3.2 Data collection and reporting

Data Sources and Surveillance Systems

Disease surveillance depends on collecting reliable data from multiple sources, then reporting it in ways that actually drive public health action. This section covers where surveillance data comes from, how cases get defined and counted, and what makes reporting effective.

Sources of Surveillance Data

No single data source gives you the full picture. Effective surveillance pulls from several streams, each with different strengths.

• Healthcare providers (hospitals, clinics, private practices) collect patient data during routine care. They're often the first to notice unusual clusters of illness, making them critical for early detection of disease trends.
• Laboratories confirm diagnoses and identify specific pathogens. Both clinical labs (serving individual patients) and public health labs (serving population-level needs) contribute. Lab data is especially important because it provides objective, pathogen-specific confirmation.
• Vital records document life events like births and deaths. Birth certificates can track congenital defects, while death certificates provide mortality data, including cause-of-death coding.
• Population surveys gather health information directly from communities. For example, the Behavioral Risk Factor Surveillance System (BRFSS) surveys adults about lifestyle choices like smoking, physical activity, and diet.
• Administrative data reveals healthcare utilization patterns. Health insurance claims and pharmacy records can show trends in medication use or healthcare-seeking behavior across large populations.
• Environmental monitoring tracks exposures that affect health, such as water quality testing or air pollution measurements. These data help link environmental hazards to disease patterns.
• Animal health data supports the One Health approach, which recognizes that human, animal, and environmental health are interconnected. Veterinary reports and wildlife surveillance help detect zoonotic diseases (those that spread from animals to humans) before they reach human populations.

Case Definitions and Ascertainment

A case definition is a standardized set of criteria used to decide whether a person has a particular disease for surveillance purposes. Without one, different jurisdictions would count cases differently, making comparisons meaningless.

Case definitions typically include three types of criteria:

• Clinical criteria (signs and symptoms)
• Laboratory criteria (test results confirming the pathogen)
• Epidemiological criteria (exposure history, such as contact with a known case or travel to an affected area)

Based on how many criteria are met, cases are classified at different levels of certainty:

• Suspected case: meets minimal criteria (e.g., compatible symptoms but no lab confirmation)
• Probable case: meets clinical criteria plus has an epidemiological link, but still lacks lab confirmation
• Confirmed case: meets definitive evidence, usually including laboratory confirmation

Case ascertainment is the process of actually finding and counting cases. There are two main approaches:

• Active surveillance: public health staff proactively seek out cases by contacting providers, reviewing records, or conducting screenings. More resource-intensive but more complete.
• Passive surveillance: relies on healthcare providers and labs to report cases to health authorities. Less expensive but tends to undercount because not all cases get reported.

The ascertainment process follows these steps:

1. Identify potential cases through one or more data sources
2. Apply the case definition criteria to each potential case
3. Verify and validate the information (e.g., confirm lab results, rule out duplicates)
4. Classify each case by certainty level (suspected, probable, confirmed)

Data Management and Reporting

Data Quality

Surveillance data is only useful if it's trustworthy. Four components define data quality:

• Accuracy: the data correctly reflects the true situation
• Timeliness: data arrives fast enough to act on it
• Completeness: all required fields are filled in and all cases are captured
• Consistency: the same criteria and methods are applied across reporters and over time

Poor data quality has real consequences. Inaccurate data can lead to misinterpreted trends, delayed outbreak detection, and misallocated resources. If half the reporting forms are missing key fields, you can't reliably track whether a disease is increasing or decreasing.

Strategies for improving data quality include using standardized reporting forms, training healthcare workers on proper reporting procedures, conducting regular data audits, and transitioning to electronic reporting systems that can flag errors automatically. Completeness should be assessed regularly by evaluating how often required fields are filled in, following up on missing data, and analyzing gaps to improve coverage.

Principles of Data Reporting

Surveillance data flows through multiple levels of the public health system: local health departments report to state agencies, which report to national bodies (like the CDC in the U.S.), which may report to international organizations (like the WHO).

Reporting timeframes depend on urgency:

• Immediate reporting (often within 24 hours) is required for urgent conditions like measles outbreaks or cases of bioterrorism agents
• Routine reporting (weekly, monthly, or quarterly) applies to non-urgent but notifiable conditions

Reporting methods range from electronic systems (the current standard for speed and accuracy) to phone, fax, or mail for settings where electronic infrastructure isn't available.

Once data is collected, analysis uses descriptive epidemiology to characterize cases by person (who is affected?), place (where are cases occurring?), and time (when did cases appear, and is the trend increasing?). Statistical methods help identify significant patterns and trends.

Dissemination means getting the findings to the people who need them. Formats include surveillance reports, epidemiological bulletins, press releases for the public, and peer-reviewed publications. Effective dissemination follows four principles:

• Timeliness: information reaches decision-makers while it's still actionable
• Accessibility: reports are understandable to their intended audience
• Confidentiality: individual patient data is protected
• Transparency: methods and limitations are clearly stated so audiences can interpret findings appropriately