Allocation Of Health Care Resources Research Articles

Cashing in: cost-benefit analysis framework for digital hospitals

BackgroundFor many countries, especially those outside the USA without incentive payments, implementing and maintaining electronic medical records (EMR) is expensive and can be controversial given the large amounts of investment. Evaluating the value of EMR implementation is necessary to understand whether or not, such investment, especially when it comes from the public source, is an efficient allocation of healthcare resources. Nonetheless, most countries have struggled to measure the return on EMR investment due to the lack of appropriate evaluation frameworks.MethodsThis paper outlines the development of an evidence-based digital health cost-benefit analysis (eHealth-CBA) framework to calculate the total economic value of the EMR implementation over time. A net positive benefit indicates such investment represents improved efficiency, and a net negative is considered a wasteful use of public resources.ResultsWe developed a three-stage process that takes into account the complexity of the healthcare system and its stakeholders, the investment appraisal and evaluation practice, and the existing knowledge of EMR implementation. The three stages include (1) literature review, (2) stakeholder consultation, and (3) CBA framework development. The framework maps the impacts of the EMR to the quadruple aim of healthcare and clearly creates a method for value assessment.ConclusionsThe proposed framework is the first step toward developing a comprehensive evaluation framework for EMRs to inform health decision-makers about the economic value of digital investments rather than just the financial value.

Healthcare use and costs in the last six months of life by level of care and cause of death

BackgroundExisting knowledge on healthcare use and costs in the last months of life is often limited to one patient group (i.e., cancer patients) and one level of healthcare (i.e., secondary care). Consequently, decision-makers lack knowledge in order to make informed decisions about the allocation of healthcare resources for all patients. Our aim is to elaborate the understanding of resource use and costs in the last six months of life by describing healthcare use and costs for all causes of death and by all levels of formal care.MethodUsing five national registers, we gained access to patient-level data for all individuals who died in Norway between 2009 and 2013. We described healthcare use and costs for all levels of formal care—namely primary, secondary, and home- and community-based care —in the last six months of life, both in total and differentiated across three time periods (6-4 months, 3-2 months, and 1-month before death). Our analysis covers all causes of death categorized in ten ICD-10 categories.ResultsDuring their last six months of life, individuals used an average of healthcare resources equivalent to €46,000, ranging from €32,000 (Injuries) to €64,000 (Diseases of the nervous system and sense organs). In terms of care level, 63% of healthcare resources were used in home- and community-based care (i.e., in-home nursing, practical assistance, or nursing home care), 35% in secondary care (mostly hospital care), and 2% in primary care (i.e., general practitioners). The amount and level of care varied by cause of death and by time to death. The proportion of home- and community-based care which individuals received during their last six months of life varied from 38% for cancer patients to 92% for individuals dying with mental diseases. The shorter the time to death, the more resources were needed: nearly 40% of all end-of-life healthcare costs were expended in the last month of life across all causes of death. The composition of care also differed depending on age. Individuals aged 80 years and older used more home- and community-based care (77%) than individuals dying at younger ages (40%) and less secondary care (old: 21% versus young: 57%).ConclusionsOur analysis provides valuable evidence on how much healthcare individuals receive in their last six months of life and the associated costs, broken down by level of care and cause of death. Healthcare use and costs varied considerably by cause of death, but were generally higher the closer a person was to death. Our findings enable decision-makers to make more informed resource-allocation decisions and healthcare planners to better anticipate future healthcare needs.

Granulicatella adiacens: An uncommon pathogen in prosthetic joint infections

Granulicatella adiacens is an uncommon culprit in prosthetic joint infections, especially when predisposing factors are absent. Here, we present the case of a 78-year-old female who developed an acute surgical site infection shortly after undergoing left total knee arthroplasty. Despite initial negative cultures, subsequent analysis identified Granulicatella adiacens in the knee fluid, marking only the second reported instance in the United States and the seventh globally. This singular case highlights the considerable challenges associated with isolating this organism, emphasizing its atypical presentation and diagnostic elusiveness. Typically associated with endocarditis and bacteremia, its involvement in isolated surgical site infections is particularly unusual. Our case underscores the necessity for heightened clinical suspicion and the adoption of specialized diagnostic methodologies to accurately detect Granulicatella adiacens infections. Given its fastidious nature and nutritional requirements, standard culture methods may fail to identify this pathogen, necessitating alternative diagnostic approaches. As such, the utilization of specialized media becomes paramount in clinical diagnostics to facilitate appropriate management and optimize healthcare resource allocation. By enhancing our ability to detect and treat infections caused by Granulicatella adiacens, we can mitigate patient morbidity and reduce the burden of unnecessary interventions, thereby improving patient outcomes and healthcare efficiency.

