Multimorbidity and associated informal care receiving characteristics for US older adults: a latent class analysis.

BACKGROUND: Older adults with varying patterns of multimorbidity may require distinct types of care and rely on informal caregiving to meet their care needs. This study aims to identify groups of older adults with distinct, empirically-determined multimorbidity patterns and compare characteristics of informal care received among estimated classes. METHODS: Data are from the 2011 National Health and Aging Trends Study (NHATS). Ten chronic conditions were included to estimate multimorbidity patterns among 7532 individuals using latent class analysis. Multinomial logistic regression model was estimated to examine the association between sociodemographic characteristics, health status and lifestyle variables, care-receiving characteristics and latent class membership. RESULTS: A four-class solution identified the following multimorbidity groups: some somatic conditions with moderate cognitive impairment (30%), cardiometabolic (25%), musculoskeletal (24%), and multisystem (21%). Compared with those who reported receiving no help, care recipients who received help with household activities only (OR = 1.44, 95% CI 1.05-1.98), mobility but not self-care (OR = 1.63, 95% CI 1.05-2.53), or self-care but not mobility (OR = 2.07, 95% CI 1.29-3.31) had greater likelihood of being in the multisystem group versus the some-somatic group. Having more caregivers was associated with higher odds of being in the multisystem group compared with the some-somatic group (OR = 1.09, 95% CI 1.00-1.18), whereas receiving help from paid helpers was associated with lower odds of being in the multisystem group (OR = 0.36, 95% CI 0.19-0.77). CONCLUSIONS: Results highlighted different care needs among persons with distinct combinations of multimorbidity, in particular the wide range of informal needs among older adults with multisystem multimorbidity. Policies and interventions should recognize the differential care needs associated with multimorbidity patterns to better provide person-centered care.

Mapping racial and ethnic healthcare disparities for persons living with dementia: A scoping review.

INTRODUCTION: We set out to map evidence of disparities in Alzheimer's disease and Alzheimer's disease related dementias healthcare, including issues of access, quality, and outcomes for racial/ethnic minoritized persons living with dementia (PLWD) and family caregivers. METHODS: We conducted a scoping review of the literature published from 2000 to 2022 in PubMed, PsycINFO, and CINAHL. The inclusion criteria were: (1) focused on PLWD and/or family caregivers, (2) examined disparities or differences in healthcare, (3) were conducted in the United States, (4) compared two or more racial/ethnic groups, and (5) reported quantitative or qualitative findings. RESULTS: Key findings include accumulating evidence that minoritized populations are less likely to receive an accurate and timely diagnosis, be prescribed anti-dementia medications, and use hospice care, and more likely to have a higher risk of hospitalization and receive more aggressive life-sustaining treatment at the end-of-life. DISCUSSION: Future studies need to examine underlying processes and develop interventions to reduce disparities while also being more broadly inclusive of diverse populations.

Multimorbidity and Functional Disability among Older Adults: The Role of Inflammation and Glycemic Status - An Observational Longitudinal Study.

INTRODUCTION: Specific multimorbidity combinations, in particular those including arthritis, stroke, and cognitive impairment, have been associated with high burden of activities of daily living (ADL)-instrumental activities of daily living (IADL) disability in older adults. The biologic underpinnings of these associations are still unclear. METHODS: Observational longitudinal study using data from the Health and Retirement Study (N = 8,618, mean age = 74 years, 58% female, 25% non-white) and negative binomial regression models stratified by sex to evaluate the role of inflammatory and glycemic biomarkers (high-sensitivity C-reactive protein (hs-CRP) and HbA1c) in the association between specific multimorbidity combinations (grouped around one of eight index diseases: arthritis, cancer, cognitive impairment, diabetes, heart disease, hypertension, lung disease, and stroke; assessed between 2006 and 2014) and prospective ADL-IADL disability (2 years later, 2008-2016). Results were adjusted for sociodemographic characteristics, body mass index, number of coexisting diseases, and baseline ADL-IADL score. RESULTS: Multimorbidity combinations indexed by arthritis (IRR = 1.1, 95% CI = 1.01-1.20), diabetes (IRR = 1.19, 95% CI = 1.09-1.30), and cognitive impairment (IRR = 1.11, 95% CI = 1.01-1.23) among men and diabetes-indexed multimorbidity combinations (IRR = 1.07, 95% CI = 1.01-1.14) among women were associated with higher ADL-IADL scores at increasing levels of HbA1c. Across higher levels of hs-CRP, multimorbidity combinations indexed by arthritis (IRR = 1.06, 95% CI = 1.02-1.11), hypertension (IRR = 1.06, 95% CI = 1.02-1.11), heart disease (IRR = 1.06, 95% CI = 1.01-1.12), and lung disease (IRR = 1.14, 95% CI = 1.07-1.23) were associated with higher ADL-IADL scores among women, while there were no significant associations among men. CONCLUSION: The findings suggest potential for anti-inflammatory management among older women and optimal glycemic control among older men with these particular multimorbidity combinations as focus for therapeutic/preventive options for maintaining functional health.

