Performance Of Health Care System Research Articles

Correlation Between CT Density, Incidence, Mortality, and Mortality-to-Incidence Ratio in Central Nervous System Cancers: An Exploratory Analysis of Global Data.

Cancers of the central nervous system (CNS) pose a significant burden, despite their relatively low incidence compared to other types of cancers. The mortality-to-incidence ratio (MIR) is a crucial indicator of long-term survival and healthcare system performance. Computed tomography (CT) plays a crucial role in the screening, diagnosis, and monitoring of brain tumors, enabling early intervention and treatment. This study aimed to explore the relationship between CT density, CNS cancer incidence, mortality, and MIR to investigate regional variations in CT utilization and their impact on CNS cancer mortality rates. Changes in MIR, referred to as δMIR, were calculated based on data from 2012 and 2018. CT density data for the year 2013 were retrieved from the Global Health Observatory data repository. The association between variables was analyzed using Spearman's rank correlation coefficient. Analysis of data from 107 countries revealed a positive association between CT density and both CNS cancer incidence and mortality. However, a trend was observed between CT density and MIR. These findings suggest that in countries with greater accessibility to CT imaging, CNS cancer cases may be detected earlier and lower mortality rates can be achieved. Our research contributes to the understanding of the impact of CT imaging on the management and outcomes of CNS cancers. It informs healthcare strategies and resource allocation to improve patient care.

Unmet need for healthcare in the Russian Federation: subnational retrospective modelling analysis (2014–2018)

IntroductionUnmet need for healthcare is a proxy indicator used to assess the performance of healthcare systems throughout the world. While the Russian Federation is committed to improving healthcare for its...

Government Health Expenditure and Policy for Public Health Outcomes: A Systematic Literature Review

INTRODUCTION: The level of good governance, the efficiency of the healthcare system, and socioeconomic vulnerabilities all affect public health outcomes. Despite potential gains in terms of health outcomes, public healthcare investment has been extremely low in low-income countries. By improving the consolidation of healthcare system performance, this study helps the government and policymakers improve the quality of life in developing countries. MATERIALS AND METHODS: This study aims to assess the government health expenditure and policy for public health outcomes. The research is based on a review of empirical studies and reports. The PubMed search engine was used to locate research publications on government health expenditure and policy for public health outcomes. The PubMed Open Database was applied to find the publications using the keywords “health expenditure” and “health policy”. All papers that were published from January 1, 2013, to January 31, 2024, were checked for the study. 121 research papers were discovered during the initial inquiry; five duplicate articles were eliminated, and 84 were irrelevant to the study. Finally, 25 research papers and reports were selected for evaluation with the study titled “ Government Health Expenditure and Policy for Public Health Outcomes: A Systematic Literature Review”. RESULTS: Raising the nation’s social health insurance (SHI) coverage does not guarantee universal health care (UHC) or improved financial security. Extremely few people in poverty have health insurance and attempts to give them coverage through exemptions and waivers have not had the intended effect. To prevent families from being ruined due to family health problems, an innovative insurance plan needs to be created. Supply-side strategies, or health infrastructure, are needed to expand access to high-quality medical treatment. CONCLUSIONS: The quality of life in developing nations is directly improved by the consolidation of healthcare system performance, suggesting that policymakers should step in and offer financial and political help through a variety of policy options. Curative and preventive care are provided by health systems, which can greatly enhance people’s health. Developing a robust health finance system has been a primary objective for numerous countries worldwide, as increased public spending on healthcare leads to better health outcomes and more effective healthcare services. Financial limitations are one of the largest barriers to obtaining high-quality healthcare. A national health insurance program increased public financing for healthcare, and the construction of community-based clinics in rural areas could all contribute to the nation’s transition to universal health care. Policymakers ought to pay greater attention to the quality of care since it significantly affects the amount of money that patients must pay out-of-pocket. Governments must develop new internal financing sources and devise strategies to refocus on health promotion and prevention.

