Data Collection
Population Health Monitoring: Climbing the Information Pyramid, Page: 59-81
2018
- 1Citations
- 21Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Book Chapter Description
As described in the previous chapters, representative and high-quality data are essential for effective health information systems and monitoring strategies. Population health monitoring covers topics in the full range of human health and its influencing factors, and these domains are typically structured into conceptual frameworks and described in terms of indicators. These indicators can be derived from several different types of data sources, such as surveys, registers, and clinical and social epidemiological studies. The aim of this chapter is to provide insight into the various types of data sources available for public health monitoring purposes, their characteristics, specific applications, potential and limitations. The main focus of the chapter will be on the two major types of data sources used for population health monitoring: health surveys and registers. The main causes of bias, influencing data quality and validity, and issues with data access and linkage are addressed as the most important factors limiting the usability of data. The role of data protection and data governance in this is explored. The chapter will conclude with an overview of the most important current and expected future developments in the field of health-related data collection.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85090376917&origin=inward; http://dx.doi.org/10.1007/978-3-319-76562-4_4; http://link.springer.com/10.1007/978-3-319-76562-4_4; http://link.springer.com/content/pdf/10.1007/978-3-319-76562-4_4; https://dx.doi.org/10.1007/978-3-319-76562-4_4; https://link.springer.com/chapter/10.1007/978-3-319-76562-4_4
Springer Nature
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