Interventions to improve medication adherence in mental health: the update of a systematic review of cost-effectiveness

Clinical Utility and Implementation of Pharmacogenomics for the Personalisation of Antipsychotic Treatments

Decades of pharmacogenetic research have revealed genetic biomarkers of clinical response to antipsychotics. Genetic variants in antipsychotic targets, dopamine and serotonin receptors in particular, and in metabolic enzymes have been associated with the efficacy and toxicity of antipsychotic treatments. However, genetic prediction of antipsychotic response based on these biomarkers is far from accurate. Despite the clinical validity of these findings, the clinical utility remains unclear. Nevertheless, genetic information on CYP metabolic enzymes responsible for the biotransformation of most commercially available antipsychotics has proven to be effective for the personalisation of clinical dosing, resulting in a reduction of induced side effects and in an increase in efficacy. However, pharmacogenetic information is rarely used in psychiatric settings as a prescription aid. Lack of studies on cost-effectiveness, absence of clinical guidelines based on pharmacogenetic biomarkers for several commonly used antipsychotics, the cost of genetic testing and the delay in results delivery hamper the implementation of pharmacogenetic interventions in clinical settings. This narrative review will comment on the existing pharmacogenetic information, the clinical utility of pharmacogenetic findings, and their current and future implementations.

Cost-Effectiveness of Anti-retroviral Adherence Interventions for People Living with HIV: A Systematic Review of Decision Analytical Models

BACKGROUND

Although safe and effective anti-retrovirals (ARVs) are readily available, non-adherence to ARVs is highly prevalent among people living with human immunodeficiency virus/acquired immunodeficiency syndrome (PLWHA). Different adherence-improving interventions have been developed and examined through decision analytic model-based health technology assessments. This systematic review aimed to review and appraise the decision analytical economic models developed to assess ARV adherence-improvement interventions.

METHODS

The review protocol was registered on PROSPERO (CRD42022270039), and reporting followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. Relevant studies were identified through searches in six generic and specialized bibliographic databases, i.e. PubMed, Embase, NHS Economic Evaluation Database, PsycINFO, Health Economic Evaluations Database, tufts CEA registry and EconLit, from their inception to 23 October 2022. The cost-effectiveness of adherence interventions is represented by the incremental cost-effectiveness ratio (ICER). The quality of studies was assessed using the quality of the health economics studies (QHES) instrument. Data were narratively synthesized in the form of tables and texts. Due to the heterogeneity of the data, a permutation matrix was used for quantitative data synthesis rather than a meta-analysis.

RESULTS

Fifteen studies, mostly conducted in North America (8/15 studies), were included in the review. The time horizon ranged from a year to a lifetime. Ten out of 15 studies used a micro-simulation, 4/15 studies employed Markov and 1/15 employed a dynamic model. The most commonly used interventions reported include technology based (5/15), nurse involved (2/15), directly observed therapy (2/15), case manager involved (1/15) and others that involved multi-component interventions (5/15). In 1/15 studies, interventions gained higher quality-adjusted life years (QALYs) with cost savings. The interventions in 14/15 studies were more effective but at a higher cost, and the overall ICER was well below the acceptable threshold mentioned in each study, indicating the interventions could potentially be implemented after careful interpretation. The studies were graded as high quality (13/15) or fair quality (2/15), with some methodological inconsistencies reported.

CONCLUSION

Counselling and smartphone-based interventions are cost-effective, and they have the potential to reduce the chronic adherence problem significantly. The quality of decision models can be improved by addressing inconsistencies in model selection, data inputs incorporated into models and uncertainty assessment methods.

Economic evaluation of interventions to improve medication adherence among patients with chronic diseases: an overview of systematic reviews

INTRODUCTION

This overview aimed to find, assess, and synthesize systematic reviews that compared the cost-effectiveness of interventions designed to improve medication adherence among patients with chronic disease.

AREAS COVERED

PubMed, Web of Science, Cochrane Database of Systematic Reviews, and Center for Review and Dissemination were searched. The quality of the included reviews was assessed using two validated checklists. The review characteristics and findings were summarized narratively. A total of 9 systematic reviews were included. Interventions reported to be cost-effective were simplification of the medication regimen, financial incentives, improved coverage or reduced out-of-pocket spending, and pharmacist care. The most common interventions were patient education and counseling, with mixed results of cost-effectiveness. This evidence comes from economic evaluations with varying degrees of quality.

