Modeling the Potential Impact of Missing Race and Ethnicity Data in Infectious Disease Surveillance Systems on Disparity Measures: Scenario Analysis of Different Imputation Strategies

Underrepresented Populations on Public Health Data Dashboards: Design Considerations for Improving Usability and Usefulness

CONTEXT

Representation of racial and ethnic minority groups and sexual and gender minority groups is limited on public health data dashboards. The small size of these populations leads to data being aggregated or suppressed to produce stable estimations and preserve individuals' information privacy. This might, however, limit the usefulness of the represented data for identifying individuals' risk factors and allocating services.

OBJECTIVE

To explore public health practitioners' concerns about underrepresented populations on public health data dashboards that emerged during a usability evaluation study.

PARTICIPANTS

20 public health practitioners from New York State, US.

SETTING

Virtual Zoom meetings were conducted to monitor the participants' use of a public health dashboard and ask about their experience using it. The collected data, in the form of researchers' notes and audio transcripts, were analyzed using the thematic analysis approach.

RESULTS

Participants were very concerned about the underrepresentation of sexual and gender minority groups and racial and ethnic minority groups on public health data dashboards. Four themes emerged regarding the consequences of the underrepresentation: (1) misinterpretation of risk for underrepresented groups, (2) exacerbating the mistrust between underrepresented populations and the government, (3) a potential disservice to underserved populations if the data is used as the basis for allocating resources, and (4) and unknown impact for the individuals whose demographic information is unknown.

CONCLUSIONS

We propose considerations for displaying underrepresented populations on public health data dashboards to improve the utility of the represented data.

Racial and geographic disparities in neonatal brain care

In this review, we explore race-based disparities in neonatology and their impact on brain injury and neurodevelopmental outcomes. We discuss the historical context of healthcare discrimination, focusing on the post-Civil War era and the segregation of healthcare facilities. We highlight the increasing disparity in infant mortality rates between Black and White infants, with premature birth being a major contributing factor, and emphasize the role of prenatal factors such as metabolic syndrome and toxic stress in affecting neonatal health. Furthermore, we examine the geographic and historical aspects of racial disparities, including the consequences of redlining and limited access to healthcare facilities or nutritious food options in Black communities. Finally, we delve into the higher incidence of brain injuries in Black neonates, as well as disparities in adverse neurodevelopmental outcome. This evidence underscores the need for comprehensive efforts to address systemic racism and provide equitable access to healthcare resources.

Validation of Multi-State EHR-Based Network for Disease Surveillance (MENDS) Data and Implications for Improving Data Quality and Representativeness

Introduction

Surveillance modernization efforts emphasize the potential use of electronic health record (EHR) data to inform public health surveillance and prevention. However, EHR data streams vary widely in their completeness, accuracy, and representativeness.

Methods

We developed a validation process for the Multi-State EHR-Based Network for Disease Surveillance (MENDS) pilot project to identify and resolve data quality issues that could affect chronic disease prevalence estimates. We examined MENDS validation processes from December 2020 through August 2023 across 5 data-contributing organizations and outlined steps to resolve data quality issues.

Results

We identified gaps in the EHR databases of data contributors and in the processes to extract, map, integrate, and analyze their EHR data. Examples of source-data problems included missing data on race and ethnicity and zip codes. Examples of data processing problems included duplicate or missing patient records, lower-than-expected volumes of data, use of multiple fields for a single data type, and implausible values.

Conclusion

Validation protocols identified critical errors in both EHR source data and in the processes used to transform these data for analysis. Our experience highlights the value and importance of data validation to improve data quality and the accuracy of surveillance estimates that use EHR data. The validation process and lessons learned can be applied broadly to other EHR-based surveillance efforts.

Integrating human-centered design in public health data dashboards: lessons from the development of a data dashboard of sexually transmitted infections in New York State

OBJECTIVE

The increased availability of public data and accessible visualization technologies enhanced the popularity of public health data dashboards and broadened their audience from professionals to the general public. However, many dashboards have not achieved their full potential due to design complexities that are not optimized to users' needs.

MATERIAL AND METHODS

We used a 4-step human-centered design approach to develop a data dashboard of sexually transmitted infections for the New York State Department of Health: (1) stakeholder requirements gathering, (2) an expert review of existing data dashboards, (3) a user evaluation of existing data dashboards, and (4) an usability evaluation of the prototype dashboard with an embedded experiment about visualizing missing race and ethnicity data.

RESULTS

Step 1 uncovered data limitations and software requirements that informed the platform choice and measures included. Step 2 yielded a checklist of general principles for dashboard design. Step 3 revealed user preferences that influenced the chart types and interactive features. Step 4 uncovered usability problems resulting in features such as prompts, data notes, and displaying imputed values for missing race and ethnicity data.

DISCUSSION

Our final design was accepted by program stakeholders. Our modifications to traditional human-centered design methodologies to minimize stakeholders' time burden and collect data virtually enabled project success despite barriers to meeting participants in-person and limited public health agency staff capacity during the COVID-19 pandemic.

CONCLUSION

Our human-centered design approach and the final data dashboard architecture could serve as a template for designing public health data dashboards elsewhere.