Prenatal Exposure to Opioids and Neurodevelopmental Disorders in Children: A Bayesian Mediation Analysis.

This study explores natural direct and joint natural indirect effects (JNIE) of prenatal opioid exposure on neurodevelopmental disorders (NDDs) in children mediated through pregnancy complications, major and minor congenital malformations, and adverse neonatal outcomes, using Medicaid claims linked to vital statistics in Rhode Island, United States, 2008-2018. A Bayesian mediation analysis with elastic net shrinkage prior was developed to estimate mean time to NDD diagnosis ratio using posterior mean and 95% credible intervals (CrIs) from Markov chain Monte Carlo algorithms. Simulation studies showed desirable model performance. Of 11,176 eligible pregnancies, 332 had ≥2 dispensations of prescription opioids anytime during pregnancy, including 200 (1.8%) having ≥1 dispensation in the first trimester (T1), 169 (1.5%) in the second (T2), and 153 (1.4%) in the third (T3). A significant JNIE of opioid exposure was observed in each trimester (T1, JNIE = 0.97, 95% CrI: 0.95, 0.99; T2, JNIE = 0.97, 95% CrI: 0.95, 0.99; T3, JNIE = 0.96, 95% CrI: 0.94, 0.99). The proportion of JNIE in each trimester was 17.9% (T1), 22.4% (T2), and 56.3% (T3). In conclusion, adverse pregnancy and birth outcomes jointly mediated the association between prenatal opioid exposure and accelerated time to NDD diagnosis. The proportion of JNIE increased as the timing of opioid exposure approached delivery.

Errors in the Calculation of the Population Attributable Fraction.

One of the common errors in the calculation of the population attributable fraction (PAF) is the use of an adjusted risk ratio in the Levin formula. In this article, we discuss the errors visually using wireframes by varying the standardized mortality ratio (SMR) and associational risk ratio (aRR) when the prevalence of exposure is fixed. When SMR >1 and SMR > aRR, the absolute bias is positive, and its magnitude increases as the difference between SMR and aRR increases. By contrast, when aRR > SMR > 1, the absolute bias is negative and its magnitude is relatively small. Moreover, when SMR > aRR, the relative bias is larger than one, whereas when SMR < aRR, the relative bias is smaller than one. Although the target population of the PAF is the total population, the target of causation of the PAF is not the total population but the exposed group.

Exchangeability of Measures of Association Before and After Exposure Status Is Flipped: Its Relationship With Confounding in the Counterfactual Model.

BACKGROUND: The counterfactual definition of confounding is often explained in the context of exchangeability between the exposed and unexposed groups. One recent approach is to examine whether the measures of association (eg, associational risk difference) are exchangeable when exposure status is flipped in the population of interest. We discuss the meaning and utility of this approach, showing their relationships with the concept of confounding in the counterfactual framework. METHODS: Three hypothetical cohort studies are used, in which the target population is the total population. After providing an overview of the notions of confounding in distribution and in measure, we discuss the approach from the perspective of exchangeability of measures of association (eg, factual associational risk difference vs counterfactual associational risk difference). RESULTS: In general, if the measures of association are non-exchangeable when exposure status is flipped, confounding in distribution is always present, although confounding in measure may or may not be present. Even if the measures of association are exchangeable when exposure status is flipped, there could be confounding both in distribution and in measure. When we use risk difference or risk ratio as a measure of interest and the exposure prevalence in the population is 0.5, testing the exchangeability of measures of association is equivalent to testing the absence of confounding in the corresponding measures. CONCLUSION: The approach based on exchangeability of measures of association essentially does not provide a definition of confounding in the counterfactual framework. Subtly differing notions of confounding should be distinguished carefully.

IMPACT OF MACULAR INTRARETINAL HEMORRHAGE AND MACULAR HOLE ON THE VISUAL PROGNOSIS OF SUBMACULAR HEMORRHAGE DUE TO RETINAL ARTERIAL MACROANEURYSM RUPTURE.

