Person:

Valeri, Linda

Mediation analysis is a popular approach in the social an biomedical sciences to examine the extent to which the effect of an exposure on an outcome is through an intermediate variable (mediator) and the extent to which the effect is direct. We first develop statistical methods and software for the estimation of direct and indirect causal effects in generalized linear models when exposure-mediator interaction may be present. We then study the bias of direct and indirect effects estimators that arise in this context when a continuous mediator is measured with error or a binary mediator is misclassified. We develop methods of correction for measurement error and misclassification coupled with sensitivity analyses for which no auxiliary information on the mediator measured with error is needed. The proposed methods are applied to a lung cancer study to evaluate the effect of genetic variants mediated through smoking on lung cancer risk and to a perinatal epidemiological study on the determinants of preterm birth.

Background: Short‐term exposures to fine (<2.5 μm aerodynamic diameter) ambient particulate‐matter (PM) have been related with increased blood pressure (BP) in controlled‐human exposure and community‐based studies. However, whether coarse (2.5 to 10 μm) PM exposure increases BP is uncertain. Recent observational studies have linked PM exposures with blood DNA hypomethylation, an epigenetic alteration that activates inflammatory and vascular responses. No experimental evidence is available to confirm those observational data and demonstrate the relations between PM, hypomethylation, and BP. Methods and Results: We conducted a cross‐over trial of controlled‐human exposure to concentrated ambient particles (CAPs). Fifteen healthy adult participants were exposed for 130 minutes to fine CAPs, coarse CAPs, or HEPA‐filtered medical air (control) in randomized order with ≥2‐week washout. Repetitive‐element (Alu, long interspersed nuclear element‐1 [LINE‐1]) and candidate‐gene (TLR4, IL‐12, IL‐6, iNOS) blood methylation, systolic and diastolic BP were measured pre‐ and postexposure. After adjustment for multiple comparisons, fine CAPs exposure lowered Alu methylation (β‐standardized=−0.74, adjusted‐P=0.03); coarse CAPs exposure lowered TLR4 methylation (β‐standardized=−0.27, adjusted‐P=0.04). Both fine and coarse CAPs determined significantly increased systolic BP (β=2.53 mm Hg, P=0.001; β=1.56 mm Hg, P=0.03, respectively) and nonsignificantly increased diastolic BP (β=0.98 mm Hg, P=0.12; β=0.82 mm Hg, P=0.11, respectively). Decreased Alu and TLR4 methylation was associated with higher postexposure DBP (β‐standardized=0.41, P=0.04; and β‐standardized=0.84, P=0.02; respectively). Decreased TLR4 methylation was associated with higher postexposure SBP (β‐standardized=1.45, P=0.01). Conclusions: Our findings provide novel evidence of effects of coarse PM on BP and confirm effects of fine PM. Our results provide the first experimental evidence of PM‐induced DNA hypomethylation and its correlation to BP.

Background: Arsenic induces neural tube defects in several animal models, but its potential to cause neural tube defects in humans is unknown. Our objective was to investigate the associations between maternal arsenic exposure, periconceptional folic acid supplementation, and risk of posterior neural tube defect (myelomeningocele) among a highly exposed population in rural Bangladesh. Methods: We performed a case–control study that recruited physician-confirmed cases from community health clinics served by Dhaka Community Hospital in Bangladesh, as well as local health facilities that treat children with myelomeningocele. Controls were selected from pregnancy registries in the same areas. Maternal arsenic exposure was estimated from drinking water samples taken from wells used during the first trimester of pregnancy. Periconceptional folic acid use was ascertained by self-report, and maternal folate status was further assessed by plasma folate levels measured at the time of the study visit. Results: Fifty-seven cases of myelomeningocele were identified along with 55 controls. A significant interaction was observed between drinking water inorganic arsenic and periconceptional folic acid use. As drinking water inorganic arsenic concentrations increased from 1 to 25 μg/L, the estimated protective effect of folic acid use declined (OR 0.22 to 1.03), and was not protective at higher concentrations of arsenic. No main effect of arsenic exposure on myelomeningocele risk was identified. Conclusions: Our study found a significant interaction between drinking water inorganic arsenic concentration from wells used during the first trimester of pregnancy and reported intake of periconceptional folic acid supplements. Results suggest that environmental arsenic exposure reduces the effectiveness of folic acid supplementation in preventing myelomeningocele.

Household air pollution (HAP) and chronic HIV infection are each associated with significant respiratory morbidity. Little is known about relationships between HAP and respiratory symptoms among people living with HIV. The objective of this study was to investigate the relationship between cooking fuel type and chronic respiratory symptoms in study participants from the Uganda AIDS Rural Treatment Outcomes Study. Study participants were enrolled at the time of antiretroviral therapy initiation and seen quarterly from 2005 to 2014 for health-focused questionnaires, CD4 count and HIV viral load. We used multivariable logistic regression and generalised estimating equations, with each study visit as a unit of observation, to investigate relationships between cooking fuel type and chronic respiratory symptoms. We observed an association between cooking with firewood (versus charcoal) and chronic cough among HIV-infected females in rural Uganda (adjusted OR 1.41, 95% CI 1.00–1.99; p=0.047). We did not observe an association between cooking fuel type and respiratory symptoms among males (adjusted OR 0.88, 95% CI 0.47–1.63; p=0.658). Associations between cooking fuel and chronic cough in this HIV-infected cohort may be influenced by sex-based roles in meal preparation. This study raises important questions about relationships between household air pollution, HIV infection and respiratory morbidity.

