Despite its severity, anaphylaxis carries a low mortality rate of less than 1%, making the cohort seen in the forensic pathology setting a small and unique subset of the majority of cases of anaphylaxis in the community. Clinically, cardiovascular disease has been recognized as a risk factor for fatal anaphylaxis; however, there is scant forensic pathology research investigating this risk factor, whereas autopsy textbooks emphasize physical respiratory changes seen in the broader clinical cohort. This 20-year retrospective study examined all fatal anaphylactic deaths in the state of Queensland, Australia, to document the underlying disease of the cases, tryptase levels, triggers, and postmortem findings. Our study found that cardiovascular disease was prevalent in 83.3% of cases of fatal anaphylaxis. Although asthma was prevalent in food-related fatal anaphylaxis (60%) in our cohort, it was poorly represented overall (28%), in contrast to clinical research. Additionally, only 43% of cases showed respiratory changes that were greater than mild. Our findings emphasize the difference between the clinical and postmortem anaphylaxis cohorts, and provide autopsy evidence of a potential role of cardiovascular disease in fatal anaphylaxis.

This paper examines peer effects in health facility quality in South Africa. Specifically, we investigate whether health facilities adapt their quality in response to changes in the quality of peer facilities, even in the absence of material incentives for doing so. Using a national census of public primary health facilities, we exploit data on structural and process components of quality, examining how these measures change from 2015 to 2017. We examine facilities strategic interactions using both a spatial econometrics approach and a more traditional quasi-experimental approach exploiting a quality improvement program as a source of exogeneous variation to estimate the response of facilities to changes in the quality of their peers. We find evidence of quality peer effects between primary health care facilities, with a 10-unit increase in average District facility quality causing facilities to increase their quality by 3.6 units. Given the lack of financial incentives, we propose prosocial motivation and reputational concerns as the mechanism inducing facilities to respond to changes in peer quality. This finding is consistent with recent literature which has stressed the role measurement and public reporting can play in improving public service, and particularly health care, provision. Importantly, our findings have significant policy implications suggesting the provision of relative performance information, allowing for peer comparisons, can induce a form of quality yardstick competition and be a credible quality improvement policy which may be considered alongside health financing reforms.

ABSTRACT:Despite its severity, anaphylaxis carries a low mortality rate of less than 1%, making the cohort seen in the forensic pathology setting a small and unique subset of the majority of cases of anaphylaxis in the community. Clinically, cardiovascular disease has been recognized as a risk factor for fatal anaphylaxis; however, there is scant forensic pathology research investigating this risk factor, whereas autopsy textbooks emphasize physical respiratory changes seen in the broader clinical cohort. This 20-year retrospective study examined all fatal anaphylactic deaths in the state of Queensland, Australia, to document the underlying disease of the cases, tryptase levels, triggers, and postmortem findings. Our study found that cardiovascular disease was prevalent in 83.3% of cases of fatal anaphylaxis. Although asthma was prevalent in food-related fatal anaphylaxis (60%) in our cohort, it was poorly represented overall (28%), in contrast to clinical research. Additionally, only 43% of cases showed respiratory changes that were greater than mild. Our findings emphasize the difference between the clinical and postmortem anaphylaxis cohorts, and provide autopsy evidence of a potential role of cardiovascular disease in fatal anaphylaxis.

Health care quality improvement (QI) initiatives are being implemented by a number of low- and middle-income countries. However, there is concern that these policies may not reduce, or may even worsen, inequities in access to high-quality care. Few studies have examined the distributional impact of QI programmes. We study the Ideal Clinic Realization and Maintenance program implemented in health facilities in South Africa, assessing whether the effects of the program are sensitive to previous quality performance. Implementing difference-in-difference-in-difference and changes-in-changes approaches we estimate the effect of the program on quality across the distribution of past facility quality performance. We find that the largest gains are realized by facilities with higher baseline quality, meaning this policy may have led to a worsening of pre-existing inequity in health care quality. Our study highlights that the full consequences of QI programmes cannot be gauged solely from examination of the mean impact.

