Resource Allocation Decisions Research Articles

Preface

The Geomatics International Conference (GeoICON) is an annual scientific meeting organized by Department of Geomatics Engineering, Institut Teknologi Sepuluh Nopember Surabaya, Indonesia, since 2016. The conference carried out a theme, “Geospatial Technology for Mapping the Future: Integrated Mapping for Sustainable Development”. This theme is chosen because we believe that harnessing geospatial technology for comprehensive mapping is instrumental in supporting sustainable development across diverse sectors. Geospatial technology provides a powerful means to collect, analyze, and visualize critical spatial data, enabling informed decision-making for environmental conservation, urban planning, disaster management, resource allocation, and many other aspects crucial to sustainable development. By exploring innovative applications and interdisciplinary approaches within geospatial technology, this conference aims to foster collaboration and knowledge exchange, ultimately contributing to a more sustainable and resilient future.The 8th Geomatics International Conference (GeoICON) 2023 presents a diverse and comprehensive agenda with eight focal topics, each addressing crucial aspects of geospatial technology and its applications. These topics such as: (A) Geodesy and Geodynamics; (B) Geology and Geophysics; (C) Spatial Data Infrastructure; (D) Geography and Urban Environment; (E) Agriculture and Forestry; (F) Mining and Energy; (G) Cadastral Mapping; and (H) Marine Environment. The 8th GeoICON 2023 has the rich and multifaceted backgrounds of our esteemed speakers, drawn from various sectors, including government agencies, industries, and esteemed academics. This convergence of expertise ensures a dynamic exchange of knowledge, perspectives, and innovations, fostering a vibrant platform for the exploration and advancement of geospatial technology across diverse domains.List of Keynote Speakers, The 8th GeoICON 2023 Committee are available in this Pdf.

OP44 Exploring The Disconnect In Relevant Outcomes For Health Technology Assessment-Related Economic Evaluation Relative To Care Commissioning: Implications For Resource Allocation

IntroductionOutcome-based commissioning – a set of arrangements to define and pay for a service based on pre-agreed outcomes – has been operationalized in some regional care settings (e.g., adult social care). However, it remains largely aspirational due to operational considerations and challenges. Outcomes-based commissioning shares a common goal with economic evaluation alongside health technology appraisal (HTA): to achieve value for money for outcomes from a finite budget.MethodsWe explored the considerations, implications, and challenges regarding the practical role of relevant outcomes in economic evaluation, relative to care commissioning, using England as a case study. Our exploration bridges a gap between economic evaluation evidence and practical resource allocation decision-making, focusing on conceptual (e.g., what are ‘relevant’ outcomes), practical considerations (e.g., quantifying and using relevant endpoints or surrogate outcomes alongside costs), and pertinent issues when linking these to commissioning based payment mechanisms.ResultsFirstly, there is a disconnect between existing economic evaluation approaches and commissioning processes. For example, using a single quality-adjusted life-year (QALY) maximum and limited consideration of affordability relative to cost effectiveness. Secondly, service-focused outcomes (e.g., seeing a specialist team) rather than person-focused outcomes (e.g., QALYs) are often desirable from a practical commissioning and service provider perspective as they make it easier to measure key performance indicators. Thirdly, both person- and service-focused payment structures could lead to market inefficiencies when activity is focused on only people for whom a prespecified outcome can be achieved or service delivered; these approaches require additional efficiency-equity tradeoff considerations (e.g., using distributional cost-effectiveness analyses).ConclusionsWe highlight payment structures as a major and complex consideration for commissioning, for which economic evaluation provides little to no consideration. Service-related outcomes and payments can be used as surrogate outcomes within economic modeling frameworks, while monitoring and evaluation can still be based on economic outcomes (e.g., QALYs and aggregated costs). Accounting for and explaining direct links from payment structures to economic outcomes is a major step to bridging a gap between economic evaluation evidence and practical resource allocation.