Rapid Prediction of Nutrient Concentration in Citrus Leaves Using Vis-NIR Spectroscopy.

The nutritional diagnosis of crops is carried out through costly foliar ionomic analysis in laboratories. However, spectroscopy is a sensing technique that could replace these destructive analyses for monitoring nutritional status. This work aimed to develop a calibration model to predict the foliar concentrations of macro and micronutrients in citrus plantations based on rapid non-destructive spectral measurements. To this end, 592 'Clementina de Nules' citrus leaves were collected during several months of growth. In these foliar samples, the spectral absorbance (430-1040 nm) was measured using a portable spectrometer, and the foliar ionomics was determined by emission spectrometry (ICP-OES) for macro and micronutrients, and the Kjeldahl method to quantify N. Models based on partial least squares regression (PLS-R) were calibrated to predict the content of macro and micronutrients in the leaves. The determination coefficients obtained in the model test were between 0.31 and 0.69, the highest values being found for P, K, and B (0.60, 0.63, and 0.69, respectively). Furthermore, the important P, K, and B wavelengths were evaluated using the weighted regression coefficients (BW) obtained from the PLS-R model. The results showed that the selected wavelengths were all in the visible region (430-750 nm) related to foliage pigments. The results indicate that this technique is promising for rapid and non-destructive foliar macro and micronutrient prediction.

The Contribution of Chronic Conditions to Hospitalization, Skilled Nursing Facility Admission, and Death: Variation by Race.

Multimorbidity (≥2 chronic conditions) is a common and important marker of aging. To better understand racial differences in multimorbidity burden and associations with important health-related outcomes, we assessed differences in the contribution of chronic conditions to hospitalization, skilled nursing facility admission, and mortality among non-Hispanic Black and non-Hispanic White older adults in the United States. We used data from a nationally representative study, the National Health and Aging Trends Study, linked to Medicare claims from 2011-2015 (n = 4,871 respondents). This analysis improved upon prior research by identifying the absolute contributions of chronic conditions using a longitudinal extension of the average attributable fraction for Black and White Medicare beneficiaries. We found that cardiovascular conditions were the greatest contributors to outcomes among White respondents, while the greatest contributor to outcomes for Black respondents was renal morbidity. This study provides important insights into racial differences in the contributions of chronic conditions to costly health-care utilization and mortality, and it prompts policy-makers to champion delivery reforms that will expand access to preventive and ongoing care for diverse Medicare beneficiaries.

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.

Prevalent Multimorbidity Combinations Among Middle-Aged and Older Adults Seen in Community Health Centers.

