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Little is known about electrocardiogram (ECG) markers of Parkinsons disease (PD) during the prodromal stage. The aim of the study was to build a generalizable ECG-based fully automatic artificial intelligence (AI) model to predict PD risk during the prodromal stage, up to 5 years before disease diagnosis. This case-control study included samples from Loyola University Chicago (LUC) and University of Tennessee-Methodist Le Bonheur Healthcare (MLH). Cases and controls were matched according to specific characteristics (date, age, sex and race). Clinical data were available from May, 2014 onward at LUC and from January, 2015 onward at MLH, while the ECG data were available as early as 1990 in both institutes. PD was denoted by at least two primary diagnostic codes (ICD9 332.0; ICD10 G20) at least 30 days apart. PD incidence date was defined as the earliest of first PD diagnostic code or PD-related medication prescription. ECGs obtained at least 6 months before PD incidence date were modeled to predict a subsequent diagnosis of PD within three time windows: 6 months-1 year, 6 months-3 years, and 6 months-5 years. We applied a novel deep neural network using standard 10-s 12-lead ECGs to predict PD risk at the prodromal phase. This model was compared to multiple feature engineering-based models. Subgroup analyses for sex, race and age were also performed. Our primary prediction model was a one-dimensional convolutional neural network (1D-CNN) that was built using 131 cases and 1058 controls from MLH, and externally validated on 29 cases and 165 controls from LUC. The model was trained on 90% of the MLH data, internally validated on the remaining 10% and externally validated on LUC data. The best performing model resulted in an external validation AUC of 0.67 when predicting future PD at any time between 6 months and 5 years after the ECG. Accuracy increased when restricted to ECGs obtained within 6 months to 3 years before PD diagnosis (AUC 0.69) and was highest when predicting future PD within 6 months to 1 year (AUC 0.74). The 1D-CNN model based on raw ECG data outperformed multiple models built using more standard ECG feature engineering approaches. These results demonstrate that a predictive model developed in one cohort using only raw 10-s ECGs can effectively classify individuals with prodromal PD in an independent cohort, particularly closer to disease diagnosis. Standard ECGs may help identify individuals with prodromal PD for cost-effective population-level early detection and inclusion in disease-modifying therapeutic trials.

BACKGROUND: Depressive symptoms are associated with age-related cognitive impairment, but the relative risk of specific subtypes of mild cognitive impairment (MCI) conferred by depressive symptoms is unclear. The purpose of this exploratory study was to determine the longitudinal association between baseline depressive symptoms and incident cases of MCI subtypes (amnestic vs. non-amnestic) and probable dementia (PD) (Alzheimers disease, vascular, mixed) among postmenopausal women. METHODS: Depressive symptoms were assessed at study baseline using an 8-item Burnam algorithm in 7043 postmenopausal women who participated in the Womens Health Initiative Memory Study (WHIMS) and the WHIMS-Epidemiology of Cognitive Health Outcomes (WHIMS-ECHO) extension study. During the median 9.4-year follow-up interval, the presence of MCI and PD was classified by a central adjudication committee. Classification of participants by MCI subtype (amnestic single and multi-domain, non-amnestic single and multi-domain) was done algorithmically based on established criteria using data from annual cognitive testing. RESULTS: At baseline, 557 women (7.9%) had clinically significant depressive symptoms based on Burnam algorithm cut-point of 0.06. Depressive symptoms at baseline were associated with an increased risk of incident amnestic MCI (hazard ratio [HR] = 1.91, 95% confidence interval [CI] 1.32-2.78, p

