Integrating cost-effectiveness evidence into clinical practice guidelines in Australia for acute myocardial infarction

The economics of the COVID-19 pandemic: economic evaluation of government mitigation and suppression policies, health system innovations, and models of care

Background

The COVID-19 pandemic has impacted the scope of health economics literature, which will increasingly examine value beyond health care interventions such as government policy and broad health system innovations.

Aim

The study analyzes economic evaluations and methodologies evaluating government policies suppressing or mitigating transmission and reducing COVID-19, broad health system innovations, and models of care. This can facilitate future economic evaluations and assist government and public health policy decisions during pandemics.

Methods

The Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) was used. Methodological quality was quantified using the scoring criteria in European Journal of Health Economics, Consolidated Health Economic Evaluation Reporting Standards (CHEERS) 2022 Checklist and the National Institute for Health and Care Excellence's (NICE) Cost Benefit Analysis Checklist. PUBMED, Medline, and Google Scholar were searched from 2020-2021.

Results

Cost utility analysis (CUA) and cost benefit analysis (CBA) analyzing mortality, morbidity, quality adjusted life year (QALY) gained, national income loss, and value of production effectively evaluate government policies suppressing or mitigating COVID-19 transmission, disease, and impacting national income loss. The WHO's pandemic economic framework facilitates economic evaluations of social and movement restrictions. Social return on investment (SROI) links benefits to health and broader social improvements. Multi-criteria decision analysis (MCDA) can facilitate vaccine prioritization, equitable health access, and technology evaluation. Social welfare function (SWF) can account for social inequalities and population-wide policy impact. It is a generalization of CBA, and operationally, it is equal to an equity-weighted CBA. It can provide governments with a guideline for achieving the optimal distribution of income, which is vital during pandemics. Economic evaluations of broad health system innovations and care models addressing COVID-19 effectively use cost effectiveness analysis (CEA) that utilize decision trees and Monte Carlo models, and CUAs that effectively utilize decision trees and Markov models, respectively.

Conclusion

These methodologies are very instructive for governments, in addition to their current use of CBA and the value of a statistical life analytical tool. CUA and CBA effectively evaluate government policies suppressing or mitigating COVID-19 transmission, disease, and impacts on national income loss. CEA and CUA effectively evaluate broad health system innovations and care models addressing COVID-19. The WHO's framework, SROI, MCDA, and SWF can also facilitate government decision-making during pandemics.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10389-023-01919-z.

International lessons in new methods for grading and integrating cost effectiveness evidence into clinical practice guidelines

Economic evidence is influential in health technology assessment world-wide. Clinical Practice Guidelines (CPG) can enable economists to include economic information on health care provision. Application of economic evidence in CPGs, and its integration into clinical practice and national decision making is hampered by objections from professions, paucity of economic evidence or lack of policy commitment. The use of state-of-art economic methodologies will improve this. Economic evidence can be graded by 'checklists' to establish the best evidence for decision making given methodological rigor. New economic evaluation checklists, Multi-Criteria Decision Analyses (MCDA) and other decision criteria enable health economists to impact on decision making world-wide. We analyse the methodologies for integrating economic evidence into CPG agencies globally, including the Agency of Health Research and Quality (AHRQ) in the USA, National Health and Medical Research Council (NHMRC) and Australian political reforms. The Guidelines and Economists Network International (GENI) Board members from Australia, UK, Canada and Denmark presented the findings at the conference of the International Health Economists Association (IHEA) and we report conclusions and developments since. The Consolidated Guidelines for the Reporting of Economic Evaluations (CHEERS) 24 item check list can be used by AHRQ, NHMRC, other CPG and health organisations, in conjunction with the Drummond ten-point check list and a questionnaire that scores that checklist for grading studies, when assessing economic evidence. Cost-effectiveness Analysis (CEA) thresholds, opportunity cost and willingness-to-pay (WTP) are crucial issues for decision rules in CEA generally, including end-of-life therapies. Limitations of inter-rater reliability in checklists can be addressed by including more than one assessor to reach a consensus, especially when impacting on treatment decisions. We identify priority areas to generate economic evidence for CPGs by NHMRC, AHRQ, and other agencies. The evidence may cover demand for care issues such as involved time, logistics, innovation price, price sensitivity, substitutes and complements, WTP, absenteeism and presentism. Supply issues may include economies of scale, efficiency changes, and return on investment. Involved equity and efficiency measures may include cost-of-illness, disease burden, quality-of-life, budget impact, cost-effective ratios, net benefits and disparities in access and outcomes. Priority setting remains essential and trade-off decisions between policy criteria can be based on MCDA, both in evidence based clinical medicine and in health planning.

Process Mining for Clinical Processes

Business process analysis and process mining, particularly within the health care domain, remain under-utilized. Applied research that employs such techniques to routinely collected health care data enables stakeholders to empirically investigate care as it is delivered by different health providers. However, cross-organizational mining and the comparative analysis of processes present a set of unique challenges in terms of ensuring population and activity comparability, visualizing the mined models, and interpreting the results. Without addressing these issues, health providers will find it difficult to use process mining insights, and the potential benefits of evidence-based process improvement within health will remain unrealized. In this article, we present a brief introduction on the nature of health care processes, a review of process mining in health literature, and a case study conducted to explore and learn how health care data and cross-organizational comparisons with process-mining techniques may be approached. The case study applies process-mining techniques to administrative and clinical data for patients who present with chest pain symptoms at one of four public hospitals in South Australia. We demonstrate an approach that provides detailed insights into clinical (quality of patient health) and fiscal (hospital budget) pressures in the delivery of health care. We conclude by discussing the key lessons learned from our experience in conducting business process analysis and process mining based on the data from four different hospitals.

