How much could anemia-related interventions reduce the HIV disparity in adverse birth outcomes?

Women and other people of childbearing potential living with HIV (WLHIV) have a higher risk of adverse birth outcomes than those without HIV (WWHIV). A higher risk of anemia in WLHIV could partially explain this disparity. Using a birth outcomes surveillance study in Botswana, we emulated target trials corresponding to currently available or feasible interventions on anemia. The first target trial evaluated two interventions: initiate multiple micronutrient supplementation (MMS), and MMS or iron and folic acid supplementation by 24 weeks gestation. The remaining target trials evaluated the interventions: eliminate anemia before pregnancy; and jointly eliminate anemia before pregnancy and initiate MMS. We estimated the observed disparity in adverse birth outcomes between WLHIV and WWHIV and compared the observed disparity measure (ODM) to the counterfactual disparity measure (CDM) under each intervention. Of 137,499 individuals (22% WLHIV), the observed risk of any adverse birth outcome was 26.0% in WWHIV and 34.5% in WLHIV (ODM, 8.5% [95% CI, 7.9-9.1%]). CDMs (95% CIs) ranged from 6.6% (4.8-8.4%) for the intervention to eliminate anemia and initiate MMS to 8.4% (7.7-9.1%) for the intervention to eliminate anemia only. Preventing anemia and expanding MMS may reduce HIV disparities in birth outcomes, but interventions with greater impact should be identified.

Confounder selection in firearm policy research: A scoping review.

Legislative firearm policies are often proposed as a way of preventing firearm-related harm. Confounding is a substantial threat to accurately estimating the causal effects of firearm policies. This scoping review characterizes selection of potential confounders in US firearm policy evaluations in the health sciences literature. We identified empirical research articles indexed in PubMed from 1/1/2000-9/1/2021 that examined any of 18 pre-specified firearm policies and extracted key study elements, including the exposure (firearm policy), outcomes, potential confounders adjusted for in analyses, and study approach (i.e., static, uncontrolled pre-post, and controlled pre-post). There was wide variation in potential confounders within study approach-policy-outcome combinations. The most common potential confounders included sociodemographic and economic variables, rurality/urbanicity, violent crime, law enforcement-related variables, alcohol use, and firearm access (mostly measured via proxies for firearm ownership). Firearm policies other than the policy being evaluated were included in the adjustment set in 23-44% of studies, depending on the study approach. Confounder selection was most often said to be based on prior research (n=49, 40%) or not explicitly stated (n=48, 39%). This scoping review provides a comprehensive resource for critically appraising the firearm policy literature and offers considerations to support more rigorous confounding control in future firearm policy research.

Use of the instrumental inequalities in simulated mendelian randomization analyses with coarsened exposures.

Mendelian randomization (MR) requires strong unverifiable assumptions to estimate causal effects. However, for categorical exposures, the MR assumptions can be falsified using a method known as the instrumental inequalities. To apply the instrumental inequalities to a continuous exposure, investigators must coarsen the exposure, a process which can itself violate the MR conditions. Violations of the instrumental inequalities for an MR model with a coarsened exposure might therefore reflect the effect of coarsening rather than other sources of bias. We aim to evaluate how exposure coarsening affects the ability of the instrumental inequalities to detect bias in MR models with multiple proposed instruments under various causal structures. To do so, we simulated data mirroring existing studies of the effect of alcohol consumption on cardiovascular disease under a variety of exposure-outcome effects in which the MR assumptions were met for a continuous exposure. We categorized the exposure based on subject matter knowledge or the observed data distribution and applied the instrumental inequalities to MR models for the effects of the coarsened exposure. In simulations of multiple binary instruments, the instrumental inequalities did not detect bias under any magnitude of exposure outcome effect when the exposure was coarsened into more than 2 categories. However, in simulations of both single and multiple proposed instruments, the instrumental inequalities were violated in some scenarios when the exposure was dichotomized. The results of these simulations suggest that the instrumental inequalities are largely insensitive to bias due to exposure coarsening with greater than 2 categories, and could be used with coarsened exposures to evaluate the required assumptions in applied MR studies, even when the underlying exposure is truly continuous.

Reporting and Description of Research Methodology in Studies Estimating Effects of Firearm Policies.

