Emergency department visits in African Americans with mild cognitive impairment and diabetes

Comorbidities in patients with vascular dementia and Alzheimer's disease with Neuropsychiatric symptoms

INTRODUCTION

This study aimed to examine baseline risk factors in Alzheimer's Disease (AD) and Vascular dementia (VaD) patients with neuropsychiatry symptoms (NPS), and determine whether specific risk factors differ by subtypes of dementia for AD and VaD patients with NPS.

METHODS

A retrospective data analysis was conducted to evaluate similarities and differences in the risk factors for AD and VaD with NPS. The analysis included 2949 patients with VaD and 6341 patients with clinical confirmation of AD and VaD with or without NPS collected between February 2016 and August 2021. The multivariate logistic regression analysis was used to determine the risk factors associated with AD and VaD with NPS, by predicting the increasing odds (odds ratios (ORs) of an association of a specific baseline risk factor with AD or VaD with NPS. The validity of the regression models was tested using a Hosmer-Lemeshow test, while the Receiver Operating Curve (ROC) was used to test the sensitivity of the models.

RESULTS

In the adjusted analysis TSH (OR = 1.781, 95 % CI, p = 0.0025) and CHF (OR = 1.620, 95 %, p = 0.016) were associated with VaD with NPS, while a history of emergency department(ED) admission (OR = 0.277, 95 % CI, p = 0.003) likely to be associated with VaD patients without NPS. For AD patients, a history of CVA (OR = 1.395, 95 % CI, p = 0.032) and cancer (OR = 1.485, 95 % CI, p = 0.013) were associated with AD patients with NPS.

DISCUSSION

The findings of this study indicate that an abnormal thyroid gland and CHF were linked to VaD patients with behavioral disturbances, while CVA and cancer were linked to AD patients with behavioral disturbances. These findings suggest the need to develop management strategies for the care of patients with AD and VaD with NPS.

Ethnic and Racial Disparities in the Association between Type II Diabetes Mellitus and Dementia

Evidence in the literature suggests an association between Type 2 Diabetes Mellitus (T2DM) and dementia, but this relationship has not been studied in the most recently available nationally representative datasets. This retrospective, observational, cross-sectional study of adults (60+ years of age) seeks to investigate this association across racial and ethnic groups in the most recently available National Ambulatory Medical Care Survey (NAMCS) datasets. A multivariable logistic regression model is employed to investigate the association between T2DM and the diagnosis of dementia and assess disparities in racial and ethnic groups, while controlling for available covariates of interest. The analysis found no evidence of a relationship between T2DM and dementia even after adjusting for available covariates of interest (OR 1.13, 95% CI = 0.81-1.57). However, evidence of differences in the proportion with dementia was observed between ethnicities and race groups. Hispanic/Latinos were found to have more than double the odds of dementia compared to Non-Hispanic/Latinos (OR 2.08, 95% CI = 1.05-4.14), while the Other race group had 74% lower odds of dementia compared to the White race group (OR 0.26, 95% CI = 0.10-0.64). This study suggests that disparities in the risk of dementia remain for ethnic/racial groups. As minority populations continue to grow, educational and preventative measures for both diabetes and dementia are vital public health priorities. Perceptions of cognitive impairment, its association with T2DM, and the interventions needed to address the deficits may vary by culture and ethnic background; therefore, specific characteristics relevant to these populations should be further evaluated.

ADscreen: A speech processing-based screening system for automatic identification of patients with Alzheimer's disease and related dementia

