Fair Utilization Research Articles

FAIRLANE: A multi-agent approach to priority lane management in diverse traffic composition

The rise of autonomous driving technologies prompts a reevaluation of traditional urban traffic control and lane management. Dedicated lanes for connected and autonomous vehicles (CAVs), with intermittent access for other vehicles, have been proven to enhance road capacity and reduce underutilization moderately. However, this assumes all non-CAVs are smart vehicles, which is different from the current baseline for the street vehicle mix. Presently, our streets feature a mix of CAVs, smart vehicles, and human-driven vehicles, and the research on dedicated lanes using the realistic mixed traffic environment is missing. In this paper, we investigate the enhancement of road utilization using realistic mixed traffic combinations and identify the penetration rate of CAVs and smart vehicles necessary to improve baseline utilization. Previous studies have focused on lane-management strategies in single-vehicle settings, neglecting the interaction of CAVs with neighboring CAVs and smart vehicles. Therefore, we propose a multi-agent reinforcement learning-based framework to facilitate fair utilization of priority lanes, considering driving comfort, traffic efficiency, and safety during lane-changing. Through multiple experiments on a realistic network, our results demonstrate that the proposed framework significantly improves traffic efficiency, particularly when the penetration rate of CAVs is below 40% and Semi-Autonomous Vehicles (SAVs) constitute 50% of the remaining vehicles. The framework outperforms traditional lane management strategies, reducing mean waiting time and increasing average speed. This study provides nuanced information on different vehicle penetration rates, enabling more informed decisions on when to install priority lanes. This highlights the importance of considering mixed traffic environments in designing autonomous vehicle infrastructure and sets the stage for future advancements in urban traffic management.

Parent-Led Applied Behavior Analysis to Impact Clinical Outcomes for Individuals on the Autism Spectrum: Retrospective Chart Review.

Autism spectrum disorder (ASD) can have traits that impact multiple domains of functioning and quality of life, which can persevere throughout life. To mitigate the impact of ASD on the long-term trajectory of an individual's life, it is imperative to seek early and adequate treatment via scientifically validated approaches, of which applied behavior analysis (ABA) is the gold standard. ABA treatment must be delivered via a behavior technician with oversight from a board-certified behavior analyst. However, shortages in certified ABA therapists create treatment access barriers for individuals on the autism spectrum. Increased ASD prevalence demands innovations for treatment delivery. Parent-led treatment models for neurodevelopmental conditions are effective yet underutilized and may be used to fill this care gap. This study reports findings from a retrospective chart review of clinical outcomes for children that received parent-led ABA treatment and intends to examine the sustained impact that modifications to ABA delivery have had on a subset of patients of Montera, Inc. dba Forta ("Forta"), as measured by progress toward skill acquisition within multiple focus areas (FAs). Parents received ≥40 hours of training in ABA prior to initiating treatment, and patients were prescribed focused (<25 hours/week) or comprehensive (>25-40 hours/week) treatment plans. Retrospective data were evaluated over ≥90 days for 30 patients. The clinical outcomes of patients were additionally assessed by age (2-5 years, 6-12 years, 13-22 years) and utilization of prescribed treatment. Treatment encompassed skill acquisition goals; to facilitate data collection consistency, successful attempts were logged within a software application built in-house. Improved goal achievement success between weeks 1-20 was observed for older age, all utilization, and both treatment plan type cohorts. Success rates increased over time for most FAs, with the exception of executive functioning in the youngest cohort and comprehensive plan cohort. Goal achievement experienced peaks and declines from week to week, as expected for ABA treatment; however, overall trends indicated increased skill acquisition success rates. Of 40 unique combinations of analysis cohorts and FAs, 20 showed statistically significant positive linear relationships (P<.05). Statistically significant positive linear relationships were observed in the high utilization cohort (communication with P=.04, social skills with P=.02); in the fair and full utilization cohorts (overall success with P=.03 for the fair utilization cohort and P=.001 for the full utilization cohort, and success in emotional regulation with P<.001 for the fair utilization cohort and P<.001 for the full utilization cohort); and in the comprehensive treatment cohort (communication with P=.001, emotional regulation with P=.045). Parent-led ABA can lead to goal achievement and improved clinical outcomes and may be a viable solution to overcome treatment access barriers that delay initiation or continuation of care.

