Epidemiology of Respiratory Syncytial Virus in Adults and Children With Medically Attended Acute Respiratory Illness Over Three Seasons.

BACKGROUND: Data on the true prevalence of respiratory syncytial virus (RSV) among medically attended acute respiratory illnesses (MAARI) has been limited by the lack of regular clinical testing of mild to moderate illnesses. Here we present a prospective evaluation of the epidemiology of RSV-associated MAARI across age groups and multimorbidity status over 3 seasons, which is informative in light of the recommendations for shared decision making for vaccination in older adults. METHODS: Ambulatory patients ≥6 months of age meeting a common MAARI case definition were prospectively enrolled in the Michigan Ford Influenza Vaccine Effectiveness (MFIVE) study, a subsite of the US Influenza Vaccine Effectiveness Network. All participants were tested by nasal-throat swab for RSV and influenza, including subtype, independently from clinician-directed testing. Participant illness characteristics and calculated multimorbidity-weighted index (MWI) were collected by in-person survey and electronic medical record review. RESULTS: Over 3 surveillance seasons (fall 2017 to spring 2020), 9.9% (n = 441) of 4442 participants had RSV detected. RSV-associated MAARI was more prevalent than influenza for participants 6 months to 4 years of age. Adults with RSV-MAARI had higher median MWI scores overall compared to influenza-MAARI and controls with neither virus (1.62, 0.40, and 0.64, respectively). CONCLUSIONS: RSV is a significant, underrecognized cause of MAARI in both children and adults presenting for ambulatory care. Multimorbidity is an important contributor to RSV-associated MAARI in outpatient adults, providing information to support shared clinical decision making for vaccination.

Prediction of Future Health Care Utilization Through Note-extracted Psychosocial Factors.

BACKGROUND: Persons with multimorbidity (≥2 chronic conditions) face an increased risk of poor health outcomes, especially as they age. Psychosocial factors such as social isolation, chronic stress, housing insecurity, and financial insecurity have been shown to exacerbate these outcomes, but are not routinely assessed during the clinical encounter. Our objective was to extract these concepts from chart notes using natural language processing and predict their impact on health care utilization for patients with multimorbidity. METHODS: A cohort study to predict the 1-year likelihood of hospitalizations and emergency department visits for patients 65+ with multimorbidity with and without psychosocial factors. Psychosocial factors were extracted from narrative notes; all other covariates were extracted from electronic health record data from a large academic medical center using validated algorithms and concept sets. Logistic regression was performed to predict the likelihood of hospitalization and emergency department visit in the next year. RESULTS: In all, 76,479 patients were eligible; the majority were White (89%), 54% were female, with mean age 73. Those with psychosocial factors were older, had higher baseline utilization, and more chronic illnesses. The 4 psychosocial factors all independently predicted future utilization (odds ratio=1.27-2.77, C -statistic=0.63). Accounting for demographics, specific conditions, and previous utilization, 3 of 4 of the extracted factors remained predictive (odds ratio=1.13-1.86) for future utilization. Compared with models with no psychosocial factors, they had improved discrimination. Individual predictions were mixed, with social isolation predicting depression and morbidity; stress predicting atherosclerotic cardiovascular disease onset; and housing insecurity predicting substance use disorder morbidity. DISCUSSION: Psychosocial factors are known to have adverse health impacts, but are rarely measured; using natural language processing, we extracted factors that identified a higher risk segment of older adults with multimorbidity. Combining these extraction techniques with other measures of social determinants may help catalyze population health efforts to address psychosocial factors to mitigate their health impacts.

Multimorbidity and Inpatient Utilization Among Older Adults with Opioid Use Disorder in New York City.