Background: The people of Bangladesh are currently exposed to high concentrations of arsenic and manganese in drinking water, as well as elevated lead in many regions. The objective of this study was to investigate associations between environmental exposure to these contaminants and neurodevelopmental outcomes among Bangladeshi children. Methods: We evaluated data from 524 children, members of an ongoing prospective birth cohort established to study the effects of prenatal and early childhood arsenic exposure in the Sirajdikhan and Pabna Districts of Bangladesh. Water was collected from the family’s primary drinking source during the first trimester of pregnancy and at ages 1, 12 and 20–40 months. At age 20–40 months, blood lead was measured and neurodevelopmental outcomes were assessed using a translated, culturally-adapted version of the Bayley Scales of Infant and Toddler Development, Third Edition (BSID-III). Results: Median blood lead concentrations were higher in Sirajdikhan than Pabna (7.6 vs. <LODμg/dL, p <0.0001) and water arsenic concentrations were lower (1.5 vs 25.7 μg/L, p <0.0001). Increased blood lead was associated with decreased cognitive scores in Sirajdikhan (β = −0.17, SE = 0.09, p = 0.05), whereas increased water arsenic was associated with decreased cognitive scores in Pabna (β = −0.06, SE = 0.03, p = 0.05). Water manganese was associated with fine motor scores in an inverse-U relationship in Pabna. Conclusion: Where blood lead levels are high, lead is associated with decreased cognitive scores on the BSID-III, and effects of other metals are not detected. In the setting of lower lead levels, the adverse effects of arsenic and manganese on neurodevelopment are observed. Electronic supplementary material The online version of this article (doi:10.1186/s12940-016-0127-y) contains supplementary material, which is available to authorized users.

Background: Lead toxicity is of particular public health concern given its near ubiquitous distribution in nature and established neurotoxicant properties. Similar in its ubiquity and ability to inhibit neurodevelopment, early childhood stunting affects an estimated 34 % of children under 5 in low- and middle-income countries. Both lead and stunting have been shown to be associated with decreased neurodevelopment, although the relationship between these childhood burdens is underexplored. The association between lead exposure and stunting has been previously established, yet limited data are available on susceptibility windows. Methods: Whole blood lead samples were collected from rural Bangladeshi children at delivery (umbilical cord blood) and at age 20–40 months (fingerstick blood). Stunting was determined using the Child Growth Standards developed from the World Health Organization Multicentre Growth Reference Study. Children with height for age < -2 z-scores below the median of the WHO Child Growth Standards were classified as stunted in all analyses. Results: Median (IQR) umbilical cord and fingerstick blood lead levels were 3.1 (1.6–6.3) μg/dl and 4.2 (1.7–7.6) μg/dl, respectively. In adjusted multivariable regression models, the odds of stunting at 20–40 months increased by 1.12 per μg/dl increase in blood lead level (OR = 1.12, 95 % CI: 1.02–1.22). No association was found between cord blood lead level and risk of stunting (OR = 0.97, 95 % CI: 0.94–1.00). Conclusions: There is a significant association between stunting and concurrent lead exposure at age 20–40 months. This association is slightly attenuated after controlling for study clinic site. Additional research including more precise timing of lead exposure during these critical 20–40 months is needed. Electronic supplementary material The online version of this article (doi:10.1186/s12940-016-0190-4) contains supplementary material, which is available to authorized users.

Background

The recent Ebola virus disease (EVD) outbreak in West Africa has spread wider than any previous human EVD epidemic. While individual-level risk factors that contribute to the spread of EVD have been studied, the population-level attributes of subnational regions associated with outbreak severity have not yet been considered.

Methods

To investigate the area-level predictors of EVD dynamics, we integrated time series data on cumulative reported cases of EVD from the World Health Organization and covariate data from the Demographic and Health Surveys. We first estimated the early growth rates of epidemics in each second-level administrative district (ADM2) in Guinea, Sierra Leone and Liberia using exponential, logistic and polynomial growth models. We then evaluated how these growth rates, as well as epidemic size within ADM2s, were ecologically associated with several demographic and socio-economic characteristics of the ADM2, using bivariate correlations and multivariable regression models.

Results

The polynomial growth model appeared to best fit the ADM2 epidemic curves, displaying the lowest residual standard error. Each outcome was associated with various regional characteristics in bivariate models, however in stepwise multivariable models only mean education levels were consistently associated with a worse local epidemic.

