With the aggravation of population aging in China, the disease spectrum of the population has changed and the coexistence of multiple chronic diseases has become the norm for the health status of the older population in China. Patient delay refers to the behaviour of an individual who fails to seek medical care in a timely manner after becoming unwell for a variety of subjective or objective reasons, resulting in a decrease in the treatment effectiveness and a decrease in the quality of the patient's survival. At present, there are few researches on patient delay and the associated factors for elderly adults with multimorbidity in China.

To explore the patient delay and the associated factors for older adults with multimorbidity, so as to provide references to further reduce the incidence of patient delay.

Eligible elderly patients attending 27 community health centers in Guangdong Province from September to December 2022 were selected for the study using multi-stage stratified whole cluster random sampling method. A self-designed questionnaire was used to collect patients' general information, disease-related information and delays in seeking medical care. Multivariate Logistic regression analysis and a decision tree model based on the CHAID algorithm were used to analyse the influencing factors of patient delay in older adults with multimorbidity.

A total of 998 patients were included in the study, of which 243 (24.35%) showed delays in seeking medical care. The multivariate Logistic regression results showed that gender (OR=0.701, 95%CI=0.504-0.977, P=0.036), type of household registration (OR=0.590, 95%CI=0.358-0.973, P=0.039), type of health insurance (OR=2.660, 95%CI=1.764-4.010, P<0.001), disease-related self-efficacy (OR=4.378, 95%CI=2.079-9.217, P<0.001), family doctor contract (OR=2.277, 95%CI=1.618-3.206, P<0.001) and self-reported health (OR=1.554, 95%CI=1.073-2.250, P=0.020) were the main factors influencing patient delay in older adults with multimorbidity (P<0.05). The decision tree model has 3 levels and 13 nodes, and a total of 5 influencing factors were screened, including type of health insurance, family doctor contract, gender, self-reported health and age. The results of the two models for predicting patient delay in older adults with multimorbidity showed that the area under receiver operating characteristic curve (AUC) was 0.729 for the multivariate Logistic regression model and 0.721 for the decision tree model. There was no significant difference in AUC between the two models for predicting patients delay in elderly patients with multimorbidity (Z=0.539, P=0.590) .

The incidence of patient delay in older adults with multimorbidity is 24.35% in Guangdong province, and the type of health insurance, the contracting rate of family doctors, gender, and self-reported health status are the main factors influencing patient delay in older adults with multimorbidity. The medical insurance system should be further improved to increase the contracting rate and utilization rate of family doctors in order to reduce the incidence of patient delay.