Dataset Information

Mass Die-Off of Saiga Antelopes, Kazakhstan, 2015.

ABSTRACT: In 2015, a mass die-off of ≈200,000 saiga antelopes in central Kazakhstan was caused by hemorrhagic septicemia attributable to the bacterium Pasteurella multocida serotype B. Previous analyses have indicated that environmental triggers associated with weather conditions, specifically air moisture and temperature in the region of the saiga antelope calving during the 10-day period running up to the event, were critical to the proliferation of latent bacteria and were comparable to conditions accompanying historically similar die-offs in the same areas. We investigated whether additional viral or bacterial pathogens could be detected in samples from affected animals using 3 different high-throughput sequencing approaches. We did not identify pathogens associated with commensal bacterial opportunisms in blood, kidney, or lung samples and thus concluded that P. multocida serotype B was the primary cause of the disease.

SUBMITTER: Fereidouni S

PROVIDER: S-EPMC6537709 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Mass Die-Off of Saiga Antelopes, Kazakhstan, 2015.

Fereidouni Sasan S Freimanis Graham L GL Orynbayev Mukhit M Ribeca Paolo P Flannery John J King Donald P DP Zuther Steffen S Beer Martin M Höper Dirk D Kydyrmanov Aidyn A Karamendin Kobey K Kock Richard R

Emerging infectious diseases 20190601 6

In 2015, a mass die-off of ≈200,000 saiga antelopes in central Kazakhstan was caused by hemorrhagic septicemia attributable to the bacterium Pasteurella multocida serotype B. Previous analyses have indicated that environmental triggers associated with weather conditions, specifically air moisture and temperature in the region of the saiga antelope calving during the 10-day period running up to the event, were critical to the proliferation of latent bacteria and were comparable to conditions acco ...[more]

PMID: 31107235

Similar Datasets

Project description:BackgroundThe health system performance assessment is a challenging process for decision-makers. In case of Kazakhstan's healthcare system, the calculation of avoidable mortality, which has been underutilized to date, could serve as an additional tool to prioritize areas for improvement. Therefore, the aim of the study is to analyse avoidable mortality in Kazakhstan.MethodsThe data was retrieved from the Bureau of National Statistics, Kazakhstan. It covers population data by age, mortality rates from disease groups based on the Joint OECD (Organisation for Economic Co-operation and Development)/Eurostat classification of preventable and treatable causes of mortality. The data spans from 2015 to 2021, categorized by gender and 5-year age groups (0, 1-4, 5-9, ..., 70-74). Standardization was performed using the 2015 OECD standard population. We used joinpoint regression analysis to calculate the average annual percentage change (AAPC).ResultsFrom 2015 to 2019, the annual percentage change (APC) in avoidable mortality per 100 000 population was -3.8 (-5.7 to -1.8), and from 2019 to 2021 it increased by 17.6 (11.3 to 24.3). Males exhibited higher avoidable mortality rates compared to females. The preventable mortality rate was consistently higher than the treatable mortality. Both preventable and treatable mortality decreased from 2015 to 2019, with preventable mortality reaching 272.17 before rising to 379.23 per 100 000 population in 2021. Between 2015 and 2021, treatable mortality rates increased from 179.3 (176.93-181.67) to 205.45 (203.08-207.81) per 100 000 population.ConclusionIn Kazakhstan, the leading causes of avoidable mortality were circulatory diseases, respiratory diseases, and cancer. To achieve the goals of universal health coverage (UHC) and improve the overall population health, there is an urgent need to amend the healthcare system and reduce avoidable mortality. While it is important to acknowledge the influence of COVID-19 on these trends, our study's focus on avoidable mortality provides valuable insights that complement the understanding of pandemic-related effects.

Dataset Information

Mass Die-Off of Saiga Antelopes, Kazakhstan, 2015.

Publications

Mass Die-Off of Saiga Antelopes, Kazakhstan, 2015.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets