And Related Dementia Patients Research Articles

CAUSAL EFFECT OF ANTI-DEMENTIA DRUGS ON PATIENTS’ ECONOMIC BURDEN USING DOUBLE/DEBIASED MACHINE LEARNING APPROACH

Abstract Alzheimer’s disease and related dementia (ADRD) present a significant public health challenge and carry a substantial financial burden. Exploring avenues for treatment, the administration of anti-dementia drugs has emerged as a potential intervention for ADRD. However, prior research has been susceptible to omitted variable bias due to data limitations and struggled to establish causal relationships due to confounding by indication. Our study aims to uncover the causal impact of anti-dementia drug utilization on the economic burden among ADRD patients through Double/Debiased Machine Learning (DML) method. Leveraging data from the Medicare Current Beneficiary Survey (MCBS) spanning 2015 to 2017, we utilized a nationally representative survey linked to Medicare data, providing comprehensive insights. DML techniques, including Random Forest (RF) regression and Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithms, were employed to investigate the causal effect. After exclusion and inclusion, a total of 218,237 ADRD patients used anti-dementia drugs, with an average total medical cost of $16,695.87; 95.51% (4,646,408 patients) of ADRD patients did not use anti-dementia drug, with an average total medical cost of $27,567.09. Results from generalized linear regression showed that anti-dementia drug use did not have a significant impact on total medial cost (β = -0.29, P = 0.10). However, results from DML demonstrated a significant true effect [RF regression (β = -0.85, P = 6.79×10-3), LASSO regression (β = -0.78, P = 9.95×10-3)]. To conclude, our analysis demonstrated the causal effect of anti-dementia drug utilization in reducing overall medical costs borne by ADRD patients.

DEMENTIA CARE PARTNER ACTIVATION PROGRAM FOR FILIPINO AMERICANS

Abstract This study aims to enhance the quality of life and health outcomes for individuals with Alzheimer’s Disease and Related Dementia (ADRD) and their care partners through a culturally tailored support program. Specifically focusing on the unique challenges Filipino and Filipino American (FA) caregivers face, we seek to address disparities in access to care and develop strategies for effective health system navigation. The research employs a multifaceted approach, combining surveys, focus groups, observational techniques, and interviews to assess the caregiving experience comprehensively. Our primary objectives include evaluating the impact of caregiving burden, socio-emotional support, and health literacy on emergency department visits and hospital admissions for ADRD patients. Preliminary results highlight the significance of culturally sensitive interventions. FA caregivers often encounter difficulties due to cultural beliefs, knowledge gaps about ADRD, and limited support networks. This study outlines the development and testing of a specialized program designed to empower FA caregivers with the skills needed for proficient care management and emergency response. By engaging these caregivers in participatory action research, the program aims to foster active involvement and enhance their caregiving capabilities. The study’s outcomes emphasize the potential for improved quality of life and health outcomes for individuals with ADRD and their caregivers. By addressing FA caregivers’ unique challenges in the context of disparity populations, our research contributes to enhancing health system navigation behaviors and promoting culturally attuned care practices. This initiative promises to reduce emergency department visits and hospital admissions, benefiting ADRD patients and their dedicated care partners.

Light Exposure as a Nonpharmacological Intervention for Alzheimer’s Disease

AbstractBackgroundIn recent years, significant advances have been made in our understanding of the non‐visible biological impact of light (Vetter et al, 2021). Many of these advances can be traced to the 2002 discovery of a previously unknown retinal photoreceptor known as the intrinsically photosensitive retinal ganglion cell, or ipRGC, which is now known to be connected directly to our master circadian clock in our superchiasmatic nucleus (SCN). Now a growing body of clinical and applied research has shown how light spectrum, intensity, and timing can be used to promote circadian entrainment, leading to significant improvements in human health outcomes, including for people with Alzheimer’s disease and related dementia (ADRD).MethodMany of these clinical studies have used lighting interventions in elder care facilities to evaluate the impact of lighting as a nonpharmacological intervention to improve mood, anxiety, sleep quality, falls and other health metrics for ADRD populations. These studies typically gather light exposure and health metric information during control periods or with control populations using traditional lighting approaches and compare results to periods or populations under “circadian‐enhanced” lighting conditions, which generally means brighter and bluer lighting during the day and dimmer and redder light in the evening and night hours.ResultThe results from these studies have consistently demonstrated significant improvements in circadian entrainment, sleep quality, mood, anxiety, falls and other health metrics for ADRD populations under circadian‐enhanced lighting conditions.ConclusionThis talk will provide an overview of our current understanding of how light exposure impacts human physiology. We will discuss several completed and ongoing clinical studies conducted with ADRD populations, including several studies on which the speaker has been the Principal Investigator. We will cover what questions remain in this area and what studies can be developed in the future to address them. Finally, we will close with a discussion of tools, products, building designs and daily routines that can be applied today by clinicians and caregivers that are most likely to improve the health and wellbeing of ADRD patients based on what is known today about the impact of light exposure.

