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Hypertrophic scar (HS) results from burns or trauma, causing aesthetic and functional issues. However, observational studies have linked inflammatory cytokines to HS, but the causal pathways involved are unclear. We aimed to determine how circulating inflammatory cytokines contribute to HS formation. Two-sample Mendelian randomization (MR) was used to identify genetic variants associated with hypertrophic scar in a comprehensive, publicly available genome-wide association study (GWAS) involving 766 patients and 207,482 controls of European descent. Additionally, data on 91 plasma proteins were drawn from a GWAS summary involving 14,824 healthy participants. Causal relationships between exposures and outcomes were investigated primarily using the inverse variance weighted (IVW) method. Furthermore, a suite of sensitivity analyses, including MR‒Egger and weighted median approaches, were concurrently employed to fortify the robustness of the conclusive findings. Finally, reverse MR analysis was conducted to evaluate the plausibility of reverse causation between hypertrophic scar and the cytokines identified in our study. In inflammatory cytokines, there was evidence of inverse associations of osteoprotegerin(OPG) levels(OR = 0.59, 95% CI = 0.41 ∼ 0.85, p = 0.01), and leukemia inhibitory factor(LIF) levels(OR = 0.51, 95% CI = 0.32 ∼ 0.82, p = 0.01) are a nominally negative association with hypertrophic scar risk, while CUB domain-domain-containing protein 1(CDCP1) level(OR = 0.59, 95% CI = 0.41 ∼ 0.85, p = 0.01) glial cell line-derived neurotrophic factor(GDNF) levels(OR = 1.42, 95% CI = 1.03 ∼ 1.96, p = 0.01) and programmed cell death 1 ligand 1(PD-L1) levels(OR = 1.47, 95% CI = 1.92 ∼ 2.11, p = 0.04) showed a positive association with hypertrophic scar risk. These associations were similar in the sensitivity analyses. According to our MR findings, OPG and LIF have a protective effect on hypertrophic scar, while CDCP1, GDNF, and PD-L1 have a risk-increasing effect on Hypertrophic scar. Our study adds to the current knowledge on the role of specific inflammatory biomarker pathways in hypertrophic scar. Further validation is needed to assess the potential of these cytokines as pharmacological or lifestyle targets for hypertrophic scar prevention and treatment. [ABSTRACT FROM AUTHOR]

Transcriptome-wide association studies (TWAS) have been increasingly applied to identify (putative) causal genes for complex traits and diseases. TWAS can be regarded as a two-sample two-stage least squares method for instrumental variable (IV) regression for causal inference. The standard TWAS (called TWAS-L) only considers a linear relationship between a gene's expression and a trait in stage 2, which may lose statistical power when not true. Recently, an extension of TWAS (called TWAS-LQ) considers both the linear and quadratic effects of a gene on a trait, which however is not flexible enough due to its parametric nature and may be low powered for nonquadratic nonlinear effects. On the other hand, a deep learning (DL) approach, called DeepIV, has been proposed to nonparametrically model a nonlinear effect in IV regression. However, it is both slow and unstable due to the ill-posed inverse problem of solving an integral equation with Monte Carlo approximations. Furthermore, in the original DeepIV approach, statistical inference, that is, hypothesis testing, was not studied. Here, we propose a novel DL approach, called DeLIVR, to overcome the major drawbacks of DeepIV, by estimating a related but different target function and including a hypothesis testing framework. We show through simulations that DeLIVR was both faster and more stable than DeepIV. We applied both parametric and DL approaches to the GTEx and UK Biobank data, showcasing that DeLIVR detected additional 8 and 7 genes nonlinearly associated with high-density lipoprotein (HDL) cholesterol and low-density lipoprotein (LDL) cholesterol, respectively, all of which would be missed by TWAS-L, TWAS-LQ, and DeepIV; these genes include BUD13 associated with HDL, SLC44A2 and GMIP with LDL, all supported by previous studies. [ABSTRACT FROM AUTHOR]

Background: The causal direction and magnitude of the associations between blood cell count and coronary heart disease (CHD) remain uncertain due to susceptibility of reverse causation and confounding. This study aimed to investigate the associations between blood cell count and CHD using Mendelian randomization (MR).Methods: In this two-sample MR study, we identified independent blood cell count associated genetic variants from a genome-wide association studies (GWAS) among European ancestry individuals. Summary level data of CHD was obtained from a GWAS consisting of 547261 subjects. Methods of inverse variance weighted (IVW), Mendelian Randomization-Egger (MR-Egger), weighted median, and outlier test (MR-PRESSO) were conducted to investigate the associations between blood cell and CHD.Results: Among all cardiovascular outcomes of interest, blood cell counts were only associated with CHD. Our findings indicated that white blood cell count and neutrophil cell count were significantly associated with increased risk of CHD [odds ratio (OR) = 1.07, 95% confidence interval (CI), 1.01–1.14; OR = 1.09, 1.02–1.16). However, there was no significant association between monocyte cell count, basophil cell count, lymphocyte cell count, eosinophil cell count, and CHD (p > 0.05). The results after excluding outliers were consistent with main results and the sensitivity analyses showed no evidence of pleiotropy (MR-Egger intercept, p > 0.05).Conclusion: Our MR study suggested that greater white blood cell count and neutrophil cell count were associated with a higher risk of CHD. Future studies are still warranted to validate the results and investigate the mechanisms underlying these associations.

