Preventing severe hypoglycemia in adults with type 2 diabetes (PHT2): Design, delivery and evaluation framework for a randomized controlled trial

BACKGROUND

Severe hypoglycemia is a common and feared complication of medications used to lower blood glucose levels in individuals with diabetes. Psychoeducational interventions can prevent severe hypoglycemia in individuals with type 1 diabetes (T1D). We aim to determine the effectiveness of this approach among adults with type 2 diabetes (T2D) at elevated risk for severe hypoglycemia.

METHODS

Preventing Hypoglycemia in Type 2 diabetes (PHT2) is a two-arm, parallel, randomized controlled trial. Participants are eligible if they are adults with T2D receiving care at an integrated group practice in Washington state and have experienced one or more episodes of severe hypoglycemia in the prior 12 months or have impaired awareness of hypoglycemia (Gold score ≥ 4). Participants are randomized to proactive nurse care management with or without my hypo compass, an evidence-based, psychoeducational intervention combining group and individual self-management training. For this study, my hypo compass was adapted to be suitable for adults with T2D and from an in-person to a virtual intervention over videoconference and telephone. The primary outcome is any self-reported severe hypoglycemia in the 12 months following the start of the intervention. Secondary outcomes include biochemical measures of hypoglycemia, self-reported hypoglycemia awareness, fear of hypoglycemia, and emergency department visits and hospitalizations for severe hypoglycemia. The study includes a process evaluation to assess implementation fidelity and clarify the causal pathway.

CONCLUSION

The PHT2 trial will compare the effectiveness of two approaches for reducing severe hypoglycemia in adults with T2D.

TRIAL REGISTRATION

clinicaltrials.gov, # NCT04863872.

Continuous Glucose Monitor Metrics Are Associated with Emergency Department Visits and Hospitalizations for Hypoglycemia and Hyperglycemia, But Have Low Predictive Value

Objective: Determine whether continuous glucose monitor (CGM) metrics can provide actionable advance warning of an emergency department (ED) visit or hospitalization for hypoglycemic or hyperglycemic (dysglycemic) events. Research Design and Methods: Two nested case-control studies were conducted among insulin-treated diabetes patients at Kaiser Permanente, who shared their CGM data with their providers. Cases included dysglycemic events identified from ED and hospital records (2016-2021). Controls were selected using incidence density sampling. Multiple CGM metrics were calculated among patients using CGM >70% of the time, using CGM data from two lookback periods (0-7 and 8-14 days) before each event. Generalized estimating equations were specified to estimate odds ratios and C-statistics. Results: Among 3626 CGM users, 108 patients had 154 hypoglycemic events and 165 patients had 335 hyperglycemic events. Approximately 25% of patients had no CGM data during either lookback; these patients had >2 × the odds of a hypoglycemic event and 3-4 × the odds of a hyperglycemic event. While several metrics were strongly associated with a dysglycemic event, none had good discrimination. Conclusion: Several CGM metrics were strongly associated with risk of dysglycemic events, and these can be used to identify higher risk patients. Also, patients who are not using their CGM device may be at elevated risk of adverse outcomes. However, no CGM metric or absence of CGM data had adequate discrimination to reliably provide actionable advance warning of an event and thus justify a rapid intervention.

Willingness to take less medication for type 2 diabetes among older patients: The Diabetes & Aging Study

BACKGROUND

To examine the willingness of older patients to take less diabetes medication (de-intensify) and to identify characteristics associated with willingness to de-intensify treatment.

METHODS

Survey conducted in 2019 in an age-stratified, random sample of older (65-100 years) adults with diabetes on glucose-lowering medications in the Kaiser Permanente Northern California Diabetes Registry. We classified survey responses to the question: "I would be willing to take less medication for my diabetes" as willing, neutral, or unwilling to de-intensify. Willingness to de-intensify treatment was examined by several clinical characteristics, including American Diabetes Association (ADA) health status categories used for individualizing glycemic targets. Analyses were weighted to account for over-sampling of older individuals.

RESULTS

A total of 1337 older adults on glucose-lowering medication(s) were included (age 74.2 ± 6.0 years, 44% female, 54.4% non-Hispanic white). The proportions of participants willing, neutral, or unwilling to take less medication were 51.2%, 27.3%, and 21.5%, respectively. Proportions of willing to take less medication varied by age (65-74 years: 54.2% vs. 85+ years: 38.5%) and duration of diabetes (0-4 years: 61.0% vs. 15+ years: 44.2%), both p < 0.001. Patients on 1-2 medications were more willing to take less medication(s) compared with patients on 10+ medications (62.1% vs. 46.6%, p = 0.03). Similar proportions of willingness to take less medications were seen across ADA health status, and HbA1c. Willingness to take less medication(s) was similar across survey responses to questions about patient-clinician relationships.

CONCLUSIONS

Clinical guidelines suggest considering treatment de-intensification in older patients with longer duration of diabetes, yet patients with these characteristics are less likely to be willing to take less medication(s).

Care Partner Engagement in Secure Messaging Between Patients With Diabetes and Their Clinicians: Cohort Study

BACKGROUND

Patient engagement with secure messaging (SM) via digital patient portals has been associated with improved diabetes outcomes, including increased patient satisfaction and better glycemic control. Yet, disparities in SM uptake exist among older patients and racial and ethnic underserved groups. Care partners (family members or friends) may provide a means for mitigating these disparities; however, it remains unclear whether and to what extent care partners might enhance SM use.

OBJECTIVE

We aim to examine whether SM use differs among older patients with diabetes based on the involvement of care partner proxies.

METHODS

This is a substudy of the ECLIPPSE (Employing Computational Linguistics to Improve Patient-Provider Secure Emails) project, a cohort study taking place in a large, fully integrated health care delivery system with an established digital patient portal serving over 4 million patients. Participants included patients with type 2 diabetes aged ≥50 years, newly registered on the patient portal, who sent ≥1 English-language message to their clinician between July 1, 2006, and December 31, 2015. Proxy SM was identified by having a registered proxy. To identify nonregistered proxies, a computational linguistics algorithm was applied to detect words and phrases more likely to appear in proxy messages compared to patient-authored messages. The primary outcome was the annual volume of secure messages (sent or received); secondary outcomes were the length of time to the first SM sent by patient or proxy and the number of annual SM exchanges (unique message topics generating ≥1 reply).

RESULTS

The mean age of the cohort (N=7659) at this study's start was 61 (SD 7.16) years; 75% (n=5573) were married, 15% (n=1089) identified as Black, 10% (n=747) Chinese, 12% (n=905) Filipino, 13% (n=999) Latino, and 30% (n=2225) White. Further, 49% (n=3782) of patients used a proxy to some extent. Compared to nonproxy users, proxy users were older (P<.001), had lower educational attainment (P<.001), and had more comorbidities (P<.001). Adjusting for patient sociodemographic and clinical characteristics, proxy users had greater annual SM volume (20.7, 95% CI 20.2-21.2 vs 10.9, 95% CI 10.7-11.2; P<.001), shorter time to SM initiation (hazard ratio vs nonusers: 1.30, 95% CI 1.24-1.37; P<.001), and more annual SM exchanges (6.0, 95% CI 5.8-6.1 vs 2.9, 95% CI 2.9-3.0, P<.001). Differences in SM engagement by proxy status were similar across patient levels of education, and racial and ethnic groups.

CONCLUSIONS

Among a cohort of older patients with diabetes, proxy SM involvement was independently associated with earlier initiation and increased intensity of messaging, although it did not appear to mitigate existing disparities in SM. These findings suggest care partners can enhance patient-clinician telecommunication in diabetes care. Future studies should examine the effect of care partners' SM involvement on diabetes-related quality of care and clinical outcomes.

Glycemic control and diabetes complications across health status categories in older adults treated with insulin or insulin secretagogues: The Diabetes & Aging Study

BACKGROUND

For older adults with type 2 diabetes (T2D) treated with insulin or sulfonylureas, Endocrine Society guideline recommends HbA1c between 7% to <7.5% for those in good health, 7.5% to <8% for those in intermediate health, and 8% to <8.5% for those in poor health. Our aim was to examine associations between attained HbA1c below, within (reference), or above recommended target range and risk of complication or mortality.

METHODS

Retrospective cohort study of adults ≥65 years old with T2D treated with insulin or sulfonylureas from an integrated healthcare delivery system. Cox proportional hazards models of complications during 2019 were adjusted for sociodemographic and clinical variables. Primary outcome was a combined outcome of any microvascular or macrovascular event, severe hypoglycemia, or mortality during 12-month follow-up.

