Evidence map›Paper›PMID 40855519›Full record

ArticleEpidemiology and infection2025

The association between rainfall and human leptospirosis in Aotearoa New Zealand.

Toni Tana, Masako Wada, Jackie Benschop, Emilie Vallee

Abstract read
In one paragraph

Article in Epidemiology and infection, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Toni TanaSurveillance and Incursion Investigation, https://ror.org/055y4y749Ministry for Primary Industries, Wallaceville, New Zealand.
Masako WadaTāwharau Ora, School of Veterinary Science, https://ror.org/052czxv31Massey University, Palmerston North, New Zealand.
Jackie BenschopTāwharau Ora, School of Veterinary Science, https://ror.org/052czxv31Massey University, Palmerston North, New Zealand.
Emilie ValleeTāwharau Ora, School of Veterinary Science, https://ror.org/052czxv31Massey University, Palmerston North, New Zealand.ORCID 0000-0002-3145-5468

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Leptospirosis remains a significant occupational zoonosis in New Zealand, and emerging serovar shifts warrant a closer examination of climate-related transmission pathways. This study aimed to examine whether total monthly rainfall is associated with reported leptospirosis in humans in New Zealand. Poisson and negative binomial models were developed to examine the relationship between rainfall at 0-, 1-, 2-, and 3-month lags and the incidence of leptospirosis during the month of the report. Total monthly rainfall was positively associated with the occurrence of human leptospirosis in the following month by a factor of 1.017 (95% CI: 1.007-1.026), 1.023 at the 2-month lag (95% CI:1.013-1.032), and 1.018 at the 3-month lag (95% CI: 1.009-1.028) for every additional cm of rainfall. Variation was present in the magnitude of association for each of the individual serovars considered, suggesting different exposure pathways. Assuming that the observed associations are causal, this study supports that additional human cases are likely to occur associated with increased levels of rainfall. This provides the first evidence for including rainfall in a leptospirosis early warning system and to design targeted communication and prevention measures and provide resource allocation, particularly after heavy rainfall in New Zealand.

Indexed as

LeptospiraLeptospirosisRainAdultFemaleHumansIncidenceMaleMiddle AgedNew ZealandSeasonsclimateleptospirosisone healthrainfallserovarsurveillancezoonosis

Identifiers

PMID40855519
PMCPMC12529419

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.