Healthcare resource allocation decisions affecting uninsured services.

Social inequalities are worsened by the decision about how resources are to be allocated. Considering that children and adolescents are likely to become future leaders and decision-makers, understanding which principles they follow regarding resource allocation in situations of inequality is crucial. This paper aims at presenting a protocol for systematic review of the relationship between perception of resource inequality and allocation decisions made by children and adolescents. Method: A systematic search will be conducted in the PsycINFO, PsycArticles, Web of Science (Core Collections), Scopus, ERIC and LILACS databases. The paper will also include empirical studies from the authors’ personal files, studies obtained through contact with the main authors of the articles included, and through backward and forward snowballing. The studies’ quality will be assessed using the Quality Assessment with Diverse Studies (QuADS). Finally, a narrative synthesis will be conducted to review and synthesize data. This protocol was outlined following the PRISMA-P guidelines. The protocol is registered at the Open Science Framework (OSF) under the code DOI: https://doi.org/10.17605/OSF.IO/D96MY. The systematic review will follow the Joanna Briggs Institute guidelines, and will be reported according to the PRISMA 2020 reporting guidelines. Discussion: The results of this review will be relevant to policy makers, decision-makers, educators, and researchers.

Resource allocation decisions in health care: A role for quality of life assessments?

PV091 / #536Poster Topic:AS11 - Epidemiology and Public HealthBackground/PurposeThe economic impact of Coronavirus Disease 2019 (COVID-19) in Systemic Lupus Erythematosus (SLE) patients remains incompletely characterized. We aimed to analyze healthcare resource utilization and costs among hospitalized SLE patients with COVID-19 compared to those without, using a nationally representative database.MethodsUsing the 2021 National Inpatient Sample, we identified adult SLE patients (International Classification of Diseases, 10th Revision code M32) and stratified by COVID-19 status. Primary outcomes included length of stay (LOS) and total hospital charges. We employed survey-weighted Poisson regression for LOS analysis and generalized linear models for cost analysis, adjusting for demographics, comorbidities, and clinical factors.ResultsAmong 170,085 hospitalized SLE patients, 12,710 (7.47%) had COVID-19. Mean LOS was significantly longer in SLE patients with COVID-19 vs without (8.48 vs 5.57 days, p < 0.001). After adjustment, COVID-19 was associated with a 53% increase in LOS (incidence rate ratio [IRR] 1.53, 95% CI 1.47-1.60, p < 0.001). Total hospital charges were substantially higher in COVID-19 patients vs non-COVID-19 patients ($103,126 vs $77,628, p < 0.001), with an adjusted cost ratio of 1.34 (95% CI 1.26-1.42, p < 0.001). Stratified analyses revealed higher rates of mechanical ventilation (11.25% vs 2.76%, p < 0.001) and acute kidney injury (29.31% vs 21.38%, p < 0.001) in COVID-19 patients. Among SLE patients with COVID-19, females vs males (88.24% vs 11.76%) showed lower hospital charges (IRR 0.91, 95% CI 0.85-0.98, p = 0.015) and shorter LOS (IRR 0.92, 95% CI 0.88-0.96, p < 0.001). African American vs Caucasian patients (29.50% vs 48.03%) demonstrated higher resource utilization (IRR 1.29, 95% CI 1.23-1.36, p < 0.001). Patients with severe vs mild comorbidity burden (41.03% vs 9.87%) had significantly higher costs (IRR 2.15, 95% CI 2.03-2.28, p < 0.001).ConclusionsCOVID-19 in SLE patients is associated with substantially increased healthcare resource utilization, manifesting as longer hospitalizations and higher costs. The economic burden is particularly pronounced in patients requiring mechanical ventilation or developing acute kidney injury. These findings highlight the significant economic impact of COVID-19 in SLE patients and can inform healthcare resource allocation and policy decisions for this high-risk population.

This chapter assesses farm household land and labour allocation decisions under imperfect factor and product markets. A non-separable farm household model is adapted to obtain econometric estimates of land and labour allocation decisions under different market participation regimes. Survey data obtained from a sample of 154 farm households producing both cash and food crops in central and eastern Ethiopia is used for the analysis. Endogenously switching regression estimation results shows that market participation, which is household specific, affects the quantity of land and labour that households allocate to food and cash crop production. There is also a significant regional difference in resource allocation decisions. Farm capital ownership (including motor pump) influences the allocation decisions. Thus, policies improving the efficiency in rural factor and product markets promote farm households' participation in these markets and orient their resource allocation decisions towards more cash crop production and higher farm income.

