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All else being equal, if we had 1 causal effect we wished to estimate, we would conduct a randomized trial with a protocol that mapped onto that causal question, or we would attempt to emulate that target trial with observational data. However, studying the social determinants of health often means there are not just 1 but several causal contrasts of simultaneous interest and importance, and each of these related but distinct causal questions may have varying degrees of feasibility in conducting trials. With this in mind, we discuss challenges and opportunities that arise when conducting and emulating such trials. We describe designing trials with the simultaneous goals of estimating the intention-to-treat effect, the per-protocol effect, effects of alternative protocols or joint interventions, effects within subgroups, and effects under interference, and we describe ways to make the most of all feasible randomized trials and emulated trials using observational data. Our comments are grounded in the study results of Courtin et al. (Am J Epidemiol. 2022;191(8):1444–1452).

Mendelian randomization (MR) is an increasingly popular approach to estimating causal effects. Although the assumptions underlying MR cannot be verified, they imply certain constraints, the instrumental inequalities, which can be used to falsify the MR conditions. However, the instrumental inequalities are rarely applied in MR. We aimed to explore whether the instrumental inequalities could detect violations of the MR conditions in case studies analyzing the effect of commonly studied exposures on coronary artery disease risk.Using 1077 single nucleotide polymorphisms (SNPs), we applied the instrumental inequalities to MR models for the effects of vitamin D concentration, alcohol consumption, C-reactive protein (CRP), triglycerides, high-density lipoprotein (HDL) cholesterol, and low-density lipoprotein (LDL) cholesterol on coronary artery disease in the UK Biobank. For their relevant exposure, we applied the instrumental inequalities to MR models proposing each SNP as an instrument individually, and to MR models proposing unweighted allele scores as an instrument. We did not identify any violations of the MR assumptions when proposing each SNP as an instrument individually. When proposing allele scores as instruments, we detected violations of the MR assumptions for 5 of 6 exposures.Within our setting, this suggests the instrumental inequalities can be useful for identifying violations of the MR conditions when proposing multiple SNPs as instruments, but may be less useful in determining which SNPs are not instruments. This work demonstrates how incorporating the instrumental inequalities into MR analyses can help researchers to identify and mitigate potential bias.

BACKGROUND: Tribal Nations experience substance misuse at high rates often attributed to historical and contemporary traumas. In response, several Tribal Nations are addressing these issues through efforts to promote recovery and prevention to substance misuse. OBJECTIVES: Partner with a Tribal Nation to develop a study to explore factors that contribute to the wellbeing of families to children with prenatal substance exposure and disseminate findings that can be translated back into the community. METHODS: We applied Community-based participatory research (CBPR), strengths-based, and community-driven approaches during this two-year study development phase. We experienced challenges and identified solutions to partnering with one Tribal Nation on an epidemiological mixed-methods study centered on families with children that have prenatal substance exposure. Key inputs were becoming familiarizing with the community setting, structural supports for CBPR research, incorporating Indigenous CBPR principles, and developing a Community Advisory Team. RESULTS: We successfully collaborated with the Confederated Salish Kootenai Tribes Early Childhood Services program to develop a robust study design and a dissemination plan to ensure translation of study findings to the community. The robust study design consisted of common themes specific to a highly stigmatized study population, substance-abusing pregnant women, to protect participant confidentiality. CONCLUSION: Research alignment with community goals, allotting meaningful time to develop a research partnership, and incorporating culturally sensitive and community-relevant measures contributed to the successful development of an effective and rigorous study to better serve the Tribal Nation on addressing substance misuse.

Background: Researchers often use random-effects or fixed-effects meta-analysis to combine findings from multiple study populations. However, the causal interpretation of these models is not always clear, and they do not easily translate to settings where bounds, rather than point estimates, are computed. Methods: If bounds on an average causal effect of interest in a well-defined population are computed in multiple study populations under specified identifiability assumptions, then under those assumptions the average causal effect would lie within all study-specific bounds and thus the intersection of the study-specific bounds. We demonstrate this by pooling bounds on the average causal effect of prenatal alcohol exposure on attention deficit-hyperactivity disorder symptoms, computed in two European cohorts and under multiple sets of assumptions in Mendelian randomization (MR) analyses. Results: For all assumption sets considered, pooled bounds were wide and did not identify the direction of effect. The narrowest pooled bound computed implied the risk difference was between-4 and 34 percentage points. Conclusions: All pooled bounds computed in our application covered the null, illustrating how strongly point estimates from prior MR studies of this effect rely on within-study homogeneity assumptions. We discuss how the interpretation of both pooled bounds and point estimation in MR is complicated by possible heterogeneity of effects across populations.

Mendelian randomization (MR) is often used to estimate effects of time-varying exposures on health outcomes using observational data. However, MR studies typically use a single measurement of exposure and apply conventional instrumental variable (IV) methods designed to handle time-fixed exposures. As such, MR effect estimates for time-varying exposures are often biased, and interpretations are unclear. We describe the instrumental conditions required for IV estimation with a time-varying exposure, and the additional conditions required to causally interpret MR estimates as a point effect, a period effect or a lifetime effect depending on whether researchers have measurements at a single or multiple time points. We propose methods to incorporate time-varying exposures in MR analyses based on g-estimation of structural mean models, and demonstrate its application by estimating the period effect of alcohol intake, high-density lipoprotein (HDL) cholesterol and low-density lipoprotein (LDL) cholesterol on intermediate coronary heart disease (CHD) outcomes using data from the Framingham Heart Study. We use this data example to highlight the challenges of interpreting MR estimates as causal effects, and describe other extensions of structural mean models for more complex data scenarios.

