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It is often of interest to decompose a total effect into an indirect effect, relayed through a particular mediator, and a direct effect. However, these effect components are not identified if there are unmeasured confounding of the mediator and the outcome. We derive nonparametric bounds on the natural direct and indirect effects, and Cornfield inequalities that the unmeasured confounders must satisfy to explain away an “observed” effect. We demonstrate, analytically and by simulation, that these bounds and Cornfield inequalities are sharper than those previously proposed in the literature. We illustrate the methods with an application to cholestyramine treatment for coronary heart disease.

In a seminal article, Ding and VanderWeele proposed a method of constructing bounds for causal effects that has become widely recognized in causal inference. This method requires the analyst to provide guesses of certain “sensitivity parameters,” loosely defined as the maximal strength of association that an unmeasured confounder may have with the exposure and with the outcome. Ding and VanderWeele stated that their bounds are sharp, but without defining this term. Using a common definition of sharpness, Sjölander A. A note on a sensitivity analysis for unmeasured confounding, and the related E-value. J Causal Inference. 2020;8(1):229–48 showed that Ding and VanderWeele’s bounds are sharp in some regions of the sensitivity parameters, but are non-sharp in other regions. In this note, we follow up the work by Sjölander A. A note on a sensitivity analysis for unmeasured confounding, and the related E-value. J Causal Inference. 2020;8(1):229–48, by deriving bounds that are guaranteed to be sharp in all regions of Ding and VanderWeele’s sensitivity parameters. We illustrate the discrepancy between Ding and VanderWeele’s bounds and the sharp bounds with a real data example on vitamin D insufficiency and urine incontinence in pregnant women.

Today, the world is facing an enormous challenge where the emissions of greenhouse gases must be reduced significantly to stop the increasing mean temperature. For the construction industry, this challenge means that structures must be designed more carefully, and materials must be used and produced more efficiently. Concrete is one of the most widely used materials, and cement production alone contributes to around 8 % of the world’s total CO2 emissions. This paper presents a pilot study in which part of the cement clinker content in accelerated shotcrete is replaced with ground granulated blast furnace slag (GGBS). Shotcrete is widely used as part of the rock support for tunnels in hard rock and has a large cement content. Thus, the environmental impact must be reduced significantly to meet future demands. However, introducing GGBS will slow down strength development, which is a crucial part of shotcrete. Thus, this paper aims to investigate the early strength development, i.e., after one and seven days. The results show that an average compressive strength of 18 MPa can be reached after 24 hours when 35 % of the cement clinker content is replaced with GGBS. Finally, some important topics for further studies are pointed out.

It is well-known that dichotomization can cause bias and loss of efficiency in estimation. One can easily construct examples where adjusting for a dichotomized confounder causes bias in causal estimation. There are additional examples in the literature where adjusting for a dichotomized confounder can be more biased than not adjusting at all. The message is clear, do not dichotomize. What is unclear is if there are scenarios where adjusting for the dichotomized confounder always leads to lower bias than not adjusting. We propose several sets of conditions that characterize scenarios where one should always adjust for the dichotomized confounder to reduce bias. We then highlight scenarios where the decision to adjust should be made more cautiously. To our knowledge, this is the first formal presentation of conditions that give information about when one should and potentially should not adjust for a dichotomized confounder.

Fibre-reinforced shotcrete is the most common support method for hard rock tunnels in the Nordic countries. The design of shotcrete is often based on empirical methods or simplified analytical equations, which neglect variations in mechanical properties and shotcrete thickness. Data collected from the field shows that significant variations in shotcrete thickness and bond strength should be expected during tunnel construction. However, how this affects the structural behaviour and capacity of the shotcrete lining is unknown. Moreover, the design philosophy for shotcrete assumes that the primary failure modes of shotcrete, i.e. bond and flexural failure, can be treated separately. This was derived based on observations of experiments in a laboratory environment. Therefore, the focus of a finalized doctoral project was to develop a numerical framework to simulate the structural behaviour of fibre-reinforced shotcrete in interaction with hard rock and rock bolts. The effect of variations in shotcrete thickness and bond strength was studied through numerical simulations to increase the understanding of its effect on the failure load of the lining. The results indicate that the most important parameter is the mean value of the shotcrete thickness and bond strength around a narrow perimeter of the block.’

Residual confounding is a common source of bias in observational studies. In this article, we build upon a series of sensitivity analyses methods for residual confounding developed by Brumback et al. and Chiba whose sensitivity parameters are constructed to quantify deviation from conditional exchangeability, given measured confounders. These sensitivity parameters are combined with the observed data to produce a “bias-corrected” estimate of the causal effect of interest. We provide important generalizations of these sensitivity analyses, by allowing for arbitrary exposures and a wide range of different causal effect measures, through the specification of the target causal effect as a parameter in a generalized linear model with the arbitrary link function. We show how our generalized sensitivity analysis can be easily implemented with standard software, and how its sensitivity parameters can be calibrated against measured confounders. We demonstrate our sensitivity analysis with an application to publicly available data from a cohort study of behavior patterns and coronary heart disease.