Alzheimer's disease and related dementias (ADRD) present a looming public health crisis, affecting roughly 5 million people and 11 % of older adults in the United States. Despite nationwide efforts for timely diagnosis of patients with ADRD, >50 % of them are not diagnosed and unaware of their disease. To address this challenge, we developed ADscreen, an innovative speech-processing based ADRD screening algorithm for the protective identification of patients with ADRD. ADscreen consists of five major components: (i) noise reduction for reducing background noises from the audio-recorded patient speech, (ii) modeling the patient's ability in phonetic motor planning using acoustic parameters of the patient's voice, (iii) modeling the patient's ability in semantic and syntactic levels of language organization using linguistic parameters of the patient speech, (iv) extracting vocal and semantic psycholinguistic cues from the patient speech, and (v) building and evaluating the screening algorithm. To identify important speech parameters (features) associated with ADRD, we used the Joint Mutual Information Maximization (JMIM), an effective feature selection method for high dimensional, small sample size datasets. Modeling the relationship between speech parameters and the outcome variable (presence/absence of ADRD) was conducted using three different machine learning (ML) architectures with the capability of joining informative acoustic and linguistic with contextual word embedding vectors obtained from the DistilBERT (Bidirectional Encoder Representations from Transformers). We evaluated the performance of the ADscreen on an audio-recorded patients' speech (verbal description) for the Cookie-Theft picture description task, which is publicly available in the dementia databank. The joint fusion of acoustic and linguistic parameters with contextual word embedding vectors of DistilBERT achieved F1-score = 84.64 (standard deviation [std] = ±3.58) and AUC-ROC = 92.53 (std = ±3.34) for training dataset, and F1-score = 89.55 and AUC-ROC = 93.89 for the test dataset. In summary, ADscreen has a strong potential to be integrated with clinical workflow to address the need for an ADRD screening tool so that patients with cognitive impairment can receive appropriate and timely care.

Emergency department visits among people with predementia highly predicts conversion to dementia

Emergency department visits (EDV) are common among older adults with and without dementia. The risk factors and demands of EDVs for people with dementia have been well studied; however, the association between EDVs and conversion to dementia among people with predementia has not been thoroughly explored. To study the predictive value of EDVs in predementia's progression to dementia. The baseline predementia cohort registered from September 2015 to August 2017, with longitudinal follow-up in the History-based Artificial Intelligent Clinical Dementia Diagnostic System database, was retrospectively analyzed. The rates of conversion among the different EDVs were compared. Multivariate logistic regression and Cox proportional hazards analyses were applied to study the influence of EDVs on progression. Age, education, sex, neuropsychological tests, activities of daily living, neuropsychiatric symptoms, parkinsonism, and multiple vascular risk factors were adjusted for. A total of 512 participants were analyzed, including 339 (66.2%) non-converters and 173 (33.8%) converters with a mean follow-up of 3.3 (range 0.4-6.1) and 2.8 (range 0.5-5.9) years, respectively. Compared to people without EDV (EDV 0), the hazard ratios for conversion to dementia were 3.6, 5.9, and 6.9 in those with EDV once (EDV 1), twice (EDV 2), and more than twice (EDV >2), respectively. In addition, older age, lower education, poorer cognition, poorer ADL performance, and longer follow-up periods also increased the conversion rates. EDVs in the predementia stages highly predict progression to dementia. Therefore, a sound public health as well as primary healthcare system that provide strategies for better management of mental and physical condition might help prevention of EDVs among older people in the predementia stages.

Impact of Statewide Mandatory Medicaid Managed Care (SMMC) Programs on Hospital Obstetric Outcomes

The state of Florida implemented mandatory managed care for Medicaid enrollees via the Statewide Medicaid Managed Care (SMMC) program in April of 2014. The objective of this study was to examine the impact of the implementation of the SMMC program on the access to care and quality of maternal care for Medicaid enrollees, as measured by several hospital obstetric outcomes. The primary data source for this retrospective observational study was the Hospital Cost and Utilization Project (HCUP) all-payer State ED (SED) visit and State Inpatient Databases (SIDs) from 2010 to 2017. The primary health outcomes for obstetric care were primary cesarean, preterm birth, postpartum preventable ED visits, postpartum preventable readmissions, and vaginal delivery after cesarean (VBAC) rates. Using difference-in-differences (DID) estimation, selected health outcomes were examined for Florida residents with Medicaid beneficiaries (treatment) and the commercially insured population (comparison), before and after the implementation of SMMC. Improvement in disparities for racial/ethnic minority Medicaid enrollees was estimated relative to whites, compared to the relative change among commercially insured patients. From the DID estimation, the findings showed that SMMC is statistically significantly associated with a higher reduction in primary cesarean rates, preterm births, preventable postpartum ED visits, and readmissions among Medicaid beneficiaries relative to their commercially insured counterparts. However, this study did not find any significant reduction in racial/ethnic disparities in obstetric outcomes. In general, this study highlights the impact of SMMC implementation on obstetric outcomes in Florida and provides important insights and potential scope for improvement in obstetric care quality and associated racial/ethnic disparities.