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.
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.
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.
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.