Cost-Effectiveness Analysis of Type 2 Diabetes Mellitus (T2DM) Treatment in Patients with Complications of Kidney and Peripheral Vascular Diseases in Indonesia
Abstract:Type 2 diabetes mellitus (T2DM) is a chronic disease with high-cost treatment. This study aimed to analyze the cost-effectiveness of T2DM treatment in hospitalized patients with complications of kidney and peripheral vascular disease (PVD) in Indonesia by focusing on patients of Health Social Security Agency (BPJS Kesehatan). An observational study was applied by collecting data retrospectively from patients’ medical record at the biggest public hospital in West Java Province, Indonesia. Two perspectives of pa… Show more
“…ZINB produced the lowest AIC in the simulation scenarios with proportion of structural zeros greater than 50% and dispersion equal to 1. Similarly, at level of dispersion equal to 1 the model that produced the lowest BIC was the NB model Tables 19,20,21,22. In scenarios with a small sample size of 50 and dispersion levels of 0.01 and 1, the ZIP model provided the smallest MAEs in nearly all simulation scenarios of proportion of structural zero, except for the scenarios with structural zero proportion of 70% and dispersion level of 0.01. Another exception were the scenarios with structural zero proportion of 10% and dispersion level of 1, where ZINB and Poisson regression models had the smallest MAEs, respectively.…”
Section: Data Generated With Zinb Regression Modelmentioning
confidence: 95%
“…Hospital LOS is often used as a measure of a post-medical procedure outcome, as a guide to the benefit of a treatment of interest, and/or as an important risk factor for adverse events, hospital readmission, and mortality [11][12][13]. Therefore, understanding hospital LOS variability across various patients' clinical and socio-demographic characteristics and hospitals' characteristics, such as geographic region and hospital sizes, is always an important public health focus [9,[14][15][16][17][18][19][20][21][22].…”
Background
Hospital length of stay (LOS) is a key indicator of hospital care management efficiency, cost of care, and hospital planning. Hospital LOS is often used as a measure of a post-medical procedure outcome, as a guide to the benefit of a treatment of interest, or as an important risk factor for adverse events. Therefore, understanding hospital LOS variability is always an important healthcare focus. Hospital LOS data can be treated as count data, with discrete and non-negative values, typically right skewed, and often exhibiting excessive zeros. In this study, we compared the performance of the Poisson, negative binomial (NB), zero-inflated Poisson (ZIP), and zero-inflated negative binomial (ZINB) regression models using simulated and empirical data.
Methods
Data were generated under different simulation scenarios with varying sample sizes, proportions of zeros, and levels of overdispersion. Analysis of hospital LOS was conducted using empirical data from the Medical Information Mart for Intensive Care database.
Results
Results showed that Poisson and ZIP models performed poorly in overdispersed data. ZIP outperformed the rest of the regression models when the overdispersion is due to zero-inflation only. NB and ZINB regression models faced substantial convergence issues when incorrectly used to model equidispersed data. NB model provided the best fit in overdispersed data and outperformed the ZINB model in many simulation scenarios with combinations of zero-inflation and overdispersion, regardless of the sample size. In the empirical data analysis, we demonstrated that fitting incorrect models to overdispersed data leaded to incorrect regression coefficients estimates and overstated significance of some of the predictors.
Conclusions
Based on this study, we recommend to the researchers that they consider the ZIP models for count data with zero-inflation only and NB models for overdispersed data or data with combinations of zero-inflation and overdispersion. If the researcher believes there are two different data generating mechanisms producing zeros, then the ZINB regression model may provide greater flexibility when modeling the zero-inflation and overdispersion.