BACKGROUND: Multimorbidity (≥ 2 chronic diseases) is associated with greater disability and higher treatment burden, as well as difficulty coordinating self-management tasks for adults with complex multimorbidity patterns. Comparatively little work has focused on assessing multimorbidity patterns among patients seeking care in community health centers (CHCs). OBJECTIVE: To identify and characterize prevalent multimorbidity patterns in a multi-state network of CHCs over a 5-year period. DESIGN: A cohort study of the 2014-2019 ADVANCE multi-state CHC clinical data network. We identified the most prevalent multimorbidity combination patterns and assessed the frequency of patterns throughout a 5-year period as well as the demographic characteristics of patient panels by prevalent patterns. PARTICIPANTS: The study included data from 838,642 patients aged ≥ 45 years who were seen in 337 CHCs across 22 states between 2014 and 2019. MAIN MEASURES: Prevalent multimorbidity patterns of somatic, mental health, and mental-somatic combinations of 22 chronic diseases based on the U.S. Department of Health and Human Services Multiple Chronic Conditions framework: anxiety, arthritis, asthma, autism, cancer, cardiac arrhythmia, chronic kidney disease (CKD), chronic obstructive pulmonary disease (COPD), congestive heart failure, coronary artery disease, dementia, depression, diabetes, hepatitis, human immunodeficiency virus (HIV), hyperlipidemia, hypertension, osteoporosis, post-traumatic stress disorder (PTSD), schizophrenia, substance use disorder, and stroke. KEY RESULTS: Multimorbidity is common among middle-aged and older patients seen in CHCs: 40% have somatic, 6% have mental health, and 24% have mental-somatic multimorbidity patterns. The most frequently occurring pattern across all years is hyperlipidemia-hypertension. The three most frequent patterns are various iterations of hyperlipidemia, hypertension, and diabetes and are consistent in rank of occurrence across all years. CKD-hyperlipidemia-hypertension and anxiety-depression are both more frequent in later study years. CONCLUSIONS: CHCs are increasingly seeing more complex multimorbidity patterns over time; these most often involve mental health morbidity and advanced cardiometabolic-renal morbidity.

The importance of chronic conditions for potentially avoidable hospitalizations among non-Hispanic Black and non-Hispanic White older adults in the US: a cross-sectional observational study.

BACKGROUND: Non-Hispanic (NH) Black older adults experience substantially higher rates of potentially avoidable hospitalization compared to NH White older adults. This study explores the top three chronic conditions preceding hospitalization and potentially avoidable hospitalization among NH White and NH Black Medicare beneficiaries in the United States. METHODS: Data on 4993 individuals (4,420 NH White and 573 NH Black individuals) aged ≥ 65 years from 2014 Medicare claims were linked with sociodemographic data from previous rounds of the Health and Retirement Study. Conditional inference random forests were used to rank the importance of chronic conditions in predicting hospitalization and potentially avoidable hospitalization separately for NH White and NH Black beneficiaries. Multivariable logistic regression with the top three chronic diseases for each outcome adjusted for sociodemographic characteristics were conducted to quantify the associations. RESULTS: In total, 22.1% of NH White and 24.9% of NH Black beneficiaries had at least one hospitalization during 2014. Among those with hospitalization, 21.3% of NH White and 29.6% of NH Black beneficiaries experienced at least one potentially avoidable hospitalization. For hospitalizations, chronic kidney disease, heart failure, and atrial fibrillation were the top three contributors among NH White beneficiaries and acute myocardial infarction, chronic obstructive pulmonary disease (COPD), and chronic kidney disease were the top three contributors among NH Black beneficiaries. These chronic conditions were associated with increased odds of hospitalization for both groups. For potentially avoidable hospitalizations, asthma, COPD, and heart failure were the top three contributors among NH White beneficiaries and fibromyalgia/chronic pain/fatigue, COPD, and asthma were the top three contributors among NH Black beneficiaries. COPD and heart failure were associated with increased odds of potentially avoidable hospitalization among NH White beneficiaries, whereas only COPD was associated with increased odds of potentially avoidable hospitalizations among NH Black beneficiaries. CONCLUSION: Having at least one hospitalization and at least one potentially avoidable hospitalization was more prevalent among NH Black than NH White Medicare beneficiaries. This suggests greater opportunity for increasing prevention efforts among NH Black beneficiaries. The importance of COPD for potentially avoidable hospitalizations further highlights the need to focus on prevention of exacerbations for patients with COPD, possibly through greater access to primary care and continuity of care.

Racial and Ethnic Differences in Multimorbidity Changes Over Time.

BACKGROUND: Our understanding of how multimorbidity progresses and changes is nascent. OBJECTIVES: Assess multimorbidity changes among racially/ethnically diverse middle-aged and older adults. DESIGN, SETTING, AND PARTICIPANTS: 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). MEASURES: Multimorbidity latent classes of: hypertension, heart disease, lung disease, diabetes, cancer, arthritis, stroke, high depressive symptoms. RESULTS: 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. CONCLUSIONS AND RELEVANCE: 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.

