A quantitative evidence base for population health: applying utilization-based cluster analysis to segment a patient population

Background: To improve population health it is crucial to understand the different care needs within a population. Traditional population groups are often based on characteristics such as age or morbidities. However, this does not take into account specific care needs across care settings, and tends to focus on high needs patients only. This paper explores the potential of using utilisation-based cluster analysis to segment a general patient population into homogenous groups. Methods: Administrative datasets covering primary and secondary care were used to construct a database of 300,000 patients, which included socio-demographics variables, morbidities, care utilisation, and cost. A k-means cluster analysis grouped the patients into segments with distinct care utilisation, based on six utilisation variables: non-elective inpatient admissions, elective inpatient admissions, outpatient visits, GP practice visits, GP home visits, and prescriptions. These segments were analysed post-hoc to understand their morbidity and demographic profile. Results: Eight population segments were identified, and utilisation of each care setting was significantly different across all segments. Each segment also presented with different morbidity patterns and demographic characteristics, creating eight distinct care user types. Comparing these segments to traditional patient groups shows the heterogeneity of these approaches, especially for lower needs patients. Conclusions: This analysis shows that utilisation-based cluster analysis segments a patient population into distinct groups with unique care priorities, providing a quantitative evidence base to improve population health. Contrary to traditional methods, this approach also segments lower needs populations, which can be used to inform preventative interventions. In addition, the identification of different care user types provides insight into needs across the care continuum

The impact of diabetes on multiple avoidable admissions: a cross-sectional study

Background Multiple admissions for ambulatory care sensitive conditions (ACSC) are responsible for an important proportion of health care expenditures. Diabetes is one of the conditions consensually classified as an ACSC being considered a major public health concern. The aim of this study was to analyse the impact of diabetes on the occurrence of multiple admissions for ACSC. Methods We analysed inpatient data of all public Portuguese NHS hospitals from 2013 to 2015 on multiple admissions for ACSC among adults aged 18 or older. Multiple ACSC users were identified if they had two or more admissions for any ACSC during the period of analysis. Two logistic regression models were computed. A baseline model where a logistic regression was performed to assess the association between multiple admissions and the presence of diabetes, adjusting for age and sex. A full model to test if diabetes had no constant association with multiple admissions by any ACSC across age groups. Results Among 301,334 ACSC admissions, 144,209 (47.9%) were classified as multiple admissions and from those, 59,436 had diabetes diagnosis, which corresponded to 23,692 patients. Patients with diabetes were 1.49 times (p < 0,001) more likely to be admitted multiple times for any ACSC than patients without diabetes. Younger adults with diabetes (18–39 years old) were more likely to become multiple users. Conclusion Diabetes increases the risk of multiple admissions for ACSC, especially in younger adults. Diabetes presence is associated with a higher resource utilization, which highlights the need for the implementation of adequate management of chronic diseases policies.NOVASaudeinfo:eu-repo/semantics/publishedVersio

Enhancing risk stratification for use in integrated care - A cluster analysis of high-risk patients in a retrospective cohort study

Objective To show how segmentation can enhance risk stratification tools for integrated care, by providing insight into different care usage patterns within the high-risk population. Design A retrospective cohort study. A risk score was calculated for each person using a logistic regression, which was then used to select the top 5% high-risk individuals. This population was segmented based on the usage of different care settings using a k-means cluster analysis. Data from 2008 to 2011 were used to create the risk score and segments, while 2012 data were used to understand the predictive abilities of the models. Setting and participants Data were collected from administrative data sets covering primary and secondary care for a random sample of 300 000 English patients. Main measures The high-risk population was segmented based on their usage of 4 different care settings: emergency acute care, elective acute care, outpatient care and GP care. Results While the risk strata predicted care usage at a high level, within the high-risk population, usage varied significantly. 4 different groups of high-risk patients could be identified. These 4 segments had distinct usage patterns across care settings, reflecting different levels and types of care needs. The 2008–2011 usage patterns of the 4 segments were consistent with the 2012 patterns. Discussion Cluster analyses revealed that the high-risk population is not homogeneous, as there exist 4 groups of patients with different needs across the care continuum. Since the patterns were predictive of future care use, they can be used to develop integrated care programmes tailored to these different groups. Conclusions Usage-based segmentation augments risk stratification by identifying patient groups with different care needs, around which integrated care programmes can be designed

Do hospitalisations for Ambulatory Care Sensitive Conditions reflect low access to primary care? An observational cohort study of primary care utilisation prior to hospitalisation

Objectives To explore whether hospitalisations for ambulatory care sensitive conditions (ACSCs) are associated with low access to primary care. Design Observational cohort study over 2008 to 2012 using the Clinical Practice Research Datalink (CPRD) and Hospital Episode Statistics (HES) databases. Setting English primary and secondary care. Participants A random sample of 300 000 patients. Main outcome measures Emergency hospitalisation for an ACSC. Results Over the long term, patients with ACSC hospitalisations had on average 2.33 (2.17 to 2.49) more general practice contacts per 6 months than patients with similar conditions who did not require hospitalisation. When accounting for the number of diagnosed ACSCs, age, gender and GP practice through a nested case–control method, the difference was smaller (0.64 contacts), but still significant (p<0.001). In the short-term analysis, measured over the 6 months prior to hospitalisation, patients used more GP services than on average over the 5 years. Cases had significantly (p<0.001) more primary care contacts in the 6 months before ACSC hospitalisations (7.12, 95% CI 6.95 to 7.30) than their controls during the same 6 months (5.57, 95% CI 5.43 to 5.72). The use of GP services increased closer to the time of hospitalisation, with a peak of 1.79 (1.74 to 1.83) contacts in the last 30 days before hospitalisation. Conclusions This study found no evidence to support the hypothesis that low access to primary care is the main driver of ACSC hospitalisations. Other causes should also be explored to understand how to use ACSC admission rates as quality metrics, and to develop the appropriate interventions