Allocation Of Public Resources Research Articles

Predictors of Early Mortality After Traumatic Spinal Cord Injury in South Africa.

Retrospective cohort study. To identify predictors for early mortality following traumatic spinal cord injury (TSCI), as recognition of factors associated with early mortality is essential for public resource allocation and optimized acute care. Retrospective Analysis of SCI patients admitted to the acute SCI ward from 2003 to 2022 was performed. Days elapsing from the date of injury to date of death established the survival time. Early mortality was defined as survival time ≤ 365days. Multivariable logistic regression was used in modeling of early death following TSCI with age, gender, neurologic deficit, type of neurologic injury, ventilation status, and count of complications as covariates. Simple logistic regression indicated a significant association of early mortality with the number of complications (P < .0001), neurological deficit (P < .0001), complete neurological injury (P < .0001), ventilation status (P < .0001), and age group (P < .0003). After adjusting for other covariates, complete neurological injury (OR: 1.75, P < .0001), ventilation (OR: 2.80, P < .0001), and Age group (over 60 OR: 17.71, P < .0001) were significantly associated with early mortality. The multivariable model showed a good overall fit (Hosmer-Lemeshow P = .315; AUC 0.85). Predictors of early mortality after TSCI are high complete spinal cord injury, age, number of complications, and the need for ventilation. Identification of high-risk patients is crucial to rationalize and improve acute care to potentially reduce mortality rates.

Evolution of endometrial cancer incidence patterns in Hong Kong: A three-decade analysis with future projections

ObjectiveThis study provides a comprehensive analysis of endometrial cancer incidence trends in Hong Kong over the past three decades. It aims to evaluate the impact of demographic shifts and epidemiological factors, including age, birth cohort, and diagnosis period, on the incidence rates. The study also projects future trends in endometrial cancer cases up to 2030 and assesses the contributions of these factors using a detailed decomposition approach. Material and methodsThe analysis is based on endometrial cancer data obtained from the Hong Kong Cancer Registry. Age-period-cohort (APC) modeling was utilized to investigate the effects of different age groups, historical periods, and birth cohorts on the changing incidence patterns. The study projects future trends using a Bayesian APC framework, integrating historical data and expert insights for robust predictions. Additionally, a decomposition analysis was conducted to disentangle the contributions of demographic changes (aging and population growth) and epidemiological shifts (risk factors such as obesity and reproductive behaviors) to the increasing cases. ResultsBetween 1992 and 2021, there were 19,214 recorded cases of endometrial cancer in Hong Kong. Age-standardized and crude incidence rates showed consistent increases, rising from 7.4 per 100,000 person-years in 1992 to 31.0 per 100,000 in 2020. Incidence trends rose significantly across all age groups, with the highest increase seen in women aged 50–65. Projections indicate that the upward trend will continue, with an estimated 1718 cases by 2030. Demographic factors, particularly population aging, and evolving epidemiological trends contribute jointly to the incidence rise. ConclusionsThe findings reveal a steady increase in endometrial cancer incidence among Hong Kong women, primarily driven by demographic aging and shifts in risk factors. The study underscores the need for targeted public health measures and resource allocation for early detection and effective management strategies, emphasizing the importance of addressing modifiable risk factors such as obesity and reproductive health behaviors.

Perceptions of key informant neurologists before implementing anti-amyloid drugs in the Spanish departments of neurology.

A deep knowledge of the healthcare system and the organization of neurology departments is important for planning and optimizing changes to facilitate the successful implementation of anti-amyloid antibodies treatments. We aimed to assess the necessary changes prior to introducing these therapies in our setting. We conducted a key informant survey among heads of departments of neurology from 16 hospitals in Spain. The questionnaire comprised questions about changes in the organization and functioning of the departments of neurology with the introduction of anti-amyloid drugs, changes in diagnosis and patient care, use of diagnostic techniques, patients, families and public information, resources allocation, and research. Sixteen key informants completed the survey. They strongly agreed that the introduction of anti-amyloid drugs will impact the functioning of neurology services, especially in hospitals with dementia units. Consensus was reached regarding referring all Alzheimer's disease patients eligible for therapy to dementia units. There was also agreement on the need to expand the neurology services, day hospital units, extend visit durations, and hire more professionals, especially neurologists, neuropsychologists, and nuclear medicine physicians. Furthermore, consensus was achieved on increasing the use of MRI, amyloid PET, cerebrospinal fluid biomarkers, APOE genotyping, and the necessity of advancing blood biomarkers and tau tracers. Our study highlights the need for extensive changes within Spanish neurological departments to effectively integrate anti-amyloid antibodies. Implementing these changes is essential for the timely and equitable adoption of novel therapies.

