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Summary: We propose a per‐cluster instrumental variable (PCIV) approach for estimating linear correlated random coefficient models in the presence of contemporaneous endogeneity and two‐way fixed effects. This approach estimates heterogeneous effects and aggregates them to population averages. We demonstrate consistency, showing robustness over standard estimators, and provide analytic standard errors for robust inference. In Monte Carlo simulation, PCIV performs relatively well in finite samples in either dimension. We apply PCIV in estimating the price elasticity of gasoline demand using state fuel taxes as instrumental variables. We find significant elasticity heterogeneity and more elastic gasoline demand on average than with standard estimators.

Mendelian randomization is an instrumental variable method that utilizes genetic information to investigate the causal effect of a modifiable exposure on an outcome. In most cases, the exposure changes over time. Understanding the time‐varying causal effect of the exposure can yield detailed insights into mechanistic effects and the potential impact of public health interventions. Recently, a growing number of Mendelian randomization studies have attempted to explore time‐varying causal effects. However, the proposed approaches oversimplify temporal information and rely on overly restrictive structural assumptions, limiting their reliability in addressing time‐varying causal problems. This article considers a novel approach to estimate time‐varying effects through continuous‐time modelling by combining functional principal component analysis and weak‐instrument‐robust techniques. Our method effectively utilizes available data without making strong structural assumptions and can be applied in general settings where the exposure measurements occur at different timepoints for different individuals. We demonstrate through simulations that our proposed method performs well in estimating time‐varying effects and provides reliable inference when the time‐varying effect form is correctly specified. The method could theoretically be used to estimate arbitrarily complex time‐varying effects. However, there is a trade‐off between model complexity and instrument strength. Estimating complex time‐varying effects requires instruments that are unrealistically strong. We illustrate the application of this method in a case study examining the time‐varying effects of systolic blood pressure on urea levels. [ABSTRACT FROM AUTHOR]

Urticaria is characterized by transient itchy symptoms on the skin, usually accompanied by swelling, which is caused by mast cell activation leading to increased vascular permeability and dilation of the dermis. Urticaria involves recurrent activation of mast cells, T cells, eosinophils, and other immune cells around lesioned venules, with complex regulatory systems affecting mast cell functions, potentially contributing to urticaria pathogenesis. The direct causal relationship between immune cells and urticaria is currently unclear. To address this, our study utilized a bidirectional Mendelian randomization analysis, employing instrumental variables (IVs) associated with immune cells and urticaria, to investigate this causal relationship. First, by utilizing Genome-wide Association Study (GWAS) data, we identified 31 immunophenotypes associated with urticaria risk, with 18 increasing and 13 decreasing the risk. Through rigorous criteria, we identified 4 immunophenotypes that have a strong causal relationship with urticaria. Notably, HLA DR+ CD4+AC, CD45 on CD8br, and HLA DR on plasmacytoid dendritic cells were associated with an increased risk, while CD8dim NKT %lymphocyte was identified as a protective factor. Sensitivity analyses, including the MR-Egger intercept test, scatter plots, funnel plots, and leave-one-out analysis, supported the robustness of the findings. Reverse MR analysis suggested an inverse causal effect of urticaria on CD8dim NKT %lymphocyte, reinforcing the potential bidirectional nature of the relationship between urticaria and immune cell phenotypes. Our research substantiates the bidirectional causal relationship between immune cells and urticaria, thus benefiting for urticaria-targeted therapy development. [ABSTRACT FROM AUTHOR]

Objective Study Setting and Design Data Sources and Analytic Sample Principal Findings Conclusions To ascertain how an instrumental variables (IV) model can improve upon the estimates obtained from traditional cost‐of‐illness (COI) models that treat health conditions as predetermined.A simulation study based on observational data compares the coefficients and average marginal effects from an IV model to a traditional COI model when an unobservable confounder is introduced. The two approaches are then applied to real data, using a kinship‐weighted family history as an instrument, and differences are interpreted within the context of the findings from the simulation study.The case study utilizes secondary data on type 2 diabetes mellitus (T2DM) status to examine healthcare costs attributable to the disease. The data come from Utah residents born between 1950 and 1970 with medical insurance coverage whose demographic information is contained in the Utah Population Database. Those data are linked to insurance claims from Utah's All‐Payer Claims Database for the analyses.The simulation confirms that estimated T2DM healthcare cost coefficients are biased when traditional COI models do not account for unobserved characteristics that influence both the risk of illness and healthcare costs. This bias can be corrected to a certain extent with instrumental variables. An IV model with a validated instrument estimates that 2014 costs for an individual age 45–64 with T2DM are 27% (95% CI: 2.9% to 51.9%) higher than those for an otherwise comparable individual who does not have T2DM.Researchers studying the COI for chronic diseases should assess the possibility that traditional estimates may be subject to bias because of unobserved characteristics. Doing so may be especially important for prevention and intervention studies that turn to COI studies to assess the cost savings associated with such initiatives. [ABSTRACT FROM AUTHOR]

Transit agencies use direct demand models (DDM) to allocate services. Since the service supply – a crucial predictor in DDMs – is endogenous to demand, including it in the model might yield biased estimations. A widely used methodology that is believed to handle this issue is Fixed Effects (FE). However, the underlying assumptions of FE are valid only if service adjustments take a considerable amount of time. This study investigates the performance of FE for estimating transit ridership. We collected 2013–2019 data and constructed 16 DDMs, employing four methodologies with a shared set of variables. We found that FE has significant limitations in handling endogeneity and will result in parameter estimates that significantly differ from those produced by methodologies that are specifically designed to control for endogeneity (such as FE-IV). Moreover, the use of FE leads to the omission of certain predictors and inaccurate ridership predictions, misguiding agencies as to what changes to implement and potentially impacting revenue projections. [ABSTRACT FROM AUTHOR]