Can green vegetation absorb air pollutants and control regional air pollution? The existing research has not reached a consistent conclusion on this issue. Using the multi-level data in China, this paper provides empirical evidence of the causal impact of greenspace on PM2.5 through the abundant fixed effect and controls. Besides, this paper applied the soil humidity, Normalized Difference Vegetation Index (NDVI) last month, NDVI in the same month last year, and NDVI prefecture-level city average as the instrumental variables, respectively, to test the robustness of the estimation. The results show that increased greenspace significantly decreases the PM2.5 concentration and other air pollutants. In further analysis, we found that the decreasing effect turns significant when NDVI exceeds 0.3. In addition, there are regional heterogeneities in the impact of greenspace on air pollution. Greenspaces show a more pronounced air pollution reduction effect in southern or higher administrative-level cities. This paper suggests that increasing greenspace is an economical and effective way to achieve co-management of multiple air pollutants.

Despite the prevalence of smoking cessation programs and public health campaigns, individuals with long-term illness, disability, or infirmity have been found to smoke more often than those without such conditions, leading to worsening health. However, the available literature has mainly focused on the association between long-term illness and smoking, which might suffer from the possible bidirectional influence, while few studies have examined the potential causal effect of long-term illness on smoking. This gap in knowledge can be addressed using an instrumental variable analysis that uses a third variable as an instrument between the endogenous independent and dependent variables and allows the identification of the direction of causality under the discussed assumptions. Our study analyzes the UK General Household Survey in 2006, covering a nationally representative 13,585 households. We exploited the number of vehicles as the instrumental variable for long-term illness, disability, or infirmity as vehicle numbers may be related to illness based on the notion that these individuals are less likely to drive, but that vehicle number may have no relationship to the likelihood of smoking. Our results suggested that chronic illness status causes a significantly 28% higher probability of smoking. The findings have wide implications for public health policymakers to design a more accessible campaign around smoking and for psychologists and doctors to take targeted care for the welfare of individuals with long-term illnesses.

IntroductionAlthough physical activity (PA) is crucial for health, the literature is mixed about how individuals' PA decisions are affected by their spouses. To fill this gap, we examined the extent to which providing care for one spouse affects the PA of the other spouse among those aged 50 or older in the United States.MethodsWe analyzed 9,173 older adults living with their spouses or partners from the 2004 to 2016 waves of the Health and Retirement Study. To identify the causal effect of spousal caregiving on the PA of older adults, we estimated individual-fixed effects models using a two-stage least squared instrumental variable approach with spousal falls as our instrument. We also estimated the models by splitting the sample by gender and race/ethnicity to identify heterogeneous impacts of spousal caregiving on PA decisions among subgroups.ResultsWe found that a one percentage point increase in the probability of providing care to spouses led to an increase in the probability of initiating moderate or vigorous PA (MVPA) by 0.34–0.52 percentage points. This effect was salient, especially among female and non-Hispanic white older adults.DiscussionCaregiving experience might provide opportunities to learn about caregiving burdens and trigger an emotional response about the salience of an event (i.e., they need care in the future). Older caregivers might start MVPA in an effort to improve or maintain their health and avoid burdening their families for caregiving in the future. This study demonstrated spousal influence on PA. Instead of delivering PA-promotion information (e.g., the harm of sedentary lifestyle and benefits of regular PA) to individuals, risk communication and education efforts on PA promotion might be more effective considering the family context. Family events such as health shocks or the emergence of caregiving needs from family members provide windows of opportunities for intervening. Subgroup differences should also be considered in targeted interventions.

Background: Trauma is a well-known risk factor for many disease, but the effect of trauma on respiratory disease is unclarified. In the present study, we aimed to evaluate the association between trauma and respiratory disease. Method: Using both United Kingdom biobank and Finnish biobank genomewide association study data (GWAS), we performed a two-sample Mendelian randomization (MR) analysis to evaluate the relationship between trauma and respiratory disease. We used four methods including inverse-variance weighted (IVW), weighted median, Maximum likelihood, and MR-Egger in this MR analysis. The IVW MR was selected as the main method. We also performed multivariable Mendelian randomization (MVMR) to simultaneously assess the independent impact of trauma exposure on respiratory disease. Results: In the main two-sample MR analysis, trauma exposure was significantly associated with increased risk of respiratory disease (OR 1.15, 95%CI: 1.05-1.25). Besides, there was no heterogeneity and horizontal pleiotropy observed in the sensitivity analysis. After adjusting for pack years of smoking and body mass index (BMI), trauma exposure retained its association with respiratory disease (OR, 1.13, 95%CI, 1.04-1.23 adjusted by pack years of smoking; and OR, 1.11, 95%CI, 1.04-1.18 adjusted by BMI). Conclusion: Our study discovered the association between trauma exposure and the increased risk of respiratory disease, suggesting the prevention and treatment with trauma to reduce the risk of respiratory disease. [ABSTRACT FROM AUTHOR]