BACKGROUND: Nationally, there is a sharp increase in older adults with opioid use disorder (OUD). However, we know little of the acute healthcare utilization patterns and medical comorbidities among this population. OBJECTIVE: This study describes the prevalence of chronic conditions, patterns of inpatient utilization, and correlates of high inpatient utilization among older adults with OUD in New York City (NYC). DESIGN: Retrospective longitudinal cohort study. PARTICIPANTS: Patients aged ≥55 with OUD hospitalized in NYC in 2012 identified using data from New York State's Statewide Planning and Research Cooperative System (SPARCS). MAIN MEASURES: The prevalence of comorbid substance use diagnoses, chronic medical disease, and mental illness was measured using admission diagnoses from the index hospitalization. We calculated the ICD-Coded Multimorbidity-Weighted Index (MWI-ICD) for each patient to measure multimorbidity. We followed the cohort through September 30, 2015 and the outcome was the number of rehospitalizations for inpatient services in NYC. We compared patient-level factors between patients with the highest use of inpatient services (≥7 rehospitalizations) during the study period to low utilizers. We used multiple logistic regression to examine possible correlates of high inpatient utilization. KEY RESULTS: Of 3669 adults aged ≥55 with OUD with a hospitalization in 2012, 76.4% (n=2803) had a subsequent hospitalization and accounted for a total of 22,801 rehospitalizations during the study period. A total of 24.7% of the cohort (n=906) were considered high utilizers and had a higher prevalence of alcohol and cocaine-related diagnoses, congestive heart failure, diabetes, schizophrenia, and chronic obstructive pulmonary disease. Multivariable predictors of high utilization included being a Medicaid beneficiary (adjusted odds ratio [aOR]=1.70, 95% confidence interval [CI]=1.37-2.11), alcohol-related diagnoses (aOR=1.43, 95% CI: 1.21-1.69), and increasing comorbidity measured by MWI-ICD (highest MWI-ICD quartile: aOR=1.98, 95% CI=1.59-2.48). CONCLUSIONS: Among older adults with OUD admitted to the hospital, multimorbidity is strongly associated with high inpatient utilization.

Association of multimorbidity patterns with incident disability and recovery of independence among middle-aged and older adults.

OBJECTIVE: to identify multimorbidity patterns among middle-aged and older adults in China and examine how these patterns are associated with incident disability and recovery of independence. METHODS: data were from The China Health and Retirement Longitudinal Study. We included 14,613 persons aged ≥45 years. Latent class analysis (LCA) was conducted to identify multimorbidity patterns with clinical meaningfulness. Multinomial logistic models were used to determine the adjusted association between multimorbidity patterns and incident disability and recovery of independence. RESULTS: we identified four multimorbidity patterns: 'low morbidity' (67.91% of the sample), 'pulmonary-digestive-rheumatic' (17.28%), 'cardiovascular-metabolic-neuro' (10.77%) and 'high morbidity' (4.04%). Compared to the 'low morbidity' group, 'high morbidity' (OR = 2.63, 95% CI = 1.97-3.51), 'pulmonary-digestive-rheumatic' (OR = 1.89, 95% CI = 1.63-2.21) and 'cardiovascular-metabolic-neuro' pattern (OR = 1.61, 95% CI = 1.31-1.97) had higher odds of incident disability in adjusted multinomial logistic models. The 'cardiovascular-metabolic-neuro' (OR = 0.60, 95% CI = 0.44-0.81), 'high morbidity' (OR = 0.68, 95% CI = 0.47-0.98) and 'pulmonary-digestive-rheumatic' group (OR = 0.75, 95% CI = 0.60-0.95) had lower odds of recovery from disability than the 'low morbidity' group. Among people without disability, the 'cardiovascular-endocrine-neuro' pattern was associated with the highest 2-year mortality (OR = 2.42, 95% CI = 1.56-3.72). CONCLUSIONS: multimorbidity is complex and heterogeneous, but our study demonstrates that clinically meaningful patterns can be obtained using LCA. We highlight four multimorbidity patterns with differential effects on incident disability and recovery from disability. These studies suggest that targeted prevention and treatment approaches are needed for people with multimorbidity.

Multimorbidity and long-term disability and physical functioning decline in middle-aged and older Americans: an observational study.

