Introduction
This book serves as a comprehensive introduction to decision analytic cost-effectiveness modeling, equipping students with essential theoretical and practical skills necessary for conducting analyses that align with the standards of health technology assessment organizations such as NICE and CADTH It offers step-by-step guidance on constructing decision trees and Markov models, while also emphasizing the interpretation of cost-effectiveness analysis results The initial chapter provides an overview of economic evaluation, including key concepts like scarcity, choice, and opportunity cost Additionally, it summarizes various types of economic evaluations, focusing on cost-effectiveness and cost-utility analyses, and introduces important concepts such as incremental cost-effectiveness ratios, dominance, and net benefit Exercises are included to reinforce learning and application of the material.
Scarcity, Choice and Opportunity Cost
Economics operates on the principle of scarcity, highlighting the limited resources in healthcare—such as a surgeon's time, specialized equipment, and available hospital beds—against the backdrop of unlimited patient needs This scarcity prompts the healthcare sector to make critical decisions regarding prescriptions, the adoption of new technologies, and the evaluation of service delivery models, all while adhering to budget constraints Public health systems allocate funds to specific departments, while private providers also face financial limitations Economic evaluation plays a crucial role in comparing healthcare programs, treatments, and interventions by analyzing their costs and outcomes Although these analyses do not provide definitive answers for resource allocation, they serve as valuable tools in the decision-making process, illustrating potential outcomes of different resource distribution strategies.
Health care spending decisions inherently involve opportunity costs, a key principle in health economics that highlights the trade-offs required when allocating limited resources Investing in one health care intervention often necessitates sacrificing another, making it crucial to assess the health improvements offered by various services While higher costs can limit funding for alternative programs, economic evaluation transcends mere cost-cutting; it focuses on comparing the benefits and costs of different health services This analysis aims to provide valuable insights for efficient resource allocation, ensuring that the advantages of implemented programs outweigh their opportunity costs and maximizing overall impact.
Types of Economic Evaluation
Cost Benefi t Analysis (CBA)
Cost-Benefit Analysis (CBA) evaluates the monetary value of benefits against the costs of an intervention, helping to determine the viability of specific goals This analysis guides decision-making on the optimal allocation of societal resources to achieve desired outcomes efficiently.
Cost-Benefit Analysis (CBA) is grounded in the concept that social welfare can be quantified and enhanced by reallocating resources to production areas that yield greater societal benefits The CBA decision rule stipulates that health interventions should only be implemented if their monetary benefits surpass the additional costs involved By selecting such interventions, it is possible to finance them in a manner that improves overall societal welfare Conversely, if the costs of an intervention outweigh its benefits, financing it would inevitably disadvantage someone in society This approach assumes the feasibility of distinguishing between different interventions, having the option to choose among them, accurately estimating their outcomes, assigning monetary values to these outcomes, and calculating the costs associated with each intervention.
Cost Effectiveness Analysis (CEA)
Cost-effectiveness analysis (CEA) originates from production theory and seeks to determine how to achieve greater health benefits at the same cost or the same benefits at a lower cost This analysis employs a social decision-making approach, grounded in the idea that economic evaluation aims to maximize the objectives of the decision maker, whether that be society, the public sector, the health sector, or individual patients and their families Consequently, only the costs and benefits deemed relevant by the decision maker are included in the analysis.
The outcomes of Cost-Effectiveness Analysis (CEA) are influenced by the decision maker's perspective, particularly in healthcare, where often only the costs associated with the healthcare system are considered This approach is based on the rationale that the healthcare budget should be allocated to maximize health outcomes (Johansson 1991) By utilizing a fixed budget, it is possible to optimize health effects through the analysis of incremental cost-effectiveness ratios across various healthcare programs or interventions.
To optimize health effects within a limited budget, decision-makers should adhere to a budget maximization approach, focusing solely on costs within the healthcare budget However, this practice may result in suboptimal societal decisions, as it overlooks costs incurred outside the healthcare system While organizations like NICE provide guidance based on cost-effectiveness from the health and social care budget perspective, there is a growing shift towards incorporating a societal perspective that considers additional factors, such as lost productivity.
In contrast to Cost-Benefit Analysis (CBA), Cost-Effectiveness Analysis (CEA) evaluates outcomes using natural units such as life years saved, cancers detected, and reductions in blood pressure or heart attacks avoided For CEA to be valid, outcomes must have a consistent value that is independent of individual characteristics, allowing for comparability Additionally, the value assigned to changes in outcomes should depend solely on the magnitude of those changes, exhibiting interval properties For instance, in a hypothetical trial using body mass index (BMI) as the primary outcome, it is suggested that risks are elevated for individuals at both higher and lower weights If BMI maintains the necessary comparability and interval properties, we would anticipate a uniform risk figure for all patients, regardless of their current BMI values However, the diagram illustrates that this consistency does not hold true across varying BMI levels.
R e duction in haz a rd fr om BMI f a ll of 1
Fig 1.1 BMI comparability and interval property
1 Economic Evaluation, Cost Effectiveness Analysis
BMI reduction is not a suitable outcome for cost-effectiveness analysis (CEA) because its value increases with higher current BMI and varies between genders While CEAs are straightforward to conduct, they focus on a single outcome, failing to account for other critical factors such as patients' quality of life This limitation makes it difficult to compare interventions with different goals, such as the cost per heart attack avoided versus the cost per hip fracture avoided Additionally, the connection between outcome measures and health can be ambiguous, particularly when using biological indicators like tumor response or prostate-specific antigen levels.
In economic evaluations, both incremental cost and incremental effectiveness are crucial for understanding the differences between two options Cost minimisation analysis (CMA) uniquely assumes no difference in effectiveness, focusing solely on the associated costs However, this foundational assumption is flawed, leading to biased results by overlooking the relationship between effect magnitude and cost Consequently, CMA has largely lost favor in the field (Brazier et al 2007; Dakin and Wordsworth 2013).
Cost Utility Analysis (CUA)
Cost-utility analysis (CUA) is often seen as a more advanced form of cost-effectiveness analysis (CEA), primarily differing in how outcomes are evaluated In CUA, outcomes are measured in terms of 'healthy years,' utilizing a multidimensional utility-based approach that combines life years gained with the quality of those years Key measures include quality-adjusted life years (QALYs) and disability-adjusted life years (DALYs), with QALYs being the widely accepted standard in CUA since the 1990s.