Obstetric Service Delivery Trends and Healthcare Staffing at a Teaching Hospital in Southwest Nigeria

Background: Maternal health is a critical component of public health worldwide, reflecting a society's commitment to the well-being of its women and children. The provision of obstetric services, encompassing antenatal care, delivery and postnatal care, plays a pivotal role in reducing maternal mortality and ensuring positive birth outcomes. However, the quality and accessibility of obstetric services are influenced by various factors, including healthcare infrastructure, staffing levels and healthcare policies. Objective: To determine obstetric service delivery trends and healthcare staffing at a teaching hospital in southwest Nigeria Methodology: This study utilized a retrospective cohort design to examine the utilization of maternity care services among women at Ekiti State University Teaching Hospital, Nigeria. Data spanning from January 2016 to December 2023 were collected from obstetric records. Analysis was done using Statistical Package for Social Sciences (SPSS) version 20. Results: a total of 9,855 deliveries were recorded, with 4,145 Caesarean sections and 5,767 spontaneous vaginal deliveries, giving a Caesarean section rate of 42%. Overall, there seems to be some fluctuations in the total number of deliveries over the years, with the highest number observed in 2019 (1482) and the lowest in 2021 (868). The number of consultants, senior residents, junior residents, house officers, nurses and other support staff varied over the years, with different staffing levels observed in each year. Conclusion: this study contributes to a better understanding of obstetric service delivery trends and healthcare staffing dynamics, offering valuable insights for healthcare resource allocation and policymaking. The findings underscore the importance of monitoring caseloads and staffing levels to ensure quality maternal and infant healthcare delivery.

Machine learning models for assessing risk factors affecting health care costs: 12-month exercise-based cardiac rehabilitation.

Exercise-based cardiac rehabilitation (ECR) has proven to be effective and cost-effective dominant treatment option in health care. However, the contribution of well-known risk factors for prognosis of coronary artery disease (CAD) to predict health care costs is not well recognized. Since machine learning (ML) applications are rapidly giving new opportunities to assist health care professionals' work, we used selected ML tools to assess the predictive value of defined risk factors for health care costs during 12-month ECR in patients with CAD. The data for analysis was available from a total of 71 patients referred to Oulu University Hospital, Finland, due to an acute coronary syndrome (ACS) event (75% men, age 61 ± 12 years, BMI 27 ± 4 kg/m2, ejection fraction 62 ± 8, 89% have beta-blocker medication). Risk factors were assessed at the hospital immediately after the cardiac event, and health care costs for all reasons were collected from patient registers over a year. ECR was programmed in accordance with international guidelines. Risk analysis algorithms (cross-decomposition algorithms) were employed to rank risk factors based on variances in their effects. Regression analysis was used to determine the accounting value of risk factors by entering first the risk factor with the highest degree of explanation into the model. After that, the next most potent risk factor explaining costs was added to the model one by one (13 forecast models in total). The ECR group used health care services during the year at an average of 1,624 ± 2,139€ per patient. Diabetes exhibited the strongest correlation with health care expenses (r = 0.406), accounting for 16% of the total costs (p < 0.001). When the next two ranked markers (body mass index; r = 0.171 and systolic blood pressure; r = - 0.162, respectively) were added to the model, the predictive value was 18% for the costs (p = 0.004). The depression scale had the weakest independent explanation rate of all 13 risk factors (explanation value 0.1%, r = 0.029, p = 0.811). Presence of diabetes is the primary reason forecasting health care costs in 12-month ECR intervention among ACS patients. The ML tools may help decision-making when planning the optimal allocation of health care resources.

Identifying determinants of spatial agglomeration of healthcare resources by using spatial econometric methods: a longitudinal study in China.

Healthcare resources are necessary for individuals to maintain their health. The Chinese government has implemented policies to optimize the allocation of healthcare resources and achieve the goal of equality in healthcare for the Chinese people since the implementation of the new medical reform in 2009. Given that no study has investigated regional differences from the perspective of healthcare resource agglomeration, this study aimed to investigate China's healthcare agglomeration from 2009 to 2017 in China and identify its determinants to provide theoretical evidence for the government to develop and implement scientific and rational healthcare policies. The study was conducted using 2009-2017 data to analyze health-resource agglomeration on institutions, beds, and workforce in China. An agglomeration index was applied to evaluate the degree of regional differences in healthcare resource allocation, and spatial econometric models were constructed to identify determinants of the spatial agglomeration of healthcare resources. From 2009 to 2017, all the agglomeration indexes of healthcare exhibited a downward trend except for the number of institutions in China. Population density (PD), government health expenditures (GHE), urban resident's disposable income (URDI), geographical location (GL), and urbanization level (UL) all had positive significant effects on the agglomeration of beds, whereas both per capita health expenditures (PCHE), number of college students (NCS), and maternal mortality rate (MMR) had significant negative effects on the agglomeration of institutions, beds, and the workforce. In addition, population density (PD) and per capita gross domestic product (PCGDP) in one province had negative spatial spillover effects on the agglomeration of beds and the workforce in neighboring provinces. However, MMR had a positive spatial spillover effect on the agglomeration of beds and the workforce in those regions. The agglomeration of healthcare resources was observed to remain at an ideal level in China from 2009 to 2017. According to the significant determinants, some corresponding targeted measures for the Chinese government and other developing countries should be fully developed to balance regional disparities in the agglomeration of healthcare resources across administrative regions.

Prevalence of Mild Cognitive Impairment and Alzheimer's Disease Identified in Veterans in the United States.

Diagnostic codes can be instrumental for case identification in Alzheimer's disease (AD) research; however, this method has known limitations and cannot distinguish between disease stages. Clinical notes may offer more detailed information including AD severity and can complement diagnostic codes for case identification. To estimate prevalence of mild cognitive impairment (MCI) and AD using diagnostics codes and clinical notes available in the electronic healthcare record (EHR). This was a retrospective study in the Veterans Affairs Healthcare System (VAHS). Health records from Veterans aged 65 years or older were reviewed during Fiscal Years (FY) 2010-2019. Overall, 274,736 and 469,569 Veterans were identified based on a rule-based algorithm as having at least one clinical note for MCI and AD, respectively; 201,211 and 149,779 Veterans had a diagnostic code for MCI and AD, respectively. During FY 2011-2018, likely MCI or AD diagnosis was defined by≥2 qualifiers (i.e., notes and/or codes)≥30 days apart. Veterans with only 1 qualifier were considered as suspected MCI/AD. Over the 8-year study, 147,106 and 207,225 Veterans had likely MCI and AD, respectively. From 2011 to 2018, yearly MCI prevalence increased from 0.9% to 2.2%; yearly AD prevalence slightly decreased from 2.4% to 2.1%; mild AD changed from 22.9% to 26.8%, moderate AD changed from 26.5% to 29.1%, and severe AD changed from 24.6% to 30.7. The relative distribution of AD severities was stable over time. Accurate prevalence estimation is critical for healthcare resource allocation and facilitating patients receiving innovative medicines.

Postoperative pneumonia after femoral fracture surgery: an in-depth retrospective analysis

