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Surveillance to improve evidence for community control decisions during the COVID-19 pandemic - Opening the animal epidemic toolbox for Public Health.

ABSTRACT: During the first few months of 2020, the COVID-19 pandemic reached Europe and spread around the world. Health systems all over the world are trying to control the outbreak in the shortest possible time. Exotic disease outbreaks are not uncommon in animal health and randomised surveillance is frequently used as support for decision-making. This editorial discusses the possibilities of practicing One Health, by using methods from animal health to enhance surveillance for COVID-19 to provide an evidence base fort decision-making in communities and countries.

SUBMITTER: Foddai A

PROVIDER: S-EPMC7104048 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Surveillance to improve evidence for community control decisions during the COVID-19 pandemic - Opening the animal epidemic toolbox for Public Health.

Foddai Alessandro A Lindberg Ann A Lubroth Juan J Ellis-Iversen Johanne J

One health (Amsterdam, Netherlands) 20200327

During the first few months of 2020, the COVID-19 pandemic reached Europe and spread around the world. Health systems all over the world are trying to control the outbreak in the shortest possible time. Exotic disease outbreaks are not uncommon in animal health and randomised surveillance is frequently used as support for decision-making. This editorial discusses the possibilities of practicing One Health, by using methods from animal health to enhance surveillance for COVID-19 to provide an evi ...[more]

PMID: 32292816

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Project description:BackgroundDemand for high-quality surveillance data for malaria, and other diseases, is greater than ever before. In Uganda, the primary source of malaria surveillance data is the Health Management Information System (HMIS). However, HMIS data may be incomplete, inaccurate or delayed. Collaborative improvement (CI) is a quality improvement intervention developed in high-income countries, which has been advocated for low-resource settings. In Kayunga, Uganda, a pilot study of CI was conducted in five public health centres, documenting a positive effect on the quality of HMIS and malaria surveillance data. A qualitative evaluation was conducted concurrently to investigate the mechanisms of effect and unintended consequences of the intervention, aiming to inform future implementation of CI.MethodsThe study intervention targeted health workers, including brief in-service training, plus CI with 'plan-do-study-act' (PDSA) cycles emphasizing self-reflection and group action, periodic learning sessions, and coaching from a CI mentor. Health workers collected data on standard HMIS out-patient registers. The qualitative evaluation (July 2015 to September 2016) included ethnographic observations at each health centre (over 12-14 weeks), in-depth interviews with health workers and stakeholders (n = 20), and focus group discussions with health workers (n = 6).ResultsThe results suggest that the intervention did facilitate improvement in data quality, but through unexpected mechanisms. The CI intervention was implemented as planned, but the PDSA cycles were driven largely by the CI mentor, not the health workers. In this context, characterized by a rigid hierarchy within the health system of limited culture of self-reflection and inadequate training and supervision, CI became an effective form of high-quality training with frequent supervisory visits. Health workers appeared motivated to improve data collection habits by their loyalty to the CI mentor and the potential for economic benefits, rather than a desire for self-improvement.ConclusionsCI is a promising method of quality improvement and could have a positive impact on malaria surveillance data. However, successful scale-up of CI in similar settings may require deployment of highly skilled mentors. Further research, focusing on the effectiveness of 'real world' mentors using robust study designs, will be required to determine whether CI can be translated effectively and sustainably to low-resource settings.

Project description:Epidemic Intelligence (EI) encompasses all activities related to early identification, verification, analysis, assessment, and investigation of health threats. It integrates an indicator-based (IBS) component using systematically collected surveillance data, and an event-based component (EBS), using non-official, non-verified, non-structured data from multiple sources. We described current EI practices in Europe by conducting a survey of national Public Health (PH) and Animal Health (AH) agencies. We included generic questions on the structure, mandate and scope of the institute, on the existence and coordination of EI activities, followed by a section where respondents provided a description of EI activities for three diseases out of seven disease models. Out of 81 gatekeeper agencies from 41 countries contacted, 34 agencies (42%) from 26 (63%) different countries responded, out of which, 32 conducted EI activities. Less than half (15/32; 47%) had teams dedicated to EI activities and 56% (18/34) had Standard Operating Procedures (SOPs) in place. On a national level, a combination of IBS and EBS was the most common data source. Most respondents monitored the epidemiological situation in bordering countries, the rest of Europe and the world. EI systems were heterogeneous across countries and diseases. National IBS activities strongly relied on mandatory laboratory-based surveillance systems. The collection, analysis and interpretation of IBS information was performed manually for most disease models. Depending on the disease, some respondents did not have any EBS activity. Most respondents conducted signal assessment manually through expert review. Cross-sectoral collaboration was heterogeneous. More than half of the responding institutes collaborated on various levels (data sharing, communication, etc.) with neighbouring countries and/or international structures, across most disease models. Our findings emphasise a notable engagement in EI activities across PH and AH institutes of Europe, but opportunities exist for better integration, standardisation, and automatization of these efforts. A strong reliance on traditional IBS and laboratory-based surveillance systems, emphasises the key role of in-country laboratories networks. EI activities may benefit particularly from investments in cross-border collaboration, the development of methods that can automatise signal assessment in both IBS and EBS data, as well as further investments in the collection of EBS data beyond scientific literature and mainstream media.

Dataset Information

Surveillance to improve evidence for community control decisions during the COVID-19 pandemic - Opening the animal epidemic toolbox for Public Health.

Publications

Surveillance to improve evidence for community control decisions during the COVID-19 pandemic - Opening the animal epidemic toolbox for Public Health.

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