Digitalization of the Medical System and Demographic Processes in Estonia

The rapid growth in information technologies has produced effects for all the walks of human and public life. The health care sector did not stay out, although it needs to be acknowledged that it has not been effectively digitalized in all the countries, even developed ones. It should be emphasized that the demographic situation heavily depends on the health care system performance. A most advanced country in terms of the health care digitalization is certainly Estonia. This country with less than 1,500,000 of population has long digitalized all the services provided to its residents. 
 The article’s objective is to investigate the impact of health care system digitalization on demographic processes in Estonia. Some innovations in the Estonian medial system, implemented with the advent of new information technologies, are discussed: for doctors it is possible to exchange paperless information about a patient in the patient’s presence in case of need (including medical records or results of tests and examinations). On the other hand, such confidential information can be closed by the patient’s desire for any medical employee. Moreover, a person is well aware of who exactly has access to his/her data: doctor, police or an official body. 
 Being a digital country, Estonia was better prepared to deal online with various problems (first and foremost, distance training, distance job and medical service: testing, identifying contacts with ill persons and vaccination later on) posed by the pandemic of COVID-19. However, a change in the demographic situation, resulting from the spread of the virus, was triggered e. g. by the health care system performance and its response on the new challenge.
 It is emphasized that important information sources for investigating the demographic situation in Estonia and the health care system’s impact on demographic change include the statistical office and other official bodies, as well as international institutions. Using information on the pandemic extracted from online registers, researchers, medics, statisticians and professionals in other fields could help heath care system in quicker adaptation to new realities and in implementing additional methods for analysis of the morbidity rate in Estonia. 
 The experience of the Estonian health care system in implementing digitalization is undoubtedly important for Ukraine. It refers to creating new registers and systems already used in Estonia and other North European countries but lacking in Ukraine, and to improving existing medical information systems and their interactions.

Home health care facility location problem under demand uncertainty

The demand for home health care services is rapidly increasing due to the growing number of older people. The uncertainty surrounding this demand affects the network design processes and the performance of the home health care system in the long term. This study aims to address the issue of demand uncertainty in a home health care location problem. While decisions regarding the location of home health care facilities must be made immediately, the determination of distribution can be postponed until actual demand is observed. In such situations, minimax/maximin robust optimization methods are commonly employed to address uncertainty and facilitate informed decision-making, even in cases where there is limited information about future demand. However, these methods are often too conservative and may lead to suboptimal solutions. To tackle this issue, we propose a regret minimization method, which is reformulated as a robust model to overcome its intractability. Additionally, we propose a column-and-constraint generation algorithm to solve the robust optimization and regret minimization models. Finally, we conduct a comprehensive set of numerical experiments to compare the performance of the models in terms of solution quality and computational time. The results demonstrate that the regret minimization model enhances solution quality and consumes less computational time when reformulated.

Abstract 16307: Does the Method of Calculating the 30-day Readmission Rate After Hospitalization for Heart Failure Matter? Data From the VancOuver CoastAL Acute Heart Failure Registry

Introduction: The 30-day readmission rate after a heart failure (HF) hospitalization is widely used for assessing healthcare quality and system performance. However, there are different methodological approaches which may influence estimated rates, and no single accepted approach. The combined impact of these methods is unknown. Goals: We calculated 30-day HF readmission rates (RRs) of hospitalized patients using different published approaches to sampling and measurement. Methods: We included 1,849 patients discharged following unplanned hospitalization with a primary diagnosis of HF between 2016 to 2018 from the VancOuver CoastAL Acute Heart Failure (VOCAL-AHF) registry. We combined five distinct methodological factors (Table 1) to create 64 unique definitions and associated HF RRs. The readmission rates were averaged over 3-years. Multiple linear regression was used to determine the impact of different factors on estimated readmission rates. Results: The calculated 30-day RR for HF varied more than twofold depending solely on the methodological approach (6.4% to 15.0%, 8.6% absolute difference, 134% relative difference). The rates were highest when including all consecutive index admissions (11.1% to 15.0%), and lowest including only one index admission per patient per year (6.4% to 11.4%). The regression model ranked variables affecting RR as: index selection method, reference period, ICD-10 codes, 30-day survival, and index day (Table 2, p<0.001 in all). Conclusions: Our findings have important implications for policy and reporting. Transparent and consistent methods are needed to calculate 30-day RRs to ensure reproducible and comparable reporting.