Biological and epidemiological phenomena are often measured with error or imperfectly captured in data. When the true state of this imperfect measure is a confounder of an outcome exposure relationship of interest, it was previously widely believed that adjustment for the mismeasured observed variables provides a less biased estimate of the true average causal effect than not adjusting. However, this is not always the case and depends on both the nature of the measurement and confounding. We describe two sets of conditions under which adjusting for a non-deferentially mismeasured proxy comes closer to the unidentifiable true average causal effect than the unadjusted or crude estimate. The first set of conditions apply when the exposure is discrete or continuous and the confounder is ordinal, and the expectation of the outcome is monotonic in the confounder for both treatment levels contrasted. The second set of conditions apply when the exposure and the confounder are categorical (nominal). In all settings, the mismeasurement must be non-differential, as differential mismeasurement, particularly an unknown pattern, can cause unpredictable results.

Unmeasured confounding is an important threat to the validity of observational studies. A common way to deal with unmeasured confounding is to compute bounds for the causal effect of interest, that is, a range of values that is guaranteed to include the true effect, given the observed data. Recently, bounds have been proposed that are based on sensitivity parameters, which quantify the degree of unmeasured confounding on the risk ratio scale. These bounds can be used to compute an E-value, that is, the degree of confounding required to explain away an observed association, on the risk ratio scale. We complement and extend this previous work by deriving analogous bounds, based on sensitivity parameters on the risk difference scale. We show that our bounds can also be used to compute an E-value, on the risk difference scale. We compare our novel bounds with previous bounds through a real data example and a simulation study.

Unmeasured confounding is one of the most important threats to the validity of observational studies. In this paper we scrutinize a recently proposed sensitivity analysis for unmeasured confounding. The analysis requires specification of two parameters, loosely defined as the maximal strength of association that an unmeasured confounder may have with the exposure and with the outcome, respectively. The E-value is defined as the strength of association that the confounder must have with the exposure and the outcome, to fully explain away an observed exposure-outcome association. We derive the feasible region of the sensitivity analysis parameters, and we show that the bounds produced by the sensitivity analysis are not always sharp. We finally establish a region in which the bounds are guaranteed to be sharp, and we discuss the implications of this sharp region for the interpretation of the E-value. We illustrate the theory with a real data example and a simulation.

In a climate of growing public concern and monitoring of business’s impact on the environment, corporations and industry groups have developed increasingly sophisticated strategies to manage their environmental reputation and to influence the outcome of environmental debates in the public sphere. In this article, we provide an exploratory overview of how the largest Swedish corporations selectively subsidise environmental news-making by supplying it with promotional materials disguised as journalistic copy. We analyse a year’s worth of public relations output from the largest 15 companies traded in the Stockholm exchange or owned by the Swedish state, in order to shed light on the environmental themes they cover, the techniques they adopt to maximise the likelihood of media coverage and the evidence they provide to support their claims. Our analysis shows that corporate voices make substantial use of environmental and ecological arguments in their strategic communication, but they provide little useful information about the company’s impact and do not usually foster forms of dialogic stakeholder engagement.

During the construction of a rock tunnel in Stockholm, several sections with leaching shotcrete (sprayed concrete) were found one year after the spraying was completed. An investigation was therefore conducted, and its results are presented in this paper. The amount of leaching after such a short time indicated that a one-sided water pressure existed in combination with a permeable shotcrete. The reason for the water pressure was likely a partly unsuccessful grouting that created sections with leaking water. The permeable shotcrete could be a combined result of insufficient curing and the use of accelerators, and the effect of in-situ curing was therefore investigated. A total of six slabs were sprayed and cured under different conditions in the tunnel. Test results according to standards indicated that curing has no significant effect on the development of mechanical strength or water penetration through the shotcrete. However, this is believed to be a result of the test method rather than the non-existing effect of curing. Lastly, some modifications to the test standard were proposed for future studies of in-situ curing.

