Michal Shimonovich

Background: Policy models increasingly guide policy decision‐making. Incorporating public and stakeholder perspectives into modelling processes can produce evidence that is more relevant, appropriate and acceptable to the public. However, public and stakeholder involvement (PSI) in modelling studies is an emerging practice in the field of health inequalities, with little guidance available to support researchers. Objective: We present a methodological reflection on a PSI process supporting development of a policy model estimating the health impacts of major income supplementation policies. We include a multi‐layered systems map, co‐developed with public and stakeholder contributors, which guided modelling strategy development. Methods: We engaged 32 people with lived experience of income insecurity and over 60 stakeholders with expertise in socio‐economic determinants of health. Perspectives were gathered through three participatory systems mapping workshops and five policy prioritisation workshops. Workshop contributions were analysed using descriptive thematic analysis. Insights were fed back to the research team, who made changes or additions to the modelling strategy. Results: We experienced successes and challenges incorporating public and stakeholder perspectives into model development, specifically: engaging people with lived experience of income insecurity; ensuring PSI settings support effective engagement; and appropriately remunerating and safeguarding public contributors. We learned the importance of breaking down methodological silos within research teams to translate rich qualitative insights into actionable modelling outputs. Conclusion: In data‐driven modelling studies, PSI requires carefully considered design, strong collaboration between team members, and appropriate resourcing to create enjoyable, productive engagement spaces where contributors can safely share experiences and expertise. Patient or Public Involvement and Engagement (PPIE) Contribution: PSI was central to this work. Our project partners at The Poverty Alliance, a third sector organisation, facilitated involvement of people with lived experience of income insecurity using their internal trauma‐informed approaches throughout all stages.

Background: Bradford Hill (BH) viewpoints are widely used to assess causality in systematic reviews, but their application has often lacked reproducibility. We describe an approach for assessing causality within systematic reviews (‘causal’ reviews), illustrating its application to the topic of income inequality and health. Our approach draws on principles of process tracing, a method used for case study research, to harness BH viewpoints to judge evidence for causal claims.Methods: In process tracing, a hypothesis may be confirmed by observing highly unique evidence and disconfirmed by observing highly definitive evidence. We drew on these principles to consider the value of finding supportive or contradictory evidence for each BH viewpoint characterised by its uniqueness and definitiveness.Results: In our exemplar systematic review, we hypothesised that income inequality adversely affects self‐rated health and all‐cause mortality. BH viewpoints ‘analogy’ and ‘coherence’ were excluded from the causal assessment because of their low uniqueness and low definitiveness. The ‘experiment’ viewpoint was considered highly unique and highly definitive, and thus could be particularly valuable. We propose five steps for using BH viewpoints in a ‘causal’ review: (1) define the hypothesis; (2) characterise each viewpoint; (3) specify the evidence expected for each BH viewpoint for a true or untrue hypothesis; (4) gather evidence for each viewpoint (e.g., systematic review meta‐analyses, critical appraisal, background knowledge); (5) consider if each viewpoint was met (supportive evidence) or unmet (contradictory evidence).Conclusions: Incorporating process tracing has the potential to provide transparency and structure when using BH viewpoints in ‘causal’ reviews.

Policy Points: Income is thought to impact a broad range of health outcomes. However, whether income inequality (how unequal the distribution of income is in a population) has an additional impact on health is extensively debated. Studies that use multilevel data, which have recently increased in popularity, are necessary to separate the contextual effects of income inequality on health from the effects of individual income on health. Our systematic review found only small associations between income inequality and poor self‐rated health and all‐cause mortality. The available evidence does not suggest causality, although it remains methodologically flawed and limited, with very few studies using natural experimental approaches or examining income inequality at the national level. Context: Whether income inequality has a direct effect on health or is only associated because of the effect of individual income has long been debated. We aimed to understand the association between income inequality and self‐rated health (SRH) and all‐cause mortality (mortality) and assess if these relationships are likely to be causal.Methods: We searched Medline, ISI Web of Science, Embase, and EconLit (PROSPERO: CRD42021252791) for studies considering income inequality and SRH or mortality using multilevel data and adjusting for individual‐level socioeconomic position. We calculated pooled odds ratios (ORs) for poor SRH and relative risk ratios (RRs) for mortality from random‐effects meta‐analyses. We critically appraised included studies using the Risk of Bias in Nonrandomized Studies – of Interventions tool. We assessed certainty of evidence using the Grading of Recommendations Assessment, Development and Evaluation framework and causality using Bradford Hill (BH) viewpoints.Findings: The primary meta‐analyses included 2,916,576 participants in 38 cross‐sectional studies assessing SRH and 10,727,470 participants in 14 cohort studies of mortality. Per 0.05‐unit increase in the Gini coefficient, a measure of income inequality, the ORs and RRs (95% confidence intervals) for SRH and mortality were 1.06 (1.03‐1.08) and 1.02 (1.00‐1.04), respectively. A total of 63.2% of SRH and 50.0% of mortality studies were at serious risk of bias (RoB), resulting in very low and low certainty ratings, respectively. For SRH and mortality, we did not identify relevant evidence to assess the specificity or, for SRH only, the experiment BH viewpoints; evidence for strength of association and dose–response gradient was inconclusive because of the high RoB; we found evidence in support of temporality and plausibility.Conclusions: Increased income inequality is only marginally associated with SRH and mortality, but the current evidence base is too methodologically limited to support a causal relationship. To address the gaps we identified, future research should focus on income inequality measured at the national level and addressing confounding with natural experiment approaches.