BackgroundParkinson's disease (PD) is a chronic, disabling neurodegenerative disorder.ObjectiveTo predict a future diagnosis of PD using questionnaires and simple non-invasive clinical tests.MethodsParticipants in the prospective Kuakini Honolulu-Asia Aging Study (HAAS) were evaluated biannually between 1995-2017 by PD experts using standard diagnostic criteria. Autopsies were sought on all deaths. We input simple clinical and risk factor variables into an ensemble-tree based machine learning algorithm and derived models to predict the probability of developing PD. We also investigated relationships of predictive models and neuropathologic features such as nigral neuron density.ResultsThe study sample included 292 subjects, 25 of whom developed PD within 3 years and 41 by 5 years. 116 (46%) of 251 subjects not diagnosed with PD underwent autopsy. Light Gradient Boosting Machine modeling of 12 predictors correctly classified a high proportion of individuals who developed PD within 3 years (area under the curve (AUC) 0.82, 95%CI 0.76-0.89) or 5 years (AUC 0.77, 95%CI 0.71-0.84). A large proportion of controls who were misclassified as PD had Lewy pathology at autopsy, including 79%of those who died within 3 years. PD probability estimates correlated inversely with nigral neuron density and were strongest in autopsies conducted within 3 years of index date (r = -0.57, p

"Race" and "ethnicity" are socially constructed terms, not based on biology - in contrast to biologic ancestry and genetic admixture - and are flexible, contested, and unstable concepts, often driven by power. Although individuals may self-identify with a given race and ethnic group, as multidimensional beings exposed to differential life influencing factors that contribute to disease risk, additional social determinants of health (SDOH) should be explored to understand the relationship of race or ethnicity to health. Potential health effects of structural racism, defined as "the structures, policies, practices, and norms resulting in differential access to goods, services, and opportunities of society by "race," have been largely ignored in medical research. The Women's Health Initiative (WHI) was expected to enroll a racially and ethnically diverse cohort of older women at 40 U.S. clinical centers between 1993 and 1998; yet, key information on the racial and ethnic make-up of the WHI cohort of 161,808 women was limited until a 2020-2021 Task Force was charged by the WHI Steering Committee to better characterize the WHI cohort and develop recommendations for WHI investigators who want to include "race" and/or "ethnicity" in papers and presentations. As the lessons learned are of relevance to most cohorts, the essence of the WHI Race and Ethnicity Language and Data Interpretation Guide is presented in this paper. Recommendations from the WHI Race and Ethnicity Language and Data Interpretation Guide include: Studies should be designed to include all populations and researchers should actively, purposefully and with cultural-relevance, commit to recruiting a diverse sample; Researchers should collect robust data on race, ethnicity and SDOH variables that may intersect with participant identities, such as immigration status, country of origin, acculturation, current residence and neighborhood, religion; Authors should use appropriate terminology, based on a participant's self-identified "race" and "ethnicity", and provide clear rationale, including a conceptual framework, for including race and ethnicity in the analytic plan; Researchers should employ appropriate analytical methods, including mixed-methods, to study the relationship of these sociocultural variables to health; Authors should address how representative study participants are of the population to which results might apply, such as by age, race and ethnicity.

Autonomic nervous system involvement precedes the motor features of Parkinson's disease (PD). Our goal was to develop a proof-of-concept model for identifying subjects at high risk of developing PD by analysis of cardiac electrical activity. We used standard 10-s electrocardiogram (ECG) recordings of 60 subjects from the Honolulu Asia Aging Study including 10 with prevalent PD, 25 with prodromal PD, and 25 controls who never developed PD. Various methods were implemented to extract features from ECGs including simple heart rate variability (HRV) metrics, commonly used signal processing methods, and a Probabilistic Symbolic Pattern Recognition (PSPR) method. Extracted features were analyzed via stepwise logistic regression to distinguish between prodromal cases and controls. Stepwise logistic regression selected four features from PSPR as predictors of PD. The final regression model built on the entire dataset provided an area under receiver operating characteristics curve (AUC) with 95% confidence interval of 0.90 [0.80, 0.99]. The five-fold cross-validation process produced an average AUC of 0.835 [0.831, 0.839]. We conclude that cardiac electrical activity provides important information about the likelihood of future PD not captured by classical HRV metrics. Machine learning applied to ECGs may help identify subjects at high risk of having prodromal PD.

ObjectiveWhile excessive daytime sleepiness (EDS) can predate the clinical diagnosis of Parkinson disease (PD), associations with underlying PD pathogenesis are unknown. Our objective is to determine if EDS is related to brain Lewy pathology (LP), a marker of PD pathogenesis, using clinical assessments of EDS with postmortem follow-up.MethodsIdentification of LP was based on staining for α-synuclein in multiple brain regions in a sample of 211 men. Data on EDS were collected at clinical examinations from 1991 to 1999 when participants were aged 72-97 years.ResultsAlthough EDS was more common in the presence vs absence of LP (p = 0.034), the association became stronger in neocortical regions. When LP was limited to the olfactory bulb, brainstem, and basal forebrain (Braak stages 1-4), frequency of EDS was 10% (4/40) vs 17.5% (20/114) in decedents without LP (p = 0.258). In contrast, compared to the absence of LP, EDS frequency doubled (36.7% [11/30], p = 0.023) when LP reached the anterior cingulate gyrus, insula mesocortex, and midfrontal, midtemporal, and inferior parietal neocortex (Braak stage 5). With further infiltration into the primary motor and sensory neocortices (Braak stage 6), EDS frequency increased threefold (51.9% [14/27], p < 0.001). Findings were similar across sleep-related features and persisted after adjustment for age and other covariates, including the removal of PD and dementia with Lewy bodies.ConclusionsThe association between EDS and PD includes relationships with extensive topographic LP expansion. The neocortex could be especially vulnerable to adverse relationships between sleep disorders and aggregation of misfolded α-synuclein and LP formation.

BackgroundOrganochlorine pesticides are associated with an increased risk of Parkinson's disease. A preliminary analysis from the Honolulu-Asia Aging Study suggested that heptachlor epoxide, a metabolite from an organochlorine pesticide extensively used in Hawaii, may be especially important. This was a cross sectional analysis to evaluate the association of heptachlor epoxide and other organochlorine compounds with Lewy pathology in an expanded survey of brain organochlorine residues from the longitudinal Honolulu-Asia Aging Study.MethodsOrganochlorines were measured in frozen occipital or temporal lobes in 705 brains using gas chromatography with mass spectrometry. Lewy pathology was identified using hematoxylin and eosin- and α-synuclein immunochemistry-stained sections from multiple brain regions.ResultsThe prevalence of Lewy pathology was nearly doubled in the presence versus the absence of heptachlor epoxide (30.1% versus 16.3%, P < 0.001). Although associations with other compounds were weaker, hexachlorobenzene (P = 0.003) and α-chlordane (P = 0.007) were also related to Lewy pathology. Most of the latter associations, however, were a result of confounding from heptachlor epoxide. Neither compound was significantly related to Lewy pathology after adjustment for heptachlor epoxide. In contrast, the association of heptachlor epoxide with Lewy pathology remained significant after adjustments for hexachlorobenzene (P = 0.013) or α-chlordane (P = 0.005). Findings were unchanged after removal of cases of PD and adjustment for age and other characteristics.ConclusionsOrganochlorine pesticides are associated with the presence of Lewy pathology in the brain, even after exclusion of PD cases. Although most of the association is through heptachlor epoxide, the role of other organochlorine compounds is in need of clarification. © 2018 International Parkinson and Movement Disorder Society.

ObjectiveTo examine the relationship between midlife milk intake and Parkinson disease (PD) incidence through associations with substantia nigra (SN) neuron density and organochlorine pesticide exposure in decedent brains from the Honolulu-Asia Aging Study.MethodsMilk intake data were collected from 1965 to 1968 in 449 men aged 45-68 years with postmortem examinations from 1992 to 2004. Neuron density (count/mm(2)) was measured in quadrants from a transverse section of the SN. Additional measures included brain residues of heptachlor epoxide, an organochlorine pesticide found at excessively high levels in the milk supply in Hawaii in the early 1980s.ResultsNeuron density was lowest in nonsmoking decedents who consumed high amounts of milk (>16 oz/d). After removing cases of PD and dementia with Lewy bodies, adjusted neuron density in all but the dorsomedial quadrant was 41.5% lower for milk intake >16 oz/d vs intake that was less (95% confidence interval 22.7%-55.7%, p < 0.001). Among those who drank the most milk, residues of heptachlor epoxide were found in 9 of 10 brains as compared to 63.4% (26/41) for those who consumed no milk (p = 0.017). For those who were ever smokers, an association between milk intake and neuron density was absent.ConclusionsMilk intake is associated with SN neuron loss in decedent brains unaffected by PD. Whether contamination of milk with organochlorine pesticides has a role in SN neurodegeneration warrants further study.

