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.

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, 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.

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.

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.

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.

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 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.

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.

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.

Roles of Postdiagnosis Accumulation of Morbidities and Lifestyle Changes in Excess Total and Cause-Specific Mortality Risk in Rheumatoid Arthritis.

OBJECTIVE: To elucidate how postdiagnosis multimorbidity and lifestyle changes contribute to the excess mortality of rheumatoid arthritis (RA). METHODS: We performed a matched cohort study among women in the Nurses' Health Study (1976-2018). We identified women with incident RA and matched each by age and year to 10 non-RA comparators at the RA diagnosis index date. Specific causes of death were ascertained via death certificates and medical record review. Lifestyle and morbidity factors were reported biennially; 61 chronic conditions were combined into the Multimorbidity Weighted Index (MWI). After adjusting for baseline confounders, we used inverse probability weighting analysis to examine the mediating influence of postindex MWI scores and lifestyle factors on total, cardiovascular, and respiratory mortality, comparing women with RA to their matched comparators. RESULTS: We identified 1,007 patients with incident RA and matched them to 10,070 non-RA comparators. After adjusting for preindex confounders, we found that hazard ratios (HRs) and 95% confidence intervals (95% CIs) were higher for total mortality (HR 1.46 [95% CI 1.32, 1.62]), as well as cardiovascular (HR 1.54 [95% CI 1.22, 1.94]) and respiratory (HR 2.75 [95% CI 2.05, 3.71]) mortality in patients with RA compared to non-RA comparators. Adjusting for postindex lifestyle factors (physical activity, body mass index, diet, smoking) attenuated but did not substantially account for this excess RA mortality. After additional adjustment for postindex MWI scores, patients with RA had HRs of 1.18 (95% CI 1.05, 1.32) for total, 1.19 (95% CI 0.94, 1.51) for cardiovascular, and 1.93 (95% CI 1.42, 2.62) for respiratory mortality. CONCLUSION: We found that MWI scores substantially accounted for the excess total and cardiovascular mortality among women with RA. This finding underscores the importance of monitoring for the total disease burden as a whole in monitoring patients with RA.

Evaluating a Widely Implemented Proprietary Deterioration Index Model among Hospitalized Patients with COVID-19.

Rationale: The Epic Deterioration Index (EDI) is a proprietary prediction model implemented in over 100 U.S. hospitals that was widely used to support medical decision-making during the coronavirus disease (COVID-19) pandemic. The EDI has not been independently evaluated, and other proprietary models have been shown to be biased against vulnerable populations. Objectives: To independently evaluate the EDI in hospitalized patients with COVID-19 overall and in disproportionately affected subgroups. Methods: We studied adult patients admitted with COVID-19 to units other than the intensive care unit at a large academic medical center from March 9 through May 20, 2020. We used the EDI, calculated at 15-minute intervals, to predict a composite outcome of intensive care unit-level care, mechanical ventilation, or in-hospital death. In a subset of patients hospitalized for at least 48 hours, we also evaluated the ability of the EDI to identify patients at low risk of experiencing this composite outcome during their remaining hospitalization. Results: Among 392 COVID-19 hospitalizations meeting inclusion criteria, 103 (26%) met the composite outcome. The median age of the cohort was 64 (interquartile range, 53-75) with 168 (43%) Black patients and 169 (43%) women. The area under the receiver-operating characteristic curve of the EDI was 0.79 (95% confidence interval, 0.74-0.84). EDI predictions did not differ by race or sex. When exploring clinically relevant thresholds of the EDI, we found patients who met or exceeded an EDI of 68.8 made up 14% of the study cohort and had a 74% probability of experiencing the composite outcome during their hospitalization with a sensitivity of 39% and a median lead time of 24 hours from when this threshold was first exceeded. Among the 286 patients hospitalized for at least 48 hours who had not experienced the composite outcome, 14 (13%) never exceeded an EDI of 37.9, with a negative predictive value of 90% and a sensitivity above this threshold of 91%. Conclusions: We found the EDI identifies small subsets of high-risk and low-risk patients with COVID-19 with good discrimination, although its clinical use as an early warning system is limited by low sensitivity. These findings highlight the importance of independent evaluation of proprietary models before widespread operational use among patients with COVID-19.

