Distribution of hospital beds in Tehran Province based on Gini coefficient and Lorenz curve from 2010 to 2012

Asl, Iravan Masoudi; Abolhallaje, Masoud; Raadabadi, Mehdi; Nazari, Hamed; Nazari, Ali; Salimi, Mojtaba; Javani, Ali

doi:10.19082/1653

Cited by 14 publications

(7 citation statements)

References 10 publications

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“…We applied the Gini coefficient to present the extent of in-depth author domains like the World Bank, UN, did for evaluating the inequality of a nation's wealth and many healthcare settings assessing the distribution of hospital beds, [ 42 ] the inefficiencies in esophageal adenocarcinoma screening, [ 43 ] the equality of medical health resource allocation on geographical areas in Chian, [ 44 ] and the child mortality in Taiwan compared with industrialized countries. [ 45 ]…”

Section: Discussionmentioning

confidence: 99%

Applying Gini coefficient to evaluate the author research domains associated with the ordering of author names

Chien¹,

Chow

Chang

et al. 2018

Medicine

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Background:Team science research includes the number of coauthors in publications. Many papers have discussed the ordering of author names and the contributions of authors to a paper. However, no paper addresses the relation between authors’ research domains and personal impact factors (PIF) with the ordering of author names. We aimed to apply Gini coefficient (GC) to evaluate the author research domains associated with the PIF and the ordering of author names on academic papers.Methods:By searching the PubMed database (Pubmed.com), we used the keyword “medicine” [journal] and downloaded 10,854 articles published from 1969 to 2018. A total number of 7502 articles labeled with complete author's countries/areas were included in data analysis. We also proposed a PIF index and jointly applied social network analysis (SNA), the GC, and Google Maps to report the following data with visual representations: the trend of author collaboration in Medicine; the dominant nations and keywords in Medicine; and the author research domains in Medicine associated with the PIF and the ordering of author names on academic papers.Results:The trend of author collaboration in Medicine is slightly declining (= −0.06) based on the number of authors per article. The mean number of individuals listed as authors in articles is 7.5. Most first authors are from China (3649, 48.64%) and Taiwan (847, 11.29%). The median of GC (0.32) and PIF (0.74) for the middle authors are obviously less than those for the first (0.53, 2.19) and the last authors (0.42, 2.61). A perfect positive linear relation with a large effect exists between GC and PIF because the correlation coefficient is 0.68 (>0.50, t = 2.48, n = 9).Conclusion:Results suggest that the corresponding author is submitting the manuscript to the target journal with a core author's academic background and the personal impact factor related to the research domain and the journal scope in the future. As such, peer reviewers can quickly determine whether the manuscript is a potentially citable research paper.

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Section: Discussionmentioning

confidence: 99%

Applying Gini coefficient to evaluate the author research domains associated with the ordering of author names

Chien¹,

Chow

Chang

et al. 2018

Medicine

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“…The unequal distribution of healthcare professionals, particularly doctors, has emerged as a growing concern in the global healthcare sector [ 4 ]. For example, in Japan, Matsumoto et al found a significant increase in the Gini coefficient after 2006, indicating an inappropriate distribution of doctors [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Inequality in the distribution of resources in the health sector before and after the Health Transformation Plan in Qazvin, Iran