BACKGROUND: Multimorbidity is highly prevalent and associated with several adverse health outcomes, including functional limitations. While maintaining physical functioning is relevant for all adults, identifying those with multimorbidity at risk for faster rates of physical functioning decline may help to target interventions to delay the onset and progression of disability. We quantified the association of multimorbidity with rates of long-term disability and objective physical functioning decline. METHODS: In the Health and Retirement Study, we computed the Multimorbidity-Weighted Index (MWI) by assigning previously validated weights (based on physical functioning) to each chronic condition. We used an adjusted negative binomial regression to assess the association of MWI with disability (measured by basic and instrumental activities of daily living [ADLs, IADLs]) over 16 years, and linear mixed effects models to assess the association of MWI with gait speed and grip strength over 8 years. RESULTS: Among 16,616 participants (mean age 67.3, SD 9.7 years; 57.8% women), each additional MWI point was associated with a 10% increase in incidence rate of disability (IRR: 1.10; 95%CI: 1.09, 1.10). In 2,748 participants with data on gait speed and grip strength, each additional MWI point was associated with a decline in gait speed of 0.004 m/s (95%CI: -0.006, -0.001). The association with grip strength was not statistically significant (-0.01 kg, 95%CI: -0.73, 0.04). The rate of decline increased with time for all outcomes, with a significant interaction between time and MWI for disability progression only. CONCLUSION: Multimorbidity, as weighted on physical functioning, was associated with long-term disability, including faster rates of disability progression, and decline in gait speed. Given the importance of maintaining physical functioning and preserving functional independence, MWI is a readily available tool that can help identify adults to target early on for interventions.

Measuring Multimorbidity: Selecting the Right Instrument for the Purpose and the Data Source.

BACKGROUND: Adults have a higher prevalence of multimorbidity-or having multiple chronic health conditions-than having a single condition in isolation. Researchers, health care providers, and health policymakers find it challenging to decide upon the most appropriate assessment tool from the many available multimorbidity measures. OBJECTIVE: The objective of this study was to describe a broad range of instruments and data sources available to assess multimorbidity and offer guidance about selecting appropriate measures. DESIGN: Instruments were reviewed and guidance developed during a special expert workshop sponsored by the National Institutes of Health on September 25-26, 2018. RESULTS: Workshop participants identified 4 common purposes for multimorbidity measurement as well as the advantages and disadvantages of 5 major data sources: medical records/clinical assessments, administrative claims, public health surveys, patient reports, and electronic health records. Participants surveyed 15 instruments and 2 public health data systems and described characteristics of the measures, validity, and other features that inform tool selection. Guidance on instrument selection includes recommendations to match the purpose of multimorbidity measurement to the measurement approach and instrument, review available data sources, and consider contextual and other related constructs to enhance the overall measurement of multimorbidity. CONCLUSIONS: The accuracy of multimorbidity measurement can be enhanced with appropriate measurement selection, combining data sources and special considerations for fully capturing multimorbidity burden in underrepresented racial/ethnic populations, children, individuals with multiple Adverse Childhood Events and older adults experiencing functional limitations, and other geriatric syndromes. The increased availability of comprehensive electronic health record systems offers new opportunities not available through other data sources.

Comprehensive review of ICD-9 code accuracies to measure multimorbidity in administrative data.

BACKGROUND: Quantifying the burden of multimorbidity for healthcare research using administrative data has been constrained. Existing measures incompletely capture chronic conditions of relevance and are narrowly focused on risk-adjustment for mortality, healthcare cost or utilization. Moreover, the measures have not undergone a rigorous review for how accurately the components, specifically the International Classification of Diseases, Ninth Revision (ICD-9) codes, represent the chronic conditions that comprise the measures. We performed a comprehensive, structured literature review of research studies on the accuracy of ICD-9 codes validated using external sources across an inventory of 81 chronic conditions. The conditions as a weighted measure set have previously been demonstrated to impact not only mortality but also physical and mental health-related quality of life. METHODS: For each of 81 conditions we performed a structured literature search with the goal to identify 1) studies that externally validate ICD-9 codes mapped to each chronic condition against an external source of data, and 2) the accuracy of ICD-9 codes reported in the identified validation studies. The primary measure of accuracy was the positive predictive value (PPV). We also reported negative predictive value (NPV), sensitivity, specificity, and kappa statistics when available. We searched PubMed and Google Scholar for studies published before June 2019. RESULTS: We identified studies with validation statistics of ICD-9 codes for 51 (64%) of 81 conditions. Most of the studies (47/51 or 92%) used medical chart review as the external reference standard. Of the validated using medical chart review, the median (range) of mean PPVs was 85% (39-100%) and NPVs was 91% (41-100%). Most conditions had at least one validation study reporting PPV ≥70%. CONCLUSIONS: To help facilitate the use of patient-centered measures of multimorbidity in administrative data, this review provides the accuracy of ICD-9 codes for chronic conditions that impact a universally valued patient-centered outcome: health-related quality of life. These findings will assist health services studies that measure chronic disease burden and risk-adjust for comorbidity and multimorbidity using patient-centered outcomes in administrative data.

