Building Data Triangulation Capacity for Routine Immunization and Vaccine Preventable Disease Surveillance Programs to Identify Immunization Coverage Inequities

Author:

Rachlin Audrey1ORCID,Adegoke Oluwasegun Joel1,Bohara Rajendra2,Rwagasore Edson3,Sibomana Hassan3,Kabeja Adeline3,Itanga Ines3,Rwunganira Samuel4,Mafende Mario Blaise5ORCID,Rosette Nahimana Marie6ORCID,Usman Obansa Ramatu7,Abah Angela Ukpojo7ORCID,Adeoye Olorunsogo Bidemi8,Sikare Ester1,Lam Eugene1,Murrill Christopher S.1,Montesanti Porter Angela1

Affiliation:

1. Global Immunization Division, Centers for Disease Control and Prevention, Atlanta, GA 30333, USA

2. World Health Organization, Dhaka 1212, Bangladesh

3. Rwanda Biomedical Centre, Ministry of Health, Kigali P.O. Box 7162, Rwanda

4. African Field Epidemiology Network, Kigali, Rwanda

5. Health Information Systems Program (HISP), Kigali, Rwanda

6. World Health Organization, Kigali P.O. Box 1324, Rwanda

7. National Stop Transmission of Polio (NSTOP) Program, African Field Epidemiology Network (AFENET), Abuja 900103, Nigeria

8. Division of Global Health Protection, Centers for Disease Control and Prevention, Atlanta, GA 30329, USA

Abstract

The Expanded Programme on Immunization (EPI) and Vaccine Preventable Disease (VPD) Surveillance (VPDS) programs generate multiple data sources (e.g., routine administrative data, VPD case data, and coverage surveys). However, there are challenges with the use of these siloed data for programmatic decision-making, including poor data accessibility and lack of timely analysis, contributing to missed vaccinations, immunity gaps, and, consequently, VPD outbreaks in populations with limited access to immunization and basic healthcare services. Data triangulation, or the integration of multiple data sources, can be used to improve the availability of key indicators for identifying immunization coverage gaps, under-immunized (UI) and un-immunized (zero-dose (ZD)) children, and for assessing program performance at all levels of the healthcare system. Here, we describe the data triangulation processes, prioritization of indicators, and capacity building efforts in Bangladesh, Nigeria, and Rwanda. We also describe the analyses used to generate meaningful data, key indicators used to identify immunization coverage inequities and performance gaps, and key lessons learned. Triangulation processes and lessons learned may be leveraged by other countries, potentially leading to programmatic changes that promote improved access and utilization of vaccination services through the identification of UI and ZD children.

Funder

Centers for Disease Control and Prevention

Publisher

MDPI AG

Reference30 articles.

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2. Improving the quality and use of immunization and surveillance data: Summary report of the Working Group of the Strategic Advisory Group of Experts on Immunization;Scobie;Vaccine,2020

3. World Health Organization (2019). Meeting of the Strategic Advisory Group of Experts on Immunization, April 2019—Conclusions and Recommendations. Wkly. Epidemiol. Rec., 94, 261–280. Available online: https://www.who.int/publications/i/item/WER9422.

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5. World Health Organization (2014). Univeral Health Coverage (UHC), World Health Organization. Available online: https://www.who.int/data/monitoring-universal-health-coverage.

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