OP173 Estimating The Marginal Productivity Of Health Technology Adoption

IntroductionDecisions to adopt health technologies rely, in part, on judgements about cost effectiveness. Cost effectiveness is commonly assessed against a willingness-to-pay threshold for health gains. Building an evidence base on the marginal productivity of health spending to inform the value of the threshold is increasingly of interest for resource allocation decision-making and technology implementation. We report on an in-progress analysis to inform a threshold for policy purposes in British Columbia, Canada.MethodsWe developed a ten-year panel-data model with instrumental variables, which lessens the degree of time-invariant confounding and addresses biased causal inferences caused by unobserved factors, to provide estimates of the marginal cost per health unit measured using quality-adjusted life-years (QALYs). We use the Johns Hopkins Adjusted Clinical Group (ACG) system and a British Columbia Health System Matrix to classify patients into six resource use bands (RUBs) ranging from ‘healthy’ to ‘very high morbidity’. Patients are also classified by chronic conditions and types of services. Place of residence and geographical region of health authorities are considered. Variables included age, gender, mortality and comorbidity rates, costs of hospitalizations, emergency department and physician visits, residential and home care, laboratory services, diagnosis and medications, and quality of life. Instrumental variables included sociodemographic characteristics as reported in the Canadian census.ResultsThe largest RUB was ‘moderate’ morbidity (39.3%), while the smallest was ‘healthy’ (1.5%). The youngest was the ‘low’ morbidity (mean 31, standard deviation [SD] 21) and the oldest was ‘very high’ (mean 69, SD 17). The healthy group had the smallest mean costs (CND563, SD CND4,121; equivalent to USD421, SD USD3,083). In contrast, the ‘very high’ group had the largest (CND20,398, SD CND36,188; equivalent to USD15,258, SD USD27,069). Age and gender standardized comorbidity index scores ranged from 0.05 to 6.41 (median 0.98). Additional analyses (e.g., costs per QALY) are ongoing and the results will be reported at the conference.ConclusionsOur empirical approach is robust and flexible, allowing estimates of marginal productivity according to factors such as disease, geographical region, service type, and care sector. This work has applications at the provincial and national levels and adds to methodological literature in the field.

Managerial background, resource allocation, and team performance in the National Football League

ABSTRACT A manager’s prior experience may affect an organization’s resource allocation decisions and its performance. I examine this issue in the context of National Football League (NFL) franchises. I find that teams with coaches from an offensive background spend 5.5% points more of their salary cap space on offensive personnel, while a defensive background does not significantly affect the share of resources allocated to defensive personnel. While additional salary resources committed to offensive and defensive personnel do improve team performance in those respective areas, marginal improvements in offensive and defensive performance associated with greater spending are generally unaffected by the coach’s background, a finding suggesting that a coach’s background does not confer an informational advantage on certain types of personnel. I further find no evidence of significant differences in the effects that spending on offence and defence respectively have on team performance, suggesting efficiency within this labour market.

What Aspects of Quality of Life are Important from Palliative Care Patients' Perspectives? A Framework Analysis to Inform Preference-Based Measures for Palliative and End-of-Life Settings.

Preference-based outcome measures are commonly applied in economic analyses to inform healthcare resource allocation decisions. Few preference-based outcome measures have been specifically developed for palliative and end-of-life settings. This study aimed to identify which quality-of-life domains are most important to Australians receiving specialised palliative care services to help determine if the development of a new condition-specific preference-based outcome measure is warranted. In-depth face-to-face interviews were conducted with 18 participants recruited from palliative care services in South Australia. Data were analysed using a framework analysis drawing on findings from a systematic review of international qualitative studies investigating the quality-of-life preferences of patients receiving palliation (domains identified included cognitive, emotional, healthcare, personal autonomy, physical, preparatory, social, spiritual). Participants identified missing or irrelevant domains in the EQ-5D and QLU-C10D questionnaires and ranked the importance of domains. A priori domains were refined into cognitive, environmental, financial, independence, physical, psychological, social and spiritual. The confirmation of the eight important quality-of-life domains across multiple international studies suggests there is a relatively high degree of convergence on the perspectives of patients in different countries. Four domains derived from the interviews are not covered by the EQ-5D and QLU-C10D (cognitive, environmental, financial, spiritual), including one of the most important (spiritual). Existing, popular, preference-based outcome measures such as the EQ-5D do not incorporate the most important, patient-valued, quality-of-life domains in the palliative and end-of-life settings. Development of a new, more relevant and comprehensive preference-based outcome measure could improve the allocation of resources to patient-valued services and have wide applicability internationally.

