A flexible model for correlated medical costs, with application to medical expenditure panel survey data

An economic evaluation of the Prioritising Responses Of Nurses To deteriorating patient Observations (PRONTO) clinical trial

BACKGROUND

Early recognition and response to clinical deterioration reduce the frequency of in-hospital cardiac arrests, mortality, and unplanned intensive care unit (ICU) admissions. This study aimed to investigate the impact of the Prioritising Responses Of Nurses To deteriorating patient Observations (PRONTO) intervention on hospital costs and patient length of stay (LOS).

METHOD

The PRONTO cluster randomised control trial was conducted to improve nurses' responses to patients with abnormal vital signs. Hospital data were collected pre-intervention (T0) at 6 months (T1) and 12 months (T2) post-intervention. The economic evaluation involved a cost-consequence analysis from the hospital's perspective. Generalised estimating equations were used to estimate the parameters for regression models of the difference in costs and LOS between study groups and time points.

RESULTS

Hospital admission data for 6065 patients (intervention group, 3102; control group, 2963) were collected from four hospitals for T0, T1 and T2. The intervention cost was 69.61 A$ per admitted patient, including the additional intervention training for nurses and associated labour costs. The results showed cost savings and a shorter LOS in the intervention group between T0 - T1 and T0 - T2 (cost differences T0 - T1: -364 (95% CI -3782; 3049) A$ and T0 - T2: -1710 (95% CI -5162; 1742) A$; and LOS differences T0 - T1: -1.10 (95% CI -2.44; 0.24) days and T0 & T2: -2.18 (95% CI -3.53; -0.82) days).

CONCLUSION

The results of the economic analysis demonstrated that the PRONTO intervention improved nurses' responses to patients with abnormal vital signs and significantly reduced hospital LOS by two days at 12 months in the intervention group compared to baseline. From the hospital's perspective, savings from reduced hospitalisations offset the costs of implementing PRONTO.

Longitudinal varying coefficient single-index model with censored covariates

It is of interest to health policy research to estimate the population-averaged longitudinal medical cost trajectory from initial cancer diagnosis to death, and understand how the trajectory curve is affected by patient characteristics. This research question leads to a number of statistical challenges because the longitudinal cost data are often non-normally distributed with skewness, zero-inflation, and heteroscedasticity. The trajectory is nonlinear, and its length and shape depend on survival, which are subject to censoring. Modeling the association between multiple patient characteristics and nonlinear cost trajectory curves of varying lengths should take into consideration parsimony, flexibility, and interpretation. We propose a novel longitudinal varying coefficient single-index model. Multiple patient characteristics are summarized in a single-index, representing a patient's overall propensity for healthcare use. The effects of this index on various segments of the cost trajectory depend on both time and survival, which is flexibly modeled by a bivariate varying coefficient function. The model is estimated by generalized estimating equations with an extended marginal mean structure to accommodate censored survival time as a covariate. We established the pointwise confidence interval of the varying coefficient and a test for the covariate effect. The numerical performance was extensively studied in simulations. We applied the proposed methodology to medical cost data of prostate cancer patients from the Surveillance, Epidemiology, and End Results-Medicare-Linked Database.

Estimating health care costs at scale in low- and middle-income countries: Mathematical notations and frameworks for the application of cost functions

Appropriate costing and economic modeling are major factors for the successful scale-up of health interventions. Various cost functions are currently being used to estimate costs of health interventions at scale in low- and middle-income countries (LMICs) potentially resulting in disparate cost projections. The aim of this study is to gain understanding of current methods used and provide guidance to inform the use of cost functions that is fit for purpose. We reviewed seven databases covering the economic and global health literature to identify studies reporting a quantitative analysis of costs informing the projected scale-up of a health intervention in LMICs between 2003 and 2019. Of the 8725 articles identified, 40 met the inclusion criteria. We classified studies according to the type of cost functions applied-accounting or econometric-and described the intended use of cost projections. Based on these findings, we developed new mathematical notations and cost function frameworks for the analysis of healthcare costs at scale in LMICs setting. These notations estimate variable returns to scale in cost projection methods, which is currently ignored in most studies. The frameworks help to balance simplicity versus accuracy and increase the overall transparency in reporting of methods.

