Allocation Of Health Care Resources Research Articles

The economic costs of orthopaedic services: a health system cost analysis of tertiary hospitals in a low-income country

BackgroundTraumatic injuries are rising globally, disproportionately affecting low- and middle-income countries, constituting 88% of the burden of surgically treatable conditions. While contributing to the highest burden, LMICs also have the least availability of resources to address this growing burden effectively. Studies on the cost-of-service provision in these settings have concentrated on the most common traumatic injuries, leaving an evidence gap on other traumatic injuries. This study aimed to address the gap in understanding the cost of orthopaedic services in low-income settings by conducting a comprehensive costing analysis in two tertiary-level hospitals in Malawi.MethodsWe used a mixed costing methodology, utilising both Top-Down and Time-Driven Activity-Based Costing approaches. Data on resource utilisation, personnel costs, medicines, supplies, capital costs, laboratory costs, radiology service costs, and overhead costs were collected for one year, from July 2021 to June 2022. We conducted a retrospective review of all the available patient files for the period under review. Assumptions on the intensity of service use were based on utilisation patterns observed in patient records. All costs were expressed in 2021 United States Dollars.ResultsWe conducted a review of 2,372 patient files, 72% of which were male. The median length of stay for all patients was 9.5 days (8–11). The mean weighted cost of treatment across the entire pathway varied, ranging from $195 ($136—$235) for Supracondylar Fractures to $711 ($389—$931) for Proximal Ulna Fractures. The main cost components were personnel (30%) and medicines and supplies (23%). Within diagnosis-specific costs, the length of stay was the most significant cost driver, contributing to the substantial disparity in treatment costs between the two hospitals.ConclusionThis study underscores the critical role of orthopaedic care in LMICs and the need for context-specific cost data. It highlights the variation in cost drivers and resource utilisation patterns between hospitals, emphasising the importance of tailored healthcare planning and resource allocation approaches. Understanding the costs of surgical interventions in LMICs can inform policy decisions and improve access to essential orthopaedic services, potentially reducing the disease burden associated with trauma-related injuries. We recommend that future studies focus on evaluating the cost-effectiveness of orthopaedic interventions, particularly those that have not been analysed within the existing literature.

Allocating health care resources in jails and prisons during COVID-19: a qualitative study of carceral decision-makers.

COVID-19 created acute demands on health resources in jails and prisons, burdening health care providers and straining capacity. However, little is known about how carceral decision-makers balanced the allocation of scarce resources to optimize access to and quality of care for incarcerated individuals. This study analyzes a national sample of semi-structured interviews with health care and custody officials (n = 32) with decision-making authority in 1 or more carceral facilities during the COVID-19 pandemic. Interviews took place between May and October 2021. We coded transcripts using a directed content analysis approach and analyzed data for emergent themes. Participants reported that facilities distributed personal protective equipment to staff before incarcerated populations due to staff's unique role as potential vectors of COVID-19. The use of testing reflected not only an initial imperative to preserve limited supplies but also more complex decision-making about the value of test results to facility operations. Participants also emphasized the difficulties caused by limited physical space, insufficient staff, and stress from modifying job roles. The rapid onset of COVID-19 confronted decision-makers with unprecedented resource allocation decisions, often with life-or-death consequences. Planning for future resource allocation decisions now may promote more equitable decisions when confronted with a future pandemic event.