BACKGROUND:Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection can result in a prolonged multisystem disorder termed long COVID, which may affect up to 10% of people following coronavirus disease 2019 (COVID-19). It is currently unclear why certain individuals do not fully recover following SARS-CoV-2 infection.SUMMARY:In this review, we examine immunological mechanisms that may underpin the pathophysiology of long COVID. These mechanisms include an inappropriate immune response to acute SARS-CoV-2 infection, immune cell exhaustion, immune cell metabolic reprogramming, a persistent SARS-CoV-2 reservoir, reactivation of other viruses, inflammatory responses impacting the central nervous system, autoimmunity, microbiome dysbiosis, and dietary factors.KEY MESSAGES:Unfortunately, the currently available diagnostic and treatment options for long COVID are inadequate, and more clinical trials are needed that match experimental interventions to underlying immunological mechanisms.

Pediatric AD patients should lead a healthy lifestyle with an emphasis on consumption of wholesome foods. Nutritional supplementation can play a role in improving AD symptoms; however, this should be evaluated on a case-by-case basis. Limitations include heterogeneity of studies.

A variety of methodologies have been developed to help health systems increase the ‘value’ created from their available resources. The urgency of creating value is heightened by population ageing, growth in people with complex morbidities, technology advancements, and increased citizen expectations. This study develops a policy framework that seeks to reconcile the various approaches towards value-based policies in health systems. The distinctive contribution is that we focus on the value created by the health system as a whole, including health promotion, thus moving from value-based health care towards a value-based health system perspective. We define health system value to be the contribution of the health system to societal wellbeing. We adopt a framework of five dimensions of value, embracing health improvement, health care responsiveness, financial protection, efficiency and equity, which we map onto a society's aggregate wellbeing. Actors within the health system make different contributions to value, and we argue that their perspectives can be aligned with a unifying concept of health system value. We provide examples of policy levers and highlight key actors and how they can promote certain aspects of health system value. We discuss advantages of value-based approach based on the notion of wellbeing and some practical obstacles to its implementation.

OBJECTIVES:To estimate the expected socio-economic value of booster vaccination in terms of averted deaths and averted closures of businesses and schools using simulation modelling. METHODS:The value of booster vaccination in Indonesia is estimated by comparing simulated societal costs under a twelve-month, 187-million-dose Moderna booster vaccination campaign to costs without boosters. The costs of an epidemic and its mitigation consist of lost lives, economic closures and lost education; cost-minimising non-pharmaceutical mitigation is chosen for each scenario. RESULTS:The cost-minimising non-pharmaceutical mitigation depends on the availability of vaccines: the differences between the two scenarios are 14 to 19 million years of in-person education and $153 to $204 billion in economic activity. The value of the booster campaign ranges from $2,500 ($1,400-$4,100) to $2,800 ($1,700-$4,600) per dose in the first year, depending on life-year valuations. CONCLUSIONS:The societal benefits of booster vaccination are substantial. Much of the value of vaccination resides in the reduced need for costly non-pharmaceutical mitigation. We propose cost minimisation as a tool for policy decision-making and valuation of vaccination, taking into account all socio-economic costs, and not averted deaths alone.

The COVID-19 pandemic has seen dramatic demand surges for hospital care that have placed a severe strain on health systems worldwide. As a result, policy makers are faced with the challenge of managing scarce hospital capacity to reduce the backlog of non-COVID patients while maintaining the ability to respond to any potential future increases in demand for COVID care. In this paper, we propose a nationwide prioritization scheme that models each individual patient as a dynamic program whose states encode the patient’s health and treatment condition, whose actions describe the available treatment options, whose transition probabilities characterize the stochastic evolution of the patient’s health, and whose rewards encode the contribution to the overall objectives of the health system. The individual patients’ dynamic programs are coupled through constraints on the available resources, such as hospital beds, doctors, and nurses. We show that the overall problem can be modeled as a grouped weakly coupled dynamic program for which we determine near-optimal solutions through a fluid approximation. Our case study for the National Health Service in England shows how years of life can be gained by prioritizing specific disease types over COVID patients, such as injury and poisoning, diseases of the respiratory system, diseases of the circulatory system, diseases of the digestive system, and cancer. This paper was accepted by Chung-Piaw Teo, optimization. Funding: G. Forchini acknowledges funding from Jan Wallanders and Tom Hedelius Foundation and the Tore Browaldh Foundation, funding from MRC Centre for Global Infectious Disease Analysis [Reference MR/R015600/1], jointly funded by the UK Medical Research Council (MRC) and the UK Foreign, Commonwealth and Development Office (FCDO), under the MRC/FCDO Concordat agreement, part of the EDCTP2 program supported by the European Union; and acknowledges funding by Community Jameel. D. Rizmie acknowledges partial funding from the MRC Centre for Global Infectious Disease Analysis [Reference MR/R015600/1]. J. C. D’Aeth acknowledges funding from the Wellcome Trust [Reference 102169/Z/13/Z]. S. Moret acknowledges partial support from the Swiss National Science Foundation (SNSF) under [Grant P2ELP2_188028]. S. Ghosal was funded by the Imperial College President’s PhD Scholarship. F. Grimm was funded by the Health Foundation as part of core staff member activity. This research was funded in whole, or in part, by the Wellcome Trust [Grant 102169/Z/13/Z]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4679 .

