Current use of estrogen-containing oral contraceptives or hormone therapy and risk of COVID-19 infection and hospitalization: a population-based cohort study.

This population-based cohort study evaluated the association between current use of oral contraceptives (OC) among women under 50 years (n=306,541), and hormone therapy (HT) among women aged 50 or older (n=323,203), and COVID-19 infection and hospitalization. Current OC/HT use was recorded monthly using prescription dispensing data. COVID-19 infections were identified March 2020-February 2021. COVID-19 infection and hospitalization were identified through diagnosis codes and laboratory tests. Weighted generalized estimating equations models estimated multivariable-adjusted odds ratios (aORs) for COVID-19 infection associated with time-varying OC/HT use. Among women with COVID-19, logistic regression models evaluated OC/HT use and COVID-19 hospitalization. Over 12 months, 11,727 (3.8%) women <50 years and 8,661 (2.7%) women ≥50 years experienced COVID-19 infections. There was no evidence of an association between OC use and infection (aOR=1.05; 95%CI: 0.97, 1.12). There was a modest association between HT use and infection (aOR=1.19; 95%CI: 1.03, 1.38). Women using OC had a 39% lower risk of hospitalization (aOR=0.61; 95%CI: 0.38, 1.00), but there was no association of HT use with hospitalization (aOR=0.89; 95%CI: 0.51, 1.53). These findings do not suggest a meaningfully greater risk of COVID-19 infection associated with OC or HT use. OC use may be associated with lower COVID-19 hospitalization risk.

Association of Traumatic Brain Injury with Late Life Neuropathological Outcomes in a Community-Based Cohort.

BACKGROUND: Prior studies into the association of head trauma with neuropathology have been limited by incomplete lifetime neurotrauma exposure characterization. OBJECTIVE: To investigate the neuropathological sequelae of traumatic brain injury (TBI) in an autopsy sample using three sources of TBI ascertainment, weighting findings to reflect associations in the larger, community-based cohort. METHODS: Self-reported head trauma with loss of consciousness (LOC) exposure was collected in biennial clinic visits from 780 older adults from the Adult Changes in Thought study who later died and donated their brain for research. Self-report data were supplemented with medical record abstraction, and, for 244 people, structured interviews on lifetime head trauma. Neuropathology outcomes included Braak stage, CERAD neuritic plaque density, Lewy body distribution, vascular pathology, hippocampal sclerosis, and cerebral/cortical atrophy. Exposures were TBI with or without LOC. Modified Poisson regressions adjusting for age, sex, education, and APOE ɛ4 genotype were weighted back to the full cohort of 5,546 participants. RESULTS: TBI with LOC was associated with the presence of cerebral cortical atrophy (Relative Risk 1.22, 95% CI 1.02, 1.42). None of the other outcomes was associated with TBI with or without LOC. CONCLUSION: TBI with LOC was associated with increased risk of cerebral cortical atrophy. Despite our enhanced TBI ascertainment, we found no association with the Alzheimer's disease-related neuropathologic outcomes among people who survived to at least age 65 without dementia. This suggests the pathophysiological processes underlying post-traumatic neurodegeneration are distinct from the hallmark pathologies of Alzheimer's disease.

Complex modeling with detailed temporal predictors does not improve health records-based suicide risk prediction.

Suicide risk prediction models can identify individuals for targeted intervention. Discussions of transparency, explainability, and transportability in machine learning presume complex prediction models with many variables outperform simpler models. We compared random forest, artificial neural network, and ensemble models with 1500 temporally defined predictors to logistic regression models. Data from 25,800,888 mental health visits made by 3,081,420 individuals in 7 health systems were used to train and evaluate suicidal behavior prediction models. Model performance was compared across several measures. All models performed well (area under the receiver operating curve [AUC]: 0.794-0.858). Ensemble models performed best, but improvements over a regression model with 100 predictors were minimal (AUC improvements: 0.006-0.020). Results are consistent across performance metrics and subgroups defined by race, ethnicity, and sex. Our results suggest simpler parametric models, which are easier to implement as part of routine clinical practice, perform comparably to more complex machine learning methods.

Association Between Diabetes Severity and Risks of COVID-19 Infection and Outcomes.

