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Alzheimer’s disease is the most common type of dementia that currently affects over 6.5 million people in the U.S. Currently there is no cure and the existing drug therapies attempt to delay the mental decline and improve cognitive abilities. Two of the most commonly prescribed such drugs are Donepezil and Memantine. We formally tested and confirmed the presence of a beneficial drug-drug interaction of Donepezil and Memantine using a causal inference analysis. We applied doubly robust estimators to one of the largest and high-quality medical databases to estimate the effect of two commonly prescribed Alzheimer’s disease (AD) medications, Donepezil and Memantine, on the average number of hospital or emergency department visits per year among patients diagnosed with AD. Our results show that, compared to the absence of medication scenario, the Memantine monotherapy, and the Donepezil monotherapy, the combined use of Donepezil and Memantine treatment significantly reduces the average number of hospital or emergency department visits per year by 0.078 (13.8%), 0.144 (25.5%), and 0.132 days (23.4%), respectively. The assessed decline in the average number of hospital or emergency department visits per year is consequently associated with a substantial reduction in medical costs. As of 2022, according to the Alzheimer’s Disease Association, there were over 6.5 million individuals aged 65 and older living with AD in the US alone. If patients who are currently on no drug treatment or using either Donepezil or Memantine alone were switched to the combined used of Donepezil and Memantine therapy, the average number of hospital or emergency department visits could decrease by over 613 thousand visits per year. This, in turn, would lead to a remarkable reduction in medical expenses associated with hospitalization of AD patients in the US, totaling over 940 million dollars per year.

Background Alzheimer’s disease (AD) is the most common neurodegenerative disease. Studying the effects of drug treatments on multiple health outcomes related to AD could be beneficial in demonstrating which drugs reduce the disease burden and increase survival. Methods We conducted a comprehensive causal inference study implementing doubly robust estimators and using one of the largest high-quality medical databases, the Oracle Electronic Health Records (EHR) Real-World Data. Our work was focused on the estimation of the effects of the two common Alzheimer’s disease drugs, Donepezil and Memantine, and their combined use on the five-year survival since initial diagnosis of AD patients. Also, we formally tested for the presence of interaction between these drugs. Results Here, we show that the combined use of Donepezil and Memantine significantly elevates the probability of five-year survival. In particular, their combined use increases the probability of five-year survival by 0.050 (0.021, 0.078) (6.4%), 0.049 (0.012, 0.085), (6.3%), 0.065 (0.035, 0.095) (8.3%) compared to no drug treatment, the Memantine monotherapy, and the Donepezil monotherapy respectively. We also identify a significant beneficial additive drug-drug interaction effect between Donepezil and Memantine of 0.064 (0.030, 0.098). Conclusions Based on our findings, adopting combined treatment of Memantine and Donepezil could extend the lives of approximately 303,000 people with AD living in the USA to be beyond five-years from diagnosis. If these patients instead have no drug treatment, Memantine monotherapy or Donepezil monotherapy they would be expected to die within five years. Plain language summary Alzheimer’s disease is the most common type of dementia, affecting millions of people worldwide. In this study, we investigated the effects of two drugs commonly prescribed to people with Alzheimer’s disease called Donepezil and Memantine to see whether they had an impact on when people died. We found that the combined use of Donepezil and Memantine significantly increased the probability of a person surviving five years compared to no drug treatment or treatment with Donepezil or Memantine alone. Our results suggest that the lives of many Alzheimer’s patients in the USA who are currently on no drug treatment or just Donepezil or Memantine could be extended if they were treated with both drugs simultaneously. Yaghmaei et al. utilize doubly robust causal inference estimators and a large medical data set to evaluate the impact of combining Donepezil and Memantine on five-year survival since initial diagnosis of Alzheimer’s disease patients. A significant benefit to survival is seen when the drugs are combined.

Objective Emergency department (ED) return visits within 72 h may be a sign of poor quality of care and entail unnecessary use of healthcare resources. In this study, we compare the performance of two leading statistical and machine learning classification algorithms, and we use the best performing approach to identify novel risk factors of ED return visits. Methods We analyzed 3.2 million ED encounters with at least one diagnosis under “injury, poisoning and certain other consequences of external causes” and “external causes of morbidity.” These encounters included patients 18 years or older from across 128 emergency room facilities in the USA. For each encounter, we calculated the 72-h ED return status and retrieved 57 features from demographics, diagnoses, procedures, and medications administered during the process of administration of medical care. We implemented a mixed-effects model to assess the effects of the covariates while accounting for the hierarchical structure of the data. Additionally, we investigated the predictive accuracy of the extreme gradient boosting tree ensemble approach and compared the performance of the two methods. Results The mixed-effects model indicates that certain blunt force and non-blunt trauma inflates the risk of a return visit. Notably, patients with trauma to the head and patients with burns and corrosions have elevated risks. This is in addition to 11 other classes of both blunt force and non-blunt force traumas. In addition, prior healthcare resource utilization, patients who have had one or more prior return visits within the last 6 months, prior ED visits, and the number of hospitalizations within the 6 months are associated with increased risk of returning to the ED after discharge. On the one hand, the area under the receiver characteristic curve (AUROC) of the mixed-effects model was 0.710 (0.707, 0.712). On the other hand, the gradient boosting tree ensemble had a lower AUROC of 0.698 CI (0.696, 0.700) on the independent test model. Conclusions The proposed mixed-effects model achieved the highest known AUC and resulted in the identification of novel risk factors. The model outperformed one of the leading machine learning ensemble classifiers, the extreme gradient boosting tree in terms of model performance. The risk factors we identified can assist emergency departments to decrease the number of unplanned return visits within 72 h.

