The correlation between surgical, anaesthetic and obstetric workforce and surgical volume: a modelling study.
7 affiliations
- 1Department of General Surgery, University Hospital Bristol and Weston Trust, Weston-Super-Mare, United Kingdom.
- 2Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway.
- 3Clinic of Surgery, Trondheim University Hospital St Olav's Hospital, Trondheim, Norway.
- 4WHO Collaborating Centre for Emergency, Critical and Operative Care, The George Institute for Global Health, New Delhi, India.
- 5Department of Statistics, BARC Hospital, Mumbai, India.
- 6Department of Global Public Health, Karolinska Institutet, Stockholm, Sweden.
- 7Centre for Leadership in Global Health, University of Global Health Equity, Butaro, Rwanda.
Abstract
Introduction
The Lancet Commission on Global Surgery has stated that two-thirds of the world's population does not have adequate access to surgical and anaesthesia care. Surgical, Obstetric and Anaesthetic (SAO) workforce and surgical volume (SV) are two of the six indicators recommended for monitoring and planning. This study aims to examine the correlation between SAO density and SV at the national level.Methodology
A cross-sectional ecological regression study was conducted with SAO workforce per 100,000 population and SV per 100,000 population, extracted from the World Bank Database. Countries that reported both indicators were included. If SAO was not available for the corresponding SV year, it was adjusted according to the population growth. A logarithmic regression model was used to describe the nonlinear association between SAO workforce density and surgical volume with SPSS. Model fit (R²) analysis, diagnostics, and a model implied marginal slope analysis were conducted.Result
A total of 57 countries were included, with 37 (64.9%) requiring adjustment. The fitted logarithmic regression model (y = 884 ln(x) + 685) had an R² of 0.524. At an SAO density of 10 per 100,000 population, the fitted model predicted approximately 2720 procedures per 100,000 population. At 20 and 40 SAO, 3333.7 (95% CI for fitted mean: 2917.2-3750.2) and 3946.5 (95% CI for fitted mean: 3470.7-4,422.3) procedures are expected, respectively. The marginal slope analysis of our model was progressively smaller at higher SAO densities.Conclusion
This study provides empirical insights into the nonlinear relationship between SAO workforce density and surgical volume. The findings highlight the need to balance workforce expansion with wider health-system capacity and may contribute to the future development of more informative surgical planning and performance indicators.
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BibTeX
@article{ng2026correlation,
title = {{The correlation between surgical, anaesthetic and obstetric workforce and surgical volume: a modelling study.}},
author = {Yuki Julius Ng and Alex J van Duinen and Håkon A Bolkan and Anita Gadgil and Priti Patil and Nobhojit Roy},
journal = {Frontiers in health services},
year = {2026},
month = oct,
doi = {10.3389/frhs.2026.1920090},
url = {https://europepmc.org/article/MED/42923312},
note = {CC BY},
}
RIS
TY - JOUR
TI - The correlation between surgical, anaesthetic and obstetric workforce and surgical volume: a modelling study.
AU - Yuki Julius Ng
AU - Alex J van Duinen
AU - Håkon A Bolkan
AU - Anita Gadgil
AU - Priti Patil
AU - Nobhojit Roy
PY - 2026
DA - 2026/10/08
JO - Frontiers in health services
DO - 10.3389/frhs.2026.1920090
UR - https://europepmc.org/article/MED/42923312
AB - Introduction The Lancet Commission on Global Surgery has stated that two-thirds of the world's population does not have adequate access to surgical and anaesthesia care. Surgical, Obstetric and Anaesthetic (SAO) workforce and surgical volume (SV) are two of the six indicators recommended for monitoring and planning. This study aims to examine the correlation between SAO density and SV at the national level.Methodology A cross-sectional ecological regression study was conducted with SAO workforce per 100,000 population and SV per 100,000 population, extracted from the World Bank Database. Countries that reported both indicators were included. If SAO was not available for the corresponding SV year, it was adjusted according to the population growth. A logarithmic regression model was used to describe the nonlinear association between SAO workforce density and surgical volume with SPSS. Model fit (R²) analysis, diagnostics, and a model implied marginal slope analysis were conducted.Result A total of 57 countries were included, with 37 (64.9%) requiring adjustment. The fitted logarithmic regression model (y = 884 ln(x) + 685) had an R² of 0.524. At an SAO density of 10 per 100,000 population, the fitted model predicted approximately 2720 procedures per 100,000 population. At 20 and 40 SAO, 3333.7 (95% CI for fitted mean: 2917.2-3750.2) and 3946.5 (95% CI for fitted mean: 3470.7-4,422.3) procedures are expected, respectively. The marginal slope analysis of our model was progressively smaller at higher SAO densities.Conclusion This study provides empirical insights into the nonlinear relationship between SAO workforce density and surgical volume. The findings highlight the need to balance workforce expansion with wider health-system capacity and may contribute to the future development of more informative surgical planning and performance indicators.
SN - 2813-0146
ER -