BackgroundWe examined the social and economic factors associated with nursing home (NH) admission in older women, overall and poststroke.MethodsThe Women's Health Initiative (WHI) included women aged 50-79 years at enrollment (1993-1998). In the WHI Extension Study (2005-2010), participants annually reported any NH admission in the preceding year. Separate multivariate logistic regression models analyzed social and economic factors associated with long-term NH admission, defined as an admission on 2 or more questionnaires, overall and poststroke.ResultsOf 103,237 participants, 8904 (8.6%) reported NH admission (2005-2010); 534 of 2225 (24.0%) women with incident stroke reported poststroke NH admission. Decreased likelihoods of NH admission overall were demonstrated for Asian, Black, and Hispanic women (versus whites, adjusted odds ratio [aOR] = .35-.44, P < .001) and women with higher income (aOR = .75, 95% confidence interval [CI] = .63-.90), whereas increased likelihoods of NH admission overall were seen for women with lower social support (aOR = 1.34, 95% CI = 1.16-1.54) and with incident stroke (aOR = 2.59, 95% CI = 2.15-3.12). Increased odds of NH admission after stroke were demonstrated for women with moderate disability after stroke (aOR = 2.76, 95% CI = 1.73-4.42). Further adjustment for stroke severity eliminated the association found for race/ethnicity, income, and social support.ConclusionsThe level of care needed after a disabling stroke may overwhelm social and economic structures in place that might otherwise enable avoidance of NH admission. We need to identify ways to provide care consistent with patients' preferences, even after a disabling stroke.

ObjectiveTo examine whether the effect of postmenopausal hormone therapy (HT) on brain volumes in women aged 65-79 years differs depending on type 2 diabetes status during postintervention follow-up of a randomized controlled clinical trial.MethodsThe Women's Health Initiative randomized clinical trials assigned women to HT (0.625 mg/day conjugated equine estrogens with or without 2.5 mg/day medroxyprogesterone acetate) or placebo for an average of 5.6 years. A total of 1,402 trial participants underwent brain MRI 2.4 years after the trials; these were repeated in 699 women 4.7 years later. General linear models were used to assess the interaction between diabetes status and HT assignment on brain volumes.ResultsWomen with diabetes at baseline or during follow-up who had been assigned to HT compared to placebo had mean decrement in total brain volume of -18.6 mL (95% confidence interval [CI] -29.6, -7.6). For women without diabetes, this mean decrement was -0.4 (95% CI -3.8, 3.0) (interaction p=0.002). This interaction was evident for total gray matter (p

ImportanceThe effect of obesity on late-age survival in women without disease or disability is unknown.ObjectiveTo investigate whether higher baseline body mass index and waist circumference affect women's survival to 85 years of age without major chronic disease (coronary disease, stroke, cancer, diabetes mellitus, or hip fracture) and mobility disability.Design, setting, and participantsExamination of 36,611 women from the Women's Health Initiative observational study and clinical trial programs who could have reached 85 years or older if they survived to the last outcomes evaluation on September 17, 2012. Recruitment was from 40 US clinical centers from October 1993 through December 1998. Multinomial logistic regression models were used to estimate odds ratios and 95% CIs for the association of baseline body mass index and waist circumference with the outcomes, adjusting for demographic, behavioral, and health characteristics.Main outcomes and measuresMutually exclusive classifications: (1) survived without major chronic disease and without mobility disability (healthy); (2) survived with 1 or more major chronic disease at baseline but without new disease or disability (prevalent diseased); (3) survived and developed 1 or more major chronic disease but not disability during study follow-up (incident diseased); (4) survived and developed mobility disability with or without disease (disabled); and (5) did not survive (died).ResultsMean (SD) baseline age was 72.4 (3.0) years (range, 66-81 years). The distribution of women classified as healthy, prevalent diseased, incident diseased, disabled, and died was 19.0%, 14.7%, 23.2%, 18.3%, and 24.8%, respectively. Compared with healthy-weight women, underweight and obese women were more likely to die before 85 years of age. Overweight and obese women had higher risks of incident disease and mobility disability. Disability risks were striking. Relative to healthy-weight women, adjusted odds ratios (95% CIs) of mobility disability were 1.6 (1.5-1.8) for overweight women and 3.2 (2.9-3.6), 6.6 (5.4-8.1), and 6.7 (4.8-9.2) for class I, II, and III obesity, respectively. Waist circumference greater than 88 cm was also associated with higher risk of earlier death, incident disease, and mobility disability.Conclusions and relevanceOverall and abdominal obesity were important and potentially modifiable factors associated with dying or developing mobility disability and major chronic disease before 85 years of age in older women.