BACKGROUND: Little is known about whether diabetes increases the risk of COVID-19 infection and whether measures of diabetes severity are related to COVID-19 outcomes. OBJECTIVE: Investigate diabetes severity measures as potential risk factors for COVID-19 infection and COVID-19 outcomes. DESIGN, PARTICIPANTS, MEASURES: In integrated healthcare systems in Colorado, Oregon, and Washington, we identified a cohort of adults on February 29, 2020 (n = 1,086,918) and conducted follow-up through February 28, 2021. Electronic health data and death certificates were used to identify markers of diabetes severity, covariates, and outcomes. Outcomes were COVID-19 infection (positive nucleic acid antigen test, COVID-19 hospitalization, or COVID-19 death) and severe COVID-19 (invasive mechanical ventilation or COVID-19 death). Individuals with diabetes (n = 142,340) and categories of diabetes severity measures were compared with a referent group with no diabetes (n = 944,578), adjusting for demographic variables, neighborhood deprivation index, body mass index, and comorbidities. RESULTS: Of 30,935 patients with COVID-19 infection, 996 met the criteria for severe COVID-19. Type 1 (odds ratio [OR] 1.41, 95% CI 1.27-1.57) and type 2 diabetes (OR 1.27, 95% CI 1.23-1.31) were associated with increased risk of COVID-19 infection. Insulin treatment was associated with greater COVID-19 infection risk (OR 1.43, 95% CI 1.34-1.52) than treatment with non-insulin drugs (OR 1.26, 95% 1.20-1.33) or no treatment (OR 1.24; 1.18-1.29). The relationship between glycemic control and COVID-19 infection risk was dose-dependent: from an OR of 1.21 (95% CI 1.15-1.26) for hemoglobin A1c (HbA1c) < 7% to an OR of 1.62 (95% CI 1.51-1.75) for HbA1c ≥ 9%. Risk factors for severe COVID-19 were type 1 diabetes (OR 2.87; 95% CI 1.99-4.15), type 2 diabetes (OR 1.80; 95% CI 1.55-2.09), insulin treatment (OR 2.65; 95% CI 2.13-3.28), and HbA1c ≥ 9% (OR 2.61; 95% CI 1.94-3.52). CONCLUSIONS: Diabetes and greater diabetes severity were associated with increased risks of COVID-19 infection and worse COVID-19 outcomes.

Association of Military Employment With Late-Life Cognitive Decline and Dementia: A Population-Based Prospective Cohort Study.

INTRODUCTION: As the number of U.S. veterans over age 65 has increased, interest in whether military service affects late-life health outcomes has grown. Whether military employment is associated with increased risk of cognitive decline and dementia remains unclear. MATERIALS AND METHODS: We used data from 4,370 participants of the longitudinal Adult Changes in Thought (ACT) cohort study, enrolled at age 65 or older, to examine whether military employment was associated with greater cognitive decline or higher risk of incident dementia in late life. We classified persons as having military employment if their first or second-longest occupation was with the military. Cognitive status was assessed at each biennial Adult Changes in Thought study visit using the Cognitive Abilities Screening Instrument, scored using item response theory (CASI-IRT). Participants meeting screening criteria were referred for dementia ascertainment involving clinical examination and additional cognitive testing. Primary analyses were adjusted for sociodemographic characteristics and APOE genotype. Secondary analyses additionally adjusted for indicators of early-life socioeconomic status and considered effect modification by age, gender, and prior traumatic brain injury with loss of consciousness TBI with LOC. RESULTS: Overall, 6% of participants had military employment; of these, 76% were males. Military employment was not significantly associated with cognitive change (difference in modeled 10-year cognitive change in CASI-IRT scores in SD units (95% confidence interval [CI]): -0.042 (-0.19, 0.11), risk of dementia (hazard ratio [HR] [95% CI]: 0.92 [0.71, 1.18]), or risk of Alzheimer's disease dementia (HR [95% CI]: 0.93 [0.70, 1.23]). These results were robust to additional adjustment and sensitivity analyses. There was no evidence of effect modification by age, gender, or traumatic brain injury with loss of consciousness. CONCLUSIONS: Among members of the Adult Changes in Thought cohort, military employment was not associated with increased risk of cognitive decline or dementia. Nevertheless, military veterans face the same high risks for cognitive decline and dementia as other aging adults.

Increased COVID-19 Infection Risk Drives Racial and Ethnic Disparities in Severe COVID-19 Outcomes.

