Effect of Collaborative Governance on Medical and Nursing Service Combination: An Evaluation Based on Delphi and Entropy Method

chang, beiquan; Yang, Yansui; Leon, Guillermo Andres Buitrago

doi:10.21203/rs.3.rs-704236/v1

Cited by 1 publication

(1 citation statement)

References 19 publications

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“…constructed a comprehensive quantitative evaluation model to effectively identify and evaluate the major risks of the food cold chain, by combining the AHP method and entropy weight method [22]. Chang et al and Mo et al both established a framework to determine the key factors by calculating the comprehensive weight of the Delphi and entropy weight method [23,24]. Therefore, it is necessary to develop a comprehensive risk factor analysis for MT-ZVL transmission applying a combined evaluation method.…”

Section: Introductionmentioning

confidence: 99%

Establishment of an indicator framework for transmission risk of the Mountain-Type Zoonotic Visceral Leishmaniasis based on the Delphi-entropy weight method

Luo¹,

Zhou²,

Hao³

et al. 2022

Preprint

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Background: Visceral leishmaniasis (VL) is one of the most important neglected tropical diseases transmitted by the biting of sandflies. Although VL has been eliminated in several regions of China during the last century, the mountain-type zoonotic visceral leishmaniasis (MT-ZVL) have reemerged in the hilly areas of China in recent decades. The purpose of this study is to set up an indicator framework for assessing the transmission risk of the MT-ZVL in China, to provide insights into the formulation of MT-ZVL control strategies.Methods: A 3-level indicator framework was established based on literature review and expert interviews. The indicator system was screened according to scientific information, necessity, operability, and the weighted importance scoring of the indicators, and the normalized weight of each indicator are calculated. The reliability of the Delphi method was evaluated by the positive coefficient of the experts, the degree of expert authority, and the coordination coefficient of experts' opinions. The comprehensive weight of the tertiary indicators was determined by the Delphi and the entropy weight method. Results: A total of 30 questionnaires were delivered, and 28 received valid responses (93.3%). The expert authority coefficients were greater than 0.80 after two rounds of expert consultation, and the coordination coefficient of the experts’ opinions was 0.250−0.277 and 0.187−0.272 in each round, respectively (all, P < 0.05). An indicator framework was finally established including four primary indicators, 11 secondary indicators, and 35 tertiary indicators. Among the primary indicators, the variable with the highest normalized weight was biological factors (0.268), followed by interventions (0.261), environmental factors (0.242), and social factors (0.229). Among the secondary indicators, the top 4 normalized weights included climatic features (0.122), geographical features (0.120), sandflies (0.097), and dogs (0.096). Among the tertiary indicators, the top 4 normalized comprehensive weights were the population density of sandflies (0.075), topography (0.057), the population density of dogs and dog tethering (0.056) and the use of bed nets or other protective measures (0.056). Conclusions: This study constructed an indicator framework for assessing the transmission risk and strengthening public health education of MT-ZVL based on the Delphi and entropy weight method, and provided insights into formulating strategies and measures for the prevention and control of MT-ZVL in China.

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Section: Introductionmentioning

confidence: 99%