RESULTS

Among 63,429 patients (mean age: 74.2 years, 46.8% women), 8773 (13.8%) experienced a complication. Complication risk was significantly elevated for patients in good health (n = 16,895) whose HbA1c was above (HR 1.97, 95% CI 1.62-2.41) or below (HR 1.29, 95% CI 1.02-1.63) compared to within recommended range. Among those in intermediate health (n = 30,129), complication risk was increased for those whose HbA1c was above (HR 1.45, 95% CI 1.30-1.60) but not those below the recommended range (HR 0.99, 95% CI 0.89-1.09). Among those in poor health (n = 16,405), complication risk was not significantly different for those whose HbA1c was below (HR 0.98, 95% CI 0.89-1.09) or above (HR 0.96, 95% CI 0.88-1.06) recommended range.

CONCLUSIONS

For older adults with T2D in good health, HbA1c below or above the recommended range was associated with significantly elevated complication risk. However, for those in poor health, achieving specific HbA1c levels may not be helpful in reducing the risk of complications.

Racial and Ethnic Differences in the Association Between Mean Glucose and Hemoglobin A1c

Background: Studies have reported significantly higher hemoglobin A1c (A1C) in African American patients than in White patients with the same mean glucose, but less is known about other racial/ethnic groups. We evaluated racial/ethnic differences in the association between mean glucose, based on continuous glucose monitor (CGM) data, and A1C. Methods: Retrospective study among 1788 patients with diabetes from Kaiser Permanente Northern California (KPNC) who used CGM devices during 2016 to 2021. In this study population, there were 5264 A1C results; mean glucose was calculated from 124,388,901 CGM readings captured during the 90 days before each A1C result. Hierarchical mixed models were specified to estimate racial/ethnic differences in the association between mean glucose and A1C. Results: Mean A1C was 0.33 (95% confidence interval: 0.23-0.44; P < 0.0001) percentage points higher among African American patients relative to White patients for a given mean glucose. A1C results for Asians, Latinos, and multiethnic patients were not significantly different from those of White patients. The slope of the association between mean glucose and A1C did not differ significantly across racial/ethnic groups. Variance for the association between mean glucose and A1C was substantially greater within groups than between racial/ethnic groups (65% vs. 9%, respectively). Conclusions: For African American patients, A1C results may overestimate glycemia and could lead to premature diabetes diagnoses, overtreatment, or invalid assessments of health disparities. However, most of the variability in the mean glucose-A1C association was within racial/ethnic groups. Treatment decisions driven by guideline-based A1C targets should be individualized and supported by direct measurement of glycemia.

Severe hypoglycemia and falls in older adults with diabetes: The Diabetes & Aging Study

Objective

To estimate rates of severe hypoglycemia and falls among older adults with diabetes and evaluate their association.

Research Design and Methods

Survey in an age-stratified, random sample adults with diabetes age 65-100 years; respondents were asked about severe hypoglycemia (requiring assistance) and falls in the past 12 months. Prevalence ratios (adjusted for age, sex, race/ethnicity) estimated the increased risk of falls associated with severe hypoglycemia.

Results

Among 2,158 survey respondents, 79 (3.7%) reported severe hypoglycemia, of whom 68 (86.1%) had no ED visit or hospitalization for hypoglycemia. Falls were reported by 847 (39.2%), of whom 745 (88.0%) had no fall documented in outpatient or inpatient records. Severe hypoglycemia was associated with a 70% greater prevalence of falls (adjusted prevalence ratio = 1.7 (95% CI, 1.3-2.2)).

Conclusion

While clinical documentation of events likely reflects severity or care-seeking behavior, severe hypoglycemia and falls are common, under-reported life-threatening events.

Development and Validation of the Life Expectancy Estimator for Older Adults with Diabetes (LEAD): the Diabetes and Aging Study

BACKGROUND

Estimated life expectancy for older patients with diabetes informs decisions about treatment goals, cancer screening, long-term and advanced care, and inclusion in clinical trials. Easily implementable, evidence-based, diabetes-specific approaches for identifying patients with limited life expectancy are needed.

OBJECTIVE

Develop and validate an electronic health record (EHR)-based tool to identify older adults with diabetes who have limited life expectancy.

DESIGN

Predictive modeling based on survival analysis using Cox-Gompertz models in a retrospective cohort.

PARTICIPANTS

Adults with diabetes aged ≥ 65 years from Kaiser Permanente Northern California: a 2015 cohort (N = 121,396) with follow-up through 12/31/2019, randomly split into training (N = 97,085) and test (N = 24,311) sets. Validation was conducted in the test set and two temporally distinct cohorts: a 2010 cohort (n = 89,563; 10-year follow-up through 2019) and a 2019 cohort (n = 152,357; 2-year follow-up through 2020).

MAIN MEASURES

Demographics, diagnoses, utilization and procedures, medications, behaviors and vital signs; mortality.

KEY RESULTS

In the training set (mean age 75 years; 49% women; 48% racial and ethnic minorities), 23% died during 5 years follow-up. A mortality prediction model was developed using 94 candidate variables, distilled into a life expectancy model with 11 input variables, and transformed into a risk-scoring tool, the Life Expectancy Estimator for Older Adults with Diabetes (LEAD). LEAD discriminated well in the test set (C-statistic = 0.78), 2010 cohort (C-statistic = 0.74), and 2019 cohort (C-statistic = 0.81); comparisons of observed and predicted survival curves indicated good calibration.

CONCLUSIONS

LEAD estimates life expectancy in older adults with diabetes based on only 11 patient characteristics widely available in most EHRs and claims data. LEAD is simple and has potential application for shared decision-making, clinical trial inclusion, and resource allocation.

Reply to: Comment on: Data-driven classification of health status of older adults with diabetes: The diabetes and aging study

This letter comments on the letter by Christiaens

Use of Real-World Data in Population Science to Improve the Prevention and Care of Diabetes-Related Outcomes

The past decade of population research for diabetes has seen a dramatic proliferation of the use of real-world data (RWD) and real-world evidence (RWE) generation from non-research settings, including both health and non-health sources, to influence decisions related to optimal diabetes care. A common attribute of these new data is that they were not collected for research purposes yet have the potential to enrich the information around the characteristics of individuals, risk factors, interventions, and health effects. This has expanded the role of subdisciplines like comparative effectiveness research and precision medicine, new quasi-experimental study designs, new research platforms like distributed data networks, and new analytic approaches for clinical prediction of prognosis or treatment response. The result of these developments is a greater potential to progress diabetes treatment and prevention through the increasing range of populations, interventions, outcomes, and settings that can be efficiently examined. However, this proliferation also carries an increased threat of bias and misleading findings. The level of evidence that may be derived from RWD is ultimately a function of the data quality and the rigorous application of study design and analysis. This report reviews the current landscape and applications of RWD in clinical effectiveness and population health research for diabetes and summarizes opportunities and best practices in the conduct, reporting, and dissemination of RWD to optimize its value and limit its drawbacks.

Data-driven classification of health status of older adults with diabetes: The diabetes and aging study

BACKGROUND

We set out to identify empirically-derived health status classes of older adults with diabetes based on clusters of comorbid conditions which are associated with future complications.

METHODS

We conducted a cohort study among 105,786 older (≥65 years of age) adults with type 2 diabetes enrolled in an integrated healthcare delivery system. We used latent class analysis of 19 baseline comorbidities to derive health status classes and then compared incident complication rates (events per 100 person-years) by health status class during 5 years of follow-up. Complications included infections, hyperglycemic events, hypoglycemic events, microvascular events, cardiovascular events, and all-cause mortality.

RESULTS

Three health status classes were identified: Class 1 (58% of the cohort) had the lowest prevalence of most baseline comorbidities, Class 2 (22%) had the highest prevalence of obesity, arthritis, and depression, and Class 3 (20%) had the highest prevalence of cardiovascular conditions. The risk for incident complications was highest for Class 3, intermediate for Class 2 and lowest for Class 1. For example, the age, sex and race-adjusted rates for cardiovascular events (per 100 person-years) for Class 3, Class 2 and Class 1 were 6.5, 2.3, and 1.6, respectively; 2.1, 1.2, 0.7 for hypoglycemia; and 8.0, 3.8, and 2.3 for mortality.

CONCLUSIONS

Three health status classes of older adults with diabetes were identified based on prevalent comorbidities and were associated with marked differences in risk of complications. These health status classes can inform population health management and guide the individualization of diabetes care.

Glycemic Control Over Multiple Decades and Dementia Risk in People With Type 2 Diabetes

Importance

The levels of glycemic control associated with the lowest risk of dementia in people with type 2 diabetes are unknown. This knowledge is critical to inform patient-centered glycemic target setting.

Objective

To examine the associations between cumulative exposure to various ranges of glycated hemoglobin (HbA1c) concentrations with dementia risk across sex and racial and ethnic groups and the association of current therapeutic glycemic targets with dementia risk.

