Multimorbidity-having two or more coexisting chronic conditions-is highly prevalent, costly, and disabling to older adults. Questions remain regarding chronic diseases accumulation over time and ...whether this differs by racial and ethnic background. Answering this knowledge gap, this study identifies differences in rates of chronic disease accumulation and multimorbidity development among non-Hispanic white, non-Hispanic black, and Hispanic study participants starting in middle-age and followed up to 16 years.
We analyzed data from the Health and Retirement Study (HRS), a biennial, ongoing, publicly-available, longitudinal nationally-representative study of middle-aged and older adults in the United States. We assessed the change in chronic disease burden among 8,872 non-Hispanic black, non-Hispanic white, and Hispanic participants who were 51-55 years of age at their first interview any time during the study period (1998-2014) and all subsequent follow-up observations until 2014. Multimorbidity was defined as having two or more of seven somatic chronic diseases: arthritis, cancer, heart disease (myocardial infarction, coronary heart disease, angina, congestive heart failure, or other heart problems), diabetes, hypertension, lung disease, and stroke. We used negative binomial generalized estimating equation models to assess the trajectories of multimorbidity burden over time for non-Hispanic black, non-Hispanic white, and Hispanic participants. In covariate-adjusted models non-Hispanic black respondents had initial chronic disease counts that were 28% higher than non-Hispanic white respondents (IRR 1.279, 95% CI 1.201, 1.361), while Hispanic respondents had initial chronic disease counts that were 15% lower than non-Hispanic white respondents (IRR 0.852, 95% CI 0.775, 0.938). Non-Hispanic black respondents had rates of chronic disease accumulation that were 1.1% slower than non-Hispanic whites (IRR 0.989, 95% CI 0.981, 0.998) and Hispanic respondents had rates of chronic disease accumulation that were 1.5% faster than non-Hispanic white respondents (IRR 1.015, 95% CI 1.002, 1.028). Using marginal effects commands, this translates to predicted values of chronic disease for white respondents who begin the study period with 0.98 chronic diseases and end with 2.8 chronic diseases; black respondents who begin the study period with 1.3 chronic diseases and end with 3.3 chronic diseases; and Hispanic respondents who begin the study period with 0.84 chronic diseases and end with 2.7 chronic diseases.
Middle-aged non-Hispanic black adults start at a higher level of chronic disease burden and develop multimorbidity at an earlier age, on average, than their non-Hispanic white counterparts. Hispanics, on the other hand, accumulate chronic disease at a faster rate relative to non-Hispanic white adults. Our findings have important implications for improving primary and secondary chronic disease prevention efforts among non-Hispanic black and Hispanic Americans to stave off greater multimorbidity-related health impacts.
Organizations that intentionally invest in research data sharing are better prepared to comply with the evolving policy landscape. ...an institution’s data sharing capacity is both an influencer and ...an indicator of scientific success. While this paper has been written for organizations, it is intended to reflect the experiences and needs of local research communities. ...for several Rules, we have included key questions research workforce members may ask, which organizations should be prepared to answer with actionable information. ...any organization can identify itself in a model and grade its current capabilities in each domain. “Research Data Sharing: A Maturity Model for Organizational Capacity” (Fig 1) describes 7 interdependent domains: governance, process and procedures, organizational culture, infrastructure, workforce development, data quality and reuse, and data ethics practices 4.
This Viewpoint discusses the benefits and potential harms of using artificial intelligence (AI) algorithms in medicine and proposes the collaborative creation of a Code of Conduct for AI in Health ...Care.
Objective
To review evidence regarding the use of Health Information Technology (health IT) interventions aimed at improving care for people living with multiple chronic conditions (PLWMCC) in order ...to identify critical knowledge gaps.
Data Sources
We searched MEDLINE, CINAHL, PsycINFO, EMBASE, Compendex, and IEEE Xplore databases for studies published in English between 2010 and 2020.
Study Design
We identified studies of health IT interventions for PLWMCC across three domains as follows: self‐management support, care coordination, and algorithms to support clinical decision making.
Data Collection/Extraction Methods
Structured search queries were created and validated. s were reviewed iteratively to refine inclusion and exclusion criteria. The search was supplemented by manually searching the bibliographic sections of the included studies. The search included a forward citation search of studies nested within a clinical trial to identify the clinical trial protocol and published clinical trial results. Data were extracted independently by two reviewers.
Principal Findings
The search yielded 1907 articles; 44 were included. Nine randomized controlled trials (RCTs) and 35 other studies including quasi‐experimental, usability, feasibility, qualitative studies, or development/validation studies of analytic models were included. Five RCTs had positive results, and the remaining four RCTs showed that the interventions had no effect. The studies address individual patient engagement and assess patient‐centered outcomes such as quality of life. Few RCTs assess outcomes such as disability and none assess mortality.
Conclusions
Despite a growing body of literature on health IT interventions or multicomponent interventions including a health IT component for chronic disease management, current evidence for applying health IT solutions to improve care for PLWMCC is limited. The body of literature included in this review provides critical information on the state of the science as well as the many gaps that need to be filled for digital health to fulfill its promise in supporting care delivery that meets the needs of PLWMCC.
Abstract
Objectives
Collider bias is a common threat to internal validity in clinical research but is rarely mentioned in informatics education or literature. Conditioning on a collider, which is a ...variable that is the shared causal descendant of an exposure and outcome, may result in spurious associations between the exposure and outcome. Our objective is to introduce readers to collider bias and its corollaries in the retrospective analysis of electronic health record (EHR) data.
Target audience
Collider bias is likely to arise in the reuse of EHR data, due to data-generating mechanisms and the nature of healthcare access and utilization in the United States. Therefore, this tutorial is aimed at informaticians and other EHR data consumers without a background in epidemiological methods or causal inference.