Towards a Standardized LCOE Calculation for Informed Decision-Making in Energy Policy and Investment: Application to the Colombian Context

The Levelized cost of electricity - LCOE represents a constant cost per unit of generated electrical energy, resulting from the analysis of the total costs throughout the life cycle of a generation plant. Its widespread use, thanks to its simplicity of calculation and potential of standardization, has made it one of the primary criteria for comparing electricity generation technologies. However, in the pursuit of simplification and generalization, certain aspects associated with the socioeconomic context of the region, as well as accounting and financial concepts, are excluded due to externalities or incentives related to regulatory and fiscal policies. These exclusions affect the accuracy of LCOE and introduce uncertainty when using it as a criterion for investment decision-making or allocation of public resources for the development of renewable sources. This article proposes a clear and standardized methodology for calculating LCOE, which allows for a fair and precise comparison of renewable energy generation options in rural areas. It contributes to informed and transparent decision-making in the fields of energy policy and public investment for expanding electricity service coverage. The proposed methodology is based on the financial cash flow matrix to address variability sources in the calculation, considering the local context of the generation project.

Societal Adaptation to Pandemic Challenges: Analysing the Role of Local Governance and Healthcare Systems in Madhya Pradesh

The COVID-19 pandemic posed unprecedented challenges, compelling communities globally to adapt rapidly to protect lives and maintain societal functions. This study examines the role of local governance and healthcare systems in Madhya Pradesh in addressing these challenges and fostering societal resilience. Drawing on quantitative data from government health agencies and qualitative insights from community feedback, this research explores how local governance structures facilitated public health response, resource allocation, and pandemic preparedness. Findings indicate that decentralized decision-making and robust healthcare infrastructure were instrumental in managing health crises, providing essential services, and supporting economic resilience at the grassroots level. Key results show that regions with stronger local governance exhibited a 30% lower infection rate compared to less governed areas, while areas with well-resourced healthcare systems saw a 20% higher recovery rate. Resource distribution, however, varied significantly, with urban areas receiving 60% more medical supplies than rural regions, creating disparities in healthcare access. Data analysis revealed that healthcare facilities that integrated digital solutions, such as telemedicine, were able to serve 45% more patients remotely, mitigating the risk of virus spread and reducing hospital overload. The study identifies the need for capacity-building initiatives, infrastructure strengthening, and data-driven decision-making to enhance future pandemic preparedness. Future prospects include continuous training programs for healthcare workers, increasing community awareness of health protocols, and leveraging technology for real-time data monitoring. These insights contribute to a comprehensive framework for building resilient healthcare systems in Madhya Pradesh, better prepared for future public health crises.

How Avoidable Mortality is influenced by health expenditure: an Italian 14-year panel data analysis