Utility is a measure of preference that reflects the value an individual or society assigns to a specific health state, ranging from 1 for full health to 0 for death, with negative values for states worse than death These utility values can be integrated with survival data to calculate Quality-Adjusted Life Years (QALYs) For instance, if an individual has a health condition with a utility value of 0.5 and a life expectancy of 4 years at a stable health state, this information can be used to assess their overall health-related quality of life.
In assessing the impact of a health intervention, a patient initially has a quality-adjusted life year (QALY) value of 2, reflecting a life expectancy of 4 years at a utility value of 0.5 However, after undergoing an operation that enhances their health to a utility value of 0.9 and extends their life expectancy to 8 years, their QALYs increase to 7.2 Consequently, the QALY gain from the operation is calculated as 5.2, illustrating the significant benefits of the procedure.
The use of Cost-Utility Analysis (CUA) offers significant advantages over Cost-Effectiveness Analysis (CEA) by allowing for comparisons both within and between different health care programs For instance, while interventions aimed at cancer detection can only be compared to similar interventions, CUA enables the comparison of diverse health interventions, such as those for cancer detection and blood pressure reduction, using Quality-Adjusted Life Years (QALYs) This approach not only incorporates quality of life considerations but also reflects preferences for various health states However, it is important to note that CUA is limited to measuring health benefits through outcomes and faces challenges in deriving health state utilities, as highlighted in studies by Brazier et al (2007), Nord (1999), Dolan (2000), and Devlin et al (2012).
Incremental Cost Effectiveness Ratios (ICERs)
Simple and Extended Dominance
When comparing multiple interventions or courses of action, identifying a dominant option becomes more complex A new intervention is considered dominant if it is both less costly and more effective, a concept known as simple dominance However, the challenge arises when evaluating more than two alternatives, necessitating a more nuanced analysis to determine the best choice.
In scenarios with multiple alternatives, the principle of simple dominance remains relevant, indicating that some options may be both less costly and more effective We can also assess whether combinations of two or more options yield better cost-effectiveness For instance, consider five diagnostic tests, labeled A through E, intended for 500 individuals, as shown in Table 1.1, arranged by increasing effectiveness Here, Option A serves as the ‘standard care’ comparator, being the least costly and least effective Figure 1.5 illustrates these options concerning their incremental costs and effects, with Option A positioned at the intersection of the axes Options B through E are progressively more effective, though not necessarily more cost-effective, and the incremental cost-effectiveness ratios (ICERs) for these comparisons are detailed in the final column of Table 1.1.
All tests remain essential, as none are more cost-effective or efficient than the others Furthermore, we can explore whether combining two or more options could provide a more affordable and effective solution compared to our current choices.
In Figure 1.5, the slope of the line connecting Options A and B illustrates the Incremental Cost-Effectiveness Ratio (ICER) between the two This line can also be interpreted as representing the potential outcomes from a random allocation of a fixed proportion of individuals to Options A and B The point labeled ‘Option A’ signifies the outcome associated with this option.
Table 1.1 ICERs between fi ve diagnostic tests (Options A–E)
Test Cost ($M) Effect (QALYs) ICER ( ∆ C / ∆ E )
1.4 Incremental Cost Effectiveness Ratios (ICERs)
In a scenario where 500 individuals are assigned to Option A and none to Option B, the outcomes will heavily favor Option A Conversely, if the allocation were reversed, with all 500 assigned to Option B, the results would reflect that preference However, if the distribution were evenly split, with 250 participants in each option, we would anticipate results that are a balanced midpoint between the two choices.
When evaluating treatment options, we can analyze combinations of different treatments, such as Options B and D, which each involve 250 participants This combination is projected to yield an average outcome of 16,500 QALYs at a cost of $145 million In contrast, Option C would incur a cost of $150 million for only 16,000 QALYs Therefore, the combination of Options B and D would be considered superior to Option C, leading to the conclusion that Option C is extended dominated when examining the incremental cost-effectiveness ratio (ICER) between the options.
By examining options B and D, along with their various combinations, we find that they yield outcomes superior to Option C Consequently, we can confidently disregard Option C For a comprehensive overview, refer to Table 1.2, which presents an updated list of options.
Table 1.2 ICERs between four diagnostic tests (Option C removed)
Incr emen tal c o st s (millions)
Incremental effectiveness (QALYs) Fig 1.5 Incremental cost effectiveness ratios for diagnostic tests compared to standard care
1 Economic Evaluation, Cost Effectiveness Analysis
11 alternatives where we have recalculated the incremental cost and effect of each remaining option relative to all the previous options.
The incremental cost-effectiveness ratios (ICERs) range from $12,500 to $60,000 per quality-adjusted life year (QALY) after excluding extended dominated alternatives Decision-makers can utilize these results to determine which tests to offer, guided by the cost-effectiveness threshold or willingness-to-pay threshold (λ) For instance, with a threshold of $20,000 per QALY, Test E is unlikely to be provided, while Tests B and D fall below this threshold Among these, Test D proves to be more cost-effective than Test B, leading us to select the test with the highest ICER that remains below the threshold.
If we had ten alternatives (Options A–J) ordered by effectiveness, then calculat- ing the ICERs would require that we consider nine pairs of options: Options A to B,
When analyzing various treatment options, it's impractical to compare every possible combination Instead, focusing on the Incremental Cost-Effectiveness Ratios (ICER) provides a clearer picture If there are no dominated or extended dominated options, the ICERs will generally rise with each more effective treatment For instance, as shown in Table 1.2, the ICERs increase significantly from $12,500 to $18,000, and then to $60,000 per Quality-Adjusted Life Year (QALY) It's important to note that lower ICERs do not always indicate greater cost-effectiveness.
In scenarios where we have identified dominated or extended dominated options, we will not observe incremental increases in Incremental Cost-Effectiveness Ratios (ICERs) with greater effectiveness For instance, negative ICERs may indicate items that should be eliminated due to simple dominance, or we may see ICERs that initially rise and then fall As illustrated in Table 1.1, the ICER increased from option B to C and subsequently decreased from C to D, indicating that option C was extended dominated even before it was assessed in combination with B and D This rule of thumb allows for quick identification of dominated or extended dominated options It is crucial to recalculate all ICERs after removing any dominant or extended dominant strategies and to reassess for dominance or extended dominance until ICERs consistently increase with effectiveness.