BackgroundFemoral fractures significantly contribute to disability, predominantly in the elderly. Despite this, data on postoperative pneumonia following femoral fracture surgeries remains sparse. Our study sought to explore the incidence and impact of postoperative pneumonia on outcomes following such surgeries.MethodsA retrospective study analyzed femoral fracture patients hospitalized from 2016 to 2022. We scrutinized postoperative outcomes, including pneumonia, hospital stay duration, intensive care unit (ICU) admissions, and in-hospital mortality. We established stringent diagnostic criteria for postoperative pneumonia, incorporating both clinical signs and radiological evidence, excluding patients with prior infections or those discharged within 24 h post-surgery. Statistical analyses involved Chi-square and t-tests, linear regression, and logestic regression using SPSS.ResultsOut of 636 patients, 10.8% were diagnosed with postoperative pneumonia. The average age was 79.55 ± 8.57 years, with a male prevalence of 47.8%. Common comorbidities were hypertension (78.3%), diabetes (60.9%), and cardiovascular diseases (40.6%). Surgical interventions were categorized as intramedullary nailing (40.6%), partial hip replacement (37.7%), and dynamic hip screw (21.7%). Postoperative pneumonia was associated with older age (AOR = 1.053, 95% CI 1.020 to 1.087, p = 0.002), ICU admission (AOR = 2.283, 95% CI 1.256 to 4.148, p = 0.007), and longer length of hospital stay (AOR = 1.079, 95% CI 1.030 to 1.130, p = 0.001). The presence of pneumonia was associated with a 2.621-day increase in hospitalization after adjusting for other variables (p < 0.001, 95% CI: 1.454 to 3.789).ConclusionThis study accentuates the clinical significance of postoperative pneumonia in femoral fracture patients, with a noted incidence of 10.8%. A notable association with older age, prolonged hospital stays, and ICU admissions was observed, underscoring the necessity of addressing this complication to improve patient outcomes and healthcare resource allocation.

Application of Data Mining Techniques in Healthcare: Identifying Inter-Disease Relationships through Association Rule Mining

This research focuses on the application of data mining techniques in a healthcare environment by utilizing patient visit data from Hospital X, coded with ICD-10 diagnoses. The purpose of this study is to explore the application of data mining techniques in a healthcare environment, specifically to identify the relationship between diseases using patient visit data from X Hospital. This research utilizes the FP-Growth algorithm method followed by Association Rule Mining to find frequent occurrences of diseases in the data set. The research process involved data pre-processing, transformation into binary format, and careful parameter setting (minimum support 0.95 and confidence 0.9). The results showed a strong association between chronic conditions such as hypertension and diabetes, which are prevalent in the patient population. This association provides insight into potential comorbidities and may assist healthcare providers in improving diagnosis accuracy and treatment effectiveness. This research has implications for the application of data mining techniques, demonstrating its potential in improving predictive analytics in healthcare and strategic planning. This approach not only aids in the efficient allocation of healthcare resources, but also aligns with the broader goal of improving personalized patient care.

Final assessment of the COVID-19 pandemic impact between the different social and economic strata population of the city of Buenos Aires

BackgroundDuring the pandemic, epidemiological communications reported an estimation of excess deaths. However, the final calculation requires a detailed analysis. The study aim was to ascertain the number and distribution of COVID-19 fatalities among various socio-economic strata in a large, moderate to low-income city. Study designObservational time series analysis in a large city, treated as a natural experiment. MethodsAnalysis of death certificates, demographic data, and health system records of positive RT-PCR COVID-19 tests from 2015 to 2021, categorizing by age, sex, and place of residence. The study measured the pandemic's impact on mortality, including COVID and non-COVID deaths, using corrected Poisson regression models for different demographics and assessing socio-economic status impact via ecological community-level analysis. ResultsCompared to the pre-pandemic period (2015–2019, IRR = 1.00), the sex- and age-adjusted rate of all-cause death increased significantly during the pandemic (2020–2021) IRR = 1.109 [1.054–1.167], p < 0.0001. This was observed in both males (IRR = 1.158 [1.1–1.219], p < 0.0001) and females (IRR = 1.068 [1.016–1.124], p = 0.01). There was no observed effect of the pandemic on the historical trend in the progressive reduction of mortality in people under 35 years of age. The increase in deaths was at the expense of COVID (+11,175 deaths) and cardiovascular causes (IRR = 1.114 [1.020–1.217] p = 0.017). During the pandemic, there was a significant increase in deaths at home (IRR = 1.219 [1197–1.242], p < 0.0001), especially in people dying of cardiovascular causes (IRR = 1.391 [1.360–1.422], p < 0.0001). The increase in the adjusted mortality rate during the pandemic was socially conditioned. ConclusionsThe pandemic not only led to increased COVID-19 mortality but also heightened fatalities from non-COVID causes, reflecting a potential bias in healthcare resource allocation towards SARS-CoV-2 at the expense of chronic pathologies care.

Stroke 5.0: A Technology Ecosystem to Support Acute Stroke Integrated Clinical Management.