Abstract Background Avoidable Mortality is a widely used public health indicator to assess the quality of healthcare systems. The aim of this study was to investigate how the allocation of private and public health expenditure (PHE) could influence avoidable mortality (AM), treatable mortality (TM) and preventable mortality (PM) of Italian citizens. Methods A pooled cross-sectional time series study was conducted. Italian healthcare data from 2009 to 2019 was extracted from ‘Health for All’, a WHO-related database released periodically by the Italian National Institute of Statistics. PHE was divided by providers area (directly provided services (DPS), general and specialist medicine, pharmaceutical, rehabilitation). Data about private health expenditure, gross domestic product per capita (GDPPC), level of education, medical drug consumption per capita (MDCPC), general practitioners per capita (GPPC), physical exercise, tobacco use, and obesity were collected. AM was divided into TM and PM using OECD/Eurostat lists of causes of death (2022). A fixed-effects regression was used to assess the effects of economic and social variables on AM, TM and PM. Results Higher spending on DPS (-0.027), higher MDCPC (-0.859) and higher GDPPC (-0.001) were associated with lower rates of AM. Higher sport activity (c -0.673) was associated with lower rates of TM. Higher levels of private health expenditure (0.022) and higher number of GPPC (0.023) were associated with higher rates of PM; these counterintuitive results could be caused by the inappropriateness of medical treatment directly requested by patients. Other variables were not significantly associated with AM, TM or PM. Conclusions The results highlight the importance of medical DPS by NHS (emergency departments, hospital care, specialized care, primary care, etc.). Analyzing health data divided by areas of NHS we could help policy makers to allocate human and economic resources more efficiently. Key messages • The allocation of public health resources could influence the health status of the polulazion. • Italian National Health System could reduce avoidable mortality through the use of directly provided services.

A Groundbreaking Prevalence Survey Conducted in Mauritania for Enhanced Sickle Cell Control

Abstract Issue/Problem Sickle cell disease is a major concern in sub-Saharan Africa, with a prevalence of 0.5 to 2% among children and a mortality rate exceeding 50% in untreated cases. However, the lack of reliable data hampers effective public health policies and resource allocation. To address this, Mauritania’s Ministry of Health, with technical support from NGO Santé Sud, conducted its first prevalence survey in 2023-24. The findings will support advocacy for a national strategy on the disease and offer valuable insights for neighboring countries with similar challenges. Description of the problem The prevalence survey comprised 3 field missions over 7 weeks, conducted across 24 primary healthcare facilities during immunization days, in Nouakchott, Dakhlet-Nouadibou, Trarza, Ghorgol, Brakna, Guidimakha and Assaba regions: 1,642 infants (0-9 months) underwent screening for sickle cell disease, with parents’ consent, and 30 paramedics and laboratory technicians were trained to administer point-of-care tests. Results The survey found that 9.7% of tested children were carriers of hemoglobinopathies, with 0.6% diagnosed with sickle cell disease. Lab technicians and nurses have improved knowledge about the disease and are better equipped for point-of-care testing. This initiative is expected to enhance sickle cell disease control by providing crucial data for advocacy and may lead to a nationwide newborn screening program, in line with WHO recommendations. Lessons This initiative highlighted the efficiency of screening infants for sickle cell disease, emphasizing the need for effective organization involving stakeholders from central to local levels. Screening during immunization days proved highly efficient in testing numerous children. Success depends on adequate human resources, technical skills, and dedicated health personnel. Additionally, having a team member to inform and counsel families, especially when test results are positive, proved crucial. Key messages • A prevalence survey on sickle cell disease to generate robust data for advocacy efforts in Mauritania. • Conducting a prevalence survey on sickle cell disease in Mauritania for a better control of the disease.

Cost-benefit analysis: just a step in the HIA process

Abstract The economic analysis within Health Impact Assessments (HIA) is a critical component in informing public policy decisions by assessing the financial implications of health outcomes associated with the different intervention options. Health metrics play a role defining the cost-effectiveness and societal value associated with the interventions. These metrics, quantifying the health impacts, include quality-adjusted life years (QALYs), years of life lost (YLLs) or disability-adjusted life years (DALYs). On another level, such metrics need to be translated into cost-effectiveness and societal value in which other methods play a role, such as the value of statistical life (VSL) or the value of a life year (VOLY). While VSL quantifies the value individuals place on reducing mortality risk, VOLY evaluates the value attributed to extending life expectancy. On another hand, uncertainties related to economic valuation pose challenges to the accuracy and reliability of cost estimates within HIA. These come from several factors, including data quality, methodological differences across studies, and subjective judgments involved in valuing health states. In fact, estimating the economic value of a prevented death involves multifaceted considerations and important assumptions. Additionally, assigning monetary values to non-fatal health outcomes, such as disability or reduced quality of life, involves subjective assessments and value judgments. By selecting appropriate metrics, considering diverse data sources, and addressing methodological steps and uncertainties, HIA practitioners can improve the utility of economic assessments, facilitating evidence-based decision-making in public health policy and resource allocation. This work was supported by National Funds through FCT - Fundação para a Ciência e a Tecnologia,I.P., within CINTESIS, R&amp;D Unit (reference UIDP/4255/2020).

