Sociodemographic characterisation of antibiotic heavy users in the Danish elderly population

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AIMS: The development of effective interventions to reduce inappropriate use of antibiotics in the elderly population requires knowledge on who can benefit from such interventions. Thus, we aimed to identify and characterise antibiotic heavy users among elderly patients in general practice with respect to sociodemographic variables.

METHODS: We conducted a retrospective nationwide register-based study on all Danish elderly citizens (⩾65 years) who redeemed an antibiotic prescription in 2017. Heavy users were defined as the 10% with the highest excess use, that is, their recorded use minus the average use for their sex, age group and comorbidity level as estimated from a linear regression model. Comparative analyses of sociodemographic characteristics (civil status, employment status, urbanity, educational level and country of origin) of heavy users and non-heavy users were performed using logistic regression models.

RESULTS: The study population consisted of 251,733 elderly individuals, who in total redeemed 573,265 prescriptions of antibiotics. Heavy users accounted for 68% of all excess use of antibiotics. In multivariable analyses, individuals with an educational level above basic schooling, non-retired, residing in an urban municipality and being born in a country outside Scandinavia all had lower odds of being a heavy user. Widowed, divorced or single individuals had higher odds of being a heavy user compared with married individuals. Relative importance analyses showed that civil status and educational level contributed considerably to the explained variance.

CONCLUSIONS: This study found an association between sociodemographic characteristics and risk of being a heavy user, indicating that sociodemographic variation exists with regard to antibiotic prescribing.

Original languageEnglish
Book seriesScandinavian Journal of Public Health
Number of pages8
ISSN1403-4956
DOIs
Publication statusE-pub ahead of print - 2022

ID: 319359094