COVID-19 inequities have been well-documented. We evaluated whether higher rates of severe COVID-19 in racial and ethnic minority groups were driven by higher infection rates by evaluating if disparities remained when analyses were restricted to people with infection. We conducted a retrospective cohort study of adults insured through Kaiser Permanente (Colorado, Northwest, Washington), follow-up in March-September 2020. Laboratory results and hospitalization diagnosis codes identified individuals with COVID-19. Severe COVID-19 was defined as invasive mechanical ventilation or mortality. Self-reported race and ethnicity, demographics, and medical comorbidities were extracted from health records. Modified Poisson regression estimated adjusted relative risks (aRRs) of severe COVID-19 in full cohort and among individuals with infection. Our cohort included 1,052,774 individuals, representing diverse racial and ethnic minority groups (e.g., 68,887 Asian, 41,243 Black/African American, 93,580 Hispanic or Latino/a individuals). Among 7,399 infections, 442 individuals experienced severe COVID-19. In the full cohort, severe COVID-19 aRRs for Asian, Black/African American, and Hispanic individuals were 2.09 (95% CI: 1.36, 3.21), 2.02 (1.39, 2.93), and 2.09 (1.57, 2.78), respectively, compared to non-Hispanic Whites. In analyses restricted to individuals with COVID-19, all aRRs were near 1, except among Asian Americans (aRR 1.82 [1.23, 2.68]). These results indicate increased incidence of severe COVID-19 among Black/African American and Hispanic individuals is due to higher infection rates, not increased susceptibility to progression. COVID-19 disparities most likely result from social, not biological, factors. Future work should explore reasons for increased severe COVID-19 risk among Asian Americans. Our findings highlight the importance of equity in vaccine distribution.

Diabetic Retinopathy and Dementia Association, Beyond Diabetes Severity.

PURPOSE: To investigate whether associations between diabetic retinopathy (DR) and dementia and Alzheimer's disease (AD) remain significant after controlling for several measures of diabetes severity. DESIGN: Retrospective cohort study. METHODS: Adult Changes in Thought (ACT) is a prospective cohort study of adults aged ≥65 years, randomly selected and recruited from the membership rolls of Kaiser Permanente Washington, who are dementia free at enrollment and followed biennially until incident dementia. The ACT participants were included in this study if they had type 2 diabetes mellitus at enrollment or developed it during follow-up, and data were collected through September, 2018 (3516 person-years of follow-up). Diabetes was defined by ≥ 2 diabetes medication fills in 1 year. Diagnosis of DR was based on International Classification of Diseases Ninth and Tenth Revision codes. Estimates of microalbuminuria, long-term glycemia, and renal function from longitudinal laboratory records were used as indicators of diabetes severity. Alzheimer's disease and dementia were diagnosed using research criteria at expert consensus meetings. RESULTS: A total of 536 participants (median baseline age 75 [interquartile range 71-80], 54% women) met inclusion criteria. Significant associations between DR >5 years duration with dementia (hazard ratio 1.81 [95% CI 1.23, 2.65]) and AD (1.80 [1.15, 2.82]) were not altered by adjustment for estimates of microalbuminuria, long-term glycemia, and renal function (dementia: 1.69 [1.14, 2.50]; AD: 1.73 [1.10, 2.74]). CONCLUSIONS: Among people with type 2 diabetes, DR itself appears to be an important biomarker of dementia risk in addition to glycemia and renal complications.

Investigation of the association of military employment and Parkinson's disease with a validated Parkinson's disease case-finding strategy.

INTRODUCTION: Persons with military involvement may be more likely to have Parkinson's disease (PD) risk factors. As PD is rare, case finding remains a challenge, contributing to our limited understanding of PD risk factors. Here, we explore the validity of case-finding strategies and whether military employment is associated with PD. MATERIALS AND METHODS: We identified Adult Changes in Thought (ACT) study participants reporting military employment as their longest or second longest occupation. We used self-report and prescription fills to identify PD cases and validated this case-finding approach against medical record review. RESULTS: At enrollment, 6% of 5,125 eligible participants had military employment and 1.8% had prevalent PD; an additional 3.5% developed PD over follow-up (mean: 8.3 years). Sensitivity of our case-finding approach was higher for incident (80%) than prevalent cases (54%). Specificity was high (>97%) for both. Military employment was not associated with prevalent PD. Among nonsmokers, point estimates suggested an increased risk of incident PD with military employment, but the result was non-significant and based on a small number of cases. CONCLUSIONS: Self-report and prescription medications can accurately identify incident PD cases relative to the reference method of medical record review. We found no association between military employment and PD.

