Racial/ethnic disparities in initiation and persistent use of anti-dementia medications

Racial and ethnic disparities in potentially inappropriate medication use in patients with dementia

INTRODUCTION

Racial and ethnic disparities in potentially inappropriate medication (PIM) use among older adults with dementia are unclear.

METHODS

Data were drawn from the baseline visits of participants who were ≥60 years old and diagnosed with dementia in the National Alzheimer's Coordinating Center Uniform Data Set (NACCUDS) recruited from National Institute on Aging (NIA)-funded Alzheimer's Disease Research Centers (ADCs) throughout the United States. PIM utilization was evaluated using the 2019 American Geriatrics Society Beers Criteria for PIM Use in Older Adults. We estimated the association between race and ethnicity and the following outcomes and estimation models: (1) any PIM use, any PIM in each drug class, and any PIM best avoided in dementia patients using logistic regression models, (2) total number of medications, total number of PIMs, and anticholinergic burden scale (ACBS) using Poisson or negative binomial regression models, and (3) proportion of total medications that were PIMs using generalized linear models (GLM).

RESULTS

Compared to White participants, Black, Hispanic, and Asian participants reported taking fewer total medications (incidence rate ratio [IRR] ± standard error[SE] = 0.903 ± 0.017, 0.875 ± 0.021, and 0.912 ± 0.041, respectively, all p < 0.01). Asian participants were less likely to be exposed to any PIM (odds ratio [OR] ± SE = 0.619 ± 0.118, p < 0.05). Compared to White participants, Black participants were less likely to be exposed to benzodiazepine (OR ± SE = 0.609 ± 0.094, p < 0.01) and antidepressant (OR ± SE = 0.416 ± 0.103, p < 0.001) PIMs, but greater antipsychotic (OR ± SE = 1.496 ± 0.204, p < 0.01), cardiovascular (OR ± SE = 2.193 ± 0.255, p < 0.001), and skeletal muscle relaxant (OR ± SE = 2.977 ± 0.860, p < 0.001) PIMs. Hispanic participants were exposed to greater skeletal muscle relaxant PIMs and had lower anticholinergic burden. Asian participants were exposed to fewer benzodiazepine PIMs.

DISCUSSION

Significant racial and ethnic disparities in exposure to PIMs and PIMs by medication category in dementia research participants who have access to dementia experts found in the study suggest that disparities may be wider in the larger community.

Inherent Bias in Electronic Health Records: A Scoping Review of Sources of Bias

Objectives

1.1Biases inherent in electronic health records (EHRs), and therefore in medical artificial intelligence (AI) models may significantly exacerbate health inequities and challenge the adoption of ethical and responsible AI in healthcare. Biases arise from multiple sources, some of which are not as documented in the literature. Biases are encoded in how the data has been collected and labeled, by implicit and unconscious biases of clinicians, or by the tools used for data processing. These biases and their encoding in healthcare records undermine the reliability of such data and bias clinical judgments and medical outcomes. Moreover, when healthcare records are used to build data-driven solutions, the biases are further exacerbated, resulting in systems that perpetuate biases and induce healthcare disparities. This literature scoping review aims to categorize the main sources of biases inherent in EHRs.

Methods

1.2We queried PubMed and Web of Science on January 19th, 2023, for peer-reviewed sources in English, published between 2016 and 2023, using the PRISMA approach to stepwise scoping of the literature. To select the papers that empirically analyze bias in EHR, from the initial yield of 430 papers, 27 duplicates were removed, and 403 studies were screened for eligibility. 196 articles were removed after the title and abstract screening, and 96 articles were excluded after the full-text review resulting in a final selection of 116 articles.

Results

1.3Systematic categorizations of diverse sources of bias are scarce in the literature, while the effects of separate studies are often convoluted and methodologically contestable. Our categorization of published empirical evidence identified the six main sources of bias: a) bias arising from past clinical trials; b) data-related biases arising from missing, incomplete information or poor labeling of data; human-related bias induced by c) implicit clinician bias, d) referral and admission bias; e) diagnosis or risk disparities bias and finally, (f) biases in machinery and algorithms.

