Dataset Information

Assessing Agreement in Exposure Classification between Proximity-Based Metrics and Air Monitoring Data in Epidemiology Studies of Unconventional Resource Development.

ABSTRACT: Recent studies of unconventional resource development (URD) and adverse health effects have been limited by distance-based exposure surrogates. Our study compared exposure classifications between air pollutant concentrations and "well activity" (WA) metrics, which are distance-based exposure proxies used in Marcellus-area studies to reflect variation in time and space of residential URD activity. We compiled Pennsylvania air monitoring data for benzene, carbon monoxide, nitrogen dioxide, ozone, fine particulates and sulfur dioxide, and combined this with data on nearly 9000 Pennsylvania wells. We replicated WA calculations using geo-coordinates of monitors to represent residences and compared exposure categories from air measurements and WA at the site of each monitor. There was little agreement between the two methods for the pollutants included in the analysis, with most weighted kappa coefficients between -0.1 and 0.1. The exposure categories agreed for about 25% of the observations and assigned inverse categories 16%-29% of the time, depending on the pollutant. Our results indicate that WA measures did not adequately distinguish categories of air pollutant exposures and employing them in epidemiology studies can result in misclassification of exposure. This underscores the need for more robust exposure assessment in future analyses and cautious interpretation of these existing studies.

SUBMITTER: Wendt Hess J

PROVIDER: S-EPMC6747456 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Assessing Agreement in Exposure Classification between Proximity-Based Metrics and Air Monitoring Data in Epidemiology Studies of Unconventional Resource Development.

Wendt Hess Judy J Bachler Gerald G Momin Fayaz F Sexton Krystal K

International journal of environmental research and public health 20190823 17

Recent studies of unconventional resource development (URD) and adverse health effects have been limited by distance-based exposure surrogates. Our study compared exposure classifications between air pollutant concentrations and "well activity" (WA) metrics, which are distance-based exposure proxies used in Marcellus-area studies to reflect variation in time and space of residential URD activity. We compiled Pennsylvania air monitoring data for benzene, carbon monoxide, nitrogen dioxide, ozone, ...[more]

PMID: 31443587

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Project description:BackgroundSeveral criteria have been used to assess agreement between replicate slide readings of malaria parasite density. Such criteria may be based on percent difference, or absolute difference, or a combination. Neither the rationale for choosing between these types of criteria, nor that for choosing the magnitude of difference which defines acceptable agreement, are clear. The current paper seeks a procedure which avoids the disadvantages of these current options and whose parameter values are more clearly justified.Methods and resultsVariation of parasite density within a slide is expected, even when it has been prepared from a homogeneous sample. This places lower limits on sensitivity and observer agreement, quantified by the Poisson distribution. This means that, if a criterion of fixed percent difference criterion is used for satisfactory agreement, the number of discrepant readings is over-estimated at low parasite densities. With a criterion of fixed absolute difference, the same happens at high parasite densities. For an ideal slide, following the Poisson distribution, a criterion based on a constant difference in square root counts would apply for all densities. This can be back-transformed to a difference in absolute counts, which, as expected, gives a wider range of acceptable agreement at higher average densities. In an example dataset from Tanzania, observed differences in square root counts correspond to a 95% limits of agreement of -2,800 and +2,500 parasites/microl at average density of 2,000 parasites/microl, and -6,200 and +5,700 parasites/microl at 10,000 parasites/microl. However, there were more outliers beyond those ranges at higher densities, meaning that actual coverage of these ranges was not a constant 95%, but decreased with density. In a second study, a trial of microscopist training, the corresponding ranges of agreement are wider and asymmetrical: -8,600 to +5,200/microl, and -19,200 to +11,700/microl, respectively. By comparison, the optimal limits of agreement, corresponding to Poisson variation, are +/- 780 and +/- 1,800 parasites/microl, respectively. The focus of this approach on the volume of blood read leads to other conclusions. For example, no matter how large a volume of blood is read, some densities are too low to be reliably detected, which in turn means that disagreements on slide positivity may simply result from within-slide variation, rather than reading errors.ConclusionsThe proposed method defines limits of acceptable agreement in a way which allows for the natural increase in variability with parasite density. This includes defining the levels of between-reader variability, which are consistent with random variation: disagreements within these limits should not trigger additional readings. This approach merits investigation in other settings, in order to determine both the extent of its applicability, and appropriate numerical values for limits of agreement.

Dataset Information

Assessing Agreement in Exposure Classification between Proximity-Based Metrics and Air Monitoring Data in Epidemiology Studies of Unconventional Resource Development.

Publications

Assessing Agreement in Exposure Classification between Proximity-Based Metrics and Air Monitoring Data in Epidemiology Studies of Unconventional Resource Development.

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