Case study research and causal inference.

Case study methodology is widely used in health research, but has had a marginal role in evaluative studies, given it is often assumed that case studies offer little for making causal inferences. We undertook a narrative review of examples of case study research from public health and health services evaluations, with a focus on interventions addressing health inequalities. We identified five types of contribution these case studies made to evidence for causal relationships. These contributions relate to: (1) evidence about system actors' own theories of causality; (2) demonstrative examples of causal relationships; (3) evidence about causal mechanisms; (4) evidence about the conditions under which causal mechanisms operate; and (5) inference about causality in complex systems. Case studies can and do contribute to understanding causal relationships. More transparency in the reporting of case studies would enhance their discoverability, and aid the development of a robust and pluralistic evidence base for public health and health services interventions. To strengthen the contribution that case studies make to that evidence base, researchers could: draw on wider methods from the political and social sciences, in particular on methods for robust analysis; carefully consider what population their case is a case 'of'; and explicate the rationale used for making causal inferences.

The use of Qualitative Comparative Analysis (QCA) to address causality in complex systems: a systematic review of research on public health interventions.

BACKGROUND: Qualitative Comparative Analysis (QCA) is a method for identifying the configurations of conditions that lead to specific outcomes. Given its potential for providing evidence of causality in complex systems, QCA is increasingly used in evaluative research to examine the uptake or impacts of public health interventions. We map this emerging field, assessing the strengths and weaknesses of QCA approaches identified in published studies, and identify implications for future research and reporting. METHODS: PubMed, Scopus and Web of Science were systematically searched for peer-reviewed studies published in English up to December 2019 that had used QCA methods to identify the conditions associated with the uptake and/or effectiveness of interventions for public health. Data relating to the interventions studied (settings/level of intervention/populations), methods (type of QCA, case level, source of data, other methods used) and reported strengths and weaknesses of QCA were extracted and synthesised narratively. RESULTS: The search identified 1384 papers, of which 27 (describing 26 studies) met the inclusion criteria. Interventions evaluated ranged across: nutrition/obesity (n = 8); physical activity (n = 4); health inequalities (n = 3); mental health (n = 2); community engagement (n = 3); chronic condition management (n = 3); vaccine adoption or implementation (n = 2); programme implementation (n = 3); breastfeeding (n = 2), and general population health (n = 1). The majority of studies (n = 24) were of interventions solely or predominantly in high income countries. Key strengths reported were that QCA provides a method for addressing causal complexity; and that it provides a systematic approach for understanding the mechanisms at work in implementation across contexts. Weaknesses reported related to data availability limitations, especially on ineffective interventions. The majority of papers demonstrated good knowledge of cases, and justification of case selection, but other criteria of methodological quality were less comprehensively met. CONCLUSION: QCA is a promising approach for addressing the role of context in complex interventions, and for identifying causal configurations of conditions that predict implementation and/or outcomes when there is sufficiently detailed understanding of a series of comparable cases. As the use of QCA in evaluative health research increases, there may be a need to develop advice for public health researchers and journals on minimum criteria for quality and reporting.

Protocol for a systematic review of the use of qualitative comparative analysis for evaluative questions in public health research.

BACKGROUND: There is an increasing recognition that health intervention research requires methods and approaches that can engage with the complexity of systems, interventions, and the relations between systems and interventions. One approach which shows promise to this end is qualitative comparative analysis (QCA), which examines casual complexity across a medium to large number of cases (between 10 and 60+), whilst also being able to generalise across those cases. Increasingly, QCA is being adopted in public health intervention research. However, there is a limited understanding of how it is being adopted. This systematic review will address this gap, examining how it is being used to understand complex causation; for what settings, populations and interventions; and with which datasets to describe cases. METHODS: We will include published and peer-reviewed studies of any public health intervention where the effects on population health, health equity, or intervention uptake are being evaluated. Electronic searches of PubMed, Scopus, Web of Science (incorporating Social Sciences Citation Index and Arts & Humanities Citation Index), Microsoft Academic, and Google Scholar will be performed. This will be supplemented with reference citation tracking and personal contact with experts to identify any additional published studies. Search results will be single screened, with machine learning used to check these results, acting as a 'second screener'. Any disagreement will be resolved through discussion. Data will be extracted from full texts of eligible studies, which will be assessed against inclusion criteria, and synthesised narratively, using thematic synthesis methods. DISCUSSION: This systematic review will provide an important map of the increasing use of QCA in public health intervention literature. This review will identify the current scope of research in this area, as well as assessing claims about the utility of the method for addressing complex causation in public health research. We will identify implications for better reporting of QCA methods in public health research and for reporting of case studies such that they can be used in future QCA studies. SYSTEMATIC REVIEW REGISTRATION: PROSPERO, CRD42019131910.

The Daily Mile as a public health intervention: a rapid ethnographic assessment of uptake and implementation in South London, UK.

BACKGROUND: Existing evidence identifies health benefits for children of additional daily physical activity (PA) on a range of cardiovascular and metabolic outcomes. The Daily Mile (TDM) is a popular scheme designed to increase children's PA within the school day. Emerging evidence indicates that participation in TDM can increase children's PA, reduce sedentarism and reduce skinfold measures. However, little is known about the potential effects of TDM as a public health intervention, and the benefits and disbenefits that might flow from wider implementation in 'real world' settings. METHODS: We aimed to identify how TDM is being implemented in a naturalistic setting, and what implications this has for its potential impact on population health. We undertook a rapid ethnographic assessment of uptake and implementation in Lewisham, south London. Data included interviews (n = 22) and focus groups (n = 11) with stakeholders; observations of implementation in 12 classes; and analysis of routine data sources to identify school level factors associated with uptake. RESULTS: Of the 69 primary schools in one borough, 33 (48%) had adopted TDM by September 2018. There were no significant differences between adopters and non-adopters in mean school population size (means 377 vs 397, P = 0.70), mean percentage of children eligible for free school meals (16.2 vs 14.3%, P = 0.39), or mean percentage of children from Black and Minority Ethnic populations (76.3 vs 78.2%, P = 0.41). Addressing obesity was a key incentive for adoption, although a range of health and educational benefits were also hypothesised to accrue from participation. Mapping TDM to the TIDierR-PHP checklist to describe the intervention in practice identified that considerable adaption happened at the level of borough, school, class and pupil. Population health effects are likely to be influenced by the interaction of intervention and context at each of these levels. CONCLUSIONS: Examining TDM in 'real world' settings surfaces adaptions and variations in implementation. This has implications for the likely effects of TDM, and points more broadly to an urgent need for more appropriate methods for evaluating public health impact and implementation in complex contexts.