Effect of doctor allocation policies on the equitable distribution of doctors in Thailand

Noree, Thinakorn; Pagaiya, Nonglak; Nimnual, Intira

doi:10.1186/s12960-022-00782-5

Cited by 6 publications

(4 citation statements)

References 15 publications

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“…While the Gini coefficient was initially designed to measure income distribution in the field of economics, it has also been used to measure workforce distribution. 13,14…”

Section: Discussionmentioning

confidence: 99%

Practice Consolidation Among US Medical Oncologists, 2015-2022

Milligan,

Erfani,

Orav

et al. 2024

JCO Oncol Pract

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PURPOSE Health care consolidation has significantly affected cancer care delivery, with oncology practices undergoing substantial consolidation over the past two decades. This study investigates practice consolidation trends among medical oncologists (MOs), factors associated with consolidation, and changes in MO geographic distribution. METHODS Medicare data from 2015 to 2022 were used to assess MO practice consolidation in hospital referral regions (HRRs), linked with regional health care market data and physician demographics. The Herfindahl-Hirschman Index (HHI) was used to measure consolidation, and the Gini coefficient was used to measure MO distribution across counties. Multivariable linear regression explored factors associated with MO practice consolidation. RESULTS Between 2015 and 2022, the number of MOs increased by 14.5% (11,727-13,433), whereas the number of MO practices decreased by 18.0% (2,774-2,276). The mean number of MOs per practice increased by 40% (4.26-5.95; P < .001). The percentage of MOs in small practices decreased, whereas larger practices saw an increase. MO consolidation, as indicated by the HHI, increased by 9% (median HHI, 0.3204-0.3480). HRRs with higher baseline hospital consolidation and more hospital beds per capita were more likely to have MO practice consolidation. Despite MO practice consolidation, the county-level distribution of MOs did not change substantially. CONCLUSION On the basis of Federal Trade Commission classifications, MO practices were highly concentrated in 2015 and consolidated even further by 2022. While distribution of MOs at the county level remained stable, further research is needed to assess the effects of rapid consolidation on cancer care cost, quality, and access. These data have important implications for policymakers and payers as they design programs that ensure high-quality, affordable cancer care.

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“…While the Gini coefficient was initially designed to measure income distribution in the field of economics, it has also been used to measure workforce distribution. 13,14…”

Section: Discussionmentioning

confidence: 99%

Practice Consolidation Among US Medical Oncologists, 2015-2022

Milligan,

Erfani,

Orav

et al. 2024

JCO Oncol Pract

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“…The copyright holder for this preprint this version posted January 12, 2024. ; https://doi.org/10.1101/2024.01.10.24301140 doi: medRxiv preprint evaluated by the policymakers for strategic planning to achieve 80% of UHC SCI by the year 2030. The spatial allocation of healthcare resources (23,24) with the use of SAI could be more helpful in low facility settings. One of the best evidence was provided from a Portuguese study and it demonstrated how the enhanced-2SFCA method was used to implement a new healthcare reform initiative for establishing emergency network points in a convenient distance to their residents (25).…”

Section: (Which Was Not Certified By Peer Review)mentioning

confidence: 99%

Primary care access and feasibility of Spatial Accessibility Index (SAI) in assessing primary care in rural Sri Lanka

Abeyrathna,

Agampodi,

Weerasinghe

et al. 2024

Preprint

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Despite decades of global health efforts and policies, inequitable access to primary care in low and middle-income countries remains an ongoing challenge. This study assessed accessibility to curative sector-primary healthcare services in the Anuradhapura district of Sri Lanka in 2020 and 2021. In this study, we included both private and state-owned primary care facilities (PCF) and their corresponding primary care doctors (PCD), covering both allopathic and complementary and alternative medicine (CAM) practices. Spatial locations of private sector PCF of the entire district were collected by conducting a comprehensive road-based survey and documenting information from publicly displayed boards. Data on state PCF were collected from provincial health departments (allopathic and Ayurveda). The Spatial Accessibility Index (SAI) of 657 GND (Grama Niladhari Division: the smallest administrative division) was calculated in ARC-GIS using the 2-Steps-Floating-Catchment-Area method in 5 km and 10 km spatial-distances from a GND. The majority of the PCF were allopathic (n=318, 78%) and belonged to the private sector (n=317, 79%) whereas CAM PCD represented one-fifth of the primary care workforce. Average SAI of 4.50X 10-4 and 4.67X 10-4 were reported for both 5 km and 10 km spatial-distance. A statistically significant difference in mean SAI was measured between urban and rural populations. The highest SAI were observed from urban populations and it gradually declined towards the rural remotes. The national primary care coverage target of 1 allopathic PCD per 5000 population was achieved only in 64% of the population in a 5 km spatial-distance and it further depleted to 25% with only of state allopathic PCD. SAI was negatively associated with the poverty headcount indices and distance to main roads and the main town (Anuradhapura). While the private allopathic sector is the main primary care provider in this study setting, a significant proportion of the population reported limited primary care access due to their geographic and economic status. Health policymakers need to address primary care workforce shortages in rural for the planning of human and physical resources. SAI can be recommended as a feasible method for healthcare planning and assessing service coverage in low-facility settings.

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“…Based on past literature (20,21), a deep comparison of the concentration of registered nurses in different regions of Guangdong province was conducted based on the population density. The regions were categorized into densely populated areas (PAD > 2), areas with moderate population (0.5 < PDA < 2), and sparsely populated areas (PAD < 0.5).…”

Section: Medical Resource Concentration (Hrdi)mentioning

confidence: 99%

Analysis of Resource Allocation Fairness of Registered Nurses in the Guangdong Province

Meng,

Wu,

et al. 2024

Preprint

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Background This study aimed to assess the fairness of resource allocation of registered nurses in the Guangdong Province, based on the Healthy China strategy. It aimed to identify the issues with resource allocation fairness and provide optimisation suggestions. Methods To assess the allocation fairness of registered nurses, the study used the Gini coefficient, health resource density index, Lorenz curve, and index of dissimilarity. Additionally, the study employed three methods to calculate the Gini coefficient to analyse equity among registered nurses. Results In 2021, the allocation of registered nurses in hospitals accounted for 71.22% of registered nurses in the province, that of registered nurses in general hospitals accounted for 72.73% of the hospitals, and that of registered nurses in tertiary medical institutions accounted for 62.28% of the medical institutions at all levels. In terms of health resource allocation density, the demographic density of registered nurses in Guangzhou and Zhuhai was greater than 1; the difference index was 0.44 and 0.43 by geography and population, respectively. Calculated by population, three methods were used to calculate the Gini coefficient, taking the mean value to be 0.31; according to the geographical distribution, the average of the three calculation methods was taken, and the Gini index of registered nurses in the functional districts was 0.39. Conclusion When considering the geographical allocation, it is evident that there are disparities in the fair distribution of registered nurses in Guangdong. Specifically, the allocation of registered nurses in the west wing of the coastal economic belt and the ecological development zone (mountain area) of northern Guangdong is insufficient, as indicated by the Gini coefficient of different functional zones. This study recommends improving regional coordinated development to enhance the fairness of registered nurses’ allocation in the Guangdong province.

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Effect of doctor allocation policies on the equitable distribution of doctors in Thailand

Cited by 6 publications

References 15 publications

Practice Consolidation Among US Medical Oncologists, 2015-2022

Practice Consolidation Among US Medical Oncologists, 2015-2022

Primary care access and feasibility of Spatial Accessibility Index (SAI) in assessing primary care in rural Sri Lanka

Analysis of Resource Allocation Fairness of Registered Nurses in the Guangdong Province

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