Disaster preparedness of health professionals at mass gatherings: a scoping review

Mass gathering disasters involving large numbers of casualties can adversely affect the performance of healthcare systems. This scoping review aims to explore the current literature on the preparedness of health professionals for disasters during mass gathering events. Four databases were searched to identify papers examining health professionals' disaster preparedness during mass gatherings between 2011 and 2022. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis for Scoping Reviews was applied to undertake this review. A total of 2024 articles were identified, of which 18 qualified for inclusion in the scoping review. Some studies reported the level of professionals' preparedness for disaster management, while others reported strategies to enhance preparedness and challenges associated with disaster preparedness during mass gatherings. More studies are needed in this field to better understand the level of disaster preparedness of health professionals to improve response and management of potential disasters during mass gathering events.

8.G. Workshop: Developments in patient experience surveys: how do we maintain relevance and maximise impact?

Abstract Background Patient and service-user experiences of healthcare are a vital component of the monitoring and assessment of the quality and performance of health and social care services and systems, as well as an important predictor of patient outcomes. The National Care Experience Programme (NCEP) is a partnership between the Irish health and social care regulator (Health Information and Quality Authority, HIQA), the national public healthcare provider (Health Service Executive, HSE), and policy makers (Department of Health). The NCEP conducts national care experience surveys with the aim of learning from people's experiences in order to improve the quality of health and social care services in Ireland. The programme includes surveys of acute hospital inpatient, maternity, maternity bereavement, nursing home and end-of-life care. Additional surveys of cancer care and mental health services are currently in development. Objectives Regular review and adaptation of existing surveys, expansion into new areas of care, and development of new methods of data collection, analysis and dissemination of survey findings and stakeholder engagement are required in order to remain agile and responsive to evolving health and social care landscapes and the changing needs of stakeholders, as well as harnessing technological advances and ensuring sustainability. The aim of this workshop is to bring together experts in patient experience from across the health system to stimulate discussion, knowledge sharing and cross-fertilisation of ideas. Presentations will focus on international developments and trends in patient experience, as well as considerations for surveying bereaved parents and families on their experiences of maternity bereavement and end-of-life care. The use of new technologies, including computational text analytics and predictive modelling to facilitate qualitative data analysis and maximise the use of survey findings will be outlined, followed by case studies of how care experience data are used to inform health and social care legislation, policy and practice in Ireland. Experiences from other jurisdictions and potential implications will be discussed with the audience. The workshop will contribute to a better understanding of the value and impact of collecting patient experience feedback and explore how new technologies can be used to generate insights and maximise the use of survey data. Format of the workshop The workshop will include a brief introduction on patient experience and the Irish National Care Experience Programme, followed by five presentations with time for audience contributions and questions after each speaker. The workshop will conclude with a discussion with the audience and presenters. Key messages • Patient experiences are a key component of the monitoring and assessment of the quality and performance of health and social care services and systems. • Regular review and adaptation of surveys and development of new methods are required to remain responsive to evolving health and social care landscapes and ensure sustainability.

Global inpatient utilization from 1990 to 2020: effects of the COVID-19 pandemic

Abstract Background Inpatient service utilization rate is a key indicator for estimating the performance of health care systems on global, regional, and national levels. Methods We collected 1378 source-years of data, including administrative sources, nationally representative surveys, and government reports covering 130 countries overall, 29 of which are in the European region from 1990 to 2020. Inpatient admission was defined as an overnight stay at a formal healthcare facility for any cause. The data underwent age- and sex-splitting as per the available level of detail; survey sources were crosswalked to the gold-standard administrative data. The resulting data set was analyzed using Spatiotemporal Gaussian Process Regression (ST-GPR), a Bayesian meta-regression tool, to model the inpatient admission rate per capita across all countries, age groups, and sexes from 1990 to 2020. Admission rates were age- and sex-standardized to compute global proportions of hospitalized persons. Results In 2019, the global age-standardized inpatient utilization rate was 0.12 (95% uncertainty interval [UI] 0.09-0.15) admissions per capita. Globally, 0.93 billion (0.72-1.18) inpatient admissions were provided in 2019 compared to 0.68 billion (0.53-0.87) in 1990. In 2020, the global age-standardized inpatient utilization rate dropped to 0.057 (0.055-0.059) with a global proportion of hospitalized patients of 0.45 billion (0.43-0.46). Conclusions The increase in inpatient utilization from 1990 to 2019 may be attributable to population growth and increased capacity of health systems across most WHO regions. Health care utilization was highly variable between regions that was partly explainable by the variation in the socio-demographic index (SDI). The pandemic-mitigating measures limiting transportation and healthcare access were associated with the reduction in utilization in 2020. Key messages • The inpatient utilization rates were rising globally with large variation across regions, while decreasing in 2020 due the COVID-19 pandemic-related mitigating measures. • Health systems may use the inpatient utilization estimates to assess the effectiveness of health care reforms, plan for future capacity, and to allocate financial and human resources appropriately.

