Guidance on the use of complex systems models for economic evaluations of public health interventions.

To help health economic modelers respond to demands for greater use of complex systems models in public health. To propose identifiable features of such models and support researchers to plan public health modeling projects using these models. A working group of experts in complex systems modeling and economic evaluation was brought together to develop and jointly write guidance for the use of complex systems models for health economic analysis. The content of workshops was informed by a scoping review. A public health complex systems model for economic evaluation is defined as a quantitative, dynamic, non-linear model that incorporates feedback and interactions among model elements, in order to capture emergent outcomes and estimate health, economic and potentially other consequences to inform public policies. The guidance covers: when complex systems modeling is needed; principles for designing a complex systems model; and how to choose an appropriate modeling technique. This paper provides a definition to identify and characterize complex systems models for economic evaluations and proposes guidance on key aspects of the process for health economics analysis. This document will support the development of complex systems models, with impact on public health systems policy and decision making.

Effect of Reflection and Deliberation on Health State Values: A Mixed-Methods Study.

BACKGROUND: Health economists ask members of the general public to value health states, but it is recognized that individuals construct their preferences during the valuation tasks. Conventional methods rely on one-off interviews that do not give participants time to reflect and deliberate on their preferences. OBJECTIVE: This study investigates the effect of reflection and deliberation on health state preferences using the EQ-5D questionnaire and time trade-off valuation method. METHODS: A novel concurrent explanatory mixed-methods design is used to investigate the explanation for the quantitative findings. RESULTS: A total of 57 participants in the United Kingdom valued health states before and after a group-based deliberation exercise. There were large changes in health state values at the individual level, but the changes canceled out at the aggregate level. The mixed-methods findings suggest deliberation did not reveal new information or reduce inconsistencies in reasoning but rather focused on an exchange of personal subjective beliefs. In cases of disagreement, the participants accepted but did not adopt other participants' opinions. Participants remained uncertain about the relevance of their experiences and about their values. CONCLUSIONS: The evidence suggests that reflection and deliberation, as designed in this study, are unlikely to result in large systematic changes of health state values. The uncertainties expressed by participants means future research should investigate whether preferences are informed or whether providing participants with more information helps them construct their preferences with more certainty. The mixed-methods design used is a promising design to help elucidate the reasons for quantitative findings.

Identification, Review, and Use of Health State Utilities in Cost-Effectiveness Models: An ISPOR Good Practices for Outcomes Research Task Force Report.

Cost-effectiveness models that present results in terms of cost per quality-adjusted life-year for health technologies are used to inform policy decisions in many parts of the world. Health state utilities (HSUs) are required to calculate the quality-adjusted life-years. Even when clinical studies assessing the effectiveness of health technologies collect data on HSUs to populate a cost-effectiveness model, which rarely happens, analysts typically need to identify at least some additional HSUs from alternative sources. When possible, HSUs are identified by a systematic review of the literature, but, again, this rarely happens. In 2014, ISPOR established a Good Practices for Outcome Research Task Force to address the use of HSUs in cost-effectiveness models. This task force report provides recommendations for researchers who identify, review, and synthesize HSUs for use in cost-effectiveness models; analysts who use the results in models; and reviewers who critically appraise the suitability and validity of the HSUs selected for use in models. The associated Minimum Reporting Standards of Systematic Review of Utilities for Cost-Effectiveness checklist created by the task force provides criteria to judge the appropriateness of the HSUs selected for use in cost-effectiveness models and is suitable for use in different international settings.

Rheumatoid arthritis treated with 6-months of first-line biologic or biosimilar therapy: an updated systematic review and network meta-analysis.

