I have recently commented on another geospatial study that such studies are not very informative (athough they get a paper published). Why? Because attribution cannot be assigned with this analytical protocol.
The reason given to support such use of geospatial information is that it simply mimics the effort John Snow and the Broad Street Well. Snow did it so why can’t we do it?
Snow marked on a map the deaths due to cholera in England and used this geospatial information to identify the contaminated well on Broad Street. But this comparison is invalid. Snow was counting real dead people at a specific location (to be blunt about it) and not using computer models to try to find where the fatalities might be. In other words, Snow was using hard data of physical location perfectly correlated with fatalities, not computer model projections.
If enough assumptions are made computer modeling studies fall into the trap of simply concluding what has already been assumed from the beginning of the study. No one wants to see adverse birth outcomes at any time. But attribution to drinking water arsenic as the cause of these effects is not supported by this study and potentially points in the wrong direction. Yet activists and overzealous regulators will do just that to sound an alarm (usually for fund raising). Focusing so much on drinking water is once again taking a rabbit trail that leads to nowhere. This is counterproductive to public health. Why not use this study to generate hypotheses to examine factors other than drinking water arsenic that might be suggested by the geospatial data analysis? If such metadata was not collected or available, then the study is inadequate indeed.
Shi X, Ayotte JD, Onda A, Miller S, Rees J, Gilbert-Diamond D, Onega T, Gui J, Karagas M, Moeschler J. Geospatial association between adverse birth outcomes and arsenic in groundwater in New Hampshire, USA. Environmental geochemistry and health. 2014 Oct 19.
There is increasing evidence of the role of arsenic in the etiology of adverse human reproductive outcomes. Because drinking water can be a major source of arsenic to pregnant women, the effect of arsenic exposure through drinking water on human birth may be revealed by a geospatial association between arsenic concentration in groundwater and birth problems, particularly in a region where private wells substantially account for water supply, like New Hampshire, USA. We calculated town-level rates of preterm birth and term low birth weight (term LBW) for New Hampshire, by using data for 1997-2009 stratified by maternal age. We smoothed the rates by using a locally weighted averaging method to increase the statistical stability. The town-level groundwater arsenic probability values are from three GIS data layers generated by the US Geological Survey: probability of local groundwater arsenic concentration >1 µg/L, probability >5 µg/L, and probability >10 µg/L. We calculated Pearson’s correlation coefficients (r) between the reproductive outcomes (preterm birth and term LBW) and the arsenic probability values, at both state and county levels. For preterm birth, younger mothers (maternal age <20) have a statewide r = 0.70 between the rates smoothed with a threshold = 2,000 births and the town mean arsenic level based on the data of probability >10 µg/L; for older mothers, r = 0.19 when the smoothing threshold = 3,500; a majority of county level r values are positive based on the arsenic data of probability >10 µg/L. For term LBW, younger mothers (maternal age <25) have a statewide r = 0.44 between the rates smoothed with a threshold = 3,500 and town minimum arsenic concentration based on the data of probability >1 µg/L; for older mothers, r = 0.14 when the rates are smoothed with a threshold = 1,000 births and also adjusted by town median household income in 1999, and the arsenic values are the town minimum based on probability >10 µg/L. At the county level for younger mothers, positive r values prevail, but for older mothers, it is a mix. For both birth problems, the several most populous counties-with 60-80 % of the state’s population and clustering at the southwest corner of the state-are largely consistent in having a positive r across different smoothing thresholds. We found evident spatial associations between the two adverse human reproductive outcomes and groundwater arsenic in New Hampshire, USA. However, the degree of associations and their sensitivity to different representations of arsenic level are variable. Generally, preterm birth has a stronger spatial association with groundwater arsenic than term LBW, suggesting an inconsistency in the impact of arsenic on the two reproductive outcomes. For both outcomes, younger maternal age has stronger spatial associations with groundwater arsenic.
The paper is here.