Reverse causation occurs when the outcome variable at an earlier timepoint, or a proximal precursor of the outcome (such as pre-clinical disease), has a causal effect on the risk factor. This confounding by pre-clinical or prior outcome can lead to bias when trying to estimate an average causal effect of the risk factor on the outcome. By proposing genetic variants as instrumental variables, Mendelian randomization can make inferences about the effect of shifting the trajectory of a risk factor on a later-measured outcome. As genetic variants are fixed at conception, it has been stated that Mendelian randomization investigations are not affected by reverse causation. While Mendelian randomization analyses have some protection against bias due to reverse causation, the claim of complete immunity is a misconception. We provide three plausible scenarios and accompanying examples of how reverse causation can influence Mendelian randomization estimates: 1) the effect of the genetic variant on the risk factor is mediated (at least in part) by the outcome, 2) the genetic association with the outcome is distorted by a feedback mechanism whereby the risk factor and outcome variable both influence each other over time, and 3) the genetic association with the outcome is distorted by an effect of the outcome in the parental generation on the outcome in the offspring generation. In these scenarios, we show that all causal effects typically estimated in Mendelian randomization analyses will be biased, though hypothesis testing can still be valid in Scenario 2 and 3 for a sharp causal null hypothesis. This underscores the general recommendation to view Mendelian randomization as primarily testing a causal null hypothesis rather than estimating a causal effect.

Background: Antenatal multiple micronutrient supplementation (MMS) with iron, folic acid, and other micronutrients might improve birth outcomes, but it is not currently universally recommended by WHO. Methods: In this observational cohort study, we surveyed pregnancies for adverse birth outcomes at eight hospitals from July, 2014, to July, 2018, and 18 hospitals from August, 2018, to December, 2020, in Botswana to assess four routine supplementation strategies in women presenting before 24 weeks’ gestation: folic acid only, iron only, iron and folic acid supplementation (IFAS), and MMS. Women with singleton pregnancies; a known HIV status, age, and delivery site; haemoglobin measured within 7 days of presenting to antenatal care; and weight measured within 31 days of presenting to care were included in our analysis. Data were abstracted from the maternity obstetric record (a record of antenatal care) at the time of birth from all women giving birth at selected hospitals throughout the country. We estimated risk differences overall and in key subgroups, adjusting for demographic and clinical factors. Findings: Between July 6, 2014, and Dec 8, 2020, 96 341 eligible women (21 659 [22·5%] of whom had HIV) were included in the study. 36 334 (37·7%) women initiated iron only supplementation, 1133 (11·8%) initiated folic acid only supplementation, 23 101 (24·0%) initiated IFAS, and 31 588 (32·8%) women initiated MMS. Women who initiated iron only and folic acid only supplementation had higher risks of stillbirth, preterm birth, very preterm birth, low and very low birthweight, and neonatal death compared with women who received IFAS (adjusted risk differences for iron only supplementation vs IFAS ranged from 0·22% [95% CI 0·04 to 0·40] for neonatal death to 2·39% [1·78 to 3·00] for preterm birth; and adjusted risk differences for folic acid only supplementation vs IFAS ranged from 0·77% [–0·80 to 2·34] for neonatal death to 5·75% [1·38 to 10·13] for preterm birth), with greater difference in women with HIV and those aged 35 years and older. Compared with IFAS, women who initiated MMS had lower risks of preterm and very preterm births, and low and very low birthweight (adjusted risk differences ranged from –0·50% [–0·77 to 0·23] for very preterm birth to –1·06% [–1·69 to –0·42] for preterm birth). Interpretation: Nationwide data from Botswana support improved birth outcomes with MMS compared with IFAS. Funding: National Institutes of Health, National Institute of Child Health and Human Development, and National Institute of Allergy and Infectious Diseases.

Importance: Little is known about the extent to which secondhand exposure to household firearms is associated with risk of suicide in adults who do not own guns, most of whom are women. Objective: To evaluate changes in risk of suicide among women living in gun-free households after one of their cohabitants became a handgun owner. Design, Setting, and Participants: This cohort study observed participants for up to 12 years and 2 months from October 18, 2004, to December 31, 2016. Data were analyzed from April to November 2021. The study population included 9.5 million adult women in California who did not own guns and who entered the study while living with 1 or more adults in a handgun-free home. Exposures: Secondhand exposure to household handguns. Main Outcomes and Measures: Suicide, firearm suicide, nonfirearm suicide. Results: Of 9.5 million women living in handgun-free homes, 331968 women (3.5% of the study population; mean [SD] age, 41.6 [18.0] years) became exposed to household handguns during the study period. In the entire study population, 294959 women died: 2197 (1%) of these were by suicide, 337 (15%) of which were suicides by firearm. Rates of suicide by any method during follow-up were higher among cohort members residing with handgun owners compared with those residing in handgun-free homes (hazard ratio, 1.43; 95% CI, 1.11-1.84). The excess suicide rate was accounted for by higher rates of suicide by firearm (hazard ratio, 4.32; 95% CI, 2.89-6.46). Women in households with and without handguns had similar rates of suicide by nonfirearm methods (hazard ratio, 0.90; 95% CI, 0.63-1.27). Conclusions and Relevance: In this study, the rate of suicide among women was significantly higher after a cohabitant of theirs became a handgun owner compared with the rate observed while they lived in handgun-free homes..

We aimed to study the effects of hypothetical interventions on systolic blood pressure (SBP) and smoking on risk of stroke and dementia using data from 15 years of follow-up in the Rotterdam Study. We used data from 4930 individuals, aged 55–80 years, with no prior history of stroke, dementia or cognitive impairment, followed for 15 years within the Rotterdam Study, a population-based cohort. We defined the following sustained interventions on SBP: (1) maintaining SBP below 120 mmHg, (2) maintaining SBP below 140 mmHg, (3) reducing SBP by 10% if above 140 mmHg, (4) reducing SBP by 20% if above 140 mmHg, and a combined intervention of quitting smoking with each of these SBP-lowering strategies. We considered incident stroke and incident dementia diagnoses as outcomes. We applied the parametric g-formula to adjust for baseline and time-varying confounding. The observed 15-year risk for stroke was 10.7%. Compared to no specified intervention (i.e., the “natural course”), all interventions that involved reducing SBP were associated with a stroke risk reduction of about 10% (e.g., reducing SBP by 20% if above 140 mmHg risk ratio: 0.89; 95% CI 0.76, 1). Jointly intervening on SBP and smoking status further decreased the risk of stroke (e.g., risk ratio: 0.83; 95% CI 0.71, 0.94). None of the specified interventions were associated with a substantive change in dementia risk. Our study suggests that a joint intervention on SBP and smoking cessation during later life may reduce stroke risk, while the potential for reducing dementia risk were not observed. Electronic supplementary material The online version of this article (10.1007/s10654-020-00694-5) contains supplementary material, which is available to authorized users. A Spanish version of the manuscript is provided as supplementary material.