Discussion

By combining two common methods—estimation of epidemic parameters using mathematical models, and estimation of associations using ecological regression models—we identified some factors predicting rapid and severe EVD epidemics in West African subnational regions. While care should be taken interpreting such results as anything more than correlational, we suggest that our approach of using data sources that were publicly available in advance of the epidemic or in real-time provides an analytic framework that may assist countries in understanding the dynamics of future outbreaks as they occur.

Background: Exposure to chemical mixtures is recognized as the real-life scenario in all populations, needing new statistical methods that can assess their complex effects. Objectives: We aimed to assess the joint effect of in utero exposure to arsenic, manganese, and lead on children’s neurodevelopment. Methods: We employed a novel statistical approach, Bayesian kernel machine regression (BKMR), to study the joint effect of coexposure to arsenic, manganese, and lead on neurodevelopment using an adapted Bayley Scale of Infant and Toddler Development™. Third Edition, in 825 mother–child pairs recruited into a prospective birth cohort from two clinics in the Pabna and Sirajdikhan districts of Bangladesh. Metals were measured in cord blood using inductively coupled plasma-mass spectrometry. Results: Analyses were stratified by clinic due to differences in exposure profiles. In the Pabna district, which displayed high manganese levels [interquartile range (IQR): 4.8, 18μg/dl], we found a statistically significant negative effect of the mixture of arsenic, lead, and manganese on cognitive score when cord blood metals concentrations were all above the 60th percentile (As≥0.7μg/dl, Mn≥6.6μg/dl, Pb≥4.2μg/dl) compared to the median (As=0.5μg/dl, Mn=5.8μg/dl, Pb=3.1μg/dl). Evidence of a nonlinear effect of manganese was found. A change in log manganese from the 25th to the 75th percentile when arsenic and manganese were at the median was associated with a decrease in cognitive score of −0.3 (−0.5, −0.1) standard deviations. Our study suggests that arsenic might be a potentiator of manganese toxicity. Conclusions: Employing a novel statistical method for the study of the health effects of chemical mixtures, we found evidence of neurotoxicity of the mixture, as well as potential synergism between arsenic and manganese. https://doi.org/10.1289/EHP614

Background: Racial/ethnic inequalities in mortality may be reducible by addressing socioeconomic factors and smoking. To our knowledge, this is the first study to estimate trends over multiple decades in (1) mediation of racial/ethnic inequalities in mortality (between Māori and Europeans in New Zealand) by socioeconomic factors, (2) additional mediation through smoking, and (3) inequalities had there never been smoking. Methods: We estimated natural (1 and 2 above) and controlled mediation effects (3 above) in census-mortality cohorts for 1981–1984 (1.1 million people), 1996–1999 (1.5 million), and 2006–2011 (1.5 million) for 25- to 74-year-olds in New Zealand, using a weighting of regression predicted outcomes. Results: Socioeconomic factors explained 46% of male inequalities in all three cohorts and made an increasing contribution over time among females from 30.4% (95% confidence interval = 18.1%, 42.7%) in 1981–1984 to 41.9% (36.0%, 48.0%). Including smoking with socioeconomic factors only modestly altered the percentage mediated for males, but more substantially increased it for females, for example, 7.7% (5.5%, 10.0%) in 2006–2011. A counterfactual scenario of having eradicated tobacco in the past (but unchanged socioeconomic distribution) lowered mortality for all sex-by-ethnic groups and resulted in a 12.2% (2.9%, 20.8%) and 21.2% (11.6%, 31.0%) reduction in the absolute mortality gap between Māori and Europeans in 2006–2011, for males and females, respectively. Conclusions: Our study predicts that, in this high-income country, reducing socioeconomic disparities between ethnic groups would greatly reduce ethnic inequalities in mortality over the long run. Eradicating tobacco would notably reduce ethnic inequalities in absolute but not relative mortality.

Abstract In this paper, we propose a structural framework for population-based cancer epidemiology and evaluate the performance of double-robust estimators for a binary exposure in cancer mortality. We conduct numerical analyses to study the bias and efficiency of these estimators. Furthermore, we compare 2 different model selection strategies based on 1) Akaike’s Information Criterion and the Bayesian Information Criterion and 2) machine learning algorithms, and we illustrate double-robust estimators’ performance in a real-world setting. In simulations with correctly specified models and near-positivity violations, all but the naive estimators had relatively good performance. However, the augmented inverse-probability-of-treatment weighting estimator showed the largest relative bias. Under dual model misspecification and near-positivity violations, all double-robust estimators were biased. Nevertheless, the targeted maximum likelihood estimator showed the best bias-variance trade-off, more precise estimates, and appropriate 95% confidence interval coverage, supporting the use of the data-adaptive model selection strategies based on machine learning algorithms. We applied these methods to estimate adjusted 1-year mortality risk differences in 183,426 lung cancer patients diagnosed after admittance to an emergency department versus persons with a nonemergency cancer diagnosis in England (2006–2013). The adjusted mortality risk (for patients diagnosed with lung cancer after admittance to an emergency department) was 16% higher in men and 18% higher in women, suggesting the importance of interventions targeting early detection of lung cancer signs and symptoms.