A systematic review of Demographic and Health Surveys: data availability and utilization for research.

OBJECTIVE: To systematically review the public health literature to assess trends in the use of Demographic and Health Survey (DHS) data for research from 1984 to 2010 and to describe the relationship between data availability and data utilization. METHODS: The MEASURE DHS web site was searched for information on all population-based surveys completed under the DHS project between 1984 and 2010. The information collected included the country, type of survey, survey design, fieldwork period and certain special features, such as inclusion of biomarkers. A search of PubMed was also conducted to identify peer-reviewed articles published during 2010 that analysed DHS data and included an English-language abstract. Trends in data availability and in the use of DHS data for research were assessed through descriptive, graphical and bivariate linear regression analyses. FINDINGS: In total, 236 household surveys under the DHS project were completed across 84 countries during 2010. The number of surveys per year has remained constant, although the scope of the survey questions has expanded. The inclusion criteria were met by 1117 peer-reviewed publications. The number of publications has increased progressively over the last quarter century, with an average annual increment of 4.3 (95% confidence interval, CI: 3.2-5.3) publications. Trends in the number of peer-reviewed publications based on the use of DHS data were highly correlated with trends in funding for health by the Government of the United States of America and globally. CONCLUSION: Published peer-reviewed articles analysing DHS data, which have increased progressively in number over the last quarter century, have made a substantial contribution to the public health evidence base in developing countries.

A regional approach to projecting land-use change and resulting ecological vulnerability.

This study explores ecological vulnerability to land-use change in the U.S. Mid-Atlantic Region by spatially extrapolating land and economic development, and overlaying these projections with maps of sensitive ecological resources. As individual extrapolations have a high degree of uncertainty, five methods with different theoretical bases are employed. Confidence in projections is increased for counties targeted by two or more projection methods. A county is considered at risk if it currently supports three or more sensitive resources, and is projected to experience significant growth by the year 2010 by two or more methods. Analysis designated 19 counties and two cities as at risk, highlighting within a large region the priority areas where state and regional efforts would contribute the most to integrating environmental considerations into the process of land development. The study also found that potentially severe ecological effects of future land-use change are not limited to the outskirts of major urban areas. Recreational demands on smaller communities with mountain and coastal resources are also significant, as are initiatives to promote economic development in rural areas of high ecological quality. This approach provides a comprehensive overview of potential regional development, leading to an objective prioritization of high-risk areas. The intent is to inform local planning and decision-making so that regional and cumulative ecological degradation are minimized.

Development of indicators to assess economic vulnerabilities to changes in ecosystem services: case study of counties in Maryland, USA.

We develop indicators showing the relative environmental burdens that human activities place on locales for a given level of economic benefits. The main purpose is to develop tools that allow us to examine the potential vulnerabilities within economies to changes in resource conditions. The indicators of pollution emission or resource consumption per job can be used to identify potential challenges to resource and industry managers and to compare areas in terms of their ability to adapt to change. For example, if a large number of area jobs are dependent on abundant water, this indicates a vulnerability to a reduction in water availability for industrial use. We develop a case study for 23 counties and 1 city in Maryland to examine the usefulness and limitations of the indicators. Our case study demonstrates that the indicators provide an informative view into patterns of local economic activity and use of an area's environmental goods and services. In contrast to patterns for total environmental burdens (e.g., total SO2 emissions) that are typically reported, the rates of environmental burden per job are not simply correlated with high or low economic output. Thus, the indicators represent distinct patterns of environmental burdens per job that reflect reliance on environmental services. The indicators have some limitations when used at this fine scale because they can misrepresent conditions in counties in which economic sectors are dominated by one or a few businesses. For this reason, the indicators are best used as a regional screening tool.

AgDRIFT: a model for estimating near-field spray drift from aerial applications.

The aerial spray prediction model AgDRIFT embodies the computational engine found in the near-wake Lagrangian model AGricultural DISPersal (AGDISP) but with several important features added that improve the speed and accuracy of its predictions. This article summarizes those changes, describes the overall analytical approach to the model, and details model implementation, application, limits, and computational utilities.

Evaluation of the AgDISP aerial spray algorithms in the AgDRIFT model.

A systematic evaluation of the AgDISP algorithms, which simulate off-site drift and deposition of aerially applied pesticides, contained in the AgDRIFT model was performed by comparing model simulations to field-trial data collected by the Spray Drift Task Force. Field-trial data used for model evaluation included 161 separate trials of typical agriculture aerial applications under a wide range of application and meteorological conditions. Input for model simulations included information on the aircraft and spray equipment, spray material, meteorology, and site geometry. The model input datasets were generated independently of the field deposition results, i.e., model inputs were in no way altered or selected to improve the fit of model output to field results. AgDRIFT shows a response similar to that of the field observations for many application variables (e.g., droplet size, application height, wind speed). However, AgDRIFT is sensitive to evaporative effects, and modeled deposition in the far-field responds to wet bulb depression whereas the field observations did not. The model tended to overpredict deposition rates relative to the field data for far-field distances, particularly under evaporative conditions. AgDRIFT was in good agreement with field results for estimating near-field buffer zones needed to manage human, crop, livestock, and ecological exposure.