A Machine Learning Framework for Length of Stay Minimization in Healthcare Emergency Department

The emergency departments (EDs) in most hospitals, especially in middle-and-low-income countries, need techniques for minimizing the waiting time of patients. The application and utilization of appropriate methods can enhance the number of patients treated, improve patients’ satisfaction, reduce healthcare costs, and lower morbidity and mortality rates which are often associated with poor healthcare facilities, overcrowding, and low availability of healthcare professionals. Modeling the length of stay (LOS) of patients in healthcare systems is a challenge that must be addressed for sound decision-making regarding capacity planning and resource allocation. This paper presents a machine learning (ML) framework for predicting a patient’s LOS within the ED. A study of the services in the ED of a tertiary healthcare facility in Uyo, Nigeria was conducted to gain insights into its operational procedures and evaluate the impact of certain parameters on LOS. Then, a computer simulation of the system was performed in R programming language using data obtained from records in the hospital. Finally, the performance of four ML classifiers involved in patients’ LOS prediction: Classification and Regression Tree (CART), Random Forest (RF), K-Nearest Neighbour (K-NN), and Support Vector Machine (SVM), were evaluated and results indicate that SVM outperforms others with the highest coefficient of determination (R2) score of 0.986984 and least mean square error (MSE) value of 0.358594. The result demonstrates the capability of ML techniques to effectively assess the performance of healthcare systems and accurately predict patients’ LOS to mitigate the low physician-patient ratio and improve throughput.

An improved fuzzy c-means-raindrop optimizer for brain magnetic resonance image segmentation

<p>The performance of healthcare systems, particularly regarding disease diagnosis and treatment planning, depends on the segmentation of medical images. Fuzzy c-means (FCM) is one of the most widely used clustering techniques for image segmentation due to its simplicity and effectiveness. FCM, on the other hand, has the disadvantages of being noise-sensitive, quickly settling on local optimal solutions, and being sensitive to initial values. This paper suggests a fuzzy c-means clustering improved with a nature-inspired raindrop optimizer for lesion extraction in brain magnetic resonance (MR) images to get around this constraint. In the preprocessing stage, the possible noises in a digital image, such as speckles, gaussian, etc., are eliminated by a hybrid filter—A combination of Gaussian, mean, and median filters. This paper presents a comparative analysis of FCM clustering and FCM-raindrop optimization (FCM-RO) approach. The algorithm performance is evaluated for images subjected to various possible noises that may affect an image during transmission and storage. The proposed FCM-RO approach is comparable to other methods now in use. The suggested system detects lesions with a partition coefficient of 0.9505 and a partition entropy of 0.0890. Brain MR images are analyzed using MATLAB software to find and extract malignancies. Image data retrieved from the public data source Kaggle are used to assess the system’s performance.</p>

Defining Priorities in Value-Based Cancer Care: Insights From the Alliance for Clinical Trials in Oncology National Cooperative Group Survey.

We determine how stakeholders prioritize the importance of oncologic outcomes, patient-reported outcomes (PROs), and cancer-related health care costs. A survey was distributed to the National Clinical Trials Network Alliance for Clinical Trials in Oncology cooperative group membership from May 14 to June 30, 2022. Respondents were asked to rate (5-point Likert scale) and rank (1-9) evidence-based value domains: overall survival, treatment toxicities/complications, quality of life (QOL), financial toxicity, access to care, compliance with evidence-based care, health system performance, scientific discovery and innovation, and cost to the health care system. A total of 514 members responded, including researchers (24.7%), nurses (19.5%), medical oncologists (17.9%), administrators (9.3%), surgical and radiation oncologists (9.1%), patient advocates (3.1%), and nonphysician providers (16.4%). Participants represented various practice settings including National Cancer Institute-designated cancer centers (29.8%), university-affiliated academic cancer centers (21%), hospital-owned oncology practices (21.8%), and others (27.4%). There was agreement in how respondents prioritized value domains (W = 0.39, P < .001). Respondents ranked patient QOL (mean rank: 2.6 ± 1.9) as most important above all other metrics including survival (mean rank: 3.5 ± 0.3) and access to care (mean rank: 3.5 ± 2.1; P < .001). Members engaged in direct patient care also ranked access to care of higher importance than nonclinicians (P = .026). Cost to the health care system (mean rank: 7.5 ± 2.1) and health system performance (mean rank: 7 ± 2) were ranked as least important (P < .001). Inclusion of PROs into therapeutic assessment (59.3%) was the most frequently selected priority of future cooperative group initiatives. Oncology community stakeholders deemed patient-centered value domains as most important and considered patient QOL the highest priority. Inclusion of PROs into clinical trials was endorsed as an important component of therapeutic assessment. These findings can be taken into consideration when creating a value framework for inclusion in cancer clinical trials.

