The role of residential greenness levels, green land cover types and diversity in overweight/obesity among older adults: A cohort study

BACKGROUND

Few studies have investigated the effects of greenness exposure, green land cover types and diversity and their interaction with particulate matter (PM) to adiposity.

METHOD

Cohort data were collected from the Chinese Longitudinal Healthy Longevity Survey (CLHLS). Baseline data on greenness levels, green land cover types and diversity were assessed by the Normalized Difference Vegetation Index (NDVI), three greenery types (trees, shrublands and grassland) and Shannon's diversity index, respectively. Body mass index (BMI) and waist circumference (WC) were separately used as dependent variables and represented for peripheral overweight/obesity and central obesity, respectively. The mixed Cox model with random intercept was used to estimate the effects of greenness levels, types and diversity on overweight/obesity using single and multiple exposure models. We also examined the interaction of PM and the aforementioned indicators on overweight/obesity on both additive and multiplicative scales.

RESULTS

Single exposure models showed that higher levels of residential greenness, tree coverage and ratio of trees to shrublands/grassland were inversely associated with peripheral overweight/obesity and central obesity. An increase in shrublands, grassland and diversity of green was related to lower odds of peripheral overweight/obesity. Multiple exposure models confirmed the association between greenness levels and peripheral overweight/obesity. Males, educated participants and elderly who lived in southern regions and areas with cleaner air environments acquired more benefits from greenspace exposure. Single and multiple exposure models indicated that an antagonistic effect of increasing PM and decreasing greenness levels on peripheral overweight/obesity and central obesity. Single exposure models showed the potential interaction of tree coverage, ratio of trees to grassland and PM_2.5 exposures on the risk of peripheral overweight/obesity.

CONCLUSION

Increasing residential greenness and diversity of green were associated with healthy weight status. The relationship between greenery and overweight/obesity varied, and the effects of greenspace exposure on overweight/obesity were associated with air pollution.

Population attributable fraction based on marginal sufficient component cause model for mediation settings

PURPOSE

Population attributable fraction (PAF), defined as the proportion of the occurrence of a disease which will be reduced by eliminating risk factors in a population, is one of the most common measurements for evaluating the benefit of a health-related policy in epidemiologic study. In this article, we propose an alternative PAF defined based on sufficient cause framework, which decompose the occurrence of a disease into several pathways including mediation and mechanistic interaction.

METHODS

We propose a formal statistical definition and regression-based estimator for PAF based on sufficient cause framework within mediation settings. Under monotonicity assumption, the proposed method can decompose the occurrence of a disease into nine PAFs corresponding to all types of mechanisms attributing to exposure and the mediator, including the portion attributing to exposure directly, to mediator, to indirect effect through mediator, to the mechanistic interaction, to both of mediation and interaction, and to none of exposure or mediator.

RESULTS

We apply the proposed method to explore the mechanism of a hepatitis C virus (HCV)-induced hepatocellular carcinoma (HCC) mediated by and/or interacted with alanine aminotransferase (ALT) and hepatitis B virus (HBV). When treating ALT as mediator, 56.77% of diseased subjects can be attributable to either HCV or abnormal ALT. When treating HBV as mediator, HCC is mainly induced by an exogenous high HBV viral load directly.

CONCLUSIONS

The proposed method can identify the impact of exposure and pathway effects, and benefit to allocate the resources on intervention strategies.

Identification and robust estimation of swapped direct and indirect effects: Mediation analysis with unmeasured mediator-outcome confounding and intermediate confounding

Counterfactual-model-based mediation analysis can yield substantial insight into the causal mechanism through the assessment of natural direct effects (NDEs) and natural indirect effects (NIEs). However, the assumptions regarding unmeasured mediator-outcome confounding and intermediate mediator-outcome confounding that are required for the determination of NDEs and NIEs present practical challenges. To address this problem, we introduce an instrumental blocker, a novel quasi-instrumental variable, to relax both of these assumptions, and we define a swapped direct effect (SDE) and a swapped indirect effect (SIE) to assess the mediation. We show that the SDE and SIE are identical to the NDE and NIE, respectively, based on a causal interpretation. Moreover, the empirical expressions of the SDE and SIE are derived with and without an intermediate mediator-outcome confounder. Then, a multiply robust estimation method is derived to mitigate the model misspecification problem. We prove that the proposed estimator is consistent, asymptotically normal, and achieves the semiparametric efficiency bound. As an illustration, we apply the proposed method to genomic datasets of lung cancer to investigate the potential role of the epidermal growth factor receptor in the treatment of lung cancer.