Multimorbidity, Mortality, and Long-Term Physical Functioning in 3 Prospective Cohorts of Community-Dwelling Adults.

Multimorbidity is prevalent, but its optimal quantification and associations with mortality rate and physical functioning in young through older adults are uncertain. We used data collected using the Short Form-36 in the Nurses' Health Study (enrollment started in 1976), Nurses' Health Study II (begun in 1989), and Health Professionals Follow-up Study (begun in 1986) to identify associations of a multimorbidity-weighted index (MWI) and common alternative indices with mortality and future physical functioning. We used Cox proportional hazard ratios to determine incident 10-year mortality and general linear models to obtain coefficients for the associations of MWI with 4- and 8-year physical functioning. At baseline, mean values for the 219,950 participants were 55.0 (standard deviation, 3.7) years for age; 3.8 (range, 0-51) for MWI; 2.7 (range, 0-23) for disease count, and 0.43 (range, 0-13) for Charlson Comorbidity Index (CCI). During follow-up, 23,709 deaths (10.8%) occurred. CCI, MWI, and disease count were 0 for 77%, 12%, and 19% of participants, respectively. When comparing persons in the highest quartiles with those in the lowest, the hazard ratios for mortality were 6.04 (95% confidence interval (CI): 6.00, 6.09; P for trend < 0.0001) for the MWI, 4.86 (95% CI: 4.81, 4.91; P for trend < 0.0001) for disease count, and 3.29 (95% CI: 3.26, 3.32; P for trend < 0.0001) for the CCI. For future physical functioning, MWI had the best model fit and explained the greatest variance. Multimorbidity has important associations with future physical functioning and mortality that are easily captured with a readily measured index.

Diverse Cumulative Impact of Chronic Diseases on Physical Health-Related Quality of Life: Implications for a Measure of Multimorbidity.

Debate continues on how to measure and weight diseases in multimorbidity. We quantified the association of a broad range of chronic diseases with physical health-related qualify of life and used these weights to develop and validate a multimorbidity weighted index (MWI). Community-dwelling adults in 3 national, prospective studies-the Nurses' Health Study (n = 121,701), Nurses' Health Study II (n = 116,686), and Health Professionals Follow-up Study (n = 51,529)-reported physician-diagnosed diseases and completed the Short Form 36 physical functioning (PF) scale over multiple survey cycles between 1992 and 2008. Mixed models were used to obtain regression coefficients for the impact of 98 morbid conditions on PF. The MWI was formed by weighting conditions by these coefficients and was validated through bootstrapping. The final sample included 612,592 observations from 216,890 participants (PF mean score = 46.5 (standard deviation, 11)). The association between diseases and PF varied severalfold (median, -1.4; range, -10.6 to 0.8). End-stage organ diseases were associated with the greatest reduction in PF. The mean MWI score was 4.8 (median, 3.7; range, 0-53), and the mean number of comorbid conditions was 3.3 (median, 2.8; range, 0-34). This validated MWI weights diseases by severity using PF, a patient-centered outcome. These results suggest that simple disease count is unlikely to capture the full impact of multimorbidity on health-related quality of life, and that the MWI is feasible and readily implemented.

Development and validation of new multimorbidity-weighted index for ICD-10-coded electronic health record and claims data: an observational study.

