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Mathematics
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Quantitative Methods in Health Care Decisions
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Quantitative Methods in Health Care Decisions

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Probability and Statistics".

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 21165

Special Issue Editors

Prof. Dr. Francisco-José Vázquez-Polo

E-Mail Website
Guest Editor
Department of Quantitative Methods, University of Las Palmas de Gran Canaria, 35017 Canary Islands, Spain
Interests: Bayesian statistics
Prof. Dr. Miguel Angel Negrín-Hernández

E-Mail Website
Guest Editor
Quantitative Methods Department, University of Las Palmas de Gran Canaria, 35001 Las Palmas, Spain
Interests: health economics; bayesian methods in health economics; cost-effectiveness analysis; meta-analysis and equity in healthcare services
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Health economics is defined as the application of the theories, concepts, and techniques of economics to any issue related to health. There has been growing interest in this field, because health concerns represent a very important part of public government spending. The growing interest in recent years can be explained partly by reduced economic growth, deficits in public budgets, and increased unemployment rates and partly by the introduction of more expensive healthcare technologies. Moreover, the increasing number of treatments that can potentially be used to improve health makes the selection of cost-effective treatments even more necessary. This has led to increased attention to economic research questions in the health sector.

In this context, optimal decision making has become even more important, but decisions in healthcare systems are very complex due to their complicated design and their nonlinear, dynamic, and unpredictable nature. These characteristics require the inclusion of many elements to the support decision process, and advanced decision-making tools are necessary. Parametric and non-parametric modeling methods, network meta-analysis, matching methods in impact evaluation, or the use of Bayesian methods in healthcare and medicine, due mainly to the popularity of MCMC techniques, are just some examples of the progress in this area of quantitative methods in recent years.

This Special Issue will serve as an outlet for research papers using advanced computational and/or statistical methods for health economics, in general, as well as other particular topics such as cost-effectiveness, health policy evaluation, etc. This Special Issue will become a valuable resource for well-founded theoretical and applied data-driven research. Submissions should contain a significant computational or statistical methodological component for data analytics. In particular, this Special Issue welcomes contributions focusing on statistics that address problems involving large and/or complex data. Emphasis will be given to comprehensive and reproducible research, including data-driven methodology, algorithms, etc. Potential topics include, but are not limited to, the following:

  • Bayesian methods in health economics;
  • Network meta-analysis;
  • Machine learning for health economics researchers;
  • Parametric and non-parametric modeling of health data;
  • Computational methods for health data.

Prof. Dr. Francisco-José Vázquez-Polo
Dr. Miguel Angel Negrín Hernández
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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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

  • Health economics
  • Quantitative methods
  • Cost-effectiveness
  • Meta-analysis
  • Healthcare decisions

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Published Papers (5 papers)