Treatment for stable coronary artery disease: a network meta-analysis of cost-effectiveness studies

Introduction and Objectives

Numerous studies have assessed cost-effectiveness of different treatment modalities for stable angina. Direct comparisons, however, are uncommon. We therefore set out to compare the efficacy and mean cost per patient after 1 and 3 years of follow-up, of the following treatments as assessed in randomized controlled trials (RCT): medical therapy (MT), percutaneous coronary intervention (PCI) without stent (PTCA), with bare-metal stent (BMS), with drug-eluting stent (DES), and elective coronary artery bypass graft (CABG).

Methods

RCT comparing at least two of the five treatments and reporting clinical and cost data were identified by a systematic search. Clinical end-points were mortality and myocardial infarction (MI). The costs described in the different trials were standardized and expressed in US $ 2008, based on purchasing power parity. A network meta-analysis was used to compare costs.

Results

Fifteen RCT were selected. Mortality and MI rates were similar in the five treatment groups both for 1-year and 3-year follow-up. Weighted cost per patient however differed markedly for the five treatment modalities, at both one year and three years (P<0.0001). MT was the least expensive treatment modality: US $3069 and 13 864 after one and three years of follow-up, while CABG was the most costly: US $27 003 and 28 670 after one and three years. PCI, whether with plain balloon, BMS or DES came in between, but was closer to the costs of CABG.

Conclusions

Appreciable savings in health expenditures can be achieved by using MT in the management of patients with stable angina.

[How should patients' utilities be incorporated into clinical decisions? 2008 SESPAS Report]

There are many clinical situations in which there is no "right" decision from a technical point of view. An example of this is elective surgery, in which patients' preferences are critical. One way to integrate patients' preferences within clinical practice is the application of decision analysis. According to this approach, preferences (utilities) are assessed and are then combined with physicians' knowledge. This combination of evidence and utilities leads to the so-called shared decision-making (SDM) model. The overview provided in the present article indicates that: a) The SDM model, if systematically applied, could improve treatment effectiveness and patients well being; b) clinical practice, nevertheless, faces barriers in the form of time and resource constraints, limiting the application of such a model; c) discrepancies between patients' and doctors' preferences could be narrowed if patients' utilities were included in clinical practice guidelines; d) the application of this kind of analysis seems to be scarce in Spain. Moreover, information provided to patients is probably insufficient; and e) patient decision aids, even though their use is rapidly growing, are subject to certain problems.

[How should health and healthcare priorities be set and evaluated? Prioritization methods and regional disparities. 2008 SESPAS Report]

The conflict between scarce resources and unlimited needs is perhaps more prominent in the healthcare sector than in any other areas. Thus, setting priorities in health care emerges as an unavoidable task. The laudable aim of adopting any health technology that improves the population's health is impossible when confronted by budgetary constraints. Therefore, the outstanding health problems of a society and the most efficient health technologies in terms of their cost-effectiveness must be identified and patients must be prioritized, bearing in mind aspects of equity and efficiency. The present article reviews the issue of setting health care priorities by examining the experiences that have been put into practice in Spain and abroad. The problem is analyzed at three levels: the "macro" level (strategic planning, identification of higher priority areas and the selection of health care interventions); the "meso" level (incorporation of cost-effectiveness analyses into clinical practice guidelines), and the "micro" level (how to design priority systems for patients on waiting lists based on clinical and social criteria). In all these levels, there is substantial heterogeneity between Spanish regional health services, the steps that need to be taken and the ground that needs to be covered. Thus, we suggest that the first steps that some regional health services have made, together with international initiatives, could serve as a reference for the definitive incorporation of new approaches in priority setting in the Spanish health system as a whole.

The risk-adjusted vision beyond casemix (DRG) funding in Australia. International lessons in high complexity and capitation

Hospitals throughout the world using funding based on diagnosis-related groups (DRG) have incurred substantial budgetary deficits, despite high efficiency. We identify the limitations of DRG funding that lack risk (severity) adjustment for State-wide referral services. Methods to risk adjust DRGs are instructive. The average price in casemix funding in the Australian State of Victoria is policy based, not benchmarked. Average cost weights are too low for high-complexity DRGs relating to State-wide referral services such as heart and lung transplantation and trauma. Risk-adjusted specified grants (RASG) are required for five high-complexity respiratory, cardiology and stroke DRGs incurring annual deficits of $3.6 million due to high casemix complexity and government under-funding despite high efficiency. Five stepwise linear regressions for each DRG excluded non-significant variables and assessed heteroskedasticity and multicollinearlity. Cost per patient was the dependent variable. Significant independent variables were age, length-of-stay outliers, number of disease types, diagnoses, procedures and emergency status. Diagnosis and procedure severity markers were identified. The methodology and the work of the State-wide Risk Adjustment Working Group can facilitate risk adjustment of DRGs State-wide and for Treasury negotiations for expenditure growth. The Alfred Hospital previously negotiated RASG of $14 million over 5 years for three trauma and chronic DRGs. Some chronic diseases require risk-adjusted capitation funding models for Australian Health Maintenance Organizations as an alternative to casemix funding. The use of Diagnostic Cost Groups can facilitate State and Federal government reform via new population-based risk adjusted funding models that measure health need.