Introduction: Reproductive endocrine disorders (RED), including polycystic ovary syndrome (PCOS), endometriosis (EMs), and female infertility (FI), significantly affect women's health globally, with varying prevalence across different regions. These conditions can be addressed through medication, surgical interventions, and lifestyle modifications. However, the limited understanding of RED's etiology and the substantial economic burden of its treatment highlight the importance of investigating its pathogenesis. Metabolites play a critical role in metabolic processes and are potentially linked to the development of RED. Despite existing studies suggesting correlations between metabolites and RED, conclusive evidence remains scarce, primarily due to the observational nature of these studies, which are prone to confounding factors. Methods: This study utilized Mendelian Randomization (MR) to explore the causal relationship between metabolites and RED, leveraging genetic variants associated with metabolite levels as instrumental variables to minimize confounding and reverse causality. Data were obtained from the Metabolomics GWAS Server and the IEU OpenGWAS project. Instrumental variables were selected based on their association with the human gut microbiota composition, and the GWAS summary statistics for metabolites, PCOS, EMs, and FI were analyzed. The MR-Egger regression and random-effects inverse-variance weighted (IVW) methods were employed to validate the causal relationship. Cochran's Q test was employed to evaluate heterogeneity, sensitivity analysis was performed using leave-one-out analysis, and for pleiotropy analysis, the intercept term of MR-Egger's method was investigated. Results: The MR analysis revealed significant associations between various metabolites and RED conditions. For instance, a positive association was found between 1-palmitoylglycerophosphocholine and PCOS, while a negative association was noted between phenylacetate and FI. The study identified several metabolites associated with an increased risk and others with protective effects against PCOS, EMs, and FI. These findings highlight the complex interplay between metabolites and RED, suggesting potential pathways through which these conditions could be influenced or treated. Conclusion: This MR study provides valuable insights into the causal relationship between metabolites and female reproductive endocrine disorders, suggesting that metabolic alterations play a significant role in the pathogenesis of PCOS, EMs, and FI, and offering a foundation for future research and therapeutic development. [ABSTRACT FROM AUTHOR]

Objectives Study Setting and Design Data Sources and Analytic Sample Principal Findings Conclusions To examine heterogeneity in physician batch ordering practices and measure the associations between a physician's tendency to batch order imaging tests on patient outcomes and resource utilization.In this retrospective study, we used comprehensive EMR data from patients who visited the Mayo Clinic of Arizona Emergency Department (ED) between October 6, 2018 and December 31, 2019. Primary outcomes are patient length of stay (LOS) in the ED, number of diagnostic imaging tests ordered during a patient encounter, and patients' return with admission to the ED within 72 h. The association between outcomes and physician batch tendency was measured using a multivariable linear regression controlling for various covariates.The Mayo Clinic of Arizona Emergency Department recorded approximately 50,836 visits, all randomly assigned to physicians during the study period. After excluding rare complaints, we were left with an analytical sample of 43,299 patient encounters.Findings show that having a physician with a batch tendency 1 standard deviation (SD) greater than the average physician was associated with a 4.5% increase in ED LOS (p < 0.001). It was also associated with a 14.8% (0.2 percentage points) decrease in the probability of a 72‐h return with admission (p < 0.001), implying that batching may lead to more comprehensive evaluations, reducing the need for short‐term revisits. A batch tendency 1SD greater than that of the average physician was also associated with an additional 8 imaging tests ordered per 100 patient encounters (p < 0.001), suggesting that batch ordering may be leading to tests that would not have been otherwise ordered had the physician waited for the results from one test before placing their next order.This study highlights the considerable impact of physicians' diagnostic test ordering strategies on ED efficiency and patient care. The results also highlight the need to develop guidelines to optimize ED test ordering practices. [ABSTRACT FROM AUTHOR]

To explore the effect of targeted poverty alleviation policies in Guangdong Province from the perspective of causal inference, this paper conducts quantitative research based on panel data of poverty-stricken counties in Guangdong Province from 2013 to 2020. Specifically, breakpoint regression is used to analyze the initial effect of the policy, instrumental variables are used to analyze the long-term effect of the policy implementation, and finally, the generalized comprehensive control method is used to make a comparative study. The results show that: (1) The targeted poverty alleviation policy in Guangdong Province has a remarkable effect on poverty reduction; (2) The effect of targeted poverty alleviation policy is still remarkable by comparing the implemented policy with the unimplemented one in Guangdong Province; (3) The improvement of market economic activity can effectively promote the increase of farmers' personal net income in poor areas of Guangdong Province. [ABSTRACT FROM AUTHOR]

Objective: Observational studies have established a connection between gut microbiota and ankylosing spondylitis (AS) risk; however, whether the observed associations are causal remains unclear. Therefore, we conducted a two-sample Mendelian randomization (MR) analysis to assess the potential causal associations of gut microbiota with AS risk. Methods: Instrumental variants of gut microbiota were obtained from the MiBioGen consortium (n = 18,340) and the Dutch Microbiome Project (n = 7738). The FinnGen consortium provided genetic association summary statistics for AS, encompassing 2860 cases and 270,964 controls. We used the inverse-variance weighted (IVW) method as the primary analysis, supplemented with the weighted median method, maximum likelihood–based method, MR pleiotropy residual sum and outlier test, and MR-Egger regression. In addition, we conducted a reverse MR analysis to assess the likelihood of reverse causality. Results: After the Bonferroni correction, species Bacteroides vulgatus remained statistically significantly associated with AS risk (odds ratio (OR) 1.55, 95% confidence interval (CI) 1.22–1.95, P = 2.55 × 10−4). Suggestive evidence of associations of eleven bacterial traits with AS risk was also observed (P < 0.05 by IVW). Among them, eight were associated with an elevated AS risk (OR 1.37, 95% CI 1.07–1.74, P = 0.011 for phylum Verrucomicrobia; OR 1.31, 95% CI 1.03–1.65, P = 0.026 for class Verrucomicrobiae; OR 1.17, 95% CI 1.01–1.36, P = 0.035 for order Bacillales; OR 1.31, 95% CI 1.03–1.65, P = 0.026 for order Verrucomicrobiales; OR 1.43, 95% CI 1.13–1.82, P = 0.003 for family Alcaligenaceae; OR 1.31, 95% CI 1.03–1.65, P = 0.026 for family Verrucomicrobiaceae; OR 1.31, 95% CI 1.03–1.65, P = 0.026 for genus Akkermansia; OR 1.55, 95% CI 1.19–2.02, P = 0.001 for species Sutterella wadsworthensis). Three traits exhibited a negative association with AS risk (OR 0.68, 95% CI 0.53–0.88, P = 0.003 for genus Dialister; OR 0.84, 95% CI 0.72–0.97, P = 0.020 for genus Howardella; OR 0.75, 95% CI 0.59–0.97, P = 0.026 for genus Oscillospira). Consistent associations were observed when employing alternate MR methods. In the reverse MR, no statistically significant correlations were detected between AS and these bacterial traits. Conclusion: Our results revealed the associations of several gut bacterial traits with AS risk, suggesting a potential causal role of gut microbiota in AS development. Nevertheless, additional research is required to clarify the mechanisms by which these bacteria influence AS risk. Key Points • The association of gut microbiota with AS risk in observational studies is unclear. • This MR analysis revealed associations of 12 gut bacterial traits with AS risk. [ABSTRACT FROM AUTHOR]