“…ZINB produced the lowest AIC in the simulation scenarios with proportion of structural zeros greater than 50% and dispersion equal to 1. Similarly, at level of dispersion equal to 1 the model that produced the lowest BIC was the NB model Tables 19,20,21,22. In scenarios with a small sample size of 50 and dispersion levels of 0.01 and 1, the ZIP model provided the smallest MAEs in nearly all simulation scenarios of proportion of structural zero, except for the scenarios with structural zero proportion of 70% and dispersion level of 0.01. Another exception were the scenarios with structural zero proportion of 10% and dispersion level of 1, where ZINB and Poisson regression models had the smallest MAEs, respectively.…”
Section: Data Generated With Zinb Regression Modelmentioning
confidence: 95%
“…Hospital LOS is often used as a measure of a post-medical procedure outcome, as a guide to the benefit of a treatment of interest, and/or as an important risk factor for adverse events, hospital readmission, and mortality [11][12][13]. Therefore, understanding hospital LOS variability across various patients' clinical and socio-demographic characteristics and hospitals' characteristics, such as geographic region and hospital sizes, is always an important public health focus [9,[14][15][16][17][18][19][20][21][22].…”
Background
Hospital length of stay (LOS) is a key indicator of hospital care management efficiency, cost of care, and hospital planning. Hospital LOS is often used as a measure of a post-medical procedure outcome, as a guide to the benefit of a treatment of interest, or as an important risk factor for adverse events. Therefore, understanding hospital LOS variability is always an important healthcare focus. Hospital LOS data can be treated as count data, with discrete and non-negative values, typically right skewed, and often exhibiting excessive zeros. In this study, we compared the performance of the Poisson, negative binomial (NB), zero-inflated Poisson (ZIP), and zero-inflated negative binomial (ZINB) regression models using simulated and empirical data.
Methods
Data were generated under different simulation scenarios with varying sample sizes, proportions of zeros, and levels of overdispersion. Analysis of hospital LOS was conducted using empirical data from the Medical Information Mart for Intensive Care database.
Results
Results showed that Poisson and ZIP models performed poorly in overdispersed data. ZIP outperformed the rest of the regression models when the overdispersion is due to zero-inflation only. NB and ZINB regression models faced substantial convergence issues when incorrectly used to model equidispersed data. NB model provided the best fit in overdispersed data and outperformed the ZINB model in many simulation scenarios with combinations of zero-inflation and overdispersion, regardless of the sample size. In the empirical data analysis, we demonstrated that fitting incorrect models to overdispersed data leaded to incorrect regression coefficients estimates and overstated significance of some of the predictors.
Conclusions
Based on this study, we recommend to the researchers that they consider the ZIP models for count data with zero-inflation only and NB models for overdispersed data or data with combinations of zero-inflation and overdispersion. If the researcher believes there are two different data generating mechanisms producing zeros, then the ZINB regression model may provide greater flexibility when modeling the zero-inflation and overdispersion.
“… f WHO guide to cost-effectiveness analysis and WHO guide for standardization of economic evaluations of immunization programmes. 23 , 24 Fig. 2 Frequency of studies by disease type.…”
Section: Resultsmentioning
confidence: 99%
“…This has been reported well in all of the studies post-2018, except for one study comparing ‘treatments’ for diabetes patients with different complications without defining the treatments. 24 Without a proper explanation of the intervention, the study may be potentially misleading.…”
“…Preventative measures are needed to improve patients' glycemic control, thereby preventing diabetes complications, which could potentially reduce the health and economic burden on the public health care system. [1][2][3] Based on International Diabetes Federation (IDF) data for 2021, Indonesia is globally ranked fifth with 19.5 million adults aged 20-79 years living with diabetes. Referring to the IDF data, the number of people with Diabetes Mellitus in Indonesia is projected to increase to 28.6 million in 2045.…”
Background: Research on the quality scale of the healthcare supply chain is still limited. This study aimed to assess the information quality of the supply chain model with a focus on construct validity. Studies related to information quality measurement generally focus on measuring the dimensions of the completeness of medical records and consumer perspectives. We intended to assess the scale based on doctors needed as care coordinators on type 2 diabetes mellitus or the Non-Insulin-Dependent-Diabetes-Mellitus (NIDDM) program in primary healthcare. Methods: Sixty-four primary healthcare doctors with an age range of 24–51 years were involved in this research. The scale obtained was formed from the assessment of the point of view of a panel of experts through the content validity index (CVI). The exploratory factor analysis (EFA) method was used to explore the scale of information quality in the information supply chain model for the NIDDM chronic disease management program. Result: The data analysis results indicated three main factors that affected the quality of the information supply chain model of NIDDM, namely accessibility, safety, and efficiency of information related to NIDDM. The results of the validity and reliability of the data showed that the scale used in this research was valid and reliable with a Cronbach alpha coefficient of 0.861. Conclusion: The scale developed in this research could be used to explore the quality of the information supply chain of NIDDM management in primary healthcare. Each item on the scale could explain the variables according to their respective groups.
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