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.

Achieving Health Equity in Preventive Services: A Systematic Review for a National Institutes of Health Pathways to Prevention Workshop.

Background: Disadvantaged populations in the United States experience disparities in the use of preventive health services. Purpose: To examine effects of barriers that create health disparities in 10 recommended preventive services for adults, and to evaluate the effectiveness of interventions to reduce them. Data Sources: English-language searches of Ovid MEDLINE, PsycINFO, SocINDEX, and the Veterans Affairs Health Services database (1 January 1996 to 5 July 2019); reference lists. Study Selection: Trials, observational studies with comparison groups, and systematic reviews of populations adversely affected by disparities that reported effects of barriers on use of any of the 10 selected preventive services or that reported the effectiveness of interventions to reduce disparities in use of a preventive service by improving intermediate or clinical outcomes. Data Extraction: Dual extraction and assessment of study quality, strength of evidence, and evidence applicability. Data Synthesis: No studies reported effects of provider-specific barriers on preventive service use. Eighteen studies reporting effects of patient barriers, such as insurance coverage or lack of a regular provider, on preventive service use had mixed and inconclusive findings. Studies of patient-provider interventions (n = 12), health information technologies (n = 11), and health system interventions (n = 88) indicated higher cancer screening rates with patient navigation; telephone calls, prompts, and other outreach methods; reminders involving lay health workers; patient education; risk assessment, counseling, and decision aids; screening checklists; community engagement; and provider training. Single studies showed that clinician-delivered and technology-assisted interventions improved rates of smoking cessation and weight loss, respectively. Limitation: Insufficient or low strength of evidence and applicability for most interventions except patient navigation, telephone calls and prompts, and reminders involving lay health workers. Conclusion: In populations adversely affected by disparities, patient navigation, telephone calls and prompts, and reminders involving lay health workers increase cancer screening. Primary Funding Source: National Institutes of Health Office of Disease Prevention through an interagency agreement with the Agency for Healthcare Research and Quality. (PROSPERO: CRD42018109263).

Accuracy of Hospital Discharge Codes in Medicare Claims for Knee and Hip Replacement Patients.

BACKGROUND: Despite the importance of the hospital discharge destination field ("discharge code" hereafter) for research and payment reform, its accuracy is not well established. OBJECTIVES: The aim of this study was to examine the accuracy of discharge codes in Medicare claims. DATA SOURCES: 2012-2015 Medicare claims of knee and hip replacement patients. RESEARCH DESIGN: We identified patients' discharge location in claims and compared it with the discharge code. We also used a mixed-effects logistic regression to examine the association of patient and hospital characteristics with discharge code accuracy. RESULTS: Approximately 9% of discharge codes were inaccurate. Long-term care hospital discharge codes had the lowest accuracy rate (41%), followed by acute care transfers (72%), inpatient rehabilitation facility (80%), and home discharges (83%). Most misclassifications occurred within 2 broad groups of postacute care settings: home-based and institutional care. The odds of inaccurate discharge codes were higher for Medicaid-enrolled patients and safety-net and low-volume hospitals. CONCLUSIONS: Inaccurate hospital discharge coding may have introduced bias in studies relying on these codes (eg, evaluations of Medicare bundled payment models). Inaccuracy was more common among Medicaid-enrolled patients and safety-net and low-volume hospitals, suggesting more potential bias in existing study findings pertaining to these patients and hospitals.

Effectiveness of Patient Navigation to Increase Cancer Screening in Populations Adversely Affected by Health Disparities: a Meta-analysis.

