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MDPI, Tropical Medicine and Infectious Disease, 4(6), p. 188, 2021

DOI: 10.3390/tropicalmed6040188

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Risk Factors for Hospitalisation amongst Leptospirosis Patients in New Zealand

Journal article published in 2021 by Maryna Sokolova ORCID, Jonathan C. Marshall ORCID, Jackie Benschop ORCID
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Data provided by SHERPA/RoMEO

Abstract

Leptospirosis is a neglected zoonotic disease that is widespread in tropical and subtropical regions such as Oceania, which includes New Zealand. The incidence rate of leptospirosis in New Zealand remains high in comparison to other high-income countries, with over half of the notified patients hospitalised, and the factors associated with hospitalisation are poorly understood. This study aimed to estimate the risk factors for hospitalisation amongst leptospirosis patients using passive surveillance data: notifications from 1 January 1999 to 31 December 2017 extracted from New Zealand’s notifiable disease database. There were 771 hospitalised and 673 non-hospitalised patients. Multivariable logistic regression was used to identify risk factors. The year of notification was significantly and positively associated with hospitalisation, with adjusted (adj.) OR 1.03 (95% CI:1.01–1.05). Occupation was significantly associated with hospitalisation, with the adjusted odds of hospitalisation amongst dairy farmers notified with leptospirosis at adj. OR 1.44 (95% CI: 1.02–2.02) times the adjusted odds of hospitalisation amongst farmers that worked with other livestock. Seropositivity for Leptospira interrogans Copenhageni (adj. OR 5.96, 95% CI: 1.68–21.17) and Pomona (adj. OR 1.14, 95% CI: 0.74–1.74)) was more likely to result in hospitalisation when compared to Leptospira borgpetersenii Ballum (baseline). Seropositivity for Leptospira borgpetersenii Hardjo (adj. OR 0.71, 95% CI: 0.49–1.01) and Tarassovi (adj. OR 0.39, 95% CI: 0.23–0.66) was less likely to result in hospitalisation when compared to Ballum (baseline). All the estimates were additionally adjusted for the effect of sex, age, ethnicity, reported occupational exposure, geographical location, reported season, and deprivation status Although passive surveillance data has limitations we have been able to identify that the New Zealand dairy farming population may benefit from a targeted awareness campaign.