PURPOSE: To compare the effects of macular intraretinal hemorrhage (IRH) and macular hole (MH) on best-corrected visual acuity (BCVA) after displacement of submacular hemorrhage (SMH) due to retinal arterial macroaneurysm (RAM) rupture. METHODS: This multicenter retrospective study included 48 eyes with SMH due to RAM rupture. Cases underwent vitrectomy to displace SMH and were followed up for 6 months. We classified cases according to the presence of IRH and MH and compared the postoperative BCVA among the groups. RESULTS: We classified the eyes into IRH(+)MH(+) group (10 eyes), IRH(+)MH(-) group (23 eyes), and IRH(-)MH(-) group (15 eyes). The postoperative BCVA was significantly worse in the IRH(+)MH(+) and IRH(+)MH(-) groups than in the IRH(-)MH(-) group (0.91 ± 0.41 in logarithm of the minimal angle of resolution units, Snellen equivalent 20/163, 0.87 ± 0.45, 20/148, and 0.18 ± 0.21, 20/30, respectively; P < 0.001). The postoperative central retinal thickness was significantly lower in the IRH(+) group (IRH(+)MH(+) and IRH(+)MH(-) groups combined) than in the IRH(-) group (IRH(-)MH(-) group) (121.4 ± 70.1 µ m and 174.3 ± 32.9 µ m, respectively, P = 0.008). The postoperative external limiting membrane and ellipsoid zone continuities were significantly discontinuous in the IRH(+) group ( P < 0.001, P = 0.001, respectively). The multiple linear regression analysis showed that both IRH(+)MH(+) and IRH(+)MH(-) were associated with the postoperative BCVA (regression coefficient, 0.799 and 0.711, respectively; P < 0.001 for both). CONCLUSION: Both IRH and MH were poor prognostic indicators in cases with SMH due to RAM rupture.

Breastfeeding at 6 months of age had a positive impact on overweight and obesity in Japanese adolescents at 15 years of age.

AIM: A number of studies have indicated the potential benefits that breastfeeding has on reducing childhood obesity, but few studies have evaluated the effect on adolescent obesity. We examined the association between breastfeeding and overweight or obesity at 15 years of age using data from a large nationwide longitudinal study launched by the Japanese Government in 2001. METHODS: We analysed data for 26 164 participants with known infant feeding practices at 6 months of age, namely the duration of breastfeeding or formula feeding. Overweight or obesity at 15 years of age were calculated based on the subject's self-reported height and weight. Multinomial logistic regression analysis adjusted the data for child factors, namely sex, siblings, birth weight and physical activity clubs and the maternal factors of age, educational attainment and smoking status. RESULTS: Formula feeding was associated with an increased risk of overweight or obesity at 15 years of age. The adjusted odds ratios (95% confidence intervals) were 0.99 (0.89-1.09) for partial breastfeeding and 1.23 (1.02-1.48) for formula feeding, when exclusive breastfeeding was the reference category. CONCLUSION: Breastfeeding during infancy had potential benefits for overweight or obesity among 15-year-old adolescents. Our results provide further evidence of the importance of breastfeeding.

Fine Particulate Matter and Diabetes Prevalence in Okayama, Japan.

Many studies have shown an association between long-term exposure to particulate matter having an aerodynamic diameter of 2.5 µm or less (PM2.5) and diabetes mellitus (DM), but few studies have focused on Asian subjects. We thus examined the association between long-term exposure to PM2.5 and DM prevalence in Okayama City, Japan. We included 76,591 participants who had received basic health checkups in 2006 and 2007. We assigned the census-level modeled PM2.5 data from 2006 and 2007 to each participant and defined DM using treatment status and the blood testing. PM2.5 was associated with DM prevalence, and the prevalence ratio (95% confidence interval) was 1.10 (1.00-1.20) following each interquartile range increase (2.1 µg/m3) in PM2.5. This finding is consistent with previous results and suggests that long-term exposure to PM2.5 is associated with an increased prevalence of DM in Okayama City, Japan, where the PM2.5 level is lower than in other cities in Asian countries.

Trends in Incidence of Intracranial and Spinal Arteriovenous Shunts: Hospital-Based Surveillance in Okayama, Japan.