Background: Bradford Hill (BH) viewpoints are widely used to assess causality in systematic reviews, but their application has often lacked reproducibility. We describe an approach for assessing causality within systematic reviews (‘causal’ reviews), illustrating its application to the topic of income inequality and health. Our approach draws on principles of process tracing, a method used for case study research, to harness BH viewpoints to judge evidence for causal claims. Methods: In process tracing, a hypothesis may be confirmed by observing highly unique evidence and disconfirmed by observing highly definitive evidence. We drew on these principles to consider the value of finding supportive or contradictory evidence for each BH viewpoint characterised by its uniqueness and definitiveness.Results: In our exemplar systematic review, we hypothesised that income inequality adversely affects self-rated health and all-cause mortality. BH viewpoints ‘analogy’ and ‘coherence’ were excluded from the causal assessment because of their low uniqueness and low definitiveness. The ‘experiment’ viewpoint was considered highly unique and highly definitive, and thus could be particularly valuable. We propose five steps for using BH viewpoints in a ‘causal’ review: 1) define the hypothesis; 2) characterise each viewpoint; 3) specify the evidence expected for each BH viewpoint for a true or untrue hypothesis; 4) gather evidence for each viewpoint (e.g., systematic review meta-analyses, critical appraisal, background knowledge); 5) consider if each viewpoint was met (supportive evidence) or unmet (contradictory evidence).Conclusions: Incorporating process tracing has the potential to provide transparency and structure when using BH viewpoints in ‘causal’ reviews.

In fields (such as population health) where randomised trials are often lacking, systematic reviews (SRs) can harness diversity in study design, settings and populations to assess the evidence for a putative causal relationship. SRs may incorporate causal assessment approaches (CAAs), sometimes called ‘causal reviews’, but there is currently no consensus on how these should be conducted. We conducted a methodological review of self‐identifying ‘causal reviews’ within the field of population health to establish: (1) which CAAs are used; (2) differences in how CAAs are implemented; (3) how methods were modified to incorporate causal assessment in SRs. Three databases were searched and two independent reviewers selected reviews for inclusion. Data were extracted using a standardised form and summarised using tabulation and narratively. Fifty‐three reviews incorporated CAAs: 46/53 applied Bradford Hill (BH) viewpoints/criteria, with the remainder taking alternative approaches: Medical Research Council guidance on natural experiments (2/53, 3.8%); realist reviews (2/53, 3.8%); horizontal SRs (1/53, 1.9%); ‘sign test’ of causal mechanisms (1/53, 1.9%); and a causal cascade model (1/53, 1.9%). Though most SRs incorporated BH, there was variation in application and transparency. There was considerable overlap across the CAAs, with a trade‐off between breadth (BH viewpoints considered a greater range of causal characteristics) and depth (many alternative CAAs focused on one viewpoint). Improved transparency in the implementation of CAA in SRs in needed to ensure their validity and allow robust assessments of causality within evidence synthesis.

Background: Income inequality has been linked to health and mortality. While there has been extensive research exploring the relationship, the evidence for whether the relationship is causal remains disputed. We describe the methods for a systematic review that will transparently assess whether a causal relationship exists between income inequality and mortality and self-rated health. Methods: We will identify relevant studies using search terms relating to income inequality, mortality, and self-rated health (SRH). Four databases will be searched: MEDLINE, ISI Web of Science, EMBASE, and the National Bureau of Economic Research. The inclusion criteria have been developed to identify the study designs best suited to assess causality: multilevel studies that have conditioned upon individual income (or a comparable measure, such as socioeconomic position) and natural experiment studies. Risk of bias assessment of included studies will be conducted using ROBINS-I. Where possible, we will convert all measures of income inequality into Gini coefficients and standardize the effect estimate of income inequality on mortality/SRH. We will conduct random-effects meta-analysis to estimate pooled effect estimates when possible. We will assess causality using modified Bradford Hill viewpoints and assess certainty using GRADE. Discussion: This systematic review protocol lays out the complexity of the relationship between income inequality and individual health, as well as our approach for assessing causality. Understanding whether income inequality impacts the health of individuals within a population has major policy implications. By setting out our methods and approach as transparently as we can, we hope this systematic review can provide clarity to an important topic for public policy and public health, as well as acting as an exemplar for other “causal reviews”.

Social prescribing schemes refer people toward personalized health/wellbeing interventions in local communities. Since schemes hold different representations of social prescribing, responses to the pandemic crisis will vary. Intersectionality states that social divisions build on one another, sustaining unequal health outcomes. We conducted and inductively analysed interviews with twenty-three professional and volunteer stakeholders across three social prescribing schemes in urban and rural Scotland at the start and end of year one of the pandemic. Concerns included identifying and digitally supporting disadvantaged and vulnerable individuals and reduced capacity statutory and third-sector services, obliging link workers to assume new practical and psychological responsibilities. Social prescribing services in Scotland, we argue, represent a collage of practices superimposed on a struggling healthcare system. Those in need of such services are unlikely to break through disadvantage whilst situated within a social texture wherein inequalities of education, health and environmental arrangements broadly intersect with one another.

Prenatal nutrition is associated with offspring autism spectrum disorder (herein referred to as autism), yet, it remains unknown if the association is causal. Triangulation may improve causal inference by integrating the results of conventional multivariate regression with several alternative approaches that have unrelated sources of bias. We systematically reviewed the literature on the relationship between prenatal multivitamin supplements and offspring autism, and evidence for the causal approaches applied. Six databases were searched up to 8 June 2020, by which time we had screened 1309 titles/abstracts, and retained 12 articles. Quality assessment was guided using Newcastle–Ottawa in individual studies, and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) for the body of evidence. The effect estimates from multivariate regression were meta-analysed in a random effects model and causal approaches were narratively synthesised. The meta-analysis of prenatal multivitamin supplements involved 904,947 children (8159 cases), and in the overall analysis showed no robust association with offspring autism; however, a reduced risk was observed in the subgroup of high-quality observational studies (RR 0.77, 95% CI (0.62, 0.96), I² = 62.4%), early pregnancy (RR 0.76, 95% CI (0.58; 0.99), I² = 79.8%) and prospective studies (RR 0.69, 95% CI (0.48, 1.00), I² = 95.9%). The quality of evidence was very low, and triangulation was of limited utility because alternative methods were used infrequently and often not robustly applied.

The nine Bradford Hill (BH) viewpoints (sometimes referred to as criteria) are commonly used to assess causality within epidemiology. However, causal thinking has since developed, with three of the most prominent approaches implicitly or explicitly building on the potential outcomes framework: directed acyclic graphs (DAGs), sufficient-component cause models (SCC models, also referred to as ‘causal pies’) and the grading of recommendations, assessment, development and evaluation (GRADE) methodology. This paper explores how these approaches relate to BH’s viewpoints and considers implications for improving causal assessment. We mapped the three approaches above against each BH viewpoint. We found overlap across the approaches and BH viewpoints, underscoring BH viewpoints’ enduring importance. Mapping the approaches helped elucidate the theoretical underpinning of each viewpoint and articulate the conditions when the viewpoint would be relevant. Our comparisons identified commonality on four viewpoints: strength of association (including analysis of plausible confounding); temporality; plausibility (encoded by DAGs or SCC models to articulate mediation and interaction, respectively); and experiments (including implications of study design on exchangeability). Consistency may be more usefully operationalised by considering an effect size’s transportability to a different population or unexplained inconsistency in effect sizes (statistical heterogeneity). Because specificity rarely occurs, falsification exposures or outcomes (i.e., negative controls) may be more useful. The presence of a dose-response relationship may be less than widely perceived as it can easily arise from confounding. We found limited utility for coherence and analogy. This study highlights a need for greater clarity on BH viewpoints to improve causal assessment.

Correction to Declared competing interests of authors.