Background: Observational studies have revealed the association between some inflammatory cytokines and the occurrence of ischemic stroke, but the causal relationships remain unclear. Methods: We conducted a two-sample Mendelian randomization (MR) analysis to assess the causal effects of thirty inflammatory cytokines and the risk of ischemic stroke. For exposure data, we collected genetic variants associated with inflammatory cytokines as instrumental variables (IVs) from a genome-wide association study (GWAS) meta-analysis from Finland (sample size up to 8,293). For the outcome data, we collected summary data of ischemic stroke from a large-scale GWAS meta-analysis involved 17 studies (34,217 cases and 406,111 controls). We further performed a series of sensitivity analyses as validation of primary MR results. Results: According to the primary MR estimations and further sensitivity analyses, we established one robust association after Bonferroni correction: the odds ratio (95% CI) per unit change in genetically increased IL-4 was 0.84 (0.89–0.95) for ischemic stroke. The chemokine MCP3 showed a nominally significant association with ischemic stroke risk (OR: 0.93, 95% CI: 0.88–0.99, unadjusted p < 0.05). There was no evidence of a causal effect of other inflammatory cytokines and the risk of ischemic stroke. Conclusions: Our study suggested that genetically increased IL-4 levels showed a protective effect on the risk of ischemic stroke, which provides important new insights into the potential therapeutic target for preventing ischemic stroke. [ABSTRACT FROM AUTHOR]

BackgroundObstructive sleep apnea (OSA) and atrial fibrillation (AF) are epidemiologically correlated, but the causal relationship between them remains elusive. We aimed to explore the causal relationships between OSA and AF.MethodUsing both the Finnish biobank and publicly available genome-wide association study data (GWAS), we conducted a two-sample Mendelian randomization (MR) analysis to estimate the causal effect of OSA on AF, both in the primary analysis and replicated analysis. The inverse variance weighted MR was selected as the main method. To further test the independent causal effect of OSA on AF, we also performed multivariable MR (MVMR), adjusting for body mass index (BMI), hypertension, and coronary artery disease (CAD), respectively.ResultsIn the primary analysis, OSA was significantly associated with the increased risk of AF (OR 1.21, 95% CI 1.11–1.32) and the replicated analysis showed consistent results (OR 1.17, 95% CI 1.05–1.30). Besides, there was no heterogeneity and horizontal pleiotropy observed both in the primary and replicated analysis. Further multivariable MR suggested that the causal relationships between OSA and AF exist independently of BMI and CAD. The MVMR result after the adjustment for hypertension is similar in magnitude and direction to the univariable MR. But it did not support a causal relationship between OSA and AF.ConclusionOur study found that genetically driven OSA causally promotes AF. This causal relationship sheds new light on taking effective measures to prevent and treat OSA to reduce the risk of AF.

Background: Observational studies have revealed the association between some inflammatory cytokines and the occurrence of ischemic stroke, but the causal relationships remain unclear.Methods: We conducted a two-sample Mendelian randomization (MR) analysis to assess the causal effects of thirty inflammatory cytokines and the risk of ischemic stroke. For exposure data, we collected genetic variants associated with inflammatory cytokines as instrumental variables (IVs) from a genome-wide association study (GWAS) meta-analysis from Finland (sample size up to 8,293). For the outcome data, we collected summary data of ischemic stroke from a large-scale GWAS meta-analysis involved 17 studies (34,217 cases and 406,111 controls). We further performed a series of sensitivity analyses as validation of primary MR results.Results: According to the primary MR estimations and further sensitivity analyses, we established one robust association after Bonferroni correction: the odds ratio (95% CI) per unit change in genetically increased IL-4 was 0.84 (0.89–0.95) for ischemic stroke. The chemokine MCP3 showed a nominally significant association with ischemic stroke risk (OR: 0.93, 95% CI: 0.88–0.99, unadjusted p < 0.05). There was no evidence of a causal effect of other inflammatory cytokines and the risk of ischemic stroke.Conclusions: Our study suggested that genetically increased IL-4 levels showed a protective effect on the risk of ischemic stroke, which provides important new insights into the potential therapeutic target for preventing ischemic stroke.

As China's economic development enters a transitional stage, the government has put forward higher requirements for efficient air pollution control. Different from the traditional environmental regulation methods, China's carbon market pilots (CMP) can use market mechanism to achieve carbon emission reduction policy goals while also providing more efficient control methods for air pollution. Using the unique daily air pollution data of 324 prefecture-level and above cities in China from December 1, 2013, to December 18, 2017, and matching them with the daily data of carbon allowance trading in each CMP, we employ the time-varying difference-in-differences (DID) method to explore the casual relationship between CMP and air pollution, and its influence mechanism. Our results show that (1) the establishment of CMP can reduce air pollution by at least 4.9%, especially on sulfur dioxide, inhalable particulate matter, fine particles, and carbon monoxide. This conclusion is still robust after we use the instrumental variable (IV) method to deal with endogeneity. (2) Mechanism analysis shows that the reducing effect of CMP on air pollution is achieved mainly through three channels, including energy consumption reducing, technological progressing, and industrial structure upgrading. (3) Since CMP have just been established, government regulation plays a stronger role than the market mechanism in the process of CMP affecting air pollution. However, with the continuous improvement of CMP, the market mechanism has also played an increasingly important dynamic effect. [ABSTRACT FROM AUTHOR]