The healthcare sector depends heavily on the informal care provided by families and friends of those who are ill. Informal caregivers may experience significant burden as well as health and well-being effects. Resource allocation decisions, in particular from a societal perspective, should account explicitly for these effects in the social environment of patients. This is not only important to make a complete welfare economic assessment of treatments, but also to ensure the lasting involvement of informal caregivers in the care-giving process. Measurement and valuation techniques for the costs and effects of informal care have been developed and their use is becoming more common. Decision-makers in healthcare – and eventually families and patients – would be helped by more uniformity in methods.

Unmanned aerial vehicles (UAVs) have been conceived as an available solution to substitute terrestrial base stations (TBSs) to provide downloading services for user equipment (i.e. mobile devices) that have difficulty communicating directly with TBSs. However, the mobility of user equipment (UE) and the random nature of the number of UE will cause several challenges including 1) the hardness of determining optimal user association and UAV resource (i.e., bandwidth and transmit power) allocation decision, 2) the burden of network function maintenance owing to the necessity of shutting down the entire system. Therefore, in this article, joint user association and resource allocation are designed for a software-defined network (SDN)-adopted leaderless softwarized UAV network, where each UAV is regarded as a flying SDN controller to enhance the control ability of the considered network. The purpose is to maximize energy efficiency (EE) with satisfying the quality of service (QoS). To this end, a joint method based on hierarchical agglomerative clustering (HAGC) and multi-agent deep deterministic policy gradient (MADDPG) is proposed. Specifically, the HAGC approach is utilized to determine the optimal MDs association with UAVs. Afterward, MADDPG approach is leveraged to obtain the best policy for resource allocation, aiming to achieve the maximum EE. Finally, the effectiveness of the proposed method is confirmed by the evaluation results.

Increasing costs for patient care may necessitate financial cuts in the health-care budget. Our aim is to examine whether the public prefers cuts for psychiatric rather than medical conditions and how resource allocation preferences are related to illness beliefs and attitudes. A telephone survey involving German adult population was conducted in 2004 (n = 1012). Participants were presented with a list of nine medical and mental diseases including alcoholism, depression, schizophrenia, Alzheimer's disease, cancer, diabetes, rheumatism and AIDS, and were asked to name three conditions where they would prefer to see health-care resources cut. For all conditions we asked about personal attitudes and illness beliefs. People were more willing to have financial resources cut for psychiatric than for medical conditions, with resources for alcoholism having the least public backing. Alzheimer's disease was rated more favourably compared to other mental disorders. Generally, the perception of the severity of a disease was associated with resource allocation decisions, favouring those conditions that were considered to be severe. Mental diseases evoked a far greater desire for social distance than most medical diseases which had considerable influence on resource allocation preferences. The perception of personal responsibility had, in contrast, only limited effect on resource allocation decisions. It varied considerably in the case of psychiatric conditions but was not fundamentally different among medical and mental diseases. Personal susceptibility, treatment effectiveness and the perceived life-threat posed by a disease also had limited effects. According to public resource allocation preferences psychiatric patients are at risk of being structurally discriminated within the health-care system.

The dominant health economic units upon which new treatment funding decisions are made are the incremental cost per life year gained (LYG) or the cost per quality-adjusted life year (QALY) gained. Neither of these units modifies the amount of health gained, by the amount of health patients would have had if they had not been given the treatment under consideration, which may unfairly undervalue the treatments for poor prognosis conditions. How certain patients make decisions about their own treatment has previously been explored, but not how they, or doctors, would allocate hypothetical resource within a healthcare system given information on disease-treatment scenarios' prognoses with and without treatment. Information on prognosis without treatment is used within the resource allocation strategies of many doctors and most patients. Individuals use this information in a variety of different ways and a single dominant strategy for quantitative modification of health units is not apparent. Information on prognosis without treatment, or prognosis with standard treatment, is available from the control arm of randomized controlled clinical trials and should be used qualitatively to facilitate decision-making around the second inflexion point on cost per QALY/LYG acceptability curves. Health economic assessments increasingly contribute to funding decisions on new treatments. Treatments for many poor prognosis conditions perform badly in such assessments because of high costs and modest effects on survival. We aimed to determine whether underlying shortness of prognosis should also be considered as a modifier in such assessments. Two hundred and eighty-three doctors and 201 oncology patients were asked to allocate treatment resource between hypothetical patients with unspecified life-shortening diseases. The prognoses with and without treatment were varied such that consistent use of one of four potential allocation strategies could be deduced: life years gained (LYGs) - which did not incorporate prognosis without treatment information; percentage increase in life years (PILY); life expectancy with treatment (LEWT) or immediate risk of death (IRD). Random choices were rare; 47% and 64% of doctors and patients, respectively, used prognosis without treatment in their strategies; while 50% and 32%, respectively, used pure LYG-based strategies. Ranking orders were LYG > PILY > IRD > LEWT (doctors) and LEWT > LYG > IRD > PILY (patients). When LYG information alone could not be used, 76% of doctors prioritized shorter prognoses, compared with 45% of patients. Information on prognosis without treatment is used within the resource allocation strategies of many doctors and most patients, and should be considered as a qualitative modifier during the health economic assessments of new treatments for life-shortening diseases. A single dominant strategy incorporating this information for any quantitative modification of health units is not apparent.

