DPH 850 Module 7 Data Equity and Vulnerable Populations Example

Who Gets Counted: Data Equity and Vulnerable Populations in Public Health Information Systems

Student Name

Doctor of Public Health Program, Aspen University

DPH 850: Health Informatics for Public Health Leaders

Instructor Name

Month Day, Year

Who Gets Counted: Data Equity and Vulnerable Populations in Public Health Information Systems

Public health data shape which problems get attention and which communities receive resources. When data about some groups are missing, lumped together or distorted, their needs become invisible. This paper examines data equity across the systems a composite state health department uses and proposes actions to ensure that vulnerable populations are counted accurately and fairly.

How Data Hide Disparities

Disparities can disappear in several ways. Race and ethnicity may be missing from many records. Broad categories may mask differences, as when all Asian Americans are grouped together despite large differences among subgroups. Small populations may be suppressed to protect privacy, leaving no data at all. And algorithms trained on biased data may reproduce inequities.

Problems and Remedies

The table summarizes data equity problems, examples, harms and remedies.

Race and Ethnicity Completeness

In the state's surveillance system, race and ethnicity are complete in only 64% of case reports, largely because laboratory reports do not carry them. During the pandemic, this gap delayed recognition of disproportionate impact on some communities. Remedies include requiring these fields in electronic case reports, matching records to immunization and vital records data and training clinics to collect self-reported data.

Reporting Standards

How race and ethnicity are reported matters as much as whether they are collected. Updated guidance for medical and scientific journals calls for explaining who classified race and ethnicity, using specific rather than broad categories where possible and avoiding treating race as a biological variable (Flanagin et al., 2021). The department will apply similar principles to its public reports.

Disaggregation

Grouping diverse populations together hides differences. The department will collect and report detailed categories where numbers allow, including subgroups of Asian American, Hispanic and Black populations, and will report Native Hawaiian and Pacific Islander data separately from Asian data.

Small Numbers and Privacy

Suppressing small counts protects privacy but can leave small populations without data. Alternatives include combining years, using larger geographic areas, reporting rates with wide intervals and granting approved partners access to detailed data under agreements. Each option balances privacy against the need to see disparities. The department will publish its suppression rules so that users understand why some cells are blank.

Algorithmic Bias

Algorithms increasingly guide public health and health care decisions. Obermeyer et al. (2019) examined a commercial risk tool that health systems used to choose patients for extra care management. It predicted future costs, and since spending on Black patients was lower than on equally sick White patients, it ranked Black patients as healthier than they were. Replacing cost with a direct measure of illness would have substantially raised the proportion of Black patients selected.

Testing Algorithms

Before using any algorithm to target outreach or allocate resources, the department will test whether its outputs differ by race, ethnicity, income or geography in ways not explained by need. It will examine what the algorithm predicts and whether that target is a fair proxy for the outcome of interest.

Tribal Data Sovereignty

Tribal nations are sovereign governments with rights over data about their citizens. The department will work with tribes through formal agreements that respect tribal authority over how their data are collected, used and published, and it will share data back with tribal health departments.

Community Involvement

Communities affected by data decisions should help make them. The department will create a data equity advisory group including representatives of immigrant communities, tribes, people with disabilities and rural residents, to review data collection, reporting and dashboards. Members will be paid for their time and receive data briefings before public release.

Population Health Records

A population health record that integrates data from many sources can fill gaps in any single system (Friedman & Parrish, 2010). Linking surveillance with immunization and vital records improves demographic completeness, since a field missing in one source may be present in another.

Action Plan

In the next two years, the department will raise race and ethnicity completeness to 85%, publish detailed categories on its dashboards, adopt an algorithm review process, sign data agreements with the state's tribes and establish the advisory group. Progress will be reported publicly.

Why Equity in Data Matters

Data equity is not an abstract concern. During the pandemic, communities whose data were incomplete often received testing sites and vaccines later than communities whose burden was well documented. Accurate, detailed data are a prerequisite for fair allocation of resources and for holding agencies accountable for reducing disparities.

Sexual Orientation and Gender Identity

Few public health systems collect sexual orientation and gender identity, leaving disparities affecting lesbian, gay, bisexual and transgender people largely invisible. Adding standard fields, training staff to ask respectfully and protecting these data carefully can make these populations visible without exposing individuals to harm.

Disability and Language

People with disabilities and those with limited English proficiency often face barriers to care and information, yet surveillance systems rarely record disability status or preferred language. Adding these fields would help the department plan accessible outreach and interpreter services, especially during emergencies.

Accountability for Equity

The department will publish an annual data equity report showing completeness of demographic fields by source, progress on disaggregation and results of algorithm reviews. Public reporting creates pressure to improve and invites communities to hold the department to its commitments.

Data Collection at the Source

Most demographic data originate in clinics and hospitals. Staff there need training to ask about race, ethnicity, language and disability respectfully, and patients need to understand why the questions matter. Registration systems should offer detailed categories and allow people to select more than one. Improvements at the source flow through to every public health system that receives the data.

Geographic Detail

Health differences often appear at the neighborhood level, yet many reports stop at the county. Geocoding addresses allows analysis by census tract, revealing pockets of high burden hidden in county averages. Tract-level data require careful privacy protection but are among the most useful tools for directing resources to communities most in need.

Conclusion

Data equity requires counting everyone accurately, in categories that reveal rather than hide differences, while protecting privacy and respecting sovereignty. Missing data, broad categories, suppression and biased algorithms can all render vulnerable populations invisible. Concrete remedies and community involvement can make the state's information systems serve those who need them most.

References

Flanagin, A., Frey, T., Christiansen, S. L., & AMA Manual of Style Committee. (2021). Updated guidance on the reporting of race and ethnicity in medical and science journals. JAMