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Record W2518267497 · doi:10.1111/jgs.14437

Depression Care Management Interventions for Older Adults with Depression Using Home Health Services: Moving the Field Forward

2016· letter· en· W2518267497 on OpenAlexaff
Maureen Markle‐Reid, Carrie McAiney

Bibliographic record

VenueJournal of the American Geriatrics Society · 2016
Typeletter
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineDepression (economics)GeriatricsPsychological interventionManagement of depressionPublic healthHealth careGerontologyMental healthPsychiatryDisease managementIntervention (counseling)Randomized controlled trialFamily medicineHealth management systemAlternative medicinePrimary careNursing

Abstract

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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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.024
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0060.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.364
Teacher spread0.345 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations2
Published2016
Admission routes1
Has abstractyes

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