Allocation Of Health Care Resources Research Articles

Abstract 4120469: Racial and ethnic disparities in catheter based interventions vs. open heart surgery in congenital heart disease

Introduction: Transcatheter cardiac interventions have emerged as a viable alternative to open heart surgery for specific congenital heart disease lesions. There is literature to support racial and ethnic disparities in medicine, indicating that patients from racially and ethnically marginalized populations are less likely to receive advanced less invasive procedures. This study seeks to delve into the existence of such inequities between transcatheter and surgical interventions, shedding light on potential inequalities in access and outcomes. Methods: We analyzed California and Florida's State Inpatient and Ambulatory Surgery Databases from 2005 to 2017 to study patients under 18 admitted with diagnosis of atrial septal defect, pulmonary stenosis, aortic stenosis, and ventricular septal defects. Multivariable logistic regression, adjusted for patient characteristics, assessed race and ethnicity's influence on procedure type and in-hospital mortality. We also used log-transformed linear regression to examine associations with length of stay, hospitalization cost, and cost per day. Results: We identified 13,771 records who had open surgeries, and 2,045 records who had CBI. Compared to non-Hispanic White patients, Black patients were significantly more likely to undergo open surgical procedures (adjusted Odds Ratio [aOR] 1.49, p < .001). Additionally, Black, Hispanic, and other race patients had higher risks of in-hospital death (aOR 1.92, p = 0.016 for Black; aOR 1.93, p < 0.01 for Hispanic; aOR 2.37, p < 0.01 for other races). Black and Hispanic patients also experienced longer lengths of stay (Adjusted Means Ratio [aMR] 1.34, p < 0.01 for Black; aMR 1.16, p < 0.01 for Hispanic), and higher costs (aMR 1.12, p = .01 for Black; aMR 1.11, p = .01 for Hispanic), but Black patients had a lower cost per day (aMR 0.96, p = .02). Conclusion: Racial and ethnic gaps persist in pediatric cardiac care, with Black patients facing reduced access to less invasive procedures, higher mortality rates, longer hospital stays, and increased costs compared to non-Hispanic whites. However, the higher costs are likely attributed to longer hospital stays rather than expensive interventions. It is imperative to tackle these disparities to prioritize patient-centered care and streamline resource allocation in healthcare.

Abstract 4132212: Trends in Atrial Fibrillation or Flutter Related Mortality Among Adults in the United States: Insights from CDC WONDER Database from 1999-2020

Background: Atrial fibrillation or flutter (Afib/AFL) incidence rates continue to rise and are linked to significant mortality, particularly in older populations. Our analysis examines Afib/AFL mortality trends across different demographics and regions in the U.S. from 1999 to 2020, highlighting the importance of understanding these patterns. Aim: To guide preventive measures that alleviate the impact of Afib/AFL and identify high-risk populations and regions, we sought to quantify trends associated with Afib/AFL related mortality in the U.S. Methods: We conducted a comprehensive search of death certificates from 1999-2020 using Centers for Disease Control and Prevention Wide-Ranging OnLine Data for Epidemiologic Research (CDC WONDER) database focusing on Afib/AFL mortality in adults with ICD-10 codes I48. Gender, race, geographical and urban-rural parameters were investigated by calculating annual percent change (APC) and age-adjusted mortality rates (AAMRs) per 100,000 persons using the Joinpoint Regression Program (Joinpoint V 4.9.0.0, National Cancer Institute). Results: A total of 2,581,488 deaths occurred in patients with Afib/AFL from 1999 to 2020. The AAMR displayed an abrupt rise from 2018 to 2020 (APC: 8.51; 95% CI: 4.84-10.47). Men consistently exhibited a higher AAMR (overall AAMR male: 79.4, 95% CI 79.3-79.6; female: 60.8, 95% CI 60.7-60.9). Non-metropolitan areas showed higher AAMRs than metropolitan areas (overall AAMR non-metropolitan areas: 74.9, 95% CI 74.7-75.1; metropolitan: 67.2, 95% CI 67.1-67.2). Disparities were also observed in AAMRs by region with the West region showing the highest mortality rate with a notable rise between 2010 and 2020 (APC: 3.70, 95% CI 3.30-5.37). Non-hispanic (NH) White population showed the highest mortality (overall AAMR: 74.2, 95% CI 74.1-74.3), followed by NH American Indian (AAMR: 50.3), NH Black (AAMR: 45.8), NH Asian (AAMR: 35.0) and Hispanic (AAMR: 37.9) populations. Conclusion: Mortality from Afib/AFL has risen from 1999 to 2020. Men, NH white populations, and residents in non-metropolitan areas and Western U.S. are at higher risk. Targeted interventions and strategic healthcare resource allocation are needed to address these disparities and improve outcomes.

