Centre for Health Policy Research, Institute for Health Systems Research, National Institutes of Health, Ministry of Health, Shah Alam, Malaysia.
Centre for Health Equity Research, Institute for Health Systems Research, National Institutes of Health, Ministry of Health, Shah Alam, Malaysia.
BMC Med Res Methodol. 2022 Oct 5;22(1):261. doi: 10.1186/s12874-022-01733-3.
Count data from the national survey captures healthcare utilisation within a specific reference period, resulting in excess zeros and skewed positive tails. Often, it is modelled using count data models. This study aims to identify the best-fitting model for outpatient healthcare utilisation using data from the Malaysian National Health and Morbidity Survey 2019 (NHMS 2019) and utilisation factors among adults in Malaysia.
The frequency of outpatient visits is the dependent variable, and instrumental variable selection is based on Andersen's model. Six different models were used: ordinary least squares (OLS), Poisson regression, negative binomial regression (NB), inflated models: zero-inflated Poisson, marginalized-zero-inflated negative binomial (MZINB), and hurdle model. Identification of the best-fitting model was based on model selection criteria, goodness-of-fit and statistical test of the factors associated with outpatient visits.
The frequency of zero was 90%. Of the sample, 8.35% of adults utilized healthcare services only once, and 1.04% utilized them twice. The mean-variance value varied between 0.14 and 0.39. Across six models, the zero-inflated model (ZIM) possesses the smallest log-likelihood, Akaike information criterion, Bayesian information criterion, and a positive Vuong corrected value. Fourteen instrumental variables, five predisposing factors, six enablers, and three need factors were identified. Data overdispersion is characterized by excess zeros, a large mean to variance value, and skewed positive tails. We assumed frequency and true zeros throughout the study reference period. ZIM is the best-fitting model based on the model selection criteria, smallest Root Mean Square Error (RMSE) and higher R2. Both Vuong corrected and uncorrected values with different Stata commands yielded positive values with small differences.
State as a place of residence, ethnicity, household income quintile, and health needs were significantly associated with healthcare utilisation. Our findings suggest using ZIM over traditional OLS. This study encourages the use of this count data model as it has a better fit, is easy to interpret, and has appropriate assumptions based on the survey methodology.
国家调查中的计数数据捕捉了特定参考期内的医疗保健利用情况，导致零值过多和正偏尾。通常，它使用计数数据模型进行建模。本研究旨在使用 2019 年马来西亚国家健康和发病率调查（NHMS 2019）的数据，以及马来西亚成年人的利用因素，确定门诊医疗保健利用情况的最佳拟合模型。
门诊就诊次数是因变量，基于 Andersen 模型进行工具变量选择。使用了六种不同的模型：普通最小二乘法（OLS）、泊松回归、负二项回归（NB）、膨胀模型：零膨胀泊松、边缘化零膨胀负二项（MZINB）和障碍模型。基于模型选择标准、拟合优度和与门诊就诊相关因素的统计检验，确定最佳拟合模型。
零的频率为 90%。在样本中，8.35%的成年人仅使用过一次医疗保健服务，1.04%的成年人使用过两次。均值-方差值在 0.14 到 0.39 之间变化。在六种模型中，零膨胀模型（ZIM）具有最小的对数似然、Akaike 信息准则、贝叶斯信息准则和正的 Vuong 校正值。确定了 14 个工具变量、5 个倾向因素、6 个促成因素和 3 个需求因素。数据过度分散的特征是零过多、均值方差值大且正偏尾。我们假设整个研究参考期内的频率和真实零值。基于模型选择标准、最小均方根误差（RMSE）和更高的 R2，ZIM 是最佳拟合模型。不同 Stata 命令的 Vuong 校正和未校正值都产生了正值，且差异较小。
居住地、种族、家庭收入五分位数和健康需求与医疗保健利用显著相关。我们的研究结果表明，与传统的 OLS 相比，应使用 ZIM。本研究鼓励使用这种计数数据模型，因为它具有更好的拟合度、易于解释且基于调查方法具有适当的假设。