Using electronic medical records to develop a predictive model of 30‐day hospital readmission for people with ADRD

AbstractBackgroundHospitals are insufficiently equipped for Alzheimer’s disease and related dementia (ADRD) patients. Thus, readmission incidence is much higher and costlier among ADRD patients than the general population. Hospital discharges often occur without adequate preparation for the complex care management needs of ADRD patients and their caregivers. This study’s objective was to develop a risk‐assessment tool for hospitalized patients with ADRD. By supporting timely and better identification of who is at risk and why, already‐scarce resources can be allocated more efficiently to reduce readmissions.MethodsWe used 2016‐2019 EMR data from the University of Michigan health system (Michigan Medicine) and applied machine learning techniques (Random Forest, XGBoost, and Logistic LASSO) to develop a readmission risk‐assessment tool. We identified 2,899 individuals with ADRD who had at least one index hospital admission. All data features available in EMR – demographics, lab results, prior counts of healthcare use, and characteristics of index hospitalization – were included in our predictive models. Additionally, we geocoded the street address of place of residence using the National Neighborhood Data Archive (NaNDA) using the U.S. Census tract‐level information to include two composite measures of socioeconomic status: disadvantage and affluence.ResultsThe readmission rate for ADRD patients was 22% versus 17% for the general population. The best predictive model was the Random Forest (area under the receiver operating characteristic curve = 0.66; sensitivity = 0.64; specificity = 0.61). The accuracy of our model (0.61) was 42% higher than the LACE score (0.43), which is currently used by the hospital for all patients. The top 5 predictors of 30‐day readmission among people with ADRD included length of hospital stay, frailty index, living in a disadvantaged neighborhood, and total prior‐year healthcare charges.ConclusionADRD patients are highly vulnerable and require many resources, with substantially greater readmission risk and elevated rates of other adverse health events. Leveraging EMR data in a readmission risk‐assessment tool can help inform appropriate and efficient coordination and transitions of care for ADRD patients. Our risk‐assessment tool can identify ADRD patients at high risk for readmission and why they are at higher risk. This can enable better decision‐making upon discharge.

National trends in osteoporosis medication use among Medicare beneficiaries with and without Alzheimer's disease/related dementias.

Osteoporotic fractures are a leading cause of disability and premature death in the elderly. Patients with Alzheimer's and related dementia (ADRD) have high rates of osteoporosis (OP) and substantial risk of osteoporotic fractures. Yet research is sparse on trends and predictors of OP medication use in ADRD. Medicare beneficiaries with OP aged ≥ 67years have Medicare parts A/B/D without HMO from 2016 to 2018. Our outcome was receipt of OP medications in 2018. A multivariable logistic regression assessed association between ADRD and OP drug prescribing, adjusted for age, sex, race, region, Medicare entitlement, dual Medicaid eligibility, chronic conditions, number of provider visits/hospitalizations, and nursing home (NH) resident status. Age/ADRD and NH residency/ADRD interactions were tested. Our sample consisted of 47,871 people with OP and ADRD and 201,840 with OP without ADRD. OP drug use was 38.6% in ADRD patients vs. 52.7% in non-ADRD. After adjustment for demographics, chronic conditions, and previous hospitalizations/physician visits, the OR for OP drug in ADRD vs. non-ADRD was 0.85 (95% CI: 0.83-0.87). NH residents had lower odds for OP medication (OR: 0.61, 95% CI: 0.58-0.64). There were significant interactions between ADRD and age, and between ADRD and NH residency. The OR for OP drug use associated with ADRD was 0.88 (95% CI: 0.86-0.90) among community-dwelling elders and 0.66 (95% CI: 0.64-0.69) among NH residents. ADRD patients received OP drugs at a lower rate than their non-ADRD counterparts. More research is needed on when to prescribe or deprescribe OP drugs in the context of different ADRD severity, patient preferences, remaining life expectancy, and time-to-benefit from OP drugs.

