Changes in Multimorbidity and Polypharmacy Patterns in Young and Adult Population over a 4-Year Period: A 2011–2015 Comparison Using Real-World Data

Mucherino, Sara; Gimeno-Miguel, Antonio; Carmona-Pírez, Jonás; González-Rubio, Francisca; Ioakeim-Skoufa, Ignatios; Moreno-Juste, Aida; Orlando, Valentina; Aza-Pascual-Salcedo, Mercedes; Poblador‐Plou, Beatriz; Menditto, Enrica; Prados‐Torres, Alexandra

doi:10.3390/ijerph18094422

Cited by 9 publications

(9 citation statements)

References 43 publications

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“…Earlier studies focusing on chronic conditions identified from electronic health records evidenced the existence of systematic associations between chronic diseases, whereby chronic diseases, often from dissimilar disease categories, coappeared within a multimorbidity pattern or cluster [24][25][26]. Importantly, though, these studies showed that the complexity of multimorbidity patterns in terms of diseases and associated drug use increased with age, which holds true for both genders.…”

Section: Discussionmentioning

confidence: 98%

Exploring Patient Multimorbidity and Complexity Using Health Insurance Claims Data: A Cluster Analysis Approach (Preprint)

Nicolet¹,

Assouline²,

Pogam³

et al. 2021

Preprint

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BACKGROUND Although the trend of progressing morbidity is widely recognized, there are numerous challenges when studying multimorbidity and patient complexity. For multimorbid or complex patients, prone to fragmented care and high health care use, novel estimation approaches need to be developed. OBJECTIVE This study aims to investigate the patient multimorbidity and complexity of Swiss residents aged ≥50 years using clustering methodology in claims data. METHODS We adopted a clustering methodology based on random forests and used 34 pharmacy-based cost groups as the only input feature for the procedure. To detect clusters, we applied hierarchical density-based spatial clustering of applications with noise. The reasonable hyperparameters were chosen based on various metrics embedded in the algorithms (out-of-bag misclassification error, normalized stress, and cluster persistence) and the clinical relevance of the obtained clusters. RESULTS Based on cluster analysis output for 18,732 individuals, we identified an outlier group and 7 clusters: individuals without diseases, patients with only hypertension-related diseases, patients with only mental diseases, complex high-cost high-need patients, slightly complex patients with inexpensive low-severity pharmacy-based cost groups, patients with 1 costly disease, and older high-risk patients. CONCLUSIONS Our study demonstrated that cluster analysis based on pharmacy-based cost group information from claims-based data is feasible and highlights clinically relevant clusters. Such an approach allows expanding the understanding of multimorbidity beyond simple disease counts and can identify the population profiles with increased health care use and costs. This study may foster the development of integrated and coordinated care, which is high on the agenda in policy making, care planning, and delivery.

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Section: Discussionmentioning

confidence: 98%

Exploring Patient Multimorbidity and Complexity Using Health Insurance Claims Data: A Cluster Analysis Approach (Preprint)

Nicolet¹,

Assouline²,

Pogam³

et al. 2021

Preprint

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show abstract

“…It is logical to think that some combinations of diseases may have a higher impact on health than others and that pharmacological management plays a vital role in clinical outcomes. The high mean number of diseases and drugs in older adults may involve disease–disease, drug–drug, and drug–disease interactions, as well as prescribing cascades [ 68 , 69 ], increasing the risk of potentially inappropriate medication and its clinical consequences [ 70 , 71 , 72 ]. An interesting finding, for example, was the high use of drugs with anticholinergic activity in clusters with dementia, especially in octogenarians and nonagenarians.…”

Section: Discussionmentioning

confidence: 99%

Multimorbidity Clusters in the Oldest Old: Results from the EpiChron Cohort

Ioakeim-Skoufa

Clerencia-Sierra

Moreno-Juste

et al. 2022

IJERPH

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Multimorbidity is challenging for both patients and healthcare systems due to its increasing prevalence and high impact on people’s health and well-being. The risk of multimorbidity increases with age, but there is still more to discover regarding the clinical profile of the oldest old. In this study, we used information from the EpiChron Cohort Study to identify multimorbidity patterns in individuals who died during the period 2010–2019 at the ages of 80–89, 90–99, and ≥100. This cohort links the demographic, clinical, and drug dispensation information of public health system users in Aragón, Spain. We saw a significantly lower number of chronic diseases and drugs and a lower prevalence of polypharmacy in centenarians compared to those aged 80–99. K-means clustering revealed different multimorbidity clusters by sex and age group. We observed clusters of cardiovascular and metabolic diseases, obstructive pulmonary conditions, and neoplasms, amongst other profiles. One in three octogenarian women had a metabolic pattern (diabetes, dyslipidaemia, and other endocrine–metabolic disorders) with the highest number of diseases (up to seven) and prevalence of polypharmacy (64%). We observed clusters of dementia and genitourinary disorders in individuals on medication with anticholinergic activity. Our study offers an opportunity to better understand the urgency of adequately addressing multimorbidity in our older adults.

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“…Additionally, it is possible that the characteristics of patients prescribed hypnotics or anxiolytics changed in the periods. By incorporating disease information, we could better understand the relationship between the trend of drug prescriptions and diseases [ 31 ]. However, one strength of this study is that we revealed the prescription trends, particularly in elderly patients, and trends observed in recent years.…”

Section: Discussionmentioning

confidence: 99%

Trends in the Prescription of Benzodiazepine Receptor Agonists from 2009 to 2020: A Retrospective Study Using Electronic Healthcare Record Data of a University Hospital in Japan

et al. 2021

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In recent years, the prescription trends of benzodiazepine receptor agonists (BZRAs) have not been investigated in Japan despite the publication of guidelines that promote cautious use of BZRAs. The prescription trend of BZRAs was assessed using the electronic healthcare records data of a University Hospital in Japan. The data from April 2009 to March 2021 were used. The following three types of outcomes were set: the proportion of patients who were prescribed with BZRAs within those prescribed hypnotics or anxiolytics; the mean number of the types of prescribed BZRAs, and the mean average daily doses of BZRAs. The same analysis was conducted for benzodiazepines (BZDs) and non-benzodiazepines (Z-drugs). As a result, we found that the proportions of patients prescribed BZRAs within those prescribed hypnotics or anxiolytics began to decrease, particularly from 2015 for patients aged <75 years and those aged ≥75 years. Further, the degree of decrease was larger in patients aged ≥75 years. The proportion for BZDs decreased particularly in the study period, and the proportion for Z-drugs also began to decrease approximately from 2016 in patients aged ≥75 years. The results suggest a possibility that guidelines affected the decreased prescriptions of BZRAs.

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Changes in Multimorbidity and Polypharmacy Patterns in Young and Adult Population over a 4-Year Period: A 2011–2015 Comparison Using Real-World Data

Cited by 9 publications

References 43 publications

Exploring Patient Multimorbidity and Complexity Using Health Insurance Claims Data: A Cluster Analysis Approach (Preprint)

Exploring Patient Multimorbidity and Complexity Using Health Insurance Claims Data: A Cluster Analysis Approach (Preprint)

Multimorbidity Clusters in the Oldest Old: Results from the EpiChron Cohort

Trends in the Prescription of Benzodiazepine Receptor Agonists from 2009 to 2020: A Retrospective Study Using Electronic Healthcare Record Data of a University Hospital in Japan

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