Machine Learning Prediction of Suicide Risk Does Not Identify Patients Without Traditional Risk Factors.

Objective: To determine whether predictions of suicide risk from machine learning models identify unexpected patients or patients without medical record documentation of traditional risk factors.Methods: The study sample included 27,091,382 outpatient mental health (MH) specialty or general medical visits with a MH diagnosis for patients aged 11 years or older from January 1, 2009, to September 30, 2017. We used predicted risk scores of suicide attempt and suicide death, separately, within 90 days of visits to classify visits into risk score percentile strata. For each stratum, we calculated counts and percentages of visits with traditional risk factors, including prior self-harm diagnoses and emergency department visits or hospitalizations with MH diagnoses, in the last 3, 12, and 60 months.Results: Risk-factor percentages increased with predicted risk scores. Among MH specialty visits, 66%, 88%, and 99% of visits with suicide attempt risk scores in the top 3 strata (respectively, 90th-95th, 95th-98th, and ≥ 98th percentiles) and 60%, 77%, and 93% of visits with suicide risk scores in the top 3 strata represented patients who had at least one traditional risk factor documented in the prior 12 months. Among general medical visits, 52%, 66%, and 90% of visits with suicide attempt risk scores in the top 3 strata and 45%, 66%, and 79% of visits with suicide risk scores in the top 3 strata represented patients who had a history of traditional risk factors in the last 12 months.Conclusions: Suicide risk alerts based on these machine learning models coincide with patients traditionally thought of as high-risk at their high-risk visits.

Agreement of Step-Based Metrics From ActiGraph and ActivPAL Accelerometers Worn Concurrently Among Older Adults.

Purpose: Our study evaluated the agreement of mean daily step counts, peak 1-min cadence, and peak 30-min cadence between the hip-worn ActiGraph GT3X+ accelerometer, using the normal filter (AGN) and the low frequency extension (AGLFE), and the thigh-worn activPAL3 micro (AP) accelerometer among older adults. Methods: Nine-hundred and fifty-three older adults (≥65 years) were recruited to wear the ActiGraph device concurrently with the AP for 4-7 days beginning in 2016. Using the AP as the reference measure, device agreement for each step-based metric was assessed using mean differences (AGN - AP and AGLFE - AP), mean absolute percentage error (MAPE), and Pearson and concordance correlation coefficients. Results: For AGN - AP, the mean differences and MAPE were: daily steps -1,851 steps/day and 27.2%, peak 1-min cadence -16.2 steps/min and 16.3%, and peak 30-min cadence -17.7 steps/min and 24.0%. Pearson coefficients were .94, .85, and .91 and concordance coefficients were .81, .65, and .73, respectively. For AGLFE - AP, the mean differences and MAPE were: daily steps 4,968 steps/day and 72.7%, peak 1-min cadence -1.4 steps/min and 4.7%, and peak 30-min cadence 1.4 steps/min and 7.0%. Pearson coefficients were .91, .91, and .95 and concordance coefficients were .49, .91, and .94, respectively. Conclusions: Compared with estimates from the AP, the AGN underestimated daily step counts by approximately 1,800 steps/day, while the AGLFE overestimated by approximately 5,000 steps/day. However, peak step cadence estimates generated from the AGLFE and AP had high agreement (MAPE ≤ 7.0%). Additional convergent validation studies of step-based metrics from concurrently worn accelerometers are needed for improved understanding of between-device agreement.

Agreement of sedentary behaviour metrics derived from hip-worn and thigh-worn accelerometers among older adults: with implications for studying physical and cognitive health.