Introduction Children with parents with psychiatric diagnoses have an increased probability for not only the same condition as their parent, but also for other conditions and behavioral and psychosocial problems. Whereas many studies have focused on parental severe mental illness due to their significant impairment, less attention has been paid to more common disorders despite their higher prevalence. In addition, because most past research only included one exposure or one outcome at a time, it remains difficult to examine and compare broad patterns of intergenerational transmission. Objectives To examine associations between six parental psychiatric diagnoses in parents, and a broad range of psychiatric diagnoses, psychotropic medications, criminality, suicide, violent victimization, accidents, and school and labor performance in their offspring. Methods Based on Swedish national registers, we linked all individuals born in Sweden between 1970 and 2000 to their biological parents (N = 3 286 293). We used a matched cohort design, analyzed with stratified Cox regression and conditional logistic regressions to examine associations between six psychiatric diagnoses in the parents, and 32 outcomes in their offspring. All exposed and unexposed children were followed from their date of birth to the date of emigration from Sweden, the death, or 31 December 2013 when the offspring were 14-44 years old. Results In terms of absolute risk, most children who had parents with psychiatric diagnoses were not diagnosed in specialist care themselves, as the proportion of having any of the 16 types of psychiatric conditions ranged from 22.17% (exposed to parental depression) to 25.05% (exposed to parental drug-related disorders) at the end of follow-up. Nevertheless, in terms of relative risk, all six parental psychiatric diagnoses increased the probability of all 32 outcomes in their offspring, with the Hazard Ratio ranging from 1.04 to 8.91 for time-to-event outcomes, and the Odds Ratio ranging from 1.29 to 3.36 for binary outcomes. Some specificities were observed for parental psychotic and substance misuse diagnoses, which strongly predicted offspring psychotic-like and externalizing-related outcomes, respectively. Conclusions The intergenerational transmission of parental psychiatric conditions appeared largely transdiagnostic, even for non-psychiatric outcomes in offspring. Given the broad spectrum of associations with the outcomes, service providers (e.g., psychiatrists, teachers, and social workers) should consider clients’ broader psychiatric family history when predicting prognosis and planning interventions/treatment. Disclosure of Interest None Declared

Background Premenstrual disorders (PMDs) and perinatal depression (PND) share symptomology and the timing of symptoms of both conditions coincide with natural hormonal fluctuations, which may indicate a shared etiology. Yet, there is a notable absence of prospective data on the potential bidirectional association between these conditions, which is crucial for guiding clinical management. Using the Swedish nationwide registers with prospectively collected data, we aimed to investigate the bidirectional association between PMDs and PND. Methods and findings With 1,803,309 singleton pregnancies of 1,041,419 women recorded in the Swedish Medical Birth Register during 2001 to 2018, we conducted a nested case-control study to examine the risk of PND following PMDs, which is equivalent to a cohort study, and transitioned that design into a matched cohort study with onward follow-up to simulate a prospective study design and examine the risk of PMDs after PND (within the same study population). Incident PND and PMDs were identified through clinical diagnoses or prescribed medications. We randomly selected 10 pregnant women without PND, individually matched to each PND case on maternal age and calendar year using incidence density sampling (N: 84,949: 849,482). We (1) calculated odds ratio (OR) and 95% confidence intervals (CIs) of PMDs using conditional logistic regression in the nested case-control study. Demographic factors (country of birth, educational level, region of residency, and cohabitation status) were adjusted for. We (2) calculated the hazard ratio (HR) and 95% CIs of PMDs subsequent to PND using stratified Cox regression in the matched cohort study. Smoking, BMI, parity, and history of psychiatric disorders were further controlled for, in addition to demographic factors. Pregnancies from full sisters of PND cases were identified for sibling comparison, which contrasts the risk within each set of full sisters discordant on PND. In the nested case-control study, we identified 2,488 PMDs (2.9%) before pregnancy among women with PND and 5,199 (0.6%) among controls. PMDs were associated with a higher risk of subsequent PND (OR 4.76, 95% CI [4.52,5.01]; p < 0.001). In the matched cohort with a mean follow-up of 7.40 years, we identified 4,227 newly diagnosed PMDs among women with PND (incidence rate (IR) 7.6/1,000 person-years) and 21,326 among controls (IR 3.8). Compared to their matched controls, women with PND were at higher risk of subsequent PMDs (HR 1.81, 95% CI [1.74,1.88]; p < 0.001). The bidirectional association was noted for both prenatal and postnatal depression and was stronger among women without history of psychiatric disorders (p for interaction < 0.001). Sibling comparison showed somewhat attenuated, yet statistically significant, bidirectional associations. The main limitation of this study was that our findings, based on clinical diagnoses recorded in registers, may not generalize well to women with mild PMDs or PND. Conclusions In this study, we observed a bidirectional association between PMDs and PND. These findings suggest that a history of PMDs can inform PND susceptibility and vice versa and lend support to the shared etiology between both disorders., Author(s): Qian Yang 1,*, Emma Bränn 1, Elizabeth R. Bertone- Johnson 2,3, Arvid Sjölander 4, Fang Fang 1, Anna Sara Oberg 4, Unnur A. Valdimarsdóttir 1,5,6, Donghao Lu 1,* Introduction [...]