The Net Benefi t Approach
Accurately measuring the Incremental Cost-Effectiveness Ratio (ICER) is crucial for comparing a new intervention with relevant alternatives, as using an inappropriate comparator can result in bias and misleading outcomes Additionally, the mathematical complexities associated with ratios can pose challenges An alternative method is the net benefit approach, as proposed by Stinnett and Mullahy (1998), which involves converting costs into the same units as effectiveness or vice versa For instance, when effectiveness is measured in Quality-Adjusted Life Years (QALYs) and costs in dollars, transforming these measures becomes essential for accurate analysis.
1.4 Incremental Cost Effectiveness Ratios (ICERs)
12 of effectiveness (in QALYs) to cost units ($), we need to have an ‘exchange rate’ between the two: this $/QALY fi gure is the ceiling ratio
To determine the Net Monetary Benefit (NMB), we multiply our measure of effectiveness by the ceiling ratio, allowing us to express effectiveness in monetary terms NMB is calculated as the difference between monetized incremental effectiveness (λ × ΔE i) and monetary incremental costs (ΔC i), measured in currency units like dollars Conversely, we can convert costs into effectiveness by dividing by the ceiling ratio, resulting in the Net Health Benefit (NHB), which is the difference between incremental effectiveness (ΔE i) and the health equivalent of costs (C/λ), expressed in effectiveness units such as QALYs Regardless of whether we focus on NMB or NHB, the most cost-effective treatment is identified by the highest Net Benefit Further details on the Net Benefit framework can be found in Chapter 11.
Identifying an appropriate value of λ for the net benefit approach can be challenging, particularly in cases with limited previous decisions or specific clinical effectiveness units, such as assessing willingness to pay for dental treatments In these situations, the cost-effectiveness acceptability curve (CEAC) has emerged as a popular alternative to presenting incremental cost-effectiveness ratios (ICERs) CEACs effectively address statistical uncertainty in ICERs and the ceiling ratio, illustrating the probability that an intervention is cost-effective across various ceiling ratio levels without requiring the analyst to pinpoint a specific value The literature extensively discusses the complexities of characterizing uncertainty surrounding estimated ICERs, with further details provided in Chapter 4.
Summary
• Economics is based on the premise of scarcity; any spending choices or deci- sions that are made about health care provision incur an opportunity cost
• Economic evaluation is a form of comparative analysis; it allows interventions to be assessed in terms of their benefi ts and costs to provide information to allocate resources effi ciently
• CEA uses one measure of effectiveness, for example, cancers detected This restricts comparison across analyses with different outcomes
Within Cost-Utility Analysis (CUA), also known as Cost-Effectiveness Analysis (CEA), the impact is evaluated by assessing healthy years, which combines both the quantity and quality of life years gained Quality-Adjusted Life Years (QALYs) are commonly recognized as the benchmark measure in this context (Gold et al 1996; NICE 2013).
In conducting a Cost-Effectiveness Analysis (CEA) or Cost-Utility Analysis (CUA), it is crucial to consider both incremental costs and incremental effectiveness The results of this analysis are typically expressed as the Incremental Cost-Effectiveness Ratio (ICER), calculated using the formula ICER = (C2 - C1) / (E2 - E1) = ∆C / ∆E.
1 Economic Evaluation, Cost Effectiveness Analysis
The Incremental Cost-Effectiveness Ratios (ICERs) can be visually represented on a cost-effectiveness plane, where a line is drawn from the origin to the intervention point The slope of this line indicates the ICER If the ICER is located in the South East quadrant, the new intervention is deemed cost-effective and dominates the control Conversely, if it falls in the North West quadrant, the intervention is not cost-effective and is considered dominated For ICERs positioned in the other two quadrants, careful consideration of trade-offs is necessary.
The net benefit approach offers an alternative to the Incremental Cost-Effectiveness Ratio (ICER) by aligning costs with effectiveness or vice versa, allowing for a more coherent comparison of economic value in healthcare decision-making.
Brazier JE, Ratcliffe J, Salomon J, Tsuchiya A (2007) Measuring and valuing health benefi ts for economic evaluation OUP, Oxford
Briggs AH, O’Brien BJ (2001) The death of cost-minimization analysis? Health Econ 10(2):179–184
Campbell MK, Torgerson D (1999) Bootstrapping: estimating confi dence intervals for cost effec- tiveness ratios QJM 92(3):177–182
Canadian Agency for Drugs and Technologies in Health (2006) Guidelines for the economic evalu- ation of health technologies, 3rd edn Canadian Agency for Drugs and Technologies in Health, Ottawa
Dakin R, Wordsworth S (2013) Cost minimisation analysis revisited Health Econ 22(1):22–34 Department of Health (2010) A new value-based approach to the pricing of branded medicines – a consultation Department of Health, London
In their 2012 study, Devlin et al compared different versions of the lead and lag time time trade-off (TTO) method, highlighting its implications for health economics Dolan (2000) emphasized the importance of measuring health-related quality of life to inform resource allocation decisions in healthcare, as discussed in the Handbook of Health Economics These studies collectively underscore the critical role of quality of life metrics in optimizing healthcare resources and improving patient outcomes.
Drummond MF, Stoddart GL, Torrance GW (1987) Methods for the economic evaluation of health care programmes, 1st edn Oxford Medical Publications, Oxford
Gold MR, Siegel JE, Russell LB, Weinstein MC (1996) Cost effectiveness in medicine and health Oxford University Press, Oxford
Johansson PO (1991) An introduction to modern welfare economics Cambridge University Press, Cambridge
National Institute for Health and Care Excellence (2013) Guide to the methods of technology appraisal National Institute for Health and Care Excellence, London
Nord E (1999) Cost-value analysis in health care: making sense out of QALYS Cambridge University Press, Cambridge
Polsky D, Glick H, Willke R, Schulman K (1997) Confi dence intervals for cost effectiveness ratios
A comparison of four methods Health Econ 6:243–252
Stinnett A, Mullahy J (1998) Net Health Benefi ts: a new framework for the analysis of uncertainty in cost effectiveness analysis Med Decis Making 18(2 Suppl):S68–S80
Sugden R, Wiliams A (1978) The principles and practice of cost benefi t analysis Oxford University Press, Oxford
Van Hout BA, Al M, Gordon GS, Rutten FFH (1994) Costs, effects and C/E-ratios alongside a clinical trial Health Econ 3:309–319
World Bank (1993) World Development Report 1993: Investing in Health New York: Oxford University Press © World Bank https://openknowledge.worldbank.org/handle/10986/5976 License: CC BY 3.0 IGO
R Edlin et al., Cost Effectiveness Modelling for Health Technology Assessment:
Finding the Evidence for Decision Analytic
Decision analytic cost-effectiveness models are essential for integrating diverse evidence regarding the safety, effectiveness, and cost of various health technologies, as well as the epidemiology of diseases and healthcare management processes The quality of a model's results heavily relies on the methods used to identify the evidence it synthesizes This chapter aims to provide guidance on best practices for sourcing evidence for cost-effectiveness models, including key considerations for selecting sources and constructing an effective search strategy.