Background: Mendelian randomisation (MR) designs apply instrumental variable techniques using genetic variants to study causal effects. MR is increasingly used to evaluate the role of maternal exposures during pregnancy on offspring health. Objectives: We review the application of MR to prenatal exposures and describe reporting of methodologic challenges in this area. Data sources: We searched PubMed, EMBASE, Medline Ovid, Cochrane Central, Web of Science, and Google Scholar. Study selection and data extraction: Eligible studies met the following criteria: (a) a maternal pregnancy exposure; (b) an outcome assessed in offspring of the pregnancy; and (c) a genetic variant or score proposed as an instrument or proxy for an exposure. Synthesis: We quantified the frequency of reporting of MR conditions stated, techniques used to examine assumption plausibility, and reported limitations. Results: Forty-three eligible studies were identified. When discussing challenges or limitations, the most common issues described were known potential biases in the broader MR literature, including population stratification (n = 29), weak instrument bias (n = 18), and certain types of pleiotropy (n = 30). Of 22 studies presenting point estimates for the effect of exposure, four defined their causal estimand. Twenty-four studies discussed issues unique to prenatal MR, including selection on pregnancy (n = 1) and pleiotropy via postnatal exposure (n = 10) or offspring genotype (n = 20). Conclusions: Prenatal MR studies frequently discuss issues that affect all MR studies, but rarely discuss problems specific to the prenatal context, including selection on pregnancy and effects of postnatal exposure. Future prenatal MR studies should report and attempt to falsify their assumptions, with particular attention to issues specific to prenatal MR. Further research is needed to evaluate the impacts of biases unique to prenatal MR in practice.

Background Strong and consistent associations between access to firearms and suicide have been found in ecologic and individual-level observational studies. For adolescents, a seminal case–control study estimated that living in a home with (vs without) a firearm was associated with a fourfold increase in the risk of death by suicide. Methods We use data from a nationally representative study of 10 123 US adolescents aged 13–18 years to (1) measure how much adolescents who live in a home with a firearm differ from those who do not in ways related to their risk of suicide, and (2) incorporate these differences into an updated effect estimate of the risk of adolescent suicide attributable to living in a home with firearms. Results Almost one-third (30.7%) of adolescents reported living in a home with firearms. Relative to those who did not, adolescents reporting living in a home with a firearm were slightly more likely to be male, older and reside in the South and rural areas, but few differences were identified for mental health characteristics. The effect size found by Brent and colleagues appeared robust to sources of possible residual confounding: updated relative risks remained above 4.0 across most sensitivity analyses and at least 3.1 in even the most conservative estimates. Conclusions Although unmeasured confounding and other biases may nonetheless remain, our updated estimates reinforce the suggestion that adolescents’ risk of suicide was increased threefold to fourfold if they had lived in homes with a firearm compared with if they had not.

In this paper we study approaches for dealing with treatment when developing a clinical prediction model. Analogous to the estimand framework recently proposed by the European Medicines Agency for clinical trials, we propose a `predictimand' framework of different questions that may be of interest when predicting risk in relation to treatment started after baseline. We provide a formal definition of the estimands matching these questions, give examples of settings in which each is useful and discuss appropriate estimators including their assumptions. We illustrate the impact of the predictimand choice in a dataset of patients with end-stage kidney disease. We argue that clearly defining the estimand is equally important in prediction research as in causal inference., Comment: under review

Dietary trans fatty acids (TFAs) are primarily industrially produced and remain abundant in processed food, particularly in low- and middle-income countries. Although TFAs are a cause of adverse cardiometabolic outcomes, little is known about exposure to TFAs in relation to brain development. We aimed to investigate the effect of maternal TFA concentration during pregnancy on offspring head growth in utero and during childhood. In a prospective population-based study in Rotterdam, the Netherlands, with 6900 mother-child dyads, maternal plasma TFA concentration was assessed using gas chromatography in mid-gestation. Offspring head circumference (HC) was measured in the second and third trimesters using ultrasonography; childhood brain morphology was assessed using magnetic resonance imaging at age 10 years. We performed regression analyses adjusting for sociodemographic and lifestyle confounders and instrumental variable (IV) analyses. Our IV analysis leveraged a national policy change that led to a substantial reduction in TFA and occurred mid-recruitment. After adjusting for covariates, maternal TFA concentration during pregnancy was inversely related to fetal HC in the third trimester (mean difference per 1% wt:wt increase: - 0.33, 95% CI - 0.51, - 0.15, cm) and to fetal HC growth from the second to the third trimester (- 0.04, 95% CI - 0.06, - 0.02, cm/week). Consistent findings were obtained with IV analyses, strengthening a causal interpretation. Association between prenatal TFA exposure and HC in the second trimester or global brain volume at age 10 years was inconclusive. Our findings are of important public health relevance as TFA levels in food remain high in many countries.