Nasiri,

Amerzadeh,

Yusefzadeh

et al. 2024

J Health Popul Nutr

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Background The equitable distribution of healthcare resources represents a paramount objective in the realm of global health systems. Thus, the present study sought to assess the fairness in the allocation of health resources at Qazvin University of Medical Sciences (QUMS), both prior to and subsequent to the implementation of the Health Transformation Plan (HTP) using the Gini coefficient and the Hirschman–Herfindahl index (HHI). Methods This descriptive-analytical study aimed to investigate the distribution of healthcare resources among general practitioners (GPs), specialists, and subspecialists employed at QUMS between 2011 and 2017. Demographic data pertaining to the cities were obtained from the statistical yearbooks of the Statistical Center of Iran, while information regarding the healthcare workforce was extracted from QUMS records. The analysis utilized two key measures, namely the Gini coefficient and the HHI, to assess the fairness of resource distribution. Data analysis was performed using Microsoft Excel 2016 and the Stata statistical software. Results The highest number of GPs, specialists, and subspecialists was observed in 2014, 2017, and 2017, respectively, while the lowest number was recorded in 2016, 2011, and 2015, respectively. From 2011 to 2017, the Gini coefficient for GPs ranged between 0.61 and 0.63. Among specialists, the lowest Gini coefficient value was observed in 2015 (0.57), while the highest was recorded in 2017 (0.60). The Gini coefficient for subspecialists remained constant at 0.52 from 2011 to 2017. The HHI revealed a high concentration of GPs in the cities of Qazvin province. Although the disparity gradually decreased in the specialties of eye, ear, nose, and throat, and pediatrics, the concentration still persists in Qazvin. In general surgery, the index value is low, indicating some level of inequality. In anesthesia and neurology, the index value decreased after the HTP and reached 5700; however, achieving equality (below 1000) still requires further efforts. No significant change in the index was observed after the HTP in specialties such as neurosurgery, rehabilitation, and nuclear medicine. Subspecialists also exhibited a concentration in the city of Qazvin. Conclusion Based on the analysis of the Gini and HHI, it is evident that the distribution of GPs has not undergone significant changes following the implementation of the HTP. The Gini coefficient, which ranges from 0.4 to 0.6, indicates a high to complete level of inequality in the distribution of specialists and subspecialists. Moreover, the HHI exceeds 1000, reflecting a concentration of resources in specific areas. As a result, the HTP has not yet achieved its goal of ensuring a fair distribution of human resources. To address this issue, it is recommended to redesign distribution policies, including the allocation of physical health resources, such as specialized hospitals, beds, and medical equipment. Additionally, increasing student admissions in specialized and subspecialized fields and implementing tariff incentives can contribute to a more equitable distribution of resources. By aligning distribution policies with the principle of fairness, the healthcare system can better address the issue of resource distribution.

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“…Nowadays, researchers and policymakers are paying increasing attention to the distribution of health resources such as beds, physicians, and equipment as indicators of public health (Asl et al, 2015). However, the heterogeneous and inappropriate allocation of these expensive resources has resulted in a waste of resources in health systems (Miao et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Identification and prioritization of indicators of hospital bed allocation in Iran

Najibi

Lotfi

Kharazmi

et al. 2022

World Med & Health Policy

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The distribution of hospital beds can be used as an indicator to assess the distribution of health services. This study aimed to determine and prioritize indicators of hospital bed allocation in Iran. In the first phase of the study, we used a scoping review to identify criteria affecting hospital bed allocation. Then, we applied an expert panel and the Delphi method to localize the criteria. Finally, we carried out Analytic Network Process method based on Decision Making Trial and Evaluation Laboratory technique (DANP), to determine the weight and importance of indicators and to evaluate the causal relationship between them. Based on the scoping review, we identified 75 criteria that influence bed allocation. In 2 Delphi cycles, 28 criteria were selected and included in the DANP phase. The population, the presence of a reference hospital in the region, the type of hospital, the common diseases in the region, and the presence of home care services were the variables that had the most interaction with other variables. The variable of home care services was at the highest level of the causal chain. Bed occupancy rate, average length of stay, waiting list, as well as type and level of the hospital had the highest priority in the allocation of hospital beds. Furthermore, given the importance of home care services, policies should be adopted to expand these services nationwide. The indicators identified in this study will be useful for the development of policies aimed at an equitable allocation of hospital beds.

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Distribution of hospital beds in Tehran Province based on Gini coefficient and Lorenz curve from 2010 to 2012

Cited by 14 publications

References 10 publications

Applying Gini coefficient to evaluate the author research domains associated with the ordering of author names

Applying Gini coefficient to evaluate the author research domains associated with the ordering of author names

Inequality in the distribution of resources in the health sector before and after the Health Transformation Plan in Qazvin, Iran

Identification and prioritization of indicators of hospital bed allocation in Iran

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