PREDICTIVE MODEL OF 30-DAY HOSPITAL READMISSION FOR PATIENTS WITH ALZHEIMER’S DISEASE

Abstract Hospitals are insufficiently equipped for patients with Alzheimer’s disease and related dementia (ADRD). Thus, 30-day hospital readmission is higher and costlier among ADRD patients than the general population of older adults. Our objective was to develop a risk-assessment tool for hospitalized patients with ADRD. We used 2016-2019 electronic medical record (EMR) data from the University of Michigan health system and applied machine learning techniques to develop a readmission risk-assessment tool. We identified 2,899 individuals with ADRD who had at least one index hospital admission. All data features available in EMR – demographics, lab results, prior counts of healthcare use, and characteristics of index hospitalization – were included in our predictive models. Additionally, we geocoded the street address of patients using the National Neighborhood Data Archive using the U.S. Census tract-level information to include two composite measures of socioeconomic status: disadvantage and affluence. The readmission rate for ADRD patients was 22% versus 17% for the general population. The best predictive model was the Random Forest (area under the receiver operating characteristic curve=0.66; sensitivity=0.64; specificity=0.61). The accuracy of our model (0.61) was 42% higher than the LACE score (0.43), which is currently used by the hospital for all patients. The top 5 predictors of 30-day readmission among people with ADRD included length of hospital stay, frailty index, living in a disadvantaged neighborhood, and total prior-year healthcare charges. Our risk-assessment tool identifies ADRD patients at high risk of readmission and why they are at higher risk. The tool enables better decision-making before discharge.

TRENDS IN OSTEOPOROSIS DRUG USE AMONG MEDICARE BENEFICIARIES WITH & WITHOUT ALZHEIMER’S DISEASE/RELATED DEMENTIAS

Abstract Background Osteoporotic fractures are a leading cause of disability and premature death in the elderly. Patients with Alzheimer’s and related dementia (ADRD) have high rates of osteoporosis (OP) and substantial risk of osteoporotic fractures. Yet research is sparse on trends and predictors of OP medication use in ADRD. Methods Medicare beneficiaries with OP aged ≥67 years with Medicare parts A/B/D without HMO from 2016–2018. Outcome was receipt of OP medications in 2018. A multivariable logistic regression assessed association between ADRD and OP drug prescribing, adjusted for age, sex, race, region, Medicare entitlement, dual Medicaid eligibility, chronic conditions, number of provider visits/hospitalizations, and nursing home (NH) resident status. Age/ADRD and NH residency/ADRD interactions were tested. Results Sample consisted of 47,871 people with OP and ADRD and 201,840 with OP without ADRD. OP drug use was 38.6% in ADRD patients vs. 52.7% in non-ADRD. After adjustment for demographics, chronic conditions, previous hospitalizations/physician visits, the OR for OP drug in ADRD vs. Non-ADRD was 0.85 (95% CI: 0.83–0.87). NH residents had lower odds for OP medication (OR: 0.61, 95% CI: 0.58–0.64). There were significant interactions between ADRD/age and between ADRD/NH residency. The OR for OP drug use associated with ADRD was 0.88 (95% CI: 0.86–0.90) among community-dwelling elders and 0.66 (95% CI: 0.64–0.69) among NH residents. Conclusions: ADRD patients received OP drugs at lower rates than non-ADRD counterparts. More research is needed on prescribing or deprescribing OP drugs in context of ADRD severity, patient preferences, remaining life expectancy and time-to-benefit from OP drugs.

RACE AND RURAL-URBAN DIFFERENCES IN HOME HEALTHCARE LATENCY IN PATIENTS WITH DEMENTIA

Abstract Given the need to ensure equity of timely access to post-acute care among minority groups in rural areas, there is a pressing need to understand disparities in the start of home healthcare among minorities with Alzheimer's disease and Related Dementia (ADRD). This study investigates differences in the timing of initiation of home healthcare services following acute hospitalization for ADRD patients by race/ethnicity and rural and urban locations. A secondary analysis was conducted among older adults with ADRD using Medicare data, discharged to home following an episode of acute hospitalization in 2016-2017. The study outcome was a delay in the start of home healthcare after two days of hospital discharge, defined as a home health latency. Compared to non-Hispanic Whites residing in urban areas, Blacks living in urban areas and Hispanics living in rural areas have a significantly higher odd of home health latency 1.15 (95%CI;1.11-1.18) and 1.06 (95%CI;1.02-1.10), respectively.