Background In many applications of instrumental variable (IV) methods, the treatments of interest are intrinsically time-varying and outcomes of interest are failure time outcomes. A common example is Mendelian randomization (MR), which uses genetic variants as proposed IVs. In this article, we present a novel application of g-estimation of structural nested cumulative failure models (SNCFTMs), which can accommodate multiple measures of a time-varying treatment when modelling a failure time outcome in an IV analysis. Methods A SNCFTM models the ratio of two conditional mean counterfactual outcomes at time k under two treatment strategies which differ only at an earlier time m. These models can be extended to accommodate inverse probability of censoring weights, and can be applied to case-control data. We also describe how the g-estimates of the SNCFTM parameters can be used to calculate marginal cumulative risks under nondynamic treatment strategies. We examine the performance of this method using simulated data, and present an application of these models by conducting an MR study of alcohol intake and endometrial cancer using longitudinal observational data from the Nurses’ Health Study. Results Our simulations found that estimates from SNCFTMs which used an IV approach were similar to those obtained from SNCFTMs which adjusted for confounders, and similar to those obtained from the g-formula approach when the outcome was rare. In our data application, the cumulative risk of endometrial cancer from age 45 to age 72 under the “never drink” strategy (4.0%) was similar to that under the “always ½ drink per day” strategy (4.3%). Conclusions SNCFTMs can be used to conduct MR and other IV analyses with time-varying treatments and failure time outcomes.

BackgroundPrevious studies using Mendelian randomization have found that fetal alcohol exposure may be associated with lower IQ and test scores in childhood.ObjectivesWe aim to replicate and extend these findings in Generation R Study, a birth cohort based in Rotterdam, the Netherlands.MethodsWe used data from Generation R which recruited pregnant women between 2002 and 2006. Alcohol use was assessed via questionnaire during each trimester. IQ was measured in the children between ages 5 and 8 using the Snijders-Oomen Non-Verbal Intelligence Test. Scores from a national standardized test administered around age 12 were used as a measure of cognition. We estimated the associations between ten genetic variants in the mothers previously found to be related to alcohol consumption and metabolism and each of the outcomes. In the children, we also estimated the association between the same genetic variants as well as two polygenic scores for alcohol consumption and the outcomes.ResultsMaternal genetic variants were not found to be related to either outcome but wide confidence intervals did not preclude important effects. A few genetic variants in the children were suggestive of a decrease in IQ. Likewise, one genetic variant and the genetic score had estimates and confidence intervals consistent with increases in standardized test scores.ConclusionsOur results provide slight support for associations between genetic variants in children related to maternal prenatal alcohol consumption and IQ and cognition outcomes. These findings are in line with two previous studies on this topic.

Supplemental Digital Content is available in the text., Background: Investigators often support the validity of Mendelian randomization (MR) studies, an instrumental variable approach proposing genetic variants as instruments, via. subject matter knowledge. However, the instrumental variable model implies certain inequalities, offering an empirical method of falsifying (but not verifying) the underlying assumptions. Although these inequalities are said to detect only extreme assumption violations in practice, to our knowledge they have not been used in settings with multiple proposed instruments. Methods: We applied the instrumental inequalities to an MR analysis of the effect of maternal pregnancy vitamin D on offspring psychiatric outcomes, proposing four independent maternal genetic variants as instruments. We assessed whether the proposed instruments satisfied the instrumental inequalities separately and jointly and explored the instrumental inequalities’ properties via simulations. Results: The instrumental inequalities were satisfied (i.e., we did not falsify the MR model) when considering each variant separately. However, the inequalities were violated when considering four variants jointly and for some combinations of two or three variants (two of 36 two-variant combinations and 18 of 24 three-variant combinations). In simulations, the inequalities detected structural biases more often when assessing proposed instruments jointly, although falsification in the absence of structural bias remained rare. Conclusions: The instrumental inequalities detected violations of the MR assumptions for genetic variants jointly proposed as instruments in our study, although the instrumental inequalities were satisfied when considering each proposed instrument separately. We discuss how investigators can assess instrumental inequalities to eliminate clearly invalid analyses in settings with many proposed instruments and provide appropriate code.

The participants in randomized trials and other studies used for causal inference are often not representative of the populations seen by clinical decision-makers. To account for differences between populations, researchers may consider standardizing results to a target population. We discuss several different types of homogeneity conditions that are relevant for standardization: Homogeneity of effect measures, homogeneity of counterfactual outcome state transition parameters, and homogeneity of counterfactual distributions. Each of these conditions can be used to show that a particular standardization procedure will result in unbiased estimates of the effect in the target population, given assumptions about the relevant scientific context. We compare and contrast the homogeneity conditions, in particular their implications for selection of covariates for standardization and their implications for how to compute the standardized causal effect in the target population. While some of the recently developed counterfactual approaches to generalizability rely upon homogeneity conditions that avoid many of the problems associated with traditional approaches, they often require adjustment for a large (and possibly unfeasible) set of covariates., Final author manuscript. To appear in European Journal of Epidemiology

Several of the hypothesized or studied exposures that may affect dementia risk are known to increase the risk of death. This may explain counterintuitive results, where exposures that are known to be harmful for mortality risk sometimes seem protective for the risk of dementia. Authors have attempted to explain these counterintuitive results as biased, but the bias associated with a particular analytic method cannot be defined or assessed if the causal question is not explicitly specified. Indeed, we can consider several causal questions when competing events like death, which cannot be prevented by design, are present. Current dementia research guidelines have not explicitly considered what constitutes a meaningful causal question in this setting or, more generally, how this choice justifies and should drive particular analytic decisions. To contextualize current practices, we first perform a systematic review of the conduct and interpretation of longitudinal studies focused on dementia outcomes where death is a competing event. We then describe and demonstrate how to address different causal questions (referred here as “the total effect” and “the controlled direct effect”) with traditional analytic approaches under explicit assumptions. Our application focuses on smoking cessation in late-midlife. To illustrate core concepts, we discuss this example both in terms of a hypothetical randomized trial and with an emulation of such a trial using observational data from the Rotterdam Study.

