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

Strong epidemiologic evidence from ecologic 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 analytic approach. The falsification strategies we focus on are versions of "negative controls" analyses in which we expect 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 within the last five years in leading peer-reviewed journals. 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.

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.

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.

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.

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.

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.

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.

Considering Questions Before Methods in Dementia Research With Competing Events and Causal Goals.

Studying causal exposure effects on dementia is challenging when death is a competing event. Researchers often interpret death as a potential source of bias, although bias cannot be defined or assessed if the causal question is not explicitly specified. Here we discuss 2 possible notions of a causal effect on dementia risk: the "controlled direct effect" and the "total effect." We provide definitions and discuss the "censoring" assumptions needed for identification in either case and their link to familiar statistical methods. We illustrate concepts in a hypothetical randomized trial on smoking cessation in late midlife, and emulate such a trial using observational data from the Rotterdam Study, the Netherlands, 1990-2015. We estimated a total effect of smoking cessation (compared with continued smoking) on 20-year dementia risk of 2.1 (95% confidence interval: -0.1, 4.2) percentage points and a controlled direct effect of smoking cessation on 20-year dementia risk had death been prevented of -2.7 (95% confidence interval: -6.1, 0.8) percentage points. Our study highlights how analyses corresponding to different causal questions can have different results, here with point estimates on opposite sides of the null. Having a clear causal question in view of the competing event and transparent and explicit assumptions are essential to interpreting results and potential bias.

Handgun Ownership and Suicide in California.

BACKGROUND: Research has consistently identified firearm availability as a risk factor for suicide. However, existing studies are relatively small in scale, estimates vary widely, and no study appears to have tracked risks from commencement of firearm ownership. METHODS: We identified handgun acquisitions and deaths in a cohort of 26.3 million male and female residents of California, 21 years old or older, who had not previously acquired handguns. Cohort members were followed for up to 12 years 2 months (from October 18, 2004, to December 31, 2016). We used survival analysis to estimate the relationship between handgun ownership and both all-cause mortality and suicide (by firearm and by other methods) among men and women. The analysis allowed the baseline hazard to vary according to neighborhood and was adjusted for age, race and ethnic group, and ownership of long guns (i.e., rifles or shotguns). RESULTS: A total of 676,425 cohort members acquired one or more handguns, and 1,457,981 died; 17,894 died by suicide, of which 6691 were suicides by firearm. Rates of suicide by any method were higher among handgun owners, with an adjusted hazard ratio of 3.34 for all male owners as compared with male nonowners (95% confidence interval [CI], 3.13 to 3.56) and 7.16 for female owners as compared with female nonowners (95% CI, 6.22 to 8.24). These rates were driven by much higher rates of suicide by firearm among both male and female handgun owners, with a hazard ratio of 7.82 for men (95% CI, 7.26 to 8.43) and 35.15 for women (95% CI, 29.56 to 41.79). Handgun owners did not have higher rates of suicide by other methods or higher all-cause mortality. The risk of suicide by firearm among handgun owners peaked immediately after the first acquisition, but 52% of all suicides by firearm among handgun owners occurred more than 1 year after acquisition. CONCLUSIONS: Handgun ownership is associated with a greatly elevated and enduring risk of suicide by firearm. (Funded by the Fund for a Safer Future and others.).

Partial Identification of the Average Causal Effect in Multiple Study Populations: The Challenge of Combining Mendelian Randomization Studies.

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.

Handgun Divestment and Risk of Suicide.