The Role of Ethics Committees in Charity Care Allocation.

Hospital ethics committees (HECs) traditionally focus on clinical ethics but are increasingly recognized for their potential role in addressing organizational ethics, particularly in the allocation of charity care resources. This commentary explores the expanded role of HECs in charity care allocation, emphasizing the core ethical principles of justice, transparency, and accountability. We discuss the need for HECs to develop expertise in organizational ethics, differentiate between emergency and chronic resource allocation, and apply value-based insurance design principles to set service boundaries. By adopting an egalitarian approach to justice, HECs can help reduce health disparities and ensure equitable access to charity care. Enhanced transparency and accountability through clear criteria, regular audits, and public reporting are also vital. This paper underscores the importance of integrating these ethical principles into healthcare resource allocation to promote health equity and maintain the financial sustainability of healthcare institutions.

Peri-injury symptomatology as predictors of brain computed tomography (CT) scan abnormalities in mild traumatic brain injury (mTBI)

ObjectiveThis study aimed to identify predictors of brain CT abnormalities in patients who sustained a mild traumatic brain injury (mTBI).MethodsRetrospective observational cohort of adult patients with mTBI (Glasgow Coma Score 13–15) that occurred within the preceding 24 h.Results2548 (91%) of the cohort had a brain CT and 698 (27%) demonstrated abnormal findings. The most frequently observed CT abnormalities were bleeding (638, 25%) and fractures (190, 7.4%). Multivariate logistic regression analysis revealed several significant predictors associated with the presence of brain CT abnormalities including older age [P < 0.0001], male sex [P < 0.0001], loss of consciousness [P = 0.0041], associated vomiting [P = 0.0011], alteration of consciousness (AOC) [P = 0102], and GCS score [P < 0.0001]. This was a robust model with an R² of 14.2%.ConclusionIn this retrospective analysis, older age, male sex, the presence of loss of consciousness or alteration in consciousness, lower GCS score, and associated vomiting were found to be significant predictors of having an abnormal brain CT. These findings highlight the importance of considering these factors when determining the necessity of brain CT scans in patients with mTBI and suggest that existing clinical decision rules may be limited. These findings may also help to inform clinical decision rules. Early identification of individuals at a higher risk of CT abnormalities may assist in appropriate management and allocation of healthcare resources.

Regional Healthcare Resource Allocation and Decision-Making: Evaluating the Effectiveness of the Three-Stage Super-Efficiency DEA Model

This study addresses the challenge of achieving a more rational allocation of medical resources at the regional level, using Guangxi Province, China, as a case study. A three-stage super-efficiency Data Envelopment Analysis (DEA) model is employed to assess and analyze the effectiveness of resource allocation. The research methodology involves identifying input, output, and environmental variable indicators to construct a healthcare resource allocation index system. The indicator data are processed using Excel software. The three-stage super-efficiency DEA model is then applied to evaluate the healthcare system in Guangxi Province, focusing on Pure Technical Efficiency Change (PEC), Scale Efficiency Change (SEC), Efficiency Change (EC), Technological Change (TC), and Total Factor Productivity (TFP). Finally, the Malmquist index method is utilized to measure and dynamically analyze the efficiency of healthcare resource allocation. The study's results show that, from a static perspective, the average comprehensive efficiency is 1.067 before adjustment and 1.054 after adjustment, indicating relatively high overall efficiency in healthcare resource allocation in Guangxi Province. However, environmental factors and random errors have led to an overestimation of healthcare resource allocation efficiency, which the three-stage super-efficiency DEA model effectively corrects. Additionally, the average SEC and PEC values are 0.997 and 0.998, respectively, both below 1. This indicates that both scale efficiency and pure technical efficiency contribute to a decline in technical efficiency. Based on the results of the sensitivity analysis, the conclusions regarding the efficiency of healthcare resource allocation in Guangxi are deemed highly reliable. Despite the influence of uncertain factors, the model consistently provides stable and coherent assessment results in most scenarios. Therefore, special attention is needed to improve scale efficiency in healthcare resource allocation within the region, alongside enhancing management and technological capabilities in the healthcare sector. Overall, this study provides valuable reference and guidance for researchers and practitioners in related fields and offers scientific decision support for healthcare resource allocation.