Post-instrument covariates are often included as controls in instrumental variable (IV) analyses to address a violation of the exclusion restriction. However, we show that such analyses are subject to biases unless strong assumptions hold. Using linear constant-effects models, we present asymptotic bias formulas for three estimators (with and without measurement error): IV with post-instrument covariates, IV without post-instrument covariates, and ordinary least squares. In large samples and when the model provides a reasonable approximation, these formulas sometimes allow the analyst to bracket the parameter of interest with two estimators and allow the analyst to choose the estimator with the least asymptotic bias. We illustrate these points with a discussion of the settler mortality IV used by Acemoglu, Johnson, and Robinson. [ABSTRACT FROM AUTHOR]

Overall, chicken consumption has increased substantially in recent decades due to farming and processing intensification as well as the consumer perception of its benefits. Although organic chicken is perceived to taste better, support higher animal welfare and have benefits for the environment, it is unclear to what extent the organic attribute in chicken carries a premium in the eyes of consumers. The purpose of this paper is to estimate this robustly. This is done by estimating a hedonic pricing model using a comprehensive dataset. The model's endogeneity is corrected using consumer characteristics as instruments. When making this correction, the value of the organic attribute is two to five times larger than without it (depending on the estimation method used). This leads to an average premium in relation to conventional chicken of 135% for the organic attribute in chicken. [ABSTRACT FROM AUTHOR]

This study investigates the impact of short-term air quality fluctuations on young people's intentions to migrate for higher education in Beijing, using Baidu search behavior as a proxy for interest in local versus non-local universities. To underpin causal inference, OLS panel estimates are supplemented by instrumental variables estimates that exploit variations in air quality driven by plausibly exogenous variations in temperature inversions. The results demonstrate that when monthly air quality in Beijing moves from 5th (excellent-day level) to 95th (moderately-polluted level) percentile, search for local education decreases by 2.51% under OLS and 8.23% under IV. Our findings provides a new perspective on how environmental factors shape educational mobility aspirations based on the causal effects, and offers policymakers insights into improving environmental quality to attract and retain talent. [ABSTRACT FROM AUTHOR]

Estimates of the impact of body mass index and obesity on health and labor market outcomes often use instrumental variables estimation (IV) to mitigate bias due to endogeneity. When these studies rely on survey data that include self‐ or proxy‐reported height and weight, there is non‐classical measurement error due to the tendency of individuals to under‐report their own weight. Mean reverting errors in weight do not cause IV to be asymptotically biased per se, but may result in bias if instruments are correlated with additive error in weight. We demonstrate the conditions under which IV is biased when there is non‐classical measurement error and derive bounds for this bias conditional on instrument strength and the severity of mean‐reverting error. We show that improvements in instrument relevance alone cannot eliminate IV bias, but reducing the correlation between weight and reporting error mitigates the bias. A solution we consider is regression calibration (RC) of endogenous variables with external validation data. In simulations, we find IV estimation paired with RC can produce consistent estimates when correctly specified. Even when RC fails to match the covariance structure of reporting error, there is still a reduction in asymptotic bias. [ABSTRACT FROM AUTHOR]

Economic literature offers several distinct explanations for the raising income inequality observed in several countries. In the debate about the causes of inequality a growing strand of research focuses on the effects of taxation on income inequality. We contribute to this literature by providing a systematic empirical account of the relationship between income inequality and personal income taxation (PIT) for a set of countries over the period 1981–2005. In order to take alternative explanations into account and to isolate the effects of tax progressivity, we include a wide range of control variables. We address potential reverse causality between inequality and PIT by using the variation in tax schedules of neighbouring countries. Our results confirm a statistically significant negative association between the progressivity of PIT and income inequality. Overall, we find that especially the average and the marginal tax rate have the potential to reduce income inequality. This finding is qualitatively robust across various different empirical specifications. [ABSTRACT FROM AUTHOR]

AbstractAmong the most important merits of modern missing data techniques such as multiple imputation (MI) and full-information maximum likelihood estimation is the possibility to include additional information about the missingness process via auxiliary variables. During the past decade, the choice of auxiliary variables has been investigated under a variety of different conditions and more recent research points to the potentially biasing effect of certain auxiliary variables, particularly colliders (Thoemmes & Rose, 2014). In this article, we further extend biasing mechanisms of certain auxiliary variables considered in previous research and thereby focus on their effects on individual diagnosis based on norming, in which the whole distribution of a variable is of interest rather than average coefficients (e.g., means). For this, we first provide the theoretical underpinnings of the mechanisms under study and then provide two focused simulations that (i) directly expand on the collider scenario in Thoemmes and Rose (2014, appendix A) by considering outcomes that are relevant to norming and (ii) extend the scenarios under consideration by instrumental variable mechanisms. We illustrate the bias mechanisms for two different norming approaches and exemplify the procedures by means of an empirical example. We end by discussing limitations and implications of our research. [ABSTRACT FROM AUTHOR]

A better understanding of the causal relationship between spousal cognitive functioning and depression levels among middle-aged and older adults is vital for effective health policymaking under the globally severe aging challenge. However, the related evidence is often limited by potential omitted-variable bias and reverse causation. This study uses an instrumental variables approach, namely the two-stage least squares (2SLS) method, to examine the impact of spousal cognitive functioning on depression levels among middle-aged and older adults in China. The data were sourced from the China Health and Retirement Longitudinal Study (CHARLS) of 2020, including a total of 3,710 couples aged 45 years and above. Depression levels were measured using the Center for Epidemiologic Studies Depression Scale (CES-D-10), while cognitive functioning was assessed using the Mini-Mental State Examination (MMSE). Spousal social participation was employed as the instrumental variable to address omitted-variable bias and reverse causation. Additionally, an interaction effect test between gender and spousal cognitive functioning was conducted. The results show that for each one-point increase in the spouse's MMSE score, the CES-D-10 score of middleaged and older adults decreased by 17.1% to 68.2%. The OLS results indicated that women, rural residents, and middle-aged individuals were more sensitive to these changes. The interaction effect test results confirmed that women were more affected by changes in spousal cognitive functioning. However, after a more reliable 2SLS analysis, the results for age groups shifted, showing that middle-aged individuals were more sensitive to these changes, with a decrease in depression levels reaching 70.0%, compared to 60.2% for the elderly group. Nonetheless, given the prevalence of depression among the elderly, the impact of spousal cognitive decline on depression in this group should not be overlooked. Our findings highlight the importance of spousal cognitive health in managing depression among both middle-aged and older adults, with particular attention to women and rural populations. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative approach to identifying and estimating the parameters of interest in the widely recognized linear-in-means regression model under conditions where the initial randomization of peers determines the observed network. We assert that peers who are initially randomized do not produce social effects. However, after randomization, agents can endogenously develop significant connections that potentially generate peer influences. We present a moment condition that compiles local heterogeneous identifying information for all agents within the population. Under the assumption of ψ-dependence in the endogenous network space, we propose a Generalized Method of Moments (GMM) estimator, which is proven to be consistent, asymptotically normally distributed, and straightforward to implement using commonly available statistical software due to its closed-form expression. Monte Carlo simulations demonstrate the GMM estimator's strong small-sample performance. An empirical analysis utilizing data from Hong Kong high school students reveals substantial positive spillover effects on math test scores among study partners in our sample, provided that their seatmates were exogenously assigned by their teachers. [ABSTRACT FROM AUTHOR]

