Evaluating hospital costs in type 2 diabetes care: does the choice of the model matter?

Prediction of total hospital expenses of patients undergoing breast cancer surgery in Shanghai, China by comparing three models

BACKGROUND

Breast cancer imposes a considerable burden on both the health care system and society, and becomes increasingly severe among women in China. To reduce the economic burden of this disease is crucial for patients undergoing the breast cancer surgery, hospital managers, and medical insurance providers. However, few studies have evidenced the prediction of the total hospital expenses (THE) for breast cancer surgery. The aim of the study is to predict THE for breast cancer surgery and identify the main influencing factors.

METHODS

Data were retrieved from the first page of medical records of 3699 patients undergoing breast cancer surgery in one tertiary hospital from 2017 to 2018. Multiple liner regression (MLR), artificial neural networks (ANNs), and classification and regression tree (CART) were constructed and compared.

RESULTS

The dataset from 3699 patients were randomly divided into training and test sets at a 70:30 ratio (2599 and 1100 records, respectively). The average total hospital expenses were 12520.54 ± 7844.88 ¥ (US$ 1929.20 ± 1208.11). MLR results revealed six factors to be significantly associated with THE: age, LOS, type of disease, having medical insurance, minimally invasive surgery, and receiving general anesthesia. After comparing three models, ANNs was the best model to predict THEs in patients undergoing breast cancer surgery, and its strong predictive performance was also validated.

CONCLUSIONS

To reduce the THEs, more attention should be paid to related factors of LOS, major and minimally invasive surgeries, and general anesthesia for these patient groups undergoing breast cancer surgery. This may reduce the information asymmetry between doctors and patients and provide more reliable cost, practical inpatient medical consumption standards and reimbursement standards reference for patients, hospital managers, and medical insurance providers ,respectively.

National trends in diabetes mellitus hospitalization in Spain 1997-2010: Analysis of over 5.4 millions of admissions

AIMS

To analyze national trends in the rates of hospitalizations (all-cause and by principal discharge diagnosis) in total diabetic population of Spain.

METHODS

We carried out a nation-wide population-based study of all diabetic patients hospitalized between 1997 and 2010. All-cause hospitalizations, hospitalizations by principal discharge diagnosis, mean age, Charlson Comorbidity Index, readmission rates and length of hospital stay were examined. Annual rates adjusted for age and sex were analyzed and trends were calculated.

RESULTS

Over 14-years-period, all-cause hospitalizations of diabetic patients increased significantly, with an average annual percentage change of 2.5 (95%CI: 1.5-3.5; P_trend < 0.01). The greatest increase was observed in heart failure (5.4; 95%CI: 4.8-6.0; P_trend < 0.001), followed by neoplasms (4.9; 95%CI: 3.6-5.8; P_trend < 0.001), pneumonia (2.7; 95%CI: 2.0-4.0; P_trend < 0.001), stroke (2.4; 95%CI: 1.6-3.4; P_trend < 0.001), chronic obstructive pulmonary disease (2.0; 95%CI: 1.4-3.4; P_trend < 0.001) and coronary artery disease (1.6; 95%CI: 1.1-2.3; P_trend < 0.01). The adjusted number of all-cause hospitalizations of patients with diabetes per 100,000 inhabitants increased 2.6-fold. The increase in hospitalizations was significantly higher among patients ≥75 years old. Males experienced a greater increase in all-cause, neoplasm, heart failure, chronic obstructive pulmonary disease, and pneumonia hospitalizations (p < 0.01 for all). Hospitalized diabetic patients were progressively older and had more comorbidities, higher readmission rates and shorter hospital stays (p < 0.05 for all).

CONCLUSIONS

Hospitalizations of diabetic patients more than doubled in Spain during the study period. Heart failure and neoplasms experienced the greatest annual increases and remained the principal causes of hospitalization, probably associated with advanced age and comorbidities of hospitalized diabetics. Coronary and cerebrovascular diseases experienced a lower annual increase, suggesting an improvement in cardiovascular care in diabetes in Spain.