Design, Setting, and Participants

This cohort study included members of the Kaiser Permanente Northern California integrated health care system with type 2 diabetes who were aged 50 years or older during the study period from January 1, 1996, to September 30, 2015. Individuals with fewer than 2 HbA1c measurements during the study period, prevalent dementia at baseline, or less than 3 years of follow-up were excluded. Data were analyzed from February 2020 to January 2023.

Exposures

Time-updated cumulative exposure to HbA1c thresholds. At each HbA1c measurement, participants were categorized based on the percentage of their HbA1c measurements that fell into the following categories: less than 6%, 6% to less than 7%, 7% to less than 8%, 8% to less than 9%, 9% to less than 10%, and 10% or more of total hemoglobin (to convert percentage of total hemoglobin to proportion of total hemoglobin, multiply by 0.01).

Main Outcomes and Measures

Dementia diagnosis was identified using International Classification of Diseases, Ninth Revision codes from inpatient and outpatient encounters. Cox proportional hazards regression models estimated the association of time-varying cumulative glycemic exposure with dementia, adjusting for age, race and ethnicity, baseline health conditions, and number of HbA1c measurements.

Results

A total of 253 211 participants were included. The mean (SD) age of participants was 61.5 (9.4) years, and 53.1% were men. The mean (SD) duration of follow-up was 5.9 (4.5) years. Participants with more than 50% of HbA1c measurements at 9% to less than 10% or 10% or more had greater risk of dementia compared with those who had 50% or less of measurements in those categories (HbA1c 9% to <10%: adjusted hazard ratio [aHR], 1.31 [95% CI, 1.15-1.51]; HbA1c≥10%: aHR, 1.74 [95% CI, 1.62-1.86]). By contrast, participants with more than 50% of HbA1c concentrations less than 6%, 6% to less than 7%, or 7% to less than 8% had lower risk of dementia (HbA1c<6%: aHR, 0.92 [95% CI, 0.88-0.97]; HbA1c 6% to <7%: aHR, 0.79 [95% CI, 0.77-0.81]; HbA1c 7% to <8%: aHR, 0.93 [95% CI, 0.89-0.97]).

Conclusions and Relevance

In this study dementia risk was greatest among adults with cumulative HbA1c concentrations of 9% or more. These results support currently recommended relaxed glycemic targets for older people with type 2 diabetes.

Development and Validation of a Diabetic Retinopathy Risk Stratification Algorithm

OBJECTIVE

Although diabetic retinopathy is a leading cause of blindness worldwide, diabetes-related blindness can be prevented through effective screening, detection, and treatment of disease. The study goal was to develop risk stratification algorithms for the onset of retinal complications of diabetes, including proliferative diabetic retinopathy, referable retinopathy, and macular edema.

RESEARCH DESIGN AND METHODS

Retrospective cohort analysis of patients from the Kaiser Permanente Northern California Diabetes Registry who had no evidence of diabetic retinopathy at a baseline diabetic retinopathy screening during 2008-2020 was performed. Machine learning and logistic regression prediction models for onset of proliferative diabetic retinopathy, diabetic macular edema, and referable retinopathy detected through routine screening were trained and internally validated. Model performance was assessed using area under the curve (AUC) metrics.

RESULTS

The study cohort (N = 276,794) was 51.9% male and 42.1% White. Mean (±SD) age at baseline was 60.0 (±13.1) years. A machine learning XGBoost algorithm was effective in identifying patients who developed proliferative diabetic retinopathy (AUC 0.86; 95% CI, 0.86-0.87), diabetic macular edema (AUC 0.76; 95% CI, 0.75-0.77), and referable retinopathy (AUC 0.78; 95% CI, 0.78-0.79). Similar results were found using a simpler nine-covariate logistic regression model: proliferative diabetic retinopathy (AUC 0.82; 95% CI, 0.80-0.83), diabetic macular edema (AUC 0.73; 95% CI, 0.72-0.74), and referable retinopathy (AUC 0.75; 95% CI, 0.75-0.76).

CONCLUSIONS

Relatively simple logistic regression models using nine readily available clinical variables can be used to rank order patients for onset of diabetic eye disease and thereby more efficiently prioritize and target screening for at risk patients.

Racial and Ethnic Differences in Medication Initiation Among Adults Newly Diagnosed with Type 2 Diabetes

OBJECTIVE

Given persistent racial/ethnic differences in type 2 diabetes outcomes and the lasting benefits conferred by early glycemic control, we examined racial/ethnic differences in diabetes medication initiation during the year following diagnosis.

METHODS

Among adults newly diagnosed with type 2 diabetes (2005-2016), we examined how glucose-lowering medication initiation differed by race/ethnicity during the year following diagnosis. We specified modified Poisson regression models to estimate the association between race/ethnicity and medication initiation in the entire cohort and within subpopulations defined by HbA1c, BMI, age at diagnosis, comorbidity, and neighborhood deprivation index (a census tract-level socioeconomic indicator).

RESULTS

Among the 77,199 newly diagnosed individuals, 47% started a diabetes medication within 12 months of diagnosis. The prevalence of medication initiation ranged from 32% among Chinese individuals to 58% among individuals of Other/Unknown races/ethnicities. Compared to White individuals, medication initiation was less likely among Chinese (relative risk: 0.78 (95% confidence interval 0.72, 0.84)) and Japanese (0.82 (0.75, 0.90)) individuals, but was more likely among Hispanic/Latinx (1.27 (1.24, 1.30)), African American (1.14 (1.11, 1.17)), other Asian (1.13 (1.08, 1.18)), South Asian (1.10 (1.04, 1.17)), Other/Unknown (1.31 (1.24, 1.39)), American Indian or Alaska Native (1.11 (1.04, 1.18)), and Native Hawaiian/Pacific Islander (1.28 (1.19, 1.37)) individuals. Racial/ethnic differences dissipated among individuals with higher HbA1c values.

CONCLUSIONS

Initiation of glucose-lowering treatment during the year following type 2 diabetes diagnosis differed markedly by race/ethnicity, particularly for those with lower HbA1c values. Future research should examine how patient preferences, provider implicit bias, and shared decision-making contribute to these early treatment differences.

Validation of a Hypoglycemia Risk Stratification Tool Using Data From Continuous Glucose Monitors

This cohort study uses data from continuous glucose monitoring to validate a hypoglycemia risk stratification tool.

Continuous Glucose Monitor Use Prevents Glycemic Deterioration in Insulin-Treated Patients with Type 2 Diabetes

Continuous glucose monitoring (CGM) is indicated in poorly controlled insulin-treated patients with type 2 diabetes (T2D) to improve glycemic control and reduce the risk of hypoglycemia, but the benefits of CGM for lower risk patients have not been well studied. Among 17,422 insulin-treated patients with T2D with hemoglobin A1c (HbA1c) <8% and no recent severe hypoglycemia (based on emergency room visits or hospitalizations), CGM initiation occurred in 149 patients (17,273 noninitiators served as reference). Changes in HbA1c and severe hypoglycemia rates for the 12 months before and after CGM initiation were calculated. CGM initiation was associated with decreased HbA1c (-0.06%), whereas noninitiation was associated with increased HbA1c (+0.32%); a weighted adjusted difference-in-difference model of change in HbA1c yielded a net benefit of -0.30%; 95% CI -0.50%, -0.10%; P = 0.004). No significant differences were observed for severe hypoglycemia. CGM may be useful in preventing glycemic deterioration in well-controlled patients with insulin-treated T2D.

Association of Low-Density Lipoprotein Testing After an Atherosclerotic Cardiovascular Event with Subsequent Statin Adherence and Intensification

PURPOSE

This study aimed to evaluate associations between outpatient low-density lipoprotein cholesterol (LDL-C) testing and subsequent statin adherence and intensification in patients after an atherosclerotic cardiovascular (ASCVD) event.

METHODS

This was a longitudinal study of adult members of Kaiser Permanente Northern California hospitalized with an ASCVD event (myocardial infarction or stroke) during January 01, 2016, to December 31, 2017, with follow-up through December 31, 2019. Outcomes were statin adherence (estimated using continuous medication gap [CMG]) and intensification (defined by an increased dose or switch to a higher-intensity statin) based on pharmacy dispensing. The exposure of interest was first outpatient LDL-C test after an ASCVD event. Baseline for follow-up was LDL-C test date or a date assigned using incidence density sampling. Multivariate logistic regression models were specified to estimate the odds ratios for statin adherence or intensification among those with vs without an LDL-C test, with adjustment for age, sex, race/ethnicity, smoking, hypertension, diabetes, body mass index, and estimated glomerular filtration rate.