A semi-parametric Bayesian model for semi-continuous longitudinal data

Semi-continuous data present challenges in both model fitting and interpretation. Parametric distributions may be inappropriate for extreme long right tails of the data. Mean effects of covariates, susceptible to extreme values, may fail to capture relevant information for most of the sample. We propose a two-component semi-parametric Bayesian mixture model, with the discrete component captured by a probability mass (typically at zero) and the continuous component of the density modeled by a mixture of B-spline densities that can be flexibly fit to any data distribution. The model includes random effects of subjects to allow for application to longitudinal data. We specify prior distributions on parameters and perform model inference using a Markov chain Monte Carlo (MCMC) Gibbs-sampling algorithm programmed in R. Statistical inference can be made for multiple quantiles of the covariate effects simultaneously providing a comprehensive view. Various MCMC sampling techniques are used to facilitate convergence. We demonstrate the performance and the interpretability of the model via simulations and analyses on the National Consortium on Alcohol and Neurodevelopment in Adolescence study (NCANDA) data on alcohol binge drinking.

Medical Cost of Cancer Care for Privately Insured Children in Chile

Medical care for children with cancer is complex and expensive, and represents a large financial burden for families around the world. We estimated the medical cost of cancer care for children under the age of 18, using administrative records of the universe of children with private insurance in Chile in the period 2007–2018, based on a sample of 3853 observations. We analyzed total cost and out-of-pocket spending by patients’ characteristics, type of cancer, and by service. Children with cancer had high annual medical costs, USD 32,287 on average for 2018. Costs were higher for the younger children in the sample. The vast majority of the cost was driven by inpatient hospital care for all types of cancer. The average total cost increased 20% in real terms over the period of study, while out-of-pocket expenses increased almost 29%. Private insurance beneficiaries faced a significant economic burden associated with medical treatment of a child with cancer. Interventions that reduce hospitalizations, as well as systemwide reforms that incorporate maximum out-of-pocket payments and prevent catastrophic expenditures, can contribute to alleviating the financial burden of childhood cancer.

The medical costs of low leisure-time physical activity among working-age adults: Gender and minority status matter

This study analyzes the direct medical costs of low physical activity by race/ethnicity and gender. Average health expenditures based on physical activity status for Black non-Hispanics (NH), Asian NHs, and Hispanics were compared to White NHs. Data from the National Health Interview Survey were merged with the Medical Expenditure Panel Survey for years 2000-2010 and 2001-2011, respectively, and weights were applied to ensure generalizability to the larger US population. The sample was restricted to non-pregnant adults between the ages of 25 and 64, with a final sample size of 44,953. The multivariate estimates reveal statistically significant lower annual health care expenditures among physically active men and women in five out of eight racial/ethnic groups relative to their inactive counterparts: on average, for men, $1041 less is spent among White NHs, $905 less is spent for Black NHs and $876 less is spent for Asian NHs. Among women, medical expenditures were $956 per year less among active White non-Hispanics relative to their inactive counterparts, and $815 per year among Hispanics. Essentially, the average reduction in health care expenditures is relatively consistent for five out of the eight groups. The absence of any reduction in average health care expenditures for three of the groups, however, suggests that there may be environmental factors at play for certain groups that mitigate the impact of physical activity on health expenditures.

Empirical likelihood-based inferences for median medical cost regression models with censored data

Recent studies show that medical cost data can be heavily censored and highly skewed, which leads to have more complex cost data analysis. In this paper, we propose influence function and empirical likelihood (EL)-based methods to construct confidence regions for regression parameters in median cost regression models with censored data. We further propose confidence intervals for the median cost with given covariates using the proposed EL-based confidence regions. Simulation studies are conducted to compare the proposed EL-based confidence regions with the existing normal approximation-based confidence regions in terms of coverage probabilities. The new EL-based methods are observed to have better finite sample performances than existing methods particularly when the censoring proportion is high. The new methods are also illustrated through a real data example.

Association of adherence to antiretroviral therapy with economic burden of cardiovascular disease in HIV-infected population

AIMS

There is a lack of studies that rigorously and systematically assess the economic burden of cardiovascular diseases (CVDs) related to the use of antiretroviral therapy (ART). We aimed to assess the association between adherence to ART and economic burden of CVDs in an HIV-infected population.