EXPERT OPINION

Future evaluations of adherence interventions' cost-effectiveness will be improved in quality as our understanding of the reasons behind intentional and unintentional nonadherence and factors associated with this behavior advances. The development of criteria for the value assessment of medication adherence-enhancing interventions will contribute to improving the quality of adherence intervention cost-effectiveness research.

Self-Care Ability of Patients With Severe Mental Disorders: Based on Community Patients Investigation in Beijing, China

Background

Severe Mental Disorders have become a topic of increasing interest in research due to their serious consequences for the quality of life and functioning. In the pages that follow, it will be argued that the self-care ability and its influencing factors among patients with severe mental disorders in Beijing, according to the questionnaire survey in 2019.

Methods

Proportionate stratified sampling was used to select representative patients as samples. The demographic characteristics of were obtained from the Management Information System for Severe Mental Disorders and the questionnaires. The self-care ability was measured by self-made scales. Descriptive statistics, t-test, and multiple linear regression were used to analyze the data.

Results

We surveyed 662 people and found that the deficiency of self-care ability is common in patients with severe mental disorders. Self-care ability was positively correlated with educated levels and guardian takes care of alone, and negatively correlated with age, course of disease and physical disease (P < 0.05). From a dimensional perspective, the daily basic activity was positively correlated with educated levels and negatively correlated with physical disease (P < 0.05); the housework ability was positively correlated with gender, educated levels and medication adherence, and negatively correlated with source of income and physical disease (P < 0.05); the social function was positively correlated with educated levels, guardian takes care of alone and medication adherence, and negatively correlated with age, source of income, course of disease and physical disease (P < 0.05).

Conclusion

The self-care ability of patients with severe mental disorders is affected by many factors, including patient characteristics and social factors. Therefore, targeted interventions are needed to help patients restore their self-care ability, which requires the joint efforts of the government and the whole society.

Interventions for improving medication adherence in community-dwelling older people with multimorbidity: A systematic review and meta-analysis

BACKGROUND

Multimorbidity is highly prevalent amongst older people. Compared with those with a single disease, older people with multimorbidity are more susceptible to medication nonadherence, which causes adverse health outcomes and increased health care costs. The effectiveness of interventions for improving medication adherence in this population is still unclear.

OBJECTIVE

To evaluate the effectiveness of interventions to improve medication adherence for community-dwelling older people with multimorbidity.

DESIGN

Systematic review and meta-analysis.

DATA SOURCES

Ten databases: Airiti Library, China National Knowledge Infrastructure, Cochrane CENTRAL, EBSCO CINAHL, OVID EMBASE, OVID MEDLINE, Proquest Central, PsycINFO, Wanfang Database and Web of Science Core Collection.

REVIEW METHODS

Studies evaluating the effects of interventions on medication adherence in community-dwelling older people with multimorbidity were included. Two researchers independently performed the study selection, data extraction and risk assessment. Intervention effects were pooled by random-effects meta-analysis.

RESULTS

A total of nine studies with 3,292 participants were included. Studies on self-management interventions revealed a significant and modest improvement in medication adherence (n = 3 studies; SMD 0.52; 95% CI 0.04 to 0.99; p = 0.03, I² = 43%). Studies using electronic health interventions demonstrated a small yet significant benefit for medication adherence (n = 2 studies; SMD 0.22; 95% CI 0.02 to 0.42; p = 0.03, I² = 0%). There was no pooled significant effect of medication review on medication adherence (n = 4 studies; SMD -0.03; 95% CI -0.21 to 0.15; p = 0.74, I² = 68%). Most of the studies failed to yield a significant improvement in patients' health outcomes.

CONCLUSIONS

Self-management interventions and electronic health interventions might be effective in improving medication adherence for older people with multimorbidity. Future adherence interventions are needed to demonstrate improvements in medication adherence and health outcomes.

PROSPERO REGISTRATION NUMBER

CRD42020150500.