Simple SummaryColorectal cancer (CRC) is a significant global health concern, ranking as the second leading cause of cancer-related deaths. Despite an overall decline in CRC rates in the United States, there is a concerning rise in early-onset cases (diagnosed before age 50). This study, utilizing comprehensive data from the Global Burden of Disease 2019, delves into the state-specific trends in CRC incidence, prevalence, and mortality over three decades (1990–2019). Notably, the research reveals varying patterns across geographic regions, gender, and age groups. Understanding these nuanced trends is crucial for targeted resource allocation and policy decisions to effectively combat CRC, a disease with an increasing impact on younger adults in the United States.CRC accounts for approximately a tenth of all cancer cases and deaths in the US. Due to large differences in demographics among the different states, we aim to determine trends in the CRC epidemiology and across different states, age groups, and genders. CRC rates, age-adjusted to the standard US population, were obtained from the GBD 2019 database. Time trends were estimated as annual percentage change (APC). A pairwise comparison was conducted between age- and gender-specific trends using the tests of parallelism and coincidence. Age-specific trends were also assessed in two age subgroups: younger adults aged 15–49 years and older adults aged 50–74 years. We also analyzed the prevalence, incidence, mortality, and DALYs in the US between 1990 and 2019. A total of 5.53 million patients were diagnosed with CRC in the US between 1990 and 2019. Overall, CRC incidence rates have significantly increased in younger adults (11.1 per 100,000 persons) and decreased in older adults (136.8 per 100,000 persons) (AAPC = 1.2 vs. −0.6; AAPC difference = 1.8, p < 0.001). Age-specific trends were neither identical (p < 0.001) nor parallel (p < 0.001), suggesting that CRC incidence rates are different and increasing at a greater rate in younger adults compared to older adults. However, for both men and women (49.4 and 35.2 per 100,000 persons), incidence rates have decreased over the past three decades at the same rate (AAPC = −0.5 vs. −0.5; AAPC difference = 0, p = 0.1). Geographically, the southern states had the highest mortality rates with Mississippi having the highest rate of 20.1 cases per 100,000 population in 2019. Massachusetts, New York, and the District of Colombia had the greatest decreases in mortality over the study period (−42.1%, −41.4%, and −40.9%). Decreased mortality was found in all states except Mississippi, where the mortality of CRC increased over the study period (+1.5%). This research provides crucial insights for policymakers to tailor resource allocation, emphasizing the dynamic nature of CRC burden across states and age groups, ultimately informing targeted strategies for prevention and intervention.

The managerial network in a multinational enterprise and the resource profiles of subsidiaries

Poster #235 SMOKING AND OTHER CARDIOVASCULAR DISEASE RISK BEHAVIOURS AMONG PEOPLE WITH SEVERE MENTAL DISORDERS

High rates of chronic disease have created challenges for public health decision-makers in resource allocation. Decision tools designed to support public health professionals can help address these challenges. This study aims to investigate the impact of different uncertainty visualization techniques on user perceptions. The study was designed for the purpose of supporting the development of CDPoRT, a chronic disease risk prediction tool. Participants performed a fictional recourse allocation decision-making task with map-based visualizations, tables with and without uncertainty data, and a table with uncertainty explanation. The findings show that CDPoRT’s user interface may benefit from the inclusion of an overlay map to visualize the rate and uncertainty information for public health resource allocation decisions. Future research can consider the development of interactive features to facilitate comparisons between multiple geographical regions and simulate interventions.

We develop an optimal control model for optimally allocating effort between detection and correction processes, during testing phase of a software development life cycle (SDLC). The objective is to propose a resource allocation plan to minimise the total testing cost with the reliability criteria using flexible software reliability growth model considering testing effort under dynamic environment. Furthermore, we have incorporated the learning curve effect in correction cost function due to its importance in cost estimation, and resource allocation decisions. A technique based on optimal control theory is proposed for the software managers to optimise the allocation of the limited resources, while genetic algorithm is used to find the approximate value of detection and correction effort. An illustrative example is presented. The optimal release policy under reliability criteria is also discussed.

Implementing priority setting frameworks: Insights from leading researchers

Financial literacy and retirees' resource allocation decisions in New Zealand