Introduction
In Chapter 1, we explored various types of economic evaluations, emphasizing cost-effectiveness analysis (CEA) and cost-utility analysis (CUA) CEA is often conducted alongside clinical trials, offering significant advantages, such as the ability to observe resource use and outcomes in a specific patient population under controlled conditions (Johnston et al 1999) However, relying solely on data from a single trial restricts the analysis to that specific population, interventions, and follow-up duration (NICE 2013) By incorporating decision analytical modeling in cost-effectiveness analyses, we can extend beyond these limitations and customize our analyses to address specific research questions, utilizing multiple data sources to inform resource allocation decisions that are typically outside the scope of a single clinical trial.
Identifying the evidence that a model will synthesize is crucial for ensuring the quality of its results This process represents a distinct area of expertise and research within the parent discipline.
Health librarianship has evolved, and today, leading practitioners are often referred to as health information specialists Regardless of their title, their expertise is crucial for teams involved in decision analytic cost-effectiveness modeling.
While this chapter does not provide extensive training in information searching, it emphasizes the importance of having a solid understanding of key methods and sources for effective engagement with information specialists in cost-effectiveness modeling Before delving into the specifics of constructing decision analytic cost-effectiveness models, this chapter aims to familiarize you with best practices for identifying evidence relevant to these models.
The processes and resources for identifying evidence in reviews differ significantly from those used in cost-effectiveness models Reviews typically begin by formulating a specific question, followed by a literature search to find relevant studies In contrast, cost-effectiveness models develop alongside the identification of care pathways and parameters, making it challenging to define search questions at the outset.
A single literature search conducted at the beginning of an economic model may not encompass all necessary data (Kaltenthaler et al 2014) Unlike review searches, model searches are typically more focused and less comprehensive, aiming to gather sufficient evidence to inform the model rather than to collect every available study (Glanville and Paisley).
This chapter emphasizes the essential requirements and methods for key searches conducted by information specialists Individual searches will be necessary to populate the model once clinical pathways and model parameters are established The modeling process relies on a diverse array of resources, necessitating extensive targeted searches Both analysts and information specialists must engage in iterative search processes that are adaptable to the model's information needs While the information specialist plays a crucial role in designing and executing searches to gather data, analysts also contribute by conducting their own searches Ultimately, models typically incorporate evidence gathered from both analysts and information specialists, along with insights from experts and key contacts.
This chapter is organized into several sections, beginning with Section 2.2, which discusses the sources of evidence and key factors for selecting them Section 2.3 outlines the construction of a search strategy based on a research question, providing foundational methods for subsequent searches Section 2.4 focuses on locating existing cost-effectiveness models, while Section 2.5 emphasizes identifying evidence related to disease epidemiology and the safety and effectiveness of technologies In Section 2.6, we explore how analysts can search for evidence regarding health-related quality of life and health state preferences Section 2.7 addresses the search for evidence on resource use and care costs Each section highlights relevant evidence sources and validated search strategies Section 2.8 details methods for logging and reporting search activities, and Section 2.9 reviews available quality assessment tools.
2 Finding the Evidence for Decision Analytic Cost Effectiveness Models
17 different types of evidence The key points covered in the chapter are summarised in Section 2.10
Choosing Resources to Search for Evidence
Cost-effectiveness models rely on diverse evidence sources, including evidence syntheses, expert judgment, observational studies, randomized controlled trials (RCTs), clinical studies, reference materials, and routine data sources (Paisley 2010) Access to this research literature and data is facilitated by international, national, and local databases and websites This chapter highlights key resources, while updated links to an extensive range of additional resources can be found on the Health Economics Core Library Recommendations website (AcademyHealth).
2011 ) and the Health Economics Resources website (University of York Library
Research studies can be effectively sourced from various databases, including multidisciplinary resources like Scopus, subject-specific databases such as CINAHL for nursing literature, and evidence-based platforms like McMaster Health Systems Evidence The selection of a database is influenced by factors such as accessibility, subject coverage, data currency, and the types of reports available Additionally, the ability to download records in bulk, access to full-text papers, and the researcher's proficiency in searching can impact this choice While some databases are freely available, others require institutional subscriptions For instance, PubMed serves as the free version of MEDLINE, offering broader coverage than subscription-based MEDLINE but with fewer advanced search features and download capabilities.
For effective health economics research, the primary databases to utilize are MEDLINE, Embase, and the NHS Economic Evaluations Database (NHSEED), which previously provided comprehensive coverage A study by Royle and Waugh (2003) revealed that these three databases captured 94.8% of the relevant studies in cost-effectiveness sections of 20 Technology Assessment Reports However, since 2015, NHSEED has ceased updates, making MEDLINE and Embase the essential resources Additionally, researchers may benefit from exploring multi-disciplinary databases like the Web of Science Core Collection to uncover cost studies that may not be indexed in MEDLINE or Embase.
Databases and websites offering population and healthcare utilization statistics, unit costs, and healthcare guidelines are essential resources for developing cost-effectiveness models While some data repositories provide access to information at no cost, others require payment for their services.
Librarians and information specialists are essential resources for identifying the most appropriate databases for specific research questions They provide insights into available subscriptions and their user-friendliness, while also offering valuable support in enhancing search skills for more effective research outcomes.
2.2 Choosing Resources to Search for Evidence
Designing Search Strategies
To effectively develop a literature search for relevant evidence related to a research question, it is essential to identify key words, phrases, and index terms from pertinent studies This process primarily utilizes bibliographic databases like MEDLINE For comprehensive guidance, researchers can consult their institution's library services and foundational texts such as Aveyard (2010) and Booth et al (2011) After identifying the relevant terms, searches are conducted in the database, and the results are combined using Boolean logic to generate a final set of relevant references.
An effective search strategy begins with an initial plan that identifies relevant terms and phrases, allowing for logical combinations of searches Breaking down the main question into distinct search concepts facilitates the collection and combination of terms For instance, the inquiry ‘What are the existing economic evaluations of hearing aids for elderly people with hearing impairments?’ can be divided into key concepts such as (A) ‘hearing aid’ and (B) ‘economic evaluations for elderly individuals with hearing impairments’.