Research-to-policy Partnerships for Evidence-Informed Resource Allocation in Health Systems in Africa: An Example Using the Thanzi Programme

ObjectivesEmpirical data on the impact of research-to-policy interventions are scant, with the few attempts mainly focusing on ensuring policymakers’ timely access to evidence and evidence-informed dialogs. MethodsThis article reflects on how the Thanzi Programme cultivates an approach of research-to-policy engagement in health economics. The program is structured around 3 interrelated pillars comprising research evidence generation, capacity and capability building, and research-and-policy engagement. Each pillar is described and examples from the Thanzi Programme are given, including illustrating how each pillar informs the other. Limitations and challenges of the approach are discussed, with examples of a way forward. ResultsThis program supports health system strengthening through addressing gaps identified by program partners. This includes providing health economics training and research and strengthened partnerships between in-country researchers and health policymakers, as well as between national and international researchers. Platforms bringing together researchers and policymakers to shape the research agenda, disseminate evidence, and foster an evidence-based dialog are institutionalized at country and regional levels. Health Economics and Policy Units have been established, which sit between the Ministries of Health and Universities, to augment policymakers and health economics researchers’ engagements on priority health policy matters and determine researchable policy questions. The establishment of the Health Economics Community of Practice as a substantive expert committee under the East Central and Southern Africa Health Community bolsters the contribution of health economics evidence in policy processes at the regional level. ConclusionsThe Thanzi Programme is an example of how a research-and-policy partnership framework is being used to support evidence-informed health resource allocation decisions in Africa. It uses a combination of high-quality multidisciplinary research, sustained research and policymakers’ engagement and capacity strengthening to use research evidence to guide and support policy makers more effectively.

The Burden of Treatment Among Delhi’s Homeless: An Analysis of Street Medicine Consultations

Background: The health challenges faced by the homeless are widely unaccounted for in the global south. In India, the lack of a primary healthcare sector in urban areas has led informal healthcare providers, such as Street Medicine, to step in. Methods: By compiling data collected by the Centre for Equity Studies’ Street Medicine teams from June 2016 to October 2018 (n =16,635), this study provides the first empirical assessment of the homeless disease burden in a global south country, while limited to only the people experiencing homelessness that the teams treat, hence being a burden of treatment. Our analysis quantifies this burden among those who seek care from Street Medicine teams and identifies variations in this burden’s distribution across demography and time. Results: The majority (n = 13,557; 81.5%) of Street Medicine cases can be attributed to 19 diagnoses or symptoms, which are mostly therapeutically-simple conditions. The distribution of disease seems to be affected by different configurations of three characteristics: demographics (age and sex), urban geography (where homeless reside), and season. Conclusion: The Street Medicine teams must reflect on the balance they wish to achieve by addressing the relatively common and mild conditions documented in the dataset, and more severe and established diseases within homeless communities. Rapid diagnostic tests for resource-constrained settings could be integrated into Street Medicine practice in order to strengthen the data on which resource allocation decisions are made and improve assessments of homeless disease burden.

Groundwater resource exploration and mapping methods: a review

Groundwater, a vital resource for various human activities and ecosystems, necessitates efficient management and sustainable utilisation. Groundwater potential zone mapping plays a pivotal role in identifying areas where groundwater resources are abundant, thereby aiding decision makers in optimal resource allocation. This review paper presents an in-depth analysis of diverse methods employed for groundwater potential zone mapping, offering a comprehensive overview of their strengths, weaknesses and recent advancements. The review covers traditional methods rooted in hydrogeological principles, as well as modern techniques that harness the power of geospatial technologies and machine learning. Furthermore, the paper explores the integration of remote sensing and geographic information systems for spatial data analysis, emphasising their role in enhancing the accuracy of potential zone mapping. In the context of recent advancements, the review sheds light on the emergence of hybrid methods that combine the strengths of multiple approaches, resulting in improved prediction accuracy and robustness. Challenges associated with each method, such as data quality, model complexity and interpretability, are critically examined, providing insights into the potential limitations and avenues for improvement. The review also emphasises the importance of validation and uncertainty assessment, ensuring the reliability of potential zone mapping results. Finally, this review paper serves as a comprehensive guide for researchers, practitioners and policymakers engaged in groundwater resource management. By offering a holistic understanding of the diverse methods available for groundwater potential zone mapping, this paper contributes to informed decision making and the advancement of sustainable groundwater-management practices.

Computing Offloading Based on TD3 Algorithm in Cache-Assisted Vehicular NOMA-MEC Networks.