A distributed end-to-end fair bandwidth allocation algorithm for multi-path networks

Multi-path transmission significantly improves network performance, yet it complicates the problem of fair resource allocation. While traditional fair bandwidth allocation schemes thrive in single-path settings, they often falter when applied to multi-path environments, highlighting the challenge of achieving fair bandwidth sharing in such networks. To tackle this issue, the concept of "max-min similarity" of queuing delays has been introduced based on insight into the intrinsic interactions between queuing packets, delays, and bandwidth allocation, which leads to a formal definition of max-min fair bandwidth allocation for multi-path environments. Theoretical analysis shows that the max-min similarity of queuing delays is a sufficient condition for max-min fair bandwidth allocation in single bottleneck environments. A novel distributed end-to-end fair bandwidth allocation algorithm, named DMFBA, is then proposed, which separates the control into flow-level and transmission path-level. In achieving max-min similarity in queuing delays by dynamically adjusting the distribution of flows’ queuing packet quotas across paths it achieves the goal of max-min fair bandwidth allocation. Two sets of numerical simulation experiments were conducted and the results show that DMFBA has less overhead and faster convergence than the traditional utility fair algorithms.

The Potential of Ketamine in Palliative Care: Ethical and Legal Considerations in Ghana

This study delves into exploring how ketamine could be beneficial, in providing care in Ghana investigating the legal considerations surrounding its usage. By utilizing the CRuPAC method (Context, Rule, Principle, Application and Conclusion) the research assesses how effective and safe ketamine is in addressing pain and depression identifies principles at play and reviews the legal frameworks regulating medication use within Ghana. The results underscore the potential of ketamine in enhancing symptom management and quality of life for patients receiving care while underscoring the importance of an approach to ensure its safe ethical and fair utilization. The study puts forth suggestions such as establishing protocols offering training initiatives and incorporating ketamine, into the National Health Insurance Scheme. Its significance lies in guiding policy making based on evidence and practices that prioritize welfare and independence.

The Potential of Ketamine in Palliative Care: Ethical and Legal Considerations in Ghana

This study delves into exploring how ketamine could be beneficial, in providing care in Ghana investigating the legal considerations surrounding its usage. By utilizing the CRuPAC method (Context, Rule, Principle, Application and Conclusion) the research assesses how effective and safe ketamine is in addressing pain and depression identifies principles at play and reviews the legal frameworks regulating medication use within Ghana. The results underscore the potential of ketamine in enhancing symptom management and quality of life for patients receiving care while underscoring the importance of an approach to ensure its safe ethical and fair utilization. The study puts forth suggestions such as establishing protocols offering training initiatives and incorporating ketamine, into the National Health Insurance Scheme. Its significance lies in guiding policy making based on evidence and practices that prioritize welfare and independence.

Impact of Corruption on Utility Prices: A Theoretical and Empirical Analysis for the Electricity Markets

This study investigates the influence of corruption on utility prices. We develop a game-theoretical model to explore the interactions between the government, an interest group formed by firms, and consumers in determining utility prices. The model suggests that an increase in corruption correlates with a rise in utility prices, a relationship confirmed through empirical analysis of electricity sectors in 21 OECD countries from 1995 to 2015. Additionally, we explore the effects of regulatory reforms on electricity prices, revealing mixed outcomes. The findings emphasise the importance of anti-corruption efforts in shaping fair utility prices.

Robust Consensus Modeling: Concerning Consensus Fairness and Efficiency with Uncertain Costs

Extensive uncertainty can affect the efficiency and fairness of consensus in the consensus reaching process (CRP), but few scholars have studied consensus modeling that focuses on fairness and efficiency in uncertain environments. Additionally, the weight of the decision maker (DM) in the CRP is influenced by multiple factors. Therefore, this paper proposes robust consensus models (EFCMs) focusing on fairness and efficiency under uncertain costs to address these issues. Firstly, this paper constructs multiple uncertainty sets to describe the uncertainty of the unit adjustment cost. Secondly, the fair utility level and opinion adjustment distance are used to measure the fairness and efficiency of reaching consensus, respectively. Furthermore, this paper uses a data-driven method based on the KDE method combined with trust propagation in social networks to determine the DMs’ weights jointly. Finally, this paper also applies the proposed models to the carbon emission reduction negotiation process between the government and enterprises. The experimental results and sensitivity analysis show that the consensus cost budget and the DMs’ jealous preference behavior particularly affects the efficiency of reaching consensus, which provides a theoretical basis for solving practical decision making problems.