Little is known about how sedentary behaviour (SB) metrics derived from hip-worn and thigh-worn accelerometers agree for older adults. Thigh-worn activPAL micro monitors were concurrently worn with hip-worn ActiGraph GT3X+ accelerometers (with SB measured using the 100 count-per-minute (cpm) cut-point; ActiGraph100cpm) by 953 older adults (age 77±6.6, 54% women) for 4-to-7 days. Device agreement for sedentary time and 5 SB pattern metrics was assessed using mean error and correlations. Logistic regression tested associations with 4 health outcomes using standardized (i.e., z-scores) and unstandardized SB metrics. Mean errors (activPAL-ActiGraph100cpm) and 95% limits of agreement were: sedentary time -54.7(-223.4,113.9) min/d; time in 30+ minute bouts 77.6(-74.8,230.1) min/d; mean bout duration 5.9(0.5,11.4) min; usual bout duration 15.2(0.4,30) min; breaks in sedentary time -35.4(-63.1,-7.6) breaks/d; and alpha -0.5(-0.6,-0.4). Respective Pearson correlations were: 0.66, 0.78, 0.73, 0.79, 0.51, 0.40. Concordance correlations were: 0.57, 0.67, 0.40, 0.50, 0.14, 0.02. The statistical significance and direction of associations was identical for ActiGraph100cpm and activPAL metrics in 46 of 48 tests, though significant differences in the magnitude of odds ratios were observed among 9 of 24 tests for unstandardized and 2 of 24 for standardized SB metrics. Caution is needed when interpreting SB metrics and associations with health from ActiGraph100cpm due to the tendency for it to overestimate breaks in sedentary time relative to activPAL. However, high correlations between activPAL and ActiGraph100cpm measures and similar standardized associations with health outcomes suggest that studies using ActiGraph100cpm are useful, though not ideal, for studying SB in older adults.

Evaluation of Electronic Health Record-Based Suicide Risk Prediction Models on Contemporary Data.

BACKGROUND: Suicide risk prediction models have been developed by using information from patients' electronic health records (EHR), but the time elapsed between model development and health system implementation is often substantial. Temporal changes in health systems and EHR coding practices necessitate the evaluation of such models in more contemporary data. OBJECTIVES: A set of published suicide risk prediction models developed by using EHR data from 2009 to 2015 across seven health systems reported c-statistics of 0.85 for suicide attempt and 0.83 to 0.86 for suicide death. Our objective was to evaluate these models' performance with contemporary data (2014-2017) from these systems. METHODS: We evaluated performance using mental health visits (6,832,439 to mental health specialty providers and 3,987,078 to general medical providers) from 2014 to 2017 made by 1,799,765 patients aged 13+ across the health systems. No visits in our evaluation were used in the previous model development. Outcomes were suicide attempt (health system records) and suicide death (state death certificates) within 90 days following a visit. We assessed calibration and computed c-statistics with 95% confidence intervals (CI) and cut-point specific estimates of sensitivity, specificity, and positive/negative predictive value. RESULTS: Models were well calibrated; 46% of suicide attempts and 35% of suicide deaths in the mental health specialty sample were preceded by a visit (within 90 days) with a risk score in the top 5%. In the general medical sample, 53% of attempts and 35% of deaths were preceded by such a visit. Among these two samples, respectively, c-statistics were 0.862 (95% CI: 0.860-0.864) and 0.864 (95% CI: 0.860-0.869) for suicide attempt, and 0.806 (95% CI: 0.790-0.822) and 0.804 (95% CI: 0.782-0.829) for suicide death. CONCLUSION: Performance of the risk prediction models in this contemporary sample was similar to historical estimates for suicide attempt but modestly lower for suicide death. These published models can inform clinical practice and patient care today.

Association of Lifetime TBI and Military Employment with Late Life ADL Functioning: A Population-Based Prospective Cohort Study.

OBJECTIVE: To determine associations of traumatic brain injury (TBI) and military employment with activities of daily living (ADL) in late life. DESIGN: Population-based prospective cohort study with biennial follow-up and censoring at the time of dementia diagnosis. SETTING: Community-based integrated health care delivery system. PARTICIPANTS: Participants (N=4953) were men (n=2066) and women (n=2887) aged ≥65 years who were dementia free. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: ADL difficulties at baseline and accumulation during follow-up. RESULTS: TBI with loss of consciousness (LOC) before the age of 40 years was associated with slightly higher ADL difficulty at baseline for women (rate ratio [RR], 1.44; 95% confidence interval [CI], 1.08-1.93; P=.01). For men, TBI with LOC at any age was associated with greater ADL difficulty at baseline (age <40y: RR, 1.58; 95% CI, 1.20-2.08; P=.001; age ≥40y: RR, 2.14; 95% CI, 1.24-3.68; P=.006). TBI with LOC was not associated with the rate of accumulation of ADL difficulties over time in men or women. There was no evidence of an association between military employment and either outcome, nor of an interaction between military employment and TBI with LOC. Findings were consistent across a variety of sensitivity analyses. CONCLUSIONS: Further investigation into factors underlying greater late life functional impairment among survivors of TBI is warranted.