BACKGROUND: This study evaluates the effectiveness of patient navigation to increase screening for colorectal, breast, and cervical cancer in populations adversely affected by health care disparities. METHODS: Eligible studies were identified through English-language searches of Ovid® MEDLINE®, PsycINFO®, SocINDEX, and Veterans Affairs Health Services database (January 1, 1996, to July 5, 2019) and manual review of reference lists. Randomized trials and observational studies of relevant populations that evaluated the effectiveness of patient navigation on screening rates for colorectal, breast, or cervical cancer compared with usual or alternative care comparison groups were included. Two investigators independently abstracted study data and assessed study quality and applicability using criteria adapted from the U.S. Preventive Services Task Force. Discrepancies were resolved by consensus with a third reviewer. Results were combined using profile likelihood random effects models. RESULTS: Thirty-seven studies met inclusion criteria (28 colorectal, 11 breast, 4 cervical cancers including 3 trials with multiple cancer types). Screening rates were higher with patient navigation for colorectal cancer overall (risk ratio [RR] 1.64; 95% confidence interval [CI] 1.42 to 1.92; I2 = 93.7%; 22 trials) and by type of test (fecal occult blood or immunohistochemistry testing [RR 1.69; 95% CI 1.33 to 2.15; I2 = 80.5%; 6 trials]; colonoscopy/endoscopy [RR 2.08; 95% CI 1.08 to 4.56; I2 = 94.6%; 6 trials]). Screening was also higher with navigation for breast cancer (RR 1.50; 95% CI 1.22 to 1.91; I2 = 98.6%; 10 trials) and cervical cancer (RR 1.11; 95% CI 1.05 to 1.19; based on the largest trial). The high heterogeneity of cervical cancer studies prohibited meta-analysis. Results were similar for colorectal and breast cancer regardless of prior adherence to screening guidelines, follow-up time, and study quality. CONCLUSIONS: In populations adversely affected by disparities, colorectal, breast, and cervical cancer screening rates were higher in patients provided navigation services. Registration: PROSPERO: CRD42018109263.

Disputes of Self-reported Chronic Disease Over Time: The Role of Race, Ethnicity, Nativity, and Language of Interview.

BACKGROUND: Respondents in longitudinal health interview surveys may inconsistently report their chronic diseases across interview waves. Racial/ethnic minority adults have an increased burden of chronic diseases and may dispute chronic disease reports more frequently. OBJECTIVE: We evaluated the longitudinal association between race/ethnicity, nativity, and language of interview with disputing previously reported chronic diseases. METHODS: We performed secondary data analysis of nationally representative longitudinal data (Health and Retirement Study, 1998-2010) of adults 51 years or older (n=23,593). We estimated multilevel mixed-effects logistic models of disputes of previously reported chronic disease (hypertension, heart disease, lung disease, diabetes, cancer, stroke, arthritis). RESULTS: Approximately 22% of Health and Retirement Study respondents disputed prior chronic disease self-reports across the entire study period; 21% of non-Latino white, 20.5% of non-Latino black, and 28% of Latino respondents disputed. In subgroup comparisons of model-predicted odds using postestimation commands, Latinos interviewed in Spanish have 34% greater odds of disputing compared with non-Latino whites interviewed in English and 35% greater odds of dispute relative to non-Latino blacks interviewed in English. CONCLUSIONS: The odds of disputing a prior chronic disease report were substantially higher for Latinos who were interviewed in Spanish compared with non-Latino white or black counterparts interviewed in English, even after accounting for other sociodemographic factors, cognitive declines, and time-in-sample considerations. Our findings point toward leveraging of multiple sources of data to triangulate information on chronic disease status as well as investigating potential mechanisms underlying the higher probability of dispute among Spanish-speaking Latino respondents.

Diabetes-Multimorbidity Combinations and Disability Among Middle-aged and Older Adults.

