Important Sources of Epidemiological Data

Epidemiology is the study of incidences, distribution, and control of diseases and health-related factors. Epidemiology is divided into descriptive and analytical epidemiology. Descriptive epidemiology characterizes the disease’s distribution in terms of time, person, and place within a population. As the name suggests, descriptive epidemiology describes the disease and health-related patterns without interfering with them.

They present raw data as it is. Analytical epidemiology tests hypotheses to test causal relationships between a risk factor or causative agent and a disease. There are primary and secondary sources of epidemiological data are the focus of this discussion.

Primary data sources are data collected by a researcher for a specific purpose. For example, a researcher can go to a community and test them for a disease such as COVID-19 to determine prevalence and incidence rates (Park et al., 2019). Such data includes clinical trials, case studies, and cohort studies. They are expensive and time-consuming and may require more time than available.

Groenewegen et al. (2020) show that primary data sources such as cohort studies produce the most reliable data. Researchers prepare research studies and implement them to produce analytical data and make inferences from the data. Primary data sources are used in the absence of secondary epidemiological data sources, which are data collected by other individuals and organizations but are relevant to the issue of interest.

Secondary sources of data are data that are already collected but for different reasons. Data is passively collected and analyzed daily in healthcare institutions. Data is passively reported from hospital registers such as birth and death registers, antenatal and postnatal clinic registers, and hospital admission registers (Wu et al., 2019). Therese registers, in addition to population census, public health department case reports, and surveys, are vital sources of secondary epidemiological data.

Secondary sources can be used singly or collectively to derive epidemiological data (Park et al., 2019). For example, a researcher can study case reports, incidence reports, and death certificates from various healthcare institutions to understand cancer epidemiology. A researcher wishing to understand perinatal mortality will consult antenatal registers, birth, and postnatal registers. Secondary data is less accurate compared to primary data but less tiresome and costly.

Tertiary sources of epidemiologic data include analysis of other studies. Various organizations produce epidemiologic data, such as the world health organization, the center for disease control and prevention, and the agency for healthcare research and quality. In addition, government websites contain vital data from various sources such as censuses and studies conducted. They collect and analyze primary and secondary data to produce reliable and comprehensive reports.

Analysis of epidemiologic data from these organizations is one of the reliable and essential sources of epidemiologic data (Baardman & Bolling, 2019). Individuals or researchers who wish to understand the epidemiology of a particular disease can rely on such websites to get reliable and accurate data. These organizations collaborate with institutions with large laboratories and research facilities that carry out studies that produce reliable data. They also fund research and collaborate with local and federal governments to produce data; hence they are some of the most critical data sources.


Primary, secondary, and tertiary sources of epidemiologic data are integral to healthcare problems. Understanding these data sources ensures that a researcher gets reliable, compatible, and representative information. The information then helps make vital decisions that produce the desired outcomes of disease surveillance and control.


Baardman, R., & Bolling, M. C. (2022). The importance of accurate epidemiological data of epidermolysis bullosa. The British Journal of Dermatology186(5), 765.

Wu, Y., Yang, Y., Nishiura, H., & Saitoh, M. (2018, June). Deep learning for epidemiological predictions. The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval (pp. 1085-1088).

Park, M., Cook, A. R., Lim, J. T., Sun, Y., & Dickens, B. L. (2020). A systematic review of COVID-19 epidemiology based on current evidence. Journal of Clinical Medicine9(4), 967.

Groenewegen, A., Rutten, F. H., Mosterd, A., & Hoes, A. W. (2020). Epidemiology of heart failure. European Journal Of Heart Failure22(8), 1342-1356.