Evaluating a Widely Implemented Proprietary Deterioration Index Model Among Hospitalized COVID-19 Patients.

INTRODUCTION: The Epic Deterioration Index (EDI) is a proprietary prediction model implemented in over 100 U.S. hospitals that was widely used to support medical decision-making during the COVID-19 pandemic. The EDI has not been independently evaluated, and other proprietary models have been shown to be biased against vulnerable populations. METHODS: We studied adult patients admitted with COVID-19 to non-ICU care at a large academic medical center from March 9 through May 20, 2020. We used the EDI, calculated at 15-minute intervals, to predict a composite outcome of ICU-level care, mechanical ventilation, or in-hospital death. In a subset of patients hospitalized for at least 48 hours, we also evaluated the ability of the EDI to identify patients at low risk of experiencing this composite outcome during their remaining hospitalization. RESULTS: Among 392 COVID-19 hospitalizations meeting inclusion criteria, 103 (26%) met the composite outcome. Median age of the cohort was 64 (IQR 53-75) with 168 (43%) African Americans and 169 (43%) women. Area under the receiver-operating-characteristic curve (AUC) of the EDI was 0.79 (95% CI 0.74-0.84). EDI predictions did not differ by race or sex. When exploring clinically-relevant thresholds of the EDI, we found patients who met or exceeded an EDI of 68.8 made up 14% of the study cohort and had a 74% probability of experiencing the composite outcome during their hospitalization with a median lead time of 24 hours from when this threshold was first exceeded. Among the 286 patients hospitalized for at least 48 hours who had not experienced the composite outcome, 14 (13%) never exceeded an EDI of 37.9, with a negative predictive value of 90% and a sensitivity above this threshold of 91%. CONCLUSION: We found the EDI identifies small subsets of high- and low-risk COVID-19 patients with fair discrimination. We did not find evidence of bias by race or sex. These findings highlight the importance of independent evaluation of proprietary models before widespread operational use among COVID-19 patients.

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.

Sex Differences in Autoimmune Multimorbidity in Type 1 Diabetes Mellitus and the Risk of Cardiovascular and Renal Disease: A Longitudinal Study in the United States, 2001-2017.

Background: Autoimmune diseases are usually more prevalent in women. The risks of cardiovascular and renal disease in those with multiple autoimmune diseases have not been fully described. Materials and Methods: Using a national database from a large health insurer in the United States (years 2001-2017) containing ∼75 million members, we calculated age- and sex-specific co-prevalence of 12 autoimmune disorders for individuals with type 1 diabetes. We then evaluated whether concomitant autoimmune diseases were associated with renal failure, ischemic stroke, and myocardial infarction. Results: Of the 179,248 people diagnosed with type 1 diabetes, 1 in 4 had a concomitant autoimmune disease (27.03%; 95% confidence interval [CI] = 26.83%-27.24%), with hypothyroidism, rheumatoid arthritis, and celiac disease being the most common. The prevalence of autoimmune disease was 1.9 times greater in female than male patients (p < 0.001). In female patients with type 1 diabetes, one in three had another autoimmune disease (35.62%; 95% CI = 35.30%-35.94%) compared with one in five male patients (19.17%; 95% CI = 18.92%-19.42%). The risk of renal failure, ischemic stroke, and myocardial infarction increased with a greater number of concomitant autoimmune diseases (p < 0.001, test for trend for both female and male patients). Patients with type 1 diabetes who had multiple sclerosis or myasthenia gravis experienced an approximate threefold increase in risk of ischemic stroke (odds ratio [OR] = 3.57, OR = 3.22, respectively). Patients with type 1 diabetes and Addison's disease had a threefold increased risk of renal failure. Conclusions: Patients with type 1 diabetes, particularly women, frequently have coexisting autoimmune diseases that are associated with higher rates of renal failure, ischemic stroke, and myocardial infarction. Additional study is warranted, as are preventive efforts in this high-risk population.