RESULTS

There were 19,604 adults hospitalized with ASCVD, including 7054 adults not on high-intensity statins. The mean age was 69.5 years and 33.0% were female. Prevalence of good adherence (continuous medication gap ≤20%) was significantly higher (80.2% vs 75.9%; odds ratio 1.38; 95% confidence interval, 1.28-1.49; P <.001) among participants who had an LDL-C test compared with participants who did not. LDL-C testing was associated with significantly higher rates of treatment intensification (16.1% vs 10.7%; odds ratio 1.51; 95% confidence interval,1.29-1.76; P <0.001).

CONCLUSIONS

Low-density lipoprotein cholesterol testing is recommended for patients with a history of ASCVD and may be a high-value and low-cost intervention to improve adherence and statin management.

Comparison of cognitive function in older adults with type 1 diabetes, type 2 diabetes, and no diabetes: results from the Study of Longevity in Diabetes (SOLID)

INTRODUCTION

The incidence of both type 1 diabetes (T1D) and type 2 diabetes (T2D) is increasing. Life expectancy is improving in T1D, resulting in a growing population of elderly adults with diabetes. While it is well established that older adults with T2D are at increased risk of cognitive impairment, little is known regarding cognitive aging in T1D and how their cognitive profiles may differ from T2D.

RESEARCH DESIGN AND METHODS

We compared baseline cognitive function and low cognitive function by diabetes status (n=734 T1D, n=232 T2D, n=247 without diabetes) among individuals from the Study of Longevity in Diabetes (mean age=68). We used factor analysis to group cognition into five domains and a composite measure of total cognition. Using linear and logistic regression models, we examined the associations between diabetes type and cognitive function, adjusting for demographics, comorbidities, depression, and sleep quality.

RESULTS

T1D was associated with lower scores on total cognition, language, executive function/psychomotor processing speed, and verbal episodic memory, and greater odds of low executive function/psychomotor processing speed (OR=2.99, 95% CI 1.66 to 5.37) and verbal episodic memory (OR=1.92, 95% CI 1.07 to 3.46), compared with those without diabetes. T2D was associated with lower scores on visual episodic memory. Compared with T2D, T1D was associated with lower scores on verbal episodic memory and executive function/psychomotor processing speed and greater odds of low executive function/psychomotor processing speed (OR=1.74, 95% CI 1.03 to 2.92).

CONCLUSIONS

Older adults with T1D had significantly poorer cognition compared with those with T2D and those without diabetes even after accounting for a range of comorbidities. Future studies should delineate how to reduce risk in this vulnerable population who are newly surviving to old age.

Precision communication: Physicians' linguistic adaptation to patients' health literacy

Little quantitative research has explored which clinician skills and behaviors facilitate communication. Mutual understanding is especially challenging when patients have limited health literacy (HL). Two strategies hypothesized to improve communication include matching the complexity of language to patients’ HL (“universal tailoring”); or always using simple language (“universal precautions”). Through computational linguistic analysis of 237,126 email exchanges between dyads of 1094 physicians and 4331 English-speaking patients, we assessed matching (concordance/discordance) between physicians’ linguistic complexity and patients’ HL, and classified physicians’ communication strategies. Among low HL patients, discordance was associated with poor understanding (P = 0.046). Physicians’ “universal tailoring” strategy was associated with better understanding for all patients (P = 0.01), while “universal precautions” was not. There was an interaction between concordance and communication strategy (P = 0.021): The combination of dyadic concordance and “universal tailoring” eliminated HL-related disparities. Physicians’ ability to adapt communication to match their patients’ HL promotes shared understanding and equity. The ‘Precision Medicine’ construct should be expanded to include the domain of ‘Precision Communication.’

Association of Type 1 Diabetes and Hypoglycemic and Hyperglycemic Events and Risk of Dementia

OBJECTIVE

To determine whether severe hypoglycemic and hyperglycemic events are associated with longitudinal dementia risk in older adults with type 1 diabetes.

METHODS

A longitudinal cohort study followed up 2,821 members of an integrated health care delivery system with type 1 diabetes from 1997 to 2015. Hypoglycemic and hyperglycemic events requiring emergency room or hospitalization were abstracted from medical records beginning January 1, 1996, through cohort entry. Participants were followed up for dementia diagnosis through September 30, 2015. Dementia risk was examined with Cox proportional hazard models adjusted for age (as time scale), sex, race/ethnicity, hemoglobin A1c, depression, stroke, and nephropathy.

RESULTS

Among 2,821 older adults (mean age 56 years) with type 1 diabetes, 398 (14%) had a history of severe hypoglycemia, 335 (12%) had severe hyperglycemia, and 87 (3%) had both. Over a mean 6.9 years of follow-up, 153 individuals (5.4%) developed dementia. In fully adjusted models, individuals with hypoglycemic events had 66% greater risk of dementia than those without a hypoglycemic event (hazard ratio [HR] 1.66, 95% confidence interval [CI] 1.09, 2.53), while those with hyperglycemic events had >2 times the risk (HR 2.11, 95% CI 1.24, 3.59) than those without a hyperglycemic event. There was a 6-fold greater risk of dementia in individuals with both severe hypoglycemia and hyperglycemia vs those with neither (HR 6.20, 95% CI 3.02, 12.70).

CONCLUSIONS

For older individuals with type 1 diabetes, severe hypoglycemic and hyperglycemic events are associated with increased future risk of dementia.

Developing and Testing Automatic Models of Patient Communicative Health Literacy Using Linguistic Features: Findings from the ECLIPPSE study

Patients with diabetes and limited health literacy (HL) may have suboptimal communication exchange with their health care providers and be at elevated risk of adverse health outcomes. These difficulties are generally attributed to patients' reduced ability to both communicate and understand health-related ideas as well as physicians' lack of skill in identifying those with limited HL. Understanding and identifying patients with barriers posed by lower HL to improve healthcare delivery and outcomes is an important research avenue. However, doing so using traditional methods has proven difficult and infeasible to scale. This study using corpus analyses, expert human ratings of HL, and natural language processing (NLP) approaches to estimate HL at the individual patient level. The goal of the study is to better understand HL from a linguistic perspective and to open new research areas to enhance population management and individualized care. Specifically, this study examines HL as a function of patients' demonstrated ability to communicate health-related information to their providers via secure messages. The study develops an NLP-based HL model and validates the model by predicting patient-related events such as medical outcomes and hospitalizations. Results indicate that the developed model predicts human ratings of HL with ~80% accuracy. Validation indicates that lower HL patients are more likely to be nonwhite and have lower educational attainment. In addition, patients with lower HL suffered more negative health outcomes and had higher healthcare service utilization.

Association of Real-time Continuous Glucose Monitoring With Glycemic Control and Acute Metabolic Events Among Patients With Insulin-Treated Diabetes

IMPORTANCE

Continuous glucose monitoring (CGM) is recommended for patients with type 1 diabetes; observational evidence for CGM in patients with insulin-treated type 2 diabetes is lacking.

OBJECTIVE

To estimate clinical outcomes of real-time CGM initiation.

DESIGN, SETTING, AND PARTICIPANTS

Exploratory retrospective cohort study of changes in outcomes associated with real-time CGM initiation, estimated using a difference-in-differences analysis. A total of 41 753 participants with insulin-treated diabetes (5673 type 1; 36 080 type 2) receiving care from a Northern California integrated health care delivery system (2014-2019), being treated with insulin, self-monitoring their blood glucose levels, and having no prior CGM use were included.

EXPOSURES

Initiation vs noninitiation of real-time CGM (reference group).

MAIN OUTCOMES AND MEASURES

Ten end points measured during the 12 months before and 12 months after baseline: hemoglobin A1c (HbA1c); hypoglycemia (emergency department or hospital utilization); hyperglycemia (emergency department or hospital utilization); HbA1c levels lower than 7%, lower than 8%, and higher than 9%; 1 emergency department encounter or more for any reason; 1 hospitalization or more for any reason; and number of outpatient visits and telephone visits.