METHODS

Taiwan's National Health Insurance Research Database 2000-2011 was utilized for analyzing 18,071 HIV-infected patients free of CVDs before HIV diagnosis. The level of adherence to ART was measured by the medication possession ratio (MPR). Generalized estimating equations analysis was applied to estimate the cost impact of a variety of CVDs. All costs were presented in 2018 US dollars.

RESULTS

The incidence of CVDs ranged from 0.17/1000 person-years (cardiogenic shock) to 2.60/1000 person-years (ischemic heart diseases (IHDs)). The mean annual medical cost for a base-case patient without CVDs was US$3000. Having cerebrovascular diseases, myocardial infarction, heart failure, arrhythmia, and IHDs increased annual costs by 41%, 33%, 30%, 16%, and 14%, respectively. The cost impact of incident CVDs in years with high adherence to ART (MPR ≥ 0.8) was significantly lower than that in years with low adherence (MPR < 0.1) (e.g. having cerebrovascular diseases in the high- versus low-adherence years increased annual costs by 21% versus 259%, respectively).

CONCLUSION

The economic burden of incident CVDs in an HIV-infected population was compelling and varied by the extent of using ART. A reduced economic impact of CVDs was found in years when patients possessed a greater adherence to ART.

A flexible quantile regression model for medical costs with application to Medical Expenditure Panel Survey Study

Medical costs are often skewed to the right and heteroscedastic, having a sophisticated relation with covariates. Mean function regression models with low-dimensional covariates have been extensively considered in the literature. However, it is important to develop a robust alternative to find the underlying relationship between medical costs and high-dimensional covariates. In this paper, we propose a new quantile regression model to analyze medical costs. We also consider variable selection, using an adaptive lasso penalized variable selection method to identify significant factors of the covariates. Simulation studies are conducted to illustrate the performance of the estimation method. We apply our method to the analysis of the Medical Expenditure Panel Survey dataset.

Semiparametric Estimation of Longitudinal Medical Cost Trajectory

Estimating the average monthly medical costs from disease diagnosis to a terminal event such as death for an incident cohort of patients is a topic of immense interest to researchers in health policy and health economics because patterns of average monthly costs over time reveal how medical costs vary across phases of care. The statistical challenges to estimating monthly medical costs longitudinally are multifold; the longitudinal cost trajectory (formed by plotting the average monthly costs from diagnosis to the terminal event) is likely to be nonlinear, with its shape depending on the time of the terminal event, which can be subject to right censoring. The goal of this paper is to tackle this statistically challenging topic by estimating the conditional mean cost at any month t given the time of the terminal event s. The longitudinal cost trajectories with different terminal event times form a bivariate surface of t and s, under the constraint t ≤ s. We propose to estimate this surface using bivariate penalized splines in an Expectation-Maximization algorithm that treats the censored terminal event times as missing data. We evaluate the proposed model and estimation method in simulations and apply the method to the medical cost data of an incident cohort of stage IV breast cancer patients from the Surveillance, Epidemiology and End Results-Medicare Linked Database.

A marginalized two-part model with heterogeneous variance for semicontinuous data

Semicontinuous data, characterized by a point mass at zero followed by a positive, continuous distribution, arise frequently in medical research. These data are typically analyzed using two-part mixtures that separately model the probability of incurring a positive outcome and the distribution of positive values among those who incur them. In such a conditional specification, however, standard two-part models do not provide a marginal interpretation of covariate effects on the overall population. We have previously proposed a marginalized two-part model that yields more interpretable effect estimates by parameterizing the model in terms of the marginal mean. In the original formulation, a constant variance was assumed for the positive values. We now extend this model to a more general framework by allowing non-constant variance to be explicitly modeled as a function of covariates, and incorporate this variance into two flexible distributional assumptions, log-skew-normal and generalized gamma, both of which take the log-normal distribution as a special case. Using simulation studies, we compare the performance of each of these models with respect to bias, coverage, and efficiency. We illustrate the proposed modeling framework by evaluating the effect of a behavioral weight loss intervention on health care expenditures in the Veterans Affairs health system.