Incorporating individual-level distributions of exposure error in epidemiologic analyses: an example using arsenic in drinking water and bladder cancer

A systematic review of quantitative bias analysis applied to epidemiological research

BACKGROUND

Quantitative bias analysis (QBA) measures study errors in terms of direction, magnitude and uncertainty. This systematic review aimed to describe how QBA has been applied in epidemiological research in 2006-19.

METHODS

We searched PubMed for English peer-reviewed studies applying QBA to real-data applications. We also included studies citing selected sources or which were identified in a previous QBA review in pharmacoepidemiology. For each study, we extracted the rationale, methodology, bias-adjusted results and interpretation and assessed factors associated with reproducibility.

RESULTS

Of the 238 studies, the majority were embedded within papers whose main inferences were drawn from conventional approaches as secondary (sensitivity) analyses to quantity-specific biases (52%) or to assess the extent of bias required to shift the point estimate to the null (25%); 10% were standalone papers. The most common approach was probabilistic (57%). Misclassification was modelled in 57%, uncontrolled confounder(s) in 40% and selection bias in 17%. Most did not consider multiple biases or correlations between errors. When specified, bias parameters came from the literature (48%) more often than internal validation studies (29%). The majority (60%) of analyses resulted in >10% change from the conventional point estimate; however, most investigators (63%) did not alter their original interpretation. Degree of reproducibility related to inclusion of code, formulas, sensitivity analyses and supplementary materials, as well as the QBA rationale.

CONCLUSIONS

QBA applications were rare though increased over time. Future investigators should reference good practices and include details to promote transparency and to serve as a reference for other researchers.

Health effects inflicted by chronic low-level arsenic contamination in groundwater: A global public health challenge

Groundwater arsenic (As) contamination is a global public health concern. The high level of As exposure (100-1000 μg/L or even higher) through groundwater has been frequently associated with serious public health hazards, e.g., skin disorders, cardiovascular diseases, respiratory problems, complications of gastrointestinal tract, liver and splenic ailments, kidney and bladder disorders, reproductive failure, neurotoxicity and cancer. However, reviews on low-level As exposure and the imperative health effects are far less documented. The World Health Organization (WHO) and the United States Environmental Protection Agency (USEPA) has set the permissible standard of As in drinking water at 10 μg/L. Considering the WHO and USEPA guidelines, most of the developed countries have established standards at or below this guideline. Worldwide many countries including India have millions of aquifers with low-level As contamination (≤50 μg/L). The exposed population of these areas might not show any As-related skin lesions (hallmark of As toxicity particularly in a population consuming As contaminated groundwater >300 μg/L) but might be subclinically affected. This review has attempted to encompass the wide range of health effects associated with chronic low-level As exposure ≤50 μg/L and the probable mechanisms that might provide a better insight regarding the underlying cause of these clinical manifestations. Therefore, there is an urgent need to create mass awareness about the health effects of chronic low-level As exposure and planning of proper mitigation strategies.

K. S, Ghosh D. Arsenic Exposures, Poisoning, and Threat to Human Health

Arsenic (As) is a naturally occurring metalloid which induces high toxicity to both human and animal health. Although As has some applications in industrial, medicinal and agricultural fields, the increasing concentrations of As in drinking water sources had made it a potential threat to living organisms. Inorganic As is naturally present in groundwater and is adsorbed by plants and crops through the irrigation system. This leads to its accumulation in crops and translocation to humans and animals through food. Increased levels of As can cause various health disorders through acute and chronic exposures such as gastrointestinal, hepatic, respiratory, cardiovascular, integumentary, renal, neurological, and reproductive disorders including stillbirth and infant mortality. Arsenic is also capable of inducing epigenetic changes, thereby causing gene mutations. This chapter focuses on the possible sources of As, leading to environmental contamination and followed by its hazardous effects which pave the way to various human health manifestations.

Local bladder cancer clusters in southeastern Michigan accounting for risk factors, covariates and residential mobility

BACKGROUND

In case control studies disease risk not explained by the significant risk factors is the unexplained risk. Considering unexplained risk for specific populations, places and times can reveal the signature of unidentified risk factors and risk factors not fully accounted for in the case-control study. This potentially can lead to new hypotheses regarding disease causation.

METHODS

Global, local and focused Q-statistics are applied to data from a population-based case-control study of 11 southeast Michigan counties. Analyses were conducted using both year- and age-based measures of time. The analyses were adjusted for arsenic exposure, education, smoking, family history of bladder cancer, occupational exposure to bladder cancer carcinogens, age, gender, and race.

RESULTS

Significant global clustering of cases was not found. Such a finding would indicate large-scale clustering of cases relative to controls through time. However, highly significant local clusters were found in Ingham County near Lansing, in Oakland County, and in the City of Jackson, Michigan. The Jackson City cluster was observed in working-ages and is thus consistent with occupational causes. The Ingham County cluster persists over time, suggesting a broad-based geographically defined exposure. Focused clusters were found for 20 industrial sites engaged in manufacturing activities associated with known or suspected bladder cancer carcinogens. Set-based tests that adjusted for multiple testing were not significant, although local clusters persisted through time and temporal trends in probability of local tests were observed.

