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One size does not fit all: an application of stochastic modeling to estimating primary healthcare needs in Ethiopia at the sub-national level
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...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10559612/ https://www.ncbi.nlm.nih.gov/pubmed/37803351 http://dx.doi.org/10.1186/s12913-023-10061-1 |
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author | Hagedorn, Brittany L. Han, Rui McCarthy, Kevin A. |
author_facet | Hagedorn, Brittany L. Han, Rui McCarthy, Kevin A. |
author_sort | Hagedorn, Brittany L. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10559612 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-105596122023-10-08 One size does not fit all: an application of stochastic modeling to estimating primary healthcare needs in Ethiopia at the sub-national level Hagedorn, Brittany L. Han, Rui McCarthy, Kevin A. BMC Health Serv Res Research 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. BioMed Central 2023-10-06 /pmc/articles/PMC10559612/ /pubmed/37803351 http://dx.doi.org/10.1186/s12913-023-10061-1 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Hagedorn, Brittany L. Han, Rui McCarthy, Kevin A. One size does not fit all: an application of stochastic modeling to estimating primary healthcare needs in Ethiopia at the sub-national level |
title | One size does not fit all: an application of stochastic modeling to estimating primary healthcare needs in Ethiopia at the sub-national level |
title_full | One size does not fit all: an application of stochastic modeling to estimating primary healthcare needs in Ethiopia at the sub-national level |
title_fullStr | One size does not fit all: an application of stochastic modeling to estimating primary healthcare needs in Ethiopia at the sub-national level |
title_full_unstemmed | One size does not fit all: an application of stochastic modeling to estimating primary healthcare needs in Ethiopia at the sub-national level |
title_short | One size does not fit all: an application of stochastic modeling to estimating primary healthcare needs in Ethiopia at the sub-national level |
title_sort | one size does not fit all: an application of stochastic modeling to estimating primary healthcare needs in ethiopia at the sub-national level |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10559612/ https://www.ncbi.nlm.nih.gov/pubmed/37803351 http://dx.doi.org/10.1186/s12913-023-10061-1 |
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