ObjectivesTo examine prestroke lifestyle factors associated with poststroke mortality and recovery in older women.DesignLongitudinal prospective cohort study.SettingThe Women's Health Initiative (WHI, clinical trials and observational study), 40 clinical centers in the United States.ParticipantsWHI participants, women aged 50 to 79, who were stroke-free at baseline (1993/98), with incident stroke before 2005.MeasurementsParticipants were followed for mortality through 2010. Prestroke characteristics were from the last examination before the stroke event. Annual follow-up for clinical events ascertained hospitalization for stroke that was subsequently physician adjudicated with medical records. Multivariable regression models were used to analyze factors associated with poststroke mortality and poststroke recovery at hospital discharge (poststroke Glasgow score), adjusting for stroke type.ResultsOf 3,173 women with incident stroke, 1,111 (35%) died. Individuals who were overweight or obese before stroke had lower poststroke mortality than those who were normal weight (obese: hazard ratio (HR) = 0.69, 95% confidence interval (CI) = 0.53-0.88; overweight: HR = 0.72, 95% CI = 0.58-0.90); individuals who were underweight before stroke had nonsignificantly greater poststroke mortality (HR = 2.02, 95% CI = 0.98-4.16, P = .06). Other prestroke factors associated with poststroke mortality included diabetes mellitus (HR = 1.28, 95% CI = 1.01-1.64), current smoking (vs nonsmoker, HR = 2.13, 95% CI = 1.53-3.00), physical inactivity (vs >150 min of exercise per week, HR = 1.39, 95% CI = 1.09-1.78), and lowest physical function quartile (vs highest, HR = 1.54, 95% CI = 1.18-2.02). Prestroke diabetes mellitus was associated with lower odds of good recovery after stroke (odds ratio (OR) = 0.60, 95% CI = 0.44-0.82). Current hormone use before stroke was associated with greater odds of moderate than of severe disability after stroke (OR = 1.29, 95% CI = 1.00-1.66).ConclusionPotentially modifiable factors before stroke, including smoking, diabetes mellitus, and being underweight, were associated with greater poststroke mortality in older women. Being overweight or obese and physical activity before stroke were associated with lower poststroke mortality in older women.

OBJECTIVE: To study how type 2 diabetes adversely affects brain volumes, changes in volume, and cognitive function. RESEARCH DESIGN AND METHODS: Regional brain volumes and ischemic lesion volumes in 1,366 women, aged 72-89 years, were measured with structural brain magnetic resonance imaging (MRI). Repeat scans were collected an average of 4.7 years later in 698 women. Cross-sectional differences and changes with time between women with and without diabetes were compared. Relationships that cognitive function test scores had with these measures and diabetes were examined. RESULTS: The 145 women with diabetes (10.6%) at the first MRI had smaller total brain volumes (0.6% less; P = 0.05) and smaller gray matter volumes (1.5% less; P = 0.01) but not white matter volumes, both overall and within major lobes. They also had larger ischemic lesion volumes (21.8% greater; P = 0.02), both overall and in gray matter (27.5% greater; P = 0.06), in white matter (18.8% greater; P = 0.02), and across major lobes. Overall, women with diabetes had slightly (nonsignificant) greater loss of total brain volumes (3.02 cc; P = 0.11) and significant increases in total ischemic lesion volumes (9.7% more; P = 0.05) with time relative to those without diabetes. Diabetes was associated with lower scores in global cognitive function and its subdomains. These relative deficits were only partially accounted for by brain volumes and risk factors for cognitive deficits. CONCLUSIONS: Diabetes is associated with smaller brain volumes in gray but not white matter and increasing ischemic lesion volumes throughout the brain. These markers are associated with but do not fully account for diabetes-related deficits in cognitive function.