BACKGROUND AND PURPOSE: To date, the incidence of intracranial and spinal arteriovenous shunts has not been thoroughly investigated. We aimed to clarify recent trends in the rates of intracranial and spinal arteriovenous shunts in Japan. METHODS: We conducted multicenter hospital-based surveillance at 8 core hospitals in Okayama Prefecture between April 1, 2009 and March 31, 2019. Patients who lived in Okayama and were diagnosed with cerebral arteriovenous malformations, dural arteriovenous fistulas (DAVFs), or spinal arteriovenous shunts (SAVSs) were enrolled. The incidence and temporal trends of each disease were calculated. RESULTS: Among a total of 393 cranial and spinal arteriovenous shunts, 201 (51.1%) cases of DAVF, 155 (39.4%) cases of cerebral arteriovenous malformation, and 34 (8.7%) cases of SAVS were identified. The crude incidence rates between 2009 and 2019 were 2.040 per 100 000 person-years for all arteriovenous shunts, 0.805 for cerebral arteriovenous malformation, 1.044 for DAVF, and 0.177 for SAVS. The incidence of all types tended to increase over the decade, with a notable increase in incidence starting in 2012. Even after adjusting for population aging, the incidence of nonaggressive DAVF increased 6.0-fold while that of SAVS increased 4.4-fold from 2010 to 2018. CONCLUSIONS: In contrast to previous studies, we found that the incidence of DAVF is higher than that of cerebral arteriovenous malformation. Even after adjusting for population aging, all of the disease types tended to increase in incidence over the last decade, with an especially prominent increase in SAVSs and nonaggressive DAVFs. Various factors including population aging may affect an increase in DAVF and SAVS.

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.

A Simple Prognostic Benefit Scoring System for Sarcoma Patients with Pulmonary Metastases: Sarcoma Lung Metastasis Score.

BACKGROUND: Pulmonary metastasectomy could be considered one of the treatment options for disease control in sarcoma patients with pulmonary metastases; however, there is little consensus regarding the suitable criteria for predicting the likely outcomes in these patients. The aim of this study was to establish a prognostic benefit scoring system based on preoperatively examined prognostic factors for sarcoma patients with pulmonary metastases. METHODS: This was a single-center, retrospective cohort study conducted in a cohort of 135 sarcoma patients who underwent a first pulmonary metastasectomy at Okayama University Hospital between January 2006 and December 2015. Based on the results of a multivariable logistic regression analysis performed to determine the factors influencing 3-year mortality, a Sarcoma Lung Metastasis Score was created and its correlation with 3-year survival was analyzed. RESULTS: The results of the multivariate analysis revealed significant differences in the disease-free interval (< 2 years vs. ≥ 2 years; odds ratio (OR) 4.22, 95% confidence interval (CI) 1.67-10.70), maximum tumor diameter (≥ 15 mm vs. < 15 mm; OR 3.86, 95% CI 1.75-8.52), and number of pulmonary metastases (≥ 6 vs. < 6; OR 2.65, 95% CI 1.06-6.620). The Sarcoma Lung Metastasis Score, which was defined as the total score of these three factors, reliably predicted 3-year survival (score: 0, 89.5%; 1, 63.2%; 2, 39.0%; 3, 10.5%). CONCLUSIONS: Our newly proposed simple Sarcoma Lung Metastasis Score appears to be a useful prognostic predictor for sarcoma patients with pulmonary metastases, in that it could be helpful for the selection of appropriate treatments for these patients.

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.

Is a sense of community belonging associated with problem gambling in Canada?

PURPOSE: Despite the increasing demand for public health measures to prevent problem gambling, few studies have examined the association between community characteristics and problem gambling. The aim of this nationally representative cross-sectional study was to investigate the relationship between a sense of community belonging and problem gambling in Canada. We also examined whether this relationship was modified by sex and marital status. METHODS: Canadian Community Health Survey (2013-2014) data from 38,968 residents of Quebec, Saskatchewan, Manitoba, and British Columbia were analyzed. Problem gambling was assessed using the Canadian Problem Gambling Index. We estimated the odds ratios (ORs) and 95% confidence intervals (CIs) for problem gambling. RESULTS: The prevalence of problem gambling was 1.4% (1.9% among males; 0.9% among females). We observed an inverse dose-response relationship between a sense of community belonging and problem gambling. Compared with those with a very strong sense of community belonging, the adjusted ORs for problem gambling were 1.07 (95% CI 0.65-1.76) for a somewhat strong sense, 1.27 (95% CI 0.77-2.11) for a somewhat weak sense, and 2.32 (95% CI 1.34-4.02) for a very weak sense of community belonging. The association was more prominent among females (except for those widowed/divorced/separated), whereas no clear association was found among males, irrespective of marital status. CONCLUSION: When implementing public health measures to reduce problem gambling, it would be useful to account for possible differential impacts of a sense of community belonging by sex and marital status, which may reflect significant social contexts among residents.