380 Sustainable Health Care to Provide Good Quality Care in the Surgical Industry- United Kingdom

Abstract Aim The surgical industry has a major contribution towards sustainable health care. The aim is to critically evaluate sustainable healthcare to provide good quality care in the surgical industry in the United Kingdom. Method A systemic review was conducted using peer-reviewed studies and articles from the United Kingdom related to surgical and anesthetic fields within the last 5 years. The most relevant journal articles were selected based on their relevance to healthcare system performance sustainability, including risks, and subsequently screened using the PRISMA 2020 model screening approach. The findings of the relevant journal articles were then evaluated critically for each theme. Results A total of 79 studies were retrieved. Fifteen of the retrieved studies met the inclusion criteria. Ten of the papers evaluated some existing sustainability practices. Only seven papers discussed significant determinants of good quality health care. Only 86.67% of the papers highlighted the implications of sustainability. The key predictors of high-quality medical care are effective resource management, moral surgical team acquisitions, professional services, integration, short hospital stays, and low mortality and morbidity rates. Conserving water, optimizing treatment routes and transportation, and creating cultural change were found to be the pillars of high-quality sustainable healthcare. Conclusions The concept of sustainability varied across these studies and limitations on sustainability through reduced mortality, morbidity, and business services were observed. The most detrimental effect on the sustainability of the surgical industry remains to be anesthetic gas emission from the theatres. A significant discrepancy was noted between the available data and its implication.

Scaling Family Voices and Engagement to Measure and Improve Systems Performance and Whole Child Health: Progress and Lessons from the Child and Adolescent Health Measurement Initiative.

The 1997 legislation authorizing the United States Child Health Insurance Program sparked progress to measure and publicly report on children's healthcare services quality and system performance. To meet the moment, the national Child and Adolescent Health Measurement Initiative (CAHMI) public-private collaboration was launched to put families at the center of defining, measuring and using healthcare performance information to drive improved services quality and outcomes. Since 1996 the CAHMI followed an intentional path of collaborative action to (1) articulate shared goals for child health and advance a comprehensive, life-course and outcomes-based healthcare performance measurement and reporting framework; (2) collaborate with families, providers, payers and government agencies to specify, validate and support national, state and local use of dozens of framework aligned measures; (3) create novel public-facing digital data query, collection and reporting tools that liberate data findings for use by families, providers, advocates, policymakers, the media and researchers (Data Resource Center, Well Visit Planner); and (4) generate field building research and systems change agendas and frameworks (Prioritizing Possibilities, Engagement In Action) to catalyze prevention, flourishing and healing centered, trauma-informed, whole child and family engaged approaches, integrated systems and supportive financing and policies. Lessons call for a restored, sustainable family and community engaged measurement infrastructure, public activation campaigns, and undeterred federal, state and systems leadership that implement policies to incentivize, resource, measure and remove barriers to integrated systems of care that scale family engagement to equitably promote whole child, youth and family well-being. Population health requires effective family engagement.

Exploring patient perspectives: A qualitative inquiry into healthcare perceptions, experiences and satisfaction in Lebanon.

