Uses of Surveillance Data

Surveillance data are used in various ways to support public health functions :

Detection and monitoring of outbreaks

Surveillance data is essential for the early identification and monitoring of outbreaks. Field epidemiologists analyze the data to detect unusual patterns or increases in disease incidence, providing a basis for further investigation and intervention. Rapid and timely surveillance data analysis helps initiate prompt public health actions, such as containment measures, contact tracing, and resource allocation.

For example, in 2014, the early detection and monitoring of the Ebola outbreak in West Africa were facilitated through the use of surveillance data, which informed the global response to the crisis .

Identifying trends and patterns

Surveillance data enables epidemiologists to track disease incidence and prevalence changes over time. This information can be used to identify long-term trends, seasonal patterns, and geographic variations in the distribution of diseases. Understanding these patterns can inform public health priorities, allocate resources, and develop targeted interventions to address specific risk factors or vulnerable populations.

For example, analysis of surveillance data helps identify the seasonal patterns of influenza activity, which informs vaccination recommendations and preparedness efforts .

Hypothesis generation

The analysis of surveillance data can help epidemiologists formulate hypotheses about potential risk factors, transmission patterns, and the effectiveness of preventive measures. These hypotheses can then be tested through epidemiological studies and investigations, such as cohort studies, case-control studies, or randomized controlled trials. Surveillance data can also be used to identify potential confounders and effect modifiers, which are critical in understanding the causal relationships between risk factors and health outcomes.

For example, examining surveillance data from multiple countries showed that the incidence of cervical cancer was significantly higher in some populations compared to others. Researchers observed that the differences in incidence rates correlated with sexual behaviour and the prevalence of sexually transmitted infections (STIs) in those populations .. Based on this observation, Dr. zur Hausen and his team hypothesized that a sexually transmitted agent, potentially a virus, might be responsible for developing cervical cancer. Through further research, they discovered that HPV was present in a high percentage of cervical cancer cases, and they were able to identify specific high-risk HPV types (HPV 16 and 18) that were strongly associated with cervical cancer .

Evaluating interventions

Surveillance data is crucial for assessing the effectiveness of public health interventions, such as vaccination campaigns, disease control programs, or health promotion initiatives. Field epidemiologists can compare data before and after an intervention, enabling them to gauge its impact and guide future efforts. Surveillance data can also be used to monitor the progress of interventions in real time, allowing for timely adjustments or modifications as needed.

For example, routine surveillance data provided evidence that the HPV vaccination program effectively reduced the prevalence of HPV infections among young females in the United States .

Estimating disease burden

Surveillance data can be used to estimate the burden of a disease in a population, providing valuable information on morbidity, mortality, and disability. These estimates are crucial for informing public health policy, setting priorities, and allocating resources. Disease burden estimates can also be used to evaluate the cost-effectiveness of interventions and guide decision-making on allocating limited public health resources.

For example, data from cancer registries worldwide were used to estimate the incidence and mortality rates for 36 types of cancer in 185 countries. The surveillance data provided valuable insights into the changing landscape of cancer globally and helped identify cancer prevention and control priorities.

Identifying health disparities

Surveillance data can reveal disparities in disease burden and risk factors among different population subgroups, such as racial and ethnic minorities, socioeconomic groups, or specific age groups. Identifying and addressing these disparities is essential for promoting health equity and reducing the overall burden of disease in a population.

For example, analysis of routine HIV surveillance data in the United States identified significant disparities in HIV diagnosis rates among different racial and ethnic groups, leading to targeted interventions and policy changes to address these disparities .

Informing policy and practice

Surveillance data is vital for informing public health policy and practice. Field epidemiologists can use the data to advocate for evidence-based policies, such as tobacco control measures, food safety regulations, or vaccination requirements. Surveillance data can also be used to identify gaps in existing policies and practices, prompting the development of new strategies or the revision of current ones.

For example, surveillance data from the national electronic injury surveillance system (NEISS) in the United States, which monitors consumer product-related injuries, was used to inform policy changes and product safety standards to prevent injuries associated with all-terrain vehicles (ATVs) .