scholarly journals Modeling Count Data for Healthcare Utilization: an Empirical Study of Outpatient Visits Among Vietnamese Older People

2020 ◽  
Author(s):  
Dung Duc Le ◽  
Roberto Leon-Gonzalez ◽  
Joseph Upile Matola
Keyword(s):  
Model Selection ◽  
Older People ◽  
Healthcare Utilization ◽  
Count Data ◽  
Selection Criteria ◽  
Cross Validation ◽  
Outpatient Visits ◽  
Best Fitting ◽  
Fold Cross Validation ◽  
Fitting Model

Abstract Background Vietnam is undergoing a fast aging process that poses potential critical issues for older people and central among those is demand for healthcare utilization. However, healthcare utilization, here measured as count data, creates challenges for modeling because such data typically has distributions that are skewed with a large mass at zero. This study compares empirical econometric strategies for the modeling of healthcare utilization (measured as the number of outpatient visits in the last 12 months), and identifies the determinants of healthcare utilization among Vietnamese older people based on the best-fitting model identified. Methods Using the Vietnam Household Living Standard Survey in 2006 (N=2426), nine econometric regression models for count data were examined to identify the best-fitting one. We used model selection criteria; statistical tests; and goodness-of-fit for in-sample model selection. In addition, we conducted 10-fold cross-validation checks to examine reliability of in-sample model selection. Finally, we utilized marginal effects to identify the factors associated with the number of outpatient visits among Vietnamese older people based on the best-fitting model identified. Results We found strong evidence in favor of hurdle negative binomial model 2 (HNB2) for both in-sample selection and 10-fold cross-validation checks. The marginal effect results of the HNB2 showed that ethnicity, region, household size, health insurance, smoking status, non-communicable diseases, and disability were significantly associated with the number of outpatient visits. The predicted probabilities for each count event showed the distinct trends of healthcare utilization among specific groups: at low count events, women and people in the younger age group used more healthcare utilization than did men and their counterparts in older age groups, but a reversed trend was found at higher count events. Conclusions Data come in all shapes and sizes, this study highlights the importance of model specification checks and model selection criteria to avoid potential biased estimates as a result of model misspecifications. This study’s findings lay the groundwork for future research on the modeling of healthcare utilization in developing countries and those findings could be used to forecast on healthcare demand and making provisions for healthcare costs.

scholarly journals Modeling Count Data for Healthcare Utilization: an Empirical Study of Outpatient Visits Among Vietnamese Older People

2020 ◽  
Author(s):  
Dung Duc Le ◽  
Roberto Leon-Gonzalez ◽  
Joseph Upile Matola
Keyword(s):  
Model Selection ◽  
Older People ◽  
Healthcare Utilization ◽  
Count Data ◽  
Aging Process ◽  
Cross Validation ◽  
Outpatient Visits ◽  
Best Fitting ◽  
Fold Cross Validation ◽  
Fitting Model

Abstract Background Vietnam is undergoing an unprecedented pace of aging process and is expected to experience the fastest aging process in region. Association between increasing age and health deterioration has been well-documented across settings. Consequently, demand for healthcare utilization is rising among older people. However, healthcare utilization, here measured as count data, creates challenges for modeling because such data typically has distributions that are skewed with a large mass at zero. This study compares empirical econometric strategies for the modeling of healthcare utilization (measured as the number of outpatient visits in the last 12 months), and identifies the determinants of healthcare utilization among Vietnamese older people based on the best-fitting model identified. Methods Using the Vietnam Household Living Standard Survey in 2006 (N=2426), nine econometric regression models for count data were examined to identify the best-fitting one. We used model selection criteria; statistical tests; and goodness-of-fit for in-sample model selection. In addition, we conducted 10-fold cross-validation checks to examine reliability of in-sample model selection. Finally, we utilized marginal effects to identify the factors associated with number of outpatient visits among Vietnamese older people based on the best-fitting model identified. Results We found strong evidence in favor of hurdle negative binomial model 2 (HNB2) for both in-sample selection and 10-fold cross-validation checks. The marginal effect results of the HNB2 showed that predisposing, enabling, need, and lifestyle factors were significantly associated with number of outpatient visits. The predicted probabilities for each count event showed the distinct trends of healthcare utilization among specific groups: at low count events, women and people in younger age group used more healthcare utilization than did men and their counterparts in older age groups, but a reversed trend was found at higher count events. Conclusions The findings here suggest that the HNB2 model should be considered for use in modeling counts of healthcare use. This study’s findings lay the groundwork for future research on the modeling of healthcare utilization in developing countries and those findings could be used to forecast on healthcare demand and making provisions for healthcare costs.

scholarly journals Modeling Count Data for Healthcare Utilization: an Empirical Study of Outpatient Visits Among Vietnamese Older People

2020 ◽  
Author(s):  
Dung Duc Le ◽  
Roberto Leon-Gonzalez ◽  
Joseph Upile Matola
Keyword(s):  
Model Selection ◽  
Older People ◽  
Healthcare Utilization ◽  
Count Data ◽  
Aging Process ◽  
Cross Validation ◽  
Outpatient Visits ◽  
Best Fitting ◽  
Fold Cross Validation ◽  
Fitting Model