Table S2: Top siRNA screen hits; Table S10: Summary of validated PRPF8-sensitive transcripts across multiple cell lines; Table S14: BE dosing schedules

The COVID-19 pandemic and the mitigation policies implemented in response to it have resulted in economic losses worldwide. Attempts to understand the relationship between economics and epidemiology has led to a new generation of integrated mathematical models. The data needs for these models transcend those of the individual fields, especially where human interaction patterns are closely linked with economic activity. In this article, we reflect upon modelling efforts to date, discussing the data needs that they have identified, both for understanding the consequences of the pandemic and policy responses to it through analysis of historic data and for the further development of this new and exciting interdisciplinary field.

To study the trade-off between economic, social and health outcomes in the management of a pandemic, DAEDALUS integrates a dynamic epidemiological model of SARS-CoV-2 transmission with a multi-sector economic model, reflecting sectoral heterogeneity in transmission and complex supply chains. The model identifies mitigation strategies that optimize economic production while constraining infections so that hospital capacity is not exceeded but allowing essential services, including much of the education sector, to remain active. The model differentiates closures by economic sector, keeping those sectors open that contribute little to transmission but much to economic output and those that produce essential services as intermediate or final consumption products. In an illustrative application to 63 sectors in the United Kingdom, the model achieves an economic gain of between £161 billion (24%) and £193 billion (29%) compared to a blanket lockdown of non-essential activities over six months. Although it has been designed for SARS-CoV-2, DAEDALUS is sufficiently flexible to be applicable to pandemics with different epidemiological characteristics.

A body of research has examined the role of fatty acid (FA), vitamin, and mineral supplementation as adjunctive treatment for atopic dermatitis (AD); however, results are conflicting and concrete recommendations are lacking. The objective of this study is to highlight the role of these nutrients in alleviating AD severity and provide the clinician with consolidated information that can be used to make recommendations to the pediatric patient and caretaker, where this topic is of high interest.

The recent incidents involving Dr. Timnit Gebru, Dr. Margaret Mitchell, and Google have triggered an important discussion emblematic of issues arising from the practice of AI Ethics research. We offer this paper and its bibliography as a resource to the global community of AI Ethics Researchers who argue for the protection and freedom of this research community. Corporate, as well as academic research settings, involve responsibility, duties, dissent, and conflicts of interest. This article is meant to provide a reference point at the beginning of this decade regarding matters of consensus and disagreement on how to enact AI Ethics for the good of our institutions, society, and individuals. We have herein identified issues that arise at the intersection of information technology, socially encoded behaviors, and biases, and individual researchers' work and responsibilities. We revisit some of the most pressing problems with AI decision-making and examine the difficult relationships between corporate interests and the early years of AI Ethics research. We propose several possible actions we can take collectively to support researchers throughout the field of AI Ethics, especially those from marginalized groups who may experience even more barriers in speaking out and having their research amplified. We promote the global community of AI Ethics researchers and the evolution of standards accepted in our profession guiding a technological future that makes life better for all.

In response to unprecedented surges in the demand for hospital care during the SARS-CoV-2 pandemic, health systems have prioritized patients with COVID-19 to life-saving hospital care to the detriment of other patients. In contrast to these ad hoc policies, we develop a linear programming framework to optimally schedule elective procedures and allocate hospital beds among all planned and emergency patients to minimize years of life lost. Leveraging a large dataset of administrative patient medical records, we apply our framework to the National Health Service in England and show that an extra 50,750–5,891,608 years of life can be gained compared with prioritization policies that reflect those implemented during the pandemic. Notable health gains are observed for neoplasms, diseases of the digestive system, and injuries and poisoning. Our open-source framework provides a computationally efficient approximation of a large-scale discrete optimization problem that can be applied globally to support national-level care prioritization policies.

