Discrimination, trust, and withholding information from providers: Implications for missing data and inequity

Public perspectives on the use of different data types for prediction in healthcare

OBJECTIVE

Understand public comfort with the use of different data types for predictive models.

MATERIALS AND METHODS

We analyzed data from a national survey of US adults (n = 1436) fielded from November to December 2021. For three categories of data (identified using factor analysis), we use descriptive statistics to capture comfort level.

RESULTS

Public comfort with data use for prediction is low. For 13 of 15 data types, most respondents were uncomfortable with that data being used for prediction. In factor analysis, 15 types of data grouped into three categories based on public comfort: (1) personal characteristic data, (2) health-related data, and (3) sensitive data. Mean comfort was highest for health-related data (2.45, SD 0.84, range 1-4), followed by personal characteristic data (2.36, SD 0.94), and sensitive data (1.88, SD 0.77). Across these categories, we observe a statistically significant positive relationship between trust in health systems' use of patient information and comfort with data use for prediction.

DISCUSSION

Although public trust is recognized as important for the sustainable expansion of predictive tools, current policy does not reflect public concerns. Low comfort with data use for prediction should be addressed in order to prevent potential negative impacts on trust in healthcare.

CONCLUSION

Our results provide empirical evidence on public perspectives, which are important for shaping the use of predictive models. Findings demonstrate a need for realignment of policy around the sensitivity of non-clinical data categories.

Intimate Partner Violence and Human Trafficking Screening and Services in Primary Care Across Underserved Communities in the United States-Initial Examination of Trends, 2020-2021

OBJECTIVES

The Health Resources and Services Administration (HRSA) began collecting data on intimate partner violence (IPV) and human trafficking (HT) in the 2020 Uniform Data System (UDS). We examined patients affected by IPV and HT served by HRSA-funded health centers in medically underserved US communities during the COVID-19 pandemic.

METHODS

We established a baseline and measured trends in patient care by analyzing data from the 2020 (N = 28 590 897) and 2021 (N = 30 193 278) UDS. We conducted longitudinal ordinal logistic regression analyses to assess the association of care trends and organization-level and patient characteristics using proportional odds ratios (PORs) and 95% CIs.

RESULTS

The number of clinical visits for patients affected by IPV and HT decreased by 29.4% and 88.3%, respectively, from 2020 to 2021. Health centers serving a higher (vs lower) percentage of pediatric patients were more likely to continuously serve patients affected by IPV (POR = 2.58; 95% CI, 1.01-6.61) and HT (POR = 6.14; 95% CI, 2.06-18.29). Health centers serving (vs not serving) patients affected by IPV were associated with a higher percentage of patients who had limited English proficiency (POR = 1.77; 95% CI, 1.02-3.05) and Medicaid beneficiaries (POR = 2.88; 95% CI, 1.48-5.62), whereas health centers serving (vs not serving) patients affected by HT were associated with a higher percentage of female patients of reproductive age (POR = 15.89; 95% CI, 1.61-157.38) and urban settings (POR = 1.74; 95% CI, 1.26-2.37).

CONCLUSIONS

The number of clinical visits for patients affected by IPV and HT during the COVID-19 pandemic declined. Delayed care will pose challenges for future health care needs of these populations.

Delivering Effective Messages in the Patient-Clinician Encounter

This JAMA Insights discusses the importance of effective patient-clinician communication and provides strategies for clinicians that can enhance accurate information gathering and exchange, encourage patient engagement, enhance comprehension, and ensure retention of the information.

Perceived discrimination in health care in Germany- results of a population survey

BACKGROUND

It has consistently been shown that perceived discrimination is associated with adverse health outcomes. Despite this uncontested relevance, there is a lack of research on the experiences of discrimination in health care. Therefore, the following research questions were addressed: (1) How often do people in Germany report having been discriminated in health care due to different reasons? (2) Which socio-demographic groups are most afflicted by perceived discrimination in health care?

