Communication of risk: health hazards from mobile phones

Summary. Possible health hazards from mobile phones arise from the use of the phones themselves and via the base stations that relay signals. Except for an increase in traffic accidents induced by the use of mobile phones in cars the evidence for a health hazard is at most indirect, but it cannot be entirely dismissed; the phones have not been widely used for sufficiently long for direct epidemiological studies to have high sensitivity for detecting any induced incidence of cancer, for example. The background and evidence are briefly reviewed and the steps taken in the UK to make information widely available described.     

Spatial variation in risk of disease: a nonparametric binary regression approach

A common problem in environmental epidemiology is the estimation and mapping of spatial variation in disease risk. In this paper we analyse data from the Walsall District Health Authority, UK, concerning the spatial distributions of cancer cases compared with controls sampled from the population register. We formulate the risk estimation problem as a nonparametric binary regression problem and consider two different methods of estimation. The first uses a standard kernel method with a cross-validation criterion for choosing the associated bandwidth parameter. The second uses the framework of the generalized additive model (GAM) which has the advantage that it can allow for additional explanatory variables, but is computationally more demanding. For the Walsall data, we obtain similar results using either the kernel method with controls stratified by age and sex to match the age–sex distribution of the cases or the GAM method with random controls but incorporating age and sex as additional explanatory variables. For cancers of the lung or stomach, the analysis shows highly statistically significant spatial variation in risk. For the less common cancers of the pancreas, the spatial variation in risk is not statistically significant.     

Assessing respondent-driven sampling: A simulation study across different networks

The purpose was to assess RDS estimators in populations simulated with diverse connectivity characteristics, incorporating the putative influence of misreported degrees and transmission processes. Four populations were simulated using different random graph models. Each population was “infected” using four different transmission processes. From each combination of population x transmission, one thousand samples were obtained using a RDS-like sampling strategy. Three estimators were used to predict the population-level prevalence of the “infection”. Several types of misreported degrees were simulated. Also, samples were generated using the standard random sampling method and the respective prevalence estimates, using the classical frequentist estimator. Estimation biases in relation to population parameters were assessed, as well as the variance. Variability was associated with the connectivity characteristics of each simulated population. Clustered populations yield greater variability and no RDS-based strategy could address the estimation biases. Misreporting degrees had modest effects, especially when RDS estimators were used. The best results for RDS-based samples were observed when the “infection” was randomly attributed, without any relation with the underlying network structure.     

Assessing the Playing Field: A Prospective Longitudinal Study of Internet Sports Gambling Behavior

Internet gambling is growing rapidly, as is concern about its possible effect on the public’s health. This paper reports the results of the first prospective longitudinal study of actual Internet sports gambling behavior during eight study months. Data include recorded fixed-odds bets on the outcome of sporting contests and live-action bets on the outcome of events within contests for 40,499 Internet sports gambling service subscribers who enrolled during February 2005. We tracked the following primary gambling behaviors: daily totals of the number of bets made, money bet, and money won. We transformed these variables into measures of gambling involvement. We analyzed behavior for both fixed-odds and live-action bets. The median betting behavior of the 39,719 fixed-odds bettors was to place 2.5 bets of €4 (approximately $5.3 US) every fourth day during the median 4 months from first to last bet. This typical pattern incurred a loss of 29% of the amount wagered. The median betting behavior of the 24,794 live-action bettors was to place 2.8 wagers of €4 every fourth day during the median duration of 6 weeks at a loss of 18% of the amount wagered. We also examined the behavior of empirically determined groups of heavily involved bettors whose activity exceeded that of 99% of the sample.     

Model-based measurement of latent risk in time series with applications

Summary.  Risk is at the centre of many policy decisions in companies, governments and other institutions. The risk of road fatalities concerns local governments in planning countermeasures, the risk and severity of counterparty default concerns bank risk managers daily and the risk of infection has actuarial and epidemiological consequences. However, risk cannot be observed directly and it usually varies over time. We introduce a general multivariate time series model for the analysis of risk based on latent processes for the exposure to an event, the risk of that event occurring and the severity of the event. Linear state space methods can be used for the statistical treatment of the model. The new framework is illustrated for time series of insurance claims, credit card purchases and road safety. It is shown that the general methodology can be effectively used in the assessment of risk.     