Patient perspectives have received increasing importance within health systems over the past four decades. Measures of patient experience and satisfaction are commonly used. However, these measures do not capture all the information that is available through engaging with patients. An improved understanding of the various types of patient perspectives and the distinctions between them is needed. The lack of such knowledge limits the usefulness of including patient perspectives as components within pay-for-performance initiatives. This study aimed to explore patient perspectives on hospital care in Lebanon. It also aimed to contribute insights that may improve the national pay-for-performance initiative and to the knowledge on engaging patients towards person-centered health systems. We conducted a qualitative study using focus group discussions with persons recently discharged after hospitalization under the coverage of the Lebanese Ministry of Public Health. This study was implemented in 2017 and involved 42 participants across eight focus groups. Qualitative content analysis was used to analyze the information provided by participants. Five overall themes supported by 17 categories were identified, capturing the meaning of the participants' perspectives: health is everything; being turned into second class citizens; money and personal connections make all the difference; wanting to be treated with dignity and respect; and tolerating letdown, for the sake of right treatment. The most frequently prioritized statement in a ranking exercise regarding patient satisfaction was regular contact with the patient's doctor. Patient perspectives include more than what is traditionally incorporated in measures of patient satisfaction and experience. Patient valuing of health and their perceptions on each of the health system, and access and quality of care should also be taken into account. Hospital pay-for-performance initiatives can be made more responsive through a broader consideration of these perspectives. More broadly, health systems would benefit from wider engagement of patients. We propose a framework relating patient perspectives to value-based healthcare and health system performance.

Machine learning-based prediction of hospital prolonged length of stay admission at emergency department: a Gradient Boosting algorithm analysis.

This study aims to develop and compare different models to predict the Length of Stay (LoS) and the Prolonged Length of Stay (PLoS) of inpatients admitted through the emergency department (ED) in general patient settings. This aim is not only to promote any specific model but rather to suggest a decision-supporting tool (i.e., a prediction framework). We analyzed a dataset of patients admitted through the ED to the "Sant"Orsola Malpighi University Hospital of Bologna, Italy, between January 1 and October 26, 2022. PLoS was defined as any hospitalization with LoS longer than 6 days. We deployed six classification algorithms for predicting PLoS: Random Forest (RF), Support Vector Machines (SVM), Gradient Boosting (GB), AdaBoost, K-Nearest Neighbors (KNN), and logistic regression (LoR). We evaluated the performance of these models with the Brier score, the area under the ROC curve (AUC), accuracy, sensitivity (recall), specificity, precision, and F1-score. We further developed eight regression models for LoS prediction: Linear Regression (LR), including the penalized linear models Least Absolute Shrinkage and Selection Operator (LASSO), Ridge and Elastic-net regression, Support vector regression, RF regression, KNN, and eXtreme Gradient Boosting (XGBoost) regression. The model performances were measured by their mean square error, mean absolute error, and mean relative error. The dataset was randomly split into a training set (70%) and a validation set (30%). A total of 12,858 eligible patients were included in our study, of whom 60.88% had a PloS. The GB classifier best predicted PloS (accuracy 75%, AUC 75.4%, Brier score 0.181), followed by LoR classifier (accuracy 75%, AUC 75.2%, Brier score 0.182). These models also showed to be adequately calibrated. Ridge and XGBoost regressions best predicted LoS, with the smallest total prediction error. The overall prediction error is between 6 and 7 days, meaning there is a 6-7 day mean difference between actual and predicted LoS. Our results demonstrate the potential of machine learning-based methods to predict LoS and provide valuable insights into the risks behind prolonged hospitalizations. In addition to physicians' clinical expertise, the results of these models can be utilized as input to make informed decisions, such as predicting hospitalizations and enhancing the overall performance of a public healthcare system.

Giving a Voice to Nurse Managers and Staff Nurses: A Two-Centres Multi-Method Research Protocol to Optimize Nurses’ Actual Scope of Practice

Introduction: Numerous studies have revealed that a limited time is devoted to value-added care activities that are part of nurses’ professional role (Déry et al., 2022). This has an impact on the performance of healthcare systems in terms of overall costs. The COVID-19 pandemic has put more pressure on healthcare organizations, on its nurse managers (NMs) and staff nurses. Objective: To mobilize key players in care units to propose effective and realistic strategies to facilitate the optimal deployment of nursing practice. Methods: This international research program encompasses 3 consecutive cross-sectional studies involving 2 healthcare centres: 1 in Canada and 1 in Switzerland. Study 1’s qualitative design will include focus groups with NMs. Study 2’s quantitative correlational design will survey staff nurses. Study 3 will include multiple meetings with NMs, staff nurses, clinical nurse specialists and educators (key players) to develop a logic model of intervention to propose effective and realistic strategies to facilitate the full deployment of nurses’ scope of practice. Discussion and Research Spin-offs: Study 1 will help describe the innovative management practices of NMs since the onset of the COVID-19 pandemic and identify their support and educational needs. Study 2 will explore the perceptions of staff nurses regarding the work psychological climate and work recognition, their actual scope of practice, their professional satisfaction at work and their perception of the quality of care. Study 3 will take advantage of this new data and mobilize key players in the identification of improvement strategies adapted to their own reality.