In this paper, in order to reduce the energy consumption and time of data transmission, the non-orthogonal multiple access (NOMA) and mobile edge caching technologies are jointly considered in mobile edge computing (MEC) networks. As for the cache-assisted vehicular NOMA-MEC networks, a problem of minimizing the energy consumed by vehicles (mobile devices, MDs) is formulated under time and resource constraints, which jointly optimize the computing resource allocation, subchannel selection, device association, offloading and caching decisions. To solve the formulated problem, we develop an effective joint computation offloading and task-caching algorithm based on the twin-delayed deep deterministic policy gradient (TD3) algorithm. Such a TD3-based offloading (TD3O) algorithm includes a designed action transformation (AT) algorithm used for transforming continuous action space into a discrete one. In addition, to solve the formulated problem in a non-iterative manner, an effective heuristic algorithm (HA) is also designed. As for the designed algorithms, we provide some detailed analyses of computation complexity and convergence, and give some meaningful insights through simulation. Simulation results show that the TD3O algorithm could achieve lower local energy consumption than several benchmark algorithms, and HA could achieve lower consumption than the completely offloading algorithm and local execution algorithm.

Location Privacy-Aware Offloading for MEC-Enabled IoT: Optimality and Heuristics

With the rapid development of 5G and the Internet of Things (IoT), cloud-based access mode is applied more and more in IoT scenarios. The emerging IoT applications put forward higher requirements on energy consumption and processing capacity of the network, as well as more strict privacy restrictions. The emergence of mobile edge computing solves the aforementioned user experience problems, which greatly improves execution efficiency and saves a lot of energy. However, existing studies pay more attention to issues such as energy consumption and delay, but ignore the privacy and imbalanced server load problems that mobile edge computing may cause. This paper made bandwidth resource allocation and task offloading decisions for multiple users via joint optimization, provided the privacy requirements of IoT terminals and load balancing requirements are satisfied. The objective was to minimize the total energy consumption of all terminal devices. By modeling the problem as an integer linear programming problem, the optimal solution is obtained. Considering the high computational complexity of solving integer linear programming problems, a more practical heuristic algorithm was proposed. The simulation results showed the effectiveness of the proposed optimal and heuristic schemes in reducing energy consumption, and demonstrated the importance of privacy protection.

Resource Allocation and Offloading Decision of UAV Based on NOMA-MEC

Resource Allocation and Offloading Decision of UAV Based on NOMA-MEC

Joint Task Offloading and Resource Allocation for Fog-Based Intelligent Transportation Systems: A UAV-Enabled Multi-Hop Collaboration Paradigm

Unmanned aerial vehicles (UAVs) have been widely used in Intelligent Transportation Systems (ITS) due to their rapid deployment and high mobility, which are considered as a promising solution to expand the scope of communication, especially in inaccessible areas. However, there is a lack of a universal and extensible multi-hop collaboration model in the existing research on UAV-involved ITS. In this paper, we innovatively introduce a novel UAV-enabled multi-hop collaborative fog computing (FC) system model, in which several moving UAVs with unpredictable locations provide effective and efficient communication and computation services for ground user equipments (UEs). With this model, we mathematically formulate a joint user association, UAV association, task offloading, transmission power, computation resource allocation, and UAV location optimization problem, which is a mixed integer nonlinear programming (MINLP) problem and challenging to deal with. To solve the non-convex problem, we propose a novel multi-hop collaborative algorithm to derive the optimal task offloading and resource allocation decisions for each UAV. Simulation results demonstrate the superiority of the UAV-enabled multi-hop collaborative FC system and validate the effectiveness of the proposed scheme.

An Enhanced Support Vector Machine based Multiclass Classification Method For Crop Prediction

Crop type classification is a fundamental task in precision agriculture, enabling informed decision-making for crop management and resource allocation. Support Vector Machines (SVMs) have emerged as robust and effective tools for multi-class classification tasks. This study explores the application of SVM-based multi-class classification techniques to accurately categorize various crop types based on remote sensing data. he SVM algorithm is employed to create decision boundaries that maximize the margin between different crop classes while minimizing classification errors. To enhance classification performance, various kernel functions such as linear, polynomial, and radial basis function are evaluated to capture complex relationships within the data. The proposed SVM-based approach is compared with other commonly used classification methods to assess its superiority in terms of accuracy, precision, recall, and F1-score.

Surgical waitlist management: Perspectives from surgeons on surgical prioritization at a paediatric hospital.