Stroke remains a significant global health burden, with substantial costs and morbidity associated with its occurrence. To address this challenge, STROKE 5.0 proposes a comprehensive approach to stroke care management, integrating advanced digital technologies and clinical expertise. This paper presents the rationale, design, and potential impact of the STROKE 5.0 platform, which aims to optimize stroke care delivery from pre-hospital assessment through acute hospitalization. The platform facilitates early symptom recognition, efficient emergency response, and streamlined hospital management through intelligent decision support systems. By leveraging predictive analytics and personalized care pathways, STROKE 5.0 seeks to enhance clinical outcomes while providing a platform capable of optimizing the efficiency of service delivery. This innovative model represents a proactive shift towards evidence-based, patient-centered stroke care, with implications for healthcare quality improvement and resource allocation in the digital health domain.

#2628 A haemodialysis patient-centred model of care with a multidisciplinary programme: what is its clinical and health efficiency impact?

Abstract Background and Aims Chronic kidney disease (CKD) constitutes a public health concern due to its notable morbidity &amp; mortality and associated costs. The Patient Care Coordination program (PCC) provides comprehensive and personalized care to haemodialysis (HD) patients. This study evaluated the cost-effectiveness of PCC compared to standard of care (SoC). Method The study included patients in 9 HD clinics (5 PCC, 4 SoC) in Catalonia, Spain, in 2020-2021. A comparative effectiveness evaluation was conducted to compare change in Individual Patient Performance Score (IPPS, score 0-100, evaluates parameters such as vascular access, adequacy of HD, anaemia, mineral bone disease, arterial hypertension, nutrition/hydration status) and number of hospitalisations of PCC patients vs SoC during the first year in the program. Instrumental variables (IV) regression analysis was performed to address potential confounding; number of interactions with program specialists was a valid IV to estimate degree of PCC use. An incremental cost-effectiveness analysis (iCEA) through microsimulation was performed using the effectiveness data from the previous step, population data from the renal patient registry of Catalonia, and the cost of hospitalisations obtained from the Public Health System. Results 127 patients in PCC and 363 patients in SoC were included. The degree of PCC use had a positive impact on IPPS variation during the first year of the program (p = 0.012). In the iCEA the PCC program resulted in a mean gain of 3.4 additional IPPS points, a reduction of 0.2 hospitalisations per patient, and reduced mean costs of €1,265 per patient in the first year compared to SoC, resulting to be a dominant alternative (Fig. 1). Conclusion The holistic and interdisciplinary approach to the patient has further highlighted the relevance of programme use and not only belonging to the programme in achieving clinical goals. The PCC program represents thus a dominant alternative, with greater effectiveness and total cost savings that could sum up to €5.5 million per year in the region due to reduced hospitalizations. This approach may be an alternative to address the challenges of CKD and optimise the allocation of healthcare resources.

Clinical prediction model for MODY type diabetes mellitus in children

BACKGROUND: MODY (maturity-onset diabetes of the young) is a rare monogenic form of diabetes mellitus, the gold standard of diagnosis is mutations detection in the genes responsible for the development of this form diabetes. Genetic test is expensive and takes a lot of time. The diagnostic criteria for MODY are well known. The development of clinical decision support system (CDSS) which allows physicians based on clinical data to determine who should have molecular genetic testing is relevant.AIM: Provided a retrospective analysis of clinical data of the patients with T1DM and MODY, from 0 to 18 years old, regardless of the duration of the disease to develop the model. Based on clinical data, a feedforward neural network (NN) was implemented - a multilayer perceptron.MATERIALS AND METHODS: Development of the most effective algorithm for predicting MODY in children based on available clinical indicators of 1710 patients with diabetes under the age of 18 years using a multilayer feedforward neural network.RESULTS: The sample consisted of 1710 children under the age of 18 years with T1DM (78%) and MODY (22%) diabetes. For the final configuration of NS the following predictors were selected: gender, age at passport age, age at the diagnosis with DM, HbA1c, BMI SDS, family history of DM, treatment. The performance (quality) assessment of the NN was carried out on a test sample (the area under the ROC (receiver operating characteristics) curve reached 0.97). The positive predictive value of PCPR was achieved at a cut-off value of 0.40 (predicted probability of MODY diabetes 40%). At which the sensitivity was 98%, specificity 93%, PCR with prevalence correction was 78%, and PCR with prevalence correction was 99%, the overall accuracy of the model was 94%.Based on the NN model, a CDSS was developed to determine whether a patient has MODY diabetes, implemented as an application.CONCLUSION: The clinical prediction model MODY developed in this work based on the NN, uses the clinical characteristic available for each patient to determine the probability of the patient having MODY. The use of the developed model in clinical practice will assist in the selection of patients for diagnostic genetic testing for MODY, which will allow for the efficient allocation of healthcare resources, the selection of personalized treatment and patient monitoring.