This study updates private returns to education in Uganda using consumption measures as an outcome variable, focusing on obtaining estimates using instrumental variables based on the introduction of the universal primary education policy. Unlike common findings from developed countries, the evidence from this study for a low-income country suggests that returns to education are smaller for liquidity-constrained individuals than for average individuals. The finding also suggests that smaller returns are observed for self-employed farmers than self-employed workers in non-agricultural sectors. The trend, showing smaller returns for liquidity-constrained individuals, is particularly obvious among self-employed farmers. [ABSTRACT FROM AUTHOR]

This paper investigates the finite sample performance of a range of parametric, semi-parametric, and non-parametric instrumental variable estimators when controlling for a fixed set of covariates to evaluate the local average treatment effect. Our simulation designs are based on empirical labor market data from the US and vary in several dimensions, including effect heterogeneity, instrument selectivity, instrument strength, outcome distribution, and sample size. Among the estimators and simulations considered, non-parametric estimation based on the random forest (a machine learner controlling for covariates in a data-driven way) performs competitive in terms of the average coverage rates of the (bootstrap-based) 95% confidence intervals, while also being relatively precise. Non-parametric kernel regression as well as certain versions of semi-parametric radius matching on the propensity score, pair matching on the covariates, and inverse probability weighting also have a decent coverage, but are less precise than the random forest-based method. In terms of the average root mean squared error of LATE estimation, kernel regression performs best, closely followed by the random forest method, which has the lowest average absolute bias. [ABSTRACT FROM AUTHOR]

This study investigates the impact of Central Bank interventions on the pricing dynamics of select stock markets. The research utilizes the instrumental variables three-stage least square (3SLS) model approach. It analyses the effects of variations in the Federal Reserve's balance sheet size across three distinct intervention scenarios: the 2008–2013 Great Recession, the 2020–2021 COVID-19 pandemic periods, and an overarching analysis spanning these timelines. Our methodology includes estimations of the Seemingly Unrelated Regression Equations (SURE), and the results are robust under the two-step Generalized Method of Moments (GMM). Our findings indicate that changes in the size of the Fed's balance sheet correlate significantly with the pricing of principal U.S. equity market indices. This correlation reflects a time-dependent effect emanating from the Fed's balance sheet expansion, marking a growing divergence between the adaptability of pricing mechanisms in equity and debt markets. Notably, the Federal Reserve's interventions during the COVID-19 crisis are associated with an increase of approximately 0.0403 basis points per billion in treasury yields. This research makes a significant contribution to the understanding of financial asset pricing, particularly by elucidating the extent to which interventions in government debt securities engender price distortions in certain equity markets. [ABSTRACT FROM AUTHOR]

This paper analyzes the effects of central bank interventions via large-scale purchases of government debt securities on the pricing of stock market indices. This study examines the effects of changes in the size of the Federal Reserve's balance sheet in three intervention scenarios: during the 2008–2013 period, the 2020–2022 period, and in the years between by using the instrumental variables three-stage least squares (3SLS) method for a time series approach, and calculates the effects of these interventions on each index in a fund of funds setup using the panel data strategy. This study confirms that large-scale purchases of government debt securities in response to the Great Recession and COVID-19 crises influenced the pricing of equity markets via their effect on the pricing of treasury bonds, with different degrees of sensitivity of each index to the effects on yields. Although the findings apply to the U.S. market, the results indicate that the pricing of small capitalization indices such as the Russell 2000 are less sensitive to changes in treasury yields caused by central bank interventions than large capitalization indices such as the DJIA. This research contributes to the understanding of financial asset pricing, particularly by identifying price distortions within equity market portfolios. [ABSTRACT FROM AUTHOR]

To date, studies on the effects of grandparenting on grandparents' health and well‐being do not reach the same conclusion and most of them have been conducted in developed countries. We add to this literature by examining the causal relationship between grandparenting and grandparents' health and well‐being in Vietnam, where the social norm and reciprocal relationships between adult children and their older parents are strong. We used instrumental variable estimations to address the endogeneity issue of the decision to provide care. Using the national survey on older persons in Vietnam, we found that grandparenting care had positive effects on psychological well‐being, life satisfaction, and self‐rated health of grandparents. We also found that the effects were heterogenous by caregiver's gender, in which grandmothers were more beneficial from caregiving tasks than grandfathers. Further exploring the mechanisms underlying the effects revealed that improvements in memory and stronger social networks were potential channels through which grandparenting might improve the health and well‐being of grandparents. Our findings support the theory of role enhancement, suggesting that grandparents can have health and well‐being benefits from grandparenting. Policies aiming at strengthening old age protection and family relationships should be advocated to sustain the subjective well‐being of older adults. [ABSTRACT FROM AUTHOR]

Selection bias is a common concern in epidemiologic studies. In the literature, selection bias is often viewed as a missing data problem. Popular approaches to adjust for bias due to missing data, such as inverse probability weighting, rely on the assumption that data are missing at random and can yield biased results if this assumption is violated. In observational studies with outcome data missing not at random, Heckman's sample selection model can be used to adjust for bias due to missing data. In this paper, we review Heckman's method and a similar approach proposed by Tchetgen Tchetgen and Wirth (2017). We then discuss how to apply these methods to Mendelian randomization analyses using individual‐level data, with missing data for either the exposure or outcome or both. We explore whether genetic variants associated with participation can be used as instruments for selection. We then describe how to obtain missingness‐adjusted Wald ratio, two‐stage least squares and inverse variance weighted estimates. The two methods are evaluated and compared in simulations, with results suggesting that they can both mitigate selection bias but may yield parameter estimates with large standard errors in some settings. In an illustrative real‐data application, we investigate the effects of body mass index on smoking using data from the Avon Longitudinal Study of Parents and Children. [ABSTRACT FROM AUTHOR]

Background: Alcoholic liver disease (ALD) significantly contributes to global morbidity and mortality. The role of inflammatory cytokines in alcohol-induced liver injury is pivotal yet not fully elucidated.Objectives: To establish a causal link between inflammatory cytokines and ALD using a Mendelian Randomization (MR) framework.Methods: This MR study utilized genome-wide significant variants as instrumental variables (IVs) for assessing the relationship between inflammatory cytokines and ALD risk, focusing on individuals of European descent. The approach was supported by comprehensive sensitivity analyses and augmented by bioinformatics tools including differential gene expression, protein-protein interactions (PPI), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and analysis of immune cell infiltration.Results: Our findings reveal that increased levels of stem cell growth factor beta (SCGF-β, beta = 0.141, p = .032) and interleukin-7 (IL-7, beta = 0.311, p = .002) are associated with heightened ALD risk, whereas higher levels of macrophage inflammatory protein-1α (MIP-1α, beta = -0.396, p = .004) and basic fibroblast growth factor (bFGF, beta = -0.628, p = .008) are linked to reduced risk. The sensitivity analyses support these robust causal relationships. Bioinformatics analyses around inflammatory cytokine-associated SNP loci suggest multiple pathways through which cytokines influence ALD.Conclusion: The genetic evidence from this study convincingly demonstrates that certain inflammatory cytokines play directional roles in ALD pathogenesis. These findings provide insights into the complex biological pathways involved and underscore the potential for developing targeted therapies that modulate these inflammatory responses, ultimately improving clinical outcomes for ALD patients. [ABSTRACT FROM AUTHOR]

Wearable devices such as the ActiGraph are now commonly used in research to monitor or track physical activity. This trend corresponds with the growing need to assess the relationships between physical activity and health outcomes, such as obesity, accurately. Device‐based physical activity measures are best treated as functions when assessing their associations with scalar‐valued outcomes such as body mass index. Scalar‐on‐function regression (SoFR) is a suitable regression model in this setting. Most estimation approaches in SoFR assume that the measurement error in functional covariates is white noise. Violating this assumption can lead to underestimating model parameters. There are limited approaches to correcting measurement errors for frequentist methods and none for Bayesian methods in this area. We present a non‐parametric Bayesian measurement error‐corrected SoFR model that relaxes all the constraining assumptions often involved with these models. Our estimation relies on an instrumental variable allowing a time‐varying biasing factor, a significant departure from the current generalized method of moment (GMM) approach. Our proposed method also permits model‐based grouping of the functional covariate following measurement error correction. This grouping of the measurement error‐corrected functional covariate allows additional ease of interpretation of how the different groups differ. Our method is easy to implement, and we demonstrate its finite sample properties in extensive simulations. Finally, we applied our method to data from the National Health and Examination Survey to assess the relationship between wearable device‐based measures of physical activity and body mass index in adults in the United States. [ABSTRACT FROM AUTHOR]

Background: Genome-wide association studies have enabled Mendelian randomization analyses to be performed at an industrial scale. Two-sample summary data Mendelian randomization analyses can be performed using publicly available data by anyone who has access to the internet. While this has led to many insightful papers, it has also fuelled an explosion of poor-quality Mendelian randomization publications, which threatens to undermine the credibility of the whole approach. Findings: We detail five pitfalls in conducting a reliable Mendelian randomization investigation: (1) inappropriate research question, (2) inappropriate choice of variants as instruments, (3) insufficient interrogation of findings, (4) inappropriate interpretation of findings, and (5) lack of engagement with previous work. We have provided a brief checklist of key points to consider when performing a Mendelian randomization investigation; this does not replace previous guidance, but highlights critical analysis choices. Journal editors should be able to identify many low-quality submissions and reject papers without requiring peer review. Peer reviewers should focus initially on key indicators of validity; if a paper does not satisfy these, then the paper may be meaningless even if it is technically flawless. Conclusions: Performing an informative Mendelian randomization investigation requires critical thought and collaboration between different specialties and fields of research. [ABSTRACT FROM AUTHOR]

This article considers the quantile regression approach for partially linear spatial autoregressive models with possibly varying coefficients. B-spline is employed for the approximation of varying coefficients. The instrumental variable quantile regression approach is employed for parameter estimation. The rank score tests are developed for hypotheses on the coefficients, including the hypotheses on the non-varying coefficients and the constancy of the varying coefficients. The asymptotic properties of the proposed estimators and test statistics are both established. Monte Carlo simulations are conducted to study the finite sample performance of the proposed method. Analysis of a real data example is presented for illustration. [ABSTRACT FROM AUTHOR]

The existence of heterogeneity in the literature that addresses the sources of economic growth, from an empirical point of view, generates a problem of uncertainty. The objective is to identify robust determinants of economic growth in Mexico by reducing the uncertainty of the model. To do so, the Bayesian Model Averaging (BMA) methodology is proposed, which analyzes many explanatory variables simultaneously. Thus, 28 possible determinants are considered in a sample that includes the 32 federal entities, to include the period 2010-2021. The BMA constructs various possible combinations of models to extract the most robust determinants. Similarly, the instrumental variables BMA (IVBMA) is used to consider possible endogeneity problems. The results show a set of significant economic, institutional, and social variables to understand economic growth in Mexico. [ABSTRACT FROM AUTHOR]