Association of residential greenness and incident depression: Investigating the mediation and interaction effects of particulate matter

BACKGROUND

Growing evidence has linked residential greenness to depression, the results from prospective cohort study are still limited. And it remains unclear whether particulate matter (PM) modify, mediate, or interact the greenness-depression relationship.

METHODS

We collected data from Yinzhou Cohort(N = 47,516) which was recruited between June 2015 and December 2017. Depression cases before April 2020 were ascertained from local Health Information System covered all residents' health care records. Residential greenness (the Normalized Difference Vegetation Index, NDVI, and the Enhanced Vegetation Index, EVI) and PM (particulate matters with diameters≤2.5 μm, PM_2.5 and particulate matters with diameters≤10 μm, PM₁₀) were estimated based on participants' residential coordinates. We conducted Cox models employing age as timescale to estimate the association between residential greenness within different buffers and incident depression. Furthermore, we explored the potential confounding, mediation and interaction relationship between greenness and PM.

RESULTS

During the 99,556 person-years of follow-up, 1043 incident depression cases occurred. In single exposure models, residential greenness was inversely associated with depression incidence (e.g. Hazard Ratio (HR) = 0.86, 95% confidence interval (CI): 0.79, 0.94 for per interquartile range (IQR) increase NDVI 250 m). The protective association between greenness was attenuated after introducing PM_2.5 and PM₁₀ into the models. We identified multiplicative interactions between greenness and PM exposure for depression (e.g. HR _interaction = 0.91, 95%CI: 0.85, 0.98 for per IQR decrease NDVI 250 m and per IQR increase PM_2.5). Besides, we found the protective association of greenness was partly mediated by PM (e.g. mediation proportion = 52.9% between NDVI 250 m and PM_2.5).

CONCLUSIONS

In this longitudinal cohort study, residents living in greener neighborhoods had a lower risk of depression incidence and the benefits were interacted and partly mediated by PM. Improvement in residential greenness could be an actionable and planning intervention to prevent depression.

The Joint Effects of Depression and Comorbid Psychiatric Disorders on Suicide Deaths: Competing Antagonism as an Explanation for Subadditivity

BACKGROUND

Previous studies of the effect of interaction between psychiatric disorders on suicide have reported mixed results. We investigated the joint effect of depression and various comorbid psychiatric disorders on suicide.

METHODS

We conducted a population-based case-cohort study with all suicide deaths occurring between 1 January 1995 and 31 December 2015 in Denmark (n = 14,103) and a comparison subcohort comprised of a 5% random sample of the source population at baseline (n = 265,183). We quantified the joint effect of pairwise combinations of depression and major psychiatric disorders (e.g., organic disorders, substance use disorders, schizophrenia, bipolar disorder, neurotic disorders, eating disorders, personality disorders, intellectual disabilities, developmental disorders, and behavioral disorders) on suicide using marginal structural models and calculated the relative excess risk due to interaction. We assessed for the presence of competing antagonism for negative relative excess risk due to interactions.

RESULTS

All combinations of depression and comorbid psychiatric disorders were associated with increased suicide risk. For example, the rate of suicide among men with depression and neurotic disorders was 20 times (95% CI = 15, 26) the rate in men with neither disorder. Most disorder combinations were associated with subadditive suicide risk, and there was evidence of competing antagonism in most of these cases.

CONCLUSIONS

Subadditivity may be explained by competing antagonism. When both depression and a comorbid psychiatric disorder are present, they may compete to cause the outcome such that having 2 disorders may be no worse than having a single disorder with respect to suicide risk.

Pharmacotherapeutic strategies for hepatitis B and hepatitis C coinfection

INTRODUCTION

Hepatitis B (HBV) and Hepatitis C (HCV) infection place a significant burden on the global health system, with chronic carriage leading to cirrhosis and hepatocellular carcinoma. HBV/HCV coinfection can be seen in highly endemic areas and present a heterogenous group given varying virologic profiles. Coinfected patients have a greater risk of advanced liver disease; hence, diagnosis and early antiviral therapy (AVT) should be a priority. Optimal treatment regimens for coinfected patients remain unknown with differing recommendations, particularly relating to the risk of HBV reactivation whilst on AVT for HCV.