Estimates from conventional Mendelian randomization (MR) analyses can be biased when the genetic variants proposed as instruments vary over age in their relationship with the exposure. For four exposures commonly studied using MR, we assessed the degree to which their relationship with genetic variants commonly used as instruments varies by age using flexible, spline-based models in UK Biobank data. Using these models, we then estimated how biased MR estimates would be due to age-varying relationships using plasmode simulations. We found that most genetic variants had age-varying relationships with the exposure for which they are a proposed instrument. Body mass index and LDL cholesterol had the most variation while alcohol consumption had very little. This variation over age led to small potential biases in some cases (e.g. alcohol consumption and C-reactive protein) and large potential biases for many proposed instruments for BMI and LDL.

Drug repurposing provides a rapid approach to meet the urgent need for therapeutics to address COVID-19. To identify therapeutic targets relevant to COVID-19, we conducted Mendelian randomization (MR) analyses, deriving genetic instruments based on transcriptomic and proteomic data for 1,263 actionable proteins that are targeted by approved drugs or in clinical phase of drug development. Using summary statistics from the Host Genetics Initiative and the Million Veteran Program, we studied 7,554 patients hospitalized with COVID-19 and >1 million controls. We found significant Mendelian randomization results for three proteins (ACE2: P=1.6×10−6, IFNAR2: P=9.8×10−11, and IL-10RB: P=1.9×10−14) using cis-eQTL genetic instruments that also had strong evidence for colocalization with COVID-19 hospitalization. To disentangle the shared eQTL signal for IL10RB and IFNAR2, we conducted phenome-wide association scans and pathway enrichment analysis, which suggested that IFNAR2 is more likely to play a role in COVID-19 hospitalization. Our findings prioritize trials of drugs targeting IFNAR2 and ACE2 for early management of COVID-19.

Purpose To quantify eating disorder (ED) stability and diagnostic transition among a community-based sample of adolescents and young adult females in the United States. Methods Using 11 prospective assessments from 9,031 U.S. females ages 9–15 years at baseline of the Growing Up Today Study, we classified cases of the following EDs involving bingeing and purging: bulimia nervosa (BN), binge ED, purging disorder (PD), and subthreshold variants defined by less frequent (monthly vs. weekly) bingeing and purging behaviors. We measured number of years symptomatic and probability of maintaining symptoms, crossing to another diagnosis, or resolving symptoms across consecutive surveys. Results Study lifetime disorder prevalence was 2.1% for BN and roughly 6% each for binge ED and PD. Most cases reported symptoms during only one survey year. Twenty-six percent of cases crossed between diagnoses during follow-up. Among participants meeting full threshold diagnostic criteria, transition from BN was most prevalent, crossing most frequently from BN to PD (12.9% of BN cases). Within each disorder phenotype, 20%–40% of cases moved between subthreshold and full threshold criteria across consecutive surveys. Conclusions Diagnostic crossover is not rare among adolescent and young adult females with an ED. Transition patterns from BN to PD add support for considering these classifications in the same diagnostic category of disorders that involve purging. The prevalence of crossover between monthly and weekly symptom frequency suggests that a continuum or staging approach may increase utility of ED classification for prognostic and therapeutic intervention.

Mendelian randomization (MR) is used to answer a variety of epidemiologic questions. One stated advantage of MR is that it estimates a “lifetime effect” of exposure, though this term remains vaguely defined. Instrumental variable analysis, on which MR is based, has focused on estimating the effects of point or time-fixed exposures rather than “lifetime effects.” Here we use an empirical example with data from the Rotterdam Study (Rotterdam, the Netherlands, 2009–2013) to demonstrate how confusion can arise when estimating “lifetime effects.” We provide one possible definition of a lifetime effect: the average change in outcome measured at time t when the entire exposure trajectory from conception to time t is shifted by 1 unit. We show that MR only estimates this type of lifetime effect under specific conditions—for example, when the effect of the genetic variants used on exposure does not change over time. Lastly, we simulate the magnitude of bias that would result in realistic scenarios that use genetic variants with effects that change over time. We recommend that investigators in future MR studies carefully consider the effect of interest and how genetic variants whose effects change with time may impact the interpretability and validity of their results.

Purpose of Review Instrumental variable (IV) methods continue to be applied to questions ranging from genetic to social epidemiology. In the epidemiologic literature, discussion of whether the assumptions underlying IV analyses hold is often limited to only certain assumptions and even then, arguments are mostly made using subject matter knowledge. To complement subject matter knowledge, there exist a variety of falsification strategies and other tools for weighing the plausibility of the assumptions underlying IV analyses. Recent Findings There are many tools that can refute the IV assumptions or help estimate the magnitude or direction of possible bias if the conditions do not hold perfectly. Many of these tools, including both recently developed strategies and strategies described decades ago, are underused or only used in specific applications of IV methods in epidemiology. Summary Although estimating causal effects with IV analyses relies on unverifiable assumptions, the assumptions can sometimes be refuted. We suggest that the epidemiologists using IV analyses employ all the falsification strategies that apply to their research question in order to avoid settings that demonstrably violate a core condition for valid inference.