We consider a situation where cause–effect relationships between variables can be described as a directed acyclic graph (DAG) and the corresponding linear structural equation model (linear SEM). When several pairs of instrumental variables (IVs) and covariates (IV-pairs; Pearl in: Proceedings of the 20th conference on uncertainty in artificial intelligence, AUAI Press, Arlington, Virginia, United States, UAI'04, 2004) are available, we propose (i) the graphical selection criteria of IV-pairs for total effects and (ii) an integrated estimator that combines them to estimate total effects with better accuracy. In this paper, in accordance with the paper by Brito and Pearl (in: Proceedings of the 18th conference on uncertainty in artificial intelligence, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, UAI'02, 2002), the proposed estimator is called an integrated generalized instrumental variable (iGIV) estimator. The proposed graphical selection criteria imply that (a) the back-door criterion achieves better estimation accuracy than the traditional instrumental variable (IV) method of estimating total effects even when the treatment and covariates are highly correlated and (b) the conditional IV method can be superior to the back-door criterion in some situations. The iGIV estimator provides a general class that includes both the ordinary least squares (OLS) estimator based on the back-door criterion and the two-stage least squares (2SLS) estimator based on the (conditional) IV method. We clarify the properties of the iGIV estimator, some of which can be read off from the DAG structure. Furthermore, through numerical experiments and an application to a case study, we show that the performance of the iGIV estimator is superior to those of the OLS and IV estimators. The iGIV estimator can be a powerful tool to estimate the total effect when the proposed graphical selection criteria of the IV-pairs are not satisfied. [ABSTRACT FROM AUTHOR]

As one of the efficacious environmental governance instruments, environmental regulations usually have been adopted to control haze pollution in most developing countries. However, as proved in recent researches, the relationship between environmental regulation and haze pollution remains uncertain. This paper develops a conceptual model and attributes this phenomenon to two reasons: the spatial spillover effect and industrial agglomeration threshold effect of effective environmental regulation. Spatial durbin model (SDM), instrumental variable (IV) and panel threshold model are used to estimate these effects based on 277 cities data in China from 2012 to 2016. The results confirm the spatial spillover effects of environmental regulations and haze pollution, which could influence the pollutant reduction effect through positive and negative spatial spillover effects. It also validates the thresholds of industrial agglomeration of approximately 0.818 and 1.271. Nevertheless, only 1.271 represents the turning point of the environmental regulation effect changing and environmental regulations could significantly reduce haze, only if it exceeds this numerical value. Moreover, the results have regional heterogeneity regarding spillover effect and threshold effect as well. The environmental regulation intensity in central and western regions plays a "multi-stage promotion" role on haze pollution emission with the adjustment of industrial agglomeration threshold, while it turns out to be a "triple-stage downward" trend in eastern China. At last, we put forward differential policies suggestions according to their industrial agglomeration levels. [ABSTRACT FROM AUTHOR]

The causal effect of a treatment is of fundamental interest in the social, biological and health sciences. Instrumental variable (IV) methods are commonly used to determine causal treatment effects in the presence of unmeasured confounding. In this work, we study a new binary IV framework with randomly censored outcomes where we propose to quantify the causal treatment effect by the concept of complier quantile causal effect (CQCE). The CQCE is identifiable under weaker conditions than the complier average causal effect when outcomes are subject to censoring, and it can provide useful insight into the dynamics of the causal treatment effect. Employing the special characteristic of the binary IV and adapting the principle of conditional score, we uncover a simple weighting scheme that can be incorporated into the standard censored quantile regression procedure to estimate CQCE. We develop robust non‐parametric estimation of the derived weights in the first stage, which permits stable implementation of the second stage estimation based on existing software. We establish rigorous asymptotic properties for the proposed estimator, and confirm its validity and satisfactory finite‐sample performance via extensive simulations. The proposed method is applied to a bone marrow transplant data set to evaluate the causal effect of rituximab in diffuse large B‐cell lymphoma patients. [ABSTRACT FROM AUTHOR]

This research analyse influence of education, job experience, level of parent education and amount of sibling to wages and family income at Indonesian family. Hereafter this research analyse how total family income to measure education and experience role give same explanation than utilizes wage measure. This research will utilize model Mincer Wage Regression on Indonesian family. Analisis's method utilizes IV (Instrumental Variable). The data will be used is panel data from IFLS (Indonesian Family Life Survey) which is IFLS 3 years 2000 and IFLS 4 years 2007. Result observation was point out that amount of sibling not significant influence to average education of employment at family, meanwhile parent education is influence signifikan to average education of employment at family. Hereafter average education and experience of employment is significant influence to income and wage at Indonesian family. Then wages measure point out stronger influence than by use of measure total income to measure how education and experience influence to family income at Indonesian.

