Effect of drug compliance on health care costs in newly-diagnosed dementia: Analysis of nationwide population-based data

Specialist Availability and Drug Adherence in Older Adults with Dementia Across Regions of the United States

BACKGROUND

Access to specialists facilitates appropriate Alzheimer's disease and related dementia (ADRD) medication use and adherence. However, there is little information on the impact of specialists' availability on ADRD medication adherence, especially in regions of the United States (US) where specialists are scarce, e.g., the Deep South (DS).

OBJECTIVE

To ascertain whether the availability of specialty physicians in the DS and other US regions predicts ADRD medication adherence among community-dwelling older adultsMethods:We conducted secondary analyses of claims data for 54,194 Medicare beneficiaries with ADRD in 2013-2015. Medication adherence was measured using the proportion of days covered (PDC). Multivariable-adjusted Modified Poisson regression was used to examine associations of adherence with physicians' availability by region.

RESULTS

The race/ethnicity distribution was 81.44% white, 9.17% black, 6.24% Hispanic, 2.25% Asian, and 1% other; 71.81% were female, and 42.36% were older than 85 years. Beneficiaries across regions differed in all individual and contextual characteristics except sex and comorbidities. Neurologists and psychiatrists' availability was not significantly associated with adherence (DS = 1.00, 0.97-1.03 & non-DS = 1.01, 1.00-1.01). Race and having ≥1 specialist visits were associated with a lower risk of adherence in both regions (p < 0.0001). Advanced age, dual Medicare/Medicaid eligibility, and living in non-large metropolitan areas, were associated with adherence in the non-DS region.

CONCLUSION

Among older Americans with ADRD, a context defined by specialist availability does not affect adherence, but other context characteristics related to socioeconomic status may. Research should further examine the influence of individual and contextual factors on ADRD treatment among older adults.

Study of Hospitalization Costs in Patients with Cerebral Ischemia Based on E-CHAID Algorithm

Background. The aging of the population has led to a rapid increase in the prevalence of most neurological diseases between 1990 and 2016, with a growth rate of up to 117%, which has put enormous pressure on medical insurance funds. As one of the core diseases of disease diagnosis grouping, the hospitalization cost composition and grouping research of patients with cerebral ischemic disease can help to determine scientific payment standards and reduce the economic burden of patients. Aim. We aimed to understand the cost composition and influencing factors of hospitalized patients with cerebral ischemic diseases and to identify a reasonable cost grouping scheme. Methods. The data come from the homepage of medical records of inpatients with cerebral ischemia in a tertiary hospital in Sichuan Province from 2018 to 2020. After cleaning the data, a total of 5,204 pieces of data were obtained. Nonparametric tests and gamma regression models were used to explore the influencing factors of hospitalization costs. Taking the influencing factors as the predictor variables and the hospitalization cost as the target variable, the exhaustive Chi-squared automatic interaction detector (E-CHAID) algorithm was used to form the costs grouping, and the payment standard of the hospitalization cost for each group was determined. The rationality of cost grouping was evaluated by coefficient of variation (CV) and Kruskal–Wallis H test. Results. From 2018 to 2020, the average hospital stay of 5,204 inpatients with cerebral ischemic disease was 10.70 days, and the average hospitalization cost was 17,206.09 RMB yuan. Among the hospitalization costs, diagnosis costs and drug costs accounted for the highest proportion, accounting for 41.18% and 22.38%, respectively, in 2020. Gender, age, admission route, comorbidities and complications, super length of stay (>30 days), and discharge mode had significant effects on hospitalization costs (P < 0.05). Patients were divided into 10 cost groups, and the grouping nodes included comorbidities and complications, discharge mode, age, gender, and admission route. The CV of 9 of the 10 cost groups is less than or equal to 1. The Kruskal–Wallis H test showed that the difference between groups was statistically significant (P < 0.05). Conclusion. The cost grouping of patients with cerebral ischemic diseases based on the E-CHAID algorithm is reasonable. This study examined the effects of super length of stay (>30 days), comorbidities and complications, and age on hospitalization cost in patients with cerebral ischemic disease. This study can provide a theoretical basis for advancing the China Healthcare Security Diagnosis Related Groups (CHS-DRG) grouping program and medical expense payment, thereby reducing the disease burden of patients.