METHODS

Analyses are based on a cross-sectional online survey conducted in Germany. An adult population sample was randomly drawn from a panel which was recruited offline (N = 2,201). Respondents were asked whether they have ever been discriminated in health care due to the following reasons: age, sex/gender, racism (i.e. migration history, religion, language problems, colour of skin), health issues or disability (i.e. overweight, mental illness/addiction, disability), socio-economic status (SES, i.e. income, education, occupation).

RESULTS

26.6% of the respondents reported discrimination experiences. Perceived discrimination due to health issues or disability was most frequent (15%), followed by age (9%) and SES (8.9%). Discrimination due to racism and sex/gender was less frequently reported (4.1% and 2.5%). Younger age groups, women, and 2nd generation migrants as well as respondents with low income and low education were more likely to report any kind of discrimination in health care. Two groups were found to be at special risk for reporting discrimination in health care across different reasons: women and younger age groups. Discrimination due to racism was more prevalent among respondents who have immigrated themselves than those who were born in Germany but whose parents have immigrated. Discrimination due to SES was significantly associated with (low) income but not with education.

CONCLUSIONS

More than a quarter of the adult population in Germany reported experiences of discrimination in health care. Such experiences were more frequent among lower SES groups, migrants, women, and younger people. Results underline the necessity of interventions to reduce the magnitude and consequences of discrimination in health care. Future studies should apply an intersectional approach to consider interactions between social inequality indicators regarding discrimination and to identify risk groups that are potentially afflicted by multiple discrimination.

How patients distinguish between clinical and administrative predictive models in health care

OBJECTIVES

To understand patient perceptions of specific applications of predictive models in health care.

STUDY DESIGN

Original, cross-sectional national survey.

METHODS

We conducted a national online survey of US adults with the National Opinion Research Center from November to December 2021. Measures of internal consistency were used to identify how patients differentiate between clinical and administrative predictive models. Multivariable logistic regressions were used to identify relationships between comfort with various types of predictive models and patient demographics, perceptions of privacy protections, and experiences in the health care system.

RESULTS

A total of 1541 respondents completed the survey. After excluding observations with missing data for the variables of interest, the final analytic sample was 1488. We found that patients differentiate between clinical and administrative predictive models. Comfort with prediction of bill payment and missed appointments was especially low (21.6% and 36.6%, respectively). Comfort was higher with clinical predictive models, such as predicting stroke in an emergency (55.8%). Experiences of discrimination were significant negative predictors of comfort with administrative predictive models. Health system transparency around privacy policies was a significant positive predictor of comfort with both clinical and administrative predictive models.

CONCLUSIONS

Patients are more comfortable with clinical applications of predictive models than administrative ones. Privacy protections and transparency about how health care systems protect patient data may facilitate patient comfort with these technologies. However, larger inequities and negative experiences in health care remain important for how patients perceive administrative applications of prediction.

Digital Determinants of Health: Health data poverty amplifies existing health disparities-A scoping review

Artificial intelligence (AI) and machine learning (ML) have an immense potential to transform healthcare as already demonstrated in various medical specialties. This scoping review focuses on the factors that influence health data poverty, by conducting a literature review, analysis, and appraisal of results. Health data poverty is often an unseen factor which leads to perpetuating or exacerbating health disparities. Improvements or failures in addressing health data poverty will directly impact the effectiveness of AI/ML systems. The potential causes are complex and may enter anywhere along the development process. The initial results highlighted studies with common themes of health disparities (72%), AL/ML bias (28%) and biases in input data (18%). To properly evaluate disparities that exist we recommend a strengthened effort to generate unbiased equitable data, improved understanding of the limitations of AI/ML tools, and rigorous regulation with continuous monitoring of the clinical outcomes of deployed tools.