The health and care sector plays a valuable role in improving population health and societal wellbeing, protecting people from the financial consequences of illness, reducing health and income inequalities, and supporting economic growth. However, there is much debate regarding the appropriate level of funding for health and care in the UK. In this Health Policy paper, we look at the economic impact of the COVID-19 pandemic and historical spending in the UK and comparable countries, assess the role of private spending, and review spending projections to estimate future needs. Public spending on health has increased by 3·7% a year on average since the National Health Service (NHS) was founded in 1948 and, since then, has continued to assume a larger share of both the economy and government expenditure. In the decade before the ongoing pandemic started, the rate of growth of government spending for the health and care sector slowed. We argue that without average growth in public spending on health of at least 4% per year in real terms, there is a real risk of degradation of the NHS, reductions in coverage of benefits, increased inequalities, and increased reliance on private financing. A similar, if not higher, level of growth in public spending on social care is needed to provide high standards of care and decent terms and conditions for social care staff, alongside an immediate uplift in public spending to implement long-overdue reforms recommended by the Dilnot Commission to improve financial protection. COVID-19 has highlighted major issues in the capacity and resilience of the health and care system. We recommend an independent review to examine the precise amount of additional funds that are required to better equip the UK to withstand further acute shocks and major threats to health.

Health Services ResearchVolume 56, Issue S3 p. 1299-1301 DEBATE-COMMENTARYOpen Access How can we make valid and useful comparisons of different health care systems? Andrew Street PhD, Corresponding Author Andrew Street PhD a.street@lse.ac.uk orcid.org/0000-0002-2540-0364 Department of Health Policy, London School of Economics and Political Science, London, UK Correspondence Andrew Street, Department of Health Policy, London School of Economics and Political Science, London WC2A 2AE, UK. Email: a.street@lse.ac.ukSearch for more papers by this authorPeter Smith PhD, Peter Smith PhD orcid.org/0000-0003-0058-7588 Centre for Health Economics, University of York, York, UKSearch for more papers by this author Andrew Street PhD, Corresponding Author Andrew Street PhD a.street@lse.ac.uk orcid.org/0000-0002-2540-0364 Department of Health Policy, London School of Economics and Political Science, London, UK Correspondence Andrew Street, Department of Health Policy, London School of Economics and Political Science, London WC2A 2AE, UK. Email: a.street@lse.ac.ukSearch for more papers by this authorPeter Smith PhD, Peter Smith PhD orcid.org/0000-0003-0058-7588 Centre for Health Economics, University of York, York, UKSearch for more papers by this author First published: 10 November 2021 https://doi.org/10.1111/1475-6773.13883AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat 1 INTRODUCTION It is important to understand and seek to reduce unwarranted variations in health treatments in order to improve health outcomes, inequalities in access and health system efficiency. Traditionally this monitoring function has been undertaken at national or subnational levels, as a means of identifying potential improvements in clinical practice and the performance of the health systems. However, international comparison of treatments is also recognized as being an important tool for assessing performance and prompting improvement, especially when examining whether the design of the health system needs reconsideration.1 However, making international comparisons is not straightforward, with two challenges standing out: first, the difficulty of making valid like-for-like comparisons; second, whether the analysis can help drive performance improvements.2 2 MAKING VALID COMPARISONS In relation to the first challenge, to make international comparisons, a key requisite is that the data used for the analysis are measured accurately and consistently for all countries subject to the comparative exercise. If not, comparative differences may derive from differences in the data rather than being a reflection of relative performance. Perhaps the most important of international standards on data specification regarding health care expenditure is the System of Health Accounts, which apply the world over.3, 4 Among high-income countries, the Organisation for Economic Co-operation and Development (OECD) has a long running series of “Health Statistics” that document trends in the macro characteristics of health systems, such as total spending and length of hospital stay.5 In 2001, the OECD initiated a Health Care Quality Indicators (HCQI) project that compares quality and safety across high-income countries.6 By 2019, the project had assembled a total of 61 indicators across 38 countries, covering the following “themes”: Primary care, prescribing, acute care, mental health care, cancer care, patient safety, and patient experiences. And The Commonwealth Fund regularly publishes its “Mirror Mirror” reports comparing the performance of the United States with that of 10 other high-income countries.7 Notwithstanding efforts to construct these datasets, they come with significant “health warnings” about the data therein, with copious footnotes noting caveats for each variable. Even the definition of the “health system” varies across countries, for example, in the extent to which long-term care is considered a part of the health system. Countries also employ different definitions of what constitutes a hospital bed, a doctor, or a nurse. Even the definition of a “patient” varies: some countries are able to track patients across institutions involved in delivering treatment and support along the care pathway; in other countries, it is very difficult to identify how patients access care in different settings. Similarly, the processes of care, such as hospital waiting times, or the outcomes of care, notably its impact on health status, are measured and reported differently, if at all. Inevitably, therefore, analyses employing inconsistently defined or inaccurately measured data may not be able to draw valid conclusions about comparative performance. 3 MAKING USEFUL COMPARISONS Many studies that make international comparisons use highly aggregated data, giving rise to the second challenge: if analyses suggest poor performance, what specific action can be taken in response? Decision makers need to know where the problems lie. Is poor performance due to the health system alone or a reflection of society more generally and the social determinants of health, such as poverty, housing, and environmental conditions? If the health system, are there problems across social, primary, secondary, and tertiary care, or are some sectors performing poorly and others relatively well? Are problems evident for all health problems or mostly driven by how care is organized for particular conditions, such as maternity or cancer care? Analyses based on aggregate data offer no insights into such questions and, hence, no intelligence as to what action should be taken. 