Home > Research > Publications & Outputs > Demographic and practice factors predicting rep...

Electronic data

  • PIIS2468266717302177

    Final published version, 516 KB, PDF-document

    Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

Links

Text available via DOI:

View graph of relations

Demographic and practice factors predicting repeated non-attendance in primary care: a national retrospective cohort analysis

Research output: Contribution to journalJournal article

Published
Close
<mark>Journal publication date</mark>4/12/2017
<mark>Journal</mark>Lancet Public Health
Issue number12
Volume2
Number of pages9
Pages (from-to)e551-559
<mark>State</mark>Published
<mark>Original language</mark>English

Abstract

Background
Addressing the causes of low engagement in health care is a prerequisite for reducing public health inequalities. People who miss multiple appointments are an under-researched group who have low engagement and place an additional financial strain on healthcare systems around the word. Individual-level patterns of missed general practice appointments may provide a risk marker for vulnerability and poor health outcomes. However, research to date has only considered non-attendance across small samples or using population-based rather than individual patient-level designs. This limited characterisation of an at-risk population makes it difficult to recommend new interventions that aim to increase patient engagement. We therefore aimed to ascertain whether patient and practice factors contributed to the likelihood of missing general practice appointments.

Methods
We have quantified appointment attendance history accurately in a large retrospective cohort of patients (N=550,083) extracted from routinely collected general practice data across Scotland.

Findings
We observed that 19·0% of patients missed more than 2 appointments on average per year. After controlling for the number of appointments made, patterns of non-attendance could be differentiated, with patients who were male (relative risk ratio (RRR) 1.05, 95% Confidence Intervals (CIs) 1.04-1.06) , aged between 16-30 (1.21 (1.19-1.23)) or over 90 years of age (2.20 (2.09-2.29)), and of low-socioeconomic status (SIMD 1 2.27(2.22-2.31)) significantly more likely to miss multiple appointments. Practice factors also play a substantial role when predicting attendance patterns. Urban practices in affluent areas that typically have appointment waiting times of 2-3 days were more likely to contain patients who serially miss appointments.

Interpretation
These findings – that both patient and practice behaviour contribute to non-attendance – raise important questions for both the management of patients who miss multiple appointments and the effectiveness of existing strategies that aim to increase attendance. Addressing these issues would, in turn, lead to improvements for public health.