Monitoring the marine environment adjacent to a petroleum refinery on Corio Bay, Victoria, Australia /Peter James Gilbert

The objective of the work reported in this thesis was to design and implement an ecological effects environmental monitoring program which would: 1) Collect baseline biological information on sessile epibiotic fouling communities from an area adjacent to a petroleum refinery located on Corio Bay, Victoria, to allow comparison with results of future monitoring for the assessment of long term temporal water quality trends. 2) Detect and — if possible - estimate the magnitude of any influence on epibiotic fouling communities within the Corio Bay marine ecosystem attributable to operations at the Shell Petroleum Refinery. 3) Investigate the extent of thermal stratification and rate of dispersal of the petroleum refinery main cooling-water outfall plume (discharging up to 350,000 tonnes of warmed seawater per day), and its effect on epibiotic communities within the EPA-defined mixing zone. A major component of the work undertaken was the design and development of artificial-substrate biological sampling stations suitable for use under the conditions prevailing in Corio Bay, and the development of appropriate quantitative underwater photographic sampling techniques to fulfil the experimental criteria outlined above. Experimental and other constraints imposed on the design of the stations precluded the simple suspension of frames from jetties or pylons, a technique widely used in previous work of this type. Artificial substrate panels were deployed along three radial transects centred within and extending beyond the petroleum refinery main cooling-water mixing zone. Identical substrate panels were deployed at a number of control sites located throughout Corio Bay, each chosen for differences in their degree of exposure to such factors as water movement, depth, shipping traffic and/or comparable industrial activity. The rate of colonisation (space utilisation) and the development of epibiotic fouling communities on artificial substrate panels was monitored over two twelve-month sampling periods using quantitative underwater photographic sampling techniques. Sampling was conducted at 4-8 week intervals with the rate of panel colonisation and community structure determined via coverage measurements. Various species of marine algae, polychaete tubeworms, hydroids, barnacles, simple and colonial ascidians, sponges, bivalve molluscs and encrusting bryozoans were all detected growing on panels. Communities which established on panels within the cooling-water mixing-zone and those at control sites were compared using statistical procedures including agglomerative hierarchical cluster analysis. A photographic sample archive has been established to allow comparison with similar future studies

Uncertainty and Cooperation: Analytical Results and a Simulated Agent Society

Uncertainty is an important factor that influences social evolution in natural and artificial environments. Here we distinguish between three aspects of uncertainty. Environmental uncertainty is the variance of resources in the environment, perceived uncertainty is the variance of the resource distribution as perceived by the organism and effective uncertainty is the variance of resources effectively enjoyed by individuals. We show analytically that perceived uncertainty is larger than environmental uncertainty and that effective uncertainty is smaller than perceived uncertainty, when cooperation is present. We use an agent society simulation in a two dimensional world for the generation of simulation data as one realisation of the analytical results. Together with our earlier theoretical work, results here show that cooperation can buffer the detrimental effects of uncertainty on the organism. The proposed conceptualisation of uncertainty can help in understanding its effects on social evolution and in designing artificial social environments.Agent-Based Modelling, Cooperation, Social Interaction Simulation, Uncertainty

Evaluating the Experience of GAPS—A Methodology for Improving Quality of Mass Immunization Campaigns in Developing Countries

The existence of pockets of under-vaccinated persons has allowed outbreaks of disease in countries that have achieved high levels of vaccination coverage. A field-based methodology—GAPS (Geographic Assessment of Planning and Services)—was developed to predict, in advance of an immunization campaign, the sites of which are most likely to have a pocket of unvaccinated persons and then use this information to improve planning, supervision, and evaluation of the campaign. At this time, there have been two applications of GAPS (Nepal and Ethiopia). The purpose of this paper was to evaluate these two applications of GAPS and make recommendations regarding its future use. Structured, expert interviews were conducted with at least three campaign organizers to evaluate each application of GAPS using purposive sampling. An evaluation of an individual campaign was considered positive when at least two of the three campaign organizers considered GAPS to be useful and worthwhile. The three campaign organizers interviewed following the GAPS application in Ethiopia responded that GAPS was useful and worth the effort. In Nepal, all four campaign organizers responded that GAPS was useful and worth the effort. Some suggestions for improvement were also identified. Although this evaluation was limited in the number of applications evaluated, GAPS appears to have promise as a practical method to help improve the quality of mass immunization campaigns. And even if no pockets of unvaccinated persons are found, the method may serve as a rapid quality-check of administrative estimates of coverage. Further applications in different settings are needed to confirm these findings or under what circumstances GAPS might best be used. GAPS may also be considered for improving other types of health campaigns, such as distribution of insecticide-treated bednets, vitamin A capsules, and deworming medications