The global, regional, and national patterns of change in the burden of congenital birth defects, 1990–2021: an analysis of the global burden of disease study 2021 and forecast to 2040

Congenital birth defects (CBDs) present enormous challenges to global healthcare systems. These conditions severely impact patients' health and underscore issues related to socioeconomic development and healthcare accessibility and efficiency. Previous studies have been geographically limited and lacked comprehensive global analysis. This study provides global, regional, and national disability-adjusted life years (DALYs) data for four major congenital birth defects-congenital heart defects (CHD), neural tube defects (NTDs), digestive congenital anomalies (DCAs), and Down syndrome (DS) from 1990 to 2021, emphasizing health inequalities. The goal is to offer scientific evidence for optimizing resource allocation, focusing on high-burden populations, and reducing disease burden. This study systematically evaluated the global, regional, and national burden of CBDs and their changes from 1990 to 2021 using the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021. To conduct a more focused analysis, four specific CBDs were selected: CHD, NTDs, DCAs, and DS. DALYs were used as the metric, combined with the sociodemographic index (SDI). Analyses included the slope index of inequality and concentration index to measure health inequalities, frontier analysis to estimate achievable outcomes based on development levels, decomposition analysis to identify drivers of disease burden changes, Joinpoint regression analysis to assess temporal trends, and the Bayesian age-period-cohort (BAPC) model to predict future disease burden trends. Compared to 1990, the global burden of the CBDs in 2021 showed a downward trend. Males had a higher burden than females, with the highest burden observed in low-SDI regions. When examining CHD, NTDs, DCAs, and DS specifically, trends in burden changes varied across different CBDs at the global, regional, and national levels. Frontier analysis revealed potential for burden improvement in various countries and territories. Decomposition analysis highlighted differences in disease burden drivers across SDI regions, showing the greatest improvement observed in low-SDI regions. Joinpoint regression analysis indicated a downward trend in DALYs burden across SDI regions, and BAPC model predictions suggested that the burden of CBDs will continue to decline in the future. CBDs pose a major challenge to global public health. Despite an overall decline in disease burden, health inequalities remain prominent, particularly in countries and territories with lower levels of development. Future public health interventions should focus on countries and territories with low levels of development by optimizing healthcare resource allocation, improving basic health infrastructure, enhancing health education, and reducing disease burden inequalities. Global collaboration and data sharing are essential to promote a lifecycle management model for CBDs research and treatment, advancing global health development. This study was supported by the National Natural Science Foundation of China (No. 82270310) and the Jiangsu Provincial Key Research and Development Program (No. BE2023662).

Cost minimization analysis of chronic kidney disease management: Evaluating economic strategies for early intervention and treatment optimization

Chronic kidney disease is a common global health condition characterized by progressive loss of kidney function and significant cost burden on the healthcare system worldwide. The disease often coexists with conditions such as diabetes and hypertension, further complicates its management and increasing associated costs. Effective economic management, particularly in the early stages of CKD, is crucial for reducing long-term healthcare expenses and improving patient outcomes. This review examines the utility of cost minimization analysis (CMA) as a strategic tool for evaluating economic options in CKD management. CMA is an economic evaluation method that compares the costs of different therapeutic interventions that have demonstrated equivalent clinical effectiveness, thereby aiding healthcare providers and policymakers in identifying the most cost-efficient options to optimize resource utilization. This review highlights the economic impact of CKD, focusing on the increasing costs linked to advanced disease management and the essential role of early diagnosis and intervention in reducing these costs and explores various cost-effective strategies for CKD management, including pharmacological treatments, lifestyle modifications, and patient education, to identify approaches that are both clinically and economically advantageous. Additionally, the broader implications of cost minimization for healthcare policy, clinical decision-making, and resource allocation are discussed for their significance in a resource-limited healthcare setting. Evidence indicates that prioritizing cost-effective CKD management through early intervention and preventive care can significantly lower healthcare expenditures, slow the disease progression, and enhance patient’s quality of life. By adopting such strategies, healthcare systems can achieve better clinical outcomes and ensure financial sustainability.

Artificial Intelligence And Big Data For Enhancing Public Health Surveillance And Disease Prevention: A Systematic Review

This systematic review explores the transformative role of Artificial Intelligence (AI) and Big Data analytics in enhancing public health outcomes, focusing on key areas such as disease surveillance, resource allocation, and personalized preventive healthcare. In the wake of increasing healthcare challenges, the integration of AI technologies and Big Data offers unprecedented opportunities for improving health monitoring, early disease detection, and strategic resource management. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a comprehensive review was conducted across multiple databases, resulting in an initial pool of 450 articles. After applying rigorous inclusion and exclusion criteria, a total of 90 high-quality studies were systematically analyzed. The findings demonstrate that AI models, particularly those leveraging machine learning, significantly enhance the early detection of outbreaks and optimize healthcare resource allocation, especially during health crises like the COVID-19 pandemic. Additionally, the use of predictive analytics in personalized preventive healthcare has shown promise in reducing the burden of chronic diseases by identifying at-risk populations and tailoring interventions based on individual risk profiles. However, challenges related to data quality, standardization, and ethical concerns continue to hinder the widespread adoption of these technologies. The review emphasizes the need for interdisciplinary collaboration and robust data governance frameworks to fully realize the potential of AI and Big Data in public health. This study not only highlights current advancements but also identifies gaps in research, offering insights into future directions for integrating AI-driven solutions to strengthen public health systems globally.

