Efficient Allocation Of Health Care Resources Research Articles

Psychological stress and influence factors in elderly patients with mild coronary heart disease: a longitudinal follow-up study in Shanghai, China

IntroductionEffective health management is crucial for elderly patients with coronary heart disease (CHD). This study applied a Psycho-Cardiology model to CHD management, aiming to assess psychological stress among patients with mild CHD and identify potential influencing factors to provide substantiating evidence.MethodsThis longitudinal study was based on a 9-year follow-up program of a community population in Shanghai, China. A total of 44,552 elderly people were included, with the average age being 74.9 (±10.35) years, and the proportion of female participants being 56.5%. To evaluate and compare the effect of the disease, individuals were categorized into four groups based on their medical records from the past 6 months, these being (I) a CHD with other chronic diseases group, (II) a CHD only group, (III) non-CHD patients with one (or more) chronic disease group, and (IV) non-patient group. Demographic characteristics, sleep quality and health status of each participants were collected using the Unified Needs Assessment Form for Elderly Care Questionnaire. A multivariate logistic regression was used for statistic analysis.ResultsDemographic characteristics differed significantly between the three chronic disease groups (Groups I, II and III) and the non-patient group. Participants in the CHD group reported poorer sleep quality, worse health status, and a more rapid health decline when compared to those with other chronic diseases. Factors such as age, gender, education level, disease duration, and family support were identified as potential influences on the self-reported subjective sleep quality in patients with mild CHD. While age, education level, living status and family support were potential factors influencing the self-assessed health status in participants without CHD (Groups III and IV).ConclusionPatients with mild CHD may experience lower subjective sleep quality, health status scores, and a faster health-sleep decline, indicating elevated psychological stress. Higher education levels offer a protective effect against this stress, highlighting the importance of psycho-emotional interventions and educational strategies. Additionally, it is important to prioritize early intervention for newly diagnosed cases to aid in illness acceptance. These findings provide crucial insights for managing patients with mild CHD and inform the efficient allocation of healthcare resources.

Health utility attributable to oral conditions in Japanese adults.

Utility values enable relative comparisons across various health conditions, providing information for efficient allocation of healthcare resources. This study aimed to (1) quantify the utility values attributable to oral health-related quality of life (OHRQoL) in Japanese adults and (2) develop models for converting the 14-item Oral Health Impact Profile (OHIP-14) scores into EuroQoL 5-dimension 5-level (EQ-5D-5L)-based utility values. This was a cross-sectional study. Data from a large-scale Internet survey of Japanese adults conducted in 2022 (n = 28 405; mean age 48.2 years) were analysed. Multiple linear regression models were fitted to investigate the association between OHIP-14 scores and EQ-5D-5L-based utility values, adjusting for confounders. Conversion models were developed using a random half of the participants, and the observed and predicted utility values in the other half were compared to evaluate the model performance. Among the participants, 55.2% scored 0, 20.9% scored 1-5 and 23.9% scored 6-56 on the OHIP-14, corresponding mean utility values of 0.93, 0.90 and 0.84, respectively. A one-point increase in the OHIP-14 score was associated with a lower utility value (coefficient: -0.0053; 95% confidence interval:health-related quality of life -0.0056, -0.0051). The estimated utility value attributable to OHIP-14 was -23.3 per 1000 individuals, greater than that for other prevalent chronic conditions, including hypertension and diabetes (-2.9 and -7.1 per 1000 individuals, respectively). The conversion model incorporated the OHIP-14 total score, age, sex and self-rated health, predicted utility scores on average and captured differences according to the number of teeth lost. However, there was a discrepancy between predicted and observed utility values in the lower utility value groups. OHRQoL substantially impacted utility values at the population level. The OHIP-14 holds the potential as a valuable tool for predicting average utility values based on oral health conditions; however, the prediction performance was relatively low for individuals with a lower health-related quality of life.

