Ensuring Causal, Not Casual, Inference

Re-envisioning, Retooling, and Rebuilding Prevention Science Methods to Address Structural and Systemic Racism and Promote Health Equity

The historic momentum from national conversations on the roots and current impacts of racism in the USA presents an incredible window of opportunity for prevention scientists to revisit how common theories, measurement tools, methodologies, and interventions can be radically re-envisioned, retooled, and rebuilt to dismantle racism and promote equitable health for minoritized communities. Recognizing this opportunity, the NIH-funded Prevention Science and Methodology Group (PSMG) launched a series of presentations focused on the role of Prevention Science to address racism and discrimination guided by a commitment to social justice and health equity. The current manuscript aims to advance the field of Prevention Science by summarizing key issues raised during the series' presentations and proposing concrete research priorities and steps that hold promise for promoting health equity by addressing systemic racism. Being anti-racist is an active practice for all of us, whether we identify as methodologists, interventionists, practitioners, funders, community members, or an intersection of these identities. We implore prevention scientists and methodologists to take on these conversations with us to promote science and practice that offers every life the right to live in a just and equitable world.

Target trial emulation: Do antimicrobials or gastrointestinal nutraceuticals prescribed at first presentation for acute diarrhoea cause a better clinical outcome in dogs under primary veterinary care in the UK?

Target trial emulation applies design principles from randomised controlled trials to the analysis of observational data for causal inference and is increasingly used within human epidemiology. Veterinary electronic clinical records represent a potentially valuable source of information to estimate real-world causal effects for companion animal species. This study employed the target trial framework to evaluate the usefulness on veterinary observational data. Acute diarrhoea in dogs was used as a clinical exemplar. Inclusion required dogs aged ≥ 3 months and < 10 years, presenting for veterinary primary care with acute diarrhoea during 2019. Treatment strategies were: 1. antimicrobial prescription compared to no antimicrobial prescription and 2. gastrointestinal nutraceutical prescription compared to no gastrointestinal nutraceutical prescription. The primary outcome was clinical resolution (defined as no revisit with ongoing diarrhoea within 30 days from the date of first presentation). Informed from a directed acyclic graph, data on the following covariates were collected: age, breed, bodyweight, insurance status, comorbidities, vomiting, reduced appetite, haematochezia, pyrexia, duration, additional treatment prescription and veterinary group. Inverse probability of treatment weighting was used to balance covariates between the treatment groups for each of the two target trials. The risk difference (RD) of 0.4% (95% CI -4.5% to 5.3%) was non-significant for clinical resolution in dogs treated with antimicrobials compared with dogs not treated with antimicrobials. The risk difference (RD) of 0.3% (95% CI -4.5% to 5.0%) was non-significant for clinical resolution in dogs treated with gastrointestinal nutraceuticals compared with dogs not treated with gastrointestinal nutraceuticals. This study successfully applied the target trial framework to veterinary observational data. The findings show that antimicrobial or gastrointestinal prescription at first presentation of acute diarrhoea in dogs causes no difference in clinical resolution. The findings support the recommendation for veterinary professionals to limit antimicrobial use for acute diarrhoea in dogs.

Is the Way Forward to Step Back? Documenting the Frequency with which Study Goals are Misaligned with Study Methods and Interpretations in the Epidemiologic Literature

In any research study, there is an underlying research process that should begin with a clear articulation of the study's goal. The study's goal drives this process; it determines many study features including the estimand of interest, the analytic approaches that can be used to estimate it, and which coefficients, if any, should be interpreted. "Misalignment" can occur in this process when analytic approaches and/or interpretations do not match the study's goal; misalignment is potentially more likely to arise when study goals are ambiguously framed. This study documented misalignment in the observational epidemiologic literature and explored how the framing of study goals contributes to its occurrence. The following misalignments were examined: 1) use of an inappropriate variable selection approach for the goal (a "goal-methods" misalignment) and 2) interpretation of coefficients of variables for which causal considerations were not made (e.g., Table 2 Fallacy, a "goal-interpretation" misalignment). A random sample of 100 articles published 2014-2018 in the top 5 general epidemiology journals were reviewed. Most reviewed studies were causal, with either explicitly stated (13/103, 13%) or associationally-framed (71/103, 69%) aims. Full alignment of goal-methods-interpretations was infrequent (9/103, 9%), although clearly causal studies (5/13, 38%) were more often fully aligned than seemingly causal ones (3/71, 4%). Goal-methods misalignments were common (34/103, 33%), but most frequently, methods were insufficiently reported to draw conclusions (47/103, 46%). Goal-interpretations misalignments occurred in 31% (32/103) of studies and occurred less often when the methods were aligned (2/103, 2%) compared with when the methods were misaligned (13/103, 13%).

