In this presentation, I examine the impacts on child health, using diarrhea as the health outcome, (among children living in households) with access to different types of water and sanitation facilities, and from socioeconomic and child specific factors. Using multiyear cross-sectional health DHS data, I employ the quasi-experimental estimators (matching) to match children belonging to different treatment groups, defined by water types and sanitation facilities, with children in a control group. Quantile regression models are used to benchmark results and to check for their robustness. The empirical framework yields strong support that access to improved sanitation has had a substantial impact on reducing the (predicted) diarrhea outcomes. This is especially true among very young children defined as those below 24 months of age whose rates of diarrhea have shown the largest declines between 2001 and 2006. These estimates serve as an input into cost-effectiveness analysis that compares the provision of increased access to sanitation with other public health interventions in developing nations (especially sub-Saharan Africa and South Asia) to underline its importance in achieving Millennium Development Goals (MDGs).

Meta-epidemiology is the use of the characteristics of individual reports of randomized trials to examine the effects on meta-analyses. Traditionally only one meta-analysis from a systematic review is included in these studies, due to a fear of what would happen because of the lack of independence if the same study was included in more than one meta-analysis.

We have some data on 64 meta-analyses but these come from only 18 systematic reviews. Papers submitted from this data have been heavily criticized because of the feared effects of this lack of independence. One suggestion for a sensitivity analysis is to randomly select one meta-analysis from each systematic review. As an extension to that I chose to bootstrap the results of interest to examine the effects of the lack of independence. Stata makes these analyses trivial. This talk will present the results of two such sensitivity analyses and show that the lack of independence appears to have little effect on the interpretation of the results.

Meta-analysis is a rapidly expanding area of research that has been relatively underutilized in animal and veterinary science. It is a quantitative, formal, epidemiological study design used to systematically assess previous research studies to derive conclusions about that body of research. Outcomes from a meta-analysis may include a more precise estimate of the effect of treatment or risk factor for disease, or other outcomes, than any individual study contributing to the pooled analysis. The examination of variability or heterogeneity in study results is also a critical outcome. Examples where meta-analyses have been repeated in animal science or veterinary medicine show good consistency in estimates of effect. Rigorously conducted meta- analyses are useful tools to improve animal well-being and productivity. The need to integrate findings from many studies ensures that meta-analytic research is desirable and the large body of research now generated makes the conduct of this research feasible.

Many of the statistical methods to conduct meta-analysis are widely used. In this presentation, we will demonstrate how Stata can provide a comprehensive suite of programs that can be used in meta-analysis. Some detail on the common statistical methods used, such as metan and metareg, is presented and examples of when these have been used in studies using cattle are provided. The post-hoc methods used to evaluate heterogeneity and publication bias (metabias, confunnel>), which include the I2 statistic, L’Abb plots, Galbraith plots, Rosenthal’s N, and influential study analysis are exclusively used in meta-analysis.

itab.ado is a new command that provides a framework to users, which allows them to create formatted output tables in a flexible way. Tables can be created in an interactive manner by using the command line or by using do-files or ado-files. Each table can be fitted with the appropriate formatting so that it can be included into the final document straightaway. No matter which of the supported output formats is used, the command applies the same look and feel to any given table and as such provides a so lution to generate the same table for different output formats. The supported output formats are Excel, LaTeX, HTML, Comma-Separated Value (CSV) files, and SMCL. The command works well with other user written commands such as the popular outreg, outreg2, estout, and tabout commands. The output of these commands can be extended or modified, for example, by adding more information or by applying a different formatting.

Mail merge is a convenient way of getting data into a report. It allows data to be interspersed between normal report texts. Graphs and table data can also be input with mail merge.

Where standard reports are required on a regular basis or large numbers of reports are required, automating the reporting process saves time and reduces the chance of errors.

Stata and Mata can generate an output that mail merge can read. This will be demonstrated using a simple example. The commands and process of doing this with Mata will be explained.

Statistical agencies are increasingly recognizing the value of configuring their data in formats that facilitate geovisualization—the representation of data across the geographic domain. For Stata users this poses a challenge because the present geovisualization capacity within the conventional Stata product is quite limited.

This presentation reports on a project completed for Statistics New Zealand on Geovisualization where graphical tools from Stata were complemented by the geovisualization capacity afforded by GeoViz. The presentation illustrates the returns to geovisualization via a specific case study and considers the advantages that could potentially accrue to Stata users if such a capacity was provided within the Stata system itself.