ObjectiveTo study how type 2 diabetes adversely affects brain volumes, changes in volume, and cognitive function.Research design and methodsRegional brain volumes and ischemic lesion volumes in 1,366 women, aged 72-89 years, were measured with structural brain magnetic resonance imaging (MRI). Repeat scans were collected an average of 4.7 years later in 698 women. Cross-sectional differences and changes with time between women with and without diabetes were compared. Relationships that cognitive function test scores had with these measures and diabetes were examined.ResultsThe 145 women with diabetes (10.6%) at the first MRI had smaller total brain volumes (0.6% less; P = 0.05) and smaller gray matter volumes (1.5% less; P = 0.01) but not white matter volumes, both overall and within major lobes. They also had larger ischemic lesion volumes (21.8% greater; P = 0.02), both overall and in gray matter (27.5% greater; P = 0.06), in white matter (18.8% greater; P = 0.02), and across major lobes. Overall, women with diabetes had slightly (nonsignificant) greater loss of total brain volumes (3.02 cc; P = 0.11) and significant increases in total ischemic lesion volumes (9.7% more; P = 0.05) with time relative to those without diabetes. Diabetes was associated with lower scores in global cognitive function and its subdomains. These relative deficits were only partially accounted for by brain volumes and risk factors for cognitive deficits.ConclusionsDiabetes is associated with smaller brain volumes in gray but not white matter and increasing ischemic lesion volumes throughout the brain. These markers are associated with but do not fully account for diabetes-related deficits in cognitive function.

Although organochlorines have been reported more frequently in Parkinson's disease (PD) brains than in controls, the association with brain Lewy pathology is unknown. Honolulu-Asia Aging Study (HAAS) participants, exposed to organochlorines from a variety of sources during midlife, represent a population well suited to determining the relationship of brain organochlorines with Lewy pathology in decedents from the longitudinal HAAS. The study design included the measurement of 21 organochlorine levels in frozen occipital lobe samples from HAAS decedents. Alpha-synuclein immunostaining performed on 225 brains was used to identify Lewy bodies and Lewy neurites. With the potential for spurious associations to appear between Lewy pathology and 17 organochlorine compounds found in at least 1 brain, initial assessments identified heptachlor epoxide isomer b, methoxychlor, and benzene hexachloride b as being most important. The prevalence of Lewy pathology was 75% (6 of 8) among brains with any 2 of the 3 compounds, 48.8% (79 of 162) among those with 1, and 32.7% (18 of 55) for those with neither (P = .007 test for trend). Although findings persisted after removing cases with PD and dementia with Lewy bodies and after adjustment for age at death, body mass index, pack-years of cigarette smoking, and coffee intake (P = .013), the results were insignificant when correcting for multiple testing. Although consistent with earlier accounts of an association between organochlorines and clinical PD, associations with Lewy pathology warrant further study.

ObjectivesTo examine the associations between depressive symptoms, antidepressant use, and duration of use with incident frailty 3 years later in nonfrail women aged 65 and older.DesignSecondary analysis of the Women's Health Initiative Observational Study (WHI-OS), a prospective cohort study.SettingWHI-OS was conducted in 40 U.S. clinical centers.ParticipantsWomen aged 65 to 79, not frail at baseline.MeasurementsAntidepressant use was assessed through medication container inspection at baseline. Four groups were created according to baseline use and Burnam depression screen (range 0-1, 0.06 cutoff): antidepressant nonusers without depressive symptoms (reference group), antidepressant nonusers with depressive symptoms, antidepressant users without depressive symptoms, and antidepressant users with depressive symptoms. Frailty components included slowness or weakness, exhaustion, low physical activity, and unintended weight loss, ascertained through self-report and physical measurements at baseline and Year 3.ResultsOf 27,652 women at baseline, 1,350 (4.9%) were antidepressant users and 1,794 (6.5%) were categorized as depressed. At Year 3, 4,125 (14.9%) were frail. All groups had a greater risk of incident frailty than the reference group. Odds ratios (ORs) ranged from 1.73 (95% confidence interval (CI) = 1.41-2.12) in antidepressant users who were not depressed to 3.63 in antidepressant users who were depressed (95% CI = 2.37-5.55). All durations of use were associated with incident frailty (3 years OR = 1.60, 95% CI = 1.20-2.14).ConclusionIn older adult women, depressive symptoms and antidepressant use were associated with frailty after 3 years of follow-up.