Clinical risk prediction models and informative cluster size: Assessing the performance of a suicide risk prediction algorithm.

Clinical visit data are clustered within people, which complicates prediction modeling. Cluster size is often informative because people receiving more care are less healthy and at higher risk of poor outcomes. We used data from seven health systems on 1,518,968 outpatient mental health visits from January 1, 2012 to June 30, 2015 to predict suicide attempt within 90 days. We evaluated true performance of prediction models using a prospective validation set of 4,286,495 visits from October 1, 2015 to September 30, 2017. We examined dividing clustered data on the person or visit level for model training and cross-validation and considered a within cluster resampling approach for model estimation. We evaluated optimism by comparing estimated performance from a left-out testing dataset to performance in the prospective dataset. We used two prediction methods, logistic regression with least absolute shrinkage and selection operator (LASSO) and random forest. The random forest model using a visit-level split for model training and testing was optimistic; it overestimated discrimination (area under the curve, AUC = 0.95 in testing versus 0.84 in prospective validation) and classification accuracy (sensitivity = 0.48 in testing versus 0.19 in prospective validation, 95th percentile cut-off). Logistic regression and random forest models using a person-level split performed well, accurately estimating prospective discrimination and classification: estimated AUCs ranged from 0.85 to 0.87 in testing versus 0.85 in prospective validation, and sensitivity ranged from 0.15 to 0.20 in testing versus 0.17 to 0.19 in prospective validation. Within cluster resampling did not improve performance. We recommend dividing clustered data on the person level, rather than visit level, to ensure strong performance in prospective use and accurate estimation of future performance at the time of model development.

Associations between physical function and device-based measures of physical activity and sedentary behavior patterns in older adults: moving beyond moderate-to-vigorous intensity physical activity.

BACKGROUND: Research supports that moderate-to-vigorous intensity physical activity (MVPA) is key to prolonged health and function. Among older adults, substantial changes to MVPA may be infeasible, thus a growing literature suggests a shift in focus to whole-day activity patterns. METHODS: With data from 795 older adults aged 65-100 in the Adult Changes in Thought Activity Monitoring study, we used linear regression to estimate associations between ActiGraph and activPAL measured activity patterns - including light intensity physical activity, steps, standing, and sedentary behaviors - and physical function as measured by a short Performance-based Physical Function (sPPF) score (range 0-12), a composite score based on three standardized physical performance tasks: gait speed, timed chair stands, and grip strength. We examined whether relationships persisted when controlling for MVPA or differed across age, gender, or quartiles of MVPA. RESULTS: In models unadjusted for MVPA, a 1-standard deviation (SD) increment of daily sitting (1.9 h more), mean sitting bout duration (8 min longer average), or time spent in sedentary activity (1.6 h more) was associated with ~ 0.3-0.4 points lower mean sPPF score (all p < 0.05). A 1-SD increment in daily steps (~ 3500 more steps) was associated with ~ 0.5 points higher mean sPPF score (95% CI: 0.22 to 0.73). MVPA adjustment attenuated all relationships. The association between physical function and steps was strongest among adults aged 75+; associations of worse function with greater sedentary behavior were more pronounced in participants with the lowest levels of MVPA. CONCLUSIONS: We found associations between function and activity metrics other than MVPA in key subgroups, findings that support research on broader activity patterns and may offer ideas regarding practical intervention opportunities for improving function in older adults.

Incident Dementia, Glycated Hemoglobin (HbA1c) Levels, and Potentially Preventable Hospitalizations in People Aged 65 and Older With Diabetes.