Epidemiology of Dementia in the Campania Region (Italy): A 2015-2020 population-based study

Abstract As the global population ages, dementia poses a significant health challenge, ranking seventh among leading causes of death worldwide. Considering the high societal and financial burden of dementia, our study aims to provide estimates of prevalence and incidence for any dementia and Alzheimer’s dementia (AD) in the Campania Region (South Italy). It is Population-based study, using routinely collected healthcare data of individuals living in the Campania Region (South Italy) from 2015 to 2020. We included individuals aged ≥65 years who had at least one administrative record for dementia and/or AD. Prevalence rates adjusted for age and sex used 2020 European population as reference. To estimate incidence, we tested three possible algorithms, which differed for the duration of the time interval between study baseline (Jan 1, 2015) and index date (first record for dementia and/or AD in administrative databases). We employed a clinical database for the validation of our algorithms towards neuropsychological test results. Among individuals, 80,392 had dementia, of which 35,748 had AD. The age- and sex-standardized prevalence rates per 1,000 individuals for any dementia and AD were 77.64 (95%CI=77.57; 77.68) and 34.05 (95%CI=34.01; 34.09), respectively. There were 82.10 incident cases of any dementia per 100,000 per year (0.79 sensitivity and 0.62 specificity), and 59.89 incident cases of AD per 100,000 per year (0.80 sensitivity and 0.59 specificity). The capture-recapture method showed low number of undetected cases (1.7% for any dementia and 3.0% for AD). Our algorithms showed acceptable performance with AUC ranging from 0.59 to 0.72, and double likelihood ratio of correctly identifying individuals above and below MMSE standard cut-offs (24 and 26). Our algorithm, integrating administrative and clinical data, holds potential for assessing dementia’s epidemiological burden, identifying risk factors, planning healthcare access, and developing prevention strategies. Key messages • Providing comprehensive estimates of dementia burden aids public health planning and resource allocation. • Integrated administrative and clinical data provide robust tools for epidemiological analysis and preventive strategies.

Cross-national characteristics and differences in type 2 diabetes: Malta versus Luxembourg

Abstract Introduction Type 2 diabetes mellitus (T2DM) is a significant global health concern, requiring a thorough understanding of its population-level features and impact. This study aims to compare the characteristics of T2DM in Malta and Luxembourg, two European nations with distinctive healthcare systems and societal attributes. Methods Data from national health surveys conducted in Malta and Luxembourg, encompassing various factors such as socio-demographics, medical history, biochemical metrics, and anthropometric measurements, were analysed to investigate the features of T2DM. Comparative analyses were conducted utilizing chi-square tests, independent t-tests, and multivariable binary regression. Results Malta (8.23%) and Luxembourg (7.82%) shared similar T2DM prevalence, with a predominance of men having T2DM. Malta exhibited a higher prevalence of obesity among the individuals having T2DM. Multivariable analysis showed no significant association between living in Malta or Luxembourg and diabetes. However, being male (OR: 1.49, CI 95%: 1.07–2.08, p = 0.02), an occasional smoker (OR: 2.49, CI 95%: 1.54–4.03, p &amp;lt; 0.01), increasing age (OR: 1.05, CI 95%: 1.03–1.67), and larger waist circumference (OR: 1.02, CI 95%: 1.01–1.04, p = 0.01) were positively associated with diabetes. Conclusions Understanding similarities and differences in T2DM between Malta and Luxembourg is crucial for identifying common challenges to formulate effective prevention and management strategies. Despite similar T2DM prevalence rates, notable differences in obesity rates and socioeconomic factors may contribute to disparities in disease burden. The complex interplay of numerous population characteristics underscores the importance of tailored public health interventions. This study accentuates the need for interventions and evidence-based policies to address modifiable risk factors, and advocate for cross-national cooperation to reduce the T2DM epidemic, especially in the context of post-COVID public health planning and resource allocation. Key messages • Malta and Luxembourg share similar T2DM prevalence despite differences in other factors, hypothesised to be attributed to differences in risk factor profiles and health care infrastructure. • This study underscores the necessity for tailored interventions and cross-national cooperation to address common challenges in T2DM management in the two small countries.