Applying anti-racist approaches to informatics: a new lens on traditional frames

Health organizations and systems rely on increasingly sophisticated informatics infrastructure. Without anti-racist expertise, the field risks reifying and entrenching racism in information systems. We consider ways the informatics field can recognize institutional, systemic, and structural racism and propose the use of the Public Health Critical Race Praxis (PHCRP) to mitigate and dismantle racism in digital forms. We enumerate guiding questions for stakeholders along with a PHCRP-Informatics framework. By focusing on (1) critical self-reflection, (2) following the expertise of well-established scholars of racism, (3) centering the voices of affected individuals and communities, and (4) critically evaluating practice resulting from informatics systems, stakeholders can work to minimize the impacts of racism. Informatics, informed and guided by this proposed framework, will help realize the vision of health systems that are more fair, just, and equitable.

Demonstrating Trustworthiness to Patients in Data-Driven Health Care

Patient data is used to drive an ecosystem of advanced digital tools in health care, like predictive models or artificial intelligence-based decision support. Patients themselves, however, receive little information about these technologies or how they affect their care. This raises important questions about patient trust and continued engagement in a health care system that extracts their data but does not treat them as key stakeholders. This essay explores these tensions and provides steps forward for health systems as they design advanced health information-technology (IT) policies and practices. It centers patients, their concerns, and the ways they perceive trustworthiness to reframe advanced health IT in service of patient interests.

Equity and bias in electronic health records data

Embedded pragmatic clinical trials (ePCTs) are conducted during routine clinical care and have the potential to increase knowledge about the effectiveness of interventions under real world conditions. However, many pragmatic trials rely on data from the electronic health record (EHR) data, which are subject to bias from incomplete data, poor data quality, lack of representation from people who are medically underserved, and implicit bias in EHR design. This commentary examines how the use of EHR data might exacerbate bias and potentially increase health inequities. We offer recommendations for how to increase generalizability of ePCT results and begin to mitigate bias to promote health equity.

Clinical Decision Support for Precision Dosing: Opportunities for Enhanced Equity and Inclusion in Health Care

Precision dosing aims to tailor doses to individual patients with the goal of improving treatment efficacy and avoiding toxicity. Clinical decision support software (CDSS) plays a crucial role in mediating this process, translating knowledge derived from clinical trials and real-world data (RWD) into actionable insights for clinicians to use at the point of care. However, not all patient populations are proportionally represented in clinical trials and other data sources that inform CDSS tools, limiting the applicability of these tools for underrepresented populations. Here, we review some of the limitations of existing CDSS tools and discuss methods for overcoming these gaps. We discuss considerations for study design and modeling to create more inclusive CDSS, particularly with an eye toward better incorporation of biological indicators in place of race, ethnicity, or sex. We also review inclusive practices for collection of these demographic data, during both study design and in software user interface design. Because of the role CDSS plays in both recording routine clinical care data and disseminating knowledge derived from data, CDSS presents a promising opportunity to continuously improve precision dosing algorithms using RWD to better reflect the diversity of patient populations.

Resident Willingness to Participate in Digital Contact Tracing in a COVID-19 Hotspot: Findings From a Detroit Panel Study

BACKGROUND

Digital surveillance tools and health informatics show promise in counteracting diseases but have limited uptake. A notable illustration of the limits of such tools is the general failure of digital contact tracing in the United States in response to COVID-19.

OBJECTIVE

We investigated the associations between individual characteristics and the willingness to use app-based contact tracing in Detroit, a majority-minority city that experienced multiple waves of COVID-19 outbreaks and deaths since the start of the pandemic. The aim of this study was to examine variations among residents in the willingness to download a contact tracing app on their phones to provide public health officials with information about close COVID-19 contact during summer 2020.