Introduction There is a longstanding debate about whether health care is more efficiently provided by the public or private sector. The debate is particularly relevant to the Military Health System (MHS), which delivers care through a combination of publicly funded federal facilities and privately contracted providers. This study compares outcomes, treatments, and costs for MHS patients obtaining care for low back pain (LBP) from public versus private providers. Materials and Methods A retrospective cohort study was completed using TRICARE Prime claims data from April 2015 to December 2018. The cohort was identified using International Classification of Diseases Ninth Revision and Tenth Revision diagnostic codes and then followed for 12 months after the index diagnosis to assess treatments, outcomes, and costs. Claims were classified as originating from either public or private providers. The primary outcome measure was resolution of LBP, defined as an absence of LBP diagnoses during the 6-to-12-month window following the index event. Instrumental variable models were used to assess the impact of care setting (i.e. private versus public), conditioning on the covariates. A regional measure of the fraction of private care was used as an instrument. Results Resolution of LBP was achieved for 79.7% of 144,866 patients in the cohort. No significant association was found between resolution of LBP and fraction of privately provided care. Higher fraction of private care was associated with a greater likelihood of opioid treatments (odds ratio, 1.22; 95% CI, 1.02-1.46) and a lower likelihood of benzodiazepine (odds ratio, 0.56; 95% CI, 0.45-0.70) and physical therapy (odds ratio 0.55; 95% CI, 0.42-0.74) treatments; manual therapy was not significantly associated with the fraction of private care. There was a significant negative association between the fraction of private care and cost (coefficient −0.27; 95% CI, −0.44, −0.10). Conclusion This study found that privately provided care was associated with significantly higher opioid prescribing, less use of benzodiazepines and physical therapy, and lower costs. No systematic differences in outcomes (as measured by resolved cases) were identified. The findings suggest that publicly funded health care within the MHS context can attain quality comparable to privately provided care, although differences in treatment choices and costs point to possibilities for improved care within both systems. [ABSTRACT FROM AUTHOR]

Introduction Congenital heart disease (CHD) has an incidence of 0.8% to 1.2% worldwide, making it the most common birth defect. Researchers have compared high-volume to low-volume hospitals and found significant hospital-level variation in major complications, health resource utilization, and mortality after CHD surgery. In addition, researchers found critical CHD patients at low-volume/non-teaching facilities to be associated with higher odds of inpatient mortality when compared to CHD patients at high-volume/teaching hospitals (odds ratio 1.76). We examined the effects of high-quality hospital (HQH) use on health care outcomes and health care costs in pediatric CHD care using an instrumental variable (IV) approach. Materials and Methods Using nationwide representative claim data from the United States Military Health System from 2016 to 2020, TRICARE beneficiaries with a diagnosis of CHD were tabulated based on relevant ICD-10 (International Classification of Diseases, 10th revision) codes. We examined the relationships between annual readmissions, annual emergency room (ER) use, and mortality and HQH use. We applied both the naive linear probability model (LPM), controlling for the observed patient and hospital characteristics, and the two-stage least squares (2SLS) model, accounting for the unobserved confounding factors. The differential distance between the patient and the closest HQH at the index date and the patient and nearest non-HQH was used as the IV. This protocol was approved by the Institutional Review Board at the University of Maryland, College Park (Approval Number: 1576246-2). Results The naive LPM indicated that HQH use was associated with a higher probability of annual readmissions (marginal effect, 18%; 95% CI, 0.12 to 0.23). The naive LPM indicated that HQH use was associated with a higher probability of mortality (marginal effect, 2.2%; 95% CI, 0.01 to 0.03). Using the differential distance of closest HQH and non-HQH, we identified a significant association between HQH use and annual ER use (marginal effect, −14%; 95% CI, −0.24 to −0.03). Conclusions After controlling for patient-level and facility-level covariates and adjusting for endogeneity, (1) HQH use did not increase the probability of more than one admission post 1-year CHD diagnosis, (2) HQH use lowered the probability of annual ER use post 1-year CHD diagnosis, and (3) HQH use did not increase the probability of mortality post 1-year CHD diagnosis. Patients who may have benefited from utilizing HQH for CHD care did not, alluding to potential barriers to access, such as health insurance restrictions or lack of patient awareness. Although we used hospital quality rating for congenital cardiac surgery as reported by the Society of Thoracic Surgeons, the contributing data span a 4-year period and may not reflect real-time changes in center performance. Since this study focused on inpatient care within the first-year post-initial CHD diagnosis, it may not reflect the full range of health system utilization. It is necessary for clinicians and patient advocacy groups to collaborate with policymakers to promote the development of an overarching HQH designation authority for CHD care. Such establishment will facilitate access to HQH for military beneficiary populations suffering from CHD. [ABSTRACT FROM AUTHOR]

We estimate the causal effect of external debt on greenhouse gas emissions in a panel of 78 emerging market and developing economies over the 1990-2015 period. Unlike previous literature, we use external instruments to address the potential endogeneity in the relationship between external debt and greenhouse gas emissions. Specifically, we use international liquidity shocks as instrumental variables for external debt. We find that dealing with the potential endogeneity problem brings about a positive and statistically significant effect of external debt on greenhouse gas emissions: a 1 percentage point (pp.) rise in external debt as a percentage of GDP causes, on average, a 0.5% increase in greenhouse gas emissions. One possible mechanism of action could be that, as external debt increases, governments are less able to enforce environmental regulations because their main priority is to increase the tax base to pay increasing debt services or because they are captured by the private sector who owns that debt and prevented from tightening such regulations. [ABSTRACT FROM AUTHOR]

Mendelian randomization (MR) addresses causal questions using genetic variants as instrumental variables. We propose a new MR method, G-Estimation under No Interaction with Unmeasured Selection (GENIUS)-MAny Weak Invalid IV, which simultaneously addresses the 2 salient challenges in MR: many weak instruments and widespread horizontal pleiotropy. Similar to MR-GENIUS, we use heteroscedasticity of the exposure to identify the treatment effect. We derive influence functions of the treatment effect, and then we construct a continuous updating estimator and establish its asymptotic properties under a many weak invalid instruments asymptotic regime by developing novel semiparametric theory. We also provide a measure of weak identification, an overidentification test, and a graphical diagnostic tool. [ABSTRACT FROM AUTHOR]

Purpose: What is the effect of having a criminal record compared to having the criminal record expunged in exchange for participating in a rehabilitative programme? The available evidence focuses on programmes comprised of the criminal record for the offence (i.e. labelling) and a punitive sanction or rehabilitative scheme. The interaction between the labelling and the sanction has made distinguishing the effect of each penological approach a challenge. Methods: We use a pretest–posttest control group design with a cohort of 341 low-harm offenders randomly assigned to either a simple, unconditional, caution or a 16-week rehabilitation treatment programme (after which the criminal record was automatically expunged). New crimes and a measure of harm were used as outcome variables. Results: Intention-to-treat analysis shows no significant difference in prevalence, crime count or crime harm. Factoring in those individuals who actually completed the programme changes this story. An instrumental variables analysis used to adjust for treatment compliance suggests that the offer to expunge the criminal record following participation in rehabilitation programmes reduces both crime count and crime harm. Conclusions: We conclude that as evidence on the adverse effects of criminal records on recidivism mounts, out of court disposals that lead to an expungement of the label 'offender' may provide promising intervention for low-harm offences. The experiment also highlighted the importance of secondary analytic strategies in experiments alongside the standard intention-to-treat model. [ABSTRACT FROM AUTHOR]

