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Automating incidence and prevalence analysis in open cohorts
Neil Cockburn1, Ben Hammond2, Illin Gani2
1Institute of Applied Health Research, University of Birmingham, Birmingham, West Midlands, UK. n.cockburn@bham.ac.uk.
Automated methods for analyzing public health data improve transparency and reproducibility. These tools streamline incidence and prevalence calculations in electronic health records, enhancing research productivity.
Area of Science:
- Public Health Informatics
- Health Data Science
Background:
- Administrative data, including electronic health records, are crucial for public health research.
- Open-cohort datasets present challenges for analyzing incidence and prevalence due to non-uniform patient entry/exit.
- Existing methods can be time-consuming and lead to analytical variations.
Purpose of the Study:
- To develop automated methods for analyzing incidence and prevalence in open cohort datasets.
- To enhance transparency, productivity, and reproducibility in public health data analysis.
Main Methods:
- A code-free ruleset for incidence and prevalence applicable to any open cohort.
- A Python Command Line Interface (CLI) implementation of these rules (Python 3.9+).
Main Results:
- The CLI enables calculation of incidence and point prevalence time series from open cohort data.
- The ruleset is adaptable for other implementations and analytical questions, such as period prevalence.
Conclusions:
- Automated analysis of incidence and prevalence in public health data is feasible and beneficial.
- The developed methods and tools offer a reproducible and efficient approach to analyzing open cohort data.
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