Cluster analysis in fibromyalgia: a systematic review.

The multifaceted nature of Fibromyalgia syndrome (FM) symptoms has been explored through clusters analysis. To synthesize the cluster research on FM (variables, methods, patient subgroups, and evaluation metrics). We performed a systematic review following the PRISMA recommendations. Independent searches were performed on PubMed, Embase, Web of Science, and Cochrane Central, employing the terms "fibromyalgia" and "cluster analysis". We included studies dated to January 2024, using the cluster analysis to assess any physical, psychological, clinical, or biomedical variables in FM subjects, and descriptively synthesized the studies in terms of design, cluster method, and resulting patient profiles. We included 39 studies. Most with a cross-sectional design aiming to classify subsets based on the severity, adjustment, symptomatic manifestations, psychological profiles, and response to treatment, based on demographic and clinical variables. Two to four different profiles were found according to the levels of severity and adjustment to FMS. According to symptom manifestation, two to three clusters described the predominance of pain versus fatigue, and thermal pain sensitivity (less versus more sensitive). Other clusters revealed profiles of personality (pathological versus non-pathological) and psychological vulnerability (suicidal ideation). Additionally, studies identified different responses to treatment (pharmacological and multimodal). Several profiles exist within FMS population, which point out to the need for specific treatment options given the different profiles and an efficient allocation of healthcare resources. We notice a need towards more objective measures, and the validation of the cluster results. Further research might investigate some of the assumptions of these findings, which are further discussed in this paper.

Precision healthcare: A deep dive into machine learning algorithms and feature selection strategies for accurate heart disease prediction

This paper presents a comprehensive exploration of machine learning algorithms (MLAs) and feature selection techniques for accurate heart disease prediction (HDP) in modern healthcare. By focusing on diverse datasets encompassing various challenges, the research sheds light on optimal strategies for early detection. MLAs such as Decision Trees (DT), Random Forests (RF), Support Vector Machines (SVM), Gaussian Naive Bayes (NB), and others were studied, with precision and recall metrics emphasized for robust predictions. Our study addresses challenges in real-world data through data cleaning and one-hot encoding, enhancing the integrity of our predictive models. Feature extraction techniques—Recursive Feature Extraction (RFE), Principal Component Analysis (PCA), and univariate feature selection—play a crucial role in identifying relevant features and reducing data dimensionality. Our findings showcase the impact of these techniques on improving prediction accuracy. Optimized models for each dataset have been achieved through grid search hyperparameter tuning, with configurations meticulously outlined. Notably, a remarkable 99.12 % accuracy was achieved on the first Kaggle dataset, showcasing the potential for accurate HDP. Model robustness across diverse datasets was highlighted, with caution against overfitting. The study emphasizes the need for validation of unseen data and encourages ongoing research for generalizability. Serving as a practical guide, this research aids researchers and practitioners in HDP model development, influencing clinical decisions and healthcare resource allocation. By providing insights into effective algorithms and techniques, the paper contributes to reducing heart disease-related morbidity and mortality, supporting the healthcare community's ongoing efforts.