Regional healthcare resource allocation and decision-making: Evaluating the effectiveness of the three-stage super-efficiency DEA model

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

Transfusion burden and willingness to pay for temporary alleviation of anemia status in transfusion-dependent beta-thalassemia patients in China

BackgroundTransfusion-dependent β-thalassemia (TDT) is one of the global public health concerns highlighted by the World Health Organization. Patients with TDT require regular blood transfusion to survive. However, the availability of blood resources is extremely limited. The purpose of this study was to investigate transfusion burden and willingness to pay (WTP) for temporary remission of anemia status among patients with TDT and to explore the associated factors.MethodsAdult patients with TDT were recruited through cluster sampling across several high-incidence provinces in China. Consenting patients completed online questionnaires on demographic information, transfusion burden and WTP with real-time WeChat communication assistance from researchers. The guiding techniques of double-bounded dichotomous choices and open-ended questions in the contingent valuation method (CVM) were used to obtain participants’ WTP for 1 unit of leukocyte-depleted red blood cells. WTP calculations were performed using maximum likelihood estimation, with further insights gained through subgroup analysis based on gender, family monthly income level and convenience of blood transfusion.ResultsThe analysis included 149 TDT patients from five high-incidence provinces, with an average monthly income of $198.5. Patients received an average of 3.7 units per transfusion, 15.4 times annually, with an average WTP of $70.4 per unit (95% CI [62.0, 78.9]). Estimated WTP for temporary anemia alleviation per transfusion totaled $260.6, exceeding monthly income by 1.32 times. Higher WTP was observed among males, higher-income households, and those with at least junior education. Lower WTP was noted among patients with lower transfusion volumes and those needing to travel for transfusion or during hospitalization for blood transfusion.ConclusionHigh WTP indicated a strong desire for temporary anemia relief. Most TDT patients faced significant economic and transfusion burden. The evident gap in meeting clinical needed underscores the urgent demand for innovative treatments to reduce transfusion dependency, potentially transforming TDT care and improving socioeconomic well-being and clinical outcomes. These findings supported evidence-based decision-making for TDT pharmacoeconomics and efficient healthcare resource allocation in China.

The promise and pitfalls of care standardization in congenital diaphragmatic hernia

The aim of standardizing care is to enhance patient outcomes and optimize healthcare delivery by minimizing variations in care and ensuring the efficient allocation of healthcare resources. Despite these potential benefits to patients, healthcare providers and the healthcare system, standardization may also disadvantage these groups. With a specific focus on congenital diaphragmatic hernia, this article will review the promise and pitfalls of standardization, as well as a potential path forward that uses standardization to improve outcomes in this rare and complex disease process.

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.

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.

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.

Healthcare Resource Allocation Optimization

Assuring high-quality patient care and making the best use of the resources at hand requires the efficient allocation of healthcare resources. This study uses a thorough examination of patient admission data, treatment plans, and staff availability to tackle the complex problem of optimizing healthcare resource allocation. We want to reduce wait times, optimize resource use, and eventually improve patient outcomes in healthcare facilities by integrating these essential elements. Our strategy makes use of cutting-edge data analytics tools, such as optimization algorithms and predictive modelling, to find underlying patterns and trends in patient demand, treatment regimens, and staffing needs. Our goal is to create prediction models that anticipate patient admissions, treatment requirements, and staffing levels by analysing historical data and real-time inputs. This will allow for proactive resource allocation techniques. The study's conclusions have important ramifications for healthcare administration since they provide practical advice on how to improve patient care delivery, cut wait times, and make the most use of the resources at hand. Healthcare institutions can enhance resource allocation procedures to meet the changing demands of patient care while guaranteeing the optimal use of resources by integrating technology, data-driven decision-making, and evidence-based practices. In summary, this study lays the groundwork for future investigations and innovations in hospital administration and operations while also supporting current initiatives to enhance healthcare resource allocation procedures. Healthcare organizations can improve patient outcomes and achieve operational excellence while providing high-quality, patient-centred care by adopting data-driven approaches and utilizing technology.

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.