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Research

15 pages, 1960 KiB  
Article
Bayesian Analysis of Population Health Data
by Dorota Młynarczyk, Carmen Armero, Virgilio Gómez-Rubio and Pedro Puig
Mathematics 2021, 9(5), 577; https://doi.org/10.3390/math9050577 - 9 Mar 2021
Cited by 4 | Viewed by 2967
Abstract
The analysis of population-wide datasets can provide insight on the health status of large populations so that public health officials can make data-driven decisions. The analysis of such datasets often requires highly parameterized models with different types of fixed and random effects to [...] Read more.
The analysis of population-wide datasets can provide insight on the health status of large populations so that public health officials can make data-driven decisions. The analysis of such datasets often requires highly parameterized models with different types of fixed and random effects to account for risk factors, spatial and temporal variations, multilevel effects and other sources on uncertainty. To illustrate the potential of Bayesian hierarchical models, a dataset of about 500,000 inhabitants released by the Polish National Health Fund containing information about ischemic stroke incidence for a 2-year period is analyzed using different types of models. Spatial logistic regression and survival models are considered for analyzing the individual probabilities of stroke and the times to the occurrence of an ischemic stroke event. Demographic and socioeconomic variables as well as drug prescription information are available at an individual level. Spatial variation is considered by means of region-level random effects. Full article
(This article belongs to the Special Issue Quantitative Methods in Health Care Decisions)
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12 pages, 1519 KiB  
Article
Non-Parametric Generalized Additive Models as a Tool for Evaluating Policy Interventions
by Jaime Pinilla and Miguel Negrín
Mathematics 2021, 9(4), 299; https://doi.org/10.3390/math9040299 - 3 Feb 2021
Cited by 4 | Viewed by 3762
Abstract
The interrupted time series analysis is a quasi-experimental design used to evaluate the effectiveness of an intervention. Segmented linear regression models have been the most used models to carry out this analysis. However, they assume a linear trend that may not be appropriate [...] Read more.
The interrupted time series analysis is a quasi-experimental design used to evaluate the effectiveness of an intervention. Segmented linear regression models have been the most used models to carry out this analysis. However, they assume a linear trend that may not be appropriate in many situations. In this paper, we show how generalized additive models (GAMs), a non-parametric regression-based method, can be useful to accommodate nonlinear trends. An analysis with simulated data is carried out to assess the performance of both models. Data were simulated from linear and non-linear (quadratic and cubic) functions. The results of this analysis show how GAMs improve on segmented linear regression models when the trend is non-linear, but they also show a good performance when the trend is linear. A real-life application where the impact of the 2012 Spanish cost-sharing reforms on pharmaceutical prescription is also analyzed. Seasonality and an indicator variable for the stockpiling effect are included as explanatory variables. The segmented linear regression model shows good fit of the data. However, the GAM concludes that the hypothesis of linear trend is rejected. The estimated level shift is similar for both models but the cumulative absolute effect on the number of prescriptions is lower in GAM. Full article
(This article belongs to the Special Issue Quantitative Methods in Health Care Decisions)
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16 pages, 375 KiB  
Article
Bayesian Variable Selection with Applications in Health Sciences
by Gonzalo García-Donato, María Eugenia Castellanos and Alicia Quirós
Mathematics 2021, 9(3), 218; https://doi.org/10.3390/math9030218 - 22 Jan 2021
Cited by 2 | Viewed by 2243
Abstract
In health sciences, identifying the leading causes that govern the behaviour of a response variable is a question of crucial interest. Formally, this can be formulated as a variable selection problem. In this paper, we introduce the basic concepts of the Bayesian approach [...] Read more.
In health sciences, identifying the leading causes that govern the behaviour of a response variable is a question of crucial interest. Formally, this can be formulated as a variable selection problem. In this paper, we introduce the basic concepts of the Bayesian approach for variable selection based on model choice, emphasizing the model space prior adoption and the algorithms for sampling from the model space and for posterior probabilities approximation; and show its application to two common problems in health sciences. The first concerns a problem in the field of genetics while the second is a longitudinal study in cardiology. In the context of these applications, considerations about control for multiplicity via the prior distribution over the model space, linear models in which the number of covariates exceed the sample size, variable selection with censored data, and computational aspects are discussed. The applications presented here also have an intrinsic statistical interest as the proposed models go beyond the standard general linear model. We believe this work will broaden the access of practitioners to Bayesian methods for variable selection. Full article
(This article belongs to the Special Issue Quantitative Methods in Health Care Decisions)
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13 pages, 370 KiB  
Article
Meta-Analysis with Few Studies and Binary Data: A Bayesian Model Averaging Approach
by Francisco-José Vázquez-Polo, Miguel-Ángel Negrín-Hernández and María Martel-Escobar
Mathematics 2020, 8(12), 2159; https://doi.org/10.3390/math8122159 - 4 Dec 2020
Cited by 3 | Viewed by 2363
Abstract
In meta-analysis, the existence of between-sample heterogeneity introduces model uncertainty, which must be incorporated into the inference. We argue that an alternative way to measure this heterogeneity is by clustering the samples and then determining the posterior probability of the cluster models. The [...] Read more.
In meta-analysis, the existence of between-sample heterogeneity introduces model uncertainty, which must be incorporated into the inference. We argue that an alternative way to measure this heterogeneity is by clustering the samples and then determining the posterior probability of the cluster models. The meta-inference is obtained as a mixture of all the meta-inferences for the cluster models, where the mixing distribution is the posterior model probabilities. When there are few studies, the number of cluster configurations is manageable, and the meta-inferences can be drawn with BMA techniques. Although this topic has been relatively neglected in the meta-analysis literature, the inference thus obtained accurately reflects the cluster structure of the samples used. In this paper, illustrative examples are given and analysed, using real binary data. Full article
(This article belongs to the Special Issue Quantitative Methods in Health Care Decisions)
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20 pages, 518 KiB  
Article
Inventory Management at a Chilean Hospital Pharmacy: Case Study of a Dynamic Decision-Aid Tool
by Fabián Silva-Aravena, Irlanda Ceballos-Fuentealba and Eduardo Álvarez-Miranda
Mathematics 2020, 8(11), 1962; https://doi.org/10.3390/math8111962 - 5 Nov 2020
Cited by 9 | Viewed by 8933
Abstract
Pharmacy inventory management is a critical process in healthcare centers. On the one hand, effective drug procurement is fundamental for fulfilling the therapeutic requirements of patients. On the other hand, as hospital pharmacies’ purchasing and storage costs comprise an important share in the [...] Read more.
Pharmacy inventory management is a critical process in healthcare centers. On the one hand, effective drug procurement is fundamental for fulfilling the therapeutic requirements of patients. On the other hand, as hospital pharmacies’ purchasing and storage costs comprise an important share in the hospital budgets, efficient inventory management may play a central role in operational cost containment. Therefore, healthcare centers should design and implement decision-aid strategies for planning the purchase of drugs with the aim of avoiding excessive purchasing volumes and optimizing warehouse capacity, while also meeting forecast demand and ensuring critical stock levels. In this study, we present the methodological features of a decision-aid tool for planning the purchases and inventory levels for the controlled medication pharmacy of the Regional Hospital of Talca, Chile. We report the results obtained after 1 year of operation; these results show that our strategy produced more than 7% savings compared to the regular inventory planning strategy and was more effective in preserving critical stock levels. Furthermore, from a computational point of view, our strategy outperforms a recently published approach for a similar application. Full article
(This article belongs to the Special Issue Quantitative Methods in Health Care Decisions)
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