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Effect and significance of incorporating access in estimating the number of required physicians: focus on differences in population density in the target area

BACKGROUND: Geographical imbalances in the health workforce, particularly the shortage of health care workers in rural areas, is an issue of social and political concern in most countries. Estimating the number of required doctors is essential for evidence-based health policy planning. In this study...

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Autores principales: Suzuki, Tatsuya, Koike, Soichi, Matsumoto, Masatoshi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8130267/
https://www.ncbi.nlm.nih.gov/pubmed/34001102
http://dx.doi.org/10.1186/s12942-021-00274-0
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author Suzuki, Tatsuya
Koike, Soichi
Matsumoto, Masatoshi
author_facet Suzuki, Tatsuya
Koike, Soichi
Matsumoto, Masatoshi
author_sort Suzuki, Tatsuya
collection PubMed
description BACKGROUND: Geographical imbalances in the health workforce, particularly the shortage of health care workers in rural areas, is an issue of social and political concern in most countries. Estimating the number of required doctors is essential for evidence-based health policy planning. In this study, we propose two methods for estimating the number of required doctors using a simple method. One is counting by unit and the other is incorporating access to medical institutions. The purpose of this study is to verify the need to incorporate access to medical institutions when estimating the number of required physicians in a region by comparing both estimation methods from the viewpoint of regional population density. METHODS: We calculated the ratio of outpatients who can access medical institutions and the number of required physicians using the travel time by car and the number of patients who can be treated per doctor per day (estimation method for the number of physicians based on the access simulation, hereinafter referred to as EAS). We compared the results of this estimation with those of a conventional method, such as the number of doctors per population (estimation method for the number of physicians based on the number of patients, hereinafter referred to as ENP) to show how important it is to incorporate the element of accessibility in such a simulation analysis. Based on the results, we discussed the applicability of the proposed method. RESULTS: ENP estimated that 38,685 outpatient primary care (PC) physicians were required and EAS estimated that 46,378 were required. There was a difference of about 8000. A comparison of the EAS-estimated number of physicians and the ENP-estimated number of physicians showed that the ENP-estimated number was small, particularly in areas with low population density. CONCLUSIONS: The results showed that it is effective to use the proposed EAS method for the estimation of PC physicians, particularly in areas with low population density. We showed that the method of allocating the number of physicians in proportion to the number of patients in a certain unit requires paying attention to the setting of the unit.
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spelling pubmed-81302672021-05-18 Effect and significance of incorporating access in estimating the number of required physicians: focus on differences in population density in the target area Suzuki, Tatsuya Koike, Soichi Matsumoto, Masatoshi Int J Health Geogr Research BACKGROUND: Geographical imbalances in the health workforce, particularly the shortage of health care workers in rural areas, is an issue of social and political concern in most countries. Estimating the number of required doctors is essential for evidence-based health policy planning. In this study, we propose two methods for estimating the number of required doctors using a simple method. One is counting by unit and the other is incorporating access to medical institutions. The purpose of this study is to verify the need to incorporate access to medical institutions when estimating the number of required physicians in a region by comparing both estimation methods from the viewpoint of regional population density. METHODS: We calculated the ratio of outpatients who can access medical institutions and the number of required physicians using the travel time by car and the number of patients who can be treated per doctor per day (estimation method for the number of physicians based on the access simulation, hereinafter referred to as EAS). We compared the results of this estimation with those of a conventional method, such as the number of doctors per population (estimation method for the number of physicians based on the number of patients, hereinafter referred to as ENP) to show how important it is to incorporate the element of accessibility in such a simulation analysis. Based on the results, we discussed the applicability of the proposed method. RESULTS: ENP estimated that 38,685 outpatient primary care (PC) physicians were required and EAS estimated that 46,378 were required. There was a difference of about 8000. A comparison of the EAS-estimated number of physicians and the ENP-estimated number of physicians showed that the ENP-estimated number was small, particularly in areas with low population density. CONCLUSIONS: The results showed that it is effective to use the proposed EAS method for the estimation of PC physicians, particularly in areas with low population density. We showed that the method of allocating the number of physicians in proportion to the number of patients in a certain unit requires paying attention to the setting of the unit. BioMed Central 2021-05-17 /pmc/articles/PMC8130267/ /pubmed/34001102 http://dx.doi.org/10.1186/s12942-021-00274-0 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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
Suzuki, Tatsuya
Koike, Soichi
Matsumoto, Masatoshi
Effect and significance of incorporating access in estimating the number of required physicians: focus on differences in population density in the target area
title Effect and significance of incorporating access in estimating the number of required physicians: focus on differences in population density in the target area
title_full Effect and significance of incorporating access in estimating the number of required physicians: focus on differences in population density in the target area
title_fullStr Effect and significance of incorporating access in estimating the number of required physicians: focus on differences in population density in the target area
title_full_unstemmed Effect and significance of incorporating access in estimating the number of required physicians: focus on differences in population density in the target area
title_short Effect and significance of incorporating access in estimating the number of required physicians: focus on differences in population density in the target area
title_sort effect and significance of incorporating access in estimating the number of required physicians: focus on differences in population density in the target area
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8130267/
https://www.ncbi.nlm.nih.gov/pubmed/34001102
http://dx.doi.org/10.1186/s12942-021-00274-0
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