Analysis of matched case-control studies.

© BMJ Publishing Group Ltd 2015. There are two common misconceptions about case-control studies: that matching in itself eliminates (controls) confounding by the matching factors, and that if matching has been performed, then a "matched analysis" is required. However, matching in a case-control study does not control for confounding by the matching factors; in fact it can introduce confounding by the matching factors even when it did not exist in the source population. Thus, a matched design may require controlling for the matching factors in the analysis. However, it is not the case that a matched design requires a matched analysis. Provided that there are no problems of sparse data, control for the matching factors can be obtained, with no loss of validity and a possible increase in precision, using a "standard" (unconditional) analysis, and a "matched" (conditional) analysis may not be required or appropriate

Effect Measures in Prevalence Studies

There is still considerable confusion and debate about the appropriate methods for analyzing prevalence studies, and a number of recent papers have argued that prevalence ratios are the preferred method and that prevalence odds ratios should not be used. These arguments assert that the prevalence ratio is obviously the better measure and the odds ratio is “unintelligible.” They have often been accompanied by demonstrations that when a disease is common the prevalence ratio and the prevalence odds ratio may differ substantially. However, this does not tell us which measure is the more valid to use. In fact, the prevalence odds ratio a) estimates the incidence rate ratio with fewer assumptions than are required for the prevalence ratio; b) can be estimated using the same methods as for the odds ratio in case–control studies, namely, the Mantel–Haenszel method and logistic regression; and c) provides practical, analytical, and theoretical consistency between analyses of a prevalence study and prevalence case–control analyses based on the same study population. For these reasons, the prevalence odds ratio will continue to be one of the standard methods for analyzing prevalence studies and prevalence case–control studies

Point: incident exposures, prevalent exposures, and causal inference: does limiting studies to persons who are followed from first exposure onward damage epidemiology?

The idea that epidemiologic studies should start from first exposure onward has been advocated in the past few years. The study of incident exposures is contrasted with studies of prevalent exposures in which follow-up may commence after first exposure. The former approach is seen as a hallmark of a good study and necessary for causal inference. We argue that studying incident exposures may be necessary in some situations, but it is not always necessary and is not the preferred option in many instances. Conducting a study involves decisions as to which person-time experience should be included. Although studies of prevalent exposures involve left truncation (missingness on the left), studies of incident exposures may involve right censoring (missingness on the right) and therefore may not be able to assess the long-term effects of exposure. These considerations have consequences for studies of dynamic (open) populations that involve a mixture of prevalent and incident exposures. We argue that studies with prevalent exposures will remain a necessity for epidemiology. The purpose of this paper is to restore the balance between the emphasis on first exposure cohorts and the richness of epidemiologic information obtained when studying prevalent exposures

К проблеме загрязнения пестицидами объектов окружающей среды в Крыму

В работе сделана попытка проследить динамику накопления пестицидов в почвах, сельскохозяйственных продуктах и в водоемах курортных зон.In this article have done attempt to follow the dynamics of pesticide accumulation in the soil, the agricultural products, and in the objects of the Crimean regions, which have the special importance as a resort

PROCESS-PROPERTY-FABRIC ARCHITECTURE RELATIONSHIPS IN FIBRE-REINFORCED COMPOSITES

The use of fibre-reinforced polymer matrix composite materials is growing at a faster rate than GDP in many countries. An improved understanding of their processing and mechanical behaviour would extend the potential applications of these materials. For unidirectional composites, it is predicted that localised absence of fibres is related to longitudinal compression failure. The use of woven reinforcements permits more effective manufacture than for unidirectional fibres. It has been demonstrated experimentally that compression strengths of woven composites are reduced when fibres are clustered. Summerscales predicted that clustering of fibres would increase the permeability of the reinforcement and hence expedite the processing of these materials. Commercial fabrics are available which employ this concept using flow-enhancing bound tows. The net effect of clustering fibres is to enhance processability whilst reducing the mechanical properties. The effects reported above were qualitative correlations. Gross differences in the appearance of laminate sections are apparent for different weave styles. For the quantification of subtle changes in fabric architecture, the use of automated image analysis is essential. Griffm used Voronoi tessellation to measure the microstructures of composites made using flow-enhancing tows. The data was presented as histograms with no single parameter to quantify microstructure. This thesis describes the use of automated image analysis for the measurement of the microstructures of woven fibre-reinforced composites, and pioneers the use of fractal dimensions as a single parameter for their quantification. It further considers the process-property- structure relationships for commercial and experimental fabric reinforcements in an attempt to resolve the processing versus properties dilemma. A new flow-enhancement concept has been developed which has a reduced impact on laminate mechanical properties.University of Bristol and Carr Reinforcements Limite

Case-control studies: basic concepts.

The purpose of this article is to present in elementary mathematical and statistical terms a simple way to quickly and effectively teach and understand case-control studies, as they are commonly done in dynamic populations-without using the rare disease assumption. Our focus is on case-control studies of disease incidence ('incident case-control studies'); we will not consider the situation of case-control studies of prevalent disease, which are published much less frequently

Quantitative estimates of work-related death, disease and injury in New Zealand.

OBJECTIVES: New Zealand lacks comprehensive statistics on work-related injury and illness, and the impact of adverse work conditions on health is therefore not known. The objective of this study was to make quantitative estimates of the annual number of deaths from work-related disease and injury in New Zealand, as well as estimate the number of incident cases of work-related disease and injury. METHODS: Wherever possible, specific data for New Zealand were used, but, where adequate national data were lacking, a combination of New Zealand data and extrapolations from other countries was used. For work-related injury mortality and incidence, published studies and reports of the New Zealand Accident Compensation Corporation were primarily used. For work-related disease mortality, the likely population attributable fractions from overseas studies were mainly used, together with mortality data from New Zealand. For work-related disease incidence, both approaches were used. RESULTS: In New Zealand about 700-1000 deaths were estimated to occur annually from work-related disease and about 100 deaths from work-related injury. About 17 000-20 000 new cases of work-related disease occur annually and about 200 000 work-related accidents result in claims made to the New Zealand Accident Compensation Corporation. CONCLUSIONS: Despite their imprecision, these conservative estimates indicate the burden of work-related death, disease, and injury in New Zealand. The estimates by gender, industry, and disease types provide useful information for policy priorities