Understanding Marginal Structural Models for Time-Varying Exposures: Pitfalls and Tips.

Epidemiologists are increasingly encountering complex longitudinal data, in which exposures and their confounders vary during follow-up. When a prior exposure affects the confounders of the subsequent exposures, estimating the effects of the time-varying exposures requires special statistical techniques, possibly with structural (ie, counterfactual) models for targeted effects, even if all confounders are accurately measured. Among the methods used to estimate such effects, which can be cast as a marginal structural model in a straightforward way, one popular approach is inverse probability weighting. Despite the seemingly intuitive theory and easy-to-implement software, misunderstandings (or "pitfalls") remain. For example, one may mistakenly equate marginal structural models with inverse probability weighting, failing to distinguish a marginal structural model encoding the causal parameters of interest from a nuisance model for exposure probability, and thereby failing to separate the problems of variable selection and model specification for these distinct models. Assuming the causal parameters of interest are identified given the study design and measurements, we provide a step-by-step illustration of generalized computation of standardization (called the g-formula) and inverse probability weighting, as well as the specification of marginal structural models, particularly for time-varying exposures. We use a novel hypothetical example, which allows us access to typically hidden potential outcomes. This illustration provides steppingstones (or "tips") to understand more concretely the estimation of the effects of complex time-varying exposures.

Causal Diagrams: Pitfalls and Tips.

Graphical models are useful tools in causal inference, and causal directed acyclic graphs (DAGs) are used extensively to determine the variables for which it is sufficient to control for confounding to estimate causal effects. We discuss the following ten pitfalls and tips that are easily overlooked when using DAGs: 1) Each node on DAGs corresponds to a random variable and not its realized values; 2) The presence or absence of arrows in DAGs corresponds to the presence or absence of individual causal effect in the population; 3) "Non-manipulable" variables and their arrows should be drawn with care; 4) It is preferable to draw DAGs for the total population, rather than for the exposed or unexposed groups; 5) DAGs are primarily useful to examine the presence of confounding in distribution in the notion of confounding in expectation; 6) Although DAGs provide qualitative differences of causal structures, they cannot describe details of how to adjust for confounding; 7) DAGs can be used to illustrate the consequences of matching and the appropriate handling of matched variables in cohort and case-control studies; 8) When explicitly accounting for temporal order in DAGs, it is necessary to use separate nodes for each timing; 9) In certain cases, DAGs with signed edges can be used in drawing conclusions about the direction of bias; and 10) DAGs can be (and should be) used to describe not only confounding bias but also other forms of bias. We also discuss recent developments of graphical models and their future directions.

On the collapsibility of measures of effect in the counterfactual causal framework.

The relationship between collapsibility and confounding has been subject to an extensive and ongoing discussion in the methodological literature. We discuss two subtly different definitions of collapsibility, and show that by considering causal effect measures based on counterfactual variables (rather than measures of association based on observed variables) it is possible to separate out the component of non-collapsibility which is due to the mathematical properties of the effect measure, from the components that are due to structural bias such as confounding. We provide new weights such that the causal risk ratio is collapsible over arbitrary baseline covariates. In the absence of confounding, these weights may be used for standardization of the risk ratio.

Effect heterogeneity and variable selection for standardizing causal effects to a target population.

The participants in randomized trials and other studies used for causal inference are often not representative of the populations seen by clinical decision-makers. To account for differences between populations, researchers may consider standardizing results to a target population. We discuss several different types of homogeneity conditions that are relevant for standardization: Homogeneity of effect measures, homogeneity of counterfactual outcome state transition parameters, and homogeneity of counterfactual distributions. Each of these conditions can be used to show that a particular standardization procedure will result in an unbiased estimate of the effect in the target population, given assumptions about the relevant scientific context. We compare and contrast the homogeneity conditions, in particular their implications for selection of covariates for standardization and their implications for how to compute the standardized causal effect in the target population. While some of the recently developed counterfactual approaches to generalizability rely upon homogeneity conditions that avoid many of the problems associated with traditional approaches, they often require adjustment for a large (and possibly unfeasible) set of covariates.

Are People With a History of Disease More Susceptible to a Short-term Exposure to Asian Dust?: A Case-Crossover Study Among the Elderly in Japan.