AREAS COVERED

This article summarizes the available data on HBV/HCV coinfection with regards to epidemiology, virologic interactions, and risk of HBV reactivation. The authors also provide a framework for the assessment and treatment of coinfected patients.

EXPERT OPINION

There is a moderate risk of HBV reactivation in hepatitis B surface antigen (HBsAg) positive patients undergoing HCV direct-acting antiviral (DAA) treatment; however, clinically significant events are rare. The risk of HBV reactivation in HBsAg negative patients undergoing HCV DAA treatment is negligible. Thus, prophylactic HBV treatment in both groups is not required. The authors recommend close monitoring with HBV treatment if there is evidence of HBV reactivation or elevated alanine aminotransferase levels.

Marginal Sufficient Component Cause Model: An Emerging Causal Model With Merits?

For decades, the sufficient cause model and the counterfactual model have shaped our understanding of causation in biomedical science, and the link between these two models has enabled us to obtain a deeper understanding of causality. Recently, a new causal model-the marginal sufficient component cause model-was proposed and applied in the context of interaction or mediation. The proponents of this model have emphasized its utility in visualizing the presence of "agonism" (a subtype of mechanistic interaction) in the counterfactual framework, claiming that the concept of agonism has not been clearly defined in causal inference and that agonistic interaction cannot be visualized by the conventional sufficient cause model. In this article, we illustrate that careful scrutiny based on the conventional sufficient cause model yields further insights into the concept of agonism in a more biologic sense. We primarily focus on the following three points: (1) "agonism" defined in the counterfactual model can be visualized as sets of sufficient causes in the conventional sufficient cause model; (2) although the so-called independent competing assumption or no redundancy assumption may seem irrelevant in the marginal sufficient component cause model, researchers do need to assume that potential completion times of relevant marginal sufficient causes differ; and (3) possibly differing potential completion times of marginal sufficient causes cannot be discerned until their hidden mechanistic paths are considered in the conventional sufficient cause model. In this rapidly progressing field of research, decades after its introduction, the sufficient cause model retains its worth.

Strength in causality: discerning causal mechanisms in the sufficient cause model

The assessment of causality is fundamental to epidemiology and biomedical sciences. One well-known approach to distinguishing causal from noncausal explanations is the nine Bradford Hill viewpoints. A recent article in this journal revisited the viewpoints to incorporate developments in causal thinking, suggesting that the sufficient cause model is useful in elucidating the theoretical underpinning of the first of the nine viewpoints-strength of association. In this article, we discuss how to discern the causal mechanisms of interest in the sufficient cause model, which pays closer attention to the relationship between the sufficient cause model and the Bradford Hill viewpoints. To this end, we explicate the link between the sufficient cause model and the potential-outcome model, both of which have become the cornerstone of causal thinking in epidemiology and biomedicine. A clearer understanding of the link between the two models provides significant implications for interpretation of the observed risks in the subpopulations defined by exposure and confounder. We also show that the concept of potential completion times of sufficient causes is useful to fully discerning completed sufficient causes, which leads us to pay closer attention to the fourth of the nine Bradford Hill viewpoints-temporality. Decades after its introduction, the sufficient cause model may be vaguely understood and thus implicitly used under unreasonably strict assumptions. To strengthen our assessment in the face of multifactorial causality, it is significant to carefully scrutinize the observed associations in a complementary manner, using the sufficient cause model as well as its relevant causal models.

Integrated multiple mediation analysis: A robustness-specificity trade-off in causal structure

Recent methodological developments in causal mediation analysis have addressed several issues regarding multiple mediators. However, these developed methods differ in their definitions of causal parameters, assumptions for identification, and interpretations of causal effects, making it unclear which method ought to be selected when investigating a given causal effect. Thus, in this study, we construct an integrated framework, which unifies all existing methodologies, as a standard for mediation analysis with multiple mediators. To clarify the relationship between existing methods, we propose four strategies for effect decomposition: two-way, partially forward, partially backward, and complete decompositions. This study reveals how the direct and indirect effects of each strategy are explicitly and correctly interpreted as path-specific effects under different causal mediation structures. In the integrated framework, we further verify the utility of the interventional analogues of direct and indirect effects, especially when natural direct and indirect effects cannot be identified or when crossworld exchangeability is invalid. Consequently, this study yields a robustness-specificity trade-off in the choice of strategies. Inverse probability weighting is considered for estimation. The four strategies are further applied to a simulation study for performance evaluation and for analyzing the Risk Evaluation of Viral Load Elevation and Associated Liver Disease/Cancer dataset from Taiwan to investigate the causal effect of hepatitis C virus infection on mortality.