4 ADDRESSING THESE CHALLENGES In fact, both challenges can be met fairly easily. All that is required is a more focused analysis. Instead of trying to analyze the health system as a whole or an entire sector within the system, a growing body of research assessing relative performance is highly focused, concentrating on how care is delivered for specific types of patients. The Dartmouth Atlas was an early leader in this endeavor, using routine data to examine variation in care across the United States.8, 9 Similar atlases of variation have been compiled by other countries, and the European Collaboration for Healthcare Optimization (ECHO) project applied the approach in making comparisons across European countries.10 Another example within Europe was the HealthBasket project, which sought to compare across nine countries the resources used and benefit packages for 10 common treatment “vignettes.”11 The key underlying principle of such research is to ensure that like-for-like comparisons of the same types of patients are being made, whether these types are defined using vignettes or patients are identified by means of precise specification of the diagnosis codes. This precision provides confidence that differences that emerge from these performance analyses are not due to differences in those being studied but to how they are being cared for. And by undertaking focused analyses of clearly defined sets of patients, the analyses direct attention: if poor performance is observed, care for these specific patients needs to be reviewed. 5 INSIGHTS FROM THE ICCONIC PROJECT The approach to the research reported in this special issue is consistent with this body of literature. The papers examine the characteristics and health care utilization and outcomes across 11 countries for people with high need and high costs (HNHC). The first paper12 sets out the methodological approach, notably the justification for focusing on two particular types of HNHC patients: older frail adults with a hip fracture and older people with complex multimorbidity including heart failure and diabetes. These are important high needs “personas,” as they are highly prevalent, and treatment is costly and delivered across multiple care settings. In each country, individual-level data about people who matched these personas were extracted from routine datasets and linked across seven settings: hospital care, primary care, outpatient care, rehabilitation, long-term care, home care, and pharmaceuticals. The paper by Figueroa et al.13 examines variations in health care utilization and spending within care settings and across countries for patients with heart failure and diabetes. This involves constructing and comparing Lorenz curves to assess whether the distributions of service use and utilization are similar, which they tend to be. The next two papers describe patterns of resource utilization and expenditure for heart failure and diabetic patients14 and hip fracture patients,15 respectively. For both personas, patients were identified when first hospitalized, and their use of services was calculated for the year prior to hospitalization and for the year immediately afterwards. Comparison of these utilization and expenditure profiles across countries yielded interesting insights. The United States stood out as having the highest expenditure, driven by a combination of three factors. First, input prices are higher. Second, patients use more services, notably having longer hospital stays and more rehabilitation than elsewhere. Third, there are different delivery patterns across settings, with patients in the United States more likely than in other countries to receive follow-up care in more expensive specialist clinics than in cheaper primary care settings. The first of these drivers has long been known16 but, by linking data across settings, this research has been able to offer novel evidence about the other two drivers. The question then arises: Is there any relationship between resource use and patient outcomes? This is examined by Papanicolas et al.,17 who assess readmission rates and mortality rates for these patients. Both outcomes are worse for those with heart failure and diabetes than for those suffering a hip fracture. But there are cross-country differences as well. Mortality rates for both personas are worse in England than elsewhere, which might partly reflect the spending in that country. But that is too simple an explanation: after England, mortality rates are highest in the United States, implying that there is no obvious return from the higher spending there. The paper by Papanicolas et al.18 provides a greater analysis of the care provided to hip fracture patients for those who survived for at least 1 year following their hospitalization. Post-acute expenditure is lower for countries, notably Germany, that substitute more expensive institutional rehabilitative care for relatively cheaper home-based care. The final paper by Blankart et al.19 provides a more in-depth analysis of service use and spending in the last 12 months of life for those hip fracture patients who died. In common with the rest of the literature on the subject,20 the paper demonstrates that utilization and costs increase as people near their deaths, given their receipt of end-of-life care (EoLC). But the paper also reveals how EoLC varies across countries. Most strikingly, the likelihood of dying in a hospital rather than at home or in a hospice is lowest in Australia and New Zealand and highest in England and Spain. The paper also shows that EoLC spending is higher in Canada and the United States, driven more by higher prices than greater use of services. 6 MOVING FORWARD Taken together, the papers in this special issue represent important advances in international comparison of treatments and outcomes. They have demonstrated that, notwithstanding major differences in data specification and collection mechanisms, routine administrative data can act as a powerful basis for comparison between health systems in high-income countries, yielding novel insights. While there are few formal international data standards, there are sufficient commonalities among information systems in the selected countries to make meaningful comparisons of how identical patients are treated in different countries. The emphasis on specifying “personas” of specific patient types obviates the need for complex risk adjustment mechanisms, which often compromises confidence in comparisons of more heterogeneous treatment groups. The drawback of using such personas is their relatively narrow focus. However, the personas used in these studies are for high prevalence and high cost patients for whom a range of possible treatment pathways exist. Findings for these groups will in themselves be directly relevant for a significant