Assessing socioeconomic bias of exposure to urban air pollution: an autopsy-based study in São Paulo, Brazil.

Background: The characterisation of individual exposure to air pollution in urban scenarios is a challenge in environmental epidemiological studies. We investigated if the city's pollution monitoring stations over or underestimate the exposure of individuals depending on their socioeconomic conditions and daily commuting times. Methods: The amount of black carbon accumulated in the lungs of 604 deceased who underwent autopsy in São Paulo was considered as a proxy for PM10. The concentrations of PM10 in the residence of the deceased were estimated by interpolating an ordinary kriging model. These two-exposure metrics allowed us to construct an environmental exposure misclassification index ranging from -1 to 1. The association between the index and daily commuting, socioeconomic context index (GeoSES), and street density as predictors was assessed by means of a multilevel linear regression model. Findings: With a decrease of 0.1 units in GeoSES, the index increases, on average, by 0.028 units and with an increase of 1 h in daily commuting, the index increases, on average, by 0.022 units indicating that individual exposure to air pollution is underestimated in the lower GeoSES and in people with many hours spent in daily commuting. Interpretation: Reduction of health consequences of air pollution demands not only alternative fuel and more efficient mobility strategies, but also should include profound rethink of cities. Funding: São Paulo Research Foundation (FAPESP-13/21728-2) and National Council for Scientific and Technological Development (CNPq-304126/2015-2, 401825/2020-5).

Measuring Precarious Employment: Type of Contract Can Lead to Serious Misclassification Error.

This study aims to assess the accuracy of temporary employment as indicator or proxy measure of precarious employment. Using sensitivity and specificity analysis, we compared type of contract (temporary versus permanent) with the Chilean version of the multidimensional Employment Precariousness Scale. Temporary employment exhibited very low sensitivity (<30%) (specificity >90%), resulting in roughly 38% of false negative results. Different EPRES-Ch cut-off scores produced similar results. The main implication of these findings is that the public health relevance of precarious employment is being underestimated both in terms of prevalence and of its association with health, making it critical that valid multidimensional measures of precarious employment be implemented.

Residential mobility in early childhood and the impact on misclassification in pesticide exposures.

Studies of environmental exposures and childhood cancers that rely on records often only use maternal address at birth or address at cancer diagnosis to assess exposures in early childhood, possibly leading to exposure misclassification and questionable validity due to residential mobility during early childhood. Our objective was to assess patterns and identify factors that may predict residential mobility in early childhood, and examine the impact of mobility on early childhood exposure assessment for agriculturally applied pesticides and childhood cancers in California. We obtained the addresses at diagnosis of all childhood cancer cases born in 1998-2011 and diagnosed at 0-5 years of age (n = 6478) from the California Cancer Registry (CCR), and their birth addresses from linked birth certificates. Controls were randomly selected from California birth records and frequency matched (20:1) to all cases by year of birth. We obtained residential histories from a public-record database LexisNexis for both case (n = 3877 with age at diagnosis 1-5 years) and control (n = 99,262) families. Logistic regression analyses were conducted to assess the socio-demographic factors in relation to residential mobility in early childhood. We employed a Geographic Information System (GIS)-based system to estimate children's first year of life exposures to agriculturally applied pesticides based on birth vs diagnosis address or residential histories based upon Lexis-Nexis Public Records and assessed agreement between exposure measures using Spearman correlations and kappa statistics. Over 20% of case and control children moved in their first year of life, and 55% of children with cancer moved between birth and diagnosis. Older age at diagnosis, younger maternal age, lower maternal education, not having a Hispanic ethnic background, use of public health insurance, and non-metropolitan residence at birth were predictors of higher residential mobility. There was moderate to strong correlation (Spearman correlation = 0.76-0.83) and good agreement (kappa = 0.75-0.81) between the first year of life exposure estimates for agricultural pesticides applied within 2 km of a residence relying on an address at birth or at diagnosis or LexisNexis addresses; this did not differ by outcome status, but agreement decreased with decreasing buffer size, and increasing distance moved or age at diagnosis. These findings suggest that residential addresses collected at one point in time may represent residential history in early childhood to a reasonable extent; nevertheless, they exposure misclassification in the first year of life remains an issue. Also, the highest proportion of women not captured by LexisNexis were Hispanic women born in Mexico and those living in the lowest SES neighborhoods, i.e. possibly those with the higher environmental exposures, as well as younger women and those with less than high school education. Though LexisNexis only captures a sub-population, its data may be useful for augmenting address information and assessing the extent of exposure misclassification when estimating environmental exposures in large record linkage studies. Future research should investigate how to correct for exposure misclassification introduced by residential mobility that is not being captured by records.

Spatiotemporal Error in Rainfall Data: Consequences for Epidemiologic Analysis of Waterborne Diseases.

The relationship between rainfall, especially extreme rainfall, and increases in waterborne infectious diseases is widely reported in the literature. Most of this research, however, has not formally considered the impact of exposure measurement error contributed by the limited spatiotemporal fidelity of precipitation data. Here, we evaluate bias in effect estimates associated with exposure misclassification due to precipitation data fidelity, using extreme rainfall as an example. We accomplished this via a simulation study, followed by analysis of extreme rainfall and incident diarrheal disease in an epidemiologic study in Ecuador. We found that the limited fidelity typical of spatiotemporal rainfall data sets biases effect estimates towards the null. Use of spatial interpolations of rain-gauge data or satellite data biased estimated health effects due to extreme rainfall (occurrence) and wet conditions (accumulated totals) downwards by 35%-45%. Similar biases were evident in the Ecuadorian case study analysis, where spatial incompatibility between exposed populations and rain gauges resulted in the association between extreme rainfall and diarrheal disease incidence being approximately halved. These findings suggest that investigators should pay greater attention to limitations in using spatially heterogeneous environmental data sets to assign exposures in epidemiologic research.