Globally exacerbated surgical waitlists have provided the opportunity to reflect on prioritization and resource allocation decisions. The unique circumstances of paediatric surgery and consequences of surgical delay prompted the study reported in this paper. As part of a larger project to attend to prioritization in our surgical waitlists, we conducted a Quality Improvement study, the purpose of which is to understand surgeon's perspectives regarding the ethical and practical realities of surgical prioritization at our institution. The study comprises semi-structured interviews with nine full-time paediatric surgeons from a variety of subspecialties conducted at our institution, which is a tertiary paediatric hospital with ten surgical subspecialties in a publicly funded healthcare system. Participants articulated how they prioritize their waitlists, and how they understand ethical prioritization. These findings resonate with the growing public concern for ethical practice in healthcare delivery and transparency in prioritization and resource allocation practices. Specifically, more transparency, consistency, and support is required in prioritization practices. This work highlights the importance of institutional dialogue regarding surgical case prioritization. Because quality improvement work is necessarily site-specific, concrete generalizations cannot be offered. However, the insights gleaned from these interviews and the process by which they were gleaned are a valuable knowledge-sharing resource for any institution that is interested in ongoing quality improvement work. The objectives here were to clarify the goals of prioritization within the institution, improve prioritization practices, and make them more ethical and transparent.

Crop Yield Prediction Using With The Simulated Annealing Convolutional Neural Networks (SACNNS) Optimization Algorithm

This paper introduces an innovative approach for precise crop yield prediction in Tamil Nadu, India, a region with diverse agricultural conditions. The Simulated Annealing Convolutional Neural Networks (SACNNs) optimization algorithm is employed to enhance the performance of Convolutional Neural Networks (CNNs). By leveraging a comprehensive agricultural dataset that includes weather information, soil characteristics, and historical crop yields, SACNNs fine-tunes CNN architecture and parameters for improved prediction accuracy. The model excels at predicting crop yields at a local level, accommodating the unique agricultural conditions of different districts in Tamil Nadu. Experimental results highlight SACNNs' superiority in prediction accuracy and robustness compared to conventional CNNs and other optimization methods. These findings offer valuable insights for informed decision-making in agricultural planning and resource allocation, benefiting farmers, policymakers, and stakeholders in the region.

An unsupervised learning approach to identify immunoglobulin utilization patterns using electronic health records.

Managing Canada's immunoglobulin (Ig) product resource allocation is challenging due to increasing demand, high expenditure, and global shortages. Detection of groups with high utilization rates can help with resource planning for Ig products. This study aims to uncover utilization subgroups among the Ig recipients using electronic health records (EHRs). The study included all Ig recipients (intravenous or subcutaneous) in Calgary from 2014 to 2020, and their EHR data, including blood inventory, recipient demographics, and laboratory test results, were analyzed. Patient clusters were derived based on patient characteristics and laboratory test data using K-means clustering. Clusters were interpreted using descriptive analyses and visualization techniques. Among 4112 recipients, six clusters were identified. Clusters 1 and 2 comprised 408 (9.9%) and 1272 (30.9%) patients, respectively, contributing to 62.2% and 27.1% of total Ig utilization. Cluster 3 included 1253 (30.5%) patients, with 86.4% of infusions administered in an inpatient setting. Cluster 4, comprising 1034 (25.1%) patients, had a median age of 4 years, while clusters 2-6 were adults with median ages of 46-60. Cluster 5 had 62 (1.5%) patients, with 77.3% infusions occurring in emergency departments. Cluster 6 contained 83 (2.0%) patients receiving subcutaneous Ig treatments. The results identified data-driven segmentations of patients with high Ig utilization rates and patients with high risk for short-term inpatient use. Our report is the first on EHR data-driven clustering of Ig utilization patterns. The findings hold the potential to inform demand forecasting and resource allocation decisions during shortages of Ig products.

Revisit intention of tourists in farm tourism sites

ABSTRACT This work offers a structural model that explains farm tourist revisit intention (RI), which highlights place attachment (PA) and memorable tourism experience (MTE) in addition to tourist satisfaction, perceived risks (PR), and tourism destination image (TDI) as antecedents. Partial least squares–structural equation modelling was used to evaluate their theoretical relationships. Findings suggest that MTE and PA are the strongest predictors of RI, with satisfaction also influencing RI. The mediation analysis shows that PA partially mediates satisfaction and RI, and satisfaction partially mediates TDI and RI. This work also frames a new theoretical lens on the relationship of PR with satisfaction, TDI, and RI in the farm tourism literature. It provides farm tourism sites a better understanding of tourist behavioural intention to revisit, inputs to the design of initiatives for tourism marketing, and aids in short- and medium-term planning and resource allocation decisions.

