Sociodemographic Characteristics of Adverse Event Reporting in the USA: An Ecologic Study.

INTRODUCTION: The Food and Drug Administration Adverse Event Reporting System (FAERS) is a vital source of new drug safety information, but whether adverse event (AE) information collected from these systems adequately captures experiences of the overall United States (US) population is unknown. OBJECTIVE: To examine determinants of consumer AE reporting in the USA. METHODS: Five-year AE reporting rate per 100,000 residents per US county were calculated, mapped, and quartiled for AE reports received directly from consumers between 2011 and 2015. Associations between county-level sociodemographic factors obtained from County Health Rankings and AE reporting rates were evaluated using negative binomial regression. RESULTS: Reporting rates were variable across US counties with > 17.6 reports versus ≤ 5.5 reports/100,000 residents in the highest and lowest reporting quartile, respectively. Controlling for drug utilization, counties with higher reporting rates had higher proportions of individuals age ≥ 65 years (e.g., 2.4% reporting increase per 1% increase in individuals age > 65, incidence rate ratio (IRR): 1.024, 95% confidence interval (CI): 1.017-1.030), higher proportions of females (IRR: 1.027, 95% CI 1.012-1.043), uninsured (IRR: 1.009, 95% CI 1.005-1.013), higher median log household incomes (IRR: 1.897, 95% CI 1.644-2.189) and more mental health providers per 100,000 residents (IRR: 1.003, 95% CI 1.001-1.004). Lower reporting was observed in counties with higher proportions of individuals age ≤ 18 years (IRR: 0.966, 95% CI 0.959-0.974), American Indian or Alaska Native individuals (IRR: 0.991, 95% CI 0.986-0.996), individuals not proficient in English (IRR: 0.978, 95% CI 0.965-0.991), and individuals residing in rural areas within a county (IRR: 0.998, 95% CI 0.997-0.998). CONCLUSIONS: Observed variations in consumer AE reporting may be related to sociodemographic factors and healthcare access. Because these factors may also correspond to AE susceptibility, voluntary AE reporting systems may be suboptimal for capturing emerging drug safety concerns among more vulnerable populations.

Estimating intervention effects on infectious disease control: The effect of community mobility reduction on Coronavirus spread.

Understanding the effects of interventions, such as restrictions on community and large group gatherings, is critical to controlling the spread of COVID-19. Susceptible-Infectious-Recovered (SIR) models are traditionally used to forecast the infection rates but do not provide insights into the causal effects of interventions. We propose a spatiotemporal model that estimates the causal effect of changes in community mobility (intervention) on infection rates. Using an approximation to the SIR model and incorporating spatiotemporal dependence, the proposed model estimates a direct and indirect (spillover) effect of intervention. Under an interference and treatment ignorability assumption, this model is able to estimate causal intervention effects, and additionally allows for spatial interference between locations. Reductions in community mobility were measured by cell phone movement data. The results suggest that the reductions in mobility decrease Coronavirus cases 4 to 7 weeks after the intervention.

Pyrimethanil and chlorpyrifos air concentrations and pregnant women's urinary metabolites in the Infants' Environmental Health Study (ISA), Costa Rica.

BACKGROUND: Only few studies have compared environmental pesticide air concentrations with specific urinary metabolites to evaluate pathways of exposure. Therefore, we compared pyrimethanil and chlorpyrifos concentrations in air with urinary 4-hydroxypyrimethanil (OHP, metabolite of pyrimethanil) and 3,5,6-trichloro-2-pyridinol (TCPy, metabolite of chlorpyrifos) among pregnant women from the Infant's Environmental Health Study (ISA) in Matina County, Costa Rica. METHODS: During pregnancy, we obtained repeat urinary samples from 448 women enrolled in the ISA study. We extrapolated pyrimethanil and chlorpyrifos concentrations measured with passive air samplers (PAS) (n = 48, from 12 schools), across space and time using a Bayesian spatiotemporal model. We subsequently compared these concentrationswith urinary OHP and TCPy in 915 samples from 448 women, usingseparatemixed models andconsidering several covariables. RESULTS: A 10% increase in air pyrimethanil (ng/m3) was associated with a 5.7% (95% confidence interval (CI 4.6, 6.8) increase in OHP (µg/L). Women living further from banana plantations had lower OHP: -0.7% (95% CI -1.2, -0.3) for each 10% increase in distance (meters) as well as women who ate rice and beans ≥15 times a week -23% (95% CI -38, -4). In addition, each 1 ng/m3 increase in chlorpyrifos in air was associated with a 1.5% (95% CI 0.2, 2.8) increase in TCPy (µg/L), and women working in agriculture tended to have increased TCPy (21%, 95% CI -2, 49). CONCLUSION: The Bayesian spatiotemporal models were useful to estimate pyrimethanil and chlorpyrifos air concentrations across space and time. Our results suggest inhalation of pyrimethanil and chlorpyrifos is a pathway of environmental exposure. PAS seems a useful technique to monitor environmental current-use pesticide exposures. For future studies, we recommend increasing the number of locations of environmental air measurements, obtaining all air and urine measurements during the same month, and, ideally, including dermal exposure estimates as well.