Residential greenness and birth outcomes: Evaluating the mediation and interaction effects of particulate air pollution

BACKGROUND

The few studies that examined the association between residential greenness and birth outcomes have produced inconsistent results, and the underlying mechanisms of these associations remain unclear.

OBJECTIVES

We examined the mediation and interaction effects of particulate matter (PM) air pollution on the relationship between greenness exposure during the first and third trimesters of pregnancy and birth outcomes, including preterm birth (PTB), term low birth weight (TLBW), small for gestational age (SGA), birth weight (BW), and head circumference (HC).

METHODS

We conducted a retrospective cohort study on 16,184 singleton live births between 2010 and 2012 in Taiwan. Residential greenness was estimated based on the normalized difference vegetation index (NDVI), and the PM information during the first and third trimesters was estimated through hybrid kriging land use regression and ordinary kriging interpolation methods. Multiple regression analyses were performed to evaluate the associations between greenness exposure and birth outcomes. We estimated the mediating effects of PM associated with greenness exposure on birth outcomes through causal mediation analyses. We also examined the potential multiplicative and additive interactions between greenness exposure and PM and their effects on birth outcomes.

RESULTS

The first trimester NDVI exposure was associated with reduced risks for PTB, TLBW, and SGA, which had an adjusted OR (aOR) of 0.93 (95% CI: 0.89-0.97), 0.91 (95% CI: 0.83-0.99), and 0.95 (95% CI: 0.91-1.00), respectively, per 0.1 unit increase in multi-pollutant models. The causal mediation analysis showed that PM mediated approximately 5-19% of the association between first and third trimester greenness and PTB and mediated approximately 15-37% of the association between greenness and SGA. We identified multiplicative interactions in log scale between first trimester PM₁₀ and NDVI exposure for SGA (aOR_interaction = 0.92, p = 0.03) and HC (estimate_interaction = 1.47, p = 0.04).

CONCLUSIONS

This study revealed beneficial associations between residential greenness and birth outcomes, including PTB, TLBW, and SGA. The associations were partly mediated by a reduction in exposure to PM air pollution.

SUMMARY

The beneficial effects of greenness on PTB and SGA are partly mediated by a reduction in exposure to PM air pollution.

Six-way decomposition of causal effects: Unifying mediation and mechanistic interaction

The sufficient component cause (SCC) model and counterfactual model are two common methods for causal inference, each with their own advantages: the SCC model allows the mechanistic interaction to be detailed, whereas the counterfactual model features a systemic framework for quantifying causal effects. Hence, integrating the SCC and counterfactual models may facilitate the conceptualization of causation. Based on the marginal SCC (mSCC) model, we propose a novel counterfactual mSCC framework that includes the steps of definition, identification, and estimation. We further propose a six-way effect decomposition for assessing mediation and the mechanistic interaction. The results demonstrate that when all variables are binary, the six-way decomposition is an extension of four-way decomposition and that without agonism, the six-way decomposition is reduced to four-way decomposition. To illustrate the utility of the proposed decomposition, we apply it to a Taiwanese cohort to examine the mechanism of hepatitis C virus (HCV)-induced hepatocellular carcinoma (HCC) with liver inflammation measured by alanine aminotransferase (ALT) as a mediator. Among the HCV-induced HCC cases, 62.27% are not explained by either mediation or interaction in relation to ALT; 9.32% are purely mediated by ALT; 16.53% are caused by the synergistic effect of HCV and ALT; and 9.31% are due to the mediated synergistic effect of HCV and ALT. In summary, we introduce an SCC model framework based on counterfactual theory and detail the required identification assumptions and estimation procedures; we also propose a six-way effect decomposition to unify mediation and mechanistic interaction analyses.