BACKGROUND: Evidence about which firearm policies work, to what extent, and for whom is hotly debated, perhaps partly because variation in research methodology has produced mixed and inconclusive effect estimates. We conducted a scoping review of firearm policy research in the health sciences in the United States, focusing on methodological considerations for causal inference. METHODS: We identified original, empirical articles indexed in PubMed from 1 January 2000 to 1 September 2021 that examined any of 18 prespecified firearm policies. We extracted key study components, including policy type(s) examined, policy operationalization, outcomes, study setting and population, study approach and design, causal language, and whether and how authors acknowledged potential sources of bias. RESULTS: We screened 7733 articles and included 124. A plurality of studies used a legislative score as their primary exposure (n = 39; 32%) and did not examine change in policies over time (n = 47; 38%). Most examined firearm homicide (n = 51; 41%) or firearm suicide (n = 40; 32%) as outcomes. One-third adjusted for other firearm policies (n = 41; 33%). Three studies (2%) explicitly mentioned that their goal was to estimate causal effects, but over half used language implying causality (n = 72; 58%). Most acknowledged causal identification assumptions of temporality (n = 91; 73%) and exchangeability (n = 111; 90%); other assumptions were less often acknowledged. One-third of studies included bias analyses (n = 42; 34%). CONCLUSIONS: We identified a range of methodologic approaches in firearm policy research in the health sciences. Acknowledging the imitations of data availability and quality, we identify opportunities to improve causal inferences about and reporting on the effects of firearm policies on population health.

Toward a clearer understanding of what works to reduce gun violence: the role of falsification strategies.

Strong epidemiologic evidence from ecological and individual-level studies in the United States supports the claim that access to firearms substantially increases the risk of dying by suicide, homicide, and firearm accidents. Less certain is how well particular interventions work to prevent these deaths and other firearm-related harms. Given the limits of existing data to study firearm violence and the infeasibility of conducting randomized trials of firearm access, it is important to do the best we can with the data we already have. We argue that falsification strategies are a critical-yet underutilized-component of any such analytical approach. The falsification strategies we focus on are versions of "negative controls" analyses in which we expect that an analysis should yield a null causal effect, and thus where not obtaining a null effect estimate raises questions about the assumptions underlying causal interpretation of a study's findings. We illustrate the saliency of this issue today with examples drawn from studies published in leading peer-reviewed journals within the last 5 years. Collecting rich, high-quality data always takes time, urgent as the need may be. On the other hand, doing better with the data we already have can start right now.

Partial Identification of the Effects of Sustained Treatment Strategies.

Although many epidemiologic studies focus on point identification, it is also possible to partially identify causal effects under consistency and the data alone. However, the literature on the so-called "assumption-free" bounds has focused on settings with time-fixed exposures. We describe assumption-free bounds for the effects of both static and dynamic sustained interventions. To provide intuition for the width of the bounds, we also discuss a mathematical connection between assumption-free bounds and clone-censor-weight approaches to causal effect estimation. The bounds, which are often wide in practice, can provide important information about the degree to which causal analyses depend on unverifiable assumptions made by investigators.

Towards a Clearer Causal Question Underlying the Association Between Cancer and Dementia.

BACKGROUND: Several observational studies have described an inverse association between cancer diagnosis and subsequent dementia risk. Multiple biologic mechanisms and potential biases have been proposed in attempts to explain this association. One proposed explanation is the opposite expression of Pin1 in cancer and dementia, and we use this explanation and potential drug target to illustrate the required assumptions and potential sources of bias for inferring an effect of Pin1 on dementia risk from analyses measuring cancer diagnosis as a proxy for Pin1 expression. METHODS: We used data from the Rotterdam Study, a population-based cohort. We estimate the association between cancer diagnosis (as a proxy for Pin1) and subsequent dementia diagnosis using two different proxy methods and with confounding and censoring for death addressed with inverse probability weights. We estimate and compare the complements of a weighted Kaplan-Meier survival estimator at 20 years of follow-up. RESULTS: Out of 3634 participants, 899 (25%) were diagnosed with cancer, of whom 53 (6%) had dementia, and 567 (63%) died. Among those without cancer, 15% (411) were diagnosed with dementia, and 667 (24%) died over follow-up. Depending on the confounding and selection bias control, and the way in which cancer was used as a time-varying proxy exposure, the risk ratio for dementia diagnosis ranged from 0.71 (95% confidence interval [CI] = 0.49, 0.95) to 1.1 (95% CI = 0.79, 1.3). CONCLUSION: Being explicit about the underlying mechanism of interest is key to maximizing what we can learn from this cancer-dementia association given available or readily collected data, and to defining, detecting, and preventing potential biases.