Exposure to common-use pesticides, manganese, lead, and thyroid function among pregnant women from the Infants' Environmental Health (ISA) study, Costa Rica.

BACKGROUND: Pesticides and metals may disrupt thyroid function, which is key to fetal brain development. OBJECTIVES: To evaluate if current-use pesticide exposures, lead and excess manganese alter free thyroxine (FT4), free triiodothyronine (FT3), and thyroid stimulating hormone (TSH) concentrations in pregnant women from the Infants' Environmental Health Study (ISA). METHODS: At enrollment, we determined women's (n = 400) specific-gravity corrected urinary pesticide (µg/L) metabolite concentrations of mancozeb (ethylene thiourea (ETU)), pyrimethanil, thiabendazole, chlorpyrifos, synthetic pyrethroids, and 2,4-D. We also measured manganese hair (MnH) (µg/g) and blood (MnB) (µg/L), and blood lead (PbB) (µg/L) concentrations. To detect an immediate and late effect on thyroid homeostasis, we determined TSH, FT4 and FT3 in serum obtained at the same visit (n = 400), and about ten weeks afterwards (n = 245). We assessed associations between exposures and outcomes with linear regression and general additive models, Bayesian multivariate linear regression, and Bayesian kernel machine regression. RESULTS: About 80%, 94%, and 100% of the women had TSH, FT4, and FT3 within clinical reference ranges, respectively. Women with higher urinary ETU, and pyrimethanil-metabolites, had lower FT4: ß = -0.79 (95%CI = -1.51, -0.08) and ß = -0.29 (95%CI = -0.62, -0.03), respectively, for each tenfold increase in exposure. MnB was positively associated with FT4 (ß = 0.04 (95%CI = 0.00, 0.07 per 1 µg/L increase), and women with high urinary pyrethroid-metabolite concentrations had decreased TSH (non-linear effects). For the late-effect analysis, metabolites of pyrethroids and chlorpyrifos, as well as MnH, and PbB were associated decreased TSH, or increased FT4 and/or FT3. DISCUSSION: Mancozeb (ETU) and pyrimethanil may inhibit FT4 secretion (hypothyroidism-like effect), while chlorpyrifos, pyrethroids, MnB, MnH, PbB and Mn showed hyperthyroidism-like effects. Some effects on thyroid homeostasis seemed to be immediate (mancozeb (ETU), pyrimethanil, MnB), others delayed (chlorpyrifos, MnH, PbB), or both (pyrethroids), possibly reflecting different mechanisms of action.

A Review of Spatial Causal Inference Methods for Environmental and Epidemiological Applications.

The scientific rigor and computational methods of causal inference have had great impacts on many disciplines but have only recently begun to take hold in spatial applications. Spatial causal inference poses analytic challenges due to complex correlation structures and interference between the treatment at one location and the outcomes at others. In this paper, we review the current literature on spatial causal inference and identify areas of future work. We first discuss methods that exploit spatial structure to account for unmeasured confounding variables. We then discuss causal analysis in the presence of spatial interference including several common assumptions used to reduce the complexity of the interference patterns under consideration. These methods are extended to the spatiotemporal case where we compare and contrast the potential outcomes framework with Granger causality and to geostatistical analyses involving spatial random fields of treatments and responses. The methods are introduced in the context of observational environmental and epidemiological studies and are compared using both a simulation study and analysis of the effect of ambient air pollution on COVID-19 mortality rate. Code to implement many of the methods using the popular Bayesian software OpenBUGS is provided.