Evidence of hospital‐based HIT infrastructure and reduced Medicare payment and racial and ethnic disparities among ADRD patients

AbstractBackgroundUnderstanding the potential cost‐saving or cost‐enhancing of HIT adoptions can help policymakers understand the capacity of HIT investment for population health and racial and ethnic groups. The objective of the study is to examine access to the hospital‐based HIT infrastructure and its association with Medicare payments by Alzheimer’s disease and related dementia (ADRD) patients’ racial and ethnic groups. ADRD is one of the most expensive health conditions in the Unites States and substantial racial and ethnic disparities are observed.MethodWe used 2017 Medicare Beneficiary Summary File, inpatient claims, and the American Hospital Annual survey. Generalized linear models were used for the payment regressions.ResultsThe adjusted total Medicare payment and acute inpatient Medicare payment per person per year were higher for ADRD beneficiaries treated in hospitals with telehealth‐post discharge functions (e.g., an incremental increase of $542 in total payment). However, it relates to a significant reduction of Medicare payments to Black ADRD patients (approximately a reduction of $1,104). The finding suggested that telehealth‐post discharge might be cost‐enhancing at the population level; however it is cost‐saving to Black patients with ADRD. The cost‐saving of Black patients exceeded the incremental cost of the adoption of HIT‐post discharge. Similar findings were found for the telehealth‐treatment, which was associated with a significant reduction of payments among Black and Hispanic ADRD patients.ConclusionsResults suggested that personalized HIT utilization and provisions (telehealth‐post‐discharge for Black and telehealth‐treatment for Black and Hispanic ADRD patients) are critical components of receiving high‐quality ADRD treatment for minority patients.

Leveraging Natural Language Processing to Identify Caregiver Availability for Patients with ADRD from Electronic Medical Records

AbstractBackgroundPrior literature has posited that caregiver availability can potentially prevent adverse health events such as hospital readmissions in the ADRD population. Identifying caregiver availability for patients with Alzheimer’s disease and related dementia (ADRD) can inform healthcare systems and hospital administrations on appropriate discharge planning. Caregiver availability is an important and influential domain in assessing risk of hospital readmissions. However, caregiver availability data is not systematically collected in our healthcare system. Our main objective was to use medical notes (from physicians, nurses, or social workers) to assess caregiver availability and types of caregivers for hospitalized patients with ADRD.MethodWe used 2016‐2019 telephone‐encounter medical notes from a single institution. Rule‐based natural language processing (NLP) was used to develop an algorithm to identify whether the patient (1) resides at home; (2) resides at an institution; (3) has a formal caregiver; and (4) has an informal caregiver. We evaluated the data at the patient level. We used chart abstraction as the “gold standard,” and examined the validity of the algorithm for both training (n = 749) and test sets (n = 227).ResultThe result from our test set indicated a high level of accuracy and reliability for identifying an informal caregiver (F1 = 0.942; accuracy = 0.947, sensitivity = 0.970, and specificity = 0.928). Identifying whether the patient lived at an institution was the least reliable measure (F1 = 0.638, accuracy = 0.899, sensitivity = 0.512, specificity = 0.978). The most common causes of misclassifications were: (1) incomplete or misspelled names of the facilities; (2) past/uncertain/undecided situations; (3) lack of specificity; (4) use of uncommon abbreviations; and (5) irregular use of templates.ConclusionOur NLP algorithm was able to identify whether the patient lived at home vs. an institution and whether the patient had a formal or informal caregiver. There is merit in continuing the NLP approach to identify more granular, caregiver‐related information (e.g., type, hours available, etc.). More effort is needed towards generalizing the algorithm. This information can be used by other healthcare systems to detect ADRD patients who do not have a caregiver at home and may be at risk of adverse events including readmission.

Measuring dose in a tailored lighting intervention to improve sleep and mood in Alzheimer's disease patients

AbstractBackgroundDaytime light helps consolidate sleep in persons with Alzheimer's disease and related dementia (ADRD), but light levels required are high and available lighting fixtures cannot deliver the light level and spectrum needed for the circadian system. Moreover, ambient light levels cannot be measured to determine treatment dose. The present study tested whether a tailored lighting intervention could improve sleep in eight ADRD patients living in long‐term care facilities and used a novel device to measure light dose (RAD).MethodIn a crossover, repeated‐measures design, we exposed ADRD participants (MMSE<24) to an active (circadian stimulus, CS=0.3) and inactive (CS=0.1) tailored lighting intervention for successive 4‐week periods, spaced by a 4‐week washout period. RAD devices, calibrated to measure circadian light, were placed in spaces where the intervention was administered. The lighting intervention was energized from wake time until 6:00 pm. Measures of sleep disturbances (Pittsburgh Sleep Quality Index, PSQI and actigraph) and mood (Cornell Scale for Depression in Dementia, CSDD) were collected at baseline and during the last weeks of each intervention periods.ResultCompared to their respective baseline conditions, the active lighting intervention significantly decreased wake percent and increased sleep percent. Compared to baseline and to the inactive intervention, PSQI scores reduced in the active intervention. Depression scores were significantly lower after the active than after the inactive intervention. The mean ± SEM PSQI scores was 10.9±1.1 and 7.3±0.7 at baseline and after active intervention and 10.0±1.0 and 10.3±1.6 at baseline and after the inactive intervention. The mean ± SEM CSDD scores was 9.0±1.7 and 8.0±1.3 at baseline and after active intervention and 12.0±1.6 and 12.0±2.1 at baseline and after the inactive intervention.ConclusionWhen carefully delivered to patients’ eyes and monitored with a calibrated instrument, daytime light can improve sleep and mood in nursing home residents with AD/ADRD. Additional data need to be collected to extend these preliminary results.