MCOTM: Mobility-aware computation offloading and task migration for edge computing in industrial IoT

Mobility-aware devices are crucial components of Industrial Internet of Things (IIoT). However, they face limitations in terms of battery capacity and computation power, which restrict their ability to provide services requiring broad bandwidth and strong computation power for computation-intensive tasks. While offloading can strengthen device computation power, ineffective offloading decisions result from device mobility and limited adaptability to changes in environmental resources, or are not applicable to the current mobile edge computing (MEC) environment. In this paper, we address these challenges by proposing a mobility-aware computation offloading and task migration approach (MCOTM) based on trajectory and resource prediction to address this issue of mobility offloading, which minimizes task turnaround time and system energy consumption. Simultaneously, our approach enhances the decision agent continuously to decrease task migration rates. MCOTM uses Lagrange interpolation equations to determine the trajectory of mobile devices, and Long Short-Term Memory (LSTM) to track the time-varying resources characteristics in IIoT. These prediction results will be used to assist Deep Deterministic Policy Gradient (DDPG) for making online computation offloading, task migration and resource allocation decisions. Experimental results show that the proposed MCOTM effectively reduces task turnaround time by at least 42% and system energy consumption by 10% while maintaining a low task migration rate of around 50%, even with an increasing number of tasks.

Comparison of the Early Childhood Oral Health Impact Scale (ECOHIS-4D) and Child Health Utility Index (CHU-9D) in children with oral diseases.

Accurate assessment of child oral health is important for guiding economic evaluations and informing healthcare decision-making. Early Childhood Oral Health Impact Scale (ECOHIS-4D) is a preference-based instrument that measures the oral health-related quality of life of young children. The aim of this study was to compare the utility scores of ECOHIS-4D and Child Health Utility Index (CHU-9D), against an oral health indicator to evaluate which utility score corresponds better with the oral health indicator. The ECOHIS-4D and CHU-9D were applied to 314 parent/child dyads from preschools in a primary healthcare setting in Perth, Western Australia. Four parameters were used to assess which instrument corresponds better with the oral health indicator (decayed, missing and filled teeth score-dmft score): (i) discrimination, the ability to discriminate between different clinical severity groups, (ii) external responsiveness, how much the utility values relate to the changes in dmft scores, (iii) correlation, the association between the two instruments and the related dimensions and (iv) differences in the utility values across the two instruments. Most participants (81%) were 2-6 years old, and nearly 50% had a dmft score <3. ECOHIS-4D demonstrated a superior ability to differentiate between dmft severity groups and respond to changes in dmft scores. A significant weak correlation was observed between dmft and ECOHIS-4D (-0.26, 95%, CI -0.36 to -0.15) compared to a non-significant very poor correlation between dmft and CHU-9D (0.01, 95% CI -0.12 to 0.10). The utility scores of the two instruments had relatively good agreement towards good health and weak agreement towards poor health. ECOHIS-4D, the oral health-specific instrument, is more sensitive in assessing children's oral health-related quality of life than the generic CHU-9D. Thus, ECOHIS-4D is more appropriate for utility estimates in economic evaluations of oral health-related interventions and resource allocation decision-making.

Face Validity of Four Preference-Weighted Quality-of-Life Measures in Residential Aged Care: A Think-Aloud Study.

There is an increased use of preference-weighted quality-of-life measures in residential aged care to guide resource allocation decisions or for quality-of-care assessments. However, little is known about their face validity (i.e., how understandable, appropriate and relevant the measures are 'on their face' when respondents complete them). The aim of this study was to assess the face validity of four preference-weighted measures (i.e., EQ-5D-5L, EQ-HWB, ASCOT, QOL-ACC) in older people living in residential aged care. Qualitative cognitive think-aloud interviews were conducted using both concurrent and retrospective think-aloud techniques. To reduce burden, each resident completed two measures, with the four measures randomised across participants. Audio recordings were transcribed and framework analysis was used for data analysis, based on an existing framework derived from the Tourangeau four-stage response model. In total, 24 interviews were conducted with residents living across three residential aged care facilities in Melbourne, Australia. Response issues were identified across all four measures, often related to comprehension and difficulty selecting a response level due to double-barrelled and ambiguous items that have different meanings in the residential aged care context. We also identified issues related to understanding instructions, non-adherence to the recall period, and noted positive responding that requires attention when interpreting the data. Our findings provide further evidence on the appropriateness of existing measures, indicating numerous response issues that require further research to guide the selection process for research and practice.