Exposure opportunity models for Agent Orange, dioxin, and other military herbicides used in Vietnam, 1961–1971

Abstract

Nearly 19.5 million gallons of herbicides were sprayed on the Republic of Vietnam between 1961 and 1971 for military purposes. Amounts of spray and patterns of applications are available in an electronic file called HERBS that contains records of 9141 defoliation missions, including detailed coordinates of US Air Force Ranch Hand aircraft flight paths, along with chemical agent and gallonage sprayed. Two classes of models for use in epidemiological and environmental studies that utilize the HERBS data for estimating relative exposure opportunity indices are presented: a discrete ''hits'' model that counts instances of proximity in time and space to known herbicide applications, and a continuous exposure opportunity index, E4, that takes into account type and amount of herbicide sprayed, distance from spray application, and time interval when exposure may have occurred. Both direct spraying and indirect exposure to herbicide (or dioxin) that may have remained in the local environment are considered, using a conservative first-order model for environmental disappearance. A correction factor for dermal versus respiratory routes of entry has been incorporated. E4 has a log-normal distribution that spans six orders of magnitude, thus providing a substantial amount of discrimination between sprayed and unsprayed areas. The models improve on earlier ones by making full use of the geometry of the HERBS spray flight paths of Ranch Hand aircraft. To the extent possible so many decades after the War, the models have been qualitatively validated by comparison with recent dioxin soil and biota samples from heavily contaminated areas of Vietnam, and quantitatively validated against adipose dioxin obtained in epidemiological studies of Vietnamese. These models are incorporated within a geographic information system (GIS) that may be used, as one would expect, to identify locations such as hamlets, villages, and military installations sprayed by herbicide. In a novel application, the GIS also facilitates quantitative risk assessment in epidemiological and ecological studies by applying the models within a framework of historical reconstruction of exposure history of individuals based upon their location histories.

Assessing exposure to allied ground troops in the Vietnam War: A comparison of AgDRIFT and Exposure Opportunity Index models

“…Note that this is the geometry described in Figure 1 of Stellman and Stellman (2004). Our work with AgDRIFT, described below, suggests that actual area where any meaningful exposure could occur is much smaller.…”

“…The general imprecision in the available data is acknowledged by the authors of the EOI model. Stellman and Stellman (2004) state that Another potential limitation is the fact that the GIS is structured so that exposure at any location within each E1.2 km 2 cell is assigned the exposure of the centroid. We justify this by noting that while the nominal precision of the HERBS coordinates is 100 m, their accuracy must be closer to 500 m. This exceeds the accuracy with which troop locations are known from military records, so that any attempt to use more ''exact'' distances could be considered an exercise in false precision.…”

“…First exposure is weighted by the reciprocal of distance from the spray path (Stellman and Stellman, 2004). The problem with a strict inverse distance weighting is that as distance tends toward zero its reciprocal tends toward infinity.…”

“…That is, if D max ¼ 0.1 km then the weight at 4 km is 1/40 but if D max ¼ 1 km then the weight is 1/4. The writings by Stellman et al (Stellman et al, 2003;Stellman and Stellman, 2004) suggest that D max may be 0.5, but this issue is not explicitly addressed. Another fix is to scale distances greater than D max by subtracting D max and adding 1 to the result to obtain a new scaled distance, D*.…”

“…2 plus first-order decay kinetics to assign exposures to points on a grid with approximately 1.2 km spacing. If more than one mission affects a grid point, the EOI scores are summed for this grid point (Stellman and Stellman, 2004). Thus, if a given point in space and time is specified, its EOI score will often be affected by several missions, and the spatial distribution EOI score for a particular mission will be hard to determine.…”