Towards a collective opinion generation approach with multiple objectives for evaluating rail transit station accessibility in urban areas

Urban rail transit can alleviate traffic congestion if sufficient ridership is achieved. The accessibility to rail transit stations largely determines public willingness to utilize urban rail transit. The current studies on the accessibility to rail transit stations tend to concentrate solely on individual rail transit lines or stations, neglecting the impact of the external macro-environment. Consequently, the outcomes of these measurements cannot accurately reflect the overall accessibility level of the region, nor do they furnish decision-makers with references for devising plans for rail transit development. This study introduces a novel approach for evaluating the accessibility to urban rail transit stations from a macro-level standpoint, utilizing expert knowledge. The present study conducts an analysis of the impact of political, economic, social, and technological factors on the accessibility to rail transit stations in urban areas. Subsequently, a comprehensive evaluation indicator system is developed based on the aforementioned analysis. Then, experts are summoned to provide their subjective evaluations for every indicator, which are depicted as probability distribution functions. The collective evaluation is derived through the aggregation of individual evaluations utilizing a bi-objective optimization approach that factors in both the collective fairness utility and the confidence level. Finally, the quantile average method is employed to consolidate the collective assessment outcomes of individual indicators, thereby deriving the level of accessibility to rail transit stations in urban areas. We design a small-scale application experiment and attempt to evaluate the accessibility to rail transit stations in Wuchang District, Wuhan with the proposed approach in order to demonstrate the feasibility of the approach.

Towards a holistic view of bias in machine learning: bridging algorithmic fairness and imbalanced learning

Machine learning (ML) is playing an increasingly important role in rendering decisions that affect a broad range of groups in society. This posits the requirement of algorithmic fairness, which holds that automated decisions should be equitable with respect to protected features (e.g., gender, race). Training datasets can contain both class imbalance and protected feature bias. We postulate that, to be effective, both class and protected feature bias should be reduced—which allows for an increase in model accuracy and fairness. Our method, Fair OverSampling (FOS), uses SMOTE (Chawla in J Artif Intell Res 16:321–357, 2002) to reduce class imbalance and feature blurring to enhance group fairness. Because we view bias in imbalanced learning and algorithmic fairness differently, we do not attempt to balance classes and features; instead, we seek to de-bias features and balance the number of class instances. FOS restores numerical class balance through the creation of synthetic minority class instances and causes a classifier to pay less attention to protected features. Therefore, it reduces bias for both classes and protected features. Additionally, we take a step toward bridging the gap between fairness and imbalanced learning with a new metric, Fair Utility, that measures model effectiveness with respect to accuracy and fairness. Our source code and data are publicly available at https://github.com/dd1github/Fair-Over-Sampling.

Quadratic Optimization Models for Balancing Preferential Access and Fairness: Formulations and Optimality Conditions

Typically, within facility location problems, fairness is defined in terms of accessibility of users. However, for facilities perceived as undesirable by communities hosting them, fairness between the usage of facilities becomes especially important. Limited research exists on this notion of fairness. To close this gap, we develop a series of optimization models for the allocation of populations of users to facilities such that access for users is balanced with a fair utilization of facilities. The optimality conditions of the underlying nonconvex quadratic models state the precise balance between accessibility and fairness. We define new classes of fairness and a metric to quantify the extent to which fairness is achieved in both optimal and suboptimal allocations. We show that a continuous relaxation of our central model is sufficient to achieve a perfect extent of fairness, while a special case reduces to the classical notion of proportional fairness. Our work is motivated by pervasive ecological challenges faced by the waste management community as policymakers seek to reduce the number of recycling centers in the last few years. As a computational case study, applying our models on data for the state of Bavaria in Germany, we find that even after the closure of a moderate number of recycling centers, large degrees of access can be ensured, provided that the closures are conducted optimally. Fairness, however, is impacted more, with facilities in rural regions shouldering larger loads of visiting populations than those in urban regions. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods &amp; Analysis. Funding: Computer resources and support provided by the Erlangen Regional Computing Center are gratefully acknowledged. B. Singh was partially financially supported by the Bavarian State Ministry for Science and Art (Bayerisches Staatsministerium für Wissenschaft und Kunst) under the Competence Network for Scientific High Performance Computing in Bavaria. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0308 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0308 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Mitigation of Harmonic Distortions in Low Voltage Distribution Grids With Fair Utilization of Resources

Mitigation of Harmonic Distortions in Low Voltage Distribution Grids With Fair Utilization of Resources