OBJECTIVES: The aim of this study was to estimate the effectiveness of first-line biologic disease modifying drugs(boDMARDs), and their approved biosimilars (bsDMARDs), compared with conventional (csDMARD) treatment, in terms of ACR (American College of Rheumatology) and EULAR (European League against Rheumatism) responses. METHODS: Systematic literature search, on eight databases to January 2017, sought ACR and EULAR data from randomized controlled trials (RCTs) of boDMARDs / bsDMARDs (in combination with csDMARDs, or monotherapy). Two adult populations: methotrexate (MTX)-naïve patients with severe active RA; and csDMARD-experienced patients with moderate-to-severe active RA. Network meta-analyses (NMA) were conducted using a Bayesian Markov chain Monte Carlo simulation using a random effects model with a probit link function for ordered categorical. RESULTS: Forty-six RCTs met the eligibility criteria. In the MTX-naïve severe active RA population, no biosimilar trials meeting the inclusion criteria were identified. MTX plus methylprednisolone (MP) was most likely to achieve the best ACR response. There was insufficient evidence that combination boDMARDs was superior to intensive (two or more) csDMARDs. In the csDMARD-experienced, moderate-to-severe RA population, the greatest effects for ACR responses were associated with tocilizumab (TCZ) monotherapy, and combination therapy (plus MTX) with bsDMARD etanercept (ETN) SB4, boDMARD ETN and TCZ. These treatments also had the greatest effects on EULAR responses. No clear differences were found between the boDMARDs and their bsDMARDs. CONCLUSIONS: In MTX-naïve patients, there was insufficient evidence that combination boDMARDs was superior to two or more csDMARDs. In csDMARD-experienced patients, boDMARDs and bsDMARDs were comparable and all combination boDMARDs / bsDMARDs were superior to single csDMARD.

ITERATIVE SIFTING IN THE SELECTION OF RESEARCH EVIDENCE: IMPLICATIONS FOR REVIEWS AND OTHER DECISION PROBLEMS.

OBJECTIVES: A rapid scoping review was performed to support the development of a new clinical technology platform. An iterative sifting approach was adopted to address the challenges posed by the nature of the review question and the extremely large volume of search results to be sifted within the timescales of the review. METHODS: This study describes the iterative sifting approach applied in the scoping review and a preliminary validation of the methods applied. RESULTS: The searches performed for the rapid scoping review retrieved 27,198 records. This was the full set of records subjected to the staged, iterative sifting approach and the subsequent validation process. The iterative sifting approach involved the screening for relevance of 17,354 (i.e., 63.8 percent) of the 27,198 records. A list of fifty-three potential biomarker names was generated as a result of this iterative sifting method, of which nineteen were selected by clinical specialists for further scrutiny. The preliminary validation involved the exhaustive sifting of the remaining 9,844 previously unsifted records. The validation process identified sixteen additional potential biomarker names not identified by the iterative sifting process. The clinical specialists subsequently concluded that none were of further clinical interest. CONCLUSIONS: This study describes an approach to the screening of search records that can be successfully applied in appropriate review and decision problems to allow the prioritization of the most relevant search records and achieve time savings. Following further refinement and standardization, this iterative sifting method may have potential for further applications in reviews and other decision problems.

Identification and review of cost-effectiveness model parameters: a qualitative study.

OBJECTIVES: Health economic models are developed as part of the health technology assessment process to determine whether health interventions represent good value for money. These models are often used to directly inform healthcare decision making and policy. The information needs for the model require the use of other types of information beyond clinical effectiveness evidence to populate the model's parameters. The purpose of this research study was to explore issues concerned with the identification and use of information for the development of such models. METHODS: Three focus groups were held in February 2011 at the University of Sheffield with thirteen UK HTA experts. Attendees included health economic modelers, information specialists and systematic reviewers. Qualitative framework analysis was used to analyze the focus group data. RESULTS: Six key themes, with related sub-themes, were identified dealing with decisions and judgments; searching methods; selection and rapid review of evidence; team communication; modeler experience and clinical input and reporting methods. There was considerable overlap between themes. CONCLUSIONS: Key issues raised by the respondents included the need for effective communication and teamwork throughout the model development process, the importance of using clinical experts as well as the need for transparent reporting of methods and decisions.

Systematic searching and selection of health state utility values from the literature.

Health state utility values (HSUVs) are important parameters in decision models in health technology assessment submissions. Reimbursement agencies, such as the National Institute for Health and Clinical Excellence, recognize that such values are obtainable from the published literature. However, to use published values in health technology assessment submissions, it should be demonstrated that HSUVs have been identified and selected systematically to avoid using biased HSUVs resulting in cost-effectiveness analyses. This article presents guidance on how to conduct a systematic literature review to identify and select HSUVs from the published literature based on the authors' experience. A case study is used to demonstrate some of the features of a systematic HSUV review. Methods are discussed in relation to identifying and selecting the evidence, performing quality and relevance assessment, and undertaking data extraction. It has been developed from a Technical Support Document produced for the National Institute for Health and Clinical Excellence by the Decision Support Unit at the School of Health and Related Research, University of Sheffield.

Reviewing the evidence to inform the population of cost-effectiveness models within health technology assessments.