Studies into the impact of social capital on welfare are currently growing. However, studies for the case of the developing countries, including Indonesia, are still very rare. Therefore, this paper aims to analyze the impact of social capital on welfare in Indonesia. In this study, social capital is measured by three indicators, namely, trust, cooperativeness and the social network (a person’s participation in community activities).Welfare is measured by household expenditure for food and non-food items. The data are acquired from the Indonesian Family Life Surveys for the years 2007 (IFLS4) and 2014 (IFLS5). This research uses instrumental variables to address the endogeneity issue on social networking (participation in community activities), which is a potential two-way causal relationship. It means that individuals with higher welfare (income) have a higher possibility of participating in community activities, since their participation in community activities is a leisure activity and the utilization of leisure is higher for higher-income people.Using the Instrumental Variables (IV) method and marital status as an instrument, the study found that social capital has a significant impact on welfare. An increased participation in community activities will improve ones welfare by 11.7 percent. Moreover, an increase of cooperativeness by one percent, would increase the welfare by 0.2 percent. On the other hand, trust has a negative relationship with welfare. It means that an increase in trust among individuals by one percent will cause household expenditure on food and non-food items to drop by 0.3 percent.It may imply that higher trust will cause lower transaction costs, which will reduce the expense of individuals buying food and non-food items. Since the coefficient of IV is larger than the coefficient in the OLS estimation, it indicates the absence of reversed causality. The results of this study have an implication for policy decision making which suggests that the policy decision makers should consider the impact of social capital on welfare and support the increase of individuals’ participating in community activities.

This brief investigates recursive instrumental variable (IV) identification of time-delayed continuous-time (CT) models from the sampled input–output data. The refined IV method is extended for the first time to identify recursively both the parameters and time delay of a CT transfer function model. It is shown that the proposed method is able to yield consistent parameter estimation. Due to the nonlinear nature of the loss function to be optimized, time-delay estimation suffers from local minima. Thus, the global convergence performance relies on the quality of initial parameters. To increase the chance of converging to the global optimum, several guidelines are suggested to help the choice of starting values. The main results derived in this brief are confirmed by numerical examples and an experimental application. [ABSTRACT FROM AUTHOR]

This paper is concerned with control-oriented modeling of a class of wireless power transfer systems with a phase-shift-controlled inverter. Two methods are investigated: the analytical modeling method, which is based on physical laws, and the refined instrumental variable method, which is based on sampled data. It shows that the former provides physical insights into the system, and that the resulting models will be accurate if the true circuit component parameters are a priori known. Otherwise, the model accuracy may decline. By contrast, the latter provides a cheap solution for accurate modeling, based only on a set of sampled input–output data, and the resulting model can capture very well the dynamic behavior of the system within a defined operating range. In real applications, it is always hard to know the actual circuit component parameters, since they may drift from the nominal values, due to some reasons such as the aging of circuit components and the variation of coil placement, making the analytical modeling method not very accurate. Therefore, to preserve the virtues of both methods, it is suggested to use them as a combination, that is, using the analytical modeling method to determine the model structure while using the data-driven modeling method to estimate the model parameters. All results derived in this paper are verified by means of both numerical simulation and experimental validation. [ABSTRACT FROM AUTHOR]

This paper identifies the dynamic pattern of East Java growth of manufacturing sector and addresses the basic questions of individual economic firms whether they would be better off if increasing physical capital or investment in human capital. To know which one of the two main inputs in industrial sector that is more needed than the other, the marginal productivity of each production factors must be identified. I estimate the models which accommodate the optimum input level by applying general method of moment (GMM) and panel instrumental variable (IV) techniques on some reduced form models. I find that on the demand function of labor and capital as the first step of IV or Two Stage Least Square (2SLS) show that the elasticity of both of them are inelastic and elasticity of labor demand is more sensitive than capital. In the production function as the second step, yields that the most productive production factors is labor so that investment in this factor production is beneficial for industrial growth in East Java. On the other side, the physical capital has not been reached the optimum level but the elasticity of capital in production is low. Hypothetically, the inelasticity of physical capital is because macroeconomic aspects which is monetary policy and expected economic situation. Considering these two arguments, quality of labor should be more concerned in the context of regional economy of East Java because capital aspect cannot be interfered at regional level at least for large capital scale. Keywords: Capital, Labor, Growth, General Method of Moment (GMM), Instrumental Variable (IV) JEL: C50, C33, C36, M51, L29, L60, O25, D22

Summary: In observational studies to estimate treatment effects, unmeasured confounding is often a concern. The instrumental variable (IV) method can control for unmeasured confounding when there is a valid IV. To be a valid IV, a variable needs to be independent of unmeasured confounders and only affect the outcome through affecting the treatment. When applying the IV method, there is often concern that a putative IV is invalid to some degree. We present an approach to sensitivity analysis for the IV method which examines the sensitivity of inferences to violations of IV validity. Specifically, we consider sensitivity when the magnitude of association between the putative IV and the unmeasured confounders and the direct effect of the IV on the outcome are limited in magnitude by a sensitivity parameter. Our approach is based on extending the Anderson–Rubin test and is valid regardless of the strength of the instrument. A power formula for this sensitivity analysis is presented. We illustrate its usage via examples about Mendelian randomization studies and its implications via a comparison of using rare versus common genetic variants as instruments. [ABSTRACT FROM AUTHOR]

