Bayesian nonparametric models for spatially indexed data of mixed type

We develop Bayesian nonparametric models for spatially indexed data of mixed type. Our work is motivated by challenges that occur in environmental epidemiology, where the usual presence of several confounding variables that exhibit complex interactions and high correlations makes it difficult to estimate and understand the effects of risk factors on health outcomes of interest. The modeling approach we adopt assumes that responses and confounding variables are manifestations of continuous latent variables, and uses multivariate Gaussians to jointly model these. Responses and confounding variables are not treated equally as relevant parameters of the distributions of the responses only are modeled in terms of explanatory variables or risk factors. Spatial dependence is introduced by allowing the weights of the nonparametric process priors to be location specific, obtained as probit transformations of Gaussian Markov random fields. Confounding variables and spatial configuration have a similar role in the model, in that they only influence, along with the responses, the allocation probabilities of the areas into the mixture components, thereby allowing for flexible adjustment of the effects of observed confounders, while allowing for the possibility of residual spatial structure, possibly occurring due to unmeasured or undiscovered spatially varying factors. Aspects of the model are illustrated in simulation studies and an application to a real data set

Sex ratio and time to pregnancy: analysis of four large European population surveys

Objective To test whether the secondary sex ratio (proportion of male births) is associated with time to pregnancy, a marker of fertility. Design Analysis of four large population surveys. Setting Denmark and the United Kingdom. Participants 49 506 pregnancies. Main outcome measure Secondary sex ratio. Results No association was found between the sex ratio and time to pregnancy and no discernible trend was found for sex ratio with time to pregnancy, either within individual datasets or in the pooled analysis. The odds ratios were 0.97 (95% confidence interval 0.90 to 1.04) for contraceptive failures, 1.01 (0.96 to 1.05) for time to pregnancy of 2-4 months, 1.02 (0.97 to 1.08) for 5-10 months, 0.98 (0.93 to 1.03) for 11 months or more, and 0.88 (0.74 to 1.06) for fertility treatment, with 0-1 months as the reference category. Conclusion No association was found between the secondary sex ratio and time to pregnancy

Health Information and Health Outcomes: An Application of the Regression Discontinuity Design to the 1995 UK Contraceptive Pill Scare Case

This paper provides a general formulation of the regression discontinuity (RD) design and shows its general applicability to many epidemiological problems. It then applies the RD method to estimate the effects of the 1995 pill scare in the UK, using individual birth records and aggregate monthly statistics. The results show that, following the announce- ment of the health warning on the þird generation" pill, conception rates increased by about 7%, with a 9% increase in abortion rates and a 6-7% rise in birth rates. No e®ect was found on still births, very low birth weight, sex ratios, or average birth weight. There is evidence of a slight increase in the rates of low birth weight births and multiple births and of a considerable reduction in the rate of births with congenital anomalies. Hetero- geneity by mother's age and social class is very pronounced, with most of the e®ects being experienced by women aged less than 25 and of lower socioeconomic status

Tracing ingestion of 'novel' foods in UK diets for possible health surveillance: a feasibility study

Objective: To investigate the feasibility of using commercially available data on household food consumption to carry out food and nutritional surveillance. Design: Taylor Nelson Sofres (TNS) collects information on foods brought home for consumption among a representative quota sample of the British population. In total, 33 177 households and 105 667 individuals provided data between 1991 and 2000. These were used to investigate sociodemographic, geographical and temporal trends in purchase patterns of the main macronutrients and four groups of marker products. Results: Sociodemographic characteristics of the TNS sample were broadly consistent with those of the British population. Estimated energy intakes were slightly low (1667 +/- 715 kcal) in comparison with other national data. However, percentage energy contributions were consistent with national trends: e.g. consumption of alcohol in the home increased between 1991 and 2000 with higher intakes among more affluent households, while fat intakes decreased slightly over the same period. Significant temporal, geographic and socio-economic trends were found for all nutrients (P < 0.0001). Intakes of marker products were sparse (purchased by < 4% of households), but significant variations were detected in the proportion of households purchasing some or all of the marker products across temporal, geographic and socio-economic strata. Conclusions: A prospective nutrient surveillance system could be used to trace consumption patterns of foods or nutrients to inform nutritional surveillance. However, existing data sources would require a number of modifications to increase their suitability for such a project. Increasing surveillance to consider ingredients would require the development of a central coding system, with electronically linked barcode, ingredient and nutrient information

Quantile regression with aggregated data

Administrative data can contain a wealth of information for empirical research. Just to cite two examples, administrative data on schools can be used to study pupils’ educational attainments while hospital data can be useful for health research. However, access to administrative information is often restricted to aggregated data and this can lead to biased results. The estimation bias caused by using aggregated rather than individual data is known as the ecological bias. In this paper we consider for the first time this issue in the context of quantile regressions. We show how data can be aggregated to obtain unbiased estimation of quantile regressions with categorical covariates and how the bias can be reduced when researchers are interested to estimate quantile regression where some of the covariates are continuous