2009-2021 年美国西南部降水事件、干旱和动物饲养与沙门氏菌感染之间的关系

IF 4.1 2区医学 Q1 INFECTIOUS DISEASES

One Health Pub Date : 2024-11-19 DOI:10.1016/j.onehlt.2024.100941

Erika Austhof , Kristen Pogreba-Brown , Alice E. White , Rachel H. Jervis , Joli Weiss , Sarah Shrum Davis , Delaney Moore , Heidi E. Brown

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引用次数: 0

摘要

背景以前，温度和降水与沙门氏菌感染有关。沙门氏菌病与降水量之间的关联可能是由先期干旱条件造成的，但很少有研究探讨这种影响。方法我们采用生态学研究设计，利用公共卫生监测、气象（总降水量 [英寸]、温度 [平均华氏度]、帕尔默干旱严重程度指数 [PDSI，类别]）和牲畜数据，探讨了 2009 年至 2021 年美国西南部 127/141 个县的降水量与沙门氏菌感染之间的关联，并确定了这种关联如何受先前干旱的影响。为了探讨降水对沙门氏菌感染的急性影响，我们使用了负二项广义估计方程，并根据气温调整了 2 周的滞后期，得出了发病率比 (IRR)。结果在对平均气温和 PDSI 进行调整后，降水量增加一英寸与两周后沙门氏菌感染率增加 2% 有关（IRR：1.02，95 % CI：1.00，1.04）。中度干旱（IRR：1.22，95 % CI：1.17，1.28）和严重干旱（IRR：1.16，95 % CI：1.10，1.22）后的降水与病例显著增加有关，而在最极端的干旱条件下，病例显著减少（IRR：0.89，95 % CI：0.85，0.94）。总体而言，降水量越多（超过 30 年正常值、第 95 百分位数和第 99 百分位数），病例增加越多，中度和严重干旱后病例增加最多。无论干旱状况如何，鸡和肉牛密度较高的县与病例增加有显著相关性，而奶牛和包括小牛在内的牛的结果则好坏参半。讨论我们的研究表明，在之前的干旱条件下出现的降水与美国西南部沙门氏菌病的增加有关。随着极端降水事件的发生，公共卫生可能会看到沙门氏菌病的增加，特别是在鸡和肉牛密度较高的县。

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Association between precipitation events, drought, and animal operations with Salmonella infections in the Southwest US, 2009–2021

Background

Temperature and precipitation have previously been associated with Salmonella infections. The association between salmonellosis and precipitation might be explained by antecedent drought conditions; however, few studies have explored this effect.

Methods

Using an ecological study design with public health surveillance, meteorological (total precipitation [inches], temperature [average °F], Palmer Drought Severity Index [PDSI, category]), and livestock data we explored the association between precipitation and Salmonella infections reported in 127/141 counties from 2009 to 2021 in the Southwest, US and determined how this association was modified by antecedent drought. To explore the acute effect of precipitation on Salmonella infections we used negative binomial generalized estimating equations adjusted for temperature with a 2-week lag resulting in Incidence Rate Ratios (IRR). Stratified analyses were used to explore the effect of antecedent drought and type of animal density on this association.

Results

A one inch increase in precipitation was associated with a 2 % increase in Salmonella infections reported two weeks later (IRR: 1.02, 95 % CI: 1.00, 1.04) after adjusting for average temperature and PDSI. Precipitation following moderate (IRR: 1.22, 95 % CI: 1.17, 1.28) and severe drought (IRR: 1.16, 95 % CI: 1.10, 1.22) was associated with a significant increase in cases, whereas in the most extreme drought conditions, cases were significantly decreased (IRR: 0.89, 95 % CI: 0.85, 0.94). Overall, more precipitation (above a 30-year normal, the 95th and 99th percentiles) were associated with greater increases in cases, with the highest increase following moderate and severe drought. Counties with a higher density of chicken and beef cattle were significantly associated with increased cases regardless of drought status, whereas dairy cattle, and cattle including calves had mixed results.

Discussion

Our study suggests precipitation following prior dry conditions is associated with an increase in salmonellosis in the Southwest, US. Public health is likely to see an increase in salmonellosis with extreme precipitation events, especially in counties with a high density of chicken and beef cattle.

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来源期刊

One Health Medicine-Infectious Diseases

CiteScore

8.10

自引率

4.00%

发文量

审稿时长

18 weeks

期刊介绍： One Health - a Gold Open Access journal. The mission of One Health is to provide a platform for rapid communication of high quality scientific knowledge on inter- and intra-species pathogen transmission, bringing together leading experts in virology, bacteriology, parasitology, mycology, vectors and vector-borne diseases, tropical health, veterinary sciences, pathology, immunology, food safety, mathematical modelling, epidemiology, public health research and emergency preparedness. As a Gold Open Access journal, a fee is payable on acceptance of the paper. Please see the Guide for Authors for more information. Submissions to the following categories are welcome: Virology, Bacteriology, Parasitology, Mycology, Vectors and vector-borne diseases, Co-infections and co-morbidities, Disease spatial surveillance, Modelling, Tropical Health, Discovery, Ecosystem Health, Public Health.