An instrumental variable (IV) solution for wideband frequency-domain identification is developed in this paper. One of the most successful IV approaches for time-domain data is known as the simple refined IV method for continuous-time model. The purpose of this paper is to present a numerically reliable simplified refined IV method able to identify (highly) resonant systems over a large bandwidth, from frequency-domain data. The proposed solution is to couple the iterative scheme of the IV approach with frequency localizing basis functions. To illustrate the performance of the proposed approach, a representative Monte Carlo simulation along with two experimental cases is presented. [ABSTRACT FROM AUTHOR]

This paper investigates the roles of formal institutions and social capital in countries’ innovation activities. The sample consists of 62 developed and developing countries, using the ordinary least squares robust standard error estimations, instrumental variable (IV) estimators, and quantile regression. The empirical results indicate that formal institutions and social capital complement one another in influencing countries’ innovations level. In terms of the relative importance of both in promoting innovation activities, the social capital has greater role compared to formal institutions. Furthermore, the empirical result suggests that innovation level tends to be higher in countries with higher social capital. We also use the quantile regression to estimate whether the relationship among formal institutions, social capital, and innovation differs at different points in the conditional distribution of innovation. The results demonstrate that formal institutions yield a significant positive impact only after exceeding in 50th quantile, whereas social capital do so at lower 10th quantile. Therefore, in addition to a policy focus on improving the formal institutions, countries with a low level of innovation should enrich social capital in their promotion of innovation activity. [ABSTRACT FROM PUBLISHER]

• The causal impact of green space on air pollution remains understudied. • Here, the Normalized Difference Vegetation Index (NDVI) was used to assess green space level in each county of China. • Two-stage least squares estimation (2SLS) and multiplex IV was employed to verify the robustness. • The increased greenspace significantly decreases the PM 2.5 concentration and other air pollutants. • Green spaces show a more pronounced air pollution reduction effect in southern or higher administrative-level cities. Can green vegetation absorb air pollutants and control regional air pollution? The existing research has not reached a consistent conclusion on this issue. Using the multi-level data in China, this paper provides empirical evidence of the causal impact of greenspace on PM 2.5 through the abundant fixed effect and controls. Besides, this paper applied the soil humidity, Normalized Difference Vegetation Index (NDVI) last month, NDVI in the same month last year, and NDVI prefecture-level city average as the instrumental variables, respectively, to test the robustness of the estimation. The results show that increased greenspace significantly decreases the PM 2.5 concentration and other air pollutants. In further analysis, we found that the decreasing effect turns significant when NDVI exceeds 0.3. In addition, there are regional heterogeneities in the impact of greenspace on air pollution. Greenspaces show a more pronounced air pollution reduction effect in southern or higher administrative-level cities. This paper suggests that increasing greenspace is an economical and effective way to achieve co-management of multiple air pollutants. [ABSTRACT FROM AUTHOR]

Background: The causal direction and magnitude of the associations between blood cell count and coronary heart disease (CHD) remain uncertain due to susceptibility of reverse causation and confounding. This study aimed to investigate the associations between blood cell count and CHD using Mendelian randomization (MR). Methods: In this two-sample MR study, we identified independent blood cell count associated genetic variants from a genome-wide association studies (GWAS) among European ancestry individuals. Summary level data of CHD was obtained from a GWAS consisting of 547261 subjects. Methods of inverse variance weighted (IVW), Mendelian Randomization-Egger (MR-Egger), weighted median, and outlier test (MR-PRESSO) were conducted to investigate the associations between blood cell and CHD. Results: Among all cardiovascular outcomes of interest, blood cell counts were only associated with CHD. Our findings indicated that white blood cell count and neutrophil cell count were significantly associated with increased risk of CHD [odds ratio (OR) = 1.07, 95% confidence interval (CI), 1.01-1.14; OR = 1.09, 1.02-1.16). However, there was no significant association between monocyte cell count, basophil cell count, lymphocyte cell count, eosinophil cell count, and CHD ( p > 0.05). The results after excluding outliers were consistent with main results and the sensitivity analyses showed no evidence of pleiotropy (MR-Egger intercept, p > 0.05). Conclusion: Our MR study suggested that greater white blood cell count and neutrophil cell count were associated with a higher risk of CHD. Future studies are still warranted to validate the results and investigate the mechanisms underlying these associations., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Zhao, Liu, Chen, Yang, Dong and Bai.)

Despite the prevalence of smoking cessation programs and public health campaigns, individuals with long-term illness, disability, or infirmity have been found to smoke more often than those without such conditions, leading to worsening health. However, the available literature has mainly focused on the association between long-term illness and smoking, which might suffer from the possible bidirectional influence, while few studies have examined the potential causal effect of long-term illness on smoking. This gap in knowledge can be addressed using an instrumental variable analysis that uses a third variable as an instrument between the endogenous independent and dependent variables and allows the identification of the direction of causality under the discussed assumptions. Our study analyzes the UK General Household Survey in 2006, covering a nationally representative 13,585 households. We exploited the number of vehicles as the instrumental variable for long-term illness, disability, or infirmity as vehicle numbers may be related to illness based on the notion that these individuals are less likely to drive, but that vehicle number may have no relationship to the likelihood of smoking. Our results suggested that chronic illness status causes a significantly 28% higher probability of smoking. The findings have wide implications for public health policymakers to design a more accessible campaign around smoking and for psychologists and doctors to take targeted care for the welfare of individuals with long-term illnesses., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Zhou, Li, Zhu and Xu.)