BACKGROUND: This study was aimed to determine whether incident dementia and HbA1c levels are associated with increased rates of potentially preventable hospitalizations (PPHs) in persons with diabetes. METHOD: A total of 565 adults aged 65+ ever treated for diabetes were enrolled from Adult Changes in Thought study. PPHs were from principal discharge diagnoses and included diabetes PPH (dPPH), respiratory PPH (rPPH), urinovolemic PPH (uPPH), cardiovascular PPH, and other PPH. Poisson generalized estimating equations estimated rate ratios (RRs) and 95% confidence intervals (CIs) for the associations between dementia or HbA1c measures and rate of PPHs. RESULTS: A total of 562 individuals contributed 3 602 dementia-free years, and 132 individuals contributed 511 dementia follow-up years. One hundred twenty-eight (23%) dementia-free individuals had 210 PPH admissions and a crude rate of 58 per 1 000 person-years, while 55 (42%) individuals with dementia had 93 PPH admissions and a crude rate of 182 per 1 000 person-years. The adjusted RR (95% CI) comparing rates between dementia and dementia-free groups were 2.27 (1.60, 3.21) for overall PPH; 5.90 (2.70, 12.88) for dPPH; 5.17 (2.49, 10.73) for uPPH; and 2.01 (1.06, 3.83) for rPPH. Compared with HbA1c of 7%-8% and adjusted for dementia, the RR (95% CI) for overall PPH was 1.43 (1.00, 2.06) for >8% HbA1c and 1.18 (0.85, 1.65) for <7% HbA1c. The uPPH RR was also increased, comparing >8% and <7% HbA1c levels. CONCLUSION: Incident dementia is associated with higher rates of PPHs among people with diabetes, especially PPHs due to diabetes, urinary tract infection (UTI), and dehydration. Potential evidence suggested that HbA1c levels of >8% versus lower levels are associated with higher rates of overall PPHs and UTI- and dehydration-related PPHs.

Glucose-Dementia Association Is Consistent Over Blood Pressure/Antihypertensive Groups.

BACKGROUND: Higher glucose levels are associated with dementia risk in people with and without diabetes. However, little is known about how this association might vary by hypertension status and antihypertensive treatment. Most studies on modifiable dementia risk factors consider each factor in isolation. OBJECTIVE: To test the hypothesis that hypertension and antihypertensive treatments may modify associations between glucose levels and dementia. METHODS: Analyses of data generated from a research study and clinical care of participants from a prospective cohort of dementia-free older adults, including glucose measures, diabetes and antihypertensive treatments, and blood pressure data. We defined groups based on blood pressure (hypertensive versus not, ≥140/90 mmHg versus <140/90 mmHg) and antihypertensive treatment intensity (0, 1, or ≥2 classes of antihypertensives). We used Bayesian joint models to jointly model longitudinal exposure and time to event data. RESULTS: A total of 3,056 participants without diabetes treatment and 480 with diabetes treatment were included (mean age at baseline, 75.1 years; mean 7.5 years of follow-up). Higher glucose levels were associated with greater dementia risk among people without and with treated diabetes. Hazard ratios for dementia were similar across all blood pressure/antihypertensive treatment groups (omnibus p = 0.82 for people without and p = 0.59 for people with treated diabetes). CONCLUSION: Hypertension and antihypertensive treatments do not appear to affect the association between glucose and dementia risk in this population-based longitudinal cohort study of community-dwelling older adults. Future studies are needed to examine this question in midlife and by specific antihypertensive treatments.

Association of remote traumatic brain injury and military employment with late-life trajectories of depressive symptom severity.

BACKGROUND: Traumatic brain injury (TBI) and military service are common lifetime exposures among current older adults that may affect late-life mental health. The objective of the present study was to evaluate the association between TBI with loss of consciousness (LOC) and military employment and late-life depressive symptom severity trajectory. METHODS: 1445 males and 2096 females adults at least 65 years old without dementia or recent TBI were enrolled and followed biennially for up to 10 years in the Adult Changes in Thought study from Kaiser Permanente Washington in Seattle, Washington. RESULTS: Using group-based trajectory modeling, we documented four distinct depressive symptom severity trajectories that followed a similar course in males and females (Minimal, Decreasing, Increasing, and Persistent). In multinomial regression analyses, TBI with LOC in males was associated with greater likelihood of Persistent versus Minimal depressive symptom severity compared to individuals without TBI (OR = 1.51, 95% CI: 1.01, 2.27; p=0.046). Males reporting past military employment had greater likelihood of Decreasing versus Minimal depressive symptom severity compared to individuals without past military employment (OR = 1.54, 95% CI: 1.03, 2.31; p=0.035). There was no association between TBI or military employment and depression trajectories in females, and no evidence of effect modification by age or between exposures. LIMITATIONS: Lifetime history of TBI was ascertained retrospectively and may be subject to recall bias. Also, past military employment does not presuppose combat exposure. CONCLUSIONS: Remote TBI and past military employment are relevant to late-life trajectories of depressive symptom severity in dementia-free older males.