Aligning expectations with real-world experiences: a talent management study on the restaurant industry in Finland

PurposeSkilled workers are crucial for an organization’s success, and managing, retaining and attracting them is vital in long-term. This study aims to explore talent management practices in the Finnish restaurant industry and to align workers' expectations with the real-world experiences of their work to reduce turnover and enhance job satisfaction.Design/methodology/approachThe study adopts a mixed methods approach, including a survey and interviews with workers and managers to gain insights into their expectations and experiences of work. The study considers themes for designing and implementing effective talent management procedures.FindingsThis study highlights the importance of employees' experiences of their work conditions, leveraging positive emotions and fair utilization of temporary agency work (TAW). Understanding the different work preferences of generational cohorts and addressing the challenges associated with owner disengagement and TAW can also contribute to attracting and retaining talent in the restaurant industry.Originality/valueSkilled workers have often been portrayed as targets that need to be managed, with insufficient consideration given to their preferences, needs and expectations. With the findings of this study, companies can establish mutual understanding with their employees and attract diverse talent.

IRS-Aided Joint Spatial Division and Multiplexing for mmWave Multiuser MISO Systems

Intelligent reflecting surface (IRS)-aided millimeter wave (mmWave) communication systems have gained considerable attention recently. However, the benefits brought by IRS require the instantaneous channel state information (I-CSI) of the cascaded base station (BS)-IRS and IRS-user channel which is difficult to obtain in practice, especially for the multiuser scenario. To address this issue, in this paper, we combine two-timescale beamforming and multi-IRS aided joint spatial division and multiplexing (JSDM) in a mmWave multiuser system. Specifically, all the users are first divided into different groups and each group is associated with an IRS. Then, we propose a novel two-stage grouping-based randomized beamforming (TS-GRB) scheme, where the analog beamformer is designed based on the statistical CSI (S-CSI) in the first stage, and the short-term digital beamformer at the BS and long-term passive beam pattern control policy at the IRSs are jointly optimized in the second stage with both S-CSI and dimension-reduced effective I-CSI. In particular, in the first stage, the analog beamformer is designed to reduce the inter-group interference (IGI) and effective channel dimension, while in the second stage, a two-timescale randomized joint beamforming (TRJB) algorithm is proposed to maximize the proportional fairness utility (PFU). We show that through two-timescale beamforming, JSDM and proper problem reformulation, the pilot overhead of our TS-GRB scheme is significantly lower than existing schemes. Finally, simulation results are presented to illustrate the effectiveness of the proposed TS-GRB scheme.

Enhancing the Efficiency of Electric Vehicles Charging Stations Based on Novel Fuzzy Integer Linear Programming

The electric vehicles (EVs) charging stations (CSs) at public premises have higher installation and power consumption costs. The potential benefits of public CSs rely on their efficient utilization. However, the conventional charging methods obligate a long waiting time and thereby deteriorate their efficiency with low utilization. This paper suggests a novel fuzzy integer linear programming and a heuristic fuzzy inference approach (FIA) for CSs utilization. The model introduces the underlying fuzzy inference system and a detailed formulation for obtaining the optimal solution. The developed fuzzy inference incorporates the uncertain and independent available power, required state-of-charge, and dwell time from the power grid and EVs domains and correlates them into weighted control variables. The FIA automates the service provision for the EVs with the most urgent requirements by resolving the objective function utilizing the weighted control variables, thereby optimizing the waiting time and the CSs utilization. To evaluate the effectiveness of the proposed FIA, several case studies were conducted, corresponding to different parking capacities and installations of CSs. Moreover, the simulations were conducted on EVs with varying battery capacities, and their performance was evaluated based on several metrics, including average waiting time, utilization of CSs, fairness, and execution time. The simulation results have confirmed that the effectiveness of the proposed FIA scheduling method is considerably higher than that of the other methods discussed.