Introduction: Although physical activity (PA) is crucial for health, the literature is mixed about how individuals' PA decisions are affected by their spouses. To fill this gap, we examined the extent to which providing care for one spouse affects the PA of the other spouse among those aged 50 or older in the United States., Methods: We analyzed 9,173 older adults living with their spouses or partners from the 2004 to 2016 waves of the Health and Retirement Study. To identify the causal effect of spousal caregiving on the PA of older adults, we estimated individual-fixed effects models using a two-stage least squared instrumental variable approach with spousal falls as our instrument. We also estimated the models by splitting the sample by gender and race/ethnicity to identify heterogeneous impacts of spousal caregiving on PA decisions among subgroups., Results: We found that a one percentage point increase in the probability of providing care to spouses led to an increase in the probability of initiating moderate or vigorous PA (MVPA) by 0.34-0.52 percentage points. This effect was salient, especially among female and non-Hispanic white older adults., Discussion: Caregiving experience might provide opportunities to learn about caregiving burdens and trigger an emotional response about the salience of an event (i.e., they need care in the future). Older caregivers might start MVPA in an effort to improve or maintain their health and avoid burdening their families for caregiving in the future. This study demonstrated spousal influence on PA. Instead of delivering PA-promotion information (e.g., the harm of sedentary lifestyle and benefits of regular PA) to individuals, risk communication and education efforts on PA promotion might be more effective considering the family context. Family events such as health shocks or the emergence of caregiving needs from family members provide windows of opportunities for intervening. Subgroup differences should also be considered in targeted interventions., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Zan and Shin.)

Abstract: Until just a few years ago, the genetic determinants of obesity and metabolic syndrome were largely unknown, with the exception of a few forms of monogenic extreme obesity. Since genome-wide association studies (GWAS) became available, large advances have been made. The first single nucleotide polymorphism robustly associated with increased body mass index (BMI) was in 2007 mapped to a gene with for the time unknown function. This gene, now known as fat mass and obesity associated (FTO) has been repeatedly replicated in several ethnicities and is affecting obesity by regulating appetite. Since the first report from a GWAS of obesity, an increasing number of markers have been shown to be associated with BMI, other measures of obesity or fat distribution and metabolic syndrome. This systematic review of obesity GWAS will summarize genome-wide significant findings for obesity and metabolic syndrome and briefly give a few suggestions of what is to be expected in the next few years. [Copyright &y& Elsevier]

Background: Trauma is a well-known risk factor for many disease, but the effect of trauma on respiratory disease is unclarified. In the present study, we aimed to evaluate the association between trauma and respiratory disease., Method: Using both United Kingdom biobank and Finnish biobank genome-wide association study data (GWAS), we performed a two-sample Mendelian randomization (MR) analysis to evaluate the relationship between trauma and respiratory disease. We used four methods including inverse-variance weighted (IVW), weighted median, Maximum likelihood, and MR-Egger in this MR analysis. The IVW MR was selected as the main method. We also performed multivariable Mendelian randomization (MVMR) to simultaneously assess the independent impact of trauma exposure on respiratory disease., Results: In the main two-sample MR analysis, trauma exposure was significantly associated with increased risk of respiratory disease (OR 1.15, 95%CI: 1.05-1.25). Besides, there was no heterogeneity and horizontal pleiotropy observed in the sensitivity analysis. After adjusting for pack years of smoking and body mass index (BMI), trauma exposure retained its association with respiratory disease (OR, 1.13, 95%CI, 1.04-1.23 adjusted by pack years of smoking; and OR, 1.11, 95%CI, 1.04-1.18 adjusted by BMI)., Conclusion: Our study discovered the association between trauma exposure and the increased risk of respiratory disease, suggesting the prevention and treatment with trauma to reduce the risk of respiratory disease., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Ma, Meng and Weng.)

Purpose: Due to substantial variation in patient followup after radical cystectomy for bladder cancer, we sought to understand the effect of urine and laboratory tests, physician visits and imaging on overall survival. Materials and Methods: We analyzed a cohort of patients treated in the fee for service Medicare population from 1992 through 2007 using Surveillance Epidemiology and End Results (SEER)-Medicare data. Using propensity score analysis, we assessed the relationship between time and geography standardized expenditures on followup care and overall survival during 3 postoperative periods, including perioperative (0 to 3 months), early followup (4 to 6 months) and later followup (7 to 24 months). Using instrumental variable analysis, we assessed the overall survival impact of the quantity of followup care by category, including physician visits, imaging, and laboratory and urine tests. Results: We found no improvement in survival due to followup care in the perioperative and early followup periods. Receiving followup care during later followup was associated with improved survival in the low, middle and high expenditure tertiles (HR 0.23, 95% CI 0.15–0.35, HR 0.27, 95% CI 0.18–0.40 and HR 0.47, 95% CI 0.31–0.71, respectively). Instrumental variable analysis suggested that only physician visits and urine testing improved survival (HR 0.96, 0.93–0.99 and 0.95, 0.91–0.99, respectively). Conclusions: Followup care after radical cystectomy in the later followup period was associated with improved survival. Physician visits and urine tests were associated with this improved survival. Our results suggest that aspects of followup care significantly improve patient outcomes but imaging could be done more judiciously after cystectomy. [Copyright &y& Elsevier]