Factors Associated with Missed Vaccination during Mass Immunization Campaigns

Achieving a high percentage of vaccination coverage with polio vaccine, while necessary, is not sufficient to eliminate or eradicate polio. The existence of pockets of under-vaccinated children has allowed outbreaks of polio in countries that have achieved high levels of vaccination coverage and in countries with no cases for many years. In a literature review, 35 articles were identified that described factors associated with missed vaccination in mass immunization campaigns. An annotated bibliography was developed for each article; these were then coded using the AnSWR program, and codes were organized into three larger thematic categories. These thematic areas were: (a) organization and implementation of mass campaigns; (b) population characteristics; and (c) knowledge and practices of caretakers. If these factors were geographically clustered, it was suspected that these clusters might have higher likelihood of becoming pockets of unvaccinated children. Immunization programme managers can target resources to identify if such clusters exist. If so, they can then ensure supervision of vaccination efforts in those sites and take further action, if indicated, to prevent or mitigate pockets of unvaccinated children

New data and the hard pomeron

New structure-function data are in excellent agreement with the existence of a hard pomeron, with intercept about 1.4. It gives a very economical description of the data. Having fixed 2 parameters from the data for the real-photon cross section $\sigma^{\gamma p}$, we need just 5 further parameters to fit the data for $F_2(x,Q^2)$ with $x\leq 0.001$. The available data range from $Q^2=0.045$ to 35 GeV$^2$. With guesses consistent with dimensional counting for the $x$ dependences of our three separate terms, the fit extends well to larger $x$ and to $Q^2=5000$ GeV$^2$. With no additional parameters, it gives a good description of data for the charm structure function $F_2^c(x,Q^2)$ from $Q^2=0$ to 130 GeV$^2$. The two pomerons also give a good description of both the $W$ and the $t$ dependence of $\gamma p\to J/\psi p$.Comment: 11 pages, plain tex, with 10 figures embedded using epsf. (Spurious figure removed.

Sensitivity Analysis for the Assessment of Causal Vaccine Effects on Viral Load in HIV Vaccine Trials

Vaccines with limited ability to prevent HIV infection may positively impact the HIV/AIDS pandemic by preventing secondary transmission and disease in vaccine recipients who become infected. To evaluate the impact of vaccination on secondary transmission and disease, efficacy trials assess vaccine effects on HIV viral load and other surrogate endpoints measured after infection. A standard test that compares the distribution of viral load between the infected subgroups of vaccine and placebo recipients does not assess a causal effect of vaccine, because the comparison groups are selected after randomization. To address this problem, we formulate clinically relevant causal estimands using the principal stratification framework developed by Frangakis and Rubin (2002), and propose a class of logistic selection bias models whose members identify the estimands. Given a selection model in the class, procedures are developed for testing and estimation of the causal effect of vaccination on viral load in the principal stratum of subjects who would be infected regardless of randomization assignment. We show how the procedures can be used for a sensitivity analysis that quantifies how the causal effect of vaccination varies with the presumed magnitude of selection bias

Nonparametric variable importance for time-to-event outcomes with application to prediction of HIV infection

In survival analysis, complex machine learning algorithms have been increasingly used for predictive modeling. Given a collection of features available for inclusion in a predictive model, it may be of interest to quantify the relative importance of a subset of features for the prediction task at hand. In particular, in HIV vaccine trials, participant baseline characteristics are used to predict the probability of infection over the intended follow-up period, and investigators may wish to understand how much certain types of predictors, such as behavioral factors, contribute toward overall predictiveness. Time-to-event outcomes such as time to infection are often subject to right censoring, and existing methods for assessing variable importance are typically not intended to be used in this setting. We describe a broad class of algorithm-agnostic variable importance measures for prediction in the context of survival data. We propose a nonparametric efficient estimation procedure that incorporates flexible learning of nuisance parameters, yields asymptotically valid inference, and enjoys double-robustness. We assess the performance of our proposed procedure via numerical simulations and analyze data from the HVTN 702 study to inform enrollment strategies for future HIV vaccine trials.Comment: 91 total pages (31 main text, 60 supplementary); 14 total figures (4 main text, 10 supplementary