When conducting research on economic evaluations of elderly individuals with hearing impairment, it is essential to focus on the most relevant concepts to enhance the retrieval of pertinent studies Selecting two or three key terms, such as 'elderly people,' 'economic evaluations,' and 'hearing aids,' can streamline the search process However, the term 'hearing impairment' poses challenges due to the variety of phrases associated with it, which can complicate data collection Additionally, searching solely for 'hearing aids' may yield studies that only pertain to individuals with hearing impairments, rendering it less effective To optimize the search strategy, it is beneficial to compile a list of potential terms for each concept while adhering to a structured search framework.
Identifying concepts within a research question is the same approach as using PICO elements to plan searches for a systematic review literature search
Table 2.1 Search concepts and terms for the question ‘What are the existing economic evaluations of hearing aids for the elderly?’
Search concept (A) Hearing aid (B) Elderly people (C) Economic evaluations
Search terms Hearing aid Elderly Economic evaluations
Hearing device Old age Cost effectiveness analysis Deaf aid Elderly Cost-benefi t analysis
Ear aid Pensioner Cost-utility analysis
Retired Over 65 Older person Older people
2 Finding the Evidence for Decision Analytic Cost Effectiveness Models
The PICO tool, as outlined by Lefebvre et al (2011), identifies four essential components for formulating an effective research question: P for patient problem or population, I for intervention, C for comparison, and O for outcome (Sackett et al 1997) For example, in a study focusing on elderly individuals with hearing impairment, P represents this population, I refers to the use of hearing aids, C is unspecified, and O denotes the goal of improved hearing Additionally, the concept of 'economic evaluations' can be integrated into this framework to enhance the analysis.
Search filters, or ready-made searches, can simplify the research process by allowing users to apply pre-designed filters instead of creating specific search concepts from scratch These filters are typically tailored to retrieve specific study types, such as randomized controlled trials (RCTs) or observational studies, and some have been validated and published in peer-reviewed literature (InterTASC ISSG 2015a) For comprehensive details on search filters, the Information Specialists’ Sub-Group (ISSG) Search Filters Resource offers extensive information and links to various filters for different study types Additionally, Section 2.5.2 provides practical examples of searches utilizing the RCT search filter, along with guidance on its effective incorporation into research queries.
After searching each concept in the database, the final combination search retrieves the most relevant references for your research question As shown in Fig 2.1, line 6 consolidates all hearing aid terms, line 12 aggregates elderly-related terms, and line 18 compiles economic evaluation terms The final combination in line 19 directs the database to fetch references that include a 'hearing aid' term, an 'elderly' term, and an 'economic evaluation' term within the same reference.
Many databases offer options to narrow searches by study or publication type, although these filters may not always be completely accurate However, they can effectively streamline the search for sufficient evidence relevant to a specific model Notably, PubMed and Ovid feature an 'additional limits' option that allows users to refine searches by publication types, age groups pertinent to the population of interest, languages, and clinical queries Subsequent sections will detail the relevant limit options available on key databases It's worth noting that limits for identifying clinical effectiveness studies are more prevalent compared to other study types, as discussed in Section 2.5.3.
Each chapter section will outline an example search question, the search con- cepts and suggested terms.
Searching for Existing Cost Effectiveness Models
Where to Look
Cost-effectiveness models are frequently detailed in health journals, HTA reports, and conference abstracts While conference abstracts may not provide the comprehensive information needed for thorough analysis, subsequent peer-reviewed papers often offer additional insights Alternatively, reaching out to the authors can yield further clarification.
Ovid MEDLINE(R)
8 (aged or elderly or geriatric*).ti,ab (507035)
9 ("old* person" or "old* people" or "old age*").ti,ab (35665)
10 (pensioner* or retired).ti,ab (4705)
11 ("over 65 y*" or "over 70 y*" or "over 80 y*").ti,ab (6587)
13 exp "Costs and Cost Analysis"/ (185208)
Exp explode subject heading (searches additional, closely related but more specific terms) / Medical Subject Heading (MeSH)
* truncated term identifying terms with the same stem
“ …” searches for the two or more words within the quotation marks as a phrase
ti, ab searches title and abstract
Or/1 − 3 set combination 1 or 2 or 3
Each chapter section will outline an example search question, the search concepts and suggested terms
Fig 2.1 Search strategy for the question ‘What are the existing economic evaluations of hearing aids for the elderly?’
2 Finding the Evidence for Decision Analytic Cost Effectiveness Models
To gather comprehensive information on cost-effectiveness models, it is beneficial to search both MEDLINE and Embase, as these databases can reveal a significant number of relevant studies Additionally, exploring specialized economic databases such as EconLit and RePEc can yield further insights Depending on the specific research question, databases focused on particular health topics may also prove valuable For instance, when developing a cost-effectiveness model for schizophrenia, utilizing the PsycINFO database is recommended to find existing models related to schizophrenia care pathways.
Search Strategy, Concepts, Terms and Combinations
To effectively identify existing cost-effectiveness models, the search strategy should incorporate key concepts related to the 'Patient/Population' and 'Intervention,' as outlined in the PICO framework Additionally, it is essential to focus on studies specifically mentioning cost-effectiveness models For instance, the inquiry into "What are the existing cost-effectiveness models of Herceptin use in breast cancer?" includes the search concepts of 'breast cancer,' 'Herceptin,' and 'cost-effectiveness model.' Table 2.2 provides a comprehensive overview of these search concepts along with potentially useful terms for the research.
To conduct individual searches for specific concepts, keywords and phrases are extracted from the titles, abstracts, and index terms of database records, such as MeSH in MEDLINE and EMTREE in Embase These individual searches are subsequently combined to pinpoint studies that include terms from each concept For example, Figure 2.2 demonstrates a targeted search structure for cost-effectiveness models of Herceptin in breast cancer, where the final search line integrates the three concepts using the 'AND' Boolean operator.
Table 2.2 Search concepts and terms for the question ’What are the existing cost effectiveness models of Herceptin use in breast cancer?’
Search concept (A) Breast cancer (B) Herceptin
Breast Cancer * Trastuzumab Economic model * Mammary Neoplasm * Herceptin Cost effectiveness model *
Malignan * Discrete event simulation * Sarcoma * Patient level simulation *
2.4 Searching for Existing Cost Effectiveness Models
C This ensures that the fi nal set of results contains a breast cancer term (A) AND a Herceptin term (B) AND a cost effectiveness model term (C) within the database record.
