Acetaminophen Use During Pregnancy and Children's Risk of Autism, ADHD, and Intellectual Disability.

Importance: Several studies suggest that acetaminophen (paracetamol) use during pregnancy may increase risk of neurodevelopmental disorders in children. If true, this would have substantial implications for management of pain and fever during pregnancy. Objective: To examine the associations of acetaminophen use during pregnancy with children's risk of autism, attention-deficit/hyperactivity disorder (ADHD), and intellectual disability. Design, Setting, and Participants: This nationwide cohort study with sibling control analysis included a population-based sample of 2 480 797 children born in 1995 to 2019 in Sweden, with follow-up through December 31, 2021. Exposure: Use of acetaminophen during pregnancy prospectively recorded from antenatal and prescription records. Main Outcomes and Measures: Autism, ADHD, and intellectual disability based on International Classification of Diseases, Ninth Revision and International Classification of Diseases, Tenth Revision codes in health registers. Results: In total, 185 909 children (7.49%) were exposed to acetaminophen during pregnancy. Crude absolute risks at 10 years of age for those not exposed vs those exposed to acetaminophen were 1.33% vs 1.53% for autism, 2.46% vs 2.87% for ADHD, and 0.70% vs 0.82% for intellectual disability. In models without sibling control, ever-use vs no use of acetaminophen during pregnancy was associated with marginally increased risk of autism (hazard ratio [HR], 1.05 [95% CI, 1.02-1.08]; risk difference [RD] at 10 years of age, 0.09% [95% CI, -0.01% to 0.20%]), ADHD (HR, 1.07 [95% CI, 1.05-1.10]; RD, 0.21% [95% CI, 0.08%-0.34%]), and intellectual disability (HR, 1.05 [95% CI, 1.00-1.10]; RD, 0.04% [95% CI, -0.04% to 0.12%]). To address unobserved confounding, matched full sibling pairs were also analyzed. Sibling control analyses found no evidence that acetaminophen use during pregnancy was associated with autism (HR, 0.98 [95% CI, 0.93-1.04]; RD, 0.02% [95% CI, -0.14% to 0.18%]), ADHD (HR, 0.98 [95% CI, 0.94-1.02]; RD, -0.02% [95% CI, -0.21% to 0.15%]), or intellectual disability (HR, 1.01 [95% CI, 0.92-1.10]; RD, 0% [95% CI, -0.10% to 0.13%]). Similarly, there was no evidence of a dose-response pattern in sibling control analyses. For example, for autism, compared with no use of acetaminophen, persons with low (<25th percentile), medium (25th-75th percentile), and high (>75th percentile) mean daily acetaminophen use had HRs of 0.85, 0.96, and 0.88, respectively. Conclusions and Relevance: Acetaminophen use during pregnancy was not associated with children's risk of autism, ADHD, or intellectual disability in sibling control analysis. This suggests that associations observed in other models may have been attributable to familial confounding.

Comparing the performance of two-stage residual inclusion methods when using physician's prescribing preference as an instrumental variable: unmeasured confounding and noncollapsibility.

Aim: The first objective is to compare the performance of two-stage residual inclusion (2SRI), two-stage least square (2SLS) with the multivariable generalized linear model (GLM) in terms of the reducing unmeasured confounding bias. The second objective is to demonstrate the ability of 2SRI and 2SPS in alleviating unmeasured confounding when noncollapsibility exists. Materials & methods: This study comprises a simulation study and an empirical example from a real-world UK population health dataset (Clinical Practice Research Datalink). The instrumental variable (IV) used is based on physicians' prescribing preferences (defined by prescribing history). Results: The percent bias of 2SRI in terms of treatment effect estimates to be lower than GLM and 2SPS and was less than 15% in most scenarios. Further, 2SRI was found to be robust to mild noncollapsibility with the percent bias less than 50%. As the level of unmeasured confounding increased, the ability to alleviate the noncollapsibility decreased. Strong IVs tended to be more robust to noncollapsibility than weak IVs. Conclusion: 2SRI tends to be less biased than GLM and 2SPS in terms of estimating treatment effect. It can be robust to noncollapsibility in the case of the mild unmeasured confounding effect.

Hemochromatosis: Ferroptosis, ROS, Gut Microbiome, and Clinical Challenges with Alcohol as Confounding Variable.