Optimizing public sector resources: A centralized DEA model for effective allocation of shared inputs

This study presents a novel centralized Data Envelopment Analysis (DEA) model with shared inputs to optimize the allocation of public sector resources and enhance management efficiency. Recognizing the need for a comprehensive approach to public sector performance evaluation, the study integrates the strengths of the centralized DEA framework and the two-stage DEA model with shared inputs. The centralized DEA model shifts the focus from individual decision-making units to optimizing resources across the entire public sector system. This allows for the reallocation of shared inputs among decision making units, leading to potential efficiency gains and improved overall performance. Incorporating shared inputs within the centralized structure enables a more nuanced understanding of the interdependencies and interactions between distinct functions and stages within the public sector. The empirical application of the proposed model in the context of public sector management and cultural subsidies provides valuable insights. The findings highlight inefficiency and offer guidance for policymakers and administrators on optimizing shared resource use. The centralized DEA model with shared inputs serves as a practical decision-support tool, informing the development of targeted policies and strategies to enhance the efficiency and effectiveness of public service delivery, particularly in resource-constrained environments. This research contributes to public sector performance evaluation's theoretical and methodological advancement, offering a comprehensive framework for resource optimization and improved management practices.

A guided twin delayed deep deterministic reinforcement learning for vaccine allocation in human contact networks

This manuscript introduces an innovative approach to optimizing the distribution of a limited vaccine resource within a population modeled as a contact network, aiming to mitigate the spread of infectious diseases. The study develops a novel methodology that combines reinforcement learning and graph neural networks. To understand the dynamics of disease propagation, the study constructs an analytical model that outlines conditions for disease eradication or endemic states. This model supports a series of simulation experiments across various scenarios, demonstrating the proposed method’s superiority over random and centrality-based approaches in reducing the average number of infections per individual during an outbreak. The adaptability of the proposed method is further emphasized by its robust performance across networks of diverse sizes and configurations, highlighting its real-world applicability. The findings of this study have significant implications for public health policy and resource allocation, offering a promising framework for managing infectious disease outbreaks in complex and dynamic environments.

A hazai gyermekvédelmi rendszer fejlesztésében rejlő potenciális gazdasági hasznok

The study approaches child protection involvement from an economic perspective, and is thus a niche study in the Hungarian literature. It seeks to quantify the social benefits to society of mitigating the negative impact of involvement in the provision of child protection, and the maximum return on investment in the child protection system for a given payback period and discount rate. As in international research, the evaluation of the project is based on a cost-benefit analysis of the increase in educational attainment. The social benefit is estimated by determining the number of people affected and the social benefit per person affected, and the social discount rate is determined using two types of elasticity estimates. The results suggest that a greater allocation of public resources to child protection provision not only benefits the individuals concerned, but can also result in a positive net present value investment for society as a whole.

A Study of the Evolution of Haze Microblog Concerns Based on a Co-Word Network Analysis

Haze is a phenomenon caused by excessive PM2.5 (air-borne particulate matter having a diameter of fewer than 2.5 μm) and other pollutants and results from the interaction between specific climatic conditions and human activities. It significantly impacts human health, transportation, and the natural environment and has aroused widespread concern. However, the influence of haze on human mental health, being hidden and indirect, is often overlooked. When haze pollution occurs, people express their feelings and concerns about haze events on media such as Weibo. At present, few studies focus on haze public opinion, as well as the changing trends in people’s discussion of haze since its emergence, which is of great significance for haze response and resource management. Based on the perspective of topic analysis, this study explores the psychological impact of haze on people by exploring the feelings of netizens in haze public opinion and investigates the evolution of people’s concerns based on long-term public opinion data. In this study, seven typical provinces and cities in China with severe haze pollution were selected as the research area. Based on data on the “haze” theme from Weibo from 2013 to 2019, first, the microblog posts were preprocessed, and the keyword co-word network was constructed. Second, the Louvain algorithm was used to detect the topic community. Based on this, the cosine similarity was calculated to realize the temporal evolution analysis of topics. The results show that with the development and change in haze pollution, the content and intensity of the topics netizens pay attention to have changed, including five types: merger, split, survival, transformation, and rebirth/extinction. People’s attention to haze shows obvious spatial differences, and it is related to the degree of haze pollution, which is bipolar. Areas with severe haze tend to pay more attention to haze itself and its influence, while areas with light haze pay more attention to haze control. The research results can provide valuable insights for governments and relevant departments in guiding public opinion and resource allocation.

