Identifying the Most Significant Microbiological Foodborne Hazards to Public Health: A New Risk Ranking Model

In order to help facilitate a risk-based food safety system, we developed the Foodborne Illness Risk Ranking Model (FIRRM), a decisionmaking tool that quantifies and compares the relative burden to society of 28 foodborne pathogens. FIRRM estimates the annual number of cases, hospitalizations, and fatalities caused by each foodborne pathogen, subsequently estimates the economic costs and QALY losses of these illnesses, and, lastly, attributes these pathogen-specific illnesses and costs to categories of food vehicles, based on outbreak data and expert judgment. The model ranks pathogen-food combinations according to five measures of societal burden. FIRRM incorporates probabilistic uncertainty within a Monte Carlo simulation framework and produces confidence intervals and statistics for all outputs. Gaps in data, most importantly in regards to food attribution and the statistical uncertainty of incidence estimates, currently limit the utility of the model. Once we address these and other problems, however, FIRRM will be a robust and useful decisionmaking tool.foodborne illness, risk ranking, pathogens, health valuation, QALYs, cost of illness, uncertainty, modeling, Monte Carlo

Joint modeling of bivariate time to event data with semi-competing risk

Indiana University-Purdue University Indianapolis (IUPUI)Survival analysis often encounters the situations of correlated multiple events including the same type of event observed from siblings or multiple events experienced by the same individual. In this dissertation, we focus on the joint modeling of bivariate time to event data with the estimation of the association parameters and also in the situation of a semi-competing risk. This dissertation contains three related topics on bivariate time to event mod els. The first topic is on estimating the cross ratio which is an association parameter between bivariate survival functions. One advantage of using cross-ratio as a depen dence measure is that it has an attractive hazard ratio interpretation by comparing two groups of interest. We compare the parametric, a two-stage semiparametric and a nonparametric approaches in simulation studies to evaluate the estimation perfor mance among the three estimation approaches. The second part is on semiparametric models of univariate time to event with a semi-competing risk. The third part is on semiparametric models of bivariate time to event with semi-competing risks. A frailty-based model framework was used to accommodate potential correlations among the multiple event times. We propose two estimation approaches. The first approach is a two stage semiparametric method where cumulative baseline hazards were estimated by nonparametric methods first and used in the likelihood function. The second approach is a penalized partial likelihood approach. Simulation studies were conducted to compare the estimation accuracy between the proposed approaches. Data from an elderly cohort were used to examine factors associated with times to multiple diseases and considering death as a semi-competing risk

Methods for checking the Markov condition in multi-state survival data

The inference in multi-state models is traditionally performed under a Markov assumption that claims that past and future of the process are independent given the present state. This assumption has an important role in the estimation of the transition probabilities. When the multi-state model is Markovian, the Aalen-Johansen estimator gives consistent estimators of the transition probabilities but this is no longer the case when the process is non-Markovian. Usually, this assumption is checked including covariates depending on the history. Since the landmark methods of the transition probabilities are free of the Markov assumption, they can also be used to introduce such tests by measuring their discrepancy to Markovian estimators. In this paper, we introduce tests for the Markov assumption and compare them with the usual approach based on the analysis of covariates depending on history through simulations. The methods are also compared with more recent and competitive approaches. Three real data examples are included for illustration of the proposed methods.This research was financed by Portuguese Funds through FCT - “Fundação para a Ciencia e a Tecnologia”, within the research grants PTDC/MAT-STA/28248/2017 and PD/BD/142887/2018

A Workflow for Software Development within Computational Epidemiology

A critical investigation into computational models developed for studying the spread of communicable disease is presented. The case in point is a spatially explicit micro-meso-macro model for the entire Swedish population built on registry data, thus far used for smallpox and for influenza-like illnesses. The lessons learned from a software development project of more than 100 person months are collected into a check list. The list is intended for use by computational epidemiologists and policy makers, and the workflow incorporating these two roles is described in detail.NOTICE: This is the author’s version of a work that was accepted for publication in Journal of Computationa Science. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Computational Science, VOL 2, ISSUE 3, 6 June 2011 DOI 10.1016/j.jocs.2011.05.004.</p

Comparison of joint modeling and landmarking for dynamic prediction under an illness‐death model

Dynamic prediction incorporates time‐dependent marker information accrued during follow‐up to improve personalized survival prediction probabilities. At any follow‐up, or “landmark”, time, the residual time distribution for an individual, conditional on their updated marker values, can be used to produce a dynamic prediction. To satisfy a consistency condition that links dynamic predictions at different time points, the residual time distribution must follow from a prediction function that models the joint distribution of the marker process and time to failure, such as a joint model. To circumvent the assumptions and computational burden associated with a joint model, approximate methods for dynamic prediction have been proposed. One such method is landmarking, which fits a Cox model at a sequence of landmark times, and thus is not a comprehensive probability model of the marker process and the event time. Considering an illness‐death model, we derive the residual time distribution and demonstrate that the structure of the Cox model baseline hazard and covariate effects under the landmarking approach do not have simple form. We suggest some extensions of the landmark Cox model that should provide a better approximation. We compare the performance of the landmark models with joint models using simulation studies and cognitive aging data from the PAQUID study. We examine the predicted probabilities produced under both methods using data from a prostate cancer study, where metastatic clinical failure is a time‐dependent covariate for predicting death following radiation therapy.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140034/1/bimj1778.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/140034/2/bimj1778_am.pd

Measuring Welfare Loss Caused by Air Pollution in Europe: A CGE Analysis

Abstract and PDF report are also available on the MIT Joint Program on the Science and Policy of Global Change website (http://globalchange.mit.edu/).To evaluate the socio-economic impacts of air pollution, we develop an integrated approach based on computable general equilibrium (CGE). Applying our approach to Europe shows that even there, where air quality is relatively high compared with other parts of the world, health-related damages caused by air pollution are substantial. We estimate that in 2005, air pollution in Europe caused a consumption loss of around 220 billion Euro (year 2000 prices, around 3 percent of consumption level) and a social welfare loss of around 370 billion Euro, measured as the sum of lost consumption and leisure (around 2 percent of welfare level). In addition, we estimated that a set of 2020-targeting air quality improvement policy scenarios, which are proposed in the 2005 CAFE program, would bring 18 European countries as a whole a welfare gain of 37 to 49 billion Euro (year 2000 prices) in year 2020 alone.This study received support from the MIT Joint Program on the Science and Policy of Global Change, which is funded by a consortium of government, industry and foundation sponsors