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Profile and dynamics of infectious diseases: a population-based observational study using multi-source big data.
Lin Zhao1,2, Hai-Tao Wang1, Run-Ze Ye1
1Institute of EcoHealth, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China.
BMC Infectious Diseases
|April 5, 2022
Summary
Big data analysis reveals a significant increase in infectious disease incidence in China, highlighting the importance of tracking non-notifiable diseases. This study provides a comprehensive overview of infectious disease dynamics.
Area of Science:
- Epidemiology
- Public Health
- Big Data Analytics
Background:
- Current infectious disease surveillance in China is limited to notifiable diseases.
- The big data era offers opportunities to study the full spectrum of infectious diseases.
Purpose of the Study:
- To estimate incidence density and describe epidemiological characteristics of infectious diseases in Shandong province.
- To utilize multi-source healthcare big data for comprehensive disease surveillance.
Main Methods:
- Population-based observational study using health data from January 2013 to June 2017.
- Analysis of data from 3,987,573 individuals in Shandong province, China.
- Estimation of incidence density and description of epidemiological dynamics for various infectious diseases.
Main Results:
- 106,289 cases of 130 infectious diseases identified, with an overall incidence density of 694.86 per 100,000 person-years.
- Significant increase in overall incidence density from 2013 to 2017 (P < 0.0001).
- Higher incidence density in urban vs. rural areas; specific age and gender groups showed higher risks for certain diseases (e.g., varicella, syphilis, zoster).
Conclusions:
- Infectious diseases pose a substantial public health challenge, including non-notifiable ones.
- Multi-source big data analytics are crucial for understanding the profile and dynamics of infectious diseases.
- Enhanced surveillance strategies are needed to address the full spectrum of infectious diseases.
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