RESULTS

The real-time CGM initiators included 3806 patients (mean age, 42.4 years [SD, 19.9 years]; 51% female; 91% type 1, 9% type 2); the noninitiators included 37 947 patients (mean age, 63.4 years [SD, 13.4 years]; 49% female; 6% type 1, 94% type 2). The prebaseline mean HbA1c was lower among real-time CGM initiators than among noninitiators, but real-time CGM initiators had higher prebaseline rates of hypoglycemia and hyperglycemia. Mean HbA1c declined among real-time CGM initiators from 8.17% to 7.76% and from 8.28% to 8.19% among noninitiators (adjusted difference-in-differences estimate, -0.40%; 95% CI, -0.48% to -0.32%; P < .001). Hypoglycemia rates declined among real-time CGM initiators from 5.1% to 3.0% and increased among noninitiators from 1.9% to 2.3% (difference-in-differences estimate, -2.7%; 95% CI, -4.4% to -1.1%; P = .001). There were also statistically significant differences in the adjusted net changes in the proportion of patients with HbA1c lower than 7% (adjusted difference-in-differences estimate, 9.6%; 95% CI, 7.1% to 12.2%; P < .001), lower than 8% (adjusted difference-in-differences estimate, 13.1%; 95% CI, 10.2% to 16.1%; P < .001), and higher than 9% (adjusted difference-in-differences estimate, -7.1%; 95% CI, -9.5% to -4.6%; P < .001) and in the number of outpatient visits (adjusted difference-in-differences estimate, -0.4; 95% CI, -0.6 to -0.2; P < .001) and telephone visits (adjusted difference-in-differences estimate, 1.1; 95% CI, 0.8 to 1.4; P < .001). Initiation of real-time CGM was not associated with statistically significant changes in rates of hyperglycemia, emergency department visits for any reason, or hospitalizations for any reason.

CONCLUSIONS AND RELEVANCE

In this retrospective cohort study, insulin-treated patients with diabetes selected by physicians for real-time continuous glucose monitoring compared with noninitiators had significant improvements in hemoglobin A1c and reductions in emergency department visits and hospitalizations for hypoglycemia, but no significant change in emergency department visits or hospitalizations for hyperglycemia or for any reason. Because of the observational study design, findings may have been susceptible to selection bias.

Employing computational linguistics techniques to identify limited patient health literacy: Findings from the ECLIPPSE study

OBJECTIVE

To develop novel, scalable, and valid literacy profiles for identifying limited health literacy patients by harnessing natural language processing.

DATA SOURCE

With respect to the linguistic content, we analyzed 283 216 secure messages sent by 6941 diabetes patients to physicians within an integrated system's electronic portal. Sociodemographic, clinical, and utilization data were obtained via questionnaire and electronic health records.

STUDY DESIGN

Retrospective study used natural language processing and machine learning to generate five unique "Literacy Profiles" by employing various sets of linguistic indices: Flesch-Kincaid (LP_FK); basic indices of writing complexity, including lexical diversity (LP_LD) and writing quality (LP_WQ); and advanced indices related to syntactic complexity, lexical sophistication, and diversity, modeled from self-reported (LP_SR), and expert-rated (LP_Exp) health literacy. We first determined the performance of each literacy profile relative to self-reported and expert-rated health literacy to discriminate between high and low health literacy and then assessed Literacy Profiles' relationships with known correlates of health literacy, such as patient sociodemographics and a range of health-related outcomes, including ratings of physician communication, medication adherence, diabetes control, comorbidities, and utilization.

PRINCIPAL FINDINGS

LP_SR and LP_Exp performed best in discriminating between high and low self-reported (C-statistics: 0.86 and 0.58, respectively) and expert-rated health literacy (C-statistics: 0.71 and 0.87, respectively) and were significantly associated with educational attainment, race/ethnicity, Consumer Assessment of Provider and Systems (CAHPS) scores, adherence, glycemia, comorbidities, and emergency department visits.

CONCLUSIONS

Since health literacy is a potentially remediable explanatory factor in health care disparities, the development of automated health literacy indicators represents a significant accomplishment with broad clinical and population health applications. Health systems could apply literacy profiles to efficiently determine whether quality of care and outcomes vary by patient health literacy; identify at-risk populations for targeting tailored health communications and self-management support interventions; and inform clinicians to promote improvements in individual-level care.

Challenges and solutions to employing natural language processing and machine learning to measure patients' health literacy and physician writing complexity: The ECLIPPSE study

OBJECTIVE

In the National Library of Medicine funded ECLIPPSE Project (Employing Computational Linguistics to Improve Patient-Provider Secure Emails exchange), we attempted to create novel, valid, and scalable measures of both patients' health literacy (HL) and physicians' linguistic complexity by employing natural language processing (NLP) techniques and machine learning (ML). We applied these techniques to > 400,000 patients' and physicians' secure messages (SMs) exchanged via an electronic patient portal, developing and validating an automated patient literacy profile (LP) and physician complexity profile (CP). Herein, we describe the challenges faced and the solutions implemented during this innovative endeavor.

MATERIALS AND METHODS

To describe challenges and solutions, we used two data sources: study documents and interviews with study investigators. Over the five years of the project, the team tracked their research process using a combination of Google Docs tools and an online team organization, tracking, and management tool (Asana). In year 5, the team convened a number of times to discuss, categorize, and code primary challenges and solutions.

RESULTS

We identified 23 challenges and associated approaches that emerged from three overarching process domains: (1) Data Mining related to the SM corpus; (2) Analyses using NLP indices on the SM corpus; and (3) Interdisciplinary Collaboration. With respect to Data Mining, problems included cleaning SMs to enable analyses, removing hidden caregiver proxies (e.g., other family members) and Spanish language SMs, and culling SMs to ensure that only patients' primary care physicians were included. With respect to Analyses, critical decisions needed to be made as to which computational linguistic indices and ML approaches should be selected; how to enable the NLP-based linguistic indices tools to run smoothly and to extract meaningful data from a large corpus of medical text; and how to best assess content and predictive validities of both the LP and the CP. With respect to the Interdisciplinary Collaboration, because the research required engagement between clinicians, health services researchers, biomedical informaticians, linguists, and cognitive scientists, continual effort was needed to identify and reconcile differences in scientific terminologies and resolve confusion; arrive at common understanding of tasks that needed to be completed and priorities therein; reach compromises regarding what represents "meaningful findings" in health services vs. cognitive science research; and address constraints regarding potential transportability of the final LP and CP to different health care settings.

DISCUSSION

Our study represents a process evaluation of an innovative research initiative to harness "big linguistic data" to estimate patient HL and physician linguistic complexity. Any of the challenges we identified, if left unaddressed, would have either rendered impossible the effort to generate LPs and CPs, or invalidated analytic results related to the LPs and CPs. Investigators undertaking similar research in HL or using computational linguistic methods to assess patient-clinician exchange will face similar challenges and may find our solutions helpful when designing and executing their health communications research.

A Randomized Encouragement Trial to Increase Mail Order Pharmacy Use and Medication Adherence in Patients with Diabetes

BACKGROUND

Mail order pharmacy (MOP) use has been linked to improved medication adherence and health outcomes among patients with diabetes. However, no large-scale intervention studies have assessed the effect of encouraging MOP use on medication adherence.

OBJECTIVE

To assess an intervention to encourage MOP services to increase its use and medication adherence.

DESIGN

Randomized encouragement trial.

PATIENTS

63,012 diabetes patients from three health care systems: Kaiser Permanente Northern California (KPNC), Kaiser Permanente Hawaii (KPHI), and Harvard Pilgrim Health Care (HPHC) who were poorly adherent to at least one class of cardiometabolic medications and had not used MOP in the prior 12 months.

INTERVENTION

Patients were randomized to receive either usual care (control arm) or outreach encouraging MOP use consisting of a mailed letter, secure email message, and automated telephone call outlining the potential benefits of MOP use (intervention arm). HPHC intervention patients received the letter only.

MEASUREMENTS

We compared the percentages of patients that began using MOP and that became adherent to cardiometabolic medication classes during a 12-month follow-up period. We also conducted a race/ethnicity-stratified analysis.

RESULTS

During follow-up, 10.6% of intervention patients began using MOP vs. 9.3% of controls (p < 0.01); the percent of cardiometabolic medication delivered via mail was 42.1% vs. 39.8% (p < 0.01). Metformin adherence improved in the intervention arm relative to control at the two KP sites (52% vs. 49%, p < 0.01). Stratified analyses suggested a significant positive effect of the intervention in White (RR: 1.12, 95% CI: 1.03, 1.22) and Asian (RR: 1.30, 95% CI: 1.17, 1.45) patients.

CONCLUSION

This pragmatic trial showed that simple outreach to encourage MOP modestly increased its use and improved adherence measured by refills to a key class of diabetes medications in some settings. Given its minimal cost, clinicians and health systems should consider outreach interventions to actively promote MOP use among diabetes patients.

TRIAL REGISTRATION

ClinicalTrials.gov registration number: NCT02621476.

Descriptive examination of secure messaging in a longitudinal cohort of diabetes patients in the ECLIPPSE study

The substantial expansion of secure messaging (SM) via the patient portal in the last decade suggests that it is becoming a standard of care, but few have examined SM use longitudinally. We examined SM patterns among a diverse cohort of patients with diabetes (N = 19 921) and the providers they exchanged messages with within a large, integrated health system over 10 years (2006-2015), linking patient demographics to SM use. We found a 10-fold increase in messaging volume. There were dramatic increases overall and for patient subgroups, with a majority of patients (including patients with lower income or with self-reported limited health literacy) messaging by 2015. Although more physicians than nurses and other providers messaged throughout the study, the distribution of health professions using SM changed over time. Given this rapid increase in SM, deeper understanding of optimizing the value of patient and provider engagement, while managing workflow and training challenges, is crucial.

