https://doi.org/10.4081/gh.2024.1335
Identification and mapping of objects targeted for surveillance and their role as risk factors for brucellosis in livestock farms in Kazakhstan
Submitted: 16 August 2024
Accepted: 3 November 2024
Published: 8 November 2024
Accepted: 3 November 2024
Abstract Views: 578
PDF: 210
HTML: 39
HTML: 39
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
Objects for Targeted Surveillance (OTS) are infrastructure entities that can be considered as focal points and conduits for transmitting infectious animal diseases, necessitating ongoing epidemiological surveillance. These entities encompass slaughterhouses, meat processing plants, animal markets, burial sites, veterinary laboratories, etc. Currently, in Kazakhstan, a funded research project is underway to establish a Geographic Information System (GIS) database of OTSs and investigate their role in the emergence and dissemination of infectious livestock diseases. This initial investigation examined the correlation between brucellosis outbreaks in cattle and small ruminant farms in the southeastern region of Kazakhstan and the presence of OTSs categorized as “slaughterhouses,” “cattle markets,” and “meat processing plants. The study area (namely Qyzylorda, Turkestan, Zhambyl, Almaty, Zhetysu, Abay and East Kazakhstan oblasts), characterized by the highest livestock density in the country, covers 335 slaughterhouses (with varying levels of biosecurity), 45 livestock markets and 15 meat processing plants. Between 2020 and 2023, 338 cases of brucellosis were reported from livestock farms in this region. The findings of the regression model reveal a statistically significant (p<0.05) positive association between the incidence of brucellosis cases and the number of OTSs in the region. Conversely, meat processing plants and livestock markets did not exhibit a significant influence on the prevalence of brucellosis cases. These results corroborate the hypothesis of an elevated risk of brucellosis transmission in regions with slaughterhouses, likely attributable to increased animal movements within and across regions, interactions with vehicles and contact with slaughterhouse staff. These outcomes mark a pivotal advancement in the national agricultural development agenda. The research will be extended to encompass the entire country, compiling a comprehensive OTS database.
Abutalip A, Bizhanov A, Matikhan N, Karabassova A, Orynbayeva B, 2024. Regional epidemiology of brucellosis infection in modern conditions of animal husbandry technology in Kazakhstan (by the degree of spread and incidence). Sci Horiz 27:20-31.
DOI: https://doi.org/10.48077/scihor5.2024.20
Al Jindan R, 2021. Scenario of pathogenesis and socioeconomic burden of human brucellosis in Saudi Arabia. Saudi J Biol Sci 28:272-9.
DOI: https://doi.org/10.1016/j.sjbs.2020.09.059
An C, Shen L, Sun M, Sun Y, Fan S, Zhao C, Nie S, Luo B, Fu T, Liu K, Shao Z, Chang W, 2023. Exploring risk transfer of human brucellosis in the context of livestock agriculture transition: A case study in Shaanxi, China. Front Public Health 27:1009854.
DOI: https://doi.org/10.3389/fpubh.2022.1009854
Ayoola MC, Akinseye VO, Cadmus E, Awosanya E, Popoola OA, Akinyemi OO, Perrett L, Taylor A, Stack J, Moriyon I, Cadmus SI, 2017. Prevalence of bovine brucellosis in slaughtered cattle and barriers to better protection of abattoir workers in Ibadan, South-Western Nigeria. Pan Afr Med J 28;10925.
DOI: https://doi.org/10.11604/pamj.2017.28.68.10925
Beauvais W, Coker R, Nurtazina G, Guitian J, 2017. Policies and livestock systems driving brucellosis re-emergence in Kazakhstan. Ecohealth 14:399-407.
DOI: https://doi.org/10.1007/s10393-015-1030-7
Belchikhina AV, Shibayev MA, Dudorova MV, 2011. The development and approbation of the information system of identification, registration and mapping of epidemiologically significant veterinary objects in the regions of the Russian Federation. (in Russian). Veterinariya I Kormlenie 6:14-17.
Bugeza JK, Roesel K, Mugizi DR, Alinaitwe L, Kivali V, Kankya C, Moriyon I, Cook EAJ, 2024. Sero-prevalence and risk factors associated with occurrence of anti-Brucella antibodies among slaughterhouse workers in Uganda. PLoS Negl Trop Dis 18:e0012046.
DOI: https://doi.org/10.1371/journal.pntd.0012046
Charypkhan D, Rüegg SR, 2022. One Health evaluation of brucellosis control in Kazakhstan. PLOS ONE 17:e0277118.