Automated Detection and Classification of Retinal Lesions Using Image Processing Techniques

Diabetic Retinopathy (DR), a diabetic eye disease, manifests as damage to the retina due to blood leakage leading to the development of Red Lesions (Microaneurysms and Hemorrhages) and Bright Lesions (Hard Exudates and Cotton Wool Spots). Chronic, uncontrolled diabetes is the primary cause, with delayed treatment potentially resulting in complete blindness. DR is clinically categorized into four stages: No DR, Mild DR, Moderate DR, and Severe DR. Manual detection of DR by ophthalmologists is time-consuming, causing prolonged suffering for patients. To address this, our research leverages advanced technological tools like MATLAB to automate lesion extraction and feature analysis. By quantifying parameters such as lesion number, area, perimeter, and solidity, our model aims to categorize categories accurately. Specifically, we focus on utilizing the area covered by lesions to determine the disease stage, providing valuable insights into disease progression. Our approach accelerates detection and enhances accuracy, potentially improving patient outcomes. By automating lesion extraction and analysis, our system reduces the burden on ophthalmologists, allowing for a more efficient allocation of healthcare resources. This research contributes to the field by offering a robust method for DR assessment, facilitating early intervention and treatment.

Incidence and risk factors for unplanned readmission after colorectal surgery: A meta-analysis

Background Unplanned readmissions (URs) after colorectal surgery (CRS) are common, expensive, and result from failure to progress in postoperative recovery. These are considered preventable, although the true extent is yet to be defined. In addition, their successful prediction remains elusive due to significant heterogeneity in this field of research. This systematic review and meta-analysis of observational studies aimed to identify the clinically relevant predictors of UR after colorectal surgery. Methods A systematic review was conducted using indexed sources (The Cochrane Database of Systematic Reviews, MEDLINE, and Embase) to search for published studies in English between 1996 and 2022. The search strategy returned 625 studies for screening of which, 150 were duplicates, and 305 were excluded for irrelevance. An additional 150 studies were excluded based on methodology and definition criteria. Twenty studies met the inclusion criteria and for the meta-analysis. Independent meta-extraction was conducted by multiple reviewers (JD & SR) in accordance with PRISMA guidelines. The primary outcome was defined as UR within 30 days of index discharge after colorectal surgery. Data were pooled using a random-effects model. Risk of bias was assessed using the Quality in Prognosis Studies tool. Results The reported 30-day UR rate ranged from 6% to 22.8%. Increased comorbidity was the strongest preoperative risk factor for UR (OR 1.39, 95% CI 1.28–1.51). Stoma formation was the strongest operative risk factor (OR 1.54, 95% CI 1.38–1.72). The occurrence of postoperative complications was the strongest postoperative and overall risk factor for UR (OR 3.03, 95% CI 1.21–7.61). Conclusions Increased comorbidity, stoma formation, and postoperative complications are clinically relevant predictors of UR after CRS. These risk factors are readily identifiable before discharge and serve as clinically relevant targets for readmission risk-reducing strategies. Successful readmission prediction may facilitate the efficient allocation of healthcare resources.

Comparison of health resource usage and costs associated with first-generation and second-generation long-acting injectable antipsychotics for patients with schizophrenia: A nationwide population-based cohort study.

The aim of this study was to examine psychiatric resource utilization, medical costs and clinical outcomes for patients with schizophrenia who received either first-generation or second-generation long-acting injectable (LAI) antipsychotics. A retrospective cohort study was conducted using data from Taiwan's National Health Insurance Research Database (NHIRD). Patients who began either first-generation or second-generation LAI treatment between 2015 and 2017 were enrolled and followed for three years. The data were evaluated using survival analysis and Cox proportional hazards regression models. Our findings demonstrated that both first- and second-generation LAI therapies led to notable reductions in the frequency of psychiatric hospitalizations and the duration of hospital stays when compared to the initial measurements. Additionally, the second-generation LAI group exhibited significantly lower rates of psychiatric emergencies and hospitalizations, as well as shorter hospital stays, compared to the first-generation LAI group. However, it is worth noting that the second-generation LAI group incurred higher pharmacy fees despite these favorable outcomes. The utilization of both first- and second-generation LAIs can enhance medication adherence and decrease the risk of acute exacerbation in patients with schizophrenia. These findings hold significant implications for schizophrenia management and the efficient allocation of healthcare resources.

Trends in prescription therapy for adolescents with depression in nine major areas of China during 2017–2021

ObjectiveTo date, no national-scale drug usage survey for adolescents with depression has been conducted in China, and the purpose of this study was to examine the national trends in prescriptions in Chinese adolescent depression patients from 2017 to 2021.MethodsPrescribing data were extracted from the Hospital Prescription Analysis Cooperative Project. The average number of patients per year, the cost of treatment, and the prescription patterns (monotherapy vs. combination therapy) were analyzed, and subgroup analyses were conducted depending on age, sex, and drug class.ResultsThe study included 674,099 patients from 136 hospitals located in nine major areas of China. Of all patients, the proportion of adolescents increased from 1.63% in 2017 to 6.75% in 2021. Visits by adolescent depression patients increased from 1,973 in 2017 to 9,751 in 2021, and the corresponding cost increased from 607,598 Chinese Yuan in 2017 to 2,228,884 Chinese Yuan in 2021. The incidence of adolescent depression among female individuals was far beyond that among male individuals. Combination therapy was more frequent than monotherapy, and the most commonly prescribed drugs were antidepressants, antipsychotics, antiepileptics, and antianxietics. Despite the use of sertraline decreasing from 47.90 to 43.39%, it was the most frequently used drug.ConclusionIn summary, the prescriptions and cost of treatment for adolescent depression patients both increased rapidly. The widespread use of those drugs with weak clinical evidence reflects the current state of China, which should arouse our attention. The study can provide references for clinical treatment decisions and a basis for more efficient allocation of healthcare resources by the government.

Impact of prediabetes with a high risk of diabetes stratified by glycated hemoglobin level on the severity of coronavirus disease 2019 during admission.

Prediabetes with a glycated hemoglobin (HbA1c) level of 5.7 - 6.4% is associated with a poor prognosis of coronavirus disease 2019 (COVID-19), but whether the degree of glycemic control is associated with COVID-19 severity is unknown. The aim of this study was to evaluate the association between the degree of glycemic control and COVID-19 severity in patients with prediabetes. We reviewed 254 patients with COVID-19 admitted to our hospital between April 2020 and September 2021. Based on their HbA1c level, patients were classified into low (HbA1c level < 5.7%), moderate (HbA1c level, 5.7 - 5.9%), and high risk of diabetes (HbA1c level, 6.0 - 6.4%). The association between risk of diabetes and the worst COVID-19 symptom in terms of severity during admission was evaluated using binary logistic regression analysis. Seventy-one and 88 patients had moderate and high risks of diabetes, respectively. Sixty-three and seven patients presented severe (requiring non-invasive oxygen therapy) or critical (intensive care unit admission or artificial respiratory management) COVID-19. The multivariate logistic regression analysis showed that a high risk of diabetes was correlated with severe COVID-19 (P = 0.01) after adjusting for baseline characteristics, whereas a moderate risk of diabetes was not (P = 0.17). Prediabetes with a high risk of diabetes is associated with the worst COVID-19 symptom in terms of severity during admission. Our findings could aid in more efficient allocation of healthcare resources to a narrower population of prediabetic patients. The online version contains supplementary material available at 10.1007/s13340-023-00643-z.

Blood gene expression predicts intensive care unit admission in hospitalised patients with COVID-19.

BackgroundThe COVID-19 pandemic has created pressure on healthcare systems worldwide. Tools that can stratify individuals according to prognosis could allow for more efficient allocation of healthcare resources and thus improved patient outcomes. It is currently unclear if blood gene expression signatures derived from patients at the point of admission to hospital could provide useful prognostic information.MethodsGene expression of whole blood obtained at the point of admission from a cohort of 78 patients hospitalised with COVID-19 during the first wave was measured by high resolution RNA sequencing. Gene signatures predictive of admission to Intensive Care Unit were identified and tested using machine learning and topological data analysis, TopMD.ResultsThe best gene expression signature predictive of ICU admission was defined using topological data analysis with an accuracy: 0.72 and ROC AUC: 0.76. The gene signature was primarily based on differentially activated pathways controlling epidermal growth factor receptor (EGFR) presentation, Peroxisome proliferator-activated receptor alpha (PPAR-α) signalling and Transforming growth factor beta (TGF-β) signalling.ConclusionsGene expression signatures from blood taken at the point of admission to hospital predicted ICU admission of treatment naïve patients with COVID-19.

Should Hungary Pay More for a QALY Gain than Higher-Income Western European Countries?

