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 may have significant unmet health needs.. Individual-level patterns of missed general practice appointments may thus 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, together with limited understanding of service factors contributing to missed appointments makes it difficult to recommend new interventions that aim to increase patient engagement. We therefore sought to ascertain the contributions of patient and practice factors to the likelihood of missing general practice appointments.
Methods
We have quantified appointment attendance history in a large retrospective cohort of patients (N=550,083) extracted from routinely collected general practice data across Scotland. A per-patient number of missed appointments was calculated from individual appointments. We investigated risk of missing a general practice appointment using a negative binomial model offset by number of appointments made.
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 aged between 16-30 (relative risk ratio (RRR) 1.21, 95% Confidence Intervals (CIs) 1.19-1.23) or over 90 years of age (2.20 (2.09-2.29)), and of low-socioeconomic status (SIMD decile 1, 2.27(2.22-2.31)) significantly more likely to miss multiple appointments. Males missed fewer appointments overall, but were somewhat more likely to miss appointments in the adjusted model (1.05, 1.04-1.06). 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 most likely to contain patients who serially miss appointments.
Interpretation
These findings – that both patient and practice characteristics 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 should lead to improvements in provision of services and public health.