CONCLUSION

Q analyses provide a powerful tool for unpacking unexplained disease risk from case-control studies. This is particularly useful when the effect of risk factors varies spatially, through time, or through both space and time. For bladder cancer in Michigan, the next step is to investigate causal hypotheses that may explain the excess bladder cancer risk localized to areas of Oakland and Ingham counties, and to the City of Jackson.

Association between lifetime exposure to inorganic arsenic in drinking water and coronary heart disease in Colorado residents

BACKGROUND

Chronic diseases, including coronary heart disease (CHD), have been associated with ingestion of drinking water with high levels of inorganic arsenic (> 1,000 μg/L). However, associations have been inconclusive in populations with lower levels (< 100 μg/L) of inorganic arsenic exposure.

OBJECTIVES

We conducted a case-cohort study based on individual estimates of lifetime arsenic exposure to examine the relationship between chronic low-level arsenic exposure and risk of CHD.

METHODS

This study included 555 participants with 96 CHD events diagnosed between 1984 and 1998 for which individual lifetime arsenic exposure estimates were determined using data from structured interviews and secondary data sources to determine lifetime residence, which was linked to a geospatial model of arsenic concentrations in drinking water. These lifetime arsenic exposure estimates were correlated with historically collected urinary arsenic concentrations. A Cox proportional-hazards model with time-dependent CHD risk factors was used to assess the association between time-weighted average (TWA) lifetime exposure to low-level inorganic arsenic in drinking water and incident CHD.

RESULTS

We estimated a positive association between low-level inorganic arsenic exposure and CHD risk [hazard ratio (HR): = 1.38, 95% CI: 1.09, 1.78] per 15 μg/L while adjusting for age, sex, first-degree family history of CHD, and serum low-density lipoprotein levels. The risk of CHD increased monotonically with increasing TWAs for inorganic arsenic exposure in water relative to < 20 μg/L (HR = 1.2, 95% CI: 0.6, 2.2 for 20-30 μg/L; HR = 2.2; 95% CI: 1.2, 4.0 for 30-45 μg/L; and HR = 3, 95% CI: 1.1, 9.1 for 45-88 μg/L).

CONCLUSIONS

Lifetime exposure to low-level inorganic arsenic in drinking water was associated with increased risk for CHD in this population.

The two-dimensional Monte Carlo: a new methodologic paradigm for dose reconstruction for epidemiological studies

Retrospective dose estimation, particularly dose reconstruction that supports epidemiological investigations of health risk, relies on various strategies that include models of physical processes and exposure conditions with detail ranging from simple to complex. Quantification of dose uncertainty is an essential component of assessments for health risk studies since, as is well understood, it is impossible to retrospectively determine the true dose for each person. To address uncertainty in dose estimation, numerical simulation tools have become commonplace and there is now an increased understanding about the needs and what is required for models used to estimate cohort doses (in the absence of direct measurement) to evaluate dose response. It now appears that for dose-response algorithms to derive the best, unbiased estimate of health risk, we need to understand the type, magnitude and interrelationships of the uncertainties of model assumptions, parameters and input data used in the associated dose estimation models. Heretofore, uncertainty analysis of dose estimates did not always properly distinguish between categories of errors, e.g., uncertainty that is specific to each subject (i.e., unshared error), and uncertainty of doses from a lack of understanding and knowledge about parameter values that are shared to varying degrees by numbers of subsets of the cohort. While mathematical propagation of errors by Monte Carlo simulation methods has been used for years to estimate the uncertainty of an individual subject's dose, it was almost always conducted without consideration of dependencies between subjects. In retrospect, these types of simple analyses are not suitable for studies with complex dose models, particularly when important input data are missing or otherwise not available. The dose estimation strategy presented here is a simulation method that corrects the previous deficiencies of analytical or simple Monte Carlo error propagation methods and is termed, due to its capability to maintain separation between shared and unshared errors, the two-dimensional Monte Carlo (2DMC) procedure. Simply put, the 2DMC method simulates alternative, possibly true, sets (or vectors) of doses for an entire cohort rather than a single set that emerges when each individual's dose is estimated independently from other subjects. Moreover, estimated doses within each simulated vector maintain proper inter-relationships such that the estimated doses for members of a cohort subgroup that share common lifestyle attributes and sources of uncertainty are properly correlated. The 2DMC procedure simulates inter-individual variability of possibly true doses within each dose vector and captures the influence of uncertainty in the values of dosimetric parameters across multiple realizations of possibly true vectors of cohort doses. The primary characteristic of the 2DMC approach, as well as its strength, are defined by the proper separation between uncertainties shared by members of the entire cohort or members of defined cohort subsets, and uncertainties that are individual-specific and therefore unshared.

Spatio-temporal epidemiology: principles and opportunities

Space-time analysis of disease data has historically involved the search for patterns in aggregated data to identify how regions of high and low risk change through time. Space-time analysis of aggregated data has great value, but represents only a subset of space-time epidemiologic applications. Technological advances for tracking and mapping individuals (e.g., global positioning systems) have introduced mobile populations as an important element in space-time epidemiology. We review five domains critical to the developing field of spatio-temporal epidemiology: (1) spatio-temporal epidemiologic theory, (2) selection of appropriate spatial scale of analysis, (3) choice of spatial/spatio-temporal method for pattern identification, (4) individual-level exposure assessment in epidemiologic studies, and (5) assessment and consideration of locational and attribute uncertainty. This review provides an introduction to principles of space-time epidemiology and highlights future research opportunities.