Estimating mortality and disability in Peru before the COVID-19 pandemic: a systematic analysis from the Global Burden of the Disease Study 2019.

Estimating and analyzing trends and patterns of health loss are essential to promote efficient resource allocation and improve Peru's healthcare system performance. Using estimates from the Global Burden of Disease (GBD), Injuries, and Risk Factors Study (2019), we assessed mortality and disability in Peru from 1990 to 2019. We report demographic and epidemiologic trends in terms of population, life expectancy at birth (LE), mortality, incidence, prevalence, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) caused by the major diseases and risk factors in Peru. Finally, we compared Peru with 16 countries in the Latin American (LA) region. The Peruvian population reached 33.9 million inhabitants (49.9% women) in 2019. From 1990 to 2019, LE at birth increased from 69.2 (95% uncertainty interval 67.8-70.3) to 80.3 (77.2-83.2) years. This increase was driven by the decline in under-5 mortality (-80.7%) and mortality from infectious diseases in older age groups (+60 years old). The number of DALYs in 1990 was 9.2 million (8.5-10.1) and reached 7.5 million (6.1-9.0) in 2019. The proportion of DALYs due to non-communicable diseases (NCDs) increased from 38.2% in 1990 to 67.9% in 2019. The all-ages and age-standardized DALYs rates and YLLs rates decreased, but YLDs rates remained constant. In 2019, the leading causes of DALYs were neonatal disorders, lower respiratory infections (LRIs), ischemic heart disease, road injuries, and low back pain. The leading risk factors associated with DALYs in 2019 were undernutrition, high body mass index, high fasting plasma glucose, and air pollution. Before the COVID-19 pandemic, Peru experienced one of the highest LRIs-DALYs rates in the LA region. In the last three decades, Peru experienced significant improvements in LE and child survival and an increase in the burden of NCDs and associated disability. The Peruvian healthcare system must be redesigned to respond to this epidemiological transition. The new design should aim to reduce premature deaths and maintain healthy longevity, focusing on effective coverage and treatment of NCDs and reducing and managing the related disability.

Respective healthcare system performances taking into account environmental quality: what are the re-rankings for OECD countries?

BackgroundEfficiency analyses have been widely used in the literature to rank countries regarding their health system performances. However, little place has been given to the environmental aspect: two countries with the same characteristics could experience completely different healthcare system outcomes just because they do not face the same environmental quality situation, which is a major determinant of the health of inhabitants.MethodsUsing a stochastic frontier model, this paper analyses the effect of environmental quality on health system outcomes in OECD countries, measured by life expectancy at birth.ResultsWe show that the healthcare system performance ranking of OECD countries changes significantly, depending on whether the environmental index is taken into account.ConclusionsThese findings, once again, underline the critical importance of the environment when addressing population health issues. In general, our results can be aligned with the messages of the One Health approach literature.

Robust Additive Value-Based Efficiency Analysis with a Hierarchical Structure of Inputs and Outputs

We introduce a novel methodological framework based on additive value-based efficiency analysis. It considers inputs and outputs organized in a hierarchical structure. Such an approach allows us to decompose the problem into manageable pieces and determine the analyzed units’ strengths and weaknesses. We provide robust outcomes by analyzing all feasible weight vectors at different hierarchy levels. The analysis concerns three complementary points of view: distances to the efficient unit, ranks, and pairwise preference relations. For each of them, we determine the exact extreme results and the distribution of probabilistic results. We apply the proposed method to a case study concerning the performance of healthcare systems in sixteen Polish voivodeships (provinces). We discuss the results based on the entire set of factors (the root of the hierarchy) and three subcategories. They concern health improvement of inhabitants, efficient financial management, and consumer satisfaction. Finally, we show the practical conclusions that can be derived from the hierarchical decomposition of the problem and robustness analysis.