Scope
We focus specifically on problems that may arise from conditioning on forms of healthcare utilization, a common collider that is an implicit selection criterion when one reuses EHR data. Directed acyclic graphs (DAGs) are introduced as a tool for identifying potential sources of bias during study design and planning. References for additional resources on causal inference and DAG construction are provided.
Primary Care Medical Home (PCMH) redesign efforts are intended to enhance primary care's ability to improve population health and well-being. PCMH transformation that is focused on "high-value ...elements" (HVEs) for cost and utilization may improve effectiveness.
The objective of this study was to determine if a focus on achieving HVEs extracted from successful primary care transformation models would reduce cost and utilization as compared with a focus on achieving PCMH quality improvement goals.
A stratified, cluster randomized controlled trial with 2 arms. All practices received equal financial incentives, health information technology support, and in-person practice facilitation. Analyses consisted of multivariable modeling, adjusting for the cluster, with difference-in-difference results.
Eight primary care clinics that were engaged in PCMH reform.
We examined: (1) total claims payments; (2) emergency department (ED) visits; and (3) hospitalizations among patients during baseline and intervention years.
In total, 16,099 patients met the inclusion criteria. Intervention clinics had significantly lower baseline ED visits (P=0.02) and claims paid (P=0.01). Difference-in-difference showed a decrease in ED visits greater in control than intervention (ED per 1000 patients: +56; 95% confidence interval: +96, +15) with a trend towards decreased hospitalizations in intervention (-15; 95% confidence interval: -52, +21). Costs were not different. In modeling monthly outcome means, the generalized linear mixed model showed significant differences for hospitalizations during the intervention year (P=0.03).
The trial had a trend of decreasing hospitalizations, increased ED visits, and no change in costs in the HVE versus quality improvement arms.
The US health care delivery system does not systematically engage or support family or friend care partners. Meanwhile, the uptake and familiarity of portals to personal health information are ...increasing among patients. Technology innovations, such as shared access to the portal, use separate identity credentials to differentiate between patients and care partners. Although not well-known, or commonly used, shared access allows patients to identify who they do and do not want to be involved in their care. However, the processes for patients to grant shared access to portals are often limited or so onerous that interested patients and care partners often circumvent the process entirely. As a result, the vast majority of care partners resort to accessing portals using a patient’s identity credentials—a “do-it-yourself” solution in conflict with a health systems’ legal responsibility to protect patient privacy and autonomy. The personal narratives in this viewpoint (shared by permission) elaborate on quantitative studies and provide first-person snapshots of challenges faced by patients and families as they attempt to gain or grant shared access during crucial moments in their lives. As digital modalities increase patient roles in health care interactions, so does the importance of making shared access work for all stakeholders involved—patients, clinicians, and care partners. Electronic health record vendors must recognize that both patients and care partners are important users of their products, and health care organizations must acknowledge and support the critical contributions of care partners as distinct from patients.
Can Personalized Care Planning Improve Primary Care? Edwards, Samuel T; Dorr, David A; Landon, Bruce E
JAMA : the journal of the American Medical Association,
07/2017, Letnik:
318, Številka:
1
Journal Article
Recenzirano
Personalized care planning, a formal process in which clinicians and patients collaborate in the creation of longitudinal treatment plans, is a frequently mentioned tool to improve the quality and ...patient-centeredness of primary care for medically complex, high-needs individuals. A systematic review of personalized care planning interventions demonstrated small improvements in quality of care for persons with diabetes, hypertension, and asthma; improvements in self-efficacy and self-care; and some improvements in psychological and general health indicators; moreover, interventions were most effective when they were integrated into routine care. Here, Edwards et al describe the essential components of care planning--defining and integrating patient goals, optimizing quality of chronic disease management, and providing a central care record--and the critical factors and steps that practices will need to address in implementing care planning to ensure it reaches its potential in improving patient care.
Our understanding of how multimorbidity progresses and changes is nascent.
Assess multimorbidity changes among racially/ethnically diverse middle-aged and older adults.
Prospective cohort study using ...latent class analysis to identify multimorbidity combinations over 16 years, and multinomial logistic models to assess change relative to baseline class membership. Health and Retirement Study respondents (age 51 y and above) in 1998 and followed through 2014 (N=17,297).
Multimorbidity latent classes of: hypertension, heart disease, lung disease, diabetes, cancer, arthritis, stroke, high depressive symptoms.
Three latent classes were identified in 1998: minimal disease (45.8% of participants), cardiovascular-musculoskeletal (34.6%), cardiovascular-musculoskeletal-mental (19.6%); and 3 in 2014: cardiovascular-musculoskeletal (13%), cardiovascular-musculoskeletal-metabolic (12%), multisystem multimorbidity (15%). Remaining participants were deceased (48%) or lost to follow-up (12%) by 2014. Compared with minimal disease, individuals in cardiovascular-musculoskeletal in 1998 were more likely to be in multisystem multimorbidity in 2014 odds ratio (OR)=1.78, P<0.001, and individuals in cardiovascular-musculoskeletal-mental in 1998 were more likely to be deceased (OR=2.45, P<0.001) or lost to follow-up (OR=3.08, P<0.001). Hispanic and Black Americans were more likely than White Americans to be in multisystem multimorbidity in 2014 (OR=1.67, P=0.042; OR=2.60, P<0.001, respectively). Black compared with White Americans were more likely to be deceased (OR=1.62, P=0.01) or lost to follow-up (OR=2.11, P<0.001) by 2014.
Racial/ethnic older adults are more likely to accumulate morbidity and die compared with White peers, and should be the focus of targeted and enhanced efforts to prevent and/or delay progression to more complex multimorbidity patterns.
PDF