Several methods have been proposed for partially or point identifying the average treatment effect (ATE) using instrumental variable (IV) type assumptions. The descriptions of these methods are widespread across the statistical, economic, epidemiologic, and computer science literature, and the connections between the methods have not been readily apparent. In the setting of a binary instrument, treatment, and outcome, we review proposed methods for partial and point identification of the ATE under IV assumptions, express the identification results in a common notation and terminology, and propose a taxonomy that is based on sets of identifying assumptions. We further demonstrate and provide software for the application of these methods to estimate bounds. Supplementary materials for this article are available online.

markdownabstractBackground Instrumental variable (IV) methods are often used to identify ‘local’ causal effects in a subgroup of the population of interest. Such ‘local’ effects may not be ideal for informing clinical or policy decision making. When the instrument is non-causal, additional difficulties arise for interpreting ‘local’ effects. Little attention has been paid to these difficulties, even though commonly proposed instruments in epidemiology are non-causal (e.g. proxies for physician’s preference; genetic variants in some Mendelian randomization studies). Methods For IV estimates obtained from both causal and non-causal instruments under monotonicity, we present results to help investigators pose four questions about the local effect estimates obtained in their studies. (1) To what subgroup of the population does the effect pertain? Can we (2) estimate the size of or (3) describe the characteristics of this subgroup relative to the study population? (4) Can the sensitivity of the effect estimate to deviations from monotonicity be quantified? Results We show that the common interpretations and approaches for answering these four questions are generally only appropriate in the case of causal instruments. Conclusions Appropriate interpretation of an IV estimate under monotonicity as a ‘local’ effect critically depends on whether the proposed instrument is causal or non-causal. The results and formal proofs presented here can help in the transparent reporting of IV results and in enhancing the use of IV estimates in informing decision-making efforts.

Sometimes instrumental variable methods are used to test whether a causal effect is null rather than to estimate the magnitude of a causal effect. However, when instrumental variable methods are applied to time-varying exposures, as in many Mendelian randomization studies, it is unclear what causal null hypothesis is tested. Here, we consider different versions of causal null hypotheses for time-varying exposures, show that the instrumental variable conditions alone are insufficient to test some of them, and describe additional assumptions that can be made to test a wider range of causal null hypotheses, including both sharp and average causal null hypotheses. Implications for interpretation and reporting of instrumental variable results are discussed. Electronic supplementary material The online version of this article (10.1007/s10654-018-0396-6) contains supplementary material, which is available to authorized users.

Contains fulltext : 200463.pdf (Publisher’s version ) (Open Access) A potential mechanism by which smoking affects ischemic stroke is through wider venules, but this mediating role of wider venules has never been quantified. Here, we aimed to estimate to what extent the effect of smoking on ischemic stroke is possibly mediated by the venules via the recently developed four-way effect decomposition. This study was part of a population-based study including 9109 stroke-free persons participated in the study in 1990, 2004, or 2006 (mean age: 63.7 years; 58% women). Smoking behavior (smoking versus non-smoking) was identified by interview. Retinal venular calibers were measured semi-automatically on retinal photographs. Incident strokes were assessed until January 2016. A regression-based approach was used with venular calibers as mediator to decompose the total effect of smoking compared to non-smoking into four components: controlled direct effect (neither mediation nor interaction), pure indirect effect (mediation only), reference interaction effect (interaction only) and mediated interaction effect (both mediation and interaction). During a mean follow-up of 12.5 years, 665 persons suffered an ischemic stroke. Smoking increased the risk of developing ischemic stroke compared to non-smoking with an excess risk of 0.41 (95% confidence interval 0.10; 0.67). With retinal venules as a potential mediator, the excess relative risk could be decomposed into 77% controlled direct effect, 4% mediation only, 4% interaction only, and 15% mediated interaction. To conclude, in the pathophysiology of ischemic stroke, the effect of smoking on ischemic stroke may partly explained by changes in the venules, where there is both pure mediation and mediated interaction.

Purpose of review When leveraging observational data to estimate treatment effects, it is useful to explicitly specify the “target trial” the investigators aspire to emulate. One concern is whether a proposed analysis plan can address the realities of the differences between the available non-randomized observational study and the target trial. When large or unknown sources of unmeasured confounding are suspected, investigators might consider turning to instrumental variable (IV) methods. Of course, the interpretation and appropriateness of IV analyses need to be considered carefully. The purpose of this review is to summarize recent methodologic advancements in how epidemiologists weigh the validity of an IV analysis and to place these methodologic advancements in the context of the feasible target trial’s protocol components. Recent findings There have been increased development and application of tools for sensitivity analyses, falsification strategies, and the identification of previously overlooked problems with IV analyses as applied in pharmacoepidemiology. Many of these recent insights can be seen as articulating restrictions on or tradeoffs between the types of target trials that can be validly emulated when using a classical IV analysis. Summary Putting classical IV methods in the context of target trials underscores the importance of recent methodologic developments and, more generally, when and how an IV analysis would be appropriate. We see that some tradeoffs in defining the target trials are unavoidable, that some tradeoffs may be offset or explored via sensitivity analyses, and that this serves as a framework for scientific discourse regarding IV and non-IV results emulating potentially different trials with different tradeoffs.

Objective Extensive evidence suggests that children and adolescents often inaccurately perceive their body size. However, the extent of this misperception is unclear. This paper describes the agreement between children's actual weight and the perception of body size (self-reported and maternally reported) and the association of actual weight with self-reported body satisfaction. Methods In a population-based cohort study of 3,408 children aged 9 to 10 years, we assessed the children's self-perception and ideal perception of their body size with the Children's Body Image Scale. Maternal perception of offspring body size was assessed with the question “How would you describe your child at the moment?” Children's height and weight were measured. Results Children tended to rate themselves toward average proportions; e.g., 83.0% of 499 children with overweight/obesity perceived themselves as less heavy then they were. Of those who underestimated their body size, most (79.2%) had a desire to be thinner; all of the children who correctly recognized their overweight/obesity had such a desire. Conclusions Despite the misperception of body size, the majority of children with overweight/obesity indicated dissatisfaction with their body size, suggesting more self-awareness than would be assumed based on the self-perception assessment of body size alone.

