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):  
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 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):  
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.

A STUDY OF EARLY STOPPING AND MODEL SELECTION APPLIED TO THE PAPERMAKING INDUSTRY

2000 ◽  
Vol 10 (01) ◽  
pp. 9-18 ◽  
Author(s):  
PETER J. EDWARDS ◽  
ALAN F. MURRAY
Keyword(s):  
Neural Network ◽  
Model Selection ◽  
Cross Validation ◽  
Model Development ◽  
Network Models ◽  
Comparison Study ◽  
Early Stopping ◽  
Neural Network Models ◽  
Model Evidence ◽  
Fold Cross Validation

This paper addresses the issues of neural network model development and maintenance in the context of a complex task taken from the papermaking industry. In particular, it describes a comparison study of early stopping techniques and model selection, both to optimise neural network models for generalisation performance. The results presented here show that early stopping via use of a Bayesian model evidence measure is a viable way of optimising performance while also making maximum use of all the data. In addition, they show that ten-fold cross-validation performs well as a model selector and as an estimator of prediction accuracy. These results are important in that they show how neural network models may be optimally trained and selected for highly complex industrial tasks where the data are noisy and limited in number.

scholarly journals Did the universal zero-markup drug policy lower healthcare expenditures? Evidence from Changde, China

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Zixuan Peng ◽  
Chaohong Zhan ◽  
Xiaomeng Ma ◽  
Honghui Yao ◽  
Xu Chen ◽  
...  
Keyword(s):  
Healthcare Utilization ◽  
Drug Policy ◽  
Negative Binomial ◽  
Public Hospitals ◽  
Hunan Province ◽  
Healthcare Expenditures ◽  
Discharge Data ◽  
Outpatient Visits ◽  
Drug Expenditures ◽  
Outpatient Drug

Abstract Background The zero-markup drug policy (also known as the universal zero-markup drug policy (UZMDP)) was implemented in stages beginning with primary healthcare facilities in 2009 and eventually encompassing city public hospitals in 2016. This policy has been a central pillar of Chinese health reforms. While the literature has examined the impacts of this policy on healthcare utilization and expenditures, a more comprehensive and detailed assessment is warranted. The purpose of this paper is to explore the impacts of the UZMDP on inpatient and outpatient visits as well as on both aggregate healthcare expenditures and its various components (including drug, diagnosis, laboratory, and medical consumables expenditures). Methods A pre-post design was applied to a dataset extracted from the Changde Municipal Human Resource and Social Security Bureau comprising discharge data on 27,246 inpatients and encounter data on 48,282 outpatients in Changde city, Hunan province, China. The pre-UZMDP period for the city public hospitals was defined as the period from October 2015 to September 2016, while the post-UZMDP period was defined as the period from October 2016 to September 2017. Difference-in-Difference negative binomial and Tobit regression models were employed to evaluate the impacts of the UZMDP on healthcare utilization and expenditures, respectively. Results Four key findings flow from our assessment of the impacts of the UZMDP: first, outpatient and inpatient visits increased by 8.89 % and 9.39 %, respectively; second, average annual inpatient and outpatient drug expenditures fell by 4,349.00 CNY and 1,262.00 CNY, respectively; third, average annual expenditures on other categories of healthcare expenditures increased by 2,500.83 CNY, 417.10 CNY, 122.98 CNY, and 143.50 CNY for aggregate inpatient, inpatient diagnosis, inpatient laboratory, and outpatient medical consumables expenditures, respectively; and fourth, men and older individuals tended to have more inpatient and outpatient visits than their counterparts. Conclusions Although the UZMDP was effective in reducing both inpatient and outpatient drug expenditures, it led to a sharp rise in other expenditure categories. Policy decision makers are advised to undertake efforts to contain the growth in total healthcare expenditures, in general, as well as to evaluate the offsetting effects of the policy on non-drug components of care.

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