Spatiotemporal patterns of influenza in Western Australia

IF 1.9 Q2 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH Public Health in Practice Pub Date : 2025-06-01 Epub Date: 2025-03-15 DOI:10.1016/j.puhip.2025.100602
Kefyalew Addis Alene , Hannah C. Moore , Archie C.A. Clements , Beth Gilmour , Dylan D. Barth , Rebecca Pavlos , Ben Scalley , Christopher C. Blyth
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Abstract

Background

Understanding the geospatial distribution of influenza infection and the risk factors associated with infection clustering can inform targeted preventive interventions. We conducted a geospatial analysis to investigate the spatial patterns and identify drivers of medically attended influenza infection across all age groups in Western Australia (WA).

Methods

Data for confirmed influenza cases were obtained from the WA Notifiable Infectious Diseases Database for the period 2017–2020. Data were also obtained for vaccination coverage, meteorological parameters, socioeconomic indicators, and healthcare access. Spatial clustering of influenza incidence was identified using Global Moran's I and Getis-Ord statistic. Bayesian spatial models were used to identify factors associated with spatial clustering of infection.

Results

Of the 36,228 influenza cases reported, over half (18,773, 51·8 %) were in individuals aged between 15 and 64 years and more than three quarters (28,545, 78·9 %) in the Perth metropolitan region. The annual incidence rate ranged from 2·7 per 1000 population in individuals aged between 15 and 64 years to 5·2 per 1000 population in children <5 years of age. For all age groups, the lowest incidence (0·4 per 1000 population) and the highest incidence rate (8·8 per 1000 population) were reported during and pre-the COVID-19 pandemic respectively. The influenza incidence rate shows both seasonal and spatial variation. Spatial clustering was significantly associated with distance to the nearest health facility in minutes (B = −0·181; 95 %CrI: 0·279, −0·088) and annual mean temperature in degrees Celsius (B = 0·171; 95 %CrI: 0·015, 0·319).

Conclusions

Spatial clustering of influenza incidence was significantly associated with climatic conditions and healthcare access.
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西澳大利亚流感的时空格局
了解流感感染的地理空间分布和与感染聚集性相关的危险因素可以为有针对性的预防干预提供信息。我们进行了一项地理空间分析,以调查西澳大利亚州(WA)所有年龄组中接受医疗护理的流感感染的空间模式和确定驱动因素。方法2017-2020年西澳法定传染病数据库中流感确诊病例数据。还获得了疫苗接种覆盖率、气象参数、社会经济指标和卫生保健可及性的数据。使用Global Moran's I和Getis-Ord统计量确定流感发病率的空间聚类。贝叶斯空间模型用于识别与感染空间聚类相关的因素。结果在报告的36228例流感病例中,超过一半(18,773例,51.8%)的个体年龄在15至64岁之间,超过四分之三(28,545例,78.9%)的个体在珀斯大都市区。年发病率在15至64岁人群中为2.7‰,在5岁儿童中为5.2‰。在所有年龄组中,在COVID-19大流行期间和前报告的发病率分别为最低(0.4 / 1000人)和最高(8.8 / 1000人)。流感发病率有季节和空间差异。空间聚类与以分钟为单位到最近卫生设施的距离显著相关(B =−0·181;95% CrI: 0·279,−0·088)和年平均气温(B = 0·171;95% CrI: 0.015, 0.319)。结论流感发病的空间聚类与气候条件和卫生保健可及性有显著相关。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Public Health in Practice
Public Health in Practice Medicine-Health Policy
CiteScore
2.80
自引率
0.00%
发文量
117
审稿时长
71 days
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