Objective The majority of persons with eating disorders (EDs) do not seek ED treatment, yet little is known about treatment-seeking barriers or facilitators. The aim of the study is to describe the characteristics associated with seeking ED treatment among U.S. adolescents with EDs. Method Data from a nationally representative cross-sectional study of U.S. adolescents ages 13–18 years were used for these analyses. Specifically, adolescents who met criteria for lifetime EDs (N = 281) were included. Sociodemographic information, characteristics of EDs, psychiatric comorbidities, and other mental health service use were assessed via interview. Results Only 20% of adolescents sought ED treatment. Females were 2.2 (95% CI 0.8, 6.4) times more likely to seek treatment than males (19.9% vs. 8.9%). Adolescents who met criteria for anorexia nervosa or bulimia nervosa were 2.4 (95% CI 0.9, 6.3) and 1.9 (95% CI 1.0, 3.8) times more likely to seek treatment than adolescents who met criteria for binge-eating disorder (27.5% and 22.3% vs. 11.6%). Specific ED behaviors (restriction and purging), ED-related impairment, and any mental health service use were also associated with adolescent treatment seeking. Discussion Adolescent treatment seeking was infrequent overall, with individuals with counter-stereotypic ED presentations least likely to have sought treatment. Adolescent treatment seeking could be promoted through increasing awareness among the public and healthcare professionals that EDs affect a heterogeneous group of people. More generally, research involving both treatment-seeking and non-treatment-seeking individuals holds great potential to refine the field's knowledge of ED etiology, prevalence, treatment, and prevention.

ObjectiveObservational data can be used to attempt to emulate a target trial of statin use and estimate analogues of intention-to-treat and per protocol effects on dementia risk.MethodsUsing data from a prospective cohort study in the Netherlands, we conceptualized a sequence of “trials” in which eligible individuals ages 55–80 years were classified as statin initiators or noninitiators for every consecutive month between 1993 and 2007 and were followed until diagnosis of dementia, death, loss to follow-up, or the end of follow-up. We estimated 2 types of effects of statin use on dementia and a combined endpoint of dementia or death: the effect of initiation vs no initiation and the effect of sustained use vs no use. We estimated risk by statin treatment strategy over time via pooled logistic regression. We used inverse-probability weighting to account for treatment-confounder feedback in estimation of per-protocol effects.ResultsOf 233,526 eligible person-trials (6,373 individuals), there were 622 initiators and 232,904 noninitiators. Comparing statin initiation with no initiation, the 10-year risk differences (95% confidence interval) were −0.1% (−2.3% to 1.8%) for dementia and 0.3% (−2.7% to 3.3%) for dementia or death. Comparing sustained statin use vs no use, the 10-year risk differences were −2.2% (−5.2% to 1.6%) for dementia and −5.1% (−10.5% to −1.1%) for dementia or death.ConclusionsIndividuals with sustained statin use, but not statin initiation alone, had reduced 10-year risks of dementia and dementia or death. Our results should be interpreted with caution due to the small number of initiators and events and potential for residual confounding.

While the number of studies using Mendelian randomization (MR) methods has grown exponentially in the last decade, the quality of reporting of these studies often has been poor. Similar to other reporting guidelines such as CONSORT (Consolidated Standards of Reporting Trials) for randomised trials and STROBE (STrenghtening the Reporting of Observational studies in Epidemiology) for observational studies in epidemiology, the STROBE-MR working group aims to provide guidance to authors on how to improve reporting of MR studies and help readers, reviewers, and journal editors to evaluate the quality of the presented evidence. Empirical evidence indicates that many reports of MR studies do not clearly state or examine the various assumptions of MR methods and report insufficient details on the data sources, which makes it hard to evaluate the quality and reliability of the results. The STROBE-MR guidance covers both one sample and two sample MR studies. At present, the draft checklist consists of 20 items, organized into the title and abstract, introduction, methods, results and discussion sections of articles. As these guidelines aim to reach the entire MR community, we would like to give everyone the opportunity to contribute their comments. The following draft of the STROBE-MR checklist is open for public discussion and all feedback will be taken into account during its next revision. For feedback, please use the comment section below this post on PeerJ Preprints. We hope the final guidelines will serve the entire community and contribute to improving the reporting of MR studies in the future.

BackgroundVirtually all existing evidence linking access to firearms to elevated risks of mortality and morbidity comes from ecological and case–control studies. To improve understanding of the health risks and benefits of firearm ownership, we launched a cohort study: the Longitudinal Study of Handgun Ownership and Transfer (LongSHOT).MethodsUsing probabilistic matching techniques we linked three sources of individual-level, state-wide data in California: official voter registration records, an archive of lawful handgun transactions and all-cause mortality data. There were nearly 28.8 million unique voter registrants, 5.5 million handgun transfers and 3.1 million deaths during the study period (18 October 2004 to 31 December 2016). The linkage relied on several identifying variables (first, middle and last names; date of birth; sex; residential address) that were available in all three data sets, deploying them in a series of bespoke algorithms.ResultsAssembly of the LongSHOT cohort commenced in January 2016 and was completed in March 2019. Approximately three-quarters of matches identified were exact matches on all link variables. The cohort consists of 28.8 million adult residents of California followed for up to 12.2 years. A total of 1.2 million cohort members purchased at least one handgun during the study period, and 1.6 million died.ConclusionsThree steps taken early may be particularly useful in enhancing the efficiency of large-scale data linkage: thorough data cleaning; assessment of the suitability of off-the-shelf data linkage packages relative to bespoke coding; and careful consideration of the minimum sample size and matching precision needed to support rigorous investigation of the study questions.

Supplemental Digital Content is available in the text., Mendelian randomization investigations are becoming more powerful and simpler to perform, due to the increasing size and coverage of genome-wide association studies and the increasing availability of summarized data on genetic associations with risk factors and disease outcomes. However, when using multiple genetic variants from different gene regions in a Mendelian randomization analysis, it is highly implausible that all the genetic variants satisfy the instrumental variable assumptions. This means that a simple instrumental variable analysis alone should not be relied on to give a causal conclusion. In this article, we discuss a range of sensitivity analyses that will either support or question the validity of causal inference from a Mendelian randomization analysis with multiple genetic variants. We focus on sensitivity analyses of greatest practical relevance for ensuring robust causal inferences, and those that can be undertaken using summarized data. Aside from cases in which the justification of the instrumental variable assumptions is supported by strong biological understanding, a Mendelian randomization analysis in which no assessment of the robustness of the findings to violations of the instrumental variable assumptions has been made should be viewed as speculative and incomplete. In particular, Mendelian randomization investigations with large numbers of genetic variants without such sensitivity analyses should be treated with skepticism.