Preference for primary care in Chinese homebound patients

ObjectiveThis study aims to describe the preference for primary healthcare (PHC) and investigate associated factors among homebound residents in both rural and urban areas of China. It provides valuable insights to facilitate the rational allocation of healthcare resources and promote the utilization of PHC.MethodsIn this nationally representative cross-sectional study, we utilized the most recent data (2020) from the China Family Panel Studies (CFPS). Participants were recruited from 25 provincial-level administrative regions in both rural and urban areas of China. Homebound patients were asked to provide details about their individual characteristics, variables related to family caregiving, and preferences for PHC. Multivariable logistic models were used to analyze potential factors associated with preference for PHC. Estimates of association were reported as odds ratios (OR) and their 95% confidence intervals (CI).ResultsThe study found that 58.43% of rural patients reported a preference for PHC, while 42.78% of urban patients favored PHC. Compared to rural participants who did not received inpatient care in the past year, those who received inpatient care in the past year had 67% lower odds of choosing PHC (OR:0.33, 95% CI:0.19–0.59); Compared to rural participants who did not received family caregiving when ill, those who received family caregiving when ill had 59% lower odds of choosing PHC (OR: 0.41, 95% CI:0.21–0.77). Correspondingly, Compared to urban participants who did not received inpatient care in the past year, those who had received inpatient care in the past year had 75% lower odds of choosing PHC (OR: 0.25, 95% CI: 0.10–0.56); Compared to urban participants who did not received family caregiving when ill, those who received family caregiving when ill had 73% lower odds of choosing PHC (OR: 0.27, 95% CI: 0.11–0.63); Compared to urban participants who with agricultural Hukou, those with Non-agricultural Hukou had 61% lower odds of choosing PHC (OR: 0.39, 95% CI:0.18–0.83); Compared to urban participants living in the eastern part of mainland China, those living in the central part of China had 188% higher odds of choosing PHC (OR: 2.88, 95% CI: 1.14–7.29).ConclusionPolicymakers should focus on tailoring PHC to vulnerable populations and prioritizing family-based public health strategies for enhancing homebound patients’ perceptions of PHC. Furthermore, further study is needed on whether the Hukou registration system affects the barriers that homebound patients experience in choosing healthcare providers.

Prediction of emergency department revisits among child and youth mental health outpatients using deep learning techniques

BackgroundThe proportion of Canadian youth seeking mental health support from an emergency department (ED) has risen in recent years. As EDs typically address urgent mental health crises, revisiting an ED may represent unmet mental health needs. Accurate ED revisit prediction could aid early intervention and ensure efficient healthcare resource allocation. We examine the potential increased accuracy and performance of graph neural network (GNN) machine learning models compared to recurrent neural network (RNN), and baseline conventional machine learning and regression models for predicting ED revisit in electronic health record (EHR) data.MethodsThis study used EHR data for children and youth aged 4–17 seeking services at McMaster Children’s Hospital’s Child and Youth Mental Health Program outpatient service to develop and evaluate GNN and RNN models to predict whether a child/youth with an ED visit had an ED revisit within 30 days. GNN and RNN models were developed and compared against conventional baseline models. Model performance for GNN, RNN, XGBoost, decision tree and logistic regression models was evaluated using F1 scores.ResultsThe GNN model outperformed the RNN model by an F1-score increase of 0.0511 and the best performing conventional machine learning model by an F1-score increase of 0.0470. Precision, recall, receiver operating characteristic (ROC) curves, and positive and negative predictive values showed that the GNN model performed the best, and the RNN model performed similarly to the XGBoost model. Performance increases were most noticeable for recall and negative predictive value than for precision and positive predictive value.ConclusionsThis study demonstrates the improved accuracy and potential utility of GNN models in predicting ED revisits among children and youth, although model performance may not be sufficient for clinical implementation. Given the improvements in recall and negative predictive value, GNN models should be further explored to develop algorithms that can inform clinical decision-making in ways that facilitate targeted interventions, optimize resource allocation, and improve outcomes for children and youth.

Trends in mortality due to tracheal, bronchial, and lung cancer across the BRICS: An age-period-cohort analysis based on the Global Burden of Disease Study 1990-2019.

Tracheal, bronchus, and lung cancer (TBL) is a major cause of mortality and top contributor to productivity loss in large emerging economies such as the BRICS (Brazil, Russia, India, China, and South Africa). We examined the time trends of TBL mortality across the BRICS to better understand the disease burden in these countries and inform public health and healthcare resource allocation. TBL mortality-related data between 1990 and 2019 were obtained from the Global Burden of Disease Study 2019 and analyzed using age-period-cohort models. Net drift (local drift) was used to describe the expected age-adjusted TBL mortality rate over time overall (each age group); the longitudinal age curve was used to reflect the age effect; the period rate ratios (RRs) were used to reflect the period effect; and the cohort RR was used to reflect the cohort effect. In 2019, there were 958.3 thousand TBL deaths across the BRICS, representing 46.9% of the global TBL deaths. From 1990 to 2019, the age-standardized mortality rate (ASMR) of TBL decreased in Russia, Brazil, and South Africa while increased in China and India, with the largest reduction reported in Russia (-29.6%) and the largest increase in China (+22.4%). India showed an overall increase (+15.7%) in TBL mortality but the mortality risk decreased among individuals born after 1990 (men) and 1995 (women). Although South Africa and Brazil experienced an overall decline in TBL mortality, their recent birth cohorts, such as Brazilian individuals born after 1985 (men) and 1980 (women), and South African men born after 1995, had an increasing TBL mortality risk. China has experienced an overall increase in TBL mortality, with the mortality risk rising among individuals born after 1995 for both men and women. Russia, which had the highest TBL mortality among the BRICS countries in 1990, has demonstrated significant improvement over the past three decades. Over the past 30years, the BRICS accounted for an increasing proportion of global TBL mortality. TBL mortality increased in older women in all the BRICS countries except Russia. Among the recent birth cohort, the risk of TBL mortality increased in Brazil, China, and South Africa. More effective efforts are needed in the BRICS to reduce the burden of TBL and help achieve the United Nation's Sustainable Development Goals.