Critical Success Factors and Acceptance of the Casemix System Implementation Within the Total Hospital Information System: Exploratory Factor Analysis of a Pilot Study.

The health care landscape is evolving rapidly due to rising costs, an aging population, and the increasing prevalence of diseases. To address these challenges, the Ministry of Health of Malaysia implemented transformation strategies such as the Casemix system and hospital information system to enhance health care quality, resource allocation, and cost-effectiveness. However, successful implementation relies not just on the technology itself but on the acceptance and engagement of the users involved. This study aims to develop and refine items of a quantitative instrument measuring the critical success factors influencing acceptance of Casemix system implementation within the Ministry of Health's Total Hospital Information System (THIS). A cross-sectional pilot study collected data from medical doctors at a hospital equipped with the THIS in the federal territory of Putrajaya, Malaysia. This pilot study's minimum sample size was 125, achieved through proportionate stratified random sampling. Data were collected using a web-based questionnaire adapted from the human, organization, and technology-fit evaluation framework and the technology acceptance model. The pilot data were analyzed using exploratory factor analysis (EFA), and the Cronbach α assessed internal reliability. Both analyses were conducted in SPSS (version 25.0; IBM Corp). This study obtained 106 valid responses, equivalent to an 84.8% (106/125) response rate. The Kaiser-Meyer-Olkin measure of sampling adequacy was 0.859, and the Bartlett test of sphericity yielded statistically significant results (P<.001). Principal component analysis identified 9 components explaining 84.07% of the total variance, surpassing the minimum requirement of 60%. In total, 9 unique slopes indicated the identification of 9 components through EFA. While no new components emerged from the other 7 constructs, only the organizational factors construct was divided into 2 components, later named organizational structure and organizational environment. In total, 98% (41/42) of the items had factor loadings of >0.6, leading to the removal of 1 item for the final instrument for the field study. EFA ultimately identified 8 main constructs influencing Casemix implementation within the THIS: system quality, information quality, service quality, organizational characteristics, perceived ease of use, perceived usefulness, intention to use, and acceptance. Internal reliability measured using the Cronbach α ranged from 0.914 to 0.969, demonstrating high reliability. This study provides insights into the complexities of EFA and the distinct dimensions underlying the constructs that influence Casemix system acceptance in the THIS. While the findings align with extensive technology acceptance literature, the results accentuate the necessity for further research to develop a consensus regarding the most critical factors for successful Casemix adoption. The developed instrument is a substantial step toward better understanding the multidimensional challenges of health care system transformations in Malaysia, postulating an underpinning for future fieldwork and broader application across other hospitals.

Novel cost-effective method for forecasting COVID-19 and hospital occupancy using deep learning

The emergence of the COVID-19 pandemic in 2019 and its rapid global spread put healthcare systems around the world to the test. This crisis created an unprecedented level of stress in hospitals, exacerbating the already complex task of healthcare management. As a result, it led to a tragic increase in mortality rates and highlighted the urgent need for advanced predictive tools to support decision-making. To address these critical challenges, this research aims to develop and implement a predictive system capable of predicting pandemic evolution with accuracy (in terms of Mean Absolute error (MAE), Root Mean Square Error (RMSE), R2, and Mean Absolute Percentage Error (MAPE)) and low computational and economic cost. It uses a set of interconnected Long Short Term-memory (LSTM) with double bidirectional LSTM (BiLSTM) layers together with a novel preprocessing based on future time windows. This model accurately predicts COVID-19 cases and hospital occupancy over long periods of time using only 40% of the set to train. This results in a long-term prediction where each day we can query the cases for the next three days with very little data. The data utilized in this analysis were obtained from the “Hospital Insular” in Gran Canaria, Spain. These data describe the spread of the coronavirus disease (COVID-19) from its initial emergence in 2020 until March 29, 2022. The results show an improvement in MAE (< 161), RMSE (< 405), and MAPE (> 0.20) compared to other studies with similar conditions. This would be a powerful tool for the healthcare system, providing valuable information to decision-makers, allowing them to anticipate and strategize for possible scenarios, ultimately improving public health outcomes and optimizing the allocation of healthcare and economic resources.