Predicting the readability of physicians' secure messages to improve health communication using novel linguistic features: Findings from the ECLIPPSE study

Background

Low literacy skills impact important aspects of communication, including health-related information exchanges. Unsuccessful communication on the part of physician or patient contributes to lower quality of care, is associated with poorer chronic disease control, jeopardizes patient safety and can lead to unfavorable healthcare utilization patterns. To date, very little research has focused on digital communication between physicians and patients, such as secure messages sent via electronic patient portals.

Method

The purpose of the current study is to develop an automated readability formula to better understand what elements of physicians' digital messages make them more or less difficult to understand. The formula is developed using advanced natural language processing (NLP) to predict human ratings of physician text difficulty.

Results

The results indicate that NLP indices that capture a diverse set of linguistic features predict the difficulty of physician messages better than classic readability tools such as Flesch Kincaid Grade Level. Our results also provide information about the textual features that best explain text readability.

Conclusion

Implications for how the readability formula could provide feedback to physicians to improve digital health communication by promoting linguistic concordance between physician and patient are discussed.

Recurrent diabetic ketoacidosis and cognitive function among older adults with type 1 diabetes: findings from the Study of Longevity in Diabetes

INTRODUCTION

Diabetic ketoacidosis (DKA) is a serious complication of diabetes. DKA is associated with poorer cognition in children with type 1 diabetes (T1D), but whether this is the case in older adults with T1D is unknown. Given the increasing life expectancy in T1D, understanding the role of DKA on brain health in older adults is crucial.

RESEARCH DESIGN AND METHODS

We examined the association of DKA with cognitive function in 714 older adults with T1D from the Study of Longevity in Diabetes. Participants self-reported lifetime exposure to DKA resulting in hospitalization; DKA was categorized into 0 hospitalization, 1 hospitalization or ≥2 hospitalizations (recurrent DKA). Global and domain-specific cognition (language, executive function/psychomotor speed, episodic memory and simple attention) were assessed. The association of DKA with cognitive function was evaluated via linear and logistic regression models.

RESULTS

Twenty-eight percent of participants (mean age=67 years; mean age at diagnosis=28 years; average duration of diabetes=39 years) reported a lifetime history of DKA resulting in hospitalization (18.5% single DKA; 9.7% recurrent DKA). In fully adjusted models, those with recurrent DKA had lower global cognitive function (β=-0.13; 95% CI -0.22 to 0.02) and lower scores on the executive function/psychomotor speed domain (β=-0.34; 95% CI -0.51 to 0.17). Individuals with recurrent DKA were also more likely to have the lowest level of cognitive function on the executive function/psychomotor speed domain (defined as 1.5 SD below the population mean; OR=3.26, 95% CI 1.43 to 7.42).

CONCLUSIONS

Among 714 older adults with T1D, recurrent DKA was associated with lower global cognitive function, lower scores on the executive function/psychomotor speed domain and 3.3 times greater risk of having the lowest level of cognitive function in our sample on the executive function/psychomotor speed domain. These findings suggest that recurrent DKA may negatively impact the brain health of older patients with T1D and highlight the importance of DKA prevention.

Initial Glycemic Control and Care Among Younger Adults Diagnosed With Type 2 Diabetes

OBJECTIVE

The prevalence of type 2 diabetes is increasing among adults under age 45. Onset of type 2 diabetes at a younger age increases an individual's risk for diabetes-related complications. Given the lasting benefits conferred by early glycemic control, we compared glycemic control and initial care between adults with younger onset (21-44 years) and mid-age onset (45-64 years) of type 2 diabetes.

RESEARCH DESIGN AND METHODS

Using data from a large, integrated health care system, we identified 32,137 adults (aged 21-64 years) with incident diabetes (first HbA_1c ≥6.5% [≥48 mmol/mol]). We excluded anyone with evidence of prior type 2 diabetes, gestational diabetes mellitus, or type 1 diabetes. We used generalized linear mixed models, adjusting for demographic and clinical variables, to examine differences in glycemic control and care at 1 year.

RESULTS

Of identified individuals, 26.4% had younger-onset and 73.6% had mid-age-onset type 2 diabetes. Adults with younger onset had higher initial mean HbA_1c values (8.9% [74 mmol/mol]) than adults with onset in mid-age (8.4% [68 mmol/mol]) (P < 0.0001) and lower odds of achieving an HbA_1c <7% (<53 mmol/mol) 1 year after the diagnosis (adjusted odds ratio [aOR] 0.70 [95% CI 0.66-0.74]), even after accounting for HbA_1c at diagnosis. Adults with younger onset had lower odds of in-person primary care contact (aOR 0.82 [95% CI 0.76-0.89]) than those with onset during mid-age, but they did not differ in telephone contact (1.05 [0.99-1.10]). Adults with younger onset had higher odds of starting metformin (aOR 1.20 [95% CI 1.12-1.29]) but lower odds of adhering to that medication (0.74 [0.69-0.80]).

CONCLUSIONS

Adults with onset of type 2 diabetes at a younger age were less likely to achieve glycemic control at 1 year following diagnosis, suggesting the need for tailored care approaches to improve outcomes for this high-risk patient population.

Severe Hypoglycemia and Cognitive Function in Older Adults With Type 1 Diabetes: The Study of Longevity in Diabetes (SOLID)

OBJECTIVE

In children with type 1 diabetes (T1D), severe hypoglycemia (SH) is associated with poorer cognition, but the association of SH with cognitive function in late life is unknown. Given the increasing life expectancy in people with T1D, understanding the role of SH in brain health is crucial.

RESEARCH DESIGN AND METHODS

We examined the association between SH and cognitive function in 718 older adults with T1D from the Study of Longevity in Diabetes (SOLID). Subjects self-reported recent SH (previous 12 months) and lifetime history of SH resulting in inpatient/emergency department utilization. Global and domain-specific cognition (language, executive function, episodic memory, and simple attention) were assessed. The associations of SH with cognitive function and impaired cognition were evaluated via linear and logistic regression models, respectively.

RESULTS

Thirty-two percent of participants (mean age 67.2 years) reported recent SH and 50% reported lifetime SH. Compared with those with no SH, subjects with a recent SH history had significantly lower global cognition scores. Domain-specific analyses revealed significantly lower scores on language, executive function, and episodic memory with recent SH exposure and significantly lower executive function with lifetime SH exposure. Recent SH was associated with impaired global cognition (odds ratio [OR] 3.22, 95% CI 1.30, 7.94) and cognitive impairment on the language domain (OR 3.15, 95% CI 1.19, 8.29).

CONCLUSIONS

Among older adults with T1D, recent SH and lifetime SH were associated with worse cognition. Recent SH was associated with impaired global cognition. These findings suggest a deleterious role of SH on the brain health of older patients with T1D and highlight the importance of SH prevention.

Risk of Cardiovascular Events in Patients With Type 2 Diabetes and Metabolic Dyslipidemia Without Prevalent Atherosclerotic Cardiovascular Disease

BACKGROUND

The relationship between achieved low-density lipoprotein cholesterol (LDL-C) levels and risk of incident atherosclerotic cardiovascular disease events among patients with diabetes and metabolic dyslipidemia has not been well described.

METHODS

We conducted an observational cohort study of statin-treated adults (ages 21-90 years) with type 2 diabetes without established atherosclerotic cardiovascular disease (as of January 1, 2006) who had metabolic dyslipidemia (elevated triglycerides ≥150 mg/dL and low high-density lipoprotein cholesterol, <50 mg/dL [women] and <40 mg/dL [men]). All subjects were members of Kaiser Permanente Northern California, an integrated health care delivery system. Adjusted multivariable Cox models were specified to estimate hazard ratios (HRs) for incident atherosclerotic cardiovascular disease events by achieved LDL-C levels (<50, 50-<70, 70-<100, and ≥100 mg/dL). Incident atherosclerotic cardiovascular disease events were defined as a composite of nonfatal myocardial infarction, ischemic stroke, or coronary heart disease death through December 31, 2013.

RESULTS

A total of 19,095 individuals met the selection criteria. Mean age was 63.4 years, 53.5% were women, and the mean follow-up was 5.9 years. Unadjusted rates of atherosclerotic cardiovascular disease events were not significantly different across specified LDL-C categories. In models adjusted for demographics and clinical characteristics, the risk was significantly lower with decreasing achieved LDL-C levels (P <0.0001 for trend). Relative to achieved LDL-C ≥100 mg/dL, LDL-C <50 mg/dL had an hazard ratio of 0.66 (95% confidence interval [CI] 0.52-0.82).

CONCLUSION

In a large, contemporary cohort of statin-treated patients with type 2 diabetes and metabolic dyslipidemia without established atherosclerotic cardiovascular disease, lower achieved LDL-C levels were associated with a monotonically lower risk of incident atherosclerotic cardiovascular disease events. The benefits of achieving very-low LDL-C (<50 mg/dL) in this population requires further evaluation in prospective interventional studies.

Use and Discontinuation of Insulin Treatment Among Adults Aged 75 to 79 Years With Type 2 Diabetes

IMPORTANCE

Among older individuals with type 2 diabetes, those with poor health have greater risk and derive less benefit from tight glycemic control with insulin.

OBJECTIVE

To examine whether insulin treatment is used less frequently and discontinued more often among older individuals with poor health compared with those in good health.

DESIGN, SETTING, AND PARTICIPANTS

This longitudinal cohort study included 21 531 individuals with type 2 diabetes followed for up to 4 years starting at age 75 years. Electronic health record data from the Kaiser Permanente Northern California Diabetes Registry was collected to characterize insulin treatment and glycemic control over time. Data were collected from January 1, 2009, through December 31, 2017, and analyzed from February 2, 2018, through June 30, 2019.

EXPOSURES

Health status was defined as good (<2 comorbid conditions or 2 comorbidities but physically active), intermediate (>2 comorbidities or 2 comorbidities and no self-reported weekly exercise), or poor (having end-stage pulmonary, cardiac, or renal disease; diagnosis of dementia; or metastatic cancer).

MAIN OUTCOMES AND MEASURES

Insulin use prevalence at age 75 years and discontinuation among insulin users over the next 4 years (or 6 months prior to death if <4 years).

RESULTS

Of 21 531 patients, 10 396 (48.3%) were women, and the mean (SD) age was 75 (0) years. Nearly one-fifth of 75-year-olds (4076 [18.9%]) used insulin. Prevalence and adjusted risk ratios (aRRs) of insulin use at age 75 years were higher in individuals with poor health (29.4%; aRR, 2.03; 95% CI, 1.87-2.20; P < .01) and intermediate health (27.5%; aRR, 1.85; 95% CI, 1.74-1.97; P < .01) relative to good health (10.5% [reference]). One-third (1335 of 4076 [32.7%]) of insulin users at age 75 years discontinued insulin within 4 years of cohort entry (and at least 6 months prior to death). Likelihood of continued insulin use was higher among individuals in poor health (aRR, 1.47; 95% CI, 1.27-1.67; P < .01) and intermediate health (aRR, 1.16; 95% CI, 1.05-1.30; P < .01) compared with good health (reference). These same prevalence and discontinuation patterns were present in the subset with tight glycemic control (hemoglobin A1c <7.0%).

CONCLUSIONS AND RELEVANCE

In older individuals with type 2 diabetes, insulin use was most prevalent among those in poor health, whereas subsequent insulin discontinuation after age 75 years was most likely in healthier patients. Changes are needed in current practice to better align with guidelines that recommend reducing treatment intensity as health status declines.

Depression in type 1 diabetes and risk of dementia

OBJECTIVE

Depression afflicts 14% of individuals with type 1 diabetes (T1D). Depression is a robust risk factor for dementia but it is unknown if this holds true for individuals with T1D, who recently started living to an age conferring dementia risk. We examined if depression is a dementia risk factor among elderly individuals with T1D.

METHODS

3,742 individuals with T1D age ≥50 were followed for dementia from 1/1/96-9/30/2015. Depression, dementia, and comorbidities were abstracted from electronic medical records. Cox proportional hazard models estimated the association between depression and dementia adjusting for demographics, glycosylated hemoglobin, severe dysglycemic epidsodes, stroke, heart disease, nephropathy, and end stage renal disease. The cumulative incidence of dementia by depression was estimated conditional on survival dementia-free to age 55.

RESULTS

Five percent (N = 182) were diagnosed with dementia and 20% had baseline depression. Depression was associated with a 72% increase in dementia (fully adjusted HR = 1.72; 95% CI:1.12-2.65). The 25-year cumulative incidence of dementia was more than double for those with versus without depression (27% vs. 12%).

CONCLUSIONS

For people with T1D, depression significantly increases dementia risk. Given the pervasiveness of depression in T1D, this has major implications for successful aging in this population recently living to old age.

Opportunities to encourage mail order pharmacy delivery service use for diabetes prescriptions: a qualitative study

Background

Medication non-adherence is a major contributor to poor outcomes in diabetes. Previous research has shown an association between use of mail order pharmacy delivery and better medication adherence, but little is known about the barriers and facilitators to mail order pharmacy use in diabetes patients. This qualitative study examined factors related to mail order pharmacy use versus traditional “brick and mortar” pharmacies to refill prescriptions.

Methods

We conducted four 90-min focus groups in 2016 among 28 diabetes patients in the Hawaii and Northern California regions of Kaiser Permanente, a large integrated health care delivery system. We queried participants on their preferred mode for refilling prescriptions and perceived barriers and facilitators of mail order pharmacy use. One researcher independently coded each focus group transcript, with two of these transcripts double-coded by a second researcher to promote reliability. We employed thematic analysis guided by the Capability, Opportunity, Motivation, and Behavior (COM-B) framework using NVivo 11 software.

Results

A total of 28 diabetes patients participated. Participants’ average age was 64.1 years; 57% were female; and racial/ethnic backgrounds included Asian/Native Hawaiian/Pacific Islander (36%), Black/African-American (21%) Hispanic/Latino (7%), and non-Hispanic White (36%). Analysis uncovered 26 themes related to the decision to use mail order pharmacy, with each theme representing a barrier or facilitator mapped to the COM-B framework. Most themes (20/26) fell into the COM-B category of ‘Opportunity.’ Opportunity barriers to mail order pharmacy use included unpredictability of medication delivery date, concerns about mail security, and difficulty coordinating refill orders for multiple prescriptions. In contrast, facilitators included greater access and convenience (e.g., no need to wait in line or arrange transportation) compared to traditional pharmacies. Motivational facilitators to mail order pharmacy use included receiving a pharmacy benefit plan incentive of a free one-month supply of prescriptions.

Conclusions

This study found that while patients with diabetes may benefit from mail order pharmacy use, they perceive numerous barriers to using the service. These findings will inform the design of interventions and quality improvement initiatives to increase mail order pharmacy use, which in turn may improve medication adherence and outcomes in diabetes patients, across health care systems.

Electronic supplementary material

The online version of this article (10.1186/s12913-019-4250-7) contains supplementary material, which is available to authorized users.

The Legacy Effect in Type 2 Diabetes: Impact of Early Glycemic Control on Future Complications (The Diabetes & Aging Study)

OBJECTIVE

To examine for a legacy effect of early glycemic control on diabetic complications and death.

RESEARCH DESIGN AND METHODS

This cohort study of managed care patients with newly diagnosed type 2 diabetes and 10 years of survival (1997-2013, average follow-up 13.0 years, N = 34,737) examined associations between HbA1c <6.5% (<48 mmol/mol), 6.5% to <7.0% (48 to <53 mmol/mol), 7.0% to <8.0% (53 to <64 mmol/mol), 8.0% to <9.0% (64 to <75 mmol/mol), or ≥9.0% (≥75 mmol/mol) for various periods of early exposure (0-1, 0-2, 0-3, 0-4, 0-5, 0-6, and 0-7 years) and incident future microvascular (end-stage renal disease, advanced eye disease, amputation) and macrovascular (stroke, heart disease/failure, vascular disease) events and death, adjusting for demographics, risk factors, comorbidities, and later HbA1c.

RESULTS

Compared with HbA1c <6.5% (<48 mmol/mol) for the 0-to-1-year early exposure period, HbA1c levels ≥6.5% (≥48 mmol/mol) were associated with increased microvascular and macrovascular events (e.g., HbA1c 6.5% to <7.0% [48 to <53 mmol/mol] microvascular: hazard ratio 1.204 [95% CI 1.063-1.365]), and HbA1c levels ≥7.0% (≥53 mmol/mol) were associated with increased mortality (e.g., HbA1c 7.0% to <8.0% [53 to <64 mmol/mol]: 1.290 [1.104-1.507]). Longer periods of exposure to HbA1c levels ≥8.0% (≥64 mmol/mol) were associated with increasing microvascular event and mortality risk.

CONCLUSIONS

Among patients with newly diagnosed diabetes and 10 years of survival, HbA1c levels ≥6.5% (≥48 mmol/mol) for the 1st year after diagnosis were associated with worse outcomes. Immediate, intensive treatment for newly diagnosed patients may be necessary to avoid irremediable long-term risk for diabetic complications and mortality.

Using natural language processing and machine learning to classify health literacy from secure messages: The ECLIPPSE study

Limited health literacy is a barrier to optimal healthcare delivery and outcomes. Current measures requiring patients to self-report limitations are time-consuming and may be considered intrusive by some. This makes widespread classification of patient health literacy challenging. The objective of this study was to develop and validate "literacy profiles" as automated indicators of patients' health literacy to facilitate a non-intrusive, economic and more comprehensive characterization of health literacy among a health care delivery system's membership. To this end, three literacy profiles were generated based on natural language processing (combining computational linguistics and machine learning) using a sample of 283,216 secure messages sent from 6,941 patients to their primary care physicians. All patients were participants in Kaiser Permanente Northern California's DISTANCE Study. Performance of the three literacy profiles were compared against a gold standard of patient self-reported health literacy. Associations were analyzed between each literacy profile and patient demographics, health outcomes and healthcare utilization. T-tests were used for numeric data such as A1C, Charlson comorbidity index and healthcare utilization rates, and chi-square tests for categorical data such as sex, race, poor adherence and severe hypoglycemia. Literacy profiles varied in their test characteristics, with C-statistics ranging from 0.61-0.74. Relations between literacy profiles and health outcomes revealed patterns consistent with previous health literacy research: patients identified via literacy profiles indicative of limited health literacy: (a) were older and more likely of minority status; (b) had poorer medication adherence and glycemic control; and (c) exhibited higher rates of hypoglycemia, comorbidities and healthcare utilization. This represents the first successful attempt to employ natural language processing to estimate health literacy. Literacy profiles can offer an automated and economical way to identify patients with limited health literacy and greater vulnerability to poor health outcomes.

Visual outcomes after cataract surgery in patients with type 2 diabetes

PURPOSE

To assess the relation between diabetic retinopathy (DR) severity, duration of diabetes, insulin dependence, and preoperative hemoglobin A_1c (HbA_1c) with visual outcome after phacoemulsification for cataract in patients with type 2 diabetes.

SETTING

Kaiser Permanente Northern California, USA.

DESIGN

Retrospective case series.

METHODS

Information was obtained from the electronic medical record for patients, June 1, 2010, through May 31, 2015. Confounding factors and clustering of eyes within patients were controlled for using linear mixed-effects regression models for continuous outcomes and general estimating equations for dichotomous outcomes.

RESULTS

The study included 65 370 patients; 28% had type 2 diabetes without DR, 5% nonproliferative DR, and 1.2% proliferative DR. Patients with diabetes and no DR were as likely as those without diabetes to achieve a corrected distance visual acuity (CDVA) of 20/20 (odds ratio, 1.01; 95% confidence interval, 0.94-1.10). The odds of a postoperative CDVA of 20/25 or worse increased with the severity of retinopathy duration of diabetes and insulin dependence, but not with the preoperative HbA_1c. Although the odds of a postoperative CDVA of 20/20 was lower in patients with DR, every DR group averaged 4 lines of CDVA improvement, the same as patients without diabetes. A longer duration of diabetes, insulin dependence, and elevated HbA_1c were not associated with worse postoperative outcomes.

CONCLUSION

Patients with DR and cataracts were less likely to achieve a CDVA of 20/20 vision but gained as many lines of CDVA from phacoemulsification as patients without diabetes, showing no evidence that cataract surgery should be delayed in diabetic patients with elevated HbA_1c.

Long-term Glycemic Control and Dementia Risk in Type 1 Diabetes

OBJECTIVE

Individuals with type 1 diabetes have experienced an increase in life expectancy, yet it is unknown what level of glycemic control is ideal for maintaining late-life brain health. We investigated the association of long-term glycemic control with dementia in older individuals with type 1 diabetes.

RESEARCH DESIGN AND METHODS

We followed 3,433 members of a health care system with type 1 diabetes, aged ≥50 years, from 1996 to 2015. Repeated measurements of hemoglobin A_1c (HbA_1c), dementia diagnoses, and comorbidities were ascertained from health records. Cox proportional hazards models were fit to evaluate the association of time-varying glycemic exposure with dementia, with adjustment for age, sex, race/ethnicity, baseline health conditions, and frequency of HbA_1c measurement.

RESULTS

Over a mean follow-up of 6.3 years, 155 individuals (4.5%) were diagnosed with dementia. Patients with ≥50% of HbA_1c measurements at 8-8.9% (64-74 mmol/mol) and ≥9% (≥75 mmol/mol) had 65% and 79% higher risk of dementia, respectively, compared with those with <50% of measurements exposed (HbA_1c 8-8.9% adjusted hazard ratio [aHR] 1.65 [95% CI 1.06, 2.57] and HbA_1c ≥9% aHR 1.79 [95% CI 1.11, 2.90]). By contrast, patients with ≥50% of HbA_1c measurements at 6-6.9% (42-52 mmol/mol) and 7-7.9% (53-63 mmol/mol) had a 45% lower risk of dementia (HbA_1c 6-6.9% aHR 0.55 [95% CI 0.34, 0.88] and HbA_1c 7-7.9% aHR 0.55 [95% CI 0.37, 0.82]).

CONCLUSIONS

Among older patients with type 1 diabetes, those with majority exposure to HbA_1c 8-8.9% and ≥9% had increased dementia risk, while those with majority exposure to HbA_1c 6-6.9% and 7-7.9% had reduced risk. Currently recommended glycemic targets for older patients with type 1 diabetes are consistent with healthy brain aging.

Traumatic brain injury associated with dementia risk among people with type 1 diabetes

OBJECTIVE

To examine the association between traumatic brain injury (TBI) and dementia risk among a cohort of middle-aged and elderly individuals with type 1 diabetes (T1D).

METHODS

We evaluated 4,049 members of an integrated health care system with T1D ≥50 years old between January 1, 1996, and September 30, 2015. Dementia and TBI diagnoses throughout the study period were abstracted from medical records. Cox proportional hazards models estimated associations between time-dependent TBI and dementia adjusting for demographics, HbA1c, nephropathy, neuropathy, stroke, peripheral artery disease, depression, and dysglycemic events. Fine and Gray regression models evaluated the association between baseline TBI and dementia risk accounting for competing risk of death.

RESULTS

A total of 178 individuals (4.4%) experienced a TBI and 212 (5.2%) developed dementia. In fully adjusted models, TBI was associated with 3.6 times the dementia risk (hazard ratio [HR] 3.64; 95% confidence interval [CI] 2.34, 5.68). When accounting for the competing risk of death, TBI was associated with almost 3 times the risk of dementia (HR 2.91; 95% CI 1.29, 5.68).

CONCLUSION

This study demonstrates a marked increase in risk of dementia associated with TBI among middle-aged and elderly people with T1D. Given the complexity of self-care for individuals with T1D, and the comorbidities that predispose them to trauma and falls, future work is needed on interventions protecting brain health in this vulnerable population, which is now living to old age.

Risk of Incident Atherosclerotic Cardiovascular DiseaseEvents by Achieved Atherogenic Lipid Levels Among62,428 Statin-Treated Individuals With Diabetes Mellitus

The relevance of low-density lipoprotein cholesterol (LDL-C) or non-high-density lipoprotein cholesterol (non-HDL-C) goals for primary prevention of atherosclerotic cardiovascular disease (ASCVD) among patients with diabetes was assessed. This retrospective cohort study included patients with type 2 diabetes, age 21 to 90years, taking statins, with no history of ASCVD as of January 1, 2006, in Kaiser Permanente Northern California, an integrated healthcare delivery system. Multivariate cox models were utilized to estimate hazard ratios (HRs) for incident ASCVD events by achieved LDL-C and non-HDL-C levels with adjustment for potential confounders. Incident ASCVD events were defined as a composite of myocardial infarction, ischemic stroke, or coronary heart disease death. A cohort of 62,428 patients, with mean age of 64.1years, 46.9% women, and mean follow-up of 6.0 years, was identified. After adjustment, the risk of incident ASCVD for these statin-treated patients was monotonically lower with decreasing achieved LDL-C levels (p<0.0001 for trend) and non-HDL-C levels (p <0.0001 for trend). Relative to achieved LDL-C ≥130 mg/dl, LDL-C <50 mg/dl had HR = 0.58 (95% confidence interval 0.49 to 0.69). Relative to achieved non-HDL-C ≥160mg/dl, non-HDL-C <80 mg/dl had HR = 0.59 (95% confidence interval 0.51 to 0.68). In a large cohort of statin-treated diabetic patients without ASCVD, a monotonically lower risk of incident ASCVD events was associated with lower achieved lipid levels. These findings support the use of LDL-C ornon-HDL-C treatment goals for ASCVD primary prevention in diabetic patients.