Epidemiological changes of scarlet fever before, during and after the COVID-19 pandemic in Chongqing, China: a 19-year surveillance and prediction study

BackgroundThis study aimed to investigate the epidemiological changes in scarlet fever before, during and after the COVID-19 pandemic (2005–2023) and predict the incidence of the disease in 2024 and 2025 in Chongqing Municipality, Southwest China.MethodsDescriptive analysis was used to summarize the characteristics of the scarlet fever epidemic. Spatial autocorrelation analysis was utilized to explore the distribution pattern of the disease, and the seasonal autoregressive integrated moving average (SARIMA) model was constructed to predict its incidence in 2024 and 2025.ResultsBetween 2005 and 2023, 9,593 scarlet fever cases were reported in Chongqing, which resulted in an annual average incidence of 1.6694 per 100,000 people. Children aged 3–7 were the primary victims of this disease, with the highest average incidence found among children aged 6 (5.0002 per 100,000 people). Kindergarten children were the dominant infected population, accounting for as much as 54.32% of cases, followed by students (34.09%). The incidence for the male was 1.51 times greater than that for the female. The monthly distribution of the incidence showed a bimodal pattern, with one peak occurring between April and June and another in November or December. The spatial autocorrelation analysis revealed that scarlet fever cases were markedly clustered; the areas with higher incidence were mainly concentrated in Chongqing’s urban areas and its adjacent districts, and gradually spreading to remote areas after 2020. The incidence of scarlet fever increased by 106.54% and 39.33% in the post-upsurge period (2015–2019) and the dynamic zero-COVID period (2020–2022), respectively, compared to the pre-upsurge period (2005–2014) (P < 0.001). During the dynamic zero-COVID period, the incidence of scarlet fever decreased by 68.61%, 25.66%, and 10.59% (P < 0.001) in 2020, 2021, and 2022, respectively, compared to the predicted incidence. In 2023, after the dynamic zero-COVID period, the reported cases decreased to 1.5168 per 100,000 people unexpectedly instead of increasing. The cases of scarlet fever are predicted to increase in 2024 (675 cases) and 2025 (705 cases).ConclusionsChildren aged 3–7 years are the most affected population, particularly males, and kindergartens and primary schools serving as transmission hotspots. It is predicted that the high incidence of scarlet fever in Chongqing will persist in 2024 and 2025, and the outer districts (counties) beyond urban zone would bear the brunt of the impact. Therefore, imminent public health planning and resource allocation should be focused within those areas.

Rehabilitation with removable dentures in adults and older people with partial or total edentulism: Chilean Public Health System Experience, 2017–2019

Statement of problemTooth loss is frequent among the Chilean population, particularly the poor and elderly. The Public's response is mostly via removable dentures (RDs), however there is limited evidence regarding its results. PurposeThe objective of this descriptive ecological study is to monitor and characterize the production of RDs within Chile's Public Health System. Material and methodsPublic records were obtained from the Department of Statistics and Health Information of Chile (DEIS) website. All records regarding RDs activities within the Public Health System for years 2017 to 2019 were retrieved and included in the analysis. ResultsBetween 2017 and 2019, the Public Health sector delivered an average of 342,261 RDs annually, which corresponds to 2.4 RD per 100 individuals. Overall, 70.3 % of beneficiaries were female, 63.2 % were for people between the ages of 20 to 64, and 35.3 % were for people aged 65 or older. Among RDs provided, 57 % were made at the primary level and 43 % at the secondary level of care. The program “More Smiles”, accounted for 52.3 % of primary healthcare RD delivery. At the secondary level, 14.9 % of RDs had metallic framework, while 85.1 % were acrylic. Older persons and males were more likely to receive acrylic RDs than their counterparts. ConclusionsRDs are regularly supplied in Chile's Public Healthcare System, mainly through primary care. The characterization regarding programs, type of RDs, gender and age of the recipients, may guide future hypothesis regarding the adequateness of the response to address tooth loss at a population level. Further investigation into the impact on patient satisfaction and RD quality is recommended to enhance understanding in this domain. Clinical implicationsThe study explores the scale and characteristics of removable prosthetic provision within Chile's Public Health System. This comprehensive overview enables the identification of areas requiring further attention to ensure the delivery of high-quality treatments and the efficient allocation of public resources.

