Suchergebnisse

AbstractMatching is a conceptually straightforward method to make groups of units comparable on observed characteristics. The method is, however, limited to settings where the study design is simple and the sample is moderately sized. We illustrate these limitations by asking what the causal effects would have been if a large-scale voter mobilization experiment that took place in Michigan for the 2006 election were scaled up to the full population of registered voters. Matching could help us answer this question, but no existing matching method can accommodate the six treatment arms and the 6,762,701 observations involved in the study. To offer a solution for this and similar empirical problems, we introduce a generalization of the full matching method that can be used with any number of treatment conditions and complex compositional constraints. The associated algorithm produces near-optimal matchings; the worst-case maximum within-group dissimilarity is guaranteed to be no more than four times greater than the optimal solution, and simulation results indicate that it comes considerably closer to the optimal solution on average. The algorithm's ability to balance the treatment groups does not sacrifice speed, and it uses little memory, terminating in linearithmic time using linear space. This enables investigators to construct well-performing matchings within minutes even in complex studies with samples of several million units.

Recently in Australia concerns have been raised regarding the contamination of municipal drinking water supplies with lead. This is of particular concern to children due to the impact of lead exposure on cognitive development and as such these findings have received much media attention. The response from legislators has been swift, and The Victorian School Building Authority has announced that all new schools and school upgrade works will only use lead-free tapware and piping systems. However, while the immediate replacement of lead-containing brass fittings may seem a logical and obvious response, it does not consider the potential implications on microbial contamination. This is particularly concerning given the increasing public health threat posed by opportunistic premise plumbing pathogens (OPPPs). This commentary explores this public health risk of lead exposure from plumbing materials compared to the potential public health risks from OPPPs. Non-tuberculous mycobacterium was chosen as the example OPPP, and the influence on plumbing material and its public health burden in Australia is explored. This commentary highlights the need for future research into the influence of plumbing material on OPPPs prior to any changes in legislation regarding plumbing material.