3.4 Case-Finding

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Potential Data Sources

Possible responses:

  • Delivery units
  • Paediatric, neurosurgery, orthopaedics units
  • Surgery units
  • Vital records (birth and death certificates)
  • Neonatal care units
  • Pathology units

Using multiple data sources, such as data from units other than the delivery unit, may improve data quality by increasing the amount and level of information available for diagnoses and case detection.

Possible responses:

  • Surgical hospitals or any other hospital that provides treatment services
  • Treatment clinics
  • Rehabilitation clinics
  • Genetic laboratories
  • Clinics
  • Foundations and associations that help children born with congenital anomalies
  • Health insurance companies’ databases
  • Vital records (birth and death certificates)
  • Health information systems
  • Hospital reporting systems
  • Pathology units

Group Discussion 3.2

Fig. 3.1. Prevalence of neural tube defects by source

NTD: neural tube defects.
Source: Birth Defects Surveillance Programme Puerto Rico Department of Health, and Auxiliary Secretariat for Planning and Development, San Juan, Puerto Rico.

Possible responses:

  • This example demonstrates how using multiple data sources increased the case detection compared to only using vital records.
  • It shows 16 years of vital records data and 10 years of data from multiple sources.
  • The data are for neural tube defects.
  • The birth prevalence of neural tube defects has declined over time.
  • Multiple sources identify more cases with neural tube defects.
  • Vital records usually include data at birth and multiple sources may include other age cut-off values that allow for identification of cases later in life.

Table of Contents

  1. Module 3: Introduction to Surveillance Approaches
  2. 3.1 Epidemiology
  3. 3.2 Population Coverage
  4. 3.3 Case Ascertainment
  5. ›3.4 Case-Finding
  6. 3.5 Case Inclusion
  7. 3.6 Inclusion Criteria
  8. 3.7 Inclusion of Pregnancy Outcomes
  9. 3.8 Description Formats for Congenital Anomalies
  10. 3.9 Core Ascertainment Variables
  11. 3.10 Data-Collection Methods and Tools
  12. 3.11 Data Collection/Management
  13. 3.12 Data-Management Protocol
  14. 3.13 Data Analysis
  15. 3.14 Data Dissemination
  16. 3.15 Evaluation Questions 3