OBJECTIVES: Health technology assessments (HTAs) typically require the development of a cost-effectiveness model, which necessitates the identification, selection, and use of other types of information beyond clinical effectiveness evidence to populate the model parameters. The reviewing activity associated with model development should be transparent and reproducible but can result in a tension between being both timely and systematic. Little procedural guidance exists in this area. The purpose of this article was to provide guidance, informed by focus groups, on what might constitute a systematic and transparent approach to reviewing information to populate model parameters. METHODS: A focus group series was held with HTA experts in the United Kingdom including systematic reviewers, information specialists, and health economic modelers to explore these issues. Framework analysis was used to analyze the qualitative data elicited during focus groups. RESULTS: Suggestions included the use of rapid reviewing methods and the need to consider the trade-off between relevance and quality. The need for transparency in the reporting of review methods was emphasized. It was suggested that additional attention should be given to the reporting of parameters deemed to be more important to the model or where the preferred decision regarding the choice of evidence is equivocal. DISCUSSION: These recommendations form part of a Technical Support Document produced for the National Institute for Health and Clinical Excellence Decision Support Unit in the United Kingdom. It is intended that these recommendations will help to ensure a more systematic, transparent, and reproducible process for the review of model parameters within HTA.

Assessing searches in NICE single technology appraisals: practice and checklist.

OBJECTIVES: No guidelines exist in the approach that Evidence Review Groups (ERGs) should take to appraise search methodologies in the manufacturer's submission (MS) in Single Technology Appraisals (STA). As a result, ERGs are left to appraise searches using their own approach. This study investigates the limitations of manufacturers' search methodologies as critiqued by ergs in published sta reports and to provide a recommended checklist. METHODS: Limitations from search critiques in 83 ERG reports published in the NIHR Web site between 2006 and May 2011 were extracted. The limitations were grouped into themes. Comparisons were made between limitations reported in the clinical effectiveness versus cost-effectiveness searches. RESULTS: Twelve themes were identified, six relating to the search strategy, source, limits, filters, translation, reporting, and missing studies. The search strategy theme contained the most limitations. Missing studies were frequently found by the ERG group in the clinical effectiveness searches. The omission of searches by manufacturers for unpublished and ongoing trials was frequently reported by the ERG. By contrast, failure of the manufacturer to report strategies was the most common limitation in the cost-effectiveness searches. Themes with the most frequent limitations in both types of searches are search strategy, reporting and source. CONCLUSIONS: It is recommended that a checklist that has reporting, source and search strategy elements be used in the appraisal of manufacturer's searches during the STA process.

Quality of life of people with mental health problems: a synthesis of qualitative research.

PURPOSE: To identify the domains of quality of life important to people with mental health problems. METHOD: A systematic review of qualitative research undertaken with people with mental health problems using a framework synthesis. RESULTS: We identified six domains: well-being and ill-being; control, autonomy and choice; self-perception; belonging; activity; and hope and hopelessness. Firstly, symptoms or 'ill-being' were an intrinsic aspect of quality of life for people with severe mental health problems. Additionally, a good quality of life was characterised by the feeling of being in control (particularly of distressing symptoms), autonomy and choice; a positive self-image; a sense of belonging; engagement in meaningful and enjoyable activities; and feelings of hope and optimism. Conversely, a poor quality life, often experienced by those with severe mental health difficulties, was characterized by feelings of distress; lack of control, choice and autonomy; low self-esteem and confidence; a sense of not being part of society; diminished activity; and a sense of hopelessness and demoralization. CONCLUSIONS: Generic measures fail to address the complexity of quality of life measurement and the broad range of domains important to people with mental health problems.

Classification of evidence in decision-analytic models of cost-effectiveness: a content analysis of published reports.

OBJECTIVES: The aim of this study was to assess systematically the scope of evidence and purposes for which evidence is used in decision-analytic models of cost-effectiveness and to assess the implications for search methods. METHODS: A content analysis of published reports of models was undertaken. Details of cited sources were extracted and categorized according to three dimensions; type of information provided by the evidence, type of source from which the evidence was drawn and type of modeling activity supported by the evidence. The analysis was used to generate a classification of evidence. Relationships within and between the categories within the classification were sought and the implications for searching considered. RESULTS: The classification generated fourteen types of information, seven types of sources of evidence and five modeling activities supported by evidence. A broad range of evidence was identified drawn from a diverse range of sources including both research-based and non-research-based sources. The use of evidence was not restricted to the population of model parameters but was used to inform the development of the modeling framework and to justify the analytical and methodological approach. CONCLUSIONS: Decision-analytic models use evidence to support all aspects of model development. The classification of evidence defines in depth the role of evidence in modeling. It can be used to inform the systematic identification of evidence.

