Depression Care Management Interventions for Older Adults with Depression Using Home Health Services: Moving the Field Forward
Notice bibliographique
Résumé
The study in this issue of the Journal of the American Geriatrics Society by Bruce and colleagues, “Integrating Depression Care Management into Medicare Home Health Reduces Risk of 30- and 60-Day Hospitalization: The Depression Care for Patients at Home Cluster-Randomized Trial,”1 is timely in view of recent national and international efforts to improve the mental health and well-being of older adults.2, 3 Depression in people of all ages is a significant public health concern. The estimated annual cost of depression in the United States is $210 billion, although only 38% of these costs are associated with depression; most are associated with comorbid conditions.4 Healthcare costs for individuals with depression and other chronic conditions are 50% higher than costs for individuals with chronic conditions alone.5 Depression is a particular concern for older adults. Depressive symptoms are highly prevalent (26–44%) in older adults using home health services6 but are poorly recognized and treated.6, 7 Older home care recipients are more vulnerable to depression because of the greater likelihood of multiple chronic conditions and limitations on physical functionality and social interaction.3 Depressive symptoms are associated with a wide range of chronic conditions, such as heart disease and diabetes mellitus,8 and present challenges for self-management and the treatment and management of other conditions.9 Recent evidence suggests that the coexistence of depression and other medical conditions significantly increases unplanned hospital visits and the use of other health services,4, 10, 11 yet most intervention studies of older adults with multiple chronic conditions do not address mental health. Moreover, despite proven strategies for identification and management of depression and randomized controlled trials demonstrating the effectiveness of depression care interventions,12-14 the rate of depression in older adults using home health services remains relatively unaffected. New and innovative models of care are needed to address this gap between research and practice to reduce the burden of depression and improve the quality of care and support in this at-risk population. The results of the study by Bruce and colleagues extend those in the literature on the feasibility and effectiveness of nurse-led depression care management interventions in older individuals with depression using home health services in several ways. First, the results of this study add to the growing evidence of the positive effect of such interventions on unplanned hospitalization admissions.15 Bruce and colleagues found that in older adults who screened positive for depression, the Depression Care for Patients at Home (CAREPATH) intervention was associated with a 35% lower risk of being hospitalized within 30 days and a 28% lower risk of being hospitalized within 60 days than in individuals receiving enhanced usual care.1 Older adults referred to home health directly from hospital benefited most, with the adjusted hazard of being rehospitalized 55% lower in CAREPATH recipients after 30 days and 44% lower after 60 days.1 This finding is significant given that hospital costs constitute the largest component of direct healthcare expenditures for depression.16 Second, the study intervention proved to be feasible within the existing infrastructure of Medicare-certified home health agencies, increasing the potential scalability of the intervention. Third, the results of this study provide evidence of the sustainability of the intervention's effects on the risk of hospitalization. This is particularly important given the chronic and recurring nature of depression in this population. Most studies on the effectiveness of depression care management interventions have analyzed only the immediate effects of the intervention. The design of an effective real-world model for implementing a nurse-led mental health promotion intervention that can be implemented in clinical practice, reach the target population, be effective across diverse providers and settings, and be able to be maintained over time,17 will require attention to three critical challenges. The first challenge centers on the need to reach older home care clients most likely to benefit from the nurse-led intervention. Future research should include older adults with any level of depression severity, dementia, and other comorbid conditions—individuals who have typically been excluded from community-based trials. The use of less-restrictive inclusion criteria increases the heterogeneity of the sample, reflecting the variability in older home healthcare recipients seen in everyday practice. The 39% enrollment rate for the in-home interviews in this study was similar to the 29% to 64% rates reported in similar studies.15 The challenges of recruiting older adults with depression are well documented.15 Future research exploring strategies that enhance recruitment of this difficult to reach population is warranted. The second challenge for the development and testing of real-world interventions relates to the need to assess the feasibility of the intervention within the local context. Many interventions found to be effective in research studies fail to translate into meaningful outcomes in multiple contexts.18 A major gap in the current evidence base is rigorous study of implementation and questions related to the maintenance of an intervention. The emerging field of implementation science focuses on generating insights that can be applied across settings to promote the uptake of research evidence. Recognizing the multiple barriers that impede the integration of research into practice, future studies should focus on the context and factors affecting implementation, implementation outcome variables that describe various aspects of how implementation occurs, and the study of implementation strategies that support the delivery of the intervention. Implementation outcome variables include acceptability, adoption, appropriateness, feasibility, fidelity, implementation cost, coverage, and sustainability, all of which can serve as indicators of the success of implementation.19 There is a notable gap in the literature related to the cost of interventions. It is imperative that future studies of depression care management interventions include an economic evaluation, which compares the costs of use of health and community support services (including all costs associated with the intervention) of participants in the intervention and usual care groups. Economic analyses will increase understanding of the use of services over time in the intervention and usual care groups, enabling determination of the specific services that the intervention affects the most and the least. Taken together, these data will provide information to make informed policy recommendations regarding the implementation of the intervention in home health, from a cost perspective. This information can be used to customize the intervention, enhance spread, and facilitate future dissemination. The third challenge centers on the need to include patient engagement as an essential component of the research. Future studies should involve individual patients in priority setting, research design and implementation, and knowledge translation. Furthermore, the use of patient-reported outcome measures in intervention studies can ensure that outcomes are meaningful and relevant to patients and that a fuller understanding of patient's experiences with the intervention is gained.20 Individual providers, settings, and sectors cannot address the myriad of methodological and operational challenges to intervention research in older adults with depression using home health services alone. To move the science forward, there is a need for extensive interprofessional and intersectoral collaboration given that many of the factors—personal, social, environmental—that promote mental health fall outside of the healthcare system's current scope of responsibility. Participating individuals, organizations, and sectors should demonstrate shared commitment for development, implementation, and evaluation; shared vision and objectives; infrastructure support; and strong leadership support in delivery of the intervention.21 Attending to these challenges may ultimately serve to enhance the relevance of study results to patients, providers, and policy-makers and help to move the field toward greater dissemination of evidence-based depression care into real-world practice settings. Conflict of Interest: The authors declare no competing interests. Author Contributions: Both authors contributed to the paper. Sponsor's Role: None.
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Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,024 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,004 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».