Knowledge and utilization of standardized stroke outcome measures among physiotherapists in tertiary health institutions in Anambra State, Nigeria

BackgroundKnowledge and utilization of standardized stroke outcome measures (SOMs) are important as it helps in the evaluation of patient response to therapy and decision of further step in patient care. This study evaluated the current knowledge and utilization of standardized stroke outcome measures and the factors that influence them.MethodsA cross-sectional survey research design was used for the study. The consecutive sampling technique was used to recruit 40 (16 females and 24 males) respondents from two tertiary hospitals located in Anambra State. Data were analyzed using the IBM Statistical Package for the Social Sciences (SPSS) version 21. The frequency counts and percentages, mean and standard deviation, chi-square test, and Spearman rank-order correlation were used in the analysis of different variables. The alpha level was set at 0.05.ResultsA total of 37.5% had poor knowledge, 30% had fair knowledge, and 32.5% had good knowledge of SOMs. The highest familiarity (i.e., very familiar) was observed as follows: 6-min walk test (72.5%), Modified Ashworth Scale (70%), Barthel Index (65.75%), Mini-mental Stroke Examination (52.5%), Functional Independence Measure (65%), Berg Balance Scale (50%), and National Institute of Health Stroke Scale (50%). Poor utilization was observed in % of the respondents, fair utilization was observed in 25%, and good was observed in 20% of them. There was a positive correlation between knowledge and utilization of standardized stroke outcome measures (p < 0.01). Knowledge and utilization were each significantly associated with gender and educational qualification. Utilization alone was significantly associated with years of experience and the center of practice.ConclusionThere is still a deficit in the awareness and utilization of standardized stroke outcome measures in the study population. There has not been an appreciable improvement in the knowledge of SOMs among the population of physiotherapists that participated in the current study. However, the utilization of SOMs is still at a low level. This requires action from critical stakeholders in Nigeria’s health system to encourage its regular use in clinical practice as it will serve to improve service delivery to patients.

Assessment of Diagnostic Accuracy and Clinical Utility of DNA Methylation (5-mC) in Detecting Severity of Occupational Lead Exposure.

This study investigated the diagnostic accuracy (DA) and clinical utility (CU) of DNA methylation (5 methylcytosine) in occupational Pb-exposure from Pb based industry. Blood Lead levels (BLLs) were measured using the ICP-OES method. The total DNA methylation (5-mC) was quantified using ELISA method. Based on their BLLs, the Pb-exposed workers were categorised into three groups: low (< 10µg/dL), moderate (10-30µg/dL), and high exposure (> 30µg/dL). DNA methylation (5-mC) was significantly lower in moderate and high Pb-exposure groups when compared to the low Pb-exposure group. Workers exposed to high levels of Pb-exposure, the DA variables of 5-mC showed that the sensitivity was 74.7% [95% CI 65.4-84.0], specificity was 69.6% [95% CI 50.8-88.4], positive predictive value (PPV) was 89.9% [95% CI 82.7-97.0], Postive likelihood ratio (LR+) was 2.454 [95% CI 1.3-4.6], and diagnostic odds ratio (DOR) is 6.3 [95% CI 6.5-7.7]. In moderate Pb-exposure, the DA variables of 5-mC revealed that the sensitivity is 64.9% [95% CI 55.2-74.5], the specificity is 69.6% [95% CI 50.8-88.4], the PPV is 89.7% [95% CI 82.5-97.0], the LR+ is 2.132 [95% CI 1.13-4.03], and the DOR is 4.2 [95% CI 3.6-5.7]. The high Pb-exposure group had higher DA metrics when compared to moderate Pb exposure. The clinical utility (CU+) of 5-mC was found to have good utility of 0.671 [95% CI 0.566-0.776] in the high Pb exposure group and fair utility of 0.582 [95% CI 0.470-0.694] in moderate Pb exposure group. In conclusion, DNA methylation (5mC) could be used as a predictive biomarker for high Pb-exposure.

Profit-Sharing Contract of the Fresh Agricultural Products Supply Chain under Community Group Purchase Mode Considering Freshness Preservation Efforts

This article constructed a four-level fresh agricultural product (FAP) supply chain with a two-stage pricing strategy under a “community group purchase (CGP) platform + direct procurement from the FAP supplier” sales model. We investigate the influence of the CGP agency’s participation in the control strategy of FAP freshness preservation efforts on the profits of supply chain stakeholders. This article discusses the effects of the FAP supplier profit-sharing ratio, the CGP agency profit-sharing ratio, and consumers’ sensitivity to FAP freshness on the supply chain stakeholders’ freshness preservation efforts. Moreover, based on the fairness preference theory, this article designed a profit-sharing contract that involves the Nash bargaining game between the FAP supplier and the CGP agency as the supply chain coordination mechanism. Modeling results revealed that: (1) The CGP agency’s freshness preservation efforts increased total supply chain profits. (2) The FAP supplier profit-sharing ratio, CGP agency profit-sharing ratio, and consumers’ sensitivity to FAP freshness have a positive correlation to the profits of the FAP supply chain and promote the coordination of the supply chain. (3) Considering fairness preferences, with the increase in FAP suppliers’ business negotiating ability, their freshness preservation efforts and fairness utility both increased gradually, while the fairness utility of the CGP agency gradually decreased.