Hypertension is a major modifiable risk factor for stroke. Associations of blood pressure with incident stroke are mostly based on single or average blood pressure levels. However, this approach does not take into account long-term trajectories of blood pressure, which can vary considerably in the elderly. Within the population-based Rotterdam Study, we examined trajectories of systolic blood pressure in 6745 participants (60.0% women) over an age-range from 55 to 106 years and jointly modeled their risk of stroke and competing causes of death using joint latent class mixed modeling. Four trajectories were identified. Class 1 was characterized by blood pressure increasing gradually from on average 120 to 160 mm Hg over 5 decades (n=4938). Compared with this class, class 2, characterized by a similar midlife blood pressure, but a steep increase (n=822, increasing from 120 to 200 mm Hg), and class 4, characterized by a high midlife blood pressure (n=115; average 160 mm Hg) and had a higher risk of stroke and death. Class 3, characterized by a moderate midlife blood pressure (n=870; average 140 mm Hg), had a similar risk of death as class 1, but the highest risk of stroke. Assessing trajectories of blood pressure provides a more nuanced understanding of the associations between blood pressure, stroke, and mortality. In particular, high blood pressure and rapidly increasing blood pressure patterns are associated with a high risk of stroke and death, whereas moderately high blood pressure is only related to an increased risk of stroke. Future studies should explore the potential pathogenic significance of these patterns.

Background Many patients with major depressive disorder (MDD) who begin antidepressant treatment discontinue use before for six months, the recommended minimum treatment length. This study sought to identify predictors of six-month antidepressant persistence including predictors utilizing patients' electronic prescription records. Methods Commercially insured children (3–17 years) and adults (18–64 years) with MDD who initiated antidepressant treatment, 1/1/2003–2/28/2010, were assessed for six-month persistence (based on prescriptions' days supply, allowing a 30-day grace period). Antidepressant persistence prediction models were developed separately for children and adults. Two additional measures, days without medication between the first and second antidepressant fill (children and adults) and prior persistence on other medications (adults only), were added to the models, concordance (c) statistics were compared and risk reclassification evaluated. Results Among children (n=8837 children) and adults (n=47,495) with MDD, six-month antidepressant persistence was low and varied by age (37%, 18–24 years to 52%, 3–12 and 50–64 years). Independent baseline predictors of persistence were identified, with model c-statistics: children=0.582, adults=0.584. Patients with more days without medication between fills were less likely to be persistent (10–30 vs. 0 days, children: RR=0.72, adults: RR=0.74), as were adults not previously persistent to other medications (RR=0.73). Limitations The definition of six-month persistence is dependent on correct days supply values and the grace period utilized; potential predictors were limited to measures available in claims data. Conclusions Six-month antidepressant persistence was low and overall prediction of persistence was poor; however, days without medication between fills and prior persistence on other medications marginally improved the ability to predict antidepressant persistence.

Observational analyses for causal inference often rely on real world data collected for purposes other than research. A frequent goal of these observational analyses is to use the data to emulate a hypothetical randomized experiment, i.e., the target trial, that mimics the design features of a true experiment, including a clear definition of time zero with synchronization of treatment assignment and determination of eligibility. We review a recent observational analysis that explicitly emulated a target trial of screening colonoscopy using insurance claims from U.S. Medicare. We then compare this explicit emulation with alternative, simpler observational analyses that do not synchronize treatment assignment and eligibility determination at time zero and/or do not allow for repeated eligibility. This empirical comparison suggests that lack of an explicit emulation of the target trial leads to biased estimates, and shows that allowing for repeated eligibility increases the statistical efficiency of the estimates.

___Purpose:___ Non-steroidal anti-inflammatory drugs (NSAIDs) are commonly used during pregnancy. Findings that prenatal NSAID exposure may affect offspring neurodevelopment have been inconsistent. We investigated the effect of prenatal NSAID exposure on childhood neurodevelopment and explored the susceptibility of our effect estimates to forms of bias via negative exposure, negative outcome, and multi-informant analyses. ___Methods:___ In a cohort of pregnant women (n = 6876), perinatal NSAID use was assessed by prescriptions and self-report. Primary neurodevelopmental outcomes included attention problems using maternal reports at 1½, 3, and 5 years. To explore potential systematic biases, we compared estimates from maternally reported attention problems to a teacher's report and a measure of nonverbal intelligence assessed at a clinic visit at age 6 years; we also used NSAID use before pregnancy and somatic problems as a “negative” exposure and outcome, respectively. ___Results:___ Maternal reports suggested that prenatal exposure to NSAIDs was associated with more attention problems at younger ages (eg, at age 3: mean difference in attention problems score: 0.30; 95% CI 0.12, 0.48). However, no strong association with attention problems was found in the teacher report, and a similarly strong association between prenatal NSAID exposure and somatic complaints suggests residual confounding by indication likely remains. Moreover, prenatal exposure to NSAIDs was not associated with an observed measure of IQ (mean difference in IQ score: −0.32; 95% CI: −1.82, 1.19). ___Conclusions:___ Jointly, our results suggest that the observed associations between prenatal exposure to NSAIDs and child attention problems reflect systematic biases of a null or small effect.

Distance to care is a common exposure and proposed instrumental variable in health research, but it is vulnerable to violations of fundamental identifiability conditions for causal inference. We used data collected from the Botswana Birth Outcomes Surveillance study between 2014 and 2016 to outline 4 challenges and potential biases when using distance to care as an exposure and as a proposed instrument: selection bias, unmeasured confounding, lack of sufficiently well-defined interventions, and measurement error. We describe how these issues can arise, and we propose sensitivity analyses for estimating the degree of bias.