OBJECTIVE: Map multimorbidity-weighted index (MWI) conditions to International Classification of Diseases, 10th Revision (ICD-10), expand the conditions and codes to develop a new ICD-10-coded MWI (MWI-ICD10) and updated MWI-ICD9, and assess their consistency. DESIGN: Population-based retrospective cohort. SETTING: Large medical centre between 2013 and 2017. PARTICIPANTS: Adults ≥18 years old with encounters in each of 4 years (2013, 2014, 2016, 2017). MAIN OUTCOME MEASURES: MWI conditions mapped to ICD-10 codes, and additional conditions and codes added to produce a new MWI-ICD10 and updated MWI-ICD9. We compared the prevalence of ICD-coded MWI conditions within the ICD-9 era (2013-2014), within the ICD-10 era (2016-2017) and across the ICD-9-ICD-10 transition in 2015 (washout period) among adults present in both sets of comparison years. We computed the prevalence and change in prevalence of conditions when using MWI-ICD10 versus MWI-ICD9. RESULTS: 88 175 adults met inclusion criteria. Participants were 60.8% female, 50.5% white, with mean age 54.7±17.3 years and baseline MWI-ICD9 4.47±6.02 (range 0-64.33). Of 94 conditions, 65 had <1% difference across the ICD-9-ICD-10 transition and similar minimal changes within ICD coding eras. CONCLUSIONS: MWI-ICD10 captured the prevalence of chronic conditions nearly identically to that of the validated MWI-ICD9, along with notable but explicable changes across the ICD-10 transition. This new comprehensive person-centred index enables quantification of cumulative disease burden and physical functioning in adults as a clinically meaningful measure of multimorbidity in electronic health record and claims data.

Capturing the care of complex community-based health center patients: A comparison of multimorbidity indices and clinical classification software.

OBJECTIVE: To compare morbidity burden captured from multimorbidity indices and aggregated measures of clinically meaningful categories captured in primary care community-based health center (CBHC) patients. DATA SOURCES AND STUDY SETTING: Electronic health records of patients seen in 2019 in OCHIN's national network of CBHCs serving patients in rural and underserved communities. STUDY DESIGN: Age-stratified analyses comparing the most common conditions captured by the Charlson, Elixhauser, and Multimorbidity Weighted (MWI) indices, and Classification Software Refined (CCSR) and Chronic Condition Indicator (CCI) algorithms. DATA COLLECTION/EXTRACTION METHODS: Active ICD-10 conditions on patients' problem list in 2019. PRINCIPAL FINDINGS: Approximately 35%-56% of patients with at least one condition are not captured by the Charlson, Elixhauser, and MWI indices. When stratified by age, this range broadens to 9%-90% with higher percentages in younger patients. The CCSR and CCI reflect a broader range of acute and chronic conditions prevalent among CBHC patients. CONCLUSION: Three commonly used indices to capture morbidity burden reflect conditions most prevalent among older adults, but do not capture those on problem lists for younger CBHC patients. An index with an expanded range of care conditions is needed to understand the complex care provided to primary care populations across the lifespan.

Multimorbidity, 30-Day Readmissions, and Postdischarge Mortality Among Medicare Beneficiaries Using a New ICD-Coded Multimorbidity-Weighted Index.

