Non-Attendance in Hospital Appointments Based on Data From the Entire Region of Southern Denmark: Descriptive Analyses and Predictive Factors.

IF 3.4 2区医学 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Clinical Epidemiology Pub Date : 2025-03-25 eCollection Date: 2025-01-01 DOI:10.2147/CLEP.S512971

Bente Mertz Nørgård, Maria Iachina, Jette Ammentorp, Daria M Schwalbe, Karin Yde Waidtløw, Luna Richardt, Morten Sodemann

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引用次数: 0

Abstract

Purpose: We aimed to analyse the characteristics related to non-attendance at general outpatient hospital clinics in patients aged ≥18 years. An increased focus has been directed towards patient non-attendance at hospital appointments as it is related to patient risk and waste of resources in the healthcare system.

Patients and methods: In this cohort study, we retrieved data from the entire Region of Southern Denmark on i) non-attendance from the electronic medical journals from January 1, 2021 until December 31, 2022, and ii) data on all attended appointments from Danish health registries in the same period. We analysed the overall proportion of non-attendance, and characteristics of patients with non-attendance, relative to those who attended an appointment. We examined hospital type and patient demographics (age, sex, comorbidity, socioeconomic factors).

Results: Included were 614,157 patients, of which 12,244 were patients with a first non-attendance and 601,913 patients with a first attended appointment. The overall non-attendance proportion was 2.0%. The most prevalent type of underlying disease among non-attendant patients was musculoskeletal/connective tissue diseases (10%), which was also the most prevalent group of diseases among patients who attended hospital appointments (11%). In the regression model, compared to those who attended, the two strongest associations for non-attendance were patients aged 18-34 years, aOR=2.69 (95% CI 2.52-2.85), and patients diagnosed with mental/behavioral disorders, aOR=2.60 (95% CI 2.39-2.82). Other sociodemographic factors were associated with non-attendance including male sex (aOR=1.90 (95% CI 1.82-1.96)), patients aged 35-54 years (aOR=1.89 (95% CI 1.78-2.01)), living alone (aOR=1.72 (95% CI 1.65-1.79)), and not Danish nationality (aOR=1.65 (95% CI 1.57-1.74)).

Conclusion: Based on data from the Region of Southern Denmark (corresponding to 20% of the Danish population), the non-attendance proportion was low (2.0%). More research is needed, including other data-sets validating our findings, validation of registration practices, and qualitative research aspects of non-attendance.

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基于丹麦南部整个地区数据的医院预约不出勤率：描述性分析和预测因素。

目的：我们的目的是分析年龄≥18岁的普通门诊患者不就诊的相关特征。越来越多的人关注病人不去医院就诊，因为这与病人的风险和医疗保健系统的资源浪费有关。患者和方法：在这项队列研究中，我们检索了整个丹麦南部地区的数据：i)从2021年1月1日至2022年12月31日的电子医学期刊中没有出诊，ii)同期丹麦卫生登记处所有出诊的数据。我们分析了不出勤的总体比例，以及不出勤患者的特征，相对于那些参加了预约的患者。我们检查了医院类型和患者人口统计数据（年龄、性别、合并症、社会经济因素）。结果：纳入614,157例患者，其中12,244例为首次未就诊患者，601,913例为首次就诊患者。总体缺席率为2.0%。在未就诊的患者中，最常见的基础疾病类型是肌肉骨骼/结缔组织疾病（10%），这也是就诊的患者中最常见的疾病组（11%）。在回归模型中，与参加治疗的患者相比，不参加治疗的两个最强关联是18-34岁的患者，aOR=2.69 (95% CI 2.52-2.85)，和诊断为精神/行为障碍的患者，aOR=2.60 （95% CI 2.39-2.82）。其他与不出席相关的社会人口学因素包括男性（aOR=1.90 (95% CI 1.82-1.96)）、35-54岁患者（aOR=1.89 (95% CI 1.78-2.01)）、独居（aOR=1.72 (95% CI 1.65-1.79)）和非丹麦国籍（aOR=1.65 (95% CI 1.57-1.74)）。结论：根据丹麦南部地区（占丹麦人口的20%）的数据，不上勤率较低（2.0%）。需要更多的研究，包括验证我们的发现的其他数据集，注册实践的验证，以及不出勤的定性研究方面。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Clinical Epidemiology Medicine-Epidemiology

CiteScore

6.30

自引率

5.10%

发文量

169

审稿时长

16 weeks

期刊介绍： Clinical Epidemiology is an international, peer reviewed, open access journal. Clinical Epidemiology focuses on the application of epidemiological principles and questions relating to patients and clinical care in terms of prevention, diagnosis, prognosis, and treatment. Clinical Epidemiology welcomes papers covering these topics in form of original research and systematic reviews. Clinical Epidemiology has a special interest in international electronic medical patient records and other routine health care data, especially as applied to safety of medical interventions, clinical utility of diagnostic procedures, understanding short- and long-term clinical course of diseases, clinical epidemiological and biostatistical methods, and systematic reviews. When considering submission of a paper utilizing publicly-available data, authors should ensure that such studies add significantly to the body of knowledge and that they use appropriate validated methods for identifying health outcomes. The journal has launched special series describing existing data sources for clinical epidemiology, international health care systems and validation studies of algorithms based on databases and registries.