The professions of pharmacy and social work are not generally assumed to directly collaborate in patient care; however, these professions are complementary. Health and wellbeing outcomes are significantly improved when care is managed by an interprofessional team that communicates and collaborates to ensure all aspects of care are effectively managed. The creation of educational opportunities for students to practice working together provides enhanced educational experiences and leads to their success as professionals. Pharmacy and social work faculty developed an interprofessional education activity specifically aimed at integrative student learning. Faculty and students based on various islands throughout the state of Hawai'i and the US territory of Guam participated in the experience. The case study encouraged interprofessional teamwork and collaboration. The case study also challenged students to share profession-specific knowledge with each other. Mean evaluation scores were compared between hybrid and fully online platforms. Evaluation scores were high and at least as good or higher in the fully online exercise compared to the hybrid exercises. Using the 20-item pre-post format, Interprofessional Collaborative Competencies Attainment Survey, results indicated statistically significant improvements in scores for all questions and domains (all P<.001). When hybrid training and fully online training were compared, there were no significant differences in pre scores, but post domain scores were significantly higher in students who experienced fully online training. This interprofessional case-based activity successfully promoted interprofessional learning and collaboration. Introducing learners to this type of collaborative practice while in school is critical for future collaboration in the workforce. [ABSTRACT FROM AUTHOR]

The article focuses on the Hawai‘i Interprofessional Education Workgroup (HIPE) and its efforts to facilitate interprofessional collaboration among health professional students from various University of Hawai‘i (UH) schools, particularly the Daniel K. Inouye College of Pharmacy (DKICP). It mentions the outcomes of distance education initiatives, including telehealth simulations and telepresence robot-assisted exercises.

ObjectivesTo define frailty using simple indicators; to identify risk factors for frailty as targets for prevention; and to investigate the predictive validity of this frailty classification for death, hospitalization, hip fracture, and activity of daily living (ADL) disability.DesignProspective study, the Women's Health Initiative Observational Study.SettingForty U.S. clinical centers.ParticipantsForty thousand six hundred fifty-seven women aged 65 to 79 at baseline.MeasurementsComponents of frailty included self-reported muscle weakness/impaired walking, exhaustion, low physical activity, and unintended weight loss between baseline and 3 years of follow-up. Death, hip fractures, ADL disability, and hospitalizations were ascertained during an average of 5.9 years of follow-up.ResultsBaseline frailty was classified in 16.3% of participants, and incident frailty at 3-years was 14.8%. Older age, chronic conditions, smoking, and depressive symptom score were positively associated with incident frailty, whereas income, moderate alcohol use, living alone, and self-reported health were inversely associated. Being underweight, overweight, or obese all carried significantly higher risk of frailty than normal weight. Baseline frailty independently predicted risk of death (hazard ratio (HR)=1.71, 95% confidence interval (CI)=1.48-1.97), hip fracture (HR=1.57, 95% CI=1.11-2.20), ADL disability (odds ratio (OR)=3.15, 95% CI=2.47-4.02), and hospitalizations (OR=1.95, 95% CI=1.72-2.22) after adjustment for demographic characteristics, health behaviors, disability, and comorbid conditions.ConclusionThese results support the robustness of the concept of frailty as a geriatric syndrome that predicts several poor outcomes in older women. Underweight, obesity, smoking, and depressive symptoms are strongly associated with the development of frailty and represent important targets for prevention.