Hemochromatosis represents clinically one of the most important genetic storage diseases of the liver caused by iron overload, which is to be differentiated from hepatic iron overload due to excessive iron release from erythrocytes in patients with genetic hemolytic disorders. This disorder is under recent mechanistic discussion regarding ferroptosis, reactive oxygen species (ROS), the gut microbiome, and alcohol abuse as a risk factor, which are all topics of this review article. Triggered by released intracellular free iron from ferritin via the autophagic process of ferritinophagy, ferroptosis is involved in hemochromatosis as a specific form of iron-dependent regulated cell death. This develops in the course of mitochondrial injury associated with additional iron accumulation, followed by excessive production of ROS and lipid peroxidation. A low fecal iron content during therapeutic iron depletion reduces colonic inflammation and oxidative stress. In clinical terms, iron is an essential trace element required for human health. Humans cannot synthesize iron and must take it up from iron-containing foods and beverages. Under physiological conditions, healthy individuals allow for iron homeostasis by restricting the extent of intestinal iron depending on realistic demand, avoiding uptake of iron in excess. For this condition, the human body has no chance to adequately compensate through removal. In patients with hemochromatosis, the molecular finetuning of intestinal iron uptake is set off due to mutations in the high-FE2+ (HFE) genes that lead to a lack of hepcidin or resistance on the part of ferroportin to hepcidin binding. This is the major mechanism for the increased iron stores in the body. Hepcidin is a liver-derived peptide, which impairs the release of iron from enterocytes and macrophages by interacting with ferroportin. As a result, iron accumulates in various organs including the liver, which is severely injured and causes the clinically important hemochromatosis. This diagnosis is difficult to establish due to uncharacteristic features. Among these are asthenia, joint pain, arthritis, chondrocalcinosis, diabetes mellitus, hypopituitarism, hypogonadotropic hypogonadism, and cardiopathy. Diagnosis is initially suspected by increased serum levels of ferritin, a non-specific parameter also elevated in inflammatory diseases that must be excluded to be on the safer diagnostic side. Diagnosis is facilitated if ferritin is combined with elevated fasting transferrin saturation, genetic testing, and family screening. Various diagnostic attempts were published as algorithms. However, none of these were based on evidence or quantitative results derived from scored key features as opposed to other known complex diseases. Among these are autoimmune hepatitis (AIH) or drug-induced liver injury (DILI). For both diseases, the scored diagnostic algorithms are used in line with artificial intelligence (AI) principles to ascertain the diagnosis. The first-line therapy of hemochromatosis involves regular and life-long phlebotomy to remove iron from the blood, which improves the prognosis and may prevent the development of end-stage liver disease such as cirrhosis and hepatocellular carcinoma. Liver transplantation is rarely performed, confined to acute liver failure. In conclusion, ferroptosis, ROS, the gut microbiome, and concomitant alcohol abuse play a major contributing role in the development and clinical course of genetic hemochromatosis, which requires early diagnosis and therapy initiation through phlebotomy as a first-line treatment.

Using instrumental variables to address unmeasured confounding in causal mediation analysis.

Mediation analysis is a strategy for understanding the mechanisms by which interventions affect later outcomes. However, unobserved confounding concerns may be compounded in mediation analyses, as there may be unobserved exposure-outcome, exposure-mediator, and mediator-outcome confounders. Instrumental variables (IVs) are a popular identification strategy in the presence of unobserved confounding. However, in contrast to the rich literature on the use of IV methods to identify and estimate a total effect of a non-randomized exposure, there has been almost no research into using IV as an identification strategy to identify mediational indirect effects. In response, we define and nonparametrically identify novel estimands-double complier interventional direct and indirect effects-when 2, possibly related, IVs are available, one for the exposure and another for the mediator. We propose nonparametric, robust, efficient estimators for these effects and apply them to a housing voucher experiment.

A Cautionary Note on Using Propensity Score Calibration to Control for Unmeasured Confounding Bias When the Surrogacy Assumption Is Absent.

Conventional propensity score methods encounter challenges when unmeasured confounding is present, as it becomes impossible to accurately estimate the gold-standard propensity score when data on certain confounders are unavailable. Propensity score calibration (PSC) addresses this issue by constructing a surrogate for the gold-standard propensity score under the surrogacy assumption. This assumption posits that the error-prone propensity score, based on observed confounders, is independent of the outcome when conditioned on the gold-standard propensity score and the exposure. However, this assumption implies that confounders cannot directly impact the outcome and that their effects on the outcome are solely mediated through the propensity score. This raises concerns regarding the applicability of PSC in practical settings where confounders can directly affect the outcome. While PSC aims to target a conditional treatment effect by conditioning on a subject's unobservable propensity score, the causal interest in the latter case lies in a conditional treatment effect conditioned on a subject's baseline characteristics. Our analysis reveals that PSC is generally biased unless the effects of confounders on the outcome and treatment are proportional to each other. Furthermore, we identify 2 sources of bias: 1) the noncollapsibility of effect measures, such as the odds ratio or hazard ratio and 2) residual confounding, as the calibrated propensity score may not possess the properties of a valid propensity score.