Instrumental variables is a popular approach for causal inference in education when randomization of treatment is not feasible. Using a first-year college program as a running example, this article reviews the five assumptions that must be met to successfully use instrumental variables to estimate a causal effect with observational data: SUTVA, as-if random assignment, exclusion restriction, nonzero average causal effect of instrument on treatment, and monotonicity, and concludes with recommendations for researchers. [ABSTRACT FROM AUTHOR]

Abstract Background Genome-wide association studies have enabled Mendelian randomization analyses to be performed at an industrial scale. Two-sample summary data Mendelian randomization analyses can be performed using publicly available data by anyone who has access to the internet. While this has led to many insightful papers, it has also fuelled an explosion of poor-quality Mendelian randomization publications, which threatens to undermine the credibility of the whole approach. Findings We detail five pitfalls in conducting a reliable Mendelian randomization investigation: (1) inappropriate research question, (2) inappropriate choice of variants as instruments, (3) insufficient interrogation of findings, (4) inappropriate interpretation of findings, and (5) lack of engagement with previous work. We have provided a brief checklist of key points to consider when performing a Mendelian randomization investigation; this does not replace previous guidance, but highlights critical analysis choices. Journal editors should be able to identify many low-quality submissions and reject papers without requiring peer review. Peer reviewers should focus initially on key indicators of validity; if a paper does not satisfy these, then the paper may be meaningless even if it is technically flawless. Conclusions Performing an informative Mendelian randomization investigation requires critical thought and collaboration between different specialties and fields of research.

This study empirically tests time-varying asset pricing models in an emerging market with individual stocks. We employ a recently proposed instrumental variables (IV) technique that uses individual stocks as test assets while consistently estimating ex-post risk premiums. This method differs from constructing test portfolios, a common practice employed to mitigate errors-in-variables bias, and, instead, uses factor sensitivity estimates from alternating even and odd months as IVs. Applying this approach, we observe statistically insignificant factor risk premiums under various multifactor models in asset pricing tests at Borsa Istanbul, after accounting for asset characteristics. Our method facilitates the inclusion of essential risk or return-related characteristics of individual stocks in tests, raising insights usually obscured by conventional test portfolios. The results contribute to empirical asset pricing by highlighting the failure of classical models to explain risk premiums at Borsa Istanbul, a significant emerging stock market, when tested with individual stocks using an IV approach.

Randomized trials offer a powerful strategy for estimating the effect of a treatment on an outcome. However, interpretation of trial results can be complicated when study subjects do not take the treatment to which they were assigned; this is referred to as nonadherence. Prior authors have described instrumental variable approaches to analyze trial data with nonadherence; under their approaches, the initial assignment to treatment is used as an instrument. However, their approaches require the assumption that initial assignment to treatment has no direct effect on the outcome except via the actual treatment received (i.e., the exclusion restriction), which may be implausible. We propose an approach to identification of a causal effect of treatment in a trial with 1-sided nonadherence without assuming exclusion restriction. The proposed approach leverages the study subjects initially assigned to control status as an unexposed reference population; we then employ a bespoke instrumental variable analysis, where the key assumption is partial exchangeability of the association between a covariate and an outcome in the treatment and control arms. We provide a formal description of the conditions for identification of causal effects, illustrate the method using simulations, and provide an empirical application.

ABSTRACTThis paper meshes 1.7 million housing transactions across three US states (Iowa, Minnesota and Texas) between 2015 and 2021 with data on broadband infrastructure to evaluate the impact of fibre broadband availability on home prices. This was a period of dramatic fibre growth in these states: prior to 2019, fibre was only available to roughly 24% of the houses sold but rose to 54% in later years. A traditional hedonic pricing model that includes a wide array of housing characteristics and census block group-level fixed effects estimates the fibre premium at around 1% in all three states for the full 2105–2021 period. The fibre premium was higher in the earlier part of this period, likely reflecting its novelty during that time. A more rigorous instrumental variable approach estimates the fibre premium at 2% in Minnesota and 9% in Texas. A conservative national estimate of the increase in housing value from deploying ubiquitous fibre is $36 billion.

Grade retention is at the core of the education debate in Spain, to the extent that its impact on students' competences has not been assessed beyond correlation. Because of that, in the present study, we analyse the influence of grade retention on students' competences, using more than 146,000 students from 6 PISA cycles (2003–2018) and an instrumental variable approach, in order to approach a causal influence. Our results show that repeating a grade in Spain seems to reduce students' competences between 1.5 and 1.7 standard deviations. Based on these results, we conclude that the Spanish educational authorities should find an alternative to grade retention, in order to prevent students from attaining a lower competence level due to repetition. [ABSTRACT FROM AUTHOR]

Parents have the option of enrolling their children in the first stage of early childhood education (from 0 to 3 years of age). However, not all parents decide to do so, waiting until the second stage of early childhood education to enrol them in the education system (from 3 to 5 years of age), or even until compulsory education when their children are around 6. We intend to analyse the influence of students' enrolment in the first stage of early childhood education on their fourth-grade reading scores. This analysis has been performed using data from the Progress in International Reading Literacy Study (PIRLS) 2011 and 2016 for 39 countries and an instrumental variable approach to go beyond simple correlation. We find that attending the first stage of early childhood education has a positive influence on students' reading scores in 18 countries, whereas it presents a null influence in 16 countries. [ABSTRACT FROM AUTHOR]

The question of why postsecondary institutions produce different labor market outcomes is difficult to answer due to unobserved student characteristics. Here, I leverage students' geographic proximity to three classifications of postsecondary institutions – earnings-enhancing, competitive, and Historically Black Colleges and Universities (HBCUs). Using a nationally representative sample, I estimate attainment and earnings effects of first attending each type. Attending an institution classified as earnings-enhancing increases humanities credit completion, degree attainment, and early-career wages. Among underrepresented students, living closest to an HBCU strongly predicts HBCU enrollment. This yields higher STEM credit completion but lower early-career wages, suggesting possible labor market bias. Abbreviations:Competitive: Barron's Top 3 Selectivity Tier Institution; HBCU:Historically Black College or University; HSI: High-Success Institution; STEM: Science; Technology; Engineering; and Mathematics; Underrepresented Minority (URM): Black; Indigenous; or Hispanic/Latinx HIGHLIGHTS Nearest-college attributes predict college choice for many high school students, especially those living near HBCUs. Colleges previously linked to students' wage mobility yield higher earnings by students' mid-20s. Higher earnings effects coincide with higher humanities credit completion, bachelor's completion, and postbaccalaureate training. HBCU attendance relative to other options yields higher STEM credit completion, but lower early-career wages. HBCU attendance relative to no college also increases humanities credit completion and bachelor's degree completion. [ABSTRACT FROM AUTHOR]