The Relationship of Loneliness to End-of-Life Experience in Older Americans: A Cohort Study.

OBJECTIVES: Little is known about the relationship between loneliness and end-of-life (EOL) experience including symptom burden, intensity of care, and advance care planning among older adults. DESIGN: Secondary analysis of the Health and Retirement Study (HRS). SETTING: Population based. PARTICIPANTS: Decedents older than 50 years who died between 2004 and 2014 (n = 8700). Exclusions included those who were ineligible for surveys assessing loneliness (n = 2932) or had missing or incomplete loneliness or symptom data (n = 2872). MEASUREMENTS: Individuals were characterized as lonely based on responses to the three-item Revised University of California, Los Angeles Loneliness Scale in the most recent HRS survey before death. Outcomes were proxy reports of total EOL symptom burden, intensity of EOL care (eg, late hospice enrollment, place of death, hospitalizations, use of life support), and advance care planning. Results were expressed as adjusted odds ratios (aORs) with 95% confidence intervals (CIs). RESULTS: One-third of 2896 decedents (n = 942) were lonely. After adjusting for demographics, socioeconomic status, multimorbidity, depressive symptoms, family and friends, and social support, loneliness was independently associated with increased total symptom burden at EOL (ß = .13; P = .004). Compared with nonlonely individuals, lonely decedents were more likely to use life support in the last 2 years of life (35.5% vs 29.4%; aOR = 1.36; 95% CI = 1.08-1.71) and more likely to die in a nursing home (18.4% vs 14.2%; aOR = 1.78; 95% CI = 1.30-2.42). No significant differences in other measures of intense care (late hospice enrollment, number of hospitalizations, or dialysis use) or likelihood of advance care planning were observed. CONCLUSION: Lonely older people may be burdened by more symptoms and may be exposed to more intense EOL care compared with nonlonely people. Interventions aiming to screen for, prevent, and mitigate loneliness during the vulnerable EOL period are necessary. J Am Geriatr Soc 68:1064-1071, 2020.

Multimorbidity in Medicare Beneficiaries: Performance of an ICD-Coded Multimorbidity-Weighted Index.

OBJECTIVES: Most older adults have multimorbidity that impairs physical functioning, but it is difficult to quantify using claims data. We previously developed and validated a multimorbidity-weighted index (MWI) that embeds physical functioning through disease weightings. We mapped these conditions to International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes and compared them with existing indices. DESIGN: Population-based prospective cohort. SETTING: Respondents to the 2006-2016 waves of the Health and Retirement Study (HRS) with linked Medicare claims data and continuous enrollment in 2006. PARTICIPANTS: Community-dwelling Medicare-eligible HRS participants (N = 9923; mean age = 75.5 ± 8.5 y). MEASUREMENTS: Individuals were followed for future physical functioning (2006-2014) and mortality (2007-2016). MWI conditions were mapped to ICD-9-CM codes to produce an ICD-coded MWI (MWI-ICD). We compared MWI-ICD, simple disease count, Charlson, Elixhauser, and the health-related quality of life comorbidity index (HRQOL-CI) through distributions, hazard ratios for mortality, and relationships with future physical functioning. RESULTS: MWI-ICD exhibited the broadest distribution and most unique values (5891). Left censoring was most pronounced for Charlson (34.3% score = 0) and Elixhauser (13.1% score = 0) vs MWI (5.0% score = 0). Hazard ratios and concordance (C)-statistics for mortality across extreme quartiles were similar for MWI-ICD, Elixhauser, and Charlson but lower for disease count and the HRQOL-CI. For physical functioning, MWI-ICD yielded the greatest contrast across extreme quartiles and overall coefficient of determination (R2 ). CONCLUSION: MWI-ICD was significantly associated with mortality and future physical functioning and comparable with established metrics for mortality prediction although not weighted to mortality. MWI-ICD successfully captures diseases accumulation and functioning in claims data. J Am Geriatr Soc 68:999-1006, 2020.