Association between radioulnar impingement and salvage surgical procedures of the distal radioulnar joint: influence of confounding variables.

We conducted an ambispective cohort study to assess the association between symptomatic radioulnar impingement syndrome (SRUIS) and distal radioulnar joint (DRUJ) salvage surgery to examine the influence of confounders on the final effect. The outcome variable was the incidence of SRUIS and the exposure variable was the surgical procedure. Seventy-two patients with median age of 48 years (IQR 25-78) were examined using bivariate and logistic regression multivariate analyses, and confounders were analysed in 15 multivariate models. Overall, SRUIS occurred in 21 patients (29%). Bivariate analysis showed a significant association between SRUIS and type of surgical procedure, observed in 71% after Sauvé-Kapandji, 50% after Bowers and 15% after Darrach procedure. When adjusted for age, aetiology and previous surgery, the significant association disappeared. Confounding is an important factor when accounting for SRUIS after DRUJ salvage surgery. The risk of SRUIS did not depend on the procedure, but rather on patient's age, aetiology and previous surgery.Level of evidence: II.

From complexity to clarity: how directed acyclic graphs enhance the study design of systematic reviews and meta-analyses.

While frameworks to systematically assess bias in systematic reviews and meta-analyses (SRMAs) and frameworks on causal inference are well established, they are less frequently integrated beyond the data analysis stages. This paper proposes the use of Directed Acyclic Graphs (DAGs) in the design stage of SRMAs. We hypothesize that DAGs created and registered a priori can offer a useful approach to more effective and efficient evidence synthesis. DAGs provide a visual representation of the complex assumed relationships between variables within and beyond individual studies prior to data analysis, facilitating discussion among researchers, guiding data analysis, and may lead to more targeted inclusion criteria or set of data extraction items. We illustrate this argument through both experimental and observational case examples.

Impact of Molar Incisor Hypomineralization on Oral Health-Related Quality of Life in Brazilian Schoolchildren Aged 8 to 10 Years

ABSTRACT Objective: To assess the impact of Molar Incisor Hypomineralization (MIH) and confounding factors on oral health-related quality of life (OHRQoL) according to the perception of 8 to 10-year-old children and their parents/caregivers. Material and Methods: A cross-sectional study including 403 students aged 8-10 years was carried out, in which OHRQoL was measured using the Child Perceptions Questionnaire administered to both children and parents/caregivers. The diagnosis of MIH was performed according to the previously proposed index. Dental caries experience, malocclusion, and sociodemographic factors were evaluated as confounders. Cluster analysis and Poisson regression with robust variance (p<0.05) were performed. Results: The prevalence of MIH was 13.4%. Parents/caregivers of children with MIH in incisors showed a higher impact prevalence in the emotional well-being domain (PR=1.92; 95%CI=1.16-3.19). Children with hypoplasia had a higher prevalence of negative impact on OHRQoL in the oral symptoms domain (PR=1.51; 95%CI=1.03-2.23). According to the perception of parents/caregivers, dental caries experience had a negative impact on the quality of life of students in the emotional well-being domain (PR=4.19; 95%CI=1.06-16.49) and in the total questionnaire score (PR=3.21; 95%CI=1.06-9.71). Conclusion: According to the perception of parents/caregivers, children with MIH in incisors showed a greater impact on OHRQoL. Additionally, the presence of hypoplasia affected the self-perception of OHRQoL in children, and caries experience influenced the OHRQoL of children, as perceived by parents/caregivers.

Understanding the impact of non-shared unmeasured confounding on the sibling comparison analysis.

BACKGROUND: The sibling comparison analysis is used to deal with unmeasured confounding. It has previously been shown that in the presence of non-shared unmeasured confounding, the sibling comparison analysis may introduce substantial bias depending on the sharedness of the unmeasured confounder and the sharedness of the exposure. We aimed to improve the awareness of this challenge of the sibling comparison analysis. METHODS: First, we simulated sibling pairs with an exposure, a confounder and an outcome. We simulated sibling pairs with no effect of the exposure on the outcome and with positive confounding. For varying degrees of sharedness of the confounder and the exposure and for varying prevalence of the exposure, we calculated the sibling comparison odds ratio (OR). Second, we provided measures for sharedness of selected treatments based on Danish health data. RESULTS: The confounded sibling comparison OR was visualized for varying degrees of sharedness of the confounder and the exposure and for varying prevalence of the exposure. The confounded sibling comparison OR was seen to increase with increasing sharedness of the exposure and the confounded sibling comparison OR decreased with an increasing prevalence of exposure. Measures for sharedness of treatments based on Danish health data showed that treatments of chronic diseases have the highest sharedness and treatments of non-chronic diseases have the lowest sharedness. CONCLUSIONS: Researchers should be aware of the challenge regarding non-shared unmeasured confounding in the sibling comparison analysis, before applying the analysis in non-randomized studies. Otherwise, the sibling comparison analysis may lead to substantial bias.

