In recent years, data-driven inference of cell-cell communication has helped reveal coordinated biological processes across cell types. Here, we integrate two tools, LIANA and Tensor-cell2cell, which, when combined, can deploy multiple existing methods and resources to enable the robust and flexible identification of cell-cell communication programs across multiple samples. In this work, we show how the integration of our tools facilitates the choice of method to infer cell-cell communication and subsequently perform an unsupervised deconvolution to obtain and summarize biological insights. We explain how to perform the analysis step by step in both Python and R and provide online tutorials with detailed instructions available at https://ccc-protocols.readthedocs.io/. This workflow typically takes ∼1.5 h to complete from installation to downstream visualizations on a graphics processing unit-enabled computer for a dataset of ∼63,000 cells, 10 cell types, and 12 samples.

Care plans are central to effective care delivery for people with multiple chronic conditions. But existing care plans-which typically are difficult to share across care settings and care team members-poorly serve people with multiple chronic conditions, who often receive care from numerous clinicians in multiple care settings. Comprehensive, shared electronic care (e-care) plans are dynamic electronic tools that facilitate care coordination and address the totality of health and social needs across care contexts. They have emerged as a potential way to improve care for individuals with multiple chronic conditions.
To review the landscape of e-care plans and care plan-related initiatives that could allow the creation of a comprehensive, shared e-care plan and inform a joint initiative by the National Institutes of Health and the Agency for Healthcare Research and Quality to develop e-care planning tools for people with multiple chronic conditions.
We conducted a scoping review, searching literature from 2015 to June 2020 using Scopus, Clinical Key, and PubMed; we also searched the gray literature. To identify initiatives potentially missing from this search, we interviewed expert informants. Relevant data were then identified and extracted in a structured format for data synthesis and analysis using an expanded typology of care plans adapted to our study context. The extracted data included (1) the perspective of the initiatives; (2) their scope, (3) network, and (4) context; (5) their use of open syntax standards; and (6) their use of open semantic standards.
We identified 7 projects for e-care plans and 3 projects for health care data standards. Each project provided critical infrastructure that could be leveraged to promote the vision of a comprehensive, shared e-care plan. All the e-care plan projects supported both broad goals and specific behaviors; 1 project supported a network of professionals across clinical, community, and home-based networks; 4 projects included social determinants of health. Most projects specified an open syntax standard, but only 3 specified open semantic standards.
A comprehensive, shared, interoperable e-care plan has the potential to greatly improve the coordination of care for individuals with multiple chronic conditions across multiple care settings. The need for such a plan is heightened in the wake of the ongoing COVID-19 pandemic. While none of the existing care plan projects meet all the criteria for an optimal e-care plan, they all provide critical infrastructure that can be leveraged as we advance toward the vision of a comprehensive, shared e-care plan. However, critical gaps must be addressed in order to achieve this vision.

BACKGROUND: The daily management of long-term conditions falls primarily on individuals and informal carers, but the impact of household context on health and social care activity among people with multiple long-term conditions (MLTCs) is understudied.
OBJECTIVE: To test whether co-residence with a person with MLTCs (compared with a co-resident without MLTCs) is associated with utilisation and cost of primary, community, secondary health care, and formal social care.
METHODS: Linked data from health providers and local government in Barking and Dagenham for a retrospective cohort of people aged ≥50 years in two-person households in 2016-2018.
METHODS: Two-part regression models were applied to estimate annualised use and cost of hospital, primary, community, mental health, and social care by MLTC status of individuals and co-residents, adjusted for age, sex, and deprivation. Applicability at the national level was tested using the Clinical Practice Research Datalink (CPRD).
RESULTS: Forty-eight per cent of people with MLTCs in two-person households were co-resident with another person with MLTCs. They were 1.14 (95% confidence interval [CI] = 1.00 to 1.30) times as likely to have community care activity and 1.24 (95% CI = 0.99 to 1.54) times as likely to have mental health care activity compared with those co-resident with a healthy person. They had more primary care visits (8.5 [95% CI = 8.2 to 8.8] versus 7.9 [95% CI = 7.7 to 8.2]) and higher primary care costs. Outpatient care and elective admissions did not differ. Findings in national data were similar.
CONCLUSIONS: Care utilisation for people with MLTCs varies by household context. There may be potential for connecting health and community service input across household members.

Many older cats often suffer concurrently from multiple conditions. By focusing on the common concerns, rather than conflicting requirements, a management program can be devised. Optimize hydration, nutrition, and ensure comfort though providing analgesia and a low-stress environment in which the patient\'s feline-specific nature is respected both in the clinic and at home. Additional requirements, such as hyperphosphatemia or hypokalemia, can be met using treatments outside of diet, if necessary.

OBJECTIVE: To predict health state utility values (HSUVs) for individuals with up to 4 conditions simultaneously.
METHODS: Person-level data were taken from the General Practice Patient Survey, a national survey of adult patients registered with general practices in England. Individuals reported whether they had any 1 of 16 chronic conditions and completed the 3-level EuroQol 5-dimensional questionnaire. Four nonparametric methods (additive, multiplicative, minimum, and the adjusted decrement estimator) and 1 parametric estimator (the linear index) were used to predict HSUVs for individuals with a joint health condition (JHC). Predicted and actual utility scores were compared for precision using root mean square error and mean absolute error. Bias was assessed using mean error.
RESULTS: The analysis included 929,565 individuals, of which 30.5% had at least 2 conditions. Of the nonparametric estimators, the multiplicative approach produced estimates with the lowest bias and most precision for 2 JHCs. For populations with a long-term mental health condition within the JHC, the multiplicative approach overestimated utility scores. All nonparametric methods produced biased results when estimating HSUVs for 3 or 4 JHCs. The linear index generally produced unbiased results with the highest precision.
CONCLUSIONS: The multiplicative approach was the best nonparametric estimator when estimating HSUVs for 2 JHCs. None of the nonparametric approaches for estimating HSUVs can be recommended with more than 2 JHCs. The linear index was found to have good predictive properties but needs external validation before being recommended for routine use.