BACKGROUND: Firearm ownership is strongly related to suicide risk, yet little is known about how much risk declines when ownership ends ("divestment"). METHODS: Using data from 523,182 handgun owners, we estimated the effect of divesting and remaining divested versus never divesting on the risk of suicide and firearm-specific suicide. We used pooled logistic regression with inverse probability weighting, adjusting for demographic and area-level measures. RESULTS: The 5-year risk of suicide death was 25.6 (95% confidence interval [CI] = 15.1, 37.2) per 10,000 persons with divestment and 15.2 (95% CI = 13.2, 17.3) per 10,000 persons with no divestment, corresponding to a risk difference of 10.4 (95% CI = 0.7, 21.1) per 10,000 persons. The 5-year risk of firearm-specific suicide death was 6.3 (95% CI = 1.4, 11.9) per 10,000 persons with divestment and 12.9 (95% CI = 11.0, 14.6) per 10,000 persons with no divestment, corresponding to a risk difference of -6.6 (95% CI = -11.4, -0.1) per 10,000 persons. Comparing divestment to no divestment, risks were elevated for deaths due to other causes proposed as negative control outcomes; we incorporated these estimates into a series of bias derivations to better understand the magnitude of unmeasured confounding. CONCLUSIONS: Collectively, these estimates suggest that divestment reduces firearm suicide risk by 50% or more and likely reduces overall suicide risk as well, although future data collection is needed to fully understand the extent of biases such as unmeasured confounding.

Bounding the average causal effect in Mendelian randomisation studies with multiple proposed instruments: An application to prenatal alcohol exposure and attention deficit hyperactivity disorder.

BACKGROUND: As large-scale observational data become more available, caution regarding causal assumptions remains critically important. This may be especially true for Mendelian randomisation (MR), an increasingly popular approach. Point estimation in MR usually requires strong, often implausible homogeneity assumptions beyond the core instrumental conditions. Bounding, which does not require homogeneity assumptions, is infrequently applied in MR. OBJECTIVES: We aimed to demonstrate computing nonparametric bounds for the causal risk difference derived from multiple proposed instruments in an MR study where effect heterogeneity is expected. METHODS: Using data from the Norwegian Mother, Father and Child Cohort Study (n = 2056) and Avon Longitudinal Study of Parents and Children (n = 6216) to study the average causal effect of maternal pregnancy alcohol use on offspring attention deficit hyperactivity disorder symptoms, we proposed 11 maternal SNPs as instruments. We computed bounds assuming subsets of SNPs were jointly valid instruments, for all combinations of SNPs where the MR model was not falsified. RESULTS: The MR assumptions were violated for all sets with more than 4 SNPs in one cohort and for all sets with more than 2 SNPs in the other. Bounds assuming one SNP was an individually valid instrument barely improved on assumption-free bounds. Bounds tightened as more SNPs were assumed to be jointly valid instruments, and occasionally identified directions of effect, though bounds from different sets varied. CONCLUSIONS: Our results suggest that, when proposing multiple instruments, bounds can contextualise plausible magnitudes and directions of effects. Computing bounds over multiple assumption sets, particularly in large, high-dimensional data, offers a means of triangulating results across different potential sources of bias within a study and may help researchers to better evaluate and emphasise which estimates are compatible with the most plausible assumptions for their specific setting.

Falsification of the instrumental variable conditions in Mendelian randomization studies in the UK Biobank.

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.

Invited Commentary: Conducting and Emulating Trials to Study Effects of Social Interventions.

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 With Repeated Measures of a Time-varying Exposure: An Application of Structural Mean Models.

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 cholesterol and low-density lipoprotein cholesterol on intermediate coronary heart disease 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.

Prenatal exposure to trans fatty acids and head growth in fetal life and childhood: triangulating confounder-adjustment and instrumental variable approaches.

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.

Diemer and Swanson Reply to "Considerations Before Using Pandemic as Instrument".

Dimitris and Platt (Am J Epidemiol. 2021;190(11):2275-2279) take on the challenging topic of using "shocks" such as the severe acute respiratory system coronavirus 2 (SARS-CoV-2) pandemic as instrumental variables to study the effect of some exposure on some outcome. Evoking our recent lived experiences, they conclude that the assumptions necessary for an instrumental variable analysis will often be violated and therefore strongly caution against such analyses. Here, we build upon this warranted caution while acknowledging that such analyses will still be pursued and conducted. We discuss strategies for evaluating or reasoning about when such an analysis is clearly inappropriate for a given research question, as well as strategies for interpreting study findings with special attention to incorporating plausible sources of bias in any conclusions drawn from a given finding.

Instrumental variable estimation for a time-varying treatment and a time-to-event outcome via structural nested cumulative failure time models.

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.

Mendelian randomisation approaches to the study of prenatal exposures: A systematic review.

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.

Hypothetical blood-pressure-lowering interventions and risk of stroke and dementia.

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.