Estimating exposure response functions using ambient pollution concentrations

This paper presents an approach to estimating the health effects of an environmental hazard. The approach is general in nature, but is applied here to the case of air pollution. It uses a computer model involving ambient pollution and temperature input to simulate the exposures experienced by individuals in an urban area, while incorporating the mechanisms that determine exposures. The output from the model comprises a set of daily exposures for a sample of individuals from the population of interest. These daily exposures are approximated by parametric distributions so that the predictive exposure distribution of a randomly selected individual can be generated. These distributions are then incorporated into a hierarchical Bayesian framework (with inference using Markov chain Monte Carlo simulation) in order to examine the relationship between short-term changes in exposures and health outcomes, while making allowance for long-term trends, seasonality, the effect of potential confounders and the possibility of ecological bias. The paper applies this approach to particulate pollution (PM10) and respiratory mortality counts for seniors in greater London (≥65 years) during 1997. Within this substantive epidemiological study, the effects on health of ambient concentrations and (estimated) personal exposures are compared. The proposed model incorporates within day (or between individual) variability in personal exposures, which is compared to the more traditional approach of assuming a single pollution level applies to the entire population for each day. Effects were estimated using single lags and distributed lag models, with the highest relative risk, RR=1.02 (1.01–1.04), being associated with a lag of two days ambient concentrations of PM10. Individual exposures to PM10 for this group (seniors) were lower than the measured ambient concentrations with the corresponding risk, RR=1.05 (1.01–1.09), being higher than would be suggested by the traditional approach using ambient concentrations

A Hybrid Model for Reducing Ecological Bias

A major drawback of epidemiological ecological studies, in which the association between area-level summaries of risk and exposure are used to make inference about individual risk, is the difficulty in characterising within-area variability in exposure and confounder variables. To avoid ecological bias, samples of individual exposure/confounder data within each area are required. Unfortunately these may be difficult or expensive to obtain, particularly if large samples are required. In this paper we propose a new approach suitable for use with small samples. We combine a Bayesian non-parametric Dirichlet process prior with an estimating functions approach, and show that this model gives a compromise between two previously-described methods. The method is investigated using simulated data, and a practical illustration is provided through an analysis of mortality and income data across England. We conclude that we require good quality prior information about the expo- sure/confounder distributions and a large between- to within-area variability ratio for an ecological study to be feasible using only small samples of individual data

Gamma Generalized Linear Models for Pharmacokinetic Data

This paper considers the modeling of single dose pharmacoki- netic data. Traditionally, so-called compartmental models have been used to analyze such data. Unfortunately the mean function of such models are sums of exponentials for which inference and computation may not be straightfor- ward. We present an alternative to these models based on generalized linear models, for which desirable statistical properties exist, with a logarithmic link and gamma distribution. The latter has a constant coefficient of variation which is often appropriate for pharmacokinetic data. Inference is convenient from either a likelihood or a Bayesian perspective. We consider models for both single and multiple individuals, the latter via generalized linear mixed models. For single individuals, Bayesian computation may be carried out with recourse to simulation. We describe a rejection algorithm that, unlike Markov chain Monte Carlo, produces independent samples from the posterior and allows straightforward calculation of Bayes factors for model compari- son. We also illustrate how prior distributions may be specified in terms of model-free pharmacokinetic parameters of interest. The methods are applied to data from 12 individuals following administration of the anti-asthmatic agent theophylline

Ethnic minority customers of the Pensions, Disability and Carers Service: an evidence review.

The aim of this project was to review and synthesise available evidence that could throw light on: why Black and Minority Ethnic (BME) customers are less satisfied with the Pension, Disability and Carers Service (PDCS); why BME individuals eligible for the PDCS benefits are less likely to apply for them; what interventions might be successful at raising levels of take-up and satisfaction with PDCS services; and what important gaps exist in research evidence to answer these questions

Enhancing the quality of published research on ethnicity and health: is journal guidance feasible and useful?

Researching ethnicity and health presents significant ethical, conceptual and methodological challenges. While the potential contribution of research evidence to tackling ethnic inequalities in health is recognised, there are widespread concerns regarding the ethical and scientific rigour of much of this research and its potential to do more harm than good. The introduction of guidance documents at critical points in the research cycle - including within the peer-review publication process - might be one way to enhance the quality of such research. This article reports the findings from the piloting of a guidance checklist within an international journal. The checklist was positively received by authors and reviewers, the majority of whom reported it to be comprehensible, relevant and potentially useful in improving the quality of published research. However, participation in the pilot was poor, suggesting that the impact of such a checklist would be very limited unless it was perceived to be an aid to authors and reviewers, rather than an additional burden, and was strongly promoted by journal editors

Effectiveness of tobacco control television advertising in changing tobacco use in England: a population‐based cross‐sectional study

AIM: To examine whether government‐funded tobacco control television advertising shown in England between 2002 and 2010 reduced adult smoking prevalence and cigarette consumption. DESIGN: Analysis of monthly cross‐sectional surveys using generalised additive models. SETTING: England. PARTICIPANTS: More than 80 000 adults aged 18 years or over living in England and interviewed in the Opinions and Lifestyle Survey. MEASUREMENTS: Current smoking status, smokers' daily cigarette consumption, tobacco control gross rating points (GRPs—a measure of per capita advertising exposure combining reach and frequency), cigarette costliness, tobacco control activity, socio‐demographic variables. FINDINGS: After adjusting for other tobacco control policies, cigarette costliness and individual characteristics, we found that a 400‐point increase in tobacco control GRPs per month, equivalent to all adults in the population seeing four advertisements per month (although actual individual‐level exposure varies according to TV exposure), was associated with 3% lower odds of smoking 2 months later [odds ratio (OR) = 0.97, 95% confidence interval (CI) = 0.95, 0.999] and accounted for 13.5% of the decline in smoking prevalence seen over this period. In smokers, a 400‐point increase in GRPs was associated with a 1.80% (95%CI = 0.47, 3.11) reduction in average cigarette consumption in the following month and accounted for 11.2% of the total decline in consumption over the period 2002–09. CONCLUSION: Government‐funded tobacco control television advertising shown in England between 2002 and 2010 was associated with reductions in smoking prevalence and smokers' cigarette consumption