ObjectivesCost-effectiveness thresholds (CETs) play a particularly important role in the reimbursement decisions of health technologies in countries with limited healthcare resources. Our goal is to develop a scientifically solid proposal for a revised cost-effectiveness threshold, as part of the planned review of the Hungarian health economic guidance.MethodsThe Threshold Working Group of the Hungarian Health Economics Association performed a targeted review on CETs in European countries. International trends on CETs served as a basis for our recommendation, which was discussed at the Association’s workshop and deliberated at an expert committee meeting with representatives from the national health technology assessment (HTA) and healthcare payer bodies, and academic HTA centres.ResultsThe current Hungarian CET is one of the highest among European countries relative to GDP per capita, and even higher in nominal value than the CET applied by NICE. As opposed to the current, single Hungarian threshold, other European countries apply multiple thresholds. The Working Group recommends that Hungary should also apply multiple CETs in the range of 1.5–3 times GDP per capita with stratification according to the relative quality-adjusted life-year (QALY) gain of the new technology. In addition, multiple CETs in the range of 3–10 times GDP per capita is recommended for technologies in rare diseases.ConclusionsCETs should be aligned with the country’s economic performance and should reflect societal preferences. Our recommendation may increase the efficiency of healthcare resource allocation in Hungary by strengthening the role of HTA in the reimbursement decisions and favouring new technologies with higher QALY gain.Supplementary InformationThe online version contains supplementary material available at 10.1007/s40258-021-00710-z.

Spatial heterogeneity in gender and age of fatal suicide in Iran.

Background: The suicide incident has had an increasing trend in Iran over the past years. This study mainly aimed to investigate and visualize the spatial variations of registered suicide cases at the province level. A two-step modeling approach was employed in order to estimate the relative risks (RRs) and model the age of fatal suicide across provinces in Iran. Study design: An applied ecological study. Methods: This study used the suicide death data recorded by the Iranian forensic medicine organization from March 21, 2016, to March 20, 2018. Furthermore, a Bayesian spatial approach - Besag, York, and Mollie (BYM) model- was applied to estimate the RR of suicide across provinces in Iran. Results: This risk was found to be significantly higher than the average in both men and women in the west of Iran. For women, higher population density (mean: 0.003; 95% CrI: 0.001-0.005) and lower urbanization rate of provinces (mean: -0.025; 95% CrI: -0.038, -0.012) were associated with increased RR of suicide. Based on the log-normal model fitted to the data, the overall mean age of the fatal suicide at the national level was 34 years. Conclusions: The magnitude of gender and age differences was quantified, and many spatial variations were identified in suicide mortality across provinces in Iran. Given the heterogeneity in suicide mortality risk among different subgroups of age and gender, our findings point to the urgent need in developing gender- and age-specific suicide prevention strategies. Moreover, efficient allocation of healthcare resources for suicide prevention can be attained by targeting provinces with higher risk.

Prescribing Pattern and Cost of Fluoroquinolones in Pyelonephritis Patients Hospitalized in Tertiary Care Hospital

Complicated urinary tract infections (cUTIs) impose a high burden on healthcare systems and are a frequent cause of hospitalization. Urinary tract infection (UTI) is a common illness affecting both males and females in almost all age groups. The objective was to determine the prescription pattern of antibiotics and cost of fluoroquinolones antibiotics in patients of UTI (pyelonephritis). We finalized the most significant 66 pyelonephritis patient’s documents. All the information, related to the prescription cost of fluoroquinolones of antibiotics needed to treat the disease was arranged. Patient medication record was obtained from the patients’ medical file, and added with the unit cost of services obtained from the hospital record mainly medication, to determine the prescription cost. Concomitant medication and medication of complications was not recorded. All costs were calculated in Pakistani rupees and in US dollar (average exchange rate in year 2018 was 1US$=121.574 PkRs. The most commonly prescribed fluoroquinolone in our study for the treatment of pyelonephritis was ciprofloxacin. Average highest Cost of antibiotic combination of ciprofloxacin/levofloxacin was PKRs 8650 ($71.150) and the average least antibiotic cost for treating pyelonephritis was PKRs1008 ($8.291) with the combination of ceftriaxone 500gm/ciprofloxacin 400mg oral. The average flouroquinolones prescription cost per hospitalized case of UTI (Pyelonephritis) varied from case to case, depending upon the type and combination of antibiotics used. A better knowledge of the reasons for variations in length of stays could facilitate a better standardised quality of care for patients with UTI (Pylonephritis) and allow a more efficient allocation of healthcare resources.