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Quantifying the Trendiness of Trends.
Andreas Kryger Jensen1, Claus Thorn Ekstrøm1
1Biostatistics, Institute of Public Health University of Copenhagen, Copenhagen, Denmark.
Summary
Quantifying trendiness in public health data is crucial. New indices, the Trend Direction Index and Expected Trend Instability, measure changes in trends, aiding analysis of health outcomes like smoking rates and COVID-19 cases.
Area of Science:
- Statistics
- Epidemiology
- Public Health
Background:
- News media frequently report changes in public health trends based on longitudinal data.
- These reported trend changes, often occurring at the most recent data points, can influence national public health decisions.
Purpose of the Study:
- To propose novel statistical measures for quantifying the "trendiness" of trends in public health data.
- To introduce a probabilistic Trend Direction Index and an Expected Trend Instability index.
Main Methods:
- Defining trends and trend changes in continuous time.
- Developing a probabilistic Trend Direction Index (probability of monotonicity change).
- Defining an Expected Trend Instability index (expected number of trend changes).
- Utilizing a latent Gaussian process model within a Bayesian framework for estimation.
Main Results:
- Demonstrated estimation of the Trend Direction Index and Expected Trend Instability.
- Applied the methods to analyze smoking proportions in Denmark over 20 years.
- Analyzed the development of new COVID-19 cases in Italy from February 24th onwards.
Conclusions:
- The proposed indices offer a quantitative approach to assessing trend changes in public health data.
- These methods provide valuable tools for analyzing complex health dynamics and informing public health strategies.
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