Resisting the heat wave: Revealing inequalities in matching between heat exposure risk and healthcare services in a megacity

Climate warming and the growing urban heat island effect are exacerbating the unequal public health crisis in megacities, and thus it is important to assess the equity of matching relationship between the healthcare services and the heat exposure risk. We selected 255 census units in Tianjin city for the study, and calculated a heat exposure index based on the universal thermal climate index (UTCI) and constructed a comprehensive urban healthcare services evaluation system to measure the degree of mismatch between the two. The results showed the following. (1) The heat exposure risk and healthcare services followed an increasing pattern from the Outer suburb area (OSA) to the Metropolitan development area (MDA) and then to the Main urban area (MUA). (2) The overall imbalance in matching was low in the OSA, whereas the healthcare services were relatively insufficient in the MDA, and the heat exposure risk and healthcare services were relatively balanced in the MUA. (3) The degree of matching between the core and peripheral parts of the MUA was severely polarized, with an overall pattern of excess healthcare in the core area and a high risk of heat exposure in the peripheral areas. The results can provide effective guidance for rationalizing the allocation of healthcare resources from a heat exposure perspective, and provide a policy basis for risk management and fund investment.

Primary care clinic visits in formerly evacuated areas due to radiation disaster following the Great East Japan Earthquake: A retrospective descriptive study.

Radiation disasters pose distinctive medical challenges, requiring diverse care approaches. Beyond radiation exposure assessment, addressing health impacts due to lifestyle changes, especially among vulnerable populations, is vital. Evacuation orders issued in radiation-affected areas introduce unique healthcare dynamics, with their duration significantly influencing the recovery process. Understanding evolving patient demographics and medical needs after lifting evacuation orders is crucial for post-disaster care planning. Minamisoma Municipal Odaka Hospital, located 13 to 20 km from Fukushima Daiichi Nuclear power plant in a post-evacuation zone, was greatly affected by the Great East Japan Earthquake and subsequent radiation disaster. Data were retrospectively collected from patient records, including age, gender, visit date, diagnoses, and addresses. Patient records from April 2014 to March 2020 were analyzed, comparing data before and after the July 2016 evacuation order lift. Data was categorized into pre and post-evacuation order lifting periods, using International Classification of Diseases, Tenth Edition codes, to identify the top diseases. Statistical analyses, including χ-square tests, assessed changes in disease distributions. Population data for Odaka Ward and Minamisoma City fluctuated after lifting evacuation orders. As of March 11, 2011, Odaka Ward had 12,842 residents (27.8% aged 65+ years), dropping to 8406 registered residents and 2732 actual residents by April 30, 2018 (49.7%). Minamisoma City also saw declines, with registered residents decreasing from 71,561 (25.9%) to 61,049 (34.1%). The study analyzed 11,100 patients, mostly older patients (75.1%), between 2014 and 2020. Post-lifting, monthly patient numbers surged from an average of 55.2 to 213.5, with female patients increasing from 33.8% to 51.7%. Disease patterns shifted, with musculoskeletal cases declining from 23.8% to 13.0%, psychiatric disorders increasing from 9.3% to 15.4%, and trauma-related cases decreasing from 14.3% to 3.9%. Hypertension (57.1%) and dyslipidemia (29.2%) prevailed post-lifting. Urgent cases decreased from 1.3% to 0.1%. This study emphasizes the importance of primary care in post-evacuation zones, addressing diverse medical needs, including trauma, noncommunicable diseases, and psychiatric disorders. Changing patient demographics require adaptable healthcare strategies and resource allocation to meet growing demands. Establishing a comprehensive health maintenance system tailored to these areas' unique challenges is crucial for future disaster recovery efforts.