Fairness-aware large-scale collective opinion generation paradigm: A case study of evaluating blockchain adoption barriers in medical supply chain

Generating collective opinion based on probability distribution function aggregation occupies crucial roles in accomplishing probabilistic risk analysis, probabilistic-forecast-based prediction, and uncertain assessment tasks. However, rare efforts have been paid to exploring collective opinion generation based on massive quantified judgments from large-scale experts. In this study, we establish a novel large-scale collective opinion generation paradigm based on probability distribution function aggregation to accomplish complicated assessment and evaluation tasks in decision analysis. To this end, we reformulate the existing bi-objective optimization model with the exclusion of the consensus dimension and inclusion of fairness concern among subject matter experts. The formulation of collective fairness utility uses the notion of aggregation functions and contributes to the establishment of large-scale collective opinion generation paradigm with capabilities of modeling distinct fairness distributions among the subject matter experts. The established fairness-aware large-scale collective opinion generation model advocates an adjusted bi-objective optimization model that maximizes the confidence level and fairness utility within the decision group for opinion aggregation on both intra-group and inter-group levels. It is endowed with the flexibility to be combined with context-specific expert classification and criterion weight assignment techniques to generate powerful large-scale group decision making prototypes that accommodate with diversifying decision-making scenarios. We apply the proposed fairness-aware large-scale collective opinion generation framework to the assessment of blockchain adoption barriers in medical supply chain to demonstrate its rationality and effectiveness.

에벤 에즈라 로버츠 건축의 영향 및 유형 분석에 관한 연구

This study aims to analyze the influence and Type of the architecture of Eben Ezra Roberts, a U.S. local architect in the early modern era, as the background of Oak Park's living culture. This aims is based on statistical quantification of the early modern architectural conditions and the role of architects in Oak Park, a local city adjacent to Chicago, the United States. Eben Ezra Roberts approached the Oak Park master architect, Frank Lloyd Wright which would not directly analyze Chicago's urban architecture or Frank Lloyd Wright's architecture. Statistics of auxiliary programs will be actively utilized to maintain the objectivity of research progress and to ensure fairness in data utilization. The conclusions drawn from this process are as follows: Unlike the early modern metropolitan city of Chicago, it was confirmed that it was a transformative period in which local architect Eben Ezra Roberts' popular functional architecture settled into a local living and cultural city. In addition, more than 250 architectures were built with more active and diverse architectural activities than master architect Frank Lloyd Wright, and it was confirmed that 137 of his architectures still function as daily living cultures of the city. Roberts can also be defined as developing his diverse architectural capabilities into the life-culture architecture of Oak Park's popular functions rather than imitating and following master architects.

Inequality of opportunity in health service utilization among middle-aged and elderly community-dwelling adults in China

BackgroundThe inequality caused by circumstances is known as "inequality of opportunity" (IOp). Many scholars have studied IOp in the health field, but few studies have quantified contributors to the IOp of health service utilization among middle-aged and elderly people. This study measured the IOp of health service utilization and decomposed the contributors to IOp present among Chinese middle-aged and elderly people.MethodsData were obtained from the China Health and Retirement Longitudinal Study (CHARLS) in 2013, 2015 and 2018. A mean-based regression method was adopted to measure the IOp of health service utilization. Shapley–Shorrocks decomposition was used to analyze the main contributors to IOp seen among the middle-aged and elderly.ResultsAlthough the absolute IOp of health service utilization decreased over time, IOp still explains the total inequality to a large extent. The absolute IOp and relative IOp were greatest in the areas of self-treatment and inpatient care utilization, respectively. Shapley decomposition results showed that the out-of-pocket (OOP) ratio contributed most to the IOp of outpatient care utilization; and the residence area highly explains the IOp of inpatient service utilization. Meanwhile, social and economic factors such as work status and income contribute more to the IOp of inpatient care utilization than outpatient and self-treatment.ConclusionsStrategies aimed at achieving equal opportunities remain necessary to ensure the fairness of health service utilization. Policies and measures should further adjust the medical insurance compensation policies, and pay more attention to the middle-aged and elderly residents in rural areas, optimize health resource allocation, improve the social security systems, and narrow the socioeconomic gap between urban and rural areas in China.