BACKGROUND: Factors influencing the susceptibility of the elderly to the adverse health effects of short-term exposure to desert dust have yet to be explored. We aimed to identify the disease histories that increase the susceptibility of the elderly to disease onset induced by dust events. METHODS: We used a time-stratified case-crossover design using data on 17,874 elderly residents (≥65 years) of Okayama, Japan, who were transported to hospital emergency rooms because of cardiovascular and respiratory diseases between 2006 and 2010. We used conditional logistic models to calculate the odds ratios (ORs) per interquartile increase of Asian dust. We then conducted stratified analyses based on patients with or without a history of chronic disease. RESULTS: Dust concentration was associated with a higher risk of cardiovascular (3-day lag), cerebrovascular (same day), and respiratory (3-day lag) disease onset. Patients with a history of respiratory disease had a higher risk of cardiovascular (OR: 1.09 [95% confidence interval (CI) = 1.00, 1.19] vs. 0.99 [0.97, 1.01]; P for interaction = 0.03) or cerebrovascular (1.15 [1.01, 1.31] vs. 0.99 [0.97, 1.01]; P = 0.02) disease onset (2-day lag) than those without. Patients with diabetes also had a higher risk of cerebrovascular disease onset (1.09 [1.00, 1.19] vs. 0.99 [0.97, 1.01]; P = 0.05) (2-day lag). In contrast, patients with a history of cerebrovascular disease had a lower risk of respiratory disease. CONCLUSIONS: People with a history of respiratory disease or diabetes might have a greater susceptibility to cardiovascular disease from Asian dust and would therefore benefit from proactive interventions during desert dust events.

Social Capital and Suicidal Ideation in Community-Dwelling Older Residents: A Multilevel Analysis of 10,094 Subjects in Japan.

OBJECTIVE: Social capital, the collective resources of groups including perceptions of trust and reciprocity, is recognized as an important contributor to suicide. We examined the association of individual- and community-level social capital with suicidal ideation after adjusting for social support among older adults living in the community. METHODS: In August 2010 we sent questionnaires to all residents aged 65 years and older living in 3 rural municipalities (N = 21,232) in Okayama Prefecture, Japan; 13,919 questionnaires were returned (response rate: 65.6%). The final analysis included 10,094 participants. The outcome variable was suicidal ideation. Exposure variables were individual-level mistrust and lack of reciprocity (level 1), and the aggregated responses of these variables from 35 communities in the municipalities (level 2). Covariates included age, sex, educational attainment, marital status, the number of cohabitants, years of residence, self-rated socioeconomic status, disability, social support, and psychological distress. Multilevel logistic regression analysis was performed to obtain odds ratios (ORs) and 95% confidence intervals (CIs). RESULTS: After adjusting for social support and psychological distress, we found that mistrust and lack of reciprocity were only associated with suicidal ideation at the individual level. Stratified analysis showed that among subjects with psychological distress, mistrust was associated with suicidal ideation at individual (OR: 1.88; 95% CI: 1.42-2.51) and community levels (OR: 1.98; 95% CI: 1.02-3.81). CONCLUSIONS: Our findings show that individual- and community-level social capital is a possible protective factor for suicidal ideation, particularly for people with psychological distress.

A typology of four notions of confounding in epidemiology.

Confounding is a major concern in epidemiology. Despite its significance, the different notions of confounding have not been fully appreciated in the literature, leading to confusion of causal concepts in epidemiology. In this article, we aim to highlight the importance of differentiating between the subtly different notions of confounding from the perspective of counterfactual reasoning. By using a simple example, we illustrate the significance of considering the distribution of response types to distinguish causation from association, highlighting that confounding depends not only on the population chosen as the target of inference, but also on the notions of confounding in distribution and confounding in measure. This point has been relatively underappreciated, partly because some literature on the concept of confounding has only used the exposed and unexposed groups as the target populations, while it would be helpful to use the total population as the target population. Moreover, to clarify a further distinction between confounding "in expectation" and "realized" confounding, we illustrate the usefulness of examining the distribution of exposure status in the target population. To grasp the explicit distinction between confounding in expectation and realized confounding, we need to understand the mechanism that generates exposure events, not the product of that mechanism. Finally, we graphically illustrate this point, highlighting the usefulness of directed acyclic graphs in examining the presence of confounding in distribution, in the notion of confounding in expectation.