BACKGROUND: Medically complex, disabled adults have high 30-day readmission rates. However, physical functioning is not routinely included in risk-adjustment models. We examined the association between multimorbidity with readmissions and mortality using a physical functioning weighted International Classification of Diseases (ICD)-coded multimorbidity-weighted index (MWI-ICD) representing 84 conditions. METHODS: We included Medicare beneficiaries with ≥1 hospitalization 2000-2015 who participated in a Health and Retirement Study interview before admission. We computed MWI-ICD by summing physical functioning weighted conditions from Medicare claims. We examined 30-, 90-, and 365-day postdischarge mortality using multivariable logistic regression and length of stay through zero-inflated negative binomials. Models adjusted for age, sex, race/ethnicity, body mass index, smoking status, physical activity, education, net worth, and marital status/living arrangement. RESULTS: The final sample of 10 737 participants had mean ± standard deviation (SD) age 75.9 ± 8.7 years, MWI-ICD 14.9 ± 9.0, and 20% had a 30-day readmission. Adults in the highest versus lowest quartile MWI-ICD had 92% increased odds of 30-day readmission (odds ratio [OR] = 1.92, 95% confidence interval [CI]: 1.65-2.22). A 1-point increase in MWI-ICD was associated with 24% increased odds of 30-day readmission (OR = 1.24, 95% CI: 1.18-1.31). A 1-point increase in MWI-ICD was associated with 32% increased odds of death within 365-day postdischarge (OR = 1.32, 95% CI: 1.25-1.40). Readmitted participants with the highest versus lowest quartile MWI-ICD had 37% increased number of expected hospitalized days (incidence rate ratio = 1.37, 95% CI: 1.17-1.59). CONCLUSION: Among Medicare beneficiaries, multimorbidity using MWI-ICD is associated with an increased risk of readmissions, mortality, and longer length of stay. MWI-ICD appears to be a valid measure of multimorbidity that embeds physical functioning and presents an opportunity to incorporate functional status into claims-based risk-adjustment models.

Readmissions and postdischarge mortality by race and ethnicity among Medicare beneficiaries with multimorbidity.

BACKGROUND: Disparities in readmission risk and reasons they might exist among diverse complex patients with multimorbidity, disability, and unmet social needs have not been clearly established. These characteristics may be underestimated in claims-based studies where individual-level data are limited. We sought to examine the risk of readmissions and postdischarge mortality by race and ethnicity after rigorous adjustment for multimorbidity, physical functioning, and sociodemographic and lifestyle characteristics. METHODS: We used Health and Retirement Study (HRS) data linked to Medicare claims. To obtain ICD-9-CM diagnostic codes to compute the ICD-coded multimorbidity-weighted index (MWI-ICD) we used Medicare Parts A and B (inpatient, outpatient, carrier) files between 1991-2015. Participants must have had at least one hospitalization between January 1, 2000 and September 30, 2015 and continuous enrollment in fee-for-service Medicare Part A 1-year prior to hospitalization. We used multivariable logistic regression to assess the association of MWI-ICD with 30-day readmissions and mortality 1-year postdischarge. Using HRS data, we adjusted for age, sex, BMI, smoking, physical activity, education, household net worth, and living arrangement/marital status, and examined for effect modification by race and ethnicity. RESULTS: The final sample of 10,737 participants had mean ± SD age 75.9 ± 8.7 years. Hispanic adults had the highest mean MWI-ICD (16.4 ± 10.1), followed by similar values for White (mean 14.8 ± 8.9) and Black (14.7 ± 8.9) adults. MWI-ICD was associated with a higher odds of readmission, and there was no significant effect modification by race and ethnicity. For postdischarge mortality, a 1-point increase MWI-ICD was associated with a 3% higher odds of mortality (OR = 1.03, 95% CI: 1.03-1.04), which did not significantly differ by race and ethnicity. CONCLUSIONS: Multimorbidity was associated with a monotonic increased odds of 30-day readmission and 1-year postdischarge mortality across all race and ethnicity groups. There was no significant difference in readmission or mortality risk by race and ethnicity after robust adjustment.

Multimorbidity and Social Participation Is Moderated by Purpose in Life and Life Satisfaction.

OBJECTIVES: We examined the association between multimorbidity and social participation and whether purpose in life and life satisfaction moderate this relationship. METHODS: Participants were 12,825 Health and Retirement Study adults. We used multiple linear regression to examine the association between a cumulative-updated multimorbidity-weighted index (MWI) and social participation. RESULTS: Among adults with average purpose in life or life satisfaction, MWI was associated with lower social participation. For those with above average purpose in life, each 1-point increase in MWI was associated with a 0.11-point (95% confidence interval [CI]: [0.07, 0.14]) better social participation score. Participants with above average life satisfaction experienced a 0.04-point (95% CI: [0.02, 0.07]) better social participation score with each 1-point increase in MWI. DISCUSSION: Multimorbidity was associated with worse social participation, but this was reversed by above average purpose in life and life satisfaction. Interventions that improve well-being should be assessed to enhance social participation among older adults with any degree of multimorbidity.