Epidemiological trends and burden analysis of cervical cancer attributable to unsafe sex: A population-based study from 1990 to 2019.

Unsafe sex is recognized as an important risk factor for cervical cancer (CC). Understanding the global disease burden of CC attributable to unsafe sex can assist policymakers in allocating healthcare resources. Data were obtained from the 2019 global burden of disease database (GBD). We examined global, regional, and national levels of CC mortality, disability-adjusted life years (DALYs), and age-standardized rates (ASRs) caused by unsafe sex. ASRs were evaluated using estimated annual percentage changes (EAPCs). Attributable to unsafe sex, there were 280,479 CC-related deaths in 2019 and 8,955,013 CC-related DALYs. In the period 1990-2019, the global ASRs of CC due to unsafe sex decreased around the world; for age-standardized mortality rate (ASMR) and age-standardized DALY rate (ASDR), the EAPCs were -0.93 and -0.95. The highest ASMRs and ASDRs were found in central sub-Saharan Africa and the lowest in Australasia. In the past few decades, the ASMR and ASDR of CC caused by unsafe sexual practices have decreased over time, with significant variations observed among different countries and regions. Increased focus is needed on spreading awareness about sexual health and promoting CC prevention and screening, particularly in low- and middle-income nations.

The Regional Total Factor Productivity Changes of Healthcare Delivery in China: A Bootstrap Malmquist Data Envelopment Analysis.

Improving the productivity of healthcare delivery and optimizing the allocation of regional healthcare resources are crucial for the health providers. The objective of this study is to evaluate the productivity dynamics of healthcare delivery at the regional (provincial) level in China, to provide evidence-based policy implications. After a review of literature, actual number of open beds, number of occupational or assistant doctors, number of registered nurses, and number of other staff were selected as input variables. The number of diagnostic visits and number of discharged inpatients were adopted as the output indicators. The panel data of 31 provinces in mainland China from 2010 to 2019 were extracted from Health Statistics Yearbook. Bootstrap-Malmquist Data Envelopment Analysis (DEA) model was used to measure the total factor productivity changes (TFPC) and its components. During the study period, the analysis of total factor productivity (TFP) in China revealed a declining trend with an average annual decline of 0.9% (ranging from 0.860 to 1.204). For each of the 31 provinces, the annual TFP scores varied from 0.971 to 1.029. On average, technical efficiency changes (TEC) had showed a downward trend from 2010-2011 (0.980) to 2013-2014 (0.982), and then an upward trend in 2014-2015 (1.029) and the following three consecutive years since 2016-2017 (1.000, 1.013, 1.009). Similarly, the trend in technological changes (TC) was consistent with the TEC from 2010-2019, which fluctuated between 0.969 and 1.011 on average per year at the provincial level. Notably, the point of inflection appeared at 2013-2014. Regional healthcare inputs and outputs in mainland China saw an upward trend from 2010 to 2019. However, TFPC, TEC, and TC decreased across all 31 provinces. TFP experienced a declining trend from 2010 to 2014, followed by growth until 2019. This may be related to the new healthcare reform being implemented since 2009, as service efficiency and capacity may undergo a reversal at the beginning of the reform.