Expert consensus on pharmacoeconomic evaluations alongside clinical trials (2024 edition)

As a technical tool to guide the optimal allocation of healthcare resources, pharmacoeconomic evaluation has been gradually applied to national healthcare decisions, including the adjustment of the National Reimbursement Drug List in China. With the acceleration of drug market approval and access to insurance coverage, the demand for pharmacoeconomic evaluation in healthcare decision-making is also increasing. Designing a pharmacoeconomic evaluation alongside clinical trials has become an important research direction and the source of evidence. In order to facilitate the standardization of this research field, the current consensus focuses on the topic of how to conduct a pharmacoeconomic evaluation alongside clinical trials from the Chinese healthcare system and decision-making context. The consensus group reconciled expert opinions and developed recommendations covering the entire process of study design, data collection and management, data analysis, and methods and results reporting. A total of 24 recommendations regarding to methodology and practice were developed from eighteen aspects including target population, selection of comparators, calculation of sample size, data collection of healthcare resource utilization, data collection of health-related quality of life, cost analysis, health outcome analysis, addressing the missing and censored data, etc., with the aim to provide reference for scholars and decision makers in this research filed.

Digital services as alternative for in-person visits among clients with long-term illnesses

Abstract Background For individuals managing long-term illnesses and often requiring complex care across health and social care sectors, the transition to digital platforms holds considerable potential to facilitate management of one’s health. This study examined factors among people with long-term illnesses associated with their perception that digital services had replaced in-person visits in health and social care. Methods A total of 11,012 Finnish residents with long-term illnesses necessitating regular care (56% female, mean age 56.9, SE .23) responded to a nationwide population survey between September 2022 and March 2023. The Inverse Probability Weighting method was used to correct for bias. Complex samples binary logistic regression was used to examine whether factors related to self-rated health, distress, digital skills and perceived benefits of digital services were associated with the replacement of in-person visits by digital services. The model was adjusted for age, gender, education, and urbanicity. Results Approximately one third (35.8%) of the respondents indicated that the use of digital services had replaced in-person visits within the past 12 months. Respondents in poor or moderate health (OR 1.17, 95% CI 1.02-1.34) had greater odds of reporting the replacement compared to those in good health. Those with proficient digital skills (OR 1.84, 1.57-2.14) and those perceiving more benefits in digital services (OR 1.56, 1.47-1.74) had greater odds of reporting of the replacement compared to their counterparts. Conclusions Transition to digital services could facilitate access to care for individuals with long-term illnesses perceiving their health poorer. Promoting digital skills and perceptions of benefits of digital services can significantly increase the use of digital services as substitutes of in-person visits and contribute to more efficient allocation of health and social care resources. Key messages • Especially those reporting poorer health seem to use digital services as replacements of in-person visits, offering the potential to optimize resource utilization in health and social services. • Promotion of digital skills and perceptions of the benefits of digital services could increase the use of digital services as replacements for traditional visits with health and social professionals.

Prevalence and social determinants of anxiety and depression among adults in Ghana systematic review

Abstract Background Anxiety and depression are major public health concerns, yet their prevalence and social determinants contributing to mental health disparities remain underexplored. This review, to our knowledge, is the first of its kind, aiming to examine the prevalence of anxiety and depression symptoms and disorders among adults in Ghana, along with social determinants associated with their occurrence. Methods A search of nine electronic databases and seven grey literature sources was conducted up to September 25, 2023. Two reviewers independently screened and appraised the studies. Forty-six studies were included. Prevalence estimates were pooled using a random-effects model. Subgroup analysis and meta-regression explored potential moderators. A narrative synthesis explored social determinants associated with anxiety and depression. Results Preliminary results showed a high prevalence of anxiety (43.7%, 95% CI: 35.6-54.4%) and depression (37.8%, 95% CI; 32.5% to 46%), with regional variations. There were significant differences in the prevalence of anxiety and depression across chronic physical conditions and method of condition assessment. Meta-regression showed significant association between average age and anxiety prevalence. Social determinants associated with anxiety included being female, non-Christian, low income, self-employed, limited social support, and basic education, while that for depression included being young (&amp;lt; 25yr), low income, government employed, childhood physical and sexual abuse, gender-based violence and urban residence. Protective factors for both were male gender, strong social support and higher education. Conclusions Preliminary results indicate a high prevalence of anxiety and depression linked to social determinants. While methodological limitations exist (eg, study heterogeneity), findings highlight a need for evidence-based interventions targeting social determinants to reduce the burden of anxiety and depression among adults in Ghana. Key messages • Our review suggests addressing the social determinants of mental health, aligned with sustainable developmental goals, may reduce the burden of anxiety and depression. • While Ghana is working towards improving mental health via its revised 12year Mental Health Policy launched in 2021, this review can facilitate planning and resource allocation for mental healthcare.