Comparing the association between multiple chronic conditions, multimorbidity, frailty, and survival among older patients with cancer.

INTRODUCTION: The high prevalence of multiple chronic conditions (MCC), multimorbidity, and frailty may affect treatment and outcomes for older adults with cancer. The goal of this study was to use three conceptually distinct measures of morbidity to examine the association between these measures and mortality. MATERIALS AND METHODS: Using Medicare claims data linked with the 2012-2016 Ohio Cancer Incidence Surveillance System we identified older adults with incident primary cancer sites of breast, colorectal, lung, or prostate (n = 29,140). We used claims data to identify their Elixhauser comorbidities, Multimorbidity-Weighted Index (MWI), and Claims Frailty Index (CFI) as measures of MCC, multimorbidity, and frailty, respectively. We used Cox proportional hazard models to examine the association between these measures and survival time since diagnosis. RESULTS: Lung cancer patients had the highest levels of MCC, multimorbidity, and frailty. There was a positive association between all three measures and a greater hazard of death after adjusting for age, sex (colorectal and lung only), and stage. Breast cancer patients with 5+ comorbidities had an adjusted hazard ratio (aHR) of 1.63 (95% confidence interval [CI]: 1.38, 1.93), and those with mild frailty had an aHR of 3.38 (95% CI; 2.12, 5.41). The C statistics for breast cancer were 0.79, 0.78, and 0.79 for the MCC, MWI, and CFI respectively. Similarly, lung cancer patients who were moderately or severely frail had an aHR of 1.82 (95% CI: 1.53, 2.18) while prostate cancer patients had an aHR of 3.39 (95% CI: 2.12, 5.41) and colorectal cancer patients had an aHR of 4.51 (95% CI: 3.23, 6.29). Model performance was nearly identical across the MCC, multimorbidity, and frailty models within cancer type. The models performed best for prostate and breast cancer, and notably worse for lung cancer. The frailty models showed the greatest separation in unadjusted survival curves. DISCUSSION: The MCC, multimorbidity, and frailty indices performed similarly well in predicting mortality among a large cohort of older cancer patients. However, there were notable differences by cancer type. This work highlights that although model performance is similar, frailty may serve as a clearer indicator in risk stratification of geriatric oncology patients than simple MCCs or multimorbidity.

Effects of eicosapentaenoic acid versus docosahexaenoic acid on serum lipids: a systematic review and meta-analysis.

Omega-3 fatty acid supplements containing both eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) have been shown to reduce triglycerides but also increase low-density lipoprotein (LDL). Whether EPA or DHA given as monotherapy has differential effects on serum lipoproteins has not been systematically evaluated. We performed a meta-analysis of randomized placebo-controlled trials of monotherapy with EPA (n=10), DHA (n=17), or EPA versus DHA (n=6). Compared with placebo, DHA raised LDL 7.23 mg/dL (95% CI, 3.98­10.5) whereas EPA non-significantly reduced LDL. In direct comparison studies, DHA raised LDL 4.63 mg/dL (95% CI, 2.15­7.10) more than EPA. Both EPA and DHA reduced triglycerides, with a greater reduction by DHA in direct comparison studies. DHA also raised high-density lipoprotein (4.49 mg/dL; 95% CI, 3.50­5.48) compared with placebo, whereas EPA did not. Although EPA and DHA both reduce triglycerides, they have divergent effects on LDL and high-density lipoprotein. Further research is needed to elucidate the mechanisms and significance of these differences.

Modular Psychotherapy Outcomes for Youth With Different Latent Profiles of Irritability and Emotion Dysregulation.

Background: Severe irritability is a common, impairing problem among youth referred for mental health services, but evidence to guide care is limited. Treatment research can be advanced by adopting a transdiagnostic perspective, leveraging existing evidence-based treatment (EBT) techniques, and situating irritability within the context of emotion dysregulation. Accordingly, this study examined treatment outcomes for youth with different levels of irritability and dysregulation who received cognitive-behavioral therapy (CBT) or behavioral parent training (BPT) in a modular EBT framework. Method: We analyzed data from a community-based implementation trial of a transdiagnostic youth psychotherapy. Two-hundred treatment-referred youths (7-15 years; 47% female; 33% White, 28% Black, 24% Latinx, 14% multiracial, 2% other) and their caregivers completed measures of clinical problems and emotion dysregulation at baseline, with repeated outcomes assessments over 18 months. First, latent profile analysis was applied to baseline irritability and emotion dysregulation data; then, latent growth curve models were used to examine outcome trajectories, controlling for covariates. Results: A two-class solution fit well, differentiating youth with high (n = 54) vs. low (n = 146) levels of dysregulation and irritability at baseline. Nearly all high-dysregulation youth received either BPT (n = 26) or CBT-Depression (n = 23). Across measures, both groups showed statistically and clinically significant improvements over time. High-dysregulation youth had greater baseline severity than low-dysregulation youth, but otherwise their longitudinal trajectories were mostly similar, with few between-group slope differences. There was virtually no evidence of differential effects for BPT vs. CBT on clinical outcomes. Conclusions: Youth with severe irritability and dysregulation, treated with a transdiagnostic, modular, EBT approach, showed significant within-person improvements over time. Their outcome trajectories did not differ according to whether they received BPT or CBT. Findings extend the literature on modular, transdiagnostic, and EBT approaches for irritability and dysregulation, suggesting comparable benefits associated with BPT and CBT when treatment selection is guided by comprehensive assessment. Clinical Trial Registration: www.ClinicalTrials.gov, identifier: NCT03153904.

Focus and features of prescribing indications spanning multiple chronic conditions in older adults: A narrative review.

Background: Inappropriate prescribing is frequent in older adults and associated with adverse outcomes. Prescribing indications aim to optimize prescribing, but little is known about the focus and features of prescribing indications for the most common chronic conditions in older adults. Understanding the conditions, medications, and issues addressed (e.g., patient perspective, drug-disease interaction, adverse drug event) in current prescribing indications may help to identify missing indications and develop standardized measures to improve prescribing quality. Methods: We searched Ovid/MEDLINE and EMBASE for articles published between 2015 and 2020 reporting prescribing indications for older adults. Prescribing indication included 1) prescribing "criteria," or statements that guide prescribing action, and 2) prescribing "measures," or prescribing actions observed in a population. We categorized their focus by conditions, medications and issues addressed, as well as level of evidence provided. Results: Among 16 sets of prescribing indications, we identified 748 criteria and 47 measures. The most common addressed medications were antihypertensives, analgesics/antirheumatics, and antiplatelets/anticoagulants. The most frequently addressed issues were drug-disease interaction, adverse drug event, administration, better therapeutic alternative, and (co-)prescription omission (20.8-36.1%). Age/functioning, drug-drug interaction, monitoring, and efficacy/safety ratio were found in only 9.9-16.5% of indications. Indications rarely focused on the patient perspective or issues with multiple providers. Conclusion: Most prescribing indications for chronic conditions in older patients are criteria rather than measures. Indications accounting for patient perspective and multiple providers are limited. The gaps identified in this review may help improve the development of prescribing measures for older adults and ultimately improve quality of care.

Innovative Strategies to Facilitate Patient-Centered Research in Multiple Chronic Conditions.

Multiple chronic conditions (MCC) are one of today's most pressing healthcare concerns, affecting 25% of all Americans and 75% of older Americans. Clinical care for individuals with MCC is often complex, condition-centric, and poorly coordinated across multiple specialties and healthcare services. There is an urgent need for innovative patient-centered research and intervention development to address the unique needs of the growing population of individuals with MCC. In this commentary, we describe innovative methods and strategies to conduct patient-centered MCC research guided by the goals and objectives in the Department of Health and Human Services MCC Strategic Framework. We describe methods to (1) increase the external validity of trials for individuals with MCC; (2) study MCC epidemiology; (3) engage clinicians, communities, and patients into MCC research; and (4) address health equity to eliminate disparities.