Use of Linagliptin for the Management of Medicine Department Inpatients with Type 2 Diabetes in Real-World Clinical Practice (Lina-Real-World Study)

The use of noninsulin antihyperglycaemic drugs in the hospital setting has not yet been fully described. This observational study compared the efficacy and safety of the standard basal-bolus insulin regimen versus a dipeptidyl peptidase-4 inhibitor (linagliptin) plus basal insulin in medicine department inpatients in real-world clinical practice. We retrospectively enrolled non-critically ill patients with type 2 diabetes with mild to moderate hyperglycaemia and no injectable treatments at home who were treated with a hospital antihyperglycaemic regimen (basal-bolus insulin, or linagliptin-basal insulin) between January 2016 and December 2017. Propensity score was used to match patients in both treatment groups and a comparative analysis was conducted to test the significance of differences between groups. After matched-pair analysis, 227 patients were included per group. No differences were shown between basal-bolus versus linagliptin-basal regimens for the mean daily blood glucose concentration after admission (standardized difference = 0.011), number of blood glucose readings between 100–140 mg/dL (standardized difference = 0.017) and >200 mg/dL (standardized difference = 0.021), or treatment failures (standardized difference = 0.011). Patients on basal-bolus insulin received higher total insulin doses and a higher daily number of injections (standardized differences = 0.298 and 0.301, respectively). Basal and supplemental rapid-acting insulin doses were similar (standardized differences = 0.003 and 0.012, respectively). There were no differences in hospital stay length (standardized difference = 0.003), hypoglycaemic events (standardized difference = 0.018), or hospital complications (standardized difference = 0.010) between groups. This study shows that in real-world clinical practice, the linagliptin-basal insulin regimen was as effective and safe as the standard basal-bolus regimen in non-critical patients with type 2 diabetes with mild to moderate hyperglycaemia treated at home without injectable therapies.

Regression models for analyzing costs and their determinants in health care: an introductory review

OBJECTIVE

This article aims to describe the various approaches in multivariable modelling of healthcare costs data and to synthesize the respective criticisms as proposed in the literature.

METHODS

We present regression methods suitable for the analysis of healthcare costs and then apply them to an experimental setting in cardiovascular treatment (COSTAMI study) and an observational setting in diabetes hospital care.

RESULTS

We show how methods can produce different results depending on the degree of matching between the underlying assumptions of each method and the specific characteristics of the healthcare problem.

CONCLUSIONS

The matching of healthcare cost models to the analytic objectives and characteristics of the data available to a study requires caution. The study results and interpretation can be heavily dependent on the choice of model with a real risk of spurious results and conclusions.

Factors affecting hospitalization costs in Type 2 diabetic patients

OBJECTIVE

To evaluate Type 2 diabetes hospitalization costs and their determinants by applying a proper methodological approach, taking into account the presence of several observations with zero costs.

METHODS

A cohort study using per-patient hospital discharge abstracts in a period of 4.5 years of follow-up (from 1/1/1996 to 30/6/2000). Potential cost predictors were age, sex, body max index, hypertension, diabetes duration, hemoglobin A1c levels, insulin treatment, retinopathy, coronary artery disease, peripheral artery disease, nephropathy, death and presence of comorbidity (cancer, chronic liver disease, chronic obstructive pulmonary disease, and psychiatric disease). Among risk factors, total cholesterol, HDL cholesterol and smoking were considered. A two-part model has been adopted in order to take into account the presence of patients with zero hospital costs: the probability of any hospitalization has been modeled via a standard logit generalized linear model (GLM); the actual level of total costs has been modeled via a GLM, with a gamma cost distribution and a LOG link function.

RESULTS

In 4.5 years the median total cost per hospitalized person was $4404 (mean $8180). In line with existing evidence, diabetes complications showed a high impact on average costs. In particular, peripheral and coronary artery diseases determined more than $1000 increase in the median costs. Chronic comorbidity were responsible for the highest incremental hospitalization costs ($1771).

CONCLUSIONS

Hospitalization costs were significantly increased by the presence of diabetes complications and chronic conditions. The adoption of a two-part model has allowed to obtain estimates not neglecting the effect of covariates on the probability of having no hospital care.