Consensus on terminology for multiple diseases is lacking. Because of the clinical relevance and social impact of multiple concurrent diseases, it is important that concepts are clear.
To highlight the diversity of terms in the literature referring to the presence of multiple concurrent diseases/conditions and make recommendations.
A bibliometric analysis of English-language publications indexed in the MEDLINE database from 1970 to 2012 for the terms comorbidity, multimorbidity, polymorbidity, polypathology, pluripathology, multipathology, and multicondition, and a review of definitions of multimorbidity found in English-language publications indexed from 1970 to 2012 in the MEDLINE and SCOPUS databases.
Comorbidity was used in 67,557 publications, multimorbidity in 434, and the other terms in three to 31 publications. At least 144 publications used the term comorbidity without referring to an index disease. Thirteen general definitions of multimorbidity were identified, but only two were frequently used (91% of publications). The most frequently used definition (48% of publications) was \"more than one or multiple chronic or long-term diseases/conditions\". Multimorbidity was not defined in 51% of the publications using the term.
Comorbidity was overwhelmingly used to describe any clinical entity coexisting with an index disease under study. Multimorbidity was the term most frequently used when no index disease was designated. Several definitions of multimorbidity were found. However, most authors using the term did not define it. The use of clearly defined terms in the literature is recommended until a general consensus on the terminology of multiple coexistent diseases is reached. Journal of Comorbidity 2013;3:4-9.

The effect of treating comorbid depression to achieve optimal management of chronic obstructive pulmonary disease (COPD) has not yet empirically tested. We examined the association between antidepressant treatment and use of and adherence to COPD maintenance medications among patients with new-onset COPD and comorbid depression.
Using 2006-2012 Medicare data, this retrospective cohort study identified patients with newly diagnosed COPD and new-onset major depression. Two exposures-antidepressant use (versus non-use) and adherence measured by proportion of days covered (PDC) (PDC ≥0.8 versus <0.8)-were assessed quarterly. We used marginal structural models to estimate the effects of prior antidepressant use and adherence on subsequent COPD maintenance inhaler use and adherence outcomes, accounting for time-varying confounders.
A total of 25 458 COPD-depression patients, 82% with antidepressant treatment, were followed for a median of 2.5 years. Nearly half (48%) used at least 1 COPD maintenance inhaler in any given quarter; among users, 3 in 5 (61%) had a PDC of <0.8. Compared to patients with no antidepressant treatment, those with antidepressant use were more likely to use (relative ratio [RR] = 1.15, 95% confidence interval [CI] = 1.12- 1.17) and adhere to (RR = 1.08, 95% = 1.03-1.14) their COPD maintenance inhalers. Patients who adhered to antidepressant treatment were more likely to use and adhere to COPD maintenance inhalers.
Regularly treated depression may increase use of and adherence to necessary maintenance medications for COPD. Antidepressant treatment may be a key determinant to improving medication-taking behaviors among COPD patients comorbid with depression.

BACKGROUND: The growing complexity of biological experiment design based on high-throughput RNA sequencing (RNA-seq) is calling for more accommodative statistical tools. We focus on differential expression (DE) analysis using RNA-seq data in the presence of multiple treatment conditions.
RESULTS: We propose a novel method, multiDE, for facilitating DE analysis using RNA-seq read count data with multiple treatment conditions. The read count is assumed to follow a log-linear model incorporating two factors (i.e., condition and gene), where an interaction term is used to quantify the association between gene and condition. The number of the degrees of freedom is reduced to one through the first order decomposition of the interaction, leading to a dramatically power improvement in testing DE genes when the number of conditions is greater than two. In our simulation situations, multiDE outperformed the benchmark methods (i.e. edgeR and DESeq2) even if the underlying model was severely misspecified, and the power gain was increasing in the number of conditions. In the application to two real datasets, multiDE identified more biologically meaningful DE genes than the benchmark methods. An R package implementing multiDE is available publicly at http://homepage.fudan.edu.cn/zhangh/softwares/multiDE .
CONCLUSIONS: When the number of conditions is two, multiDE performs comparably with the benchmark methods. When the number of conditions is greater than two, multiDE outperforms the benchmark methods.

BACKGROUND: Following a workshop with people with multiple conditions, the Chronic Illness Alliance undertook a literature review to explore current literature about multiple conditions.
METHODS: The literature search was performed using Medline, CINAHL, Google Scholar and Cochrane Library employing an extensive list of search terms and limited to English language journals between 1999 and 2009. Inclusion criteria for articles were those articles focussing on issues identified by consumers with more than one chronic illness and the health services working with them.
RESULTS: The results reported in this article are definitions of multiple conditions, safety and quality of services, risks and benefits of treatments for multiple and rare conditions and coordination of services.
CONCLUSIONS: The impact of multiple conditions or multimorbidities on health services has been researched internationally and identifies the barriers to good health care when multiple conditions are not recognized. While the issues for consumers with more than one condition are not well recognized, the barriers identified by the literature are of great importance to consumers.
CONCLUSIONS: This review demonstrates that services and policies in Australia require specific reforms to better meet the needs of people with multiple conditions.