tetano
Editor, Senior Moderator
BMJ Open. 2019 Aug 30;9(8):e026997. doi: 10.1136/bmjopen-2018-026997.
Observational study to assess the effects of social networks on the seasonal influenza vaccine uptake by early career doctors.
Edge R[SUP]1[/SUP], Keegan T[SUP]2[/SUP], Isba R[SUP]2[/SUP], Diggle P[SUP]2,[/SUP][SUP]3[/SUP].
Author information
1 Lancaster Medical School, Lancaster University, Lancaster, UK r.edge@lancs.ac.uk. 2 Lancaster Medical School, Lancaster University, Lancaster, UK. 3 Epidemiology and Population Health, University of Liverpool, Liverpool, UK.
Abstract
OBJECTIVES:
To evaluate the effect of social network influences on seasonal influenza vaccination uptake by healthcare workers.
DESIGN:
Cross-sectional, observational study.
SETTING:
A large secondary care NHS Trust which includes four hospital sites in Greater Manchester.
PARTICIPANTS:
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.
PRIMARY OUTCOME MEASURES:
Self-reported seasonal influenza vaccination status.
RESULTS:
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.
CONCLUSIONS:
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.
? Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.
KEYWORDS:
auto-logistic regression; influenza vaccination; occupational health; social network analysis
PMID: 31471430 DOI: 10.1136/bmjopen-2018-026997
Free full text
Observational study to assess the effects of social networks on the seasonal influenza vaccine uptake by early career doctors.
Edge R[SUP]1[/SUP], Keegan T[SUP]2[/SUP], Isba R[SUP]2[/SUP], Diggle P[SUP]2,[/SUP][SUP]3[/SUP].
Author information
1 Lancaster Medical School, Lancaster University, Lancaster, UK r.edge@lancs.ac.uk. 2 Lancaster Medical School, Lancaster University, Lancaster, UK. 3 Epidemiology and Population Health, University of Liverpool, Liverpool, UK.
Abstract
OBJECTIVES:
To evaluate the effect of social network influences on seasonal influenza vaccination uptake by healthcare workers.
DESIGN:
Cross-sectional, observational study.
SETTING:
A large secondary care NHS Trust which includes four hospital sites in Greater Manchester.
PARTICIPANTS:
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.
PRIMARY OUTCOME MEASURES:
Self-reported seasonal influenza vaccination status.
RESULTS:
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.
CONCLUSIONS:
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.
? Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.
KEYWORDS:
auto-logistic regression; influenza vaccination; occupational health; social network analysis
PMID: 31471430 DOI: 10.1136/bmjopen-2018-026997
Free full text