Identifying economic evaluations for health technology assessment.

OBJECTIVES AND METHODS: Health technology assessment (HTA) often requires the identification and review of economic evaluations and models. This study surveys the available specific and general resources to search to identify economic evaluations. It also provides information on efficient searching of those resources and comments on the current evidence-base. RESULTS: Published checklists recommend searching for economic evaluations in specific information resources which collect economic evaluations such as NHS EED and HEED, followed by top-up searches of large biomedical bibliographic databases (such as MEDLINE and EMBASE). Other resources such as the HTA and DARE databases can yield reports of economic evaluations. Searches within NHS EED and HEED can be made more efficient by using database-specific search options. Searches within large biomedical databases such as MEDLINE and EMBASE require the use of economic search terms called search filters. Search filters are highly sensitive, retrieving most economic evaluations, but suffer from low precision returning many irrelevant records which need to be assessed. CONCLUSIONS: It is relatively easy to identify rapidly a high proportion of economic evaluations but more research is required to improve the efficiency of this process. There are key high yield resources to search but more evidence is required on their overlap and unique contribution to searches. The value of other resources, particularly those providing access to gray literature, should be explored. Research into efficient retrieval requires clear definitions of economic evaluations to allow comparison across studies.

The Cost-Effectiveness of Expanding the UK Newborn Bloodspot Screening Programme to Include Five Additional Inborn Errors of Metabolism.

Glutaric aciduria type 1, homocystinuria, isovaleric acidaemia, long-chain hydroxyacyl CoA dehydrogenase deficiency and maple syrup urine disease are all inborn errors of metabolism that can be detected through newborn bloodspot screening. This evaluation was undertaken in 2013 to provide evidence to the UK National Screening Committee for the cost-effectiveness of including these five conditions in the UK Newborn Bloodspot Screening Programme. A decision-tree model with lifetable estimates of outcomes was built with the model structure and parameterisation informed by a systematic review and expert clinical judgment. A National Health Service/Personal Social Services perspective was used, and lifetime costs and quality-adjusted life years (QALYs) were discounted at 1.5%. Uncertainty in the results was explored using expected value of perfect information analysis methods together with a sensitivity analysis using the screened incidence rate in the UK from 2014 to 2018. The model estimates that screening for all the conditions is more effective and cost saving when compared to not screening for each of the conditions, and the results were robust to the updated incidence rates. The key uncertainties included the sensitivity and specificity of the screening test and the estimated costs and QALYs.

The SIPHER Consortium: Introducing the new UK hub for systems science in public health and health economic research.

The conditions in which we are born, grow, live, work and age are key drivers of health and inequalities in life chances. To maximise health and wellbeing across the whole population, we need well-coordinated action across government sectors, in areas including economic, education, welfare, labour market and housing policy. Current research struggles to offer effective decision support on the cross-sector strategic alignment of policies, and to generate evidence that gives budget holders the confidence to change the way major investment decisions are made. This open letter introduces a new research initiative in this space. The SIPHER ( Systems Science in Public Health and Health Economics Research) Consortium brings together a multi-disciplinary group of scientists from across six universities, three government partners at local, regional and national level, and ten practice partner organisations. The Consortium's vision is a shift from health policy to healthy public policy, where the wellbeing impacts of policies are a core consideration across government sectors. Researchers and policy makers will jointly tackle fundamental questions about: a) the complex causal relationships between upstream policies and wellbeing, economic and equality outcomes; b) the multi-sectoral appraisal of costs and benefits of alternative investment options; c) public values and preferences for different outcomes, and how necessary trade-offs can be negotiated; and d) creating the conditions for intelligence-led adaptive policy design that maximises progress against economic, social and health goals. Whilst our methods will be adaptable across policy topics and jurisdictions, we will initially focus on four policy areas: Inclusive Economic Growth, Adverse Childhood Experiences, Mental Wellbeing and Housing.

Estimating the health benefits and costs associated with ezetimibe coadministered with statin therapy compared with higher dose statin monotherapy in patients with established cardiovascular disease: results of a Markov model for UK costs using data registries.

