Assessment of performance for a key indicator of One Health: evidence based on One Health index for zoonoses in Sub-Saharan Africa

Abstract

Background Zoonoses are public health threats that cause severe damage worldwide. Zoonoses constitute a key indicator of One Health (OH) and the OH approach is being applied for zoonosis control programmes of zoonotic diseases. In a very recent study, we developed an evaluation system for OH performance through the global OH index (GOHI). This study applied the GOHI to evaluate OH performance for zoonoses in sub-Saharan Africa. Methods The framework for the OH index on zoonoses (OHIZ) was constructed including five indicators, 15 subindicators and 28 datasets. Publicly available data were referenced to generate the OHIZ database which included both qualitative and quantitative indicators for all sub-Sahara African countries (n = 48). The GOHI algorithm was used to estimate scores for OHIZ. Indicator weights were calculated by adopting the fuzzy analytical hierarchy process. Results Overall, five indicators associated with weights were generated as follows: source of infection (23.70%), route of transmission (25.31%), targeted population (19.09%), capacity building (16.77%), and outcomes/case studies (15.13%). Following the indicators, a total of 37 sub-Sahara African countries aligned with OHIZ validation, while 11 territories were excluded for unfit or missing data. The OHIZ average score of sub-Saharan Africa was estimated at 53.67/100. The highest score was 71.99 from South Africa, while the lowest score was 40.51 from Benin. It is also worth mentioning that Sub-Sahara African countries had high performance in many subindicators associated with zoonoses, e.g., surveillance and response, vector and reservoir interventions, and natural protected areas, which suggests that this region had a certain capacity in control and prevention or responses to zoonotic events. Conclusions This study reveals that it is possible to perform OH evaluation for zoonoses in sub-Saharan Africa by OHIZ. Findings from this study provide preliminary research information in advancing knowledge of the evidenced risks to strengthen strategies for effective control of zoonoses and to support the prevention of zoonotic events.

Increased interleukin-6 is associated with long COVID-19: a systematic review and meta-analysis

“…The newly emerged coronavirus of likely bat origin, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), causes a disease known as coronavirus disease 2019 (COVID-19) [ 1 , 2 ]. The disease has affected hundreds of millions of people along with the devastating global consequences of an unprecedented public health crisis.…”

Global One Health index for zoonoses: A performance assessment in 160 countries and territories

“…Overall, the strengths of this study lie in the rigorous framework design and the selection of indicators used. 16 , 60 The datasets used were sourced from authoritative and public databases ( Table 1 ). The methodology that employed algorithms has proven to be effective.…”

“…The methodology that employed algorithms has proven to be effective. 60 , 61 , 62 To the best of our knowledge, this study is the first global and comprehensive assessment of the performance of zoonoses based on the OH concept. The scores under each GOHI-Zoonoses indicator reflected the strengths and weaknesses in controlling or responding to zoonotic threats by country and region, which are positively associated with economic, sociodemographic, environmental, and climatic factors.…”

“…The framework for the GOHI-Zoonoses was formulated as reported previously 60 and is composed of key indicators, subindicators, and datasets.…”

“…To establish the GOHI-Zoonoses framework and populate its datasets, we referred to authoritative publicly available databases of the World Health Organization (WHO), World Organization for Animal Health (WOAH), World Bank, Global Health Security Index (GHS index), and Global Health Data Exchange (GHDx). 60 The construction of the dataset was based on the principles of relevance, authoritative sources, open access, completeness, timeliness, comparability, and country-level data. 16 The datasets met the following criteria.…”