A Novel Framework to Forecast COVID-19 Incidence Based on Google Trends Search Data

The global outbreak of coronavirus disease 2019 (COVID-19) has spread to more than 200 countries worldwide, leading to severe health and socioeconomic consequences. As such, the topic of monitoring and predicting epidemics has been attracting a lot of interest. Previous work reported search volumes from Google Trends are beneficial in decoding influenza dynamics, implying its potential for COVID-19 prediction. Therefore, a predictive model using the Wiener methods was built based on epidemic-related search queries from Google Trends, along with climate variables, aiming to forecast the dynamics of the weekly COVID-19 incidence in Washington, DC, USA. The Wiener model, which shares the merits of interpretability, low computation costs, and adaptation to nonlinear fluctuations, was used in this study. Models with multiple sets of features were constructed and further optimized by the highest weight selecting strategy. Furthermore, comparisons to the other two commonly used prediction models based on the autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) were also performed. Our results showed the predicted COVID-19 trends significantly correlated with the actual (rho <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$=$</tex-math> </inline-formula> 0.88, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p $</tex-math> </inline-formula> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$&lt;$</tex-math> </inline-formula> 0.0001), outperforming those with ARIMA and LSTM approaches, indicating Google Trends data as a useful tool in terms of COVID-19 prediction. Also, the model using 20 search queries with the highest weighting outperformed all other models, supporting the highest weight feature selection as a feasible criterion. Google Trends search query data can be used to forecast the outbreak of COVID-19, which might assist health policymakers to allocate health care resources and taking preventive strategies.

Global burden and trends of disability‐adjusted life years and mortality for decubitus ulcer: A systematic analysis

AbstractThe management of chronic wounds has presented a significant dilemma, which is evident not only in clinical treatment but also in the substantial burden it places on medical resources. The global COVID‐19 pandemic in 2020 is likely to further exacerbate this trend. Therefore, it is imperative to delve deeper into the impact of chronic wound on disease burden across different regions and populations. In this study, we focused on decubitus ulcers (DU) as representative chronic wounds and utilized data from the Global Burden of Disease (GBD) 2019 database (http://ghdx.healthdata.org/gbd-results-tool) pertaining to age, gender, region, year and socio‐demographic index (SDI) group. Disability‐adjusted life years (DALYs) and mortality were utilized as indicators to assess the burden of DU. The analysis and visualization were performed using R software (version 4.2.3). A decrease in the global ASRs of DALYs and mortality for DU was observed across most regions between 1990 and 2019. The reduction in burden was particularly significant in regions characterized by a high SDI, while regions with a high‐middle SDI experienced an increase. The burden of DU increased with age for both males and females, with males generally experiencing a higher burden compared to females. Strengthening population‐based data on the prevalence of DU and implementing dynamic monitoring at the public health level will enable policymakers to develop evidence‐based strategies for efficient allocation of healthcare resources.

Primary care doctors retirements in the context of an ageing population in Italy.

Ongoing shortages in primary care doctors/primary care paediatricians and increasing healthcare needs due to ageing of the population represent a great challenge for healthcare providers, managers, and policymakers. To support planning of primary healthcare resource allocation we analyzed the geographic distribution of primary care doctors/primary care paediatricians across Italian regions, accounting for area-specific number and age of the population. Additionally, we estimated the number of primary care doctors/primary care paediatricians expected to retire over the next 25 years, with a focus on the next five years. Ecological study. We gathered the list of Italian general practitioners and primary care paediatricians and combined them with the data from the National Federation of Medical Doctors, Surgeons and Dentists. Using data from the National Institutes of Statistics, we calculated the average number of patients per doctor for each region using the number of residents above and under 14 years of age for general practitioners and primary care paediatricians respectively. We also calculated the number of residents over-65 and over-75 years of age per general practitioner, as elderly patients typically have higher healthcare needs. On average the number of patients per general practitioner was 1,447 (SD: 190), while for paediatricians it was 1,139 (SD: 241), with six regions above the threshold of 1,500 patients per general practitioner and only one region under the threshold of 880 patients per paediatrician. We estimated that on average 2,228 general practitioners and 444 paediatricians are going to retire each year for the next five years, reaching more than 70% among the current workforce for some southern regions. The number of elderly patients per general practitioner varies substantially between regions, with two regions having >15% more patients aged over 65 years compared to the expected number. over 65 years compared to the expected number. Conclusions. The study highlighted that some regions do not currently have the required primary care workforce, and the expec-ted retirements and the ageing of the population will exacerbate the pressure on the already over-stretched healthcare services. A response from healthcare administrations and policymakers is urgently required to allow equitable access to quality primary care across the country.

Predicting colorectal cancer risk: a novel approach using anemia and blood test markers.

