Household presentation of influenza and acute respiratory illnesses to a primary care sentinel network: retrospective database studies (2013–2018)

Abstract

Background Direct observation of the household spread of influenza and respiratory infections is limited; much of our understanding comes from mathematical models. The study aims to determine household incidence of influenza-like illness (ILI), lower (LRTI) and upper (URTI) respiratory infections within a primary care routine data and identify factors associated with the diseases’ incidence. Methods We conducted two five-year retrospective analyses of influenza-like illness (ILI), lower (LRTI) and upper (URTI) respiratory infections using the England Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) primary care sentinel network database; a cross-sectional study reporting incident rate ratio (IRR) from a negative binomial model and a retrospective cohort study, using a shared gamma frailty survival model, reporting hazard ratios (HR). We reported the following household characteristics: children < 5 years old, each extra household member, gender, ethnicity (reference white), chronic disease, pregnancy, and rurality. Results The IRR where there was a child < 5 years were 1·62 (1·38–1·89, p < 0·0001), 2·40 (2.04–2.83, p < 0·0001) and 4·46 (3.79–5.255, p < 0·0001) for ILI, LRTI and URTI respectively. IRR also increased with household size, rurality and presentations and by female gender, compared to male. Household incidence of URTI and LRTI changed little between years whereas influenza did and were greater in years with lower vaccine effectiveness. The HR where there was a child < 5 years were 2·34 (95%CI 1·88–2·90, p < 0·0001), 2·97 (95%CI 2·76–3·2, p < 0·0001) and 10·32 (95%CI 10.04–10.62, p < 0·0001) for ILI, LRTI and URTI respectively. HR were increased with female gender, rurality, and increasing household size. Conclusions Patterns of household incidence can be measured from routine data and may provide insights for the modelling of disease transmission and public health policy.

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