A New Natural Language Processing-Inspired Methodology (Detection, Initial Characterization, and Semantic Characterization) to Investigate Temporal Shifts (Drifts) in Health Care Data: Quantitative Study.

Proper analysis and interpretation of health care data can significantly improve patient outcomes by enhancing services and revealing the impacts of new technologies and treatments. Understanding the substantial impact of temporal shifts in these data is crucial. For example, COVID-19 vaccination initially lowered the mean age of at-risk patients and later changed the characteristics of those who died. This highlights the importance of understanding these shifts for assessing factors that affect patient outcomes. This study aims to propose detection, initial characterization, and semantic characterization (DIS), a new methodology for analyzingchanges in health outcomes and variables over time while discovering contextual changes for outcomes in large volumes of data. The DIS methodology involves 3 steps: detection, initial characterization, and semantic characterization. Detection uses metrics such as Jensen-Shannon divergence to identify significant data drifts. Initial characterization offers a global analysis of changes in data distribution and predictive feature significance over time. Semantic characterization uses natural language processing-inspired techniques to understand the local context of these changes, helping identify factors driving changes in patient outcomes. By integrating the outcomes from these 3 steps, our results can identify specific factors (eg, interventions and modifications in health care practices) that drive changes in patient outcomes. DIS was applied to the Brazilian COVID-19 Registry and the Medical Information Mart for Intensive Care, version IV (MIMIC-IV) data sets. Our approach allowed us to (1) identify drifts effectively, especially using metrics such as the Jensen-Shannon divergence, and (2) uncover reasons for the decline in overall mortality in both the COVID-19 and MIMIC-IV data sets, as well as changes in the cooccurrence between different diseases and this particular outcome. Factors such as vaccination during the COVID-19 pandemic and reduced iatrogenic events and cancer-related deaths in MIMIC-IV were highlighted. The methodology also pinpointed shifts in patient demographics and disease patterns, providing insights into the evolving health care landscape during the study period. We developed a novel methodology combining machine learningand natural language processing techniques to detect, characterize, and understand temporal shifts in health care data. This understanding can enhance predictive algorithms, improve patient outcomes, and optimize health care resource allocation, ultimatelyimproving the effectiveness of machine learning predictive algorithms applied to health care data. Our methodology can be applied to a variety of scenarios beyond those discussed in this paper.

Ferric derisomaltose versus usual care in patients with heart failure, reduced ejection fraction, and iron deficiency: a short-term UK cost-utility analysis based on the IRONMAN RCT

Abstract Background In the IRONMAN trial, patients with heart failure (HF), a reduced ejection fraction and iron deficiency who were randomly assigned to ferric derisomaltose (FDI), rather than usual care, had a lower rate of the composite of hospitalization for heart failure (HHF) and cardiovascular (CV) death (rate ratio, RR, 0.82; 95% CI 0.66 to 1.02; p=0.07). A COVID-19 sensitivity analysis, censoring data at 1 year of follow-up showed a greater magnitude of treatment effect (RR 0.66; 95% CI 0.48 to 0.91; p=0.011). Purpose Patient-level data from IRONMAN were used to evaluate the effectiveness and cost-utility of FDI compared to usual care for patients with a current or recent HHF, or raised plasma natriuretic peptide concentrations, from a UK National Health Service (NHS) perspective. Methods A cost-utility analysis (CUA) was conducted covering the first year after randomization. Numbers-needed-to-treat (NNTs) were calculated for the primary endpoint censored at 1 year. In the CUA model, patient survival and rates of (re)hospitalization due to HF, other CV events, infections, respiratory conditions, and other non-CV events were modeled based directly on data from the IRONMAN RCT. UK-specific costs were obtained from the national schedule of NHS costs. Quality of life was estimated based on EQ-5D-5L data collected from patients included in the IRONMAN RCT. Results were undiscounted and reported as absolute and incremental costs in 2023 pounds sterling, and quality-adjusted life expectancy in quality-adjusted life years (QALYs). A willingness-to-pay threshold of £20,000 per QALY was adopted to allow calculation of a net monetary benefit (NMB), a summary measure of the overall value of FDI in monetary terms, capturing changes in both cost and QALY outcomes. Results The NNT analysis showed that 9 patients would need to be treated with FDI to avoid one composite primary endpoint event (CV death and HHF) over 1 year. Relative to usual care, FDI was associated with an improvement of 0.01 QALYs per patient over 1 year (0.58 versus 0.57 QALYs). Total average costs per-patient were £1,672 for FDI, versus £1,975 for usual care, leading to a saving of £302 per patient with FDI. The cost of iron procurement was more than offset by reductions in costs of (re)hospitalizations (Figure). FDI was therefore dominant over usual care, yielding an NMB of £483 over 1 year. Conclusions This short-term, within-trial CUA suggests that FDI both improves QALYs and reduces healthcare expenditure versus usual care when administered to patients with HF and iron deficiency in the UK. Iron repletion with FDI may be a prudent allocation of healthcare resources in this patient population.Breakdown of costs