BACKGROUND: Ezetimibe has been reported to improve lipid control in patients with established cardiovascular disease (CVD). OBJECTIVE: The aim of this study was to estimate the potential long-term impact on health status of prescribing ezetimibe in combination with statin therapy in patients with established CVD and evaluate its cost-effectiveness in a health economic model. METHODS: A Markov model was used to compare ezetimibe and statin combination therapy with statin monotherapy. A published relationship linking changes in low-density lipoprotein cholesterol and cardiovascular events was used to estimate the cardiovascular events avoided through lipid-lowering therapies. The model was populated using results of extensive literature searches and a meta-analysis of clinical evidence. An adjustment was applied to model second-line lipid-lowering benefits. Conservative assumptions were used to extend the patient pathway beyond the clinical evidence. The analysis took the perspective of the UK Department of Health; therefore, only direct costs were included. Costs were calculated as year-2006 British pounds. RESULTS: For a cohort of 1,000 hypothetical male patients aged 55 years, ezetimibe coadministered with current statin therapy was estimated to prevent a mean of 43 nonfatal myocardial infarctions, 7 nonfatal strokes, and 26 cardiovascular deaths over a lifetime, compared with doubling the current statin dose. The events avoided would provide a mean of 134 additional quality-adjusted life-years (QALYs). With a mean incremental cost of pound 3,693,000, the lifetime discounted cost per QALY gained would be pound 27,475 (95% CI, pound 27,331- pound 27,620) and would rise to pound 32,000 for men aged 75 years. CONCLUSIONS: The results suggest that, in some instances, ezetimibe coadministration may be cost-effective compared with statin monotherapy, but there are several limitations with this model. The economic effects of ezetimibe must be revisited when long-term effectiveness and safety data become available.

Cost effectiveness of ezetimibe in patients with cardiovascular disease and statin intolerance or contraindications: a Markov model.

OBJECTIVE: To evaluate the cost effectiveness of long-term ezetimibe monotherapy in patients with established cardiovascular disease (CVD) who do not tolerate statins or in whom they are contraindicated. METHODS: A Markov model was used to estimate the potential costs and benefits associated with ezetimibe monotherapy compared with no treatment. The benefits associated with ezetimibe treatment were informed by a systematic review of clinical evidence and a published relationship linking changes in low-density lipoprotein cholesterol (LDL-C) levels to cardiovascular events. RESULTS: In the absence of data from clinical outcome trials, surrogate endpoints such as changes in lipid levels were used as indicators of clinical outcomes. A meta-analysis of seven placebo-controlled trials included in the review showed that ezetimibe was associated with a statistically significant mean reduction (from baseline to endpoint) in LDL-C of 18.56% (95% CI -19.68, -17.44; p < 0.00001) compared with placebo. Using 10,000 Monte Carlo simulations, it is estimated that ezetimibe monotherapy would prevent an average of 49 nonfatal myocardial infarctions, 11 nonfatal strokes, and 37 cardiovascular deaths in a cohort of 1,000 patients aged 55 years with a baseline LDL-C concentration of 4.0 mmol/L. Events avoided provide an additional 211 quality-adjusted life-years (QALYs) over the 45 years modeled. With a mean incremental cost of pound 4,861,000 (year 2006 value), the discounted cost per QALY is pound 23,026 (Jackknife CI 22 979, 23 074). The model is reasonably robust to variations in key parameters. Incremental cost-effectiveness ratios fall below pound 20,000 per QALY for cohorts with baseline LDL-C values >4.5 mmol/L. CONCLUSION: Ezetimibe monotherapy compared with no treatment is a cost-effective alternative for individuals with a history of CVD and high LDL-C levels, who do not tolerate statins or in whom they are contraindicated.

So many filters, so little time: the development of a search filter appraisal checklist.

OBJECTIVES: The authors developed a tool to assess the quality of search filters designed to retrieve records for studies with specific research designs (e.g., diagnostic studies). METHODS: The UK InterTASC Information Specialists' Sub-Group (ISSG), a group of experienced health care information specialists, reviewed the literature to evaluate existing search filter appraisal tools and determined that existing tools were inadequate for their needs. The group held consensus meetings to develop a new filter appraisal tool consisting of a search filter appraisal checklist and a structured abstract. ISSG members tested the final checklist using three published search filters. RESULTS: The detailed ISSG Search Filter Appraisal Checklist captures relevance criteria and methods used to develop and test search filters. The checklist includes categorical and descriptive responses and is accompanied by a structured abstract that provides a summary of key quality features of a filter. DISCUSSION: The checklist is a comprehensive appraisal tool that can assist health sciences librarians and others in choosing search filters. The checklist reports filter design methods and search performance measures, such as sensitivity and precision. The checklist can also aid filter developers by indicating information on core methods that should be reported to help assess filter suitability. The generalizability of the checklist for non-methods filters remains to be explored.