DOI: https://doi.org/10.1371/journal.pone.0277118
Charypkhan D, Sultanov AA, Ivanov NP, Baramova SA, Taitubayev MK, Torgerson PR, 2019. Economic and health burden of brucellosis in Kazakhstan. Zoonoses Public Health 66:487-94.
DOI: https://doi.org/10.1111/zph.12582
Corbel MJ, 1997. Brucellosis: an overview. Emerg Infect Dis 3:213-21.
DOI: https://doi.org/10.3201/eid0302.970219
Counotte MJ, Minbaeva G, Usubalieva J, Abdykerimov K, Torgerson PR, 2016. The Burden of Zoonoses in Kyrgyzstan: A Systematic Review. PLoS Negl Trop Dis 10:e0004831.
DOI: https://doi.org/10.1371/journal.pntd.0004831
de Araújo LCSDSC, da Costa MM, do Nascimento JA, da Silva FDA, Peixoto RM, 2022. Bovine brucellosis seroprevalence and flow network analysis in slaughterhouses in the state of Ceará. Braz J Vet Med 44:e003521.
DOI: https://doi.org/10.29374/2527-2179.bjvm003521
Dudnikov SA, Lyadsky MM, Belchikhina AV, Gulenkin VM, Vystavkina ES, Lisitcyn VV, 2008. Epidemically significant objects of the Vladimir Oblast. Veterinary Atlas. (in Russian). Rosselkhoznadzor. Accessed 01 Nov 2024. Available from: https://fsvps.gov.ru/sites/default/files/fsvps-docs/ru/iac/publications/iac_public8.pdf
Dzhupina SI, 2004. Lessons of epizootological studies (in Russian). Moscow, RUDN. 299 p.
Ergazina AM, Piontkovsky VI, Kazimieras L, 2013. Cattle brucellosis: epizootiology, diagnosis, prevention and control measures in Kazakhstan. World Appl Sci J 28:81-6.
Godfroid J, Scholz HC, Barbier T, Nicolas C, Wattiau P, Fretin D, Whatmore AM, Cloeckaert A, Blasco JM, Moriyon I, Saegerman C, Muma JB, Al Dahouk S, Neubauer H, Letesson JJ, 2011. Brucellosis at the animal/ecosystem/human interface at the beginning of the 21st century. Prev Vet Med 102:118-31.
DOI: https://doi.org/10.1016/j.prevetmed.2011.04.007
Hasanjani Roushan MR, Ebrahimpour S, 2015. Human brucellosis: An overview. Caspian J Internal Med 6;46–47.
Jin M, Fan Z, Gao R, Li X, Gao Z, Wang Z, 2023. Research progress on complications of Brucellosis. Front Cell Infect Microbiol 13:1136674.
DOI: https://doi.org/10.3389/fcimb.2023.1136674
Juarez-Colunga E, Dean CB, 2024. Negative Binomial Regression. In Wiley StatsRef: Statistics Reference Online (eds N. Balakrishnan, T. Colton, B. Everitt, W. Piegorsch, F. Ruggeri and J.L. Teugels). doi:10.1002/9781118445112.stat08246
DOI: https://doi.org/10.1002/9781118445112.stat08246
Kakooza S, Watuwa J, Ipola PA, et al., 2022. Seromonitoring of brucellosis in goats and sheep slaughtered at an abattoir in Kampala, Uganda. J Vet Diagnostic Investig 34:964-967.
DOI: https://doi.org/10.1177/10406387221126658
Laine CG, Johnson VE, Scott HM, Arenas-Gamboa AM, 2023. Global Estimate of Human Brucellosis Incidence. Emerging Infect Dis 29:1789–97.
DOI: https://doi.org/10.3201/eid2909.230052
Liu Z, Gao L, Wang M, Yuan M, Li Z, 2024. Long ignored but making a comeback: a worldwide epidemiological evolution of human brucellosis. Emerg Microbes Infect 13:2290839.
DOI: https://doi.org/10.1080/22221751.2023.2290839
Madut NA, Ocan M, Muwonge A, Muma JB, Nasinyama GW, Godfroid J, Jubara AS, Kankya C, 2019. Sero-prevalence of brucellosis among slaughterhouse workers in Bahr el Ghazal region, South Sudan. BMC Infect Dis 19:450.