Abstract C004: Geopolitical and racial-ethnic trends of social vulnerability impact on oropharyngeal cancer mortality in the United States

Abstract Background: Oropharyngeal cancer (OPC) disparities exist across race-ethnicity, socioeconomic status, and sociobehavioral characteristics, as well as across different US regions. However, no investigations have interactively assessed these multilevel factors in the polarizing dynamics of region-specific political leanings and affiliated local policies. Methods: This retrospective ecological analysis used the NPCR-SEER database to evaluate county-area-summarized OPC age-adjusted annual mortality rates from 2000-2020 and the CDC-Social Vulnerability Index to assess 15 country-level SDH-vulnerability factors across all 3143 US counties. OPC-mortality rates for White, Black, and all race-ethnic groups were regionally stratified based on the Presidential and midterm (categorized into Democrat, Republican) electoral results of their counties’ respective states for each biannual cycle. These stratified mortality rates were regressed against socioeconomic status, minority-language status, household composition, housing-transportation SDH-vulnerabilities, as well as their total composite. Results: Across the US, yearly age-adjusted OPC-mortality mean rate was 2.94 deaths/100k patients from 2000 to 2020, with counties in Republican-leaning states having higher mortality rates (3.09/100k) compared to Democratic ones (2.72/100k). Mortality rates for Black patients were substantially higher in Republican-leaning areas (4.02/100k) than Democratic ones (3.08/100k). Non-Hispanic white patients also showed lesser increases in mortality in Republican-areas (3.04/100k) compared to Democratic ones (2.68/100k). With increasing total SDH-vulnerability, white populations in Republican-leaning areas suffered 0.27 age-adjusted added deaths/100k annually &amp; 0.22/100k in Democratic ones, whereas black populations in Republican states suffered 0.51/100k annually &amp; 0.34/100k in Democratic ones. For white patients, vulnerabilities in socioeconomic status primarily contributed to these mortality disparities in Democratic-leaning areas while both socioeconomic status and household composition vulnerabilities influenced disparities in Republican ones. For black patients, household composition primarily contributed in Democratic areas, whereas socioeconomic status, housing-transportation, and household composition-vulnerabilities all contributed equivalently in Republican ones. Conclusions: The results of this study showcase multilevel, population effects of political leanings and racial-ethnic demographic makeup in conferring SDH-derived OPC-mortality disparities over the past two decades. In particular, black OPC patients from Republican-leaning areas suffered exacerbated associations of SDH-vulnerability-related disparities driven by varied SDH-types. Through this nuanced modeling of the interactions between geopolitical and sociodemographic factors, targeted public health resource allocation and policy-lobbying can more specifically address local legislature through this systematic, large-data approach. Citation Format: David J. Fei-Zhang, Mary J. Xu, Jeffrey C. Rastatter, Anthony M. Sheyn, Daniel C. Chelius, Jill N. D'Souza, Sandeep Samant, Urjeet A. Patel, Caryn E. Peterson, Olga Garcia-Bedoya, Patrick D. Smith, Kate Klein, Mamoudou Maiga, Warren A. Kibbe, Lifang Hou. Geopolitical and racial-ethnic trends of social vulnerability impact on oropharyngeal cancer mortality in the United States [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C004.

