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Related Concept Videos

Vaccinations01:51

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Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
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Articles linked to this work by shared authors, journal, and citation graph.

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Assessing vaccination coverage quality in Jamaica during COVID-19 using vcqiR: insights from the 2022 MICS.

Revista panamericana de salud publica = Pan American journal of public health·2026
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Correction: Loayza et al. Monitoring the Performance of National Immunization Programs: Innovative Methodology and Tool for Countries' Self-Assessment. <i>Vaccines</i> 2026, <i>14</i>, 258.

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Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
10:58

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Published on: January 2, 2011

Vaccination Coverage Quality Indicators (VCQI): a flexible open-source tool for survey data analysis.

Dale A Rhoda1, Caitlin B Clary1, Mary Kay Trimner1

  • 1Biostat Global Consulting Worthington, Ohio United States of America Biostat Global Consulting, Worthington, Ohio, United States of America.

Revista Panamericana De Salud Publica = Pan American Journal of Public Health
|July 14, 2026
PubMed
Summary

The Vaccination Coverage Quality Indicators (VCQI) software offers standardized analysis for vaccination coverage surveys. Now available as an R package (vcqiR), it enhances data interpretation for global health initiatives.

Keywords:
Surveys and questionnairesregistriessoftwarevaccination coverage

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Area of Science:

  • Public Health
  • Biostatistics
  • Epidemiology

Background:

  • Standardized analysis of vaccination coverage is crucial for global health.
  • Existing tools may lack flexibility or accessibility for diverse survey data.
  • The World Health Organization (WHO) and Pan American Health Organization (PAHO) developed VCQI to address these needs.

Purpose of the Study:

  • To introduce the Vaccination Coverage Quality Indicators (VCQI) software and its capabilities.
  • To announce the availability of vcqiR, an R package for VCQI.
  • To describe the vision, design, and future potential of standardized vaccination coverage analysis.

Main Methods:

  • VCQI analyzes data from child vaccination records and caregiver recall.
  • It calculates indicators for vaccination access, coverage, continuity, and quality.
  • The software supports various survey types (routine, post-campaign, maternal tetanus) and data sources (DHS, MICS, EPI).

Main Results:

  • VCQI provides output tables and figures for direct use in reports.
  • It augments survey datasets with child-level outcome indicator variables.
  • VCQI has been available as Stata scripts since 2015 and as an R package (vcqiR) since 2025.

Conclusions:

  • VCQI offers a flexible and standardized approach to analyzing vaccination coverage data.
  • The new vcqiR package expands accessibility for researchers and public health professionals.
  • Future development aims to include analysis of electronic immunization registries.