Universal Difference-in-Differences for Causal Inference in Epidemiology.

Difference-in-differences is undoubtedly one of the most widely used methods for evaluating the causal effect of an intervention in observational (i.e., nonrandomized) settings. The approach is typically used when pre- and postexposure outcome measurements are available, and one can reasonably assume that the association of the unobserved confounder with the outcome has the same absolute magnitude in the two exposure arms and is constant over time; a so-called parallel trends assumption. The parallel trends assumption may not be credible in many practical settings, for example, if the outcome is binary, a count, or polytomous, as well as when an uncontrolled confounder exhibits nonadditive effects on the distribution of the outcome, even if such effects are constant over time. We introduce an alternative approach that replaces the parallel trends assumption with an odds ratio equi-confounding assumption under which an association between treatment and the potential outcome under no treatment is identified with a well-specified generalized linear model relating the pre-exposure outcome and the exposure. Because the proposed method identifies any causal effect that is conceivably identified in the absence of confounding bias, including nonlinear effects such as quantile treatment effects, the approach is aptly called universal difference-in-differences. We describe and illustrate both fully parametric and more robust semiparametric universal difference-in-differences estimators in a real-world application concerning the causal effects of a Zika virus outbreak on birth rate in Brazil. A supplementary digital video is available at: http://links.lww.com/EDE/C90.

Addressing unmeasured confounders in cohort studies: Instrumental variable method for a time-fixed exposure on an outcome trajectory.

Instrumental variable methods, which handle unmeasured confounding by targeting the part of the exposure explained by an exogenous variable not subject to confounding, have gained much interest in observational studies. We consider the very frequent setting of estimating the unconfounded effect of an exposure measured at baseline on the subsequent trajectory of an outcome repeatedly measured over time. We didactically explain how to apply the instrumental variable method in such setting by adapting the two-stage classical methodology with (1) the prediction of the exposure according to the instrumental variable, (2) its inclusion into a mixed model to quantify the exposure association with the subsequent outcome trajectory, and (3) the computation of the estimated total variance. A simulation study illustrates the consequences of unmeasured confounding in classical analyses and the usefulness of the instrumental variable approach. The methodology is then applied to 6224 participants of the 3C cohort to estimate the association of type-2 diabetes with subsequent cognitive trajectory, using 42 genetic polymorphisms as instrumental variables. This contribution shows how to handle endogeneity when interested in repeated outcomes, along with a R implementation. However, it should still be used with caution as it relies on instrumental variable assumptions hardly testable in practice.

Daly and colleagues have overestimated the magnitude of the "Cinderella effect" in lethal child abuse, and underestimated the role of confounding variables in its explanation: A reply to Daly (2022).

Nobes et al. (2019) used updated data from the same source-the British Home Office's Homicide Index-as that used by Daly and Wilson (1994) to investigate the Cinderella effect (increased risk to stepchildren), and in particular their claim (e.g., Daly, 2022; Daly & Wilson, 1994, 2001, 2008) that stepfathers fatally assault their young children at rates more than 100 times those of genetic fathers. Nobes et al. reported much lower-though still substantial-increased risk to young stepchildren, and little or none to older children, particularly when they took the mislabeling of noncohabiting perpetrators into account. In his Commentary, Daly (2022) largely accepts this analysis, but does not acknowledge its implications for his own findings and claims. Nobes et al. also reported that controlling for father's age accounted for much of the remaining increased risk, and argued that this and other confounding variables are likely to explain most or all of the Cinderella effect. Daly says very little about this too, but instead responds with a series of criticisms, many of which misrepresent Nobes et al.'s account, and most of which are incorrect. Young stepchildren are at increased risk, but if stepparenthood per se (i.e., lack of genetic relatedness) contributes to the explanation, its influence is considerably less than Daly claims. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Inclusion of binary proxy variables in logistic regression improves treatment effect estimation in observational studies in the presence of binary unmeasured confounding variables.

