3.1 Public health surveillance systems

Public health surveillance systems are the backbone of monitoring and responding to health threats. They collect, analyze, and interpret data on diseases, injuries, and risk factors to guide public health action. These systems detect outbreaks, track trends, and measure the impact of interventions.

Surveillance data comes from various sources like disease reports, medical records, and surveys. It's analyzed to spot patterns in time, place, and person. Good data quality is crucial for timely and accurate insights. Surveillance informs emergency response and helps target interventions to protect public health.

Public health surveillance systems

Components and functions

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• Public health surveillance is the ongoing, systematic collection, analysis, and interpretation of health-related data essential to planning, implementation, and evaluation of public health practice
• The main functions of public health surveillance systems:
  • Detect and monitor health events
  • Provide early warning of public health emergencies
  • Document the impact of interventions or track progress towards goals
  • Serve as a foundation for epidemiologic research
• Key components of surveillance systems:
  • Clear objectives
  • Case definitions
  • Data sources
  • Data management
  • Analysis
  • Interpretation
  • Dissemination of findings
  • Evaluation

Types of surveillance systems

• Surveillance systems can be passive, relying on reporting from healthcare providers or laboratories, or active, involving outreach by public health to stimulate reporting
• Sentinel surveillance focuses on a pre-arranged sample of reporting sources to monitor specific health events (influenza-like illness)
• Syndromic surveillance uses clinical data about symptoms to detect outbreaks early before diagnoses are confirmed (emergency department visits for respiratory symptoms)

Surveillance data types

Data collected and analyzed

• Surveillance systems can collect data on:
  • Infectious diseases (COVID-19, measles)
  • Chronic diseases and conditions (diabetes, cancer)
  • Injuries (motor vehicle crashes, falls)
  • Exposures to health threats (lead poisoning, foodborne illness)
  • Risk factors (smoking, obesity)
  • Other health-related events (birth defects, drug overdoses)
• Data collected may include:
  • Demographic information (age, sex, race/ethnicity)
  • Clinical information (signs, symptoms, laboratory results, diagnosis)
  • Exposures (travel history, animal contact)
  • Risk factors (underlying medical conditions, vaccination status)
  • Treatment (medications, procedures)
• Data sources can include:
  • Notifiable disease reports
  • Electronic health records
  • Vital records (birth and death certificates)
  • Surveys (Behavioral Risk Factor Surveillance System)
  • Registries (cancer registries, immunization information systems)
  • Administrative data systems (hospital discharge data, insurance claims)

Epidemiologic data analysis

• Epidemiologic data analysis in surveillance examines time, place, and person to characterize health events and detect meaningful increases or clusters
  • Time: Temporal trends and seasonality (influenza activity peaking in winter months)
  • Place: Geographic distribution and clusters (mapping cases of Legionnaires' disease to identify a common exposure source)
  • Person: Demographic characteristics and risk factors (higher rates of sexually transmitted infections among adolescents and young adults)
• Advanced analytic methods in surveillance:
  • Statistical algorithms to detect aberrations in time-series data (CUSUM, EWMA)
  • Spatial analysis to detect geographic clusters (SaTScan)
  • Data mining to discover associations (market basket analysis)

Data quality in surveillance

Timeliness, accuracy, and completeness

• Timeliness in reporting and analyzing surveillance data is critical for early detection and rapid response to public health threats. Delays can impede control efforts (reporting of measles cases within 24 hours)
• Accuracy of surveillance data depends on correct diagnosis, laboratory testing, and case classification. Inaccurate data can lead to missed outbreaks or inappropriate responses (false-positive COVID-19 rapid antigen tests)
• Completeness of reporting by healthcare providers, facilities, and laboratories is necessary to ensure surveillance systems are sensitive enough to detect real problems. Incomplete reporting can cause underestimation of disease burden (underreporting of sexually transmitted infections due to stigma)

Data quality assurance

• Data quality monitoring and follow-up with reporting sources are important for ensuring timeliness, accuracy, and completeness
• Electronic reporting and data exchange improve timeliness and completeness but may pose challenges for accuracy if data are not validated (automated electronic laboratory reporting of positive COVID-19 tests)

Surveillance for public health threats

Preparedness and emergency response

• Surveillance systems are the foundation of public health preparedness and emergency response, providing situational awareness before, during, and after public health emergencies
• Surveillance can detect outbreaks of known and emerging infectious diseases, allowing rapid investigation and control measures to limit spread (early detection of Zika virus in the Americas)
• Syndromic surveillance can provide early warning of bioterrorism, chemical exposures, or other threats before diagnoses are confirmed, enabling a timely response (increased sales of over-the-counter antidiarrheal medications during a waterborne disease outbreak)

Informing public health interventions

• Surveillance data inform decision-making about public health interventions:
  • Vaccination campaigns (influenza, measles)
  • Health education (promoting handwashing during a norovirus outbreak)
  • Environmental remediation (lead abatement in housing)
• During an emergency response, ongoing surveillance is used to:
  • Track the progression of the outbreak or incident (monitoring case counts and geographic spread)
  • Monitor the effectiveness of control measures (decrease in new cases after implementing social distancing measures)
  • Guide resource allocation (deploying additional medical personnel to affected areas)
• After an emergency, surveillance is used to:
  • Identify long-term health consequences (monitoring for complications among COVID-19 survivors)
  • Support recovery efforts (mental health surveillance after a natural disaster)