DOI: https://doi.org/10.1186/s12879-019-4066-4
Mamadaliyev SM, Matveeva VM, Koshemetov ZhK, Khairullin BM, Orynbayev MB, Sandybayev NT, Kydyrbayev ZhK, Zaitsev VL, Zhilin ES, Nurabayev SSh, Koryagina MI, 2010. The monitoring of especially dangerous viral diseases of animals and birds on the territory of Central Asian republics (in Russian). Actual problems of Veterinary Biology. 2(6).
Mioni MSR, Vicente AF, Peres MG, Appolinário CM, Ribeiro BLD, Pantoja JCF, de Almeida JP, Pinto N, Mathias LA, Megid J, 2018. Brucellosis Prevalence in Brazilian Slaughterhouses with Different Meat Inspection Systems. J Food Prot 81:1073-8.
DOI: https://doi.org/10.4315/0362-028X.JFP-17-451
Muminov A, 2019. Agriculture is a basis of a strong Economics. (in Russian). Accessed 16 Aug 2024. Available from: https://kapital.kz/economic/77001/sel-skoye-khozyaystvo-osnova-sil-noy-ekonomiki.html
Papadatos SS, Bazoukis G, Deligiannis G, Mylonas S, Zissis C, 2017. Respiratory symptoms as prominent manifestation of brucellosis: a case series. J Clin Diagnostic Res 11:OR01–3.
DOI: https://doi.org/10.7860/JCDR/2017/26117.9848
Pappas G, Akritidis N, Bosilkovski M, Tsianos E, 2005. Brucellosis. New Engl J Med 352:2325–36.
DOI: https://doi.org/10.1056/NEJMra050570
R Core Team, 2022. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Accessed 01 Nov 2024. Available from: https://www.R-project.org/.
Rubach MP, Halliday JE, Cleaveland S, Crump JA, 2013. Brucellosis in low-income and middle-income countries. Curr Opin Infect Dis 26:404-12.
DOI: https://doi.org/10.1097/QCO.0b013e3283638104
Ryskeldinova S, Zinina N, Kydyrbayev Z, Yespembetov B, Kozhamkulov Y, Inkarbekov D, Assanzhanova N, Mailybayeva A, Bugybayeva D, Sarmykova M, Khairullin B, Tabynov K, Bulashev A, Aitzhanov B, Abeuov K, Sansyzbay A, Yespolov T, Renukaradhya GJ, Olsen S, Oñate A, Tabynov K, 2021. Registered influenza viral vector based brucella abortus vaccine for cattle in Kazakhstan: age-wise safety and efficacy studies. Front Cell Infect Microbiol 1:669196.
DOI: https://doi.org/10.3389/fcimb.2021.669196
Shaikenov BS, Torgerson PR, Usenbayev AE, Baitursynov KK, Rysmukhambetova AT, Abdybekova AM, Karamendin KO, 2003. The changing epidemiology of echinococcosis in Kazakhstan due to transformation of farming practices. Acta Trop 85:287-93.
DOI: https://doi.org/10.1016/S0001-706X(02)00236-X
Shevtsova E, Shevtsov A, Mukanov K, Filipenko M, Kamalova D, Sytnik I, et al., 2016. Epidemiology of Brucellosis and Genetic Diversity of Brucella abortus in Kazakhstan. PLoS ONE 11:e0167496.
DOI: https://doi.org/10.1371/journal.pone.0167496
Shevtsova E, Vergnaud G, Shevtsov A, Shustov A, Berdimuratova K, Mukanov K, Syzdykov M, Kuznetsov A, Lukhnova L, Izbanova U, Filipenko M, Ramankulov Y, 2019. Genetic Diversity of Brucella melitensis in Kazakhstan in Relation to World-Wide Diversity. Front Microbiol 13:1897.
DOI: https://doi.org/10.3389/fmicb.2019.01897
Syrym NS, Yespembetov BA, Sarmykova MK, Konbayeva GM, Koshemetov ZhK, Akmatova EK, Bazarbaev M, Siyabekov ST, 2019. Reasons behind the epidemiological situation of brucellosis in the Republic of Kazakhstan. Acta Tropica 191:98-107.
DOI: https://doi.org/10.1016/j.actatropica.2018.12.028
UNECE Data Portal. Share of agriculture in GDP. Accessed 01 Nov 2024. Available from: https://w3.unece.org/PXWeb/en/Table?IndicatorCode=6
Venables WN, Ripley BD, 2002. Modern Applied Statistics with S. Fourth edition. XII, 498. Springer New York, NY.
