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Real-World Data for Health Research: The Potential of Longitudinal Data Mining

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A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Health Care Sciences & Services".

Deadline for manuscript submissions: closed (30 December 2021) | Viewed by 14168

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Special Issue Editors

Dr. Sara Malo

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Guest Editor

Preventive Medicine and Public Health Department, University of Zaragoza, 50009 Zaragoza, Spain
Interests: health services research; pharmacoepidemiology; drug utilization studies; drug adherence; cardiovascular drug prevention

Dr. Antonio Gimeno-Miguel

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Guest Editor

1. EpiChron Research Group, Aragon Health Sciences Institute (IACS), Institute for Health Research Aragón (IIS Aragón), REDISSEC, Miguel Servet University Hospital, Zaragoza, Spain
2. Research Network on Chronicity, Primary Care and Health Promotion (RICAPPS), Institute of Health Carlos III, Madrid, Spain
Interests: epidemiology; public health; chronic diseases; multimorbidity; real-world data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the era of digitalization, both the availability and the utilization of demographic, social, clinical, health, work and lifestyle information are increasing and essential for obtaining real-world evidence (RWE). The evidence coming from large-scale observational studies complements well that traditionally obtained through clinical trials.

Integration and analysis of large real-world data (RWD) sets is performed by means of data mining processes. In this way, data mining allows to find repetitive patterns that explain data behaviour. In the field of health research, for instance, it represents an opportunity to conduct clinical-epidemiological studies aimed at identifying profiles of patients with different probabilities of presenting a specific heatlh outcome, subjects who would benefit the most from a public health intervention, or those who would respond better than others to a pharmacological treatment. Longitudinal data mining can also be useful to explore how diseases tend to cluster in the form of morbidity patterns in the population and how these patterns evolve over time following specific disease trajectories.

Generating RWE on this area may help us fill the existing knowledge gaps and support different stakeholders involved in healthcare decisions to take the most effective measures for the improvement of both patients’ quality of life and healthcare systems’ performance.

On this basis, we consider of special relevance the potential of integration and mining of health-related longitudinal data in the achievement of the best RWE on health research. Papers addressing this topic are invited for this Special Issue, especially those combining a high academic standard and a practical focus on providing relevant RWE for clinical paractice and health systems.

Dr. Sara Malo
Dr. Antonio Gimeno-Miguel
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

real-world data
data management
data analyses
big data
health care utilization
drug utilization study

Published Papers (5 papers)

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Research

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10 pages, 323 KiB

Open AccessArticle

Baseline Drug Treatments as Indicators of Increased Risk of COVID-19 Mortality in Spain and Italy

by Kevin Bliek-Bueno, Sara Mucherino, Beatriz Poblador-Plou, Francisca González-Rubio, Mercedes Aza-Pascual-Salcedo, Valentina Orlando, Mercedes Clerencia-Sierra, Ignatios Ioakeim-Skoufa, Enrico Coscioni, Jonás Carmona-Pírez, Alessandro Perrella, Ugo Trama, Alexandra Prados-Torres, Enrica Menditto and Antonio Gimeno-Miguel

Int. J. Environ. Res. Public Health 2021, 18(22), 11786; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph182211786 - 10 Nov 2021

Cited by 5 | Viewed by 2454

Abstract

This study aims to identify baseline medications that, as a proxy for the diseases they are dispensed for, are associated with increased risk of mortality in COVID-19 patients from two regions in Spain and Italy using real-world data. We conducted a cross-country, retrospective, observational study including 8570 individuals from both regions with confirmed SARS-CoV-2 infection between 4 March and 17 April 2020, and followed them for a minimum of 30 days to allow sufficient time for the studied event, in this case death, to occur. Baseline demographic variables and all drugs dispensed in community pharmacies three months prior to infection were extracted from the PRECOVID Study cohort (Aragon, Spain) and the Campania Region Database (Campania, Italy) and analyzed using logistic regression models. Results show that the presence at baseline of potassium-sparing agents, antipsychotics, vasodilators, high-ceiling diuretics, antithrombotic agents, vitamin B12, folic acid, and antiepileptics were systematically associated with mortality in COVID-19 patients from both countries. Treatments for chronic cardiovascular and metabolic diseases, systemic inflammation, and processes with increased risk of thrombosis as proxies for the conditions they are intended for can serve as timely indicators of an increased likelihood of mortality after the infection, and the assessment of pharmacological profiles can be an additional approach to the identification of at-risk individuals in clinical practice. Full article