proportion of health system spending, and are also likely to be indirectly relevant to a wide range of other high needs patients. The challenges associated with identifying comparable personas across countries should not be underestimated. There remain differences in the use of the International Classification of Diseases, and there are variations in the extent to which comorbidities are recorded. Linkage of patient pathways across health care providers is highly variable, and there is little standardization of procedure codes and resource utilization metrics. So far as is feasible, the methodology used in these studies successfully exploits commonalities between countries and highlights where comparisons remain unreliable or impossible. Perhaps the biggest weakness identified in these studies was the shortage of useful measures of patient outcome, relying primarily on examining differences in mortality. There remain few international standards regarding patient-reported outcomes21 or process measures such as waiting times.22 Such metrics are becoming increasingly important indicators of health care quality, and a failure to consider them leads to an incomplete picture of health system performance. There is a clear need for the development of widely accepted quality metrics that can be used for clinical management as well as comparisons within and across health systems. Of course, the ultimate touchstone for the success of initiatives such as the ICCONIC project is the extent to which they promote real change in health systems and data collection. The researchers and funders will need to work hard to ensure that the key messages from these published papers reach audiences who can take appropriate action to better support health care users with high needs and high costs. REFERENCES 1 I Papanicolas, PC Smith, eds. Health System Performance Comparison. An Agenda for Policy, Information and Research. European Observatory on Health Systems and Policies Series. Open University Press; 2013. https://www.euro.who.int/__data/assets/pdf_file/0009/244836/Health-System-Performance-Comparison.pdfGoogle Scholar 2Hollingsworth B, Street A. The market for efficiency analysis of health care organisations. Health Econ. 2006; 15(10): 1055- 1059. https://doi.org/10.1002/hec.1169Wiley Online LibraryCASPubMedWeb of Science®Google Scholar 3Berman PA. National health accounts in developing countries: appropriate methods and recent applications. Health Econ. 1997; 6(1): 11- 30. https://doi.org/10.1002/(SICI)1099-1050(199701)6:1〈11::AID-HEC238〉3.0.CO;2-7Wiley Online LibraryCASPubMedWeb of Science®Google Scholar 4 OECD, Eurostat, WHO. A System of Health Accounts 2011 Revised Edition. OECD Publishing; 2017. https://www.oecd.org/publications/a-system-of-health-accounts-2011-9789264270985-en.htmCrossrefGoogle Scholar 5 OECD. OECD Health Statistics 2021. Secondary OECD Health Statistics 2021. https://www.oecd.org/els/health-systems/health-data.htmGoogle Scholar 6 OECD. OECD Health Care Quality Indicators Project - Background. Secondary OECD Health Care Quality Indicators Project - Background 2021. https://www.oecd.org/els/health-systems/oecdhealthcarequalityindicatorsproject-background.htmGoogle Scholar 7 The Commonwealth Fund. Mirror, Mirror 2021: Reflecting Poorly. Secondary Mirror, Mirror 2021: Reflecting Poorly 2021. https://www.commonwealthfund.org/publications/fund-reports/2021/aug/mirror-mirror-2021-reflecting-poorlyGoogle Scholar 8Wennberg JE. Time to tackle unwarranted variations in practice. BMJ. 2011; 342:d1513. https://doi.org/10.1136/bmj.d1513CrossrefPubMedWeb of Science®Google Scholar 9 Dartmouth Atlas Project. The Dartmouth Atlas of Health Care. Secondary The Dartmouth Atlas of Health Care 2021. https://www.dartmouthatlas.org/Google Scholar 10Bernal-Delgado E, Christiansen T, Bloor K, et al. ECHO: health care performance assessment in several European health systems. Eur J Public Health. 2015; 25(suppl_1): 3- 7. https://doi.org/10.1093/eurpub/cku219CrossrefPubMedWeb of Science®Google Scholar 11Schreyögg J, Stargardt T, Velasco-Garrido M, Busse R. Defining the “Health Benefit Basket” in nine European countries. Eur J Health Econ. 2005; 6(1): 2- 10. https://doi.org/10.1007/s10198-005-0312-3CrossrefPubMedGoogle Scholar 12Figueroa J. A methodology for identifying high-need, high-cost patient personas for international comparisons. Health Serv Res. 2021. Wiley Online LibraryWeb of Science®Google Scholar 13Figueroa J, Papanicolas I, Riley K, et al. International comparison of health care spending & utilization among people with complex multimorbidity. Health Serv Res. 2021. https://doi.org/10.1111/1475-6773.13708Google Scholar 14Or Z, Shatrov K, Penneau A, et al. Within and across country variations in treatment of patients with heart failure and diabetes. Health Serv Res. 2021. https://doi.org/10.1111/1475-6773.13854Wiley Online LibraryWeb of Science®Google Scholar 15Wodchis W, Or Z, Blankart CR, et al. An international comparison of long-term care trajectories and spending following hip fracture. Health Serv Res. 2021. https://doi.org/10.1111/1475-6773.13864Wiley Online LibraryWeb of Science®Google Scholar 16Anderson GF, Reinhardt UE, Hussey PS, Petrosyan V. It's the prices, stupid: why the United States is so different from other countries. Health Aff. 2003; 22(3): 89- 105. https://doi.org/10.1377/hlthaff.22.3.89CrossrefPubMedWeb of Science®Google Scholar 17Papanicolas I, Riley K, Abiona O, et al. Differences in health outcomes for high-need, high-cost patients across high-income countries. Health Serv Res. 2021. https://doi.org/10.1111/1475-6773.13735Wiley Online LibraryWeb of Science®Google Scholar 18Papanicolas I, Figueroa JF, Schoenfeld AJ, et al. Differences in health care spending & utilization among frail elders in high-income countries: ICCONIC hip fracture persona. Health Serv Res. 2021. https://doi.org/10.1111/1475-6773.13739Wiley Online LibraryWeb of Science®Google Scholar 19Blankart C, van Gool K, Papanicolas I, et al. International comparison of spending and utilization at the end of life for hip fracture patients. Health Serv Res. 2021. https://doi.org/10.1111/1475-6773.13734Wiley Online LibraryWeb of Science®Google Scholar 20Howdon D, Rice N. Health care expenditures, age, proximity to death and morbidity: implications for an ageing population. J Health Econ. 2018; 57: 60- 74. https://doi.org/10.1016/j.jhealeco.2017.11.001CrossrefPubMedWeb of Science®Google Scholar 21Gutacker N, Street A. Calls for routine collection of patient-reported outcome measures are getting louder. J Health Serv Res Policy. 2019; 24(1): 1- 2. https://doi.org/10.1177/1355819618812239CrossrefPubMedWeb of Science®Google Scholar 22Siciliani L, Moran V, Borowitz M. Measuring and comparing health care waiting times in OECD countries. Health Policy. 2014; 118(3): 292- 303. https://doi.org/10.1016/j.healthpol.2014.08.011CrossrefPubMedWeb of Science®Google Scholar Volume56, IssueS3Special Issue: International Comparisons of High-Need, High-Cost Patients: New Directions in Research and PolicyDecember 2021Pages 1299-1301 ReferencesRelatedInformation