Abstract Background Vietnam is undergoing an unprecedented pace of aging process and is expected to experience the fastest aging process in region. Association between increasing age and health deterioration has been well-documented across settings. Consequently, demand for healthcare utilization is rising among older people. However, healthcare utilization, here measured as count data, creates challenges for modeling because such data typically has distributions that are skewed with a large mass at zero. This study compares empirical econometric strategies for the modeling of healthcare utilization (measured as the number of outpatient visits in the last 12 months), and identifies the determinants of healthcare utilization among Vietnamese older people based on the best-fitting model identified. Methods Using the Vietnam Household Living Standard Survey in 2006 (N = 2426), nine econometric regression models for count data were examined to identify the best-fitting one. We used model selection criteria; statistical tests; and goodness-of-fit for in-sample model selection. In addition, we conducted 10-fold cross-validation checks to examine reliability of in-sample model selection. Finally, we utilized marginal effects to identify the factors associated with number of outpatient visits among Vietnamese older people based on the best-fitting model identified. Results We found strong evidence in favor of hurdle negative binomial model 2 (HNB2) for both in-sample selection and 10-fold cross-validation checks. The marginal effect results of the HNB2 showed that predisposing, enabling, need, and lifestyle factors were significantly associated with number of outpatient visits. The predicted probabilities for each count event showed the distinct trends of healthcare utilization among specific groups: at low count events, women and people in younger age group used more healthcare utilization than did men and their counterparts in older age groups, but a reversed trend was found at higher count events. Conclusions The findings here suggest that the HNB2 model should be considered for use in modeling counts of healthcare use. This study’s findings lay the groundwork for future research on the modeling of healthcare utilization in developing countries and those findings could be used to forecast on healthcare demand and making provisions for healthcare costs.

scholarly journals Modeling count data for health care utilization: an empirical study of outpatient visits among Vietnamese older people

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Duc Dung Le ◽  
Roberto Leon Gonzalez ◽  
Joseph Upile Matola
Keyword(s):  
Model Selection ◽  
Older People ◽  
Healthcare Utilization ◽  
Count Data ◽  
Cross Validation ◽  
Negative Binomial ◽  
Outpatient Visits ◽  
Best Fitting ◽  
Fold Cross Validation ◽  
Fitting Model

Abstract Background Vietnam is undergoing a fast-aging process that poses potential critical issues for older people and central among those is demand for healthcare utilization. However, healthcare utilization, here measured as count data, creates challenges for modeling because such data typically has distributions that are skewed with a large mass at zero. This study compares empirical econometric strategies for the modeling of healthcare utilization (measured as the number of outpatient visits in the last 12 months) and identifies the determinants of healthcare utilization among Vietnamese older people based on the best-fitting model identified. Methods Using the Vietnam Household Living Standard Survey in 2006 (N = 2426), nine econometric regression models for count data were examined to identify the best-fitting one. We used model selection criteria, statistical tests and goodness-of-fit for in-sample model selection. In addition, we conducted 10-fold cross-validation checks to examine reliability of the in-sample model selection. Finally, we utilized marginal effects to identify the factors associated with the number of outpatient visits among Vietnamese older people based on the best-fitting model identified. Results We found strong evidence in favor of hurdle negative binomial model 2 (HNB2) for both in-sample selection and 10-fold cross-validation checks. The marginal effect results of the HNB2 showed that ethnicity, region, household size, health insurance, smoking status, non-communicable diseases, and disability were significantly associated with the number of outpatient visits. The predicted probabilities for each count event revealed the distinct trends of healthcare utilization among specific groups: at low count events, women and people in the younger age group used more healthcare utilization than did men and their counterparts in older age groups, but a reverse trend was found at higher count events. Conclusions The high degree of skewness and dispersion that typically characterizes healthcare utilization data affects the appropriateness of the econometric models that should be used in modeling such data. In the case of Vietnamese older people, our study findings suggest that hurdle negative binomial models should be used in the modeling of healthcare utilization given that the data-generating process reflects two different decision-making processes.

scholarly journals Modeling Count Data for Healthcare Utilization: an Empirical Study of Outpatient Visits Among Vietnamese Older People

2020 ◽  
Author(s):  
Dung Duc Le ◽  
Roberto Leon-Gonzalez ◽  
Joseph Upile Matola
Keyword(s):  
Empirical Study ◽  
Older People ◽  
Healthcare Utilization ◽  
Count Data ◽  
Outpatient Visits

Abstract The authors have withdrawn this preprint due to erroneous posting.

Model selection for direct marketing: performance criteria and validation methods

2008 ◽  
Vol 26 (3) ◽  
pp. 275-292 ◽  
Author(s):  
Geng Cui ◽  
Man Leung Wong ◽  
Guichang Zhang ◽  
Lin Li
Keyword(s):  
Neural Networks ◽  
Model Selection ◽  
Cross Validation ◽  
Direct Marketing ◽  
Latent Class Models ◽  
Performance Criteria ◽  
Content Type ◽  
Validation Methods ◽  
Practical Implications ◽  
Fold Cross Validation

PurposeThe purpose of this paper is to assess the performance of competing methods and model selection, which are non‐trivial issues given the financial implications. Researchers have adopted various methods including statistical models and machine learning methods such as neural networks to assist decision making in direct marketing. However, due to the different performance criteria and validation techniques currently in practice, comparing different methods is often not straightforward.Design/methodology/approachThis study compares the performance of neural networks with that of classification and regression tree, latent class models and logistic regression using three criteria – simple error rate, area under the receiver operating characteristic curve (AUROC), and cumulative lift – and two validation methods, i.e. bootstrap and stratified k‐fold cross‐validation. Systematic experiments are conducted to compare their performance.FindingsThe results suggest that these methods vary in performance across different criteria and validation methods. Overall, neural networks outperform the others in AUROC value and cumulative lifts, and the stratified ten‐fold cross‐validation produces more accurate results than bootstrap validation.Practical implicationsTo select predictive models to support direct marketing decisions, researchers need to adopt appropriate performance criteria and validation procedures.Originality/valueThe study addresses the key issues in model selection, i.e. performance criteria and validation methods, and conducts systematic analyses to generate the findings and practical implications.

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