(This article belongs to the Special Issue Real-World Data for Health Research: The Potential of Longitudinal Data Mining)

14 pages, 6288 KiB

Open AccessArticle

Evolution of Cardiovascular Risk Factors in a Worker Cohort: A Cluster Analysis

by Sara Castel-Feced, Lina Maldonado, Isabel Aguilar-Palacio, Sara Malo, Belén Moreno-Franco, Eusebio Mur-Vispe, José-Tomás Alcalá-Nalvaiz and María José Rabanaque-Hernández

Int. J. Environ. Res. Public Health 2021, 18(11), 5610; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18115610 - 24 May 2021

Viewed by 1835

Abstract

The identification of the cardiovascular risk factor (CVRF) profile of individual patients is key to the prevention of cardiovascular disease (CVD), and the development of personalized preventive approaches. Using data from annual medical examinations in a cohort of workers, the aim of the study was to characterize the evolution of CVRFs and the CVD risk score (SCORE) over three time points between 2009 and 2017. For descriptive analyses, mean, standard deviation, and quartile values were used for quantitative variables, and percentages for categorical ones. Cluster analysis was performed using the Kml3D package in R software. This algorithm, which creates distinct groups based on similarities in the evolution of variables of interest measured at different time points, divided the cohort into 2 clusters. Cluster 1 comprised younger workers with lower mean body mass index, waist circumference, blood glucose values, and SCORE, and higher mean HDL cholesterol values. Cluster 2 had the opposite characteristics. In conclusion, it was found that, over time, subjects in cluster 1 showed a higher improvement in CVRF control and a lower increase in their SCORE, compared with cluster 2. The identification of subjects included in these profiles could facilitate the development of better personalized medical approaches to CVD preventive measures. Full article

(This article belongs to the Special Issue Real-World Data for Health Research: The Potential of Longitudinal Data Mining)

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10 pages, 379 KiB

Open AccessArticle

Drug Prescription Profiles in Patients with Polypharmacy in Spain: A Large-Scale Pharmacoepidemiologic Study Using Real-World Data

by Miguel Ángel Hernández-Rodríguez, Ermengol Sempere-Verdú, Caterina Vicens-Caldentey, Francisca González-Rubio, Félix Miguel-García, Vicente Palop-Larrea, Ramón Orueta-Sánchez, Óscar Esteban-Jiménez, Mara Sempere-Manuel, María Pilar Arroyo-Aniés, Buenaventura Fernández-San José, José Ignacio de Juan-Roldán and Ignatios Ioakeim-Skoufa

Int. J. Environ. Res. Public Health 2021, 18(9), 4754; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18094754 - 29 Apr 2021

Cited by 12 | Viewed by 4070

Abstract

We aimed to identify and compare medication profiles in populations with polypharmacy between 2005 and 2015. We conducted a cross-sectional study using information from the Computerized Database for Pharmacoepidemiologic Studies in Primary Care (BIFAP, Spain). We estimated the prevalence of therapeutic subgroups in all individuals 15 years of age and older with polypharmacy (≥5 drugs during ≥6 months) using the Anatomical Therapeutic Chemical classification system level 4, by sex and age group, for both calendar years. The most prescribed drugs were proton-pump inhibitors (PPIs), statins, antiplatelet agents, benzodiazepine derivatives, and angiotensin-converting enzyme inhibitors. The greatest increases between 2005 and 2015 were observed in PPIs, statins, other antidepressants, and β-blockers, while the prevalence of antiepileptics was almost tripled. We observed increases in psychotropic drugs in women and cardiovascular medications in men. By patient´s age groups, there were notable increases in antipsychotics, antidepressants, and antiepileptics (15–44 years); antidepressants, PPIs, and selective β-blockers (45–64 years); selective β-blockers, biguanides, PPIs, and statins (65–79 years); and in statins, selective β-blockers, and PPIs (80 years and older). Our results revealed important increases in the use of specific therapeutic subgroups, like PPIs, statins, and psychotropic drugs, highlighting opportunities to design and implement strategies to analyze such prescriptions’ appropriateness. Full article

(This article belongs to the Special Issue Real-World Data for Health Research: The Potential of Longitudinal Data Mining)

20 pages, 389 KiB

Open AccessArticle

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

by Sara Mucherino, Antonio Gimeno-Miguel, Jonas Carmona-Pirez, Francisca Gonzalez-Rubio, Ignatios Ioakeim-Skoufa, Aida Moreno-Juste, Valentina Orlando, Mercedes Aza-Pascual-Salcedo, Beatriz Poblador-Plou, Enrica Menditto and Alexandra Prados-Torres

Int. J. Environ. Res. Public Health 2021, 18(9), 4422; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18094422 - 21 Apr 2021

Cited by 5 | Viewed by 2327

Abstract

The pressing problem of multimorbidity and polypharmacy is aggravated by the lack of specific care models for this population. We aimed to investigate the evolution of multimorbidity and polypharmacy patterns in a given population over a 4-year period (2011–2015). A cross-sectional, observational study among the EpiChron Cohort, including anonymized demographic, clinical and drug dispensation information of all users of the public health system ≥65 years in Aragon (Spain), was performed. An exploratory factor analysis, stratified by age and sex, using an open cohort was carried out based on the tetra-choric correlations among chronic diseases and dispensed drugs during 2011 and compared with 2015. Seven baseline patterns were identified during 2011 named as: mental health, respiratory, allergic, mechanical pain, cardiometabolic, osteometabolic, and allergic/derma. Of the epidemiological patterns identified in 2015, six were already present in 2011 but a new allergic/derma one appeared. Patterns identified in 2011 were more complex in terms of both disease and drugs. Results confirmed the existing association between age and clinical complexity. The systematic associations between diseases and drugs remain similar regarding their clinical nature over time, helping in early identification of potential interactions in multimorbid patients with a high risk of negative health outcomes due to polypharmacy. Full article

(This article belongs to the Special Issue Real-World Data for Health Research: The Potential of Longitudinal Data Mining)

Review

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21 pages, 1886 KiB

Open AccessReview

Real-World Utilization of Target- and Immunotherapies for Lung Cancer: A Scoping Review of Studies Based on Routinely Collected Electronic Healthcare Data

by Andrea Spini, Giulia Hyeraci, Claudia Bartolini, Sandra Donnini, Pietro Rosellini, Rosa Gini, Marina Ziche, Francesco Salvo and Giuseppe Roberto

Int. J. Environ. Res. Public Health 2021, 18(14), 7679; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18147679 - 19 Jul 2021

Cited by 2 | Viewed by 2171

Abstract

Routinely collected electronic healthcare data (rcEHD) have a tremendous potential for enriching pre-marketing evidence on target- and immunotherapies used to treat lung cancer (LC). A scoping review was performed to provide a structured overview of available rcEHD-based studies on this topic and to support the execution of future research by facilitating access to pertinent literature both for study design and benchmarking. Eligible studies published between 2016 and 2020 in PubMed and ISI Web of Science were searched. Data source and study characteristics, as well as evidence on drug utilization and survival were extracted. Thirty-two studies were included. Twenty-six studies used North American data, while three used European data only. Thirteen studies linked ≥1 data source types among administrative/claims data, cancer registries and medical/health records. Twenty-nine studies retrieved cancer-related information from medical records/cancer registries and 31 studies retrieved information on drug utilization or survival from medical records or administrative/claim data. Most part of studies concerned non-small-cell-LC patients (29 out of 32) while none focused on small-cell-LC. Study cohorts ranged between 85 to 81,983 patients. Only two studies described first-line utilization of immunotherapies. Results from this review will serve as a starting point for the execution of future rcEHD-based studies on innovative LC pharmacotherapies. Full article

(This article belongs to the Special Issue Real-World Data for Health Research: The Potential of Longitudinal Data Mining)

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