Lancaster Medical School, Lancaster University, Lancaster, UK
Lancaster Medical School, Lancaster University, Lancaster, UK.
BMJ Open. 2019 Aug 30;9(8):e026997. doi: 10.1136/bmjopen-2018-026997.
To evaluate the effect of social network influences on seasonal influenza vaccination uptake by healthcare workers.
Cross-sectional, observational study.
A large secondary care NHS Trust which includes four hospital sites in Greater Manchester.
Foundation doctors (FDs) working at the Pennine Acute Hospitals NHS Trust during the study period. Data collection took place during compulsory weekly teaching sessions, and there were no exclusions. Of the 200 eligible FDs, 138 (70%) provided complete data.
Self-reported seasonal influenza vaccination status.
Among participants, 100 (72%) reported that they had received a seasonal influenza vaccination. Statistical modelling demonstrated that having a higher proportion of vaccinated neighbours increased an individual's likelihood of being vaccinated. The coefficient for γ, the social network parameter, was 0.965 (95% CI: 0.248 to 1.682; odds: 2.625 (95% CI: 1.281 to 5.376)), that is, a diffusion effect. Adjusting for year group, geographical area and sex did not account for this effect.
This population exhibited higher than expected vaccination coverage levels-providing protection both in the workplace and for vulnerable patients. The modelling approach allowed covariate effects to be incorporated into social network analysis which gave us a better understanding of the network structure. These techniques have a range of applications in understanding the role of social networks on health behaviours.
评估社交网络对医护人员季节性流感疫苗接种率的影响。
横断面观察性研究。
大曼彻斯特区的一个大型二级保健国民保健服务信托机构,其中包括四家医院。
在研究期间在 Pennine 急性医院国民保健服务信托机构工作的基础医生。数据收集在强制性的每周教学会议期间进行,没有排除任何对象。在 200 名符合条件的基础医生中,有 138 名(70%)提供了完整的数据。
自我报告的季节性流感疫苗接种状况。
在参与者中,有 100 名(72%)报告他们已接种季节性流感疫苗。统计建模表明,邻居中有更高比例的接种者会增加个人接种疫苗的可能性。γ,即社交网络参数的系数为 0.965(95%CI:0.248 至 1.682;优势比:2.625(95%CI:1.281 至 5.376)),即扩散效应。调整年龄组、地理区域和性别并不能说明这种影响。
该人群的疫苗接种率高于预期,为工作场所和脆弱患者提供了保护。建模方法允许将协变量的影响纳入社交网络分析,从而更好地了解网络结构。这些技术在理解社交网络对健康行为的作用方面具有广泛的应用。