TEMPORAL TRENDS AND REGIONAL VARIATION OF HIP ARTHROSCOPY AND PELVIC OSTEOTOMY IN ENGLAND FROM 2010 TO 2023

Hip arthroscopy (HA) and pelvic osteotomy (PO) are surgical procedures used to treat a variety of hip pathology affecting young adults, including femoroacetabular impingement and hip dysplasia respectively. This study aimed to investigate the trends and regional variation in the provision of HA and PO across England from 2010 to 2023 to inform healthcare resource allocation.We analysed the National Hospital Episode Statistics database for all HA and PO procedures in NHS England using specific OPCS-4 codes: HA: ‘W83+Z843’ or ‘W84+Z843’; PO: ‘X222+Z75’. We collected patient demographics, age, sex, and region of treatment. We performed descriptive and regression analyses to evaluate temporal trends in PO volume, age, sex and regional variation.22,401 HAs and 1,348 POs were recorded between 2010 and 2023. The annual number of HAs declined by 28.4%, whilst the number of POs increased by 64% (p&lt;0.001). Significantly more females underwent PO vs HA (90% vs 61.3%) and were older than males undergoing the same procedure (PO: 29.0±8.7 vs 25.8±9.2 years; HA: 36.8±12.0 years vs 35.8±11.2 years, p&lt;0.001). For HA, the mean age of both sexes decreased by 3.3 and 2.9 years respectively (p&lt;0.001), whereas the age of PO patients did not change significantly over the study period. There were significant regional variations with a mean incidence of 1.60/100,00 for HA (ranging from 0.70–2.66 per 100,000) and 0.43/100,000 for PO (ranging from 0.08–2.07 per 100,000).We have observed a decline in HA volume in England, likely due to improved patient selection and the impact of COVID-19, whilst PO volume has significantly increased, with regional variation persisting for both procedures. These trends highlight the need for equitable HA and PO access to improve patient outcomes and call for strategic healthcare planning and resource allocation to reduce disparities and improve training opportunities.

Projecting the economic burden of chronic kidney disease at the patient level (Inside CKD): a microsimulation modelling study

The growing burden of chronic kidney disease (CKD) places substantial financial pressures on patients, healthcare systems, and society. An understanding of the costs attributed to CKD and kidney replacement therapy (KRT) is essential for evidence-based policy making. Inside CKD maps and projects the economic burden of CKD across 31 countries/regions from 2022 to2027. A microsimulation model was developed that generated virtual populations using national demographics, relevant literature, and renal registries for the 31 countries/regions included. Patient-level country/region-specific cost data were extracted via a pragmatic local literature review and under advisement from local experts. Direct cost projections were generated for diagnosed CKD (by age, stage 3a-5), KRT (by modality), cardiovascular complications (heart failure, myocardial infarction, stroke), and comorbidities (hypertension, type 2 diabetes). For the 31 countries/regions, Inside CKD projected that annual direct costs (US$) of diagnosed CKD and KRT would increase by 9.3% between 2022 and 2027, from $372.0 billion to $406.7 billion. Annual KRT-associated costs were projected to increase by 10.0% from $169.6 billion to $186.6 billion between 2022 and 2027. By 2027, patients receiving KRT are projected to constitute 5.3% of the diagnosed CKD population but contribute 45.9% of the total costs. The economic burden of CKD is projected to increase from 2022 to 2027. KRT contributes disproportionately to this burden. Earlier diagnosis and proactive management could slow disease progression, potentially alleviating the substantial costs associated with later CKD stages. Data presented here can be used to inform healthcare resource allocation and shape future policy. AstraZeneca.

Spatial distribution and influencing factors of CDC health resources in China: a study based on panel data from 2016-2021.

Measuring the development of Chinese centers for disease control and prevention only by analyzing human resources for health seems incomplete. Moreover, previous studies have focused more on the quantitative changes in healthcare resources and ignored its determinants. Therefore, this study aimed to analyze the allocation of healthcare resources in Chinese centers for disease control and prevention from the perspective of population and spatial distribution, and to further explore the characteristics and influencing factors of the spatial distribution of healthcare resources. Disease control personnel density, disease control and prevention centers density, and health expenditures density were used to represent human, physical, and financial resources for health, respectively. First, health resources were analyzed descriptively. Then, spatial autocorrelation was used to analyze the spatial distribution characteristics of healthcare resources. Finally, we used spatial econometric modeling to explore the influencing factors of healthcare resources. The global Moran index for disease control and prevention centers density decreased from 1.3164 to 0.2662 (p < 0.01), while the global Moran index for disease control personnel density increased from 0.4782 to 0.5067 (p < 0.01), while the global Moran index for health expenditures density was statistically significant only in 2016 (p < 0.1). All three types of healthcare resources showed spatial aggregation. Population density and urbanization have a negative impact on the disease control and prevention centers density. There are direct and indirect effects of disease control personnel density and health expenditures density. Population density and urbanization had significant negative effects on local disease control personnel density. Urbanization has an indirect effect on health expenditures density. There were obvious differences in the spatial distribution of healthcare resources in Chinese centers for disease control and prevention. Social, economic and policy factors can affect healthcare resources. The government should consider the rational allocation of healthcare resources at the macro level.