Ethical and Methodological Considerations for Evaluating Participant Views on Alzheimer's and Dementia Research.

The urgent need to expand enrollment in Alzheimer's disease and related dementia (ADRD) research has synergized calls for an empiric science of research recruitment, yet, progress in this area is hindered by challenges to measuring views toward ADRD research. This paper reports ethical and methodological considerations identified through a prospective qualitative study investigating ADRD patient and caregiver views on research recruitment and participation surrounding acute illness. Ethical and methodological considerations were identified through a combination of memoing, collaboration with a Community Advisory Board (CAB), and analysis of interview data from ADRD patients (N = 3) and/or caregivers (N = 28). These included risk for undue influence attributable to role ambiguity/motivational misconceptions, divergent decision-making preferences, bias contributing to low referrals of ADRD participants, and difficulty answering abstract/hypothetical questions. Many considerations were successfully addressed with multifaceted, proactive strategies, and CAB input. Findings have implications for recruitment science research and the validity of inferences regarding research preferences.

Use of Direct-Acting Antiviral Agents and Survival Among Medicare Beneficiaries with Dementia and Chronic Hepatitis C.

Many patients with Alzheimer's disease and related dementia (ADRD) have chronic hepatitis C due to the high prevalence of both conditions among elderly populations. Direct-acting antivirals (DAAs) are effective in treating hepatitis C virus (HCV). However, the complexity of ADRD care may affect DAA use and outcomes among patients with HCV and ADRD. Little information exists on uptake of DAAs, factors associated with DAA use, and health benefits of DAAs among patients with HCV and ADRD. To examine use and survival benefits of DAAs in Medicare patients with HCV and ADRD. The study included Medicare patients with HCV between 2014 and 2017. We estimated Cox proportional hazards regressions to examine the association between having ADRD and DAA use, and the relation between DAA use and survival among patients with HCV and ADRD. The adjusted hazard of initiating a DAA was 50% lower in patients with ADRD than those without ADRD (adjusted HR = 0.50, 95% CI: 0.46-0.54). The hazard of DAA use among ADRD patients with behavioral disturbances was 68% lower than non-ADRD patients (adjusted HR = 0.32, 95% CI: 0.28-0.37). DAA treatment was associated with a significant reduction in mortality among ADRD patients (adjusted HR = 0.52, 95% CI: 0.44-0.61). The rate of DAA treatment in patients with HCV and ADRD was low, particularly among those with behavioral disturbance. The survival benefits of DAA treatment for patients with ADRD were substantial.

Application of distributed lighting control architecture in dementia-friendly smart homes

Dementia is a growing problem in societies with aging populations, not only for patients, but also for family members and for the society in terms of the associated costs of providing health care. Helping patients to maintain a degree of independence in their home environment while ensuring their safeties is considered as a positive step forward for addressing individual needs of dementia patients. A common symptom for dementia patients including those with Alzheimer's Disease and Related Dementia (ADRD) is sleep disturbance, patients being awake at night and asleep during the day. One of the problems with night time sleep disturbance in dementia patients is the possible accidental falls of patients in the dark environment. An issue associated with un-hourly sleeping behavior in these patients is the lighting condition of their surroundings. Clinical studies indicate that appropriate level of lightning can help to restore the rest-activity cycles of ADRD patients. This study tackles this problem by generating machine learning solutions for controlling the lighting conditions of multiple rooms in the house in different hours based on patterns of behaviors generated for the patient. Several neural network oriented classification methods are investigated and their feasibilities are assessed with a collection of synthetic data capturing two conditions of balanced and unbalanced inter-class samples. The classifiers are utilized within two centric and distributed lighting control architectures. The results indicate the feasibility of the distributed architecture in achieving a high level of classification performance resulting in adequate control over lighting conditions of the house in various time periods.