Visual displays of data in the parasitology literature are often presented in a way which is not very informative regarding the distribution of the data. An example being simple barcharts with half an error bar on top to display the distribution of parasitaemia and biomarkers of host immunity. Such displays obfuscate the shape of the data distribution through displaying too few statistical measures to explain the spread of all the data and selecting statistical measures which are influenced by skewness and outliers. We describe more informative, yet simple, visual representations of the data distribution commonly used in statistics and provide guidance with regards to the display of estimates of population parameters (e.g. population mean) and measures of precision (e.g. 95% confidence interval) for statistical inference. In this article we focus on visual displays for numerical data and demonstrate such displays using an example dataset consisting of total IgG titres in response to three Plasmodium blood antigens measured in pregnant women and parasitaemia measurements from the same study. This tutorial aims to highlight the importance of displaying the data distribution appropriately and the role such displays have in selecting statistics to summarize its distribution and perform statistical inference.

Objectives: The aim of this study was to analyze the EuroScan Database and to describe and compare the characteristics of the included technologies and participating agencies.

Methods: Data of interest were exported from the EuroScan Database to Excel and to SPSS. A descriptive analysis depending on the agency, type of technology, stage of diffusion, and technology purpose was conducted. A frequency distribution analysis of the diffusion stage for different technology types and assigned purposes was made with the EpiCalc 2000 statistical calculator. A p value of less than .05 was considered to be statistically significant.

Results: Four agencies introduced the great majority of the technologies (81 percent), with drugs representing the 46.26 percent of the total, followed by devices (21.21 percent). The purpose of 24.45 percent of the identified technologies was not specified, and 34.58 percent of them were identified at the investigational or phase III stage. The frequency distribution of diffusion stage at identification was found to be similar for devices and diagnostics (p = .543), whereas drugs were identified earlier than devices (p <.001). Some agencies were found to focus their work on drugs, whereas others focused mainly on devices. Interagency differences were also observed with regard to the stage of diffusion at which technologies were identified.

Conclusions: This is the first analysis of one of the most important databases on new and emerging health technologies. Our study suggests that more active strategies should be designed to provide an earlier identification, mainly in the case of devices.