Evaluation of the collaborative integrated surveillance system (ViCo) in Guatemala: a qualitative study on lessons learned and future perspectives

Abstract

Background The collaborative integrated surveillance system known as Vigilancia Integrada Comunitaria (ViCo) was implemented in 2007 to better understand and characterize the burden of diarrheal, respiratory and febrile illnesses in Guatemala. Methods To evaluate the usefulness of ViCo and inform a redesign of the system and new surveillance activities in the Central American region, personnel from the United States Centers for Disease Control and Prevention (CDC) conducted thirty-nine in-depth interviews from June—December 2018 with key stakeholders responsible for the design and implementation of ViCo in Guatemala. A semi-structured questionnaire adapted from the Updated CDC Guidelines for Evaluating Public Health Surveillance Systems was used for data collection. We used a grounded theory approach to explore stakeholder perceptions of ViCo and generate recommendations for improvement. Primary qualitative findings were organized based on thematic areas using ATLAS.ti version 8 software. Results Emergent themes relevant to the usefulness of ViCo were organized across strengths, weaknesses, and recommendations pertaining to the: (1) Size and Complexity of ViCo, (2) Stakeholder Expectations About the Objectives of ViCo, (3) Data Management and Structure of the Information System, (4) Local Control of Data, (5) Integration of ViCo within the Ministry of Health, and, (6) Improvement of the Operational and Design Aspects of ViCo across System, Process, and Output levels. Conclusions Stakeholders perceived ViCo to be useful. They recommended measures to improve system performance and quality, including simplifying the surveillance system, routine data analysis and feedback, and channeling efforts towards integrating surveillance data into the national health information system. To create a well-performing surveillance system and achieve the intended objective of surveillance for public health action, ongoing evaluation and assessment of surveillance activities are necessary.

Data collection and sharing for pathogen surveillance: Making sense of a fragmented global system

“…Even where data is available across regions and over time, changes in case definition or surveillance systems over time could present challenges in generating reliable, compatible and comparable data [8,31,32]. Along with making data linkages more difficult, this means that additional resources are then required to clean and merge datasets.…”

“…For instance, initiatives such as the Early Warning, Alert and Response System and the Global Polio Eradication Initiative experienced technical difficulties related to the lack of an electronic data management system, or constraints related to technical capability or infrastructure to analyse and disseminate data [5,8,17,18,22,23]. Some systems for pathogen surveillance are more difficult to maintain than others -one article discussing challenges relating to an integrated pathogen surveillance system in Guatemala, for example, cited the size and complexity of the system (in terms of the number of different data items collected and the number of stakeholders involved in data management) as a factor that made the system more difficult to manage [31]. This indicates that although more complicated systems can provide more comprehensive surveillance, there is a need to ensure that there is sufficient capacity to manage the data.…”

“…Where initiatives are able to provide support to partner laboratories and other stakeholders, this has the potential to improve data quality and consistency [5,31,42]. Initiatives such as the EU Sewage Sentinel System and the Global Antimicrobial Resistance and Use Surveillance System provide training material, guidance on best practice, protocols and workflow guidelines to data collection sites [6,12,42,43].…”

“…However, at least 13 initiatives were reported to be limited by a lack of timely collection and sharing, including large international initiatives such as the Global Polio Laboratory Network and AusTrakka, along with national systems in Guatemala and Yemen [2,3,23,31]. For example, the system in Yemen relied on manual data entry and analysis [2], which can cause delays in tracking diseases, along with errors in data entry.…”