estimate the primary healthcare clinical workload for seven regions and two chartered cities in Ethiopia from 2021 to 2035. Inputs included region-specific fertility, mortality, and disease burden data. Key service categories included pregnancy, non-communicable diseases, and sick child care. Sensitivity analysis showed fertility assumptions were critical. Limitations include reliance on estimated data and projected trends over a long horizon.

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other

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public health

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ORIGINAL PAPER

PMC10559612

One size does not fit all: an application of stochastic modeling to estimating primary healthcare needs in Ethiopia at the sub-national level

BMC Health Services Research

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ABSTRACT

Background Primary healthcare systems require adequate staffing to meet the needs of their local population. Guidelines typically use population ratio targets for healthcare workers, such as Ethiopia’s goal of two health extension workers for every five thousand people. However, fixed ratios do not reflect local demographics, fertility rates, disease burden (e.g., malaria endemicity), or trends in these values. Recognizing this, we set out to estimate the clinical workload to meet the primary healthcare needs in Ethiopia by region. Methods We utilize the open-source R package PACE-HRH for our analysis, which is a stochastic Monte Carlo simulation model that estimates workload for a specified service package and population. Assumptions and data inputs for region-specific fertility, mortality, disease burden were drawn from literature, DHS, and WorldPop. We project workload until 2035 for seven regions and two charted cities of Ethiopia. Results All regions and charted cities are expected to experience increased workload between 2021 and 2035 for a starting catchment of five thousand people. The expected (mean) annual clinical workload varied from 2,930 h (Addis) to 3,752 h (Gambela) and increased by 19–28% over fifteen years. This results from a decline in per capita workload (due to declines in fertility and infectious diseases), overpowered by total population growth. Pregnancy, non-communicable diseases, sick child care, and nutrition remain the largest service categories, but their priority shifts substantially in some regions by 2035. Sensitivity analysis shows that fertility assumptions have major implications for workload. We incorporate seasonality and estimate monthly variation of up to 8.9% (Somali), though most services with high variability are declining. Conclusions Regional variation in demographics, fertility, seasonality, and disease trends all affect the workload estimates. This results in differences in expected clinical workload, the level of uncertainty in those estimates, and relative priorities between service categories. By showing these differences, we demonstrate the inadequacy of a fixed population ratio for staffing allocation. Policy-makers and regulators need to consider these factors in designing their healthcare systems, or they risk sub-optimally allocating workforce and creating inequitable access to care. Supplementary Information The online version contains supplementary material available at 10.1186/s12913-023-10061-1.

CITATION EXPORT

Brittany L. Hagedorn, Rui Han, Kevin A. McCarthy. One size does not fit all: an application of stochastic modeling to estimating primary healthcare needs in Ethiopia at the sub-national level. BMC Health Services Research. (2023). https://doi.org/10.1186/s12913-023-10061-1

AUTHORS

3 · 1 CENTRES

Brittany L. Hagedorn, Rui Han, Kevin A. McCarthy

https://ror.org/0532gcg76grid.508089.c0000 0004 8340 3146Institute for Disease Modeling, Bill & Melinda Gates Foundation, 500 5th Ave N, Seattle, WA 98109 USA