Search Filters, Database Limits and Clinical Queries
Currently, there is no validated search filter specifically for cost effectiveness models, and existing databases lack the necessary limits or clinical queries to narrow searches to studies focused on cost effectiveness.
Ovid MEDLINE(R) Search Strategy:
2 (breast adj5 (neoplasm* or cancer* or tumour* or tumor* or carcinoma* or adenocarcinoma* or malignan* or sarcoma*)).ti,ab.(215009)
3 (mammary adj5 (neoplasm* or cancer* or tumour* or tumor* or carcinoma* or adenocarcinoma* or malignan* or sarcoma*)).ti,ab.(27024)
12 (econom* adj2 model*).ti,ab.(2650)
13 ("cost effectiveness" adj2 model*).ti,ab.(969)
14 (markov* adj5 model*).ti,ab.(7743)
15 (decision* adj8 model*).ti,ab.(10953)
16 (discrete event* adj8 model*).ti,ab.(308)
17 (discrete event* adj5 simulat*).ti,ab.(381)
18 (patient level adj8 simulat*).ti,ab.(38)
Exp explode subject heading (searches additional, closely related but more specific terms) / Medical Subject Heading (MeSH)
Adj 5 terms must be within 5 words of each other
*truncated term identifying terms with the same stem
“ …” searches for the two or more words within the quotation marks as a phrase
ti, ab searches title and abstract
Or/1 − 3 set combination 1 or 2 or 3
Fig 2.2 Example search for cost effectiveness models of Herceptin in breast cancer (in Ovid
2 Finding the Evidence for Decision Analytic Cost Effectiveness Models
Searching for Clinical Evidence
Finding the Evidence on Incidence, Prevalence
and Natural History of a Disease
Accurate disease prediction and prevalence assessment in a population rely on essential information Understanding the natural history of a disease offers insights into resource allocation over time and highlights potential alternative care pathways.
Large sets of routine data are essential for understanding the current epidemiology of diseases Key resources, such as the Health and Social Care Information Centre (HSCIC) in England, offer access to valuable datasets like UK Hospital Episode Statistics and the Compendium of Population Health Indicators Additionally, charities, pressure groups, and research centers play a vital role in providing quick access to epidemiological data.
Researching the epidemiology of diseases can be effectively conducted using books, journal articles, and their indexed databases Longitudinal and observational studies serve as valuable sources of epidemiological data Evidence indicates that utilizing two databases, specifically MEDLINE and Embase, is often sufficient for identifying relevant epidemiological information (Royle et al 2005) Additionally, exploring subject-specific databases, such as PEDro for physiotherapy (The George Institute for Global Health 2014), or non-English language databases like LILACs (BIREME 2015), can uncover pertinent studies that may not be available through other sources.
Brief, targeted literature searches for epidemiology studies consist of a search concept for the disease or health care condition (e.g hearing loss), and a second
Table 2.3 Selected health statistics resources
Resource Geographic coverage URL (accessed 29-9-14)
Health Data Tools and Statistics
International and the USA http://phpartners.org/health_stats.html
International http://stats.oecd.org/
Australian Institute of Health and
Australia http://www.aihw.gov.au/
Statistics Canada Canada http://www5.statcan.gc.ca/subject-sujet/theme- theme.action?pid)66&lang=eng&more=0&MM
European regions http://ec.europa.eu/health/indicators/echi/index_en.htm
UK http://www.hscic.gov.uk/
USA http://www.cdc.gov/nchs/
24 concept is epidemiology (to cover incidence, prevalence and natural history)
A geographic region or contextual concept can enhance search strategies for studies on hearing loss in the UK Table 2.4 outlines effective search concepts and terms designed to identify relevant research on the natural history, incidence, and prevalence of hearing loss This strategy employs a focused selection of MeSH headings and targets terms specifically in the title, ensuring that the results yield studies most pertinent to the topic Unlike the exhaustive search used in cost-effectiveness models, this approach aims to find a limited number of highly relevant papers containing essential epidemiological data.
Figure 2.3 outlines the search strategy, highlighting that each search concept is explored before combining A (line 7), B (line 9), and C (line 17) When utilizing MEDLINE, the epidemiology subheading can be paired with the health care condition MeSH, as demonstrated in search line 10 (exp *Hearing Loss/ep), which retrieves studies indexed under the ‘Hearing Loss – Epidemiology’ subject heading This search line effectively combines A (epidemiology) and B (hearing loss) Currently, there are no validated search filters specifically for epidemiology studies; however, existing research suggests using the ‘Epidemiology’ subheading when searching health care condition subject headings (as shown in line 10 of Fig 2.3) Additionally, specific epidemiology subject headings can be utilized (refer to lines 1–5 in Fig 2.3), and the ‘causation-aetiology’ clinical query can serve as an extra limit in MEDLINE, PubMed, and Embase to identify risk studies.
Finding the Evidence on the Clinical Effectiveness
Clinical effectiveness evidence is essential for understanding health intervention outcomes, encompassing data on safety, adverse events, and complications Randomized controlled trials (RCTs), along with evidence syntheses and systematic reviews, provide the most reliable evidence regarding the clinical effectiveness of these interventions Conducting a literature search for relevant syntheses, reviews, and RCTs can yield sufficient data to inform clinical effectiveness assessments.
Table 2.4 Search concepts and search terms for the question ’What is the incidence and prevalence of hearing loss in the UK?’
Search concept (A) Epidemiology (B) Hearing loss (C) UK
Search terms Epidemiology Hearing loss Great Britain
2 Finding the Evidence for Decision Analytic Cost Effectiveness Models
When developing a model, it's crucial to include at least 25 required studies In cases where randomized controlled trials (RCTs) are scarce or absent, it is necessary to expand the search to include various study types Additionally, RCT data may not be suitable if the population or condition differs from your model's context Incorporating evidence from observational studies, such as cohort studies, is vital for obtaining real-world effectiveness data Therefore, initial searches for systematic reviews and RCTs should be complemented by targeted searches for relevant observational studies.
Systematic reviews and trials assessing the effectiveness and safety of interventions can be found in various sources, including journal articles, conference abstracts, theses, and unpublished reports Relying solely on MEDLINE for identifying trials for systematic reviews is insufficient, as it may not yield an unbiased or comprehensive set of effectiveness studies To ensure thoroughness, the Cochrane Collaboration recommends using MEDLINE, Embase, and the Cochrane Library as a minimum set of databases Additionally, the Centre for Reviews and Dissemination advises searching these databases along with others relevant to specific questions, such as PsycINFO for mental health intervention evidence.