BACKGROUND: Older adults with diabetes rarely have only one chronic disease. As a result, there is a need to re-conceptualize research and clinical practice to address the growing number of older Americans with diabetes and concurrent chronic diseases (diabetes-multimorbidity). OBJECTIVE: To identify prevalent multimorbidity combinations and examine their association with poor functional status among a nationally representative sample of middle-aged and older adults with diabetes. DESIGN: A prospective cohort study of the 2012-2014 Health and Retirement Study (HRS) data. We identified the most prevalent diabetes-multimorbidity combinations and estimated negative binomial models of diabetes-multimorbidity on prospective disability. PARTICIPANTS: Analytic sample included 3841 HRS participants with diabetes, aged 51 years and older. MAIN MEASURES: The main outcome measure was the combined activities of daily living (ADL)-instrumental activities of daily living (IADL) index (range 0-11; higher index denotes higher disability). The main independent variables were diabetes-multimorbidity combination groups, defined as the co-occurrence of diabetes and at least one of six somatic chronic diseases (hypertension, cardiovascular disease, lung disease, cancer, arthritis, and stroke) and/or two mental chronic conditions (cognitive impairment and high depressive symptoms (CESD score ≥ 4). KEY RESULTS: The three most prevalent multimorbidity combinations were, in rank-order diabetes-arthritis-hypertension (n = 694, 18.1%); diabetes-hypertension (n = 481, 12.5%); and diabetes-arthritis-hypertension-heart disease (n = 383, 10%). Diabetes-multimorbidity combinations that included high depressive symptoms or stroke had significantly higher counts of ADL-IADL limitations compared with diabetes-only. In head-to-head comparisons of diabetes-multimorbidity combinations, high depressive symptoms or stroke added to somatic multimorbidity combinations was associated with a higher count of ADL-IADL limitations (diabetes-arthritis-hypertension-high depressive symptoms vs. diabetes-arthritis-hypertension: IRR = 1.95 [1.13, 3.38]; diabetes-arthritis-hypertension-stroke vs. diabetes-arthritis-hypertension: IRR = 2.09 [1.15, 3.82]) even after adjusting for age, gender, education, race/ethnicity, BMI, baseline ADL-IADL, and diabetes duration. Coefficients were robust to further adjustment for diabetes treatment. CONCLUSIONS: Depressive symptoms or stroke added onto other multimorbidity combinations may pose a substantial functional burden for middle-aged and older adults with diabetes.

Associations between prevalent multimorbidity combinations and prospective disability and self-rated health among older adults in Europe.

BACKGROUND: Multimorbidity is associated with greater likelihood of disability, health-related quality of life, and mortality, greater than the risk attributable to individual diseases. The objective of this study is to examine the association between unique multimorbidity combinations and prospective disability and poor self-rated health (SRH) in older adults in Europe. METHODS: We conducted a prospective analysis using data from the Survey of Health, Ageing and Retirement in Europe in 2013 and 2015. We used hierarchical models to compare respondents with multiple chronic conditions to healthy respondents and respondents reporting only one chronic condition and made within-group comparisons to examine the marginal contribution of specific chronic condition combinations. RESULTS: Less than 20% of the study population reported having zero chronic conditions, while 50% reported having at least two chronic conditions. We identified 380 unique disease combinations among people who reported having at least two chronic conditions. Over 35% of multimorbidity could be attributed to five specific multimorbidity combinations, and over 50% to ten specific combinations. Overall, multimorbidity combinations that included high depressive symptoms were associated with increased odds of reporting poor SRH, and increased rates of ADL-IADL disability. CONCLUSIONS: Multimorbidity groups that include high depressive symptoms may be more disabling than combinations that include only somatic conditions. These findings argue for a continued integration of both mental and somatic chronic conditions in the conceptualization of multimorbidity, with important implications for clinical practice and healthcare delivery.

Health Disparities in Veterans: A Map of the Evidence.

BACKGROUND: Goals for improving the quality of care for all Veterans and eliminating health disparities are outlined in the Veterans Health Administration Blueprint for Excellence, but the degree to which disparities in utilization, health outcomes, and quality of care affect Veterans is not well understood. OBJECTIVES: To characterize the research on health care disparities in the Veterans Health Administration by means of a map of the evidence. RESEARCH DESIGN: We conducted a systematic search for research studies published from 2006 to February 2016 in MEDLINE and other data sources. We included studies of Veteran populations that examined disparities in 3 outcome categories: utilization, quality of health care, and patient health. MEASURES: We abstracted data on study design, setting, population, clinical area, outcomes, mediators, and presence of disparity for each outcome category. We grouped the data by population characteristics including race, disability status, mental illness, demographics (age, era of service, rural location, and distance from care), sex identity, socioeconomic status, and homelessness, and created maps illustrating the evidence. RESULTS: We reviewed 4249 citations and abstracted data from 351 studies which met inclusion criteria. Studies examining disparities by race/ethnicity comprised by far the vast majority of the literature, followed by studies examining disparities by sex, and mental health condition. Very few studies examined disparities related to lesbian, gay, bisexual, or transgender identity or homelessness. Disparities findings vary widely by population and outcome. CONCLUSIONS: Our evidence maps provide a "lay of the land" and identify important gaps in knowledge about health disparities experienced by different Veteran populations.