Epidemiological aspects of diabetes in the aging male

Type 2 diabetes mellitus is a common chronic disease of older men, affecting an estimated 40% of American men over age 60¹. This paper reviews epidemiological aspects of diabetes mellitus in the aging male, with emphasis on the impact of diabetes in older American men. The first part of the report provides an overview of diabetes in relation to health-care costs, key demographic factors and population dynamics. The second part uses data examples from the Health, Aging and Body Composition Study to illustrate several areas of interest to researchers who study diabetes in older men.     

A case–control study relating railroad worker mortality to diesel exhaust exposure using a threshold regression model

A case–control study of lung cancer mortality in U.S. railroad workers in jobs with and without diesel exhaust exposure is reanalyzed using a new threshold regression methodology. The study included 1256 workers who died of lung cancer and 2385 controls who died primarily of circulatory system diseases. Diesel exhaust exposure was assessed using railroad job history from the US Railroad Retirement Board and an industrial hygiene survey. Smoking habits were available from next-of-kin and potential asbestos exposure was assessed by job history review. The new analysis reassesses lung cancer mortality and examines circulatory system disease mortality. Jobs with regular exposure to diesel exhaust had a survival pattern characterized by an initial delay in mortality, followed by a rapid deterioration of health prior to death. The pattern is seen in subjects dying of lung cancer, circulatory system diseases, and other causes. The unique pattern is illustrated using a new type of Kaplan–Meier survival plot in which the time scale represents a measure of disease progression rather than calendar time. The disease progression scale accounts for a healthy-worker effect when describing the effects of cumulative exposures on mortality.     

Sexual assessment and the epidemiology of AIDS

Research on the transmission dynamics of AIDS necessitates an extraordinarily rigorous sexual science. Thus, the purpose of the present paper is to isolate sexual parameters which are crucial to the epidemiology of AIDS. Several representative mathematical models of the proliferation of HIV are described. From these models, three consistent sexual parameters emerge (sexual orientation, anal intercourse, and selection of sexual partners). Consequently, each parameter is examined for the feasibility of assessment and the reliability of existing data. However, as the present review suggests, the relevant data on sexual parameters are either marginal or nonexistent. Therefore, alternatives are described, and the broader implications of measurement error on epidemiological predictions is developed.     

Development and Application of a Stochastic Epidemic Model for the Transmission of Salmonella Typhimurium at the Farm Level of the Pork Production Chain

In previous work a deterministic model for the compartment level was built, taking into account the two different syndromes with which  Salmonella  Typhimurium appears at pig farms. Based on this model, a stochastic one was built in this work that simulated different compartmental sizes, taking into account compartments of 200 to 400 pigs. Multiple scenarios of starting conditions of infection (SCI) ranging from 0.25 to 100% were tested for each population size. The effect of each of these two factors on the probability of disease extinctions and the prevalence of each of the classes of the model and the risk groups of pigs were estimated. The results showed that the compartment population had an inverse effect on the probability of disease extinction. On the other hand, low SCI resulted in high levels of early extinctions reaching 45%, while higher SCI led to high levels of late extinctions. Early extinctions resulted in the absence of the pathogen from the compartment, while late extinctions did not assure it. This effect shows that reducing the population of the compartment combined with appropriate cleaning and good farming practices could have a positive effect in the reduction of the risk of introducing  S.  Typhimurium into the slaughtering procedure. On the other hand, the profile of seroprevalence at slaughter age allows for risk characterization of the farm, given the relative stability and the small variation for higher SCI.     

Census data issues for epidemiology and health risk assessment: experiences from the Small Area Health Statistics Unit

Summary.  Census data are vital components of epidemiological studies, but the issues that are involved in using these data in such studies are often not fully appreciated. The paper describes some of the problems and uncertainties that arise, and some of the approaches that can be used to address them, based on experience in the Small Area Health Statistics Unit at Imperial College London. Issues considered include the geography of census data (zone design systems, recasting and the role of postcodes), temporal aspects of census data (especially in relation to migration and population change) and information content (especially in relation to characterization of socio-economic status). In the light of these issues, opportunities to improve the resolution and utility of census data for epidemiological studies are discussed.