Ovid MEDLINE(R) Search Strategy:
6 (epidemiology or incidence or prevalence or "risk factor*" or "natural histor*").ti (265815)
10 exp *Hearing Loss/ep[Epidemiology](1561)
13 ("united kingdom*" or uk or "U.K." or "UK." or "U.K" or britain).ti (30789)
14 (british or english or scottish or welsh or irish).ti (24557)
15 (england or wales or scotland or ireland).ti (23640)
16 (nhs or "national health service").ti (8234)
Exp explode subject heading (searches additional, closely related but more specific terms) / Medical Subject Heading (MeSH)
* truncated term identifying terms with the same stem
“ …” searches for the two or more words within the quotation marks as a phrase
Or/1 − 3 set combination 1 or 2 or 3
Fig 2.3 Example search for incidence and prevalence of hearing loss in the UK
A comprehensive search of MEDLINE, Embase, and NHSEED revealed that 87.3% of clinical effectiveness studies in technology appraisal reports (TARs) were identified (Royle and Waugh 2003) For efficient evidence retrieval, user-friendly web portals like Trip and NHS Evidence provide access to selected evidence-based health studies and reports While many studies are available in MEDLINE, Embase, and the Cochrane Library, these portals offer quick access to key reports sufficient for populating models Essential health databases for identifying randomized controlled trials (RCTs), systematic reviews, and other evaluative studies are outlined in Table 2.5 Additionally, trials have been documented in numerous national and international databases, encompassing both published and unpublished literature (AUHE 2015).
The search strategy to identify evidence of effectiveness is likely to include search concepts for the ‘Patient/Population’ and ‘Intervention’ under consideration
To enhance search results for effectiveness studies, such as randomized controlled trials (RCTs), systematic reviews, and observational studies, a third search concept can be incorporated This can be achieved through the use of search filters, database limits, or by crafting a specific search strategy While database limits provide a quick way to narrow down results to a small number of highly relevant studies, they may overlook important research that can be identified using search filters.
Where the size of available evidence is small, then a search of just ‘population’ and ‘intervention’ without a limit to study type will suffi ce The analyst can view a
Table 2.5 Selected clinical effectiveness resources
Resource Study type URL (accessed 29-9-14)
Systematic reviews www.thecochranelibrary.com/
Systematic reviews www.thecochranelibrary.com/
Reviews and syntheses www.thecochranelibrary.com/
NHSEED Economic evaluations www.thecochranelibrary.com/
Cochrane Central Register of Controlled Trials
Controlled trials www.thecochranelibrary.com/
MEDLINE/PubMed Trials, reviews and other evaluative studies www.ncbi.nlm.nih.gov/pubmed
Embase Trials, reviews and other evaluative studies www.elsevier.com/online-tools/Embase
NHS Evidence Trials, reviews and other evaluative studies www.evidence.nhs.uk
Reviews and syntheses www.mcmasterhealthforum.org/hse/
Trip Database Trials, reviews and other evaluative studies http://www.tripdatabase.com/
2 Finding the Evidence for Decision Analytic Cost Effectiveness Models
To assess the effectiveness of Herceptin in breast cancer, analysts must evaluate a wide array of evidence and select the most relevant data for their model For instance, when exploring the question, "How effective is Herceptin in breast cancer?", the search concepts include (A) breast cancer, (B) Herceptin, and (C) effectiveness studies If relevant trials are identified, the optimal search combination would be A AND B AND C, ensuring records include all three terms In cases where trials are absent, a simplified search using A AND B can still yield studies that mention both breast cancer and Herceptin, allowing analysts to choose the most suitable information Additionally, various clinical effectiveness search filters are available in certain databases, offering an alternative to the analyst creating their own search strategy.
To enhance search efficiency, researchers can utilize validated search filters to specifically retrieve study types such as randomized controlled trials (RCTs), eliminating the need to create their own search terms The Cochrane Handbook provides tested RCT search filters for Ovid MEDLINE and PubMed, which can be easily copied and pasted For instance, the Cochrane RCT filter has successfully identified clinical effectiveness studies, including those on Herceptin for breast cancer It is essential to document the use of these search filters in the methods section of the final report A variety of 'study type' filters are available on the InterTASC ISSG Search Filters Resource webpage, with some being validated and peer-reviewed These filters can assist in locating effectiveness studies across different types, such as systematic reviews and observational studies However, researchers must ensure that the filters are compatible with the specific database being used, as search strategies designed for MEDLINE may not yield effective results in Embase due to differing indexing terms like MeSH and EMTREE.
Table 2.6 Search concepts and terms for effectiveness studies of Herceptin use in breast cancer
Search concept (A) Breast cancer (B) Herceptin
Breast Cancer* Trastuzumab Randomised controlled trial*
Database Limits and Clinical Queries
Ovid databases have an ‘additional limits’ feature containing the publication-type limit ‘randomised controlled trial’ and the clinical query limits for ‘reviews’ and
Therapy studies can be effectively identified in Ovid MEDLINE using specific clinical queries and publication type limits Similar options are available in PubMed and EBSCOhost databases like CINAHL, which offer database limit features Additionally, NHS Evidence provides filters for 'types of information' that help narrow searches to effectiveness studies, including primary research and health technology assessments.
Ovid MEDLINE(R) Search Strategy:
2 (breast adj5 (neoplasm* or cancer* or tumour* or tumor* or carcinoma* or adenocarcinoma* or malignan* or sarcoma*)).ti,ab.(214346)
3 (mammary adj5 (neoplasm* or cancer* or tumour* or tumor* or carcinoma* or adenocarcinoma* or malignan* or sarcoma*)).ti,ab.(26991)
16 9 or 10 or 11 or 12 or 13 or 14 or 15 (885427)
18 16 not 17 [Cochrane RCT filter -precision maximising] (813525)
Exp explode subject heading (searches additional, closely related but more specific terms) / Medical Subject Heading (MeSH)
Adj 5 terms must be within 5 words of each other
* truncated term identifying terms with the same stem
“ …” searches for the two or more words within the quotation marks as a phrase
ti, ab searches title and abstract
Or/1 − 3 set combination 1 or 2 or 3
Fig 2.4 Clinical effectiveness of Herceptin for breast cancer search using Cochrane RCT fi lter
2 Finding the Evidence for Decision Analytic Cost Effectiveness Models
Adverse events data, including side effects, complications, treatment failures, and safety issues, are crucial for cost-effectiveness models The absence of this data can lead to biased cost-effectiveness estimates (Heather et al 2014) While clinical trials and systematic reviews may report adverse events, follow-up studies often provide a more suitable timeframe for data collection Resources for assessing clinical effectiveness, as detailed in Table 2.5 and Section 2.5.2, are relevant for adverse events data Recent evaluations suggest that searching MEDLINE and Embase with specific filters can effectively retrieve most studies on adverse effects (Golder et al 2014) Additionally, industry submissions, reference lists, and specialized databases like TOXLINE and Derwent Drug Index are important sources for drug safety data (Golder and Loke 2012b) Subject-specific databases may also offer valuable insights.
