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 R-2 value (0.66) than models using either group of factors separately (patients only R-2 = 0.54; practice only R-2 = 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.