In many low- and middle-income countries, geographical accessibility continues to be a barrier to health care utilization. In this paper, we aim to better understand the full relationship between distance to providers and utilization of maternal delivery services. We address three methodological challenges: non-linear effects between distance and utilization; unobserved heterogeneity through non-random distance "assignment"; and heterogeneous effects of distance. Linking Malawi Demographic Health Survey household data to Service Provision Assessment facility data, we consider distance as a continuous treatment variable, estimating a Dose-Response Function based on generalized propensity scores, allowing exploration of non-linearities in the effect of an increment in distance at different distance exposures. Using an instrumental variables approach, we examine the potential for unobserved differences between women residing at different distances to health facilities. Our results suggest distance significantly reduces the probability of having a facility delivery, with evidence of non-linearities in the effect. The negative relationship is shown to be particularly strong for women with poor health knowledge and lower socio-economic status, with important implications for equity. We also find evidence of potential unobserved confounding, suggesting that methods that ignore such confounding may underestimate the effect of distance on the utilization of health services.

Thomas, Ranjeeta; Burger, Ronelle; Harper, Abigail; Kanema, Sarah; Mwenge, Lawrence; Vanqa, Nosivuyile; Bell-Mandla, Nomtha; Smith, Peter C; Floyd, Sian; Bock, Peter; +7 more... Ayles, Helen; Beyers, Nulda; Donnell, Deborah; Fidler, Sarah; Hayes, Richard; Hauck, Katharina; HPTN 071 (PopART) Study Team; (2017) Differences in health-related quality of life between HIV-positive and HIV-negative people in Zambia and South Africa: a cross-sectional baseline survey of the HPTN 071 (PopART) trial. The Lancet Global health, 5 (11). e1133-e1141. ISSN 2214-109X DOI: https://doi.org/10.1016/S2214-109X(17)30367-4 Downloaded from: http://researchonline.lshtm.ac.uk/id/eprint/4468778/