Doubly-robust Nonparametric Inference on the Average Treatment Effect

Doubly-robust estimators are widely used to draw inference about the average effect of a treatment. Such estimators are consistent for the effect of interest if either one of two nuisance parameters is consistently estimated. However, if flexible, data-adaptive estimators of these nuisance parameters are used, double-robustness does not readily extend to inference. We present a general theoretical study of the behavior of doubly-robust estimators of an average treatment effect when one of the nuisance parameters is inconsistently estimated. We contrast different approaches for constructing such estimators and investigate the extent to which they may be modified to also allow doubly-robust inference. We find that while targeted maximum likelihood estimation can be used to solve this problem very naturally, common alternative frameworks appear to be inappropriate for this purpose. We provide a theoretical study and a numerical evaluation of the alternatives considered. Our simulations highlight the need and usefulness of these approaches in practice, while our theoretical developments have broad implications for the construction of estimators that permit doubly-robust inference in other problems

The effects of entry on incumbent innovation and productivity

How does firm entry affect innovation incentives in incumbent firms? Microdata suggest that there is heterogeneity across industries. Specifically, incumbent productivity growth and patenting is positively correlated with lagged greenfield foreign firm entry in technologically advanced industries, but not in laggard industries. In this paper we provide evidence that these correlations arise from a causal effect predicted by Schumpeterian growth theory—the threat of technologically advanced entry spurs innovation incentives in sectors close to the technology frontier, where successful innovation allows incumbents to survive the threat, but discourages innovation in laggard sectors, where the threat reduces incumbents' expected rents from innovating. We find that the empirical patterns hold using rich micro panel data for the United Kingdom. We control for the endogeneity of entry by exploiting major European and U.K. policy reforms, and allow for endogeneity of additional factors. We complement the analysis for foreign entry with evidence for domestic entry and entry through imports

Major epidemiological changes in sudden infant death syndrome : a 20-year population-based study in the UK

Background Results of case-control studies in the past 5 years suggest that the epidemiology of sudden infant death syndrome (SIDS) has changed since the 1991 UK Back to Sleep campaign. The campaign's advice that parents put babies on their back to sleep led to a fall in death rates. We used a longitudinal dataset to assess these potential changes. Methods Population-based data from home visits have been collected for 369 consecutive unexpected infant deaths (300 SIDS and 69 explained deaths) in Avon over 20 years (1984—2003). Data obtained between 1993 and 1996 from 1300 controls with a chosen “reference” sleep before interview have been used for comparison. Findings Over the past 20 years, the proportion of children who died from SIDS while co-sleeping with their parents, has risen from 12% to 50% (p<0·0001), but the actual number of SIDS deaths in the parental bed has halved (p=0·01). The proportion seems to have increased partly because the Back to Sleep campaign led to fewer deaths in infants sleeping alone—rather than because of a rise in deaths of infants who bed-shared, and partly because of an increase in the number of deaths in infants sleeping with their parents on a sofa. The proportion of deaths in families from deprived socioeconomic backgrounds has risen from 47% to 74% (p=0·003), the prevalence of maternal smoking during pregnancy from 57% to 86% (p=0·0004), and the proportion of pre-term infants from 12% to 34% (p=0·0001). Although many SIDS infants come from large families, first-born infants are now the largest group. The age of infants who bed-share is significantly smaller than that before the campaign, and fewer are breastfed. Interpretation Factors that contribute to SIDS have changed in their importance over the past 20 years. Although the reasons for the rise in deaths when a parent sleeps with their infant on a sofa are still unclear, we strongly recommend that parents avoid this sleeping environment. Most SIDS deaths now occur in deprived families. To better understand contributory factors and plan preventive measures we need control data from similarly deprived families, and particularly, infant sleep environments