Related Experiment Video
Updated: Feb 28, 2026
Watershed Planning within a Quantitative Scenario Analysis Framework
Published on: July 24, 2016
Building Data and Information Capacity in Environmental Public Health: A Best-Worst Scaling Experiment
Lauren E Wallar1, Jan M Sargeant, Scott A McEwen
1Centre for Public Health and Zoonoses (Dr Sargeant) and Department of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, Ontario, Canada (Drs Wallar, Sargeant, McEwen, and Papadopoulos); and Wellington-Dufferin-Guelph Public Health, Guelph, Ontario, Canada (Dr Mercer).
Context:
Environmental public health practitioners rely on information technology (IT) to maintain and improve environmental health. However, current systems have limited capacity. A better understanding of the importance of IT features is needed to enhance data and information capacity.
Objective:
(1) Rank IT features according to the percentage of respondents who rated them as essential to an information management system and (2) quantify the relative importance of a subset of these features using best-worst scaling.
Design:
Information technology features were initially identified from a previously published systematic review of software evaluation criteria and a list of software options from a private corporation specializing in inspection software. Duplicates and features unrelated to environmental public health were removed. The condensed list was refined by a working group of environmental public health management to a final list of 57 IT features. The essentialness of features was electronically rated by environmental public health managers. Features where 50% to 80% of respondents rated them as essential (n = 26) were subsequently evaluated using best-worst scaling.
Setting:
Ontario, Canada.
Participants:
Environmental public health professionals in local public health.
Main Outcome Measure:
Importance scores of IT features.
Results:
The majority of IT features (47/57) were considered essential to an information management system by at least half of the respondents (n = 52). The highest-rated features were delivery to printer, software encryption capability, and software maintenance services. Of the 26 features evaluated in the best-worst scaling exercise, the most important features were orientation to all practice areas, off-line capability, and ability to view past inspection reports and results.
Conclusions:
The development of a single, unified environmental public health information management system that fulfills the reporting and functionality needs of system users is recommended. This system should be implemented by all public health units to support data and information capacity in local environmental public health. This study can be used to guide vendor evaluation, negotiation, and selection in local environmental public health, and provides an example of academia-practice partnerships and the use of best-worst scaling in public health research.
Related Concept Videos
01:18Steps in Outbreak Investigation
01:09Censoring Survival Data
01:25Statistical Methods for Analyzing Epidemiological Data
01:13Data Collection by Experiments
An example of the experimental method is a public...
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
00:55Ordinal Level of Measurement
Data measured using an ordinal scale are similar to nominal scale data, but there is one major difference. The ordinal scale data can be ordered. An example of ordinal scale data is a list of the top five national parks...