Digital health in the EP lab: routine implementation of remote access and technical support for complex ablation procedures

Abstract Background Complex electrophysiological procedures are performed using three-dimensional (3D) electroanatomical mapping (EAM) systems, which are usually operated on site, by field technical engineers (FTE). Due to a lack of skilled personnel, the engineers usually must travel between different hospitals and the procedures have to be planed in advance. Remote access (RA) technologies were recently implemented in clinical practice, aiming to increase the flexibility in the EP lab and maximise healthcare resources use. Methods Electroanatomical mapping was performed remotely by means of an integrated audio-visual solution for RA. The operator and the FTE communicated exclusively using the RA system, while the latter worked only in home office. Consecutive procedures were prospectively enrolled and analysed in terms of indication, procedural characteristics, feasibility and saved travelled time by the FTA. Results Between September 2022 and July 2023, a total of 133 EP procedures were performed in a high volume, tertiary electrophysiological centre in Germany. The mean age was 67 (57.5, 75.5) years. Women represented 39.8% of the population. The median BMI was 26.2 (23.1, 30.3) Kg/m2. A total of 56 (42.1%) patients underwent an atrial fibrillation ablation procedure (37.5% paroxysmal), 22 (16.5%) an atrial tachycardia ablation, 13 (9.8%) cavotricuspid isthmus ablation, 15 (11.3%) supraventricular tachycardia ablation, 14 (10.5%) a premature ventricular contraction ablation, 12 (9%) ventricular tachycardia ablations and 1 patient an electrophysiological diagnostic study. The median time length of procedure was 130 (96.5, 168) minutes. The median number of radiofrequency applications was 63.5 (22.3, 109.5), while the median RF time was 14.2 (6.9, 25.1) minutes. The fluoroscopy time was 9.2 (6.5, 12.6) minutes, with a median radiation dose of 408.5 (233.0, 631.8) cGy x cm2. The median amount of contrast media was 40 (8.75, 50.0) ml and the median amount of heparin administered was 12000 (8000, 16000) UI. Protamine was used in 9 cases, with a median of 7000 (5000, 7000) UI. Except for one supraventricular procedure, where no arrhythmia induction was possible, all procedures were successful. One major complication was observed. In one case, the internet-based headphone connection was not stable. Therefore, the communication was established via a conventional smartphone. No change to on-site support was needed. The travel time reduction was available in 130 cases. In total, 290 travel hours could be saved. Conclusion The routine implementation of RA for complex EP procedures proved to be safe and efficient. This approach has the potential to increase the feasibility in case of emergency procedures, reduce travel times for FTE and streamline healthcare resources allocation.

Global, Regional, and National Burden of Intracerebral Hemorrhage in Young Adults From 1990 to 2021: A Population-Based Study.

Intracerebral hemorrhage (ICH) in young adults demands greater attention, given its poor prognosis; however, there has been a paucity of epidemiological data. The objective of this study is to estimate the temporal trends and distribution characteristics of the disease burden in 204 countries or territories between 1990 and 2021. The data, including incidence, mortality, prevalence, and disability-adjusted life year (DALY) rates were sourced from the Global Burden of Diseases Study 2021. From 1990 to 2021, there was a significant decline in the global incidence (estimated annual percentage change=-1.05), mortality (estimated annual percentage change=-0.92), and DALY (estimated annual percentage change=-1.00) rates of ICH in young adults. In 2021, the highest incidence, mortality, and DALY rates were observed in Southeast Asia, East Asia, and Oceania. Globally, high systolic blood pressure, smoking, and ambient particulate matter pollution were identified as the primary contributors to the largest proportion of DALYs associated with ICH in young adults. The health inequality of ICH in young adults has been reduced over the past 3 decades. There is a considerable degree of heterogeneity in the global burden of ICH in young adults. A decline in the incidence, mortality, prevalence, and DALY rates has been observed from 1990 to 2021, however, the number of absolute cases has increased. These results will enable health care professionals, policymakers, and researchers to refine the implementation of cost-effective policies, the allocation of health care resources, and the management of patients to further mitigate the burden of ICH in young adults.

Headache disorders: a persistent public health challenge for the under 50s.

