Alene Kefyalew Addis, Moore Hannah C, Clements Archie C A, Gilmour Beth, Barth Dylan D, Pavlos Rebecca, Scalley Ben, Blyth Christopher C
School of Population Health, Faculty of Health Sciences, Curtin University, Perth, Western Australia, Australia.
Wesfarmers Centre of Vaccines and Infectious Diseases, The Kids Research Institute Australia, Perth, Western Australia, Australia.
Public Health Pract (Oxf). 2025 Mar 15;9:100602. doi: 10.1016/j.puhip.2025.100602. eCollection 2025 Jun.
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).
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.
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 ( = -0·181; 95 %CrI: 0·279, -0·088) and annual mean temperature in degrees Celsius ( = 0·171; 95 %CrI: 0·015, 0·319).
Spatial clustering of influenza incidence was significantly associated with climatic conditions and healthcare access.
了解流感感染的地理空间分布以及与感染聚集相关的风险因素可为有针对性的预防干预措施提供依据。我们进行了一项地理空间分析，以调查西澳大利亚州（WA）所有年龄组中就医流感感染的空间模式并确定其驱动因素。
确诊流感病例的数据来自2017 - 2020年WA法定传染病数据库。还获取了疫苗接种覆盖率、气象参数、社会经济指标和医疗服务可及性的数据。使用全局莫兰指数（Global Moran's I）和Getis - Ord统计量确定流感发病率的空间聚集情况。采用贝叶斯空间模型确定与感染空间聚集相关的因素。
在报告的36228例流感病例中，超过一半（18773例，51.8%）为15至64岁的个体，超过四分之三（28545例，78.9%）在珀斯都会区。年发病率范围从15至64岁个体的每1000人口2.7例到5岁以下儿童的每1000人口5.2例。对于所有年龄组，在新冠疫情期间和疫情前分别报告了最低发病率（每1000人口0.4例）和最高发病率（每1000人口8.至8例）。流感发病率呈现季节性和空间变化。空间聚集与到最近医疗机构的距离（分钟）显著相关（β = -0.181；95%可信区间：-0.279，-0.088）以及年平均温度（摄氏度）显著相关（β = 0.171；95%可信区间：0.015，0.319）。
流感发病率的空间聚集与气候条件和医疗服务可及性显著相关。
