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

BACKGROUND: Addressing the causes of low engagement in health care is a prerequisite for reducing health inequalities. People who miss multiple appointments are an under-researched group who might have substantial unmet health needs. Individual-level patterns of missed general practice appointments might thus provide a risk marker for vulnerability and poor health outcomes. We sought to ascertain the contributions of patient and practice factors to the likelihood of missing general practice appointments. METHODS: For this national retrospective cohort analysis, we extracted UK National Health Service general practice data that were routinely collected across Scotland between Sept 5, 2013, and Sept 5, 2016. We calculated the per-patient number of missed appointments from individual appointments and investigated the risk of missing a general practice appointment using a negative binomial model offset by number of appointments made. We then analysed the effect of patient-level factors (including age, sex, and socioeconomic status) and practice-level factors (including appointment availability and geographical location) on the risk of missing appointments. FINDINGS: The full dataset included information from 909 073 patients, of whom 550 083 were included in the analysis after processing. We observed that 104 461 (19·0%) patients missed more than two appointments in the 3 year study period. After controlling for the number of appointments made, patterns of non-attendance could be differentiated, with patients who were aged 16-30 years (relative risk ratio [RRR] 1·21, 95% CI 1·19-1·23) or older than 90 years (2·20, 2·09-2·29), and of low socioeconomic status (Scottish Index of Multiple Deprivation decile 1: RRR 2·27, 2·22-2·31) significantly more likely to miss multiple appointments. Men missed fewer appointments overall than women, but were somewhat more likely to miss appointments in the adjusted model (1·05, 1·04-1·06). Practice factors also substantially affected attendance patterns, with urban practices in affluent areas that typically have appointment waiting times of 2-3 days the most likely to have patients who serially miss appointments. The combination of both patient and practice factors to predict appointments missed gave a higher pseudo R2 value (0·66) than models using either group of factors separately (patients only R2=0·54; practice only R2=0·63). INTERPRETATION: The findings that both patient and practice characteristics contribute to non-attendance of general practice appointments 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 should lead to improvements in provision of services and public health. FUNDING: Scottish Government Chief Scientist Office and Data Sharing and Linkage Service of the Scottish Government.

Understanding repeated non-attendance in health services: a pilot analysis of administrative data and full study protocol for a national retrospective cohort.

INTRODUCTION: Understanding the causes of low engagement in healthcare is a pre-requisite for improving health services' contribution to tackling health inequalities. Low engagement includes missing healthcare appointments. Serially (having a pattern of) missing general practice (GP) appointments may provide a risk marker for vulnerability and poorer health outcomes. METHODS AND ANALYSIS: A proof of concept pilot using GP appointment data and a focus group with GPs informed the development of missed appointment categories: patients can be classified based on the number of appointments missed each year. The full study, using a retrospective cohort design, will link routine health service and education data to determine the relationship between GP appointment attendance, health outcomes, healthcare usage, preventive health activity and social circumstances taking a life course approach and using data from the whole journey in the National Health Service (NHS) healthcare. 172 practices will be recruited (∼900 000 patients) across Scotland. The statistical analysis will focus on 2 key areas: factors that predict patients who serially miss appointments, and serial missed appointments as a predictor of future patient outcomes. Regression models will help understand how missed appointment patterns are associated with patient and practice characteristics. We shall identify key factors associated with serial missed appointments and potential interactions that might predict them. ETHICS AND DISSEMINATION: The results of the project will inform debates concerning how best to reduce non-attendance and increase patient engagement within healthcare systems. Significant non-academic beneficiaries include governments, policymakers and medical practitioners. Results will be disseminated via a combination of academic outputs (papers, conferences), social media and through collaborative public health/policy fora.