Currently, neurological diseases has surpassed cardiovascular diseases as the primary cause of global disease burden. Among these, headache disorders are the most prevalent and have emerged as the main cause of disability in people under 50 years old in recent years. Since the release of GBD 2021, there has been no comprehensive systematic exposition on the burden of headache among individuals under 50 years old and a forecast for future burdens. This study aims to quantify the global, regional, and national burden of headache disorders among people under 50 from 1992 to 2021 and to predict future trends in order to provide policy makers with precise and effective epidemiological evidence. This study extracted the incidence, prevalence, and DALYs data related to headache disorders in the 5-50 age group from the GBD 2021. After age-standardizing the data, we used joinpoint regression analysis and health inequity analysis to analyze the burden and temporal trend of headache disorders and predicted the future disease burden and changes based on the age-period-cohort model. By 2021, the case number of global incidence, prevalence and DALYs have increased by 35, 39, and 41%, respectively, over the past 30 years. The incidence and prevalence of tension-type headache (TTH) are significantly higher than those of migraine, but migraine causes greater health burdens. The burden is higher for female than for male. In terms of age, youth aged 25-29 years had the highest incidence in 2021, with an age-standardized rate (ASR) of 13,454.64 (95%CI, 9,546.96-18,361.36) per 100,000 population. Nationally, the highest ASR of incidence and prevalence are found in Norway, and the most damaging to health is found in Belgium. Among the five sociodemographic index (SDI) categories, the middle SDI has the highest number of cases (190 million in 2021). It is worth noting that the forecast shows that by 2046 the three indicators will reach 680 million, 2.33 billion, and 41 million, respectively, indicating that the burden of headache disorders in this age group will continue to persist. Globally, the burden of headache disorders in people under 50 years of age remains significant and has not improved over the past 30 years, especially in regions with high SDI. Headache problems will continue to pose a serious public health challenge for this age group for some time to come. This study reveals the burden and distribution of headache disorders in this age group, providing important basis for governments and policymakers to accurately and effectively allocate health care resources, strengthen prevention and management strategies, and respond to this global health problem.

Data-driven collaborative healthcare resource allocation in pandemics

Severe shortages of healthcare resources are major challenges in pandemics, especially in their early stages. To improve emergency management efficiency, this paper proposes a novel rolling predict-then-optimize framework that includes three interactive modules, i.e., data-driven demand prediction, healthcare resource allocation, and parameter rolling update. Such a framework uses historical data to dynamically update the control parameters of the proposed Net-SEIHRD model, which predicts the healthcare needs of each region by jointly considering government interventions and cross-regional travel behaviors. Based on the forecasted healthcare resource demand in real-time, an optimization model is then formulated to realize coordinated resource allocation across multiple regions by minimizing the total generalized cost. To facilitate model solving, the proposed mixed integer nonlinear programming model is converted into an equivalent mixed integer linear model by using some linearization techniques. Finally, the proposed method is applied to the SARS-CoV-2 emergency response and collaborative allocation of healthcare resources in Shanghai, China. The results show that the proposed prediction model can effectively predict the peak and scale of the spread of the virus. Compared with the traditional LM and SEIHR models, the prediction accuracy of the Net-SEIHRD model is improved by 10.76% and 24.11%, respectively. Moreover, coordinated relief activities across regions, such as patient transfer and drug-sharing can improve the efficiency of pandemic control and save social costs.

Key Factors Driving Physiotherapy Use in Patients with Nonspecific Low Back Pain: Retrospective Clinical Data Analysis

Background/objectives: Understanding the factors that influence physiotherapy (PT) service use among patients with nonspecific lower back pain (LBP) is necessary to optimize treatment strategies, healthcare resource allocation, and the planning of value-based initiatives. We report factors that influence the number of PT visits per episode of care (defined as a referral from a physician) for an LBP population in Saudi Arabia, and compare them with patients experiencing their first and recurrent episodes of LBP. Methods: LBP patients were retrospectively enrolled from a clinical dataset derived from an outpatient PT clinic in Saudi Arabia. The primary outcome variable was the number of PT visits performed per episode of care. Multiple linear regression analysis was performed to examine the relationships between the numbers of PT visits per episode of care and independent variables. Results: The number of PT sessions per week (β 0.34, p &lt; 0.001), compliance with PT sessions (β 0.31, p &lt; 0.001), and pre-pain scores (β 0.29, p &lt; 0.001) explained 41.8% (adjusted R2 0.32) of the variance in the total number of PT visits per episode of care (p &lt; 0.001). Conclusions: Factors that might improve value-based care for LBP patients are reported. The more PT sessions per week, compliance with these sessions, and higher baseline pain scores predict a higher number of PT visits per episode of care among these patients. While reported for a Saudi Arabian population, there is no reason to believe that these findings do not apply internationally.