Identifying optimal treatment options with respect to survival of Alzheimer's disease patients is crucially important and previously uninvestigated research question. Our objective was to estimate the causal effects of the most prevalent classes of Alzheimer’s disease drugs, Donepezil and Memantine, and their combined use on Survival and General Health Status Measures of Alzheimer's disease patients for the first five years after initial diagnosis. We carried out a thorough causal inference study using doubly robust estimators, nonparametric bootstrap confidence intervals, Bonferroni corrections for multiple comparisons and analyzing one of the largest high-quality medical databases containing millions of de-identified electronic health records obtained from over 110 health systems in the United States. We also analyzed demographic variables, preexisting conditions, and drug treatments. Our results show that the combined use of Donepezil and Memantine significantly increased the probability of three, four and five-year survival of AD patients, significantly decreases in the average number of hospital or emergency department visits per year, and significantly decreased the hospital length of stay. The adoption of the combined treatment of Memantine and Donepezil could extend the lives of approximately 174,000, 215,000 and 303,000 current Alzheimer's disease patients above the three, four and five-year survival thresholds since initial diagnosis that would have otherwise expired due to being presently treated with no drugs, the Memantine monotherapy, or the Donepezil monotherapy respectively. Switching the patients that are currently on no drug treatment or on Donepezil and Memantine monotherapies to the combined treatment of Donepezil and Memantine will decrease the total number of patient days in hospitals and emergency departments by over 1.47 million per year and lower the yearly medical expenses in the U.S. due to hospitalization of AD patients by over 984 million dollars.

Background: Marginalized low-income racial and ethnic minority residents of public housing represent an intersectional population with multiple health needs and low healthcare utilization. Telehealth has been proposed as a solution to address healthcare access disparities, yet the role of telehealth attitudes, including perceived benefits and self-efficacy, in influencing telehealth use of this population remains unknown. Objective: This study investigates whether two domains of telehealth attitudes, namely self-efficacy and perceived benefits (expectancy), mediate the relationship between demographic, health, and social determinants of telehealth use among low-income racial and ethnic minority residents of public housing in Los Angeles. Methods: This cross-sectional study analyzed data collected from low-income racial and ethnic minority residents of public housing in Los Angeles. Measures included demographic factors (age and gender), social determinants of health (e.g., education, language, and primacy care provider), health (chronic illnesses and physical mobility), telehealth attitudes (perceived benefits and self-efficacy), and telehealth use frequency. Mediation analysis was conducted to test whether telehealth attitudes explained the association between demographic, social, and health determinants of telehealth use. Results: The findings revealed that neither of the two domains of telehealth attitudes, including perceived benefits and self-efficacy, were significantly associated with telehealth use. As such, these attitude domains did not operate as mediators of the relationship between demographic, health, and social determinants of telehealth use. Conclusion: The study results suggest that structural barriers, rather than individual attitudes, primarily drive telehealth use disparities among this intersectional population. Interventions aimed at increasing telehealth adoption should prioritize addressing systemic inequities rather than focusing solely on changing individual attitudes. These findings underscore the importance of structural solutions to promote equitable telehealth access in marginalized communities.

The electrocardiogram (ECG) is a widely used, non-invasive tool for diagnosing a range of cardiovascular conditions, including arrhythmia and heart disease-related structural changes. Despite its critical role in clinical care, racial and ethnic differences in ECG readings are often underexplored or inadequately addressed in research. Variations in key ECG parameters, such as PR interval, QRS duration, QT interval, and T-wave morphology, have been noted across different racial groups. However, the limited research in this area has hindered the development of diagnostic criteria that account for these differences, potentially contributing to healthcare disparities, as ECG interpretation algorithms largely developed from major population data may lead to misdiagnoses or inappropriate treatments for minority groups. This review aims to help cardiac researchers and cardiovascular specialists better understand, explore, and address the impact of racial and ethnic differences in ECG readings. By identifying potential causes—ranging from genetic factors to environmental influences—and exploring the resulting disparities in healthcare outcomes, we propose strategies such as the development of race-specific ECG norms, the application of artificial intelligence (AI) to improve diagnostic accuracy, and the diversification of ECG databases. Through these efforts, the medical community can advance toward more personalized and equitable cardiovascular care.