Multimorbidity and Cognitive Decline Over 14 Years in Older Americans.

BACKGROUND: Multimorbidity is associated with greater disability and accelerated declines in physical functioning over time in older adults. However, less is known about its effect on cognitive decline. METHODS: Participants without dementia from the Health and Retirement Study were interviewed about physician-diagnosed conditions, from which their multimorbidity-weighted index (MWI) that weights diseases to physical functioning was computed. We used linear mixed-effects models to examine the predictor MWI with the modified Telephone Interview for Cognitive Status (TICSm, global cognition), 10-word immediate recall and delayed recall, and serial 7s outcomes biennially after adjusting for baseline cognition and covariates. RESULTS: Fourteen thousand two hundred sixty-five participants, 60% female, contributed 73,700 observations. Participants had a mean ± SD age 67 ± 9.3 years and MWI 4.4 ± 3.9 at baseline. Each point increase in MWI was associated with declines in global cognition (0.04, 95% CI: 0.03-0.04 TICSm), immediate recall (0.01, 95% CI: 0.01-0.02 words), delayed recall (0.01, 95% CI: 0.01-0.02 words), and working memory (0.01, 95% CI: 0.01-0.02 serial 7s; all p < .001). Multimorbidity was associated with faster declines in global cognition (0.003 points/year faster, 95% CI: 0.002-0.004), immediate recall (0.001 words/year faster, 95% CI: 0.001-0.002), and working memory (0.006 incorrect serial 7s/year faster, 95% CI: 0.004-0.009; all p < .001), but not delayed recall compared with premorbid slopes. CONCLUSIONS: Multimorbidity using a validated index weighted to physical functioning was associated with acute decline in cognition and accelerated and persistent cognitive decline over 14 years. This study supports an ongoing geriatric syndrome of coexisting physical and cognitive impairment in adults with multimorbidity. Clinicians should monitor and address both domains in older multimorbid adults.

Physical Functioning Decline and Mortality in Older Adults With Multimorbidity: Joint Modeling of Longitudinal and Survival Data.

Background: Multimorbidity is common among older adults and strongly associated with physical functioning decline and increased mortality. However, the full spectrum of direct and indirect effects of multimorbidity on physical functioning and survival has not been quantified. We aimed to determine the longitudinal relationship of multimorbidity on physical functioning and quantify the impact of multimorbidity and multimorbidity-attributed changes in physical functioning on mortality risk. Methods: The Health and Retirement Study (HRS) is a nationally representative population-based prospective cohort of adults aged 51 or older. In 2000, participants were interviewed about physician-diagnosed chronic conditions, from which their multimorbidity-weighted index (MWI) was computed. Between 2000 and 2011, participants reported their current physical functioning using a modified Short Form-36. With MWI as a time-varying exposure, we jointly modeled its associations with physical functioning and survival. Results: The final sample included 74,037 observations from 18,174 participants. At baseline, participants had a weighted mean MWI of 4.6 ± 4.2 (range 0-36.8). During follow-up, physical functioning declined: -1.72 (95% confidence interval [CI] -1.77, -1.67, p < .001) HRS physical functioning units per point MWI in adjusted models. Over follow-up, 6,362 (34%) participants died. Mortality risk increased 8% (hazard ratio 1.08, 95% CI 1.07-1.08, p < .001) per point MWI in adjusted models. Across all population subgroups, MWI was associated with greater physical functioning decline and mortality risk. Conclusions: Multimorbidity and its associated decline in physical functioning were significantly associated with increased mortality. These associations can be predicted with an easily interpreted and applied multimorbidity index that can better identify and target adults at increased risk for disability and death.