Colorectal cancer remains an important public health problem in the context of the COVID-19 (Corona virus disease 2019) pandemic. The decline in detection rates and delayed diagnosis of the disease necessitate the exploration of novel approaches to identify individuals with a heightened risk of developing colorectal cancer. The study aids clinicians in the rational allocation and utilization of healthcare resources, thereby benefiting patients, physicians, and the healthcare system. The present study retrospectively analyzed the clinical data of colorectal cancer cases diagnosed at the Affiliated Hospital of Guilin Medical University from September 2022 to September 2023, along with a control group. The study employed univariate and multivariate logistic regression as well as LASSO (Least absolute shrinkage and selection operator) regression to screen for predictors of colorectal cancer risk. The optimal predictors were selected based on the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. These predictors were then utilized in constructing a Nomogram Model for predicting colorectal cancer risk. The accuracy of the risk prediction Nomogram Model was assessed through calibration curves, ROC curves, and decision curve analysis (DCA) curves. Clinical data of 719 patients (302 in the case group and 417 in the control group) were included in this study. Based on univariate logistic regression analysis, there is a correlation between Body Mass Index (BMI), red blood cell count (RBC), anemia, Mean Corpuscular Volume (MCV), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), platelet count (PLT), Red Cell Distribution Width-Standard Deviation (RDW-SD), and the incidence of colorectal cancer. Based on the findings of multivariate logistic regression analysis, the variables of BMI and RBC exhibit a decrease, while anemia and PLT demonstrate an increase, all of which are identified as risk factors for the occurrence of colorectal cancer. LASSO regression selected BMI, RBC, anemia, and PLT as prediction factors. LASSO regression and multivariate logistic regression analysis yielded the same results. A nomogram was constructed based on the 4 prediction factors identified by LASSO regression analysis to predict the risk of colorectal cancer. The AUC of the nomogram was 0.751 (95% CI, OR: 0.708-0.793). The calibration curves in the validation and training sets showed good performance, indicating that the constructed nomogram model has good predictive ability. Additionally, the DCA demonstrated that the nomogram model has diagnostic accuracy. The Nomogram Model offers precise prognostications regarding the likelihood of Colorectal Cancer in patients, thereby helping healthcare professionals in their decision-making processes and promoting the rational categorization of patients as well as the allocation of medical resources.

Sensitivity to change in well-being and health-related quality of life in adults with eating disorder symptoms: ICECAP-A versus EQ-5D-5L.

Economic evaluations of treatments help to inform decisions on allocating health care resources. These evaluations involve comparing costs and effectiveness in terms of quality of life. To calculate quality-adjusted life years, generic health related quality of life is often used, but is criticized for not being sensitive to change in mental health populations. Another approach, using experienced well-being measured through capabilities with the ICECAP-A, has been proposed as an alternative. The aim of this study was to investigate whether changes in individuals with eating disorder (ED) symptoms can be better captured using health related quality of life (EQ-5D-5L) or well-being (ICECAP-A). Measurements at two time points with an interval of 1 year were used from a sample of 233 participants with self-reported ED symptoms. An analysis of variance was used to test whether the EQ-5D-5L and ICECAP-A differed in sensitivity to change over time. In order to compare the two questionnaires in terms of clinically significant outcome, the reliable change index and clinical cut-off score were calculated. The two questionnaires did not differ in sensitivity to change. More individuals had recovered but also more had deteriorated according to the EQ-5D-5L compared to the ICECAP. The present study revealed no differences in sensitivity to change in health-related quality of life or well-being in individuals with ED symptoms in the context of mild clinical change. Results corroborated the pervasiveness of low quality of life in this population, even after alleviation of ED symptoms. Measuring treatment benefits in terms of improvements in quality of life is an integral part of economic evaluations in health care. It was expected that these treatment benefits would be better captured as changes in well-being (measured with the ICECAP-A) than as changes in health-related quality of life (measured with the EQ-5D-5L) for individuals with ED symptoms. Based on the results of this study, no preference for one of the two approaches was found.

Prevalence and Factors Associated with Long COVID Symptoms among U.S. Adults, 2022.