This paper examines the causal effect of childcare provision on grandparents’ health in the United States. We use the sex ratio among older adults’ children as an instrument for grandparental childcare provision. Our instrument exploits that parents of daughters transition to grandparenthood earlier and invest more in their grandchildren than parents of sons. We estimate 2SLS regressions using data from the Health and Retirement Study. The results suggest that providing childcare is detrimental to grandparents’ physical functioning and subjective health. We show that these effects increase with the intensity of grandchild care provision, and the effects are driven primarily by grandmothers. [ABSTRACT FROM AUTHOR]

Abstract Background PM2.5 can induce and aggravate the occurrence and development of cardiovascular diseases (CVDs). The objective of our study is to estimate the causal effect of PM2.5 on mortality rates associated with CVDs using the instrumental variables (IVs) method. Methods We extracted daily meteorological, PM2.5 and CVDs death data from 2016 to 2020 in Binzhou. Subsequently, we employed the general additive model (GAM), two-stage predictor substitution (2SPS), and control function (CFN) to analyze the association between PM2.5 and daily CVDs mortality. Results The 2SPS estimated the association between PM2.5 and daily CVDs mortality as 1.14% (95% CI: 1.04%, 1.14%) for every 10 µg/m3 increase in PM2.5. Meanwhile, the CFN estimated this association to be 1.05% (95% CI: 1.02%, 1.10%). The GAM estimated it as 0.85% (95% CI: 0.77%, 1.05%). PM2.5 also exhibited a statistically significant effect on the mortality rate of patients with ischaemic heart disease, myocardial infarction, or cerebrovascular accidents (P

Abstract This study focuses on identifying the determinants of cluster farming participation decisions and the impact of cluster farming on smallholder wheat farmer’s productivity in the two districts of Arsi Zone, Ethiopia. The study utilized cross-sectional data and a multistage sampling procedure to select a total of 381 respondents from the two districts of Arsi Zone, Ethiopia. The endogenous switching regression model was employed to achieve the research objectives. The study result indicates that sex of the household head, education level, size of cultivated land, access to training, membership in a farmer's cooperative, access to information and social responsibility of the household head influenced cluster farming participation positively and significantly, while distance from the nearest market had a negative and significant effect on cluster farming participation. Furthermore, the study shows that, if participants had decided not to practice cluster farming, their wheat yield and net benefit would have decreased by 33.57 and 40.08%, respectively. Similarly, had non-participants decided to participate, their wheat yield and net benefit would have increased by 46.79 and 102.49%, respectively. The study recommends that policymakers and development organizations should consider cluster farming as a main strategy to increase smallholder farmer’s productivity. Moreover, the study calls for government and institutional assistance in the areas of education services, training, extension services, infrastructure (particularly access to markets), and cooperative development. Moreover, policy and development measures should address the issue of gender disparities in cluster farming participation.

Abstract Background Earlier studies have estimated the impact of increased body mass index (BMI) on healthcare costs. Various methods have been used to avoid potential biases and inconsistencies. Each of these methods measure different local effects and have different strengths and weaknesses. Methods In the current study we estimate the impact of increased BMI on healthcare costs using nine common methods from the literature: multivariable regression analyses (ordinary least squares, generalized linear models, and two-part models), and instrumental variable models (using previously measured BMI, offspring BMI, and three different weighted genetic risk scores as instruments for BMI). We stratified by sex, investigated the implications of confounder adjustment, and modelled both linear and non-linear associations. Results There was a positive effect of increased BMI in both males and females in each approach. The cost of elevated BMI was higher in models that, to a greater extent, account for endogenous relations. Conclusion The study provides solid evidence that there is an association between BMI and healthcare costs, and demonstrates the importance of triangulation.

Poverty remains a concern for policymakers across the globe because a considerable number of individuals are still poor and unable to make a decent living for themselves. Among the efforts to combat poverty is to enhance the income of individuals by improving access to credit. Extant studies have investigated the nexus of poverty and credit access, but the majority of them tend to focus on microcredit without considering the broader theme of credit. Moreover, most of the studies ignored the potential endogeneity inherent in credit access. Again, studies that focused on Ghana are concentrated on some specific areas, districts, or municipalities of the country which does not reflect the country-wide effect of credit access on poverty. To bridge these study gaps, we analyzed the impact of accessing credit on poverty using more representative data within an instrumental variable (IV) framework that is robust to endogeneity. Besides, we extend the discussion by showing how the poverty impact of accessing credit from formal sources differs from informal ones. Using data from the 2016/2017 round of the Ghana Living Standards Survey (GLSS), we found that accessing credit generally deepens the poverty status of Ghanaians. However, the specific impacts of credit sources indicate that accessing credit from formal sources is more effective in reducing the propensity to be poor in Ghana. Additional findings show that age, income, and location of the individual have significant impacts on their ability to access credit to better their lives. It follows that efforts by the government to improve financial inclusion to roll in more people will increase credit access from formal financial institutions and help reduce poverty in Ghana. [ABSTRACT FROM AUTHOR]

Objective: This Mendelian Randomization (MR) study aims to explore the potential causal relationships between four sleep traits and pain in 10 different body sites. Materials and methods: The study utilizes exposure and outcome data from the GWAS database, employing the Inverse Variance Weighting Method (IVW) for primary causal estimates. Cochran Q and Rücker Q heterogeneity tests are conducted using IVW and MR-Egger methods, with the Egger-intercept method for pleiotropy testing, leave-one-out sensitivity analysis, and calculation of F-statistics to assess the presence of weak instrument bias. Results: The study reveals that genetically predicted insomnia significantly increases the risk of unspecified pain, chest pain, gum pain, upper abdominal pain, and lower abdominal pain occurrence. Daytime napping is associated with a moderate reduction in the likelihood of joint pain but may concomitantly elevate the risk of chest pain, upper abdominal pain, and generalized abdominal pain. Neither sleep chronotype nor sleep duration demonstrated a definitive causal relationship with pain perception. Conclusion: This study elucidates the causal relationships between four sleep characteristics and pain across 10 different body regions. Overall, the contribution of insomnia and sleep deficiency to pain in multiple body regions is more pronounced. Conversely, the association between adequate sleep and the likelihood of somatic pain is relatively lower and less significant. [ABSTRACT FROM AUTHOR]