Analyzing Individual-Level Secondary Data with Instrumental Variable Methods Is Useful for Studying the Effects of Air Pollution on Dementia.

We hypothesize that analyzing individual-level secondary data with instrumental variable (IV) methods can advance knowledge of the long-term effects of air pollution on dementia. We discuss issues in measurement using secondary data and how IV estimation can overcome biases due to measurement error and unmeasured variables. We link air-quality data from the Environmental Protection Agency's monitors with Medicare claims data to illustrate the use of secondary data to document associations.

Revisiting the temperature-economic growth relationship using global subnational data.

Previous studies have used national data to demonstrate that higher annual temperatures negatively affect economic output and growth. Yet, annual temperatures and productivity can also vary greatly across space within countries. With this in mind, we revisit the relationship between temperature and economic growth using subnational short panel data for 10,597 grid cells across the terrestrial Earth.

Choice Inconsistencies among the Elderly: Evidence from Plan Choice in the Medicare Part D Program: Comment.

Consumers' enrollment decisions in Medicare Part D can be explained by Abaluck and Gruber’s (2011) model of utility maximization with psychological biases or by a neoclassical version of their model that precludes such biases. We evaluate these competing hypotheses by applying nonparametric tests of utility maximization and model validation tests to administrative data. We find that 79 percent of enrollment decisions from 2006 to 2010 satisfied basic axioms of consumer theory under the assumption of full information.

Measured voluntary avoidance behaviour during the 2009 A/H1N1 epidemic.

Managing infectious disease is among the foremost challenges for public health policy. Interpersonal contacts play a critical role in infectious disease transmission, and recent advances in epidemiological theory suggest a central role for adaptive human behaviour with respect to changing contact patterns. However, theoretical studies cannot answer the following question: are individual responses to disease of sufficient magnitude to shape epidemiological dynamics and infectious disease risk? We provide empirical evidence that Americans voluntarily reduced their time spent in public places during the 2009 A/H1N1 swine flu, and that these behavioural shifts were of a magnitude capable of reducing the total number of cases.

Merging economics and epidemiology to improve the prediction and management of infectious disease.

Mathematical epidemiology, one of the oldest and richest areas in mathematical biology, has significantly enhanced our understanding of how pathogens emerge, evolve, and spread. Classical epidemiological models, the standard for predicting and managing the spread of infectious disease, assume that contacts between susceptible and infectious individuals depend on their relative frequency in the population. The behavioral factors that underpin contact rates are not generally addressed.

Skip the trip: air travelers' behavioral responses to pandemic influenza.

Theory suggests that human behavior has implications for disease spread. We examine the hypothesis that individuals engage in voluntary defensive behavior during an epidemic. We estimate the number of passengers missing previously purchased flights as a function of concern for swine flu or A/H1N1 influenza using 1.