Adherence improves long-term prognosis and medical costs in Japan

Medical costs and the burden associated with cardiovascular disease are on the rise. In Japan, due to changes in lifestyle and other factors, the number of inpatients with cardiovascular disease and other conditions is increasing at a rate of 10,000 per year.

With regard to the management of medical resources, it has become clear that adherence to medication and moral hazards among patients are closely related to clinical outcomes and have a significant impact not only on health behaviours but also on socioeconomic factors, including medical costs [1, 2].

Therefore, to improve the overall economy and quality assessment of healthcare systems, Japanese researchers developed a predictive model of integrated healthcare resource consumption (Adherence Score for Healthcare Resource Outcome, ASHRO) that incorporates patient health behaviours, and examined its association with clinical outcomes [3].

This study used information from a large-scale database on health insurance claims, long-term care insurance and health check-ups. Participants comprised patients who received inpatient medical care for diseases of the circulatory system (ICD-10 codes I00-I99).

The predictive model used broadly defined composite adherence as the explanatory variable and medical and long-term care costs as the objective variable. Predictive models used random forest learning (AI: artificial intelligence) to adjust for predictors and multiple regression analysis to construct ASHRO scores.

The ability of discrimination and calibration of the prediction model were evaluated using the area under the curve and the Hosmer-Lemeshow test. The overall mortality of the two ASHRO 50% cut-off groups adjusted for clinical risk factors by propensity score matching over a 48-month follow-up period was compared.

Overall, 48,456 patients were discharged from the hospital with cardiovascular disease (mean age, 68.3 ± 9.9 years; male, 61.9%). The broad adherence score classification, adjusted as an index of the predictive model by machine learning, was an index of eight: secondary prevention, rehabilitation intensity, guidance, proportion of days covered, overlapping outpatient visits/clinical laboratory and physiological tests, medical attendance and generic drug rate.

Multiple regression analysis showed an overall coefficient of determination of 0.313 (p < 0.001). Logistic regression analysis with cut-off values of 50% and 25%/75% for medical and long-term care costs showed that the overall coefficient of determination was statistically significant (p < 0.001).

The predictive model (ASHRO) calculates a score that predicts healthcare resource consumption. The ASHRO score was associated with the incidence of all deaths between the two 50% cut-off groups (2% vs 7%; p < 0.001). Promotion of the generic drug rate index has been shown to reduce future medical and long-term care costs (standard partial regression coefficient: 0.016, < 0.001).

The integrated parameter, which was named a broad adherence in this study, is complemented by moral hazards and social cooperation (public nature). These broadly defined adherence indicators may be collaborated to studies of substantive behavioural change models that improve self-efficacy.

ASHRO is a tool that is expected to be utilized by those in charge of healthcare budget management and specialists in the clinical field. Predictive models enable the insured to share the level of future cost burden and the goal of reducing disease risk with the public insurer and medical professionals.

Analysis of explanatory variables is expected to be used by public insurers in conducting various promotions for health check-ups and high scoring groups with low generic drug rates and in promoting guidance provided by medical professionals.

The authors concluded that ASHRO accurately predicted future integrated healthcare resource consumption and was associated with clinical outcomes. It can be a valuable tool for evaluating the economic usefulness of individual adherence behaviours and optimizing clinical outcomes.

Conflict of interest
The authors of the research paper [1] reported conflict of interest, including having received report grants from Nihon Medi-Physic, Terumo Corporation and Fujifilm Toyama Chemical.

For full details of the authors’ conflict of interest, see the research paper [3].

Abstracted by Professor Tomoyuki Takura, MD, PhD, Department of Healthcare Economics and Health Policy, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan.

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References
1. Cleemput I, Kesteloot K, DeGeest S. A review of the literature on the economics of noncompliance. Room for methodological improvement. Health Policy. 2002;59(1):65-94.
2. Neiman AB, Ruppar T, Ho M, et al. CDC grand rounds: improving medication adherence for chronic disease management - innovations and opportunities. MMWR Morb Mortal Wkly Rep. 2017;66(45):1248-51.
3. Takura T, Hirano Goto K, Honda A. Development of a predictive model for integrated medical and long-term care resource consumption based on health behaviour: application of healthcare big data of patients with circulatory diseases. BMC Med. 2021;19(1):15.

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