Long COVID and its symptoms have not been examined in different subpopulations of U.S. adults. Using the 2022 BRFSS (n = 445,132), we assessed long COVID and each symptom by sociodemographic characteristics and health-related variables. Multivariable logistic regression was conducted to examine factors associated with long COVID and the individual symptoms. Prevalence differences were conducted to examine differences in long COVID by vaccination status. Overall, more than one in five adults who ever had COVID-19 reported symptoms consistent with long COVID (21.8%). The most common symptom was tiredness or fatigue (26.2%), followed by difficulty breathing or shortness of breath (18.9%), and loss of taste or smell (17.0%). Long COVID was more common among adults under 65 years, women, American Indian or Alaska Native or other/multi race group, smokers, and people with a disability, depression, overweight or obesity compared to their respective counterparts. The prevalence of long COVID was higher among unvaccinated adults (25.6%) than vaccinated adults (21.6%) overall, and for 20 of 32 subgroups assessed. These findings underscore the benefits of vaccination, the importance of early treatment, and the need to better inform health care resource allocation and support services for those experiencing long COVID.

Predicting COVID-19 hospitalizations: The importance of healthcare hotlines, test positivity rates and vaccination coverage

In this study, we developed a negative binomial regression model for one-week ahead spatio-temporal predictions of the number of COVID-19 hospitalizations in Uppsala County, Sweden. Our model utilized weekly aggregated data on testing, vaccination, and calls to the national healthcare hotline. Variable importance analysis revealed that calls to the national healthcare hotline were the most important contributor to prediction performance when predicting COVID-19 hospitalizations. Our results support the importance of early testing, systematic registration of test results, and the value of healthcare hotline data in predicting hospitalizations. The proposed models may be applied to studies modeling hospitalizations of other viral respiratory infections in space and time assuming count data are overdispersed. Our suggested variable importance analysis enables the calculation of the effects on the predictive performance of each covariate. This can inform decisions about which types of data should be prioritized, thereby facilitating the allocation of healthcare resources.

Benchmarking non-attendance patterns in paediatric medical imaging: A retrospective cohort study spotlighting First Nations children

IntroductionNon-attendance at Medical Imaging (MI) appointments can result in inefficiencies in healthcare resource allocation, increased financial burdens, and lead to potential barriers to effective healthcare delivery. We evaluated factors associated with non-attendance of MI appointments for children including variables: gender; age groups; residential postcodes; Indigenous status; appointment dates; appointment reminders and socio-economic status. MethodRetrospective cohort study of children with scheduled MI appointments at a Tertiary paediatric hospital in Australia, between January and December 2022. Data were extracted from the Radiology Information System and integrated with socio-economic census data through linking with postcode. Chi-squared, and logistic regression analysis were performed to identify significant predictors of non-attendance. ResultsOut of 17,962 scheduled outpatient appointments, 6.2 % did not attend. Males were less likely to attend than females (7.3 % vs. 5.8 %; p < 0.001). Older children had the highest frequency of non-attendance (p < 0.001). First Nations identified children had a higher likelihood of non-attendance at 14.5 % compared to non-First Nations at 5.8 %, and the odds ratio (OR) of First Nation children not attending was 2.54 (CI 2.13–3.03; p < 0.001) higher than non-First Nations children. Children from areas of disadvantage were less likely to attend (p < 0.001). Bone mineral densitometry had the highest odds of non-attendance (19.4 % of bookings) compared to other imaging modalities (p < 0.001). ConclusionThe following characteristics were associated with non-attendance: older male gender, residing in areas of socio-economic disadvantage, or identifying as First Nations Australians. By reviewing these findings with the cultural and professional experience of our Indigenous co-author, we have identified some strategies for improving attendance amongst First Nations children. Implications for practiceFactors associated with non-attendance, or “missed opportunities for care”, provide opportunities for intervention to improve attendance for vulnerable groups of children who require medical imaging.

The Correlation between Economic Convergence and Health Indices in Developed Countries.

Economic convergence signifies diminishing income disparities among global or regional economies and their eventual disappearance. It is also linked to economic growth and key health indicators. We aimed to assess the association between economic convergence and key health indicators in developed countries called G7 (USA, UK, Germany, France, Italy, Japan, and Canada). We examined G7 health and economic indicators from 2000 to 2021 using panel data analysis. We compared balanced and unbalanced panel datasets to address missing data and applied suitable methods to handle missing health indicators. Little's MCAR test (X 2 = 3.2872, P - value = 0.3494) confirmed random missing data in the unbalanced panel, enabling us to impute missing values as missing observations were below 5%. Unit root tests on balanced and unbalanced panel data validated the health convergence hypothesis, showing no unit roots in economic growth rate, current health expenditure, and female and male population indicators (P<0.05). Interestingly, the hypothesis for hospital bed counts in the unbalanced panel, differing from the balanced panel, offers new insights into addressing incomplete health data. While G7 have economic similarities, their health indicators diverge (excluding hospital bed counts). Variations in health indicators stem from healthcare system structures, funding mechanisms, resource allocation, and health investments, even among economies of similar size. Therefore, G7 member states should develop tailored national health policies based on their specific circumstances and priorities, utilizing economic convergence data for effective health resource planning.