DOI: https://doi.org/10.1007/978-0-387-21706-2
Yespembetov BA, Syrym NS, Syzdykov MS, Kuznetsov AN, Koshemetov ZhK, Mussayeva AK, Basybekov SZ, Kanatbayev SG, Mankibaev AT, Romashev CM, 2019. Impact of geographical factors on the spread of animal brucellosis in the Republic of Kazakhstan. Compar Immunol Microbiol Infect Dis 67:101349.
DOI: https://doi.org/10.1016/j.cimid.2019.101349
Supporting Agencies
This study was conducted within the framework of the project: BR19678128 «Development of measures to reduce the epidemiological risks of the spread of zooanthroponoses in Kazakhstan».How to Cite
Mukhanbetkaliyeva, A. A., Kadyrov, A. S., Mukhanbetkaliyev, Y. Y., Adilbekov, Z. S., Zhanabayev, A. A., Abenova, A. Z., … Abdrakhmanov, S. K. (2024). Identification and mapping of objects targeted for surveillance and their role as risk factors for brucellosis in livestock farms in Kazakhstan. Geospatial Health, 19(2). https://doi.org/10.4081/gh.2024.1335
Copyright (c) 2024 the Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
Similar Articles
- Cristian Cáceres, Marcelo Leiva-Bianchi, Yony Ormazábal, Carlos Mena, Juan Carlos Cantillana, Post-traumatic stress in people from the interior drylands of the Maule region, Chile in the context of climate change , Geospatial Health: Vol. 17 No. 1 (2022)
- Kamukama Ismail, Gilbert Maiga, Denis Ssebuggwawo, Peter Nabende, Ali Mansourian, Spatio-temporal trends and distribution patterns of typhoid disease in Uganda from 2012 to 2017 , Geospatial Health: Vol. 15 No. 2 (2020)
- Enrique Ibarra-Zapata, Darío Gaytán-Hernández, Verònica Gallegos-García, Claudia Elena González-Acevedo, Thuluz Meza-Menchaca, María Judith Rios-Lugo, Héctor Hernández-Mendoza, Geospatial modelling to estimate the territory at risk of establishment of influenza type A in Mexico - An ecological study , Geospatial Health: Vol. 16 No. 1 (2021)
- Francis P. Boscoe, Thomas O. Talbot, Martin Kulldorff, Public domain small-area cancer incidence data for New York State, 2005-2009 , Geospatial Health: Vol. 11 No. 1 (2016): Valencia Issue
- Stefan Dongus, Dickson Nyika, Khadija Kannady, Deo Mtasiwa, Hassan Mshinda, Laura Gosoniu, Axel W. Drescher, Ulrike Fillinger, Marcel Tanner, Gerry F. Killeen, Marcia C. Castro, Urban agriculture and Anopheles habitats in Dar es Salaam, Tanzania , Geospatial Health: Vol. 3 No. 2 (2009)
- Benjamin G. Jacob, Weidong Gu, Erik X. Caamano, Robert J. Novak, Developing operational algorithms using linear and non-linear squares estimation in Python® for the identification of Culex pipiens and Culex restuans in a mosquito abatement district (Cook County, Illinois, USA) , Geospatial Health: Vol. 3 No. 2 (2009)
- Zar Chi Htwe, Wongsa Laohasiriwong, Kittipong Sornlorm, Roshan Mahato, Spatial pattern and heterogeneity of chronic respiratory diseases and relationship to socio-demographic factors in Thailand in the period 2016 to 2019 , Geospatial Health: Vol. 18 No. 1 (2023)
- Nicola A. Wardrop, Chi-Chien Kuo, Hsi-Chieh Wang, Archie C. A. Clements, Pei-Fen Lee, Peter M. Atkinson, Bayesian spatial modelling and the significance of agricultural land use to scrub typhus infection in Taiwan , Geospatial Health: Vol. 8 No. 1 (2013)
- Marjan Zare, Ali Semati, Alireza Mirahmadizadeh, Abdulrasool Hemmati, Mostafa Ebrahimi, Spatial epidemiology and meteorological risk factors of COVID-19 in Fars Province, Iran , Geospatial Health: Vol. 17 No. s1 (2022): Special issue on COVID-19
- Ei Sandar U, Wongsa Laohasiriwong, Kittipong Sornlorm, Spatial autocorrelation and heterogenicity of demographic and healthcare factors in the five waves of COVID-19 epidemic in Thailand , Geospatial Health: Vol. 18 No. 1 (2023)
<< < 19 20 21 22 23 24 25 26 27 28 > >>
You may also start an advanced similarity search for this article.