Advances in Statistical Methods for Causal Inference in Prevention Science: Introduction to the Special Section

The board of the Society for Prevention Research noted recently that extant methods for the analysis of causality mechanisms in prevention may still be too rudimentary for detailed and sophisticated analysis of causality hypotheses. This Special Section aims to fill some of the current voids, in particular in the domain of statistical methods of the analysis of causal inference. In the first article, Bray et al. propose a novel methodological approach in which they link propensity score techniques and Latent Class Analysis. In the second article, Kelcey et al. discuss power analysis tools for the study of causal mediation effects in cluster-randomized interventions. Wiedermann et al. present, in the third article, methods of Direction Dependence Analysis for the identification of confounders and for inference concerning the direction of causal effects in mediation models. A more general approach to the identification of causal structures in non-experimental data is presented by Shimizu in the fourth article. This approach is based on linear non-Gaussian acyclic models. Molenaar introduces vector-autoregressive methods for the optimal representation of Granger causality in time-dependent data. The Special Section concludes with a commentary by Musci and Stuart. In this commentary, the contributions of the articles in the Special Section are highlighted from the perspective of the experimental causal research tradition.

Patient Selection After Mandatory Bundled Payments for Hip and Knee Replacement: Limited Evidence of Lemon-Dropping or Cherry-Picking

BACKGROUND

On April 1, 2016, the Centers for Medicare & Medicaid Services (CMS) introduced bundled-payment programs for hip replacement and knee replacement (HKR) in selected metropolitan statistical areas (MSAs) to decrease the costs and cost variability of HKR and to increase the quality of care. Early program analyses showed cost savings; however, studies also demonstrated a trend toward the selection of healthier patients for HKR performed under the bundled system. We compared the characteristics of patients who underwent HKR before implementation of the bundled-payment system (pre-policy) with those of patients who underwent HKR after implementation (post-policy).

METHODS

Patients who underwent HKR from 2015 to 2016 were identified from Medicare inpatient claims files. After matching for MSA characteristics, we used a difference-in-difference design to evaluate changes in patient case mix from pre-policy to post-policy by comparing Medicare beneficiaries receiving HKR in bundled MSAs (bMSAs) with those receiving HKR in non-bundled MSAs (nbMSAs). The main characteristics of interest were race, dual eligibility (for Medicare and Medicaid), tobacco use, obesity, presence of diabetes with or without complications, and Charlson Comorbidity Index (CCI) value. We also evaluated pre-policy to post-policy changes in patient case mix by comparing Medicare beneficiaries in bMSAs who underwent HKR compared with those who underwent hip hemiarthroplasty. Hip hemiarthroplasty was used as a control to determine whether there were changes in access to HKR.

RESULTS

We found significant differences in the unadjusted baseline characteristics between the bMSA and nbMSA cohorts, both for unmatched and matched samples. We found no significant post-policy changes in the characteristics of patients undergoing HKR. Patients undergoing hemiarthroplasty had significantly higher CCI values than did those undergoing HKR in bMSAs post-policy, although the difference was small (0.36-point higher CCI value; p < 0.01). Patients undergoing hemiarthroplasty were also 2.4% more likely to have diabetes mellitus without complications compared with those who underwent HRK post-policy (p < 0.01).

CONCLUSIONS

In contrast to previous investigators, we found little to no significant change in the characteristics (including race, dual eligibility, tobacco use, obesity, presence of diabetes with or without complications, and CCI value) of Medicare beneficiaries who underwent HKR after the initiation of the CMS mandatory bundled-payment policy.

Approaches to Improve Causal Inference in Physical Activity Epidemiology

BACKGROUND

It is not always clear whether physical activity is causally related to health outcomes, or whether the associations are induced through confounding or other biases. Randomized controlled trials of physical activity are not feasible when outcomes of interest are rare or develop over many years. Thus, we need methods to improve causal inference in observational physical activity studies.

METHODS

We outline a range of approaches that can improve causal inference in observational physical activity research, and also discuss the impact of measurement error on results and methods to minimize this.

RESULTS

Key concepts and methods described include directed acyclic graphs, quantitative bias analysis, Mendelian randomization, and potential outcomes approaches which include propensity scores, g methods, and causal mediation.

CONCLUSIONS

We provide a brief overview of some contemporary epidemiological methods that are beginning to be used in physical activity research. Adoption of these methods will help build a stronger body of evidence for the health benefits of physical activity.