Toward a theory-led metaframework for considering socioeconomic health inequalities within systematic reviews.

OBJECTIVES: To develop a theory-led framework to inform reviewers' understanding of what, how, and why health care interventions may lead to differential effects across socioeconomic groups. STUDY DESIGN AND SETTING: A metaframework approach combined two theoretical perspectives (socioeconomic health inequalities and complex interventions) into a single framework to inform socioeconomic health inequality considerations in systematic reviews. RESULTS: Four theories relating to complexity within systematic reviews and 16 health inequalities intervention theories informed the development of a metaframework. Factors relating to the type of intervention, implementation, context, participant response, and mechanisms associated with differential effects across socioeconomic groups were identified. The metaframework can inform; reviewer discussions around how socioeconomic status (SES) can moderate intervention effectiveness during question formulation, approaches to data extraction and help identify a priori analysis considerations. CONCLUSION: The metaframework offers a transparent, practical, theory-led approach to inform a program theory for what, how, and why interventions work for different SES groups in systematic reviews. It can enhance existing guidance on conducting systematic reviews that consider health inequalities, increase awareness of how SES can moderate intervention effectiveness, and encourage a greater engagement with theory throughout the review process.

The Identification, Review and Synthesis of Health State Utility Values from the Literature.

Systematic literature reviews of health-related quality of life (HRQoL) evidence that are to inform economic models can be challenging due to the volume of hits identified in searches using generic terms for HRQoL. Nevertheless, a robust review of the literature is required to ensure that the health state utility values (HSUVs) used in the economic model are the most appropriate available. This article provides a synopsis of literature relating to identifying, reviewing and synthesising HSUVs. The process begins with scoping the needs of the economic model, including the definitions of health states and the requirements of any reimbursement agencies. A sequence of searches may be required as the economic model evolves. The terminology used for HRQoL measures may be problematic, and as there is no robust HRQoL filter [equivalent to that applied for randomised control trial (RCTs)], sifting the results of sensitive searches can be resource intensive. Alternative approaches such as forward and backward citation searches may reduce the resources required, while maintaining the integrity of the search. Any included studies should be assessed in terms of quality using a recommended checklist, and insufficient detail in the primary studies should be noted as a short-coming in this exercise. Subject to homogeneity (similar populations, same measure and preference weights) evidence can be pooled in some way, although methodological research into the appropriateness of alternative techniques for meta-analysis is in its infancy. Reporting standards are key and as a minimum should include details on searches, inclusion/exclusion criteria (together with rationale for exclusion at each stage), assessment of quality and relevance of included studies, and justification for the choice of final HSUVs.

Use of programme theory to understand the differential effects of interventions across socio-economic groups in systematic reviews-a systematic methodology review.

BACKGROUND: Systematic review guidance recommends the use of programme theory to inform considerations of if and how healthcare interventions may work differently across socio-economic status (SES) groups. This study aimed to address the lack of detail on how reviewers operationalise this in practice. METHODS: A methodological systematic review was undertaken to assess if, how and the extent to which systematic reviewers operationalise the guidance on the use of programme theory in considerations of socio-economic inequalities in health. Multiple databases were searched from January 2013 to May 2016. Studies were included if they were systematic reviews assessing the effectiveness of an intervention and included data on SES. Two reviewers independently screened all studies, undertook quality assessment and extracted data. A narrative approach to synthesis was adopted. RESULTS: A total of 37 systematic reviews were included, 10 of which were explicit in the use of terminology for 'programme theory'. Twenty-nine studies used programme theory to inform both their a priori assumptions and explain their review findings. Of these, 22 incorporated considerations of both what and how interventions do/do not work in SES groups to both predict and explain their review findings. Thirteen studies acknowledged 24 unique theoretical references to support their assumptions of what or how interventions may have different effects in SES groups. Most reviewers used supplementary evidence to support their considerations of differential effectiveness. The majority of authors outlined a programme theory in the "Introduction" and "Discussion" sections of the review to inform their assumptions or provide explanations of what or how interventions may result in differential effects within or across SES groups. About a third of reviews used programme theory to inform the review analysis and/or synthesis. Few authors used programme theory to inform their inclusion criteria, data extraction or quality assessment. Twenty-one studies tested their a priori programme theory. CONCLUSIONS: The use of programme theory to inform considerations of if, what and how interventions lead to differential effects on health in different SES groups in the systematic review process is not yet widely adopted, is used implicitly, is often fragmented and is not implemented in a systematic way.