Assessing the Burden of Treatment.

Current healthcare systems and guidelines are not designed to adapt to care for the large and growing number of patients with complex care needs and those with multimorbidity. Minimally disruptive medicine (MDM) is an approach to providing care for complex patients that advances patients' goals in health and life while minimizing the burden of treatment. Measures of treatment burden assess the impact of healthcare workload on patient function and well-being. At least two of these measures are now available for use with patients living with chronic conditions. Here, we describe these measures and how they can be useful for clinicians, researchers, managers, and policymakers. Their work to improve the care of high-cost, high-use, complex patients using innovative patient-centered models such as MDM should be supported by periodic large-scale assessments of treatment burden.

Racial and ethnic differences in smoking changes after chronic disease diagnosis among middle-aged and older adults in the United States.

BACKGROUND: Middle-aged and older Americans from underrepresented racial and ethnic backgrounds are at risk for greater chronic disease morbidity than their white counterparts. Cigarette smoking increases the severity of chronic illness, worsens physical functioning, and impairs the successful management of symptoms. As a result, it is important to understand whether smoking behaviors change after the onset of a chronic condition. We assessed the racial/ethnic differences in smoking behavior change after onset of chronic diseases among middle-aged and older adults in the US. METHODS: We use longitudinal data from the Health and Retirement Study (HRS 1992-2010) to examine changes in smoking status and quantity of cigarettes smoked after a new heart disease, diabetes, cancer, stroke, or lung disease diagnosis among smokers. RESULTS: The percentage of middle-aged and older smokers who quit after a new diagnosis varied by racial/ethnic group and disease: for white smokers, the percentage ranged from 14% after diabetes diagnosis to 32% after cancer diagnosis; for black smokers, the percentage ranged from 15% after lung disease diagnosis to 40% after heart disease diagnosis; the percentage of Latino smokers who quit was only statistically significant after stoke, where 38% quit. In logistic models, black (OR = 0.43, 95% CI: 0.19-0.99) and Latino (OR = 0.26, 95% CI: 0.11-0.65) older adults were less likely to continue smoking relative to white older adults after a stroke, and Latinos were more likely to continue smoking relative to black older adults after heart disease onset (OR = 2.69, 95% CI [1.05-6.95]). In models evaluating changes in the number of cigarettes smoked after a new diagnosis, black older adults smoked significantly fewer cigarettes than whites after a new diagnosis of diabetes, heart disease, stroke or cancer, and Latino older adults smoked significantly fewer cigarettes compared to white older adults after newly diagnosed diabetes and heart disease. Relative to black older adults, Latinos smoked significantly fewer cigarettes after newly diagnosed diabetes. CONCLUSIONS: A large majority of middle-aged and older smokers continued to smoke after diagnosis with a major chronic disease. Black participants demonstrated the largest reductions in smoking behavior. These findings have important implications for tailoring secondary prevention efforts for older adults.

Determination of persimmon leaf chloride contents using near-infrared spectroscopy (NIRS).

Early diagnosis of specific chloride toxicity in persimmon trees requires the reliable and fast determination of the leaf chloride content, which is usually performed by means of a cumbersome, expensive and time-consuming wet analysis. A methodology has been developed in this study as an alternative to determine chloride in persimmon leaves using near-infrared spectroscopy (NIRS) in combination with multivariate calibration techniques. Based on a training dataset of 134 samples, a predictive model was developed from their NIR spectral data. For modelling, the partial least squares regression (PLSR) method was used. The best model was obtained with the first derivative of the apparent absorbance and using just 10 latent components. In the subsequent external validation carried out with 35 external data this model reached r(2) = 0.93, RMSE = 0.16% and RPD = 3.6, with standard error of 0.026% and bias of -0.05%. From these results, the model based on NIR spectral readings can be used for speeding up the laboratory determination of chloride in persimmon leaves with only a modest loss of precision. The intermolecular interaction between chloride ions and the peptide bonds in leaf proteins through hydrogen bonding, i.e. N-H···Cl, explains the ability for chloride determinations on the basis of NIR spectra.