Greater understanding of the burden of paediatric allergic rhinitis (AR) may help optimise management.[1] This study aimed to better define the parent-perceived burden of AR amongst children in Australia. Non-interventional, national on-line survey of 1541 parents of children aged 2–15 years. Children were allocated to case (AR) or control (No AR) analysis groups based on a screening questionnaire. The primary endpoint was health-related quality of life (HRQoL) in children aged 6–15 years in case versus control groups. Health status, measured using a 10cm visual analogue scale [0=poor; 10=excellent],[2] was used as a proxy for HRQoL. Children were aged 6–15 years (N=1111) or 2–5 years (N=430). The odds of being diagnosed with AR increased with age (odds ratio [OR] 0.51; 95%CI 0.41,0.65 at 2–5 years versus 1.67; 95%CI 1.32,2.10 at 12–15 years). The majority of AR cases were classified as having moderate-severe, intermittent AR (737/1040; 70.2%). Most were treated (939/1040; 90.3%); half reported adequate symptom control in the prior 2 weeks (515/1040, 49.5%; OR 4.04; 95%CI 2.24,7.31). Having AR negatively impacted on schoolwork (p<0.05), other activities (p<0.05) and sleep (p<0.05), increased the likelihood of having comorbidities (p<0.05) and increased household economic burden (p<0.05). Among children aged 6–15 years, having AR was associated with worse overall health status (7.41 vs 8.40, p<0.0001), fewer days being happy (22.24 vs 25.92, p<0.001) and more days of poor physical (2.82 vs 0.78, p<0.001) and emotional health (2.14 vs 0.67, p<0.001). Each of these outcomes was significantly (p<0.001) worse in children who reported inadequate symptom control. Our study confirms the extent to which AR impacts on HRQoL in children and provides a measure of the overall health status of Australian children with and without AR. This impact extends to many areas of the child’s life (emotional health, physical health, school, and sleep) and the household economy (parental time off work to care for their child or take them to the doctor). Adequacy of AR symptom control was identified as an important driver of HRQoL. Optimising treatment and ensuring adequate symptom control are potential strategies to reduce the burden of AR.