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Using Routine Data Sources to Feed an Immunization Information System for High-Risk Patients-A Pilot Study.

Domenico Martinelli1, Francesca Fortunato1, Stefania Iannazzo2

  • 1Department of Medical and Surgical Sciences, University of Foggia, Foggia, Italy.

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|March 6, 2018
PubMed
Summary

This study developed an automated method to identify high-risk patients for vaccinations. The approach successfully identified individuals with chronic diseases eligible for immunizations, improving public health efforts.

Keywords:
chronic illnesscomorbid disordersdata-linkagehigh-risk patientsimmunization information systemunderlying medical conditionsvaccination

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Area of Science:

  • Public Health
  • Health Informatics
  • Epidemiology

Background:

  • Vaccine-preventable diseases pose a significant public health challenge, particularly for high-risk populations.
  • National immunization programs aim to vaccinate these groups, but they are often hard-to-reach, leading to poor coverage data.
  • An automated approach was piloted to identify individuals with underlying medical conditions for immunization information systems (IIS).

Purpose of the Study:

  • To develop and test an automated method for identifying high-risk patients eligible for specific vaccinations.
  • To improve the data quality within a regional immunization information system (IIS).

Main Methods:

  • Reviewed vaccination recommendations for influenza, pneumococcal, meningococcal, hepatitis A, and B.
  • Mapped medical conditions to ICD-9-CM codes, user fee exempt codes, and ATC codes.
  • Extracted patient data from hospital discharge, user fee exempt, and drug prescription registries (2001-2010).
  • Linked registry data to the regional IIS using unique personal identification numbers.
  • Validated the approach by comparing identified patients with general practitioners' (GPs) records for influenza vaccination eligibility.

Main Results:

  • A total of 1,204,496 subjects with underlying medical conditions eligible for vaccination were identified.
  • Patients were identified across one (73%), two (18%), or all three (9%) data sources.
  • The automated process demonstrated high completeness (88.9%) in identifying high-risk patients for influenza vaccination.
  • Sensitivity was 69.2% and positive predictive value (PPV) was 85.7% in GP record validation.

Conclusions:

  • The developed automated methodology effectively identifies high-risk patients from existing data sources.
  • The high completeness and accuracy support its application for populating the regional IIS.
  • This approach can enhance vaccination program targeting and improve public health outcomes.