Healthcare resource allocation decisions and non-emergency treatments in the aftermath of Covid-19 pandemic. How should children with chronic illness feature in prioritisation processes?

In the aftermath of the Coronavirus disease 2019 (Covid-19) pandemic, allocation of non-urgent medical interventions is a persistent ethical challenge as health systems currently face an unprecedented backlog of patients requiring treatment. Difficult decisions must be made that prioritise certain patients over others. Ethical resource allocation requires that the needs of all patients are considered properly, but at present there is no guidance that can help support such decision-making which explicitly considers the needs of children with chronic and complex conditions. This paper reviews the NHS guidance for priorities and operational planning and examines how the needs of children with chronic illness are addressed in NHS objectives for restoring services and meeting elective care demands. The usual criteria for prioritisation featured in the NHS guidance fail to account for the distinct needs of children with chronic illnesses and fail to match more general considerations of what constitutes fair resource allocation decisions. To address this issue, two considerations, namely 'protecting age-related opportunity' and 'recognising complexity of care,' are proposed as additions to the existing approach. By providing a broader conception of needs, these criteria address inefficiencies of the current guidance and relevant ethical frameworks and help to embed a currently missing children-related ethical approach to healthcare policy making in general.

Ethical Shortcomings of QALY: Discrimination Against Minorities in Public Health.

Despite progress, discrimination in public health remains a problem. A significant aspect of this problem relates to how medical resources are allocated. The paradigm of quality-adjusted-life-year (QALY) dictates that medical resources should be allocated on the basis of units measured as length of life and quality of life that are expected after the implementation of a treatment. In this article, I discuss some of the ethical shortcomings of QALY, by focusing on some of its flawed moral aspects, as well as the way it relates to discrimination on the basis of age, race, and disability status. I argue that while this approach seeks to maximize efficiency, it does not place sufficient value on the preservation of life itself. Even more concerning is the fact that the use of QALY disproportionately harms minorities. While QALY is a well-intentioned approach to the allocation of scarce healthcare resources, new alternatives must be sought.

Face Transplantation, Social Death , and Bias in Health Care Resource Allocation.

Face Transplantation, Social Death , and Bias in Health Care Resource Allocation.

A pharmacoeconomic analysis from Italian guidelines for the management of prolactinomas.

Prolactinoma, the most common pituitary adenoma, is usually treated with dopamine agonist (DA) therapy like cabergoline. Surgery is second-line therapy, and radiotherapy is used if surgical treatment fails or in relapsing macroprolactinoma. This study aimed to provide economic evidence for the management of prolactinoma in Italy, using a cost-of-illness and cost-utility analysis that considered various treatment options, including cabergoline, bromocriptine, temozolomide, radiation therapy, and surgical strategies. The researchers conducted a systematic literature review for each research question on scientific databases and surveyed a panel of experts for each therapeutic procedure's specific drivers that contributed to its total cost. The average cost of the first year of treatment was €2,558.91 and €3,287.40 for subjects with microprolactinoma and macroprolactinoma, respectively. Follow-up costs from the second to the fifth year after initial treatment were €798.13 and €1,084.59 per year in both groups. Cabergoline had an adequate cost-utility profile, with an incremental cost-effectiveness ratio (ICER) of €3,201.15 compared to bromocriptine, based on a willingness-to-pay of €40,000 per quality-adjusted life year (QALY) in the reference economy. Endoscopic surgery was more cost-effective than cabergoline, with an ICER of €44,846.64. Considering a willingness-to-pay of €40,000/QALY, the baseline findings show cabergoline to have high cost utility and endoscopic surgery just a tad abovethat. Due to the favorable cost-utility profile and safety of surgical treatment, pituitary surgery should be considered more frequently as the initial therapeutic approach. This management choice could lead to better outcomes and an appropriate allocation of healthcare resources.