We present a simulation study and application that shows inclusion of binary proxy variables related to binary unmeasured confounders improves the estimate of a related treatment effect in binary logistic regression. The simulation study included 60,000 randomly generated parameter scenarios of sample size 10,000 across six different simulation structures. We assessed bias by comparing the probability of finding the expected treatment effect relative to the modeled treatment effect with and without the proxy variable. Inclusion of a proxy variable in the logistic regression model significantly reduced the bias of the treatment or exposure effect when compared to logistic regression without the proxy variable. Including proxy variables in the logistic regression model improves the estimation of the treatment effect at weak, moderate, and strong association with unmeasured confounders and the outcome, treatment, or proxy variables. Comparative advantages held for weakly and strongly collapsible situations, as the number of unmeasured confounders increased, and as the number of proxy variables adjusted for increased.

Investigation of the structure and magnitude of time-varying uncontrolled confounding in simulated cohort data analyzed using g-computation.

BACKGROUND: When estimating the effect of time-varying exposures on longer-term outcomes, the assumption of conditional exchangeability or no uncontrolled confounding extends beyond baseline confounding to include time-varying confounding. We illustrate the structures and magnitude of uncontrolled time-varying confounding in exposure effect estimates obtained from g-computation when sequential conditional exchangeability is violated. METHODS: We used directed acyclic graphs (DAGs) to depict time-varying uncontrolled confounding. We performed simulations and used g-computation to quantify the effects of each time-varying exposure for each DAG type. Models adjusting all time-varying confounders were considered the true (bias-adjusted) estimate. The exclusion of time-varying uncontrolled confounders represented the biased effect estimate and an unmet 'no uncontrolled confounding' assumption. True and biased estimates were compared across DAGs, with different magnitudes of uncontrolled confounding. RESULTS: Time-varying uncontrolled confounding can present in several scenarios, including relationships into subsequently measured exposure(s), outcome, unmeasured confounder(s) and other measured confounder(s). In simulations, effect estimates obtained from g-computation were more biased in DAGs when the uncontrolled confounders were directly related to the outcome. Complex DAGs that included relationships between uncontrolled confounders and other variables and relationships where exposures caused uncontrolled confounders at the next time point resulted in the most biased effect estimates. In these complex DAGs, excluding uncontrolled confounders affected the multiple effect estimates. CONCLUSIONS: Time-varying uncontrolled confounding has the potential to substantially impact observed effect estimates. Given the importance of longitudinal studies in advising public health, the impact of time-varying uncontrolled confounding warrants more recognition and evaluation using quantitative bias analysis.

The use of the E-value for sensitivity analysis.

Observational research designs enable clinicians to investigate topics for which randomized-controlled trials may be difficult to conduct. However, the lack of randomization in observational studies increases the likelihood of confounders introducing bias to study results. Analytical methods such as propensity score matching and regression analysis are employed to reduce the effects of such confounding, mainly by determining characteristics of patient groups and adjusting for measured confounders. Sensitivity analyses are subsequently applied to elucidate the extent to which study results could still be affected by unmeasured confounding. The E-value is one such approach. By presenting a value that quantifies the strength of unmeasured confounding necessary to negate the observed results, the E-value is a useful heuristic concept for assessing the robustness of observational studies. This article provides an introductory overview of how the E-value can be evaluated and presented in clinical research studies.

The Role of Confounders in Statistical Analysis.

Researchers can limit the effects of study design limitations on research findings through statistical analysis that includes crucial covariates and accounts for confounding.

Identifiability of causal effects in test-negative design studies.

Causal directed acyclic graphs (DAGs) are often used to select variables in a regression model to identify causal effects. Outcome-based sampling studies, such as the 'test-negative design' used to assess vaccine effectiveness, present unique challenges that are not addressed by the common back-door criterion. Here we discuss intuitive, graphical approaches to explain why the common back-door criterion cannot be used for identification of population average causal effects with outcome-based sampling studies. We also describe graphical rules that can be used instead in outcome-based sampling studies when the objective is limited to determining if the causal odds ratio is identifiable, and illustrate recent changes to the free online software Dagitty which incorporate these principles.

[Statistical methods of unmeasured confounder control based on negative control theory].

Controlling unmeasured confounders in non-randomized controlled studies is challenging. Negative control theory is based on the theoretical concept that the test result of negative controls must be negative. Setting appropriate negative control incorporates the specificity of association into population studies for the identification and control of unmeasured confounders. This paper explains the principles to control unmeasured confounders using negative control theory from a statistical perspective. A detailed introduction of derived methods based on negative control theory is also introduced, including adjusted standardized mortality ratio method, calibrating P-value method, generalized difference-in-difference model and double negative control method. The reasonable application of those derived methods is also comprehensively summarized based on representative case studies. Negative control is an important statistical design to identify, revise and control unmeasured confounders and a valuable method for comparative effectiveness research based on real-world data.