The Impacts of Urban Population Growth and Shrinkage on the Urban Land Use Efficiency: A Case Study of the Northeastern Region of China

In the context of rapid urbanization, urban population differentiation has become increasingly pronounced. Regional development strategies based on growth scenarios often lead to continuous expansion, regardless of urban population status. Such “one-size-fits-all” models exacerbate resource waste and negatively impact urban land use efficiency (ULUE). This study aims to explore the mechanisms by which urban population growth and shrinkage (UPGS) affect ULUE, with the goal of enhancing ULUE and promoting sustainable urban development. We analyzed 34 prefecture-level cities in China’s three northeastern provinces. First, we identified UPGS using population data. We then employed a three-stage SBM-DEA model to measure ULUE from 2000 to 2020. Spatial analysis methods were used to examine the spatiotemporal characteristics and correlations between UPGS and ULUE. Additionally, mediating effect models and spatial Durbin models were utilized to empirically test the impact processes, mechanisms, and spatial heterogeneity. Our findings reveal that: (1) Over the past 20 years, urban population shrinkage in northeastern China has intensified, and significant regional disparities in urban development are evident. (2) Population growth positively influences ULUE, while population shrinkage inhibits its improvement. (3) Economic development, technological innovation, and industrial structure upgrading are key factors in enhancing ULUE in this region, while the impact of public services on ULUE varies significantly at different stages of urban development. (4) Economic development, technological innovation, and industrial structure upgrading exhibit spatial spillover effects, whereas public services are constrained by regional limitations, resulting in minimal spatial spillover effects. To foster coordinated regional development, this study proposes policy recommendations, including strengthening support for resource-dependent cities, optimizing the allocation of public resources, and promoting technological innovation and industrial diversification.

The influence of fiscal transparency on expenditure by regional and provincial governments in Vietnam

The intricate relationship between budget transparency and government expenditure has been confirmed, with a particular focus on how these dynamics are influenced by spatial and temporal factors. This study deals with the spatio-temporal effects of transparent communal budgets on local government spending, specifically across the 63 provinces in Vietnam. By analyzing data over time, the study uncovers critical insights into how the disclosure of budget information influences government spending patterns, both in the short term and the long term. The findings reveal a nuanced relationship, where both the direct and indirect effects of budget transparency on expenditure tend to be negative, suggesting that increased transparency may lead to a reduction in government spending. This trend is observed from both a short-term perspective, where immediate changes in budget disclosure affect spending, and a long-term perspective, where the effects of sustained transparency become more evident. The study highlights significant regional disparities in the impact of budget transparency on government expenditure. It finds that the influence of budget disclosure is less pronounced in northern Vietnam compared to the southern regions. The results advocate for policies that support targeted resource allocation at the provincial or regional levels. Such tailored approaches are essential for achieving more effective and equitable government budget management, ensuring that transparency initiatives are adapted to the specific needs and conditions of different regions within Vietnam. Overall, this research contributes to the broader understanding of how transparency in budgetary processes can influence government spending. By highlighting the spatial and temporal dimensions of this relationship, the study provides valuable insights for policymakers seeking to enhance fiscal accountability and optimize public resource allocation in Vietnam.

Predicting costs of local public bus transport services through machine learning methods

The present study developed several machine learning-based cost models to predict an efficient total economic cost per vehicle revenue-mile of urban public bus transport. The models were trained on a built-in dataset from 269 transit agencies providing urban services in the United States from 2015 to 2019. A feature selection strategy was implemented, finding that, for each proposed model, a subset of features determined a large impact on unit cost. These “core” features included commercial speed, average salary expenses per employee, vehicle productivity, and fleet ownership cost per vehicle. Machine learning techniques outperformed the linear regression method in terms of predictive power and robustness (understood as the dispersion of predictive power measures over the training sets). Based on SHAP values, the sensitivity analyses showed that the proposed models could be used to predict the impact of changes in some critical features on corresponding unit costs. The results may be useful for: (i) introducing regulatory constraints to the allocation of national public resources to local public bus transport services, aimed at minimizing the resources needed to provide a given level of service; (ii) defining the maximum economic compensation required by firms involved in competitive tendering for the allotment of service concessions, or firms with monopoly rights (by political choice and/or local public ownership); and (iii) improving service contract management and design by identifying key cost drivers of transit services.