Abstract: Height is associated with mortality from many diseases, but it remains unclear whether the association is causal or due to confounding by social factors, genetic pleiotropy, 1 [1] Pleiotropy is the influence of one gene on multiple phenotypic traits. or existing ill-health. The authors investigated whether the association of height with mortality is causal by using a son's height as an instrumental variable (IV) for parents’ height among the parents of a cohort of 1,036,963 Swedish men born between 1951 and 1980 who had their height measured at military conscription, aged around 18, between 1969 and 2001. In a two-sample IV analysis adjusting for son's age at examination and secular trends in height, as well as parental age, and socioeconomic position, the hazard ratio (HR) for all-cause paternal mortality per standard deviation (SD, 6.49cm) of height was 0.96 (95% confidence interval (CI): 0.95, 0.96). The results of IV analyses of mortality from all causes, cardiovascular disease (CVD), respiratory disease, cancer, external causes and suicide were comparable to those obtained using son's height as a simple proxy for own height and to conventional analyses of own height in the present data and elsewhere, suggesting that such conventional analyses are not substantially confounded by existing ill-health. [Copyright &y& Elsevier]

Abstract: Determination of the order of a model is the key first step towards modeling any dynamic systems, particularly two-dimensional processes. In this paper, a new method for two-dimensional (2-D) Gaussian ARMA model order determination is proposed. In the proposed method, the AR and MA orders are first independently determined, then the procedure for model order determination of the 2-D ARMA model is outlined. The model is assumed to be causal, stable, linear, and spatial shift-invariant with quarter-plane (QP) support. Numerical simulations are presented to show the effectiveness of the proposed new approach. [Copyright &y& Elsevier]

Background: Obstructive sleep apnea (OSA) and atrial fibrillation (AF) are epidemiologically correlated, but the causal relationship between them remains elusive. We aimed to explore the causal relationships between OSA and AF., Method: Using both the Finnish biobank and publicly available genome-wide association study data (GWAS), we conducted a two-sample Mendelian randomization (MR) analysis to estimate the causal effect of OSA on AF, both in the primary analysis and replicated analysis. The inverse variance weighted MR was selected as the main method. To further test the independent causal effect of OSA on AF, we also performed multivariable MR (MVMR), adjusting for body mass index (BMI), hypertension, and coronary artery disease (CAD), respectively., Results: In the primary analysis, OSA was significantly associated with the increased risk of AF (OR 1.21, 95% CI 1.11-1.32) and the replicated analysis showed consistent results (OR 1.17, 95% CI 1.05-1.30). Besides, there was no heterogeneity and horizontal pleiotropy observed both in the primary and replicated analysis. Further multivariable MR suggested that the causal relationships between OSA and AF exist independently of BMI and CAD. The MVMR result after the adjustment for hypertension is similar in magnitude and direction to the univariable MR. But it did not support a causal relationship between OSA and AF., Conclusion: Our study found that genetically driven OSA causally promotes AF. This causal relationship sheds new light on taking effective measures to prevent and treat OSA to reduce the risk of AF., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Li, Leng, Tang, Deng, Wang, Yuan, Miao and Mu.)

In genomic studies with both genotypes and gene or protein expression profile available, causal effects of gene or protein on clinical outcomes can be inferred through using genetic variants as instrumental variables (IVs). The goal of introducing IV is to remove the effects of unobserved factors that may confound the relationship between the biomarkers and the outcome. A valid inference under the IV framework requires pairwise associations and pathway exclusivity. Among these assumptions, the IV expression association needs to be strong for the casual effect estimates to be unbiased. However, a small number of single nucleotide polymorphisms (SNPs) often provide limited explanation of the variability in the gene or protein expression and can only serve as weak IVs. In this study, we propose to replace SNPs with haplotypes as IVs to increase the variant-expression association and thus improve the casual effect inference of the expression. In the classical two-stage procedure, we developed a haplotype regression model combined with a model selection procedure to identify optimal instruments. The performance of the new method was evaluated through simulations and compared with the IV approaches using observed multiple SNPs. Our results showed the gain of power to detect a causal effect of gene or protein on the outcome using haplotypes compared with using only observed SNPs, under either complete or missing genotype scenarios. We applied our proposed method to a study of the effect of interleukin-1 beta (IL-1β) protein expression on the 90-day survival following sepsis and found that overly expressed IL-1β is likely to increase mortality., (© 2015 WILEY PERIODICALS, INC.)