Conclusions

1.4Machine learning and data-driven solutions can potentially transform healthcare delivery, but not without limitations. The core inputs in the systems (data and human factors) currently contain several sources of bias that are poorly documented and analyzed for remedies. The current evidence heavily focuses on data-related biases, while other sources are less often analyzed or anecdotal. However, these different sources of biases add to one another exponentially. Therefore, to understand the issues holistically we need to explore these diverse sources of bias. While racial biases in EHR have been often documented, other sources of biases have been less frequently investigated and documented (e.g. gender-related biases, sexual orientation discrimination, socially induced biases, and implicit, often unconscious, human-related cognitive biases). Moreover, some existing studies lack causal evidence, illustrating the different prevalences of disease across groups, which does not per se prove the causality. Our review shows that data-, human- and machine biases are prevalent in healthcare and they significantly impact healthcare outcomes and judgments and exacerbate disparities and differential treatment. Understanding how diverse biases affect AI systems and recommendations is critical. We suggest that researchers and medical personnel should develop safeguards and adopt data-driven solutions with a "bias-in-mind" approach. More empirical evidence is needed to tease out the effects of different sources of bias on health outcomes.

Mapping racial and ethnic healthcare disparities for persons living with dementia: A scoping review

INTRODUCTION

We set out to map evidence of disparities in Alzheimer's disease and Alzheimer's disease related dementias healthcare, including issues of access, quality, and outcomes for racial/ethnic minoritized persons living with dementia (PLWD) and family caregivers.

METHODS

We conducted a scoping review of the literature published from 2000 to 2022 in PubMed, PsycINFO, and CINAHL. The inclusion criteria were: (1) focused on PLWD and/or family caregivers, (2) examined disparities or differences in healthcare, (3) were conducted in the United States, (4) compared two or more racial/ethnic groups, and (5) reported quantitative or qualitative findings.

RESULTS

Key findings include accumulating evidence that minoritized populations are less likely to receive an accurate and timely diagnosis, be prescribed anti-dementia medications, and use hospice care, and more likely to have a higher risk of hospitalization and receive more aggressive life-sustaining treatment at the end-of-life.

DISCUSSION

Future studies need to examine underlying processes and develop interventions to reduce disparities while also being more broadly inclusive of diverse populations.

Anti-dementia medication use in Aotearoa New Zealand: An exploratory study using health data from the Integrated Data Infrastructure (IDI)

OBJECTIVE

Anti-dementia medications such as acetylcholinesterase inhibitors are an important part of the management pathway for dementia. However, there are limited data in New Zealand that have examined the rates and patterns of use of funded anti-dementia medication and how use differs with ethnicity, age and sex.

METHODS

This was a retrospective population-based descriptive study. Using the Integrated Data Infrastructure, we identified individuals of all ages coded for a diagnosis of dementia and estimated the proportion dispensed funded anti-dementia medication - donepezil tablets and rivastigmine patches - between 1 July 2016 and 30 June 2020. Rates of medication use in five main ethnic groups (Māori, Pacific peoples, Asian, European, and Middle Eastern, Latin American and African) in the <65, 65-79 and 80 and over (80+) age groups were compared and also between males and females in all sub-groups. Log-binomial models were used to calculate relative risks to determine any differences in anti-dementia medication use in the five ethnic groups and the three age groups and between males and females in each of the four study years.

RESULTS

Overall, one-third of the dementia population received a funded anti-dementia medication in the total population (all ages) between 2016 and 2020. Donepezil tablets were dispensed in 31.6-34.0% and rivastigmine patches in 1.4-2.1% across the four study years. Compared to people of European ethnicity, Māori, Pacific peoples, and Middle Eastern, Latin American and African groups were less likely to be dispensed an anti-dementia medication (Māori: relative risk = 0.79-0.81, p < 0.0001; Pacific peoples: relative risk = 0.72-0.74, p < 0.0001; Middle Eastern, Latin American and African: relative risk = 0.73-0.78, p < 0.05). Compared to the age 80+ group, the 65-79 age group was more likely (relative risk = 1.50-1.54, p < 0.0001), while the age <65 group was less likely (relative risk = 0.67-0.71, p < 0.0001) to be dispensed an anti-dementia medication. There were no statistically significant differences in anti-dementia medication use between males and females.

CONCLUSION

This study provides important information about funded anti-dementia medication use in New Zealand and how this differs by ethnicity, age and sex. Despite higher dementia prevalence in Māori and Pacific peoples, these groups were less likely to receive funded anti-dementia medication.