Creating a successful search strategy for identifying adverse events can be challenging due to the wide range of terms and synonyms used, such as adverse effects, complications, harms, and specific symptoms like rash or nausea To enhance the effectiveness of your search, it is recommended to consult with a librarian or information specialist, as this field is continuously evolving.
Ovid MEDLINE(R)
2 (breast adj5 (neoplasm* or cancer* or tumour* or tumor* or carcinoma* or adenocarcinoma* or malignan* or sarcoma*)).ti,ab.(214346)
3 (mammary adj5 (neoplasm* or cancer* or tumour* or tumor* or carcinoma* or adenocarcinoma* or malignan* or sarcoma*)).ti,ab.(26991)
10 limit 9 to "reviews (maximizes specificity)" (90)
11 limit 9 to "therapy (maximizes specificity)" (184)
12 limit 9 to randomized controlled trial (182)
Exp explode subject heading (searches additional, closely related but more specific terms) / Medical Subject Heading (MeSH)
Adj 5 terms must bewithin 5 words of each other
* truncated term identifying terms with the same stem
ti, absearches title and abstract
Or/1 − 3 set combination 1 or 2 or 3
Fig 2.5 Clinical effectiveness of Herceptin for breast cancer search using Ovid ‘Additional
When conducting an adverse events search, it is essential to include the intervention under consideration and, if relevant, a Patient/Population search concept To refine the search, incorporate a concept that specifically targets studies mentioning adverse events Utilizing Medical Subject Headings (MeSH) in MEDLINE can enhance the search by applying the adverse events subheading, which focuses on studies related to the intervention's adverse effects (Golder and Loke 2012a) For instance, searching with the MeSH term ‘Hearing Aids/ae [Adverse Effects]’ will yield studies on complications associated with hearing aids Additional strategies include employing available adverse effects search filters from the ISSG Search Filter Resource (InterTASC ISSG 2015b), using the adverse effects floating subheading, or crafting a tailored search strategy with relevant adverse event terminology NHS Evidence also offers a filter to narrow results to studies on 'drug prescribing and safety.'
2.6 Finding the Evidence on Health-Related Quality of Life and Health State Preferences
In Chapter 1, Section 1.3.3, we discussed the measurement of health effects in terms of ‘healthy years’ using a multidimensional utility-based approach that combines life years gained with quality assessments of those years For evaluating health-related quality of life (HRQoL), generic preference-based measures like EQ-5D, SF-6D, HUI2, and HUI3 are commonly utilized across various health conditions Additionally, condition-specific utility measures, such as those developed for pressure ulcers and incontinence, offer more precise assessments of the impact of specific health conditions and the benefits of interventions When constructing a cost-effectiveness model, analysts often require HRQoL evidence; however, preference-based measures may sometimes be unavailable or unsuitable In such instances, analysts may resort to non-preference-based measures, as demonstrated by Fairburn et al in their cost-effectiveness analysis comparing transcatheter aortic valve implantation to surgical replacement, where they utilized New York Heart Association class transitions assigned with mean EQ-5D utility values.
Where to Look
Health utilities, health state preferences, and HRQoL data can be found in journal articles and various databases or registries These sources encompass general health databases, specialty databases, and instrument-specific resources, providing valuable insights into health-related quality of life.
2 Finding the Evidence for Decision Analytic Cost Effectiveness Models
A comprehensive list of health technology assessment resources can be found in the Etext by Paisley et al (2005), which includes 31 selected websites Additionally, exploring subject-specific databases may yield valuable health utility data.
Search Strategy, Concepts, Terms and Combinations
The Intervention and Patient/Population are likely to form two search concepts (similar to the clinical effectiveness, epidemiology and the existing model search)
To effectively narrow down research to studies that reference Health-Related Quality of Life (HRQoL) or health state preference terms, it is essential to utilize both generic and specific terminology related to these measures The selection of terms can significantly influence the volume and specificity of the search results Table 2.8 provides examples of these terms, which have been recommended by health economists and identified through textual analysis, including selections from the Etext on Health Technology Assessment Information Resources (Paisley et al 2005) However, this list is not exhaustive, and there are numerous additional specific and generic instrument terms that can be incorporated into the search as needed.
To find studies related to health-related quality of life (HRQoL) or health state preferences in breast cancer patients treated with Herceptin, you can perform a search by combining the key concepts with 'AND' This means you would search for 'breast cancer' AND 'Herceptin' to retrieve relevant results.
Table 2.7 Selected sources of reports of health utilities, HRQoL and health state preferences
ScHARRHUD Studies of health state utility values www.scharrhud.org
PROQOLID Patient-reported outcome and quality of life instruments http://www.proqolid.org/
MEDLINE General health database with greater N American journal coverage www.ncbi.nlm.nih.gov/pubmed
Embase General health database with greater European journal coverage www.elsevier.com/online-tools/Embase
NHSEED Economic evaluations, UK focussed www.crd.york.ac.uk/CRDWeb/
RePEc Published and working papers in economics http://repec.org/
CEA Registry CEA studies https://research.tufts-nemc.org/cear4/ EQ-5D Individual instrument website http://www.euroqol.org/
SF-36 Individual instrument website http://www.sf-36.org/
2.6 Finding the Evidence on Health-Related Quality of Life and Health State Preferences
HRQoL, or health-related quality of life, is illustrated in Figure 2.6 A comparison of search line 29 and search line 30 reveals that using generic terms in search line 29 yields significantly more records (142) compared to the limited search for specific measures, which only retrieves two records This highlights the importance of broad search parameters in capturing a wider array of data.
Search Filters, Database Limits and Clinical Queries
Currently, there are no validated search filters specifically for health utilities However, the ISSG Search Filters Resource provides guidance for searching health-related quality of life (HRQoL) studies, outcome studies, and the properties of measurement instruments Notably, databases lack pre-made clinical queries or options that facilitate quick searches focused on HRQoL or health state preference studies.