METHODS

To examine residents' willingness to participate in digital contact tracing, we analyzed data from 2 waves of the Detroit Metro Area Communities Study, a population-based survey of Detroit, Michigan residents. The data captured 1873 responses from 991 Detroit residents collected in June and July 2020. We estimated a series of multilevel logit models to gain insights into differences in the willingness to participate in digital contact tracing across a variety of individual attributes, including race/ethnicity, degree of trust in the government, and level of education, as well as interactions among these variables.

RESULTS

Our results reflected widespread reluctance to participate in digital contact tracing in response to COVID-19, as less than half (826/1873, 44.1%) of the respondents said they would be willing to participate in app-based contact tracing. Compared to White respondents, Black (odds ratio [OR] 0.45, 95% CI 0.23-0.86) and Latino (OR 0.32, 95% CI 0.11-0.99) respondents were significantly less willing to participate in digital contact tracing. Trust in the government was positively associated with the willingness to participate in digital contact tracing (OR 1.17, 95% CI 1.07-1.27), but this effect was the strongest for White residents (OR 2.14, 95% CI 1.55-2.93). We found similarly divergent patterns of the effects of education by race. While there were no significant differences among noncollege-educated residents, White college-educated residents showed greater willingness to use app-based contact tracing (OR 6.12, 95% CI 1.86-20.15) and Black college-educated residents showed less willingness (OR 0.46, 95% CI 0.26-0.81).

CONCLUSIONS

Trust in the government and education contribute to Detroit residents' wariness of digital contact tracing, reflecting concerns about surveillance that cut across race but likely arise from different sources. These findings point to the importance of a culturally informed understanding of health hesitancy for future efforts hoping to leverage digital contact tracing. Though contact tracing technologies have the potential to advance public health, unequal uptake may exacerbate disparate impacts of health crises.

An Ecosystem Approach to Earning and Sustaining Trust in Health Care-Too Big to Care

This Viewpoint describes the decline in trust in medical institutions in the US and suggests approches to rebuilding and maintaining trust.

Lecanemab: Appropriate Use Recommendations

Lecanemab (Leqembi®) is approved in the United States for the treatment of Alzheimer's disease (AD) to be initiated in early AD (mild cognitive impairment [MCI] due to AD or mild AD dementia) with confirmed brain amyloid pathology. Appropriate Use Recommendations (AURs) are intended to help guide the introduction of new therapies into real-world clinical practice. Community dwelling patients with AD differ from those participating in clinical trials. Administration of lecanemab at clinical trial sites by individuals experienced with monoclonal antibody therapy also differs from the community clinic-based administration of lecanemab. These AURs use clinical trial data as well as research and care information regarding AD to help clinicians administer lecanemab with optimal safety and opportunity for effectiveness. Safety and efficacy of lecanemab are known only for patients like those participating in the phase 2 and phase 3 lecanemab trials, and these AURs adhere closely to the inclusion and exclusion criteria of the trials. Adverse events may occur with lecanemab including amyloid related imaging abnormalities (ARIA) and infusion reactions. Monitoring guidelines for these events are detailed in this AUR. Most ARIA with lecanemab is asymptomatic, but a few cases are serious or, very rarely, fatal. Microhemorrhages and rare macrohemorrhages may occur in patients receiving lecanemab. Anticoagulation increases the risk of hemorrhage, and the AUR recommends that patients requiring anticoagulants not receive lecanemab until more data regarding this interaction are available. Patients who are apolipoprotein E ε4 (APOE4) gene carriers, especially APOE4 homozygotes, are at higher risk for ARIA, and the AUR recommends APOE genotyping to better inform risk discussions with patients who are lecanemab candidates. Clinician and institutional preparedness are mandatory for use of lecanemab, and protocols for management of serious events should be developed and implemented. Communication between clinicians and therapy candidates or those on therapy is a key element of good clinical practice for the use of lecanemab. Patients and their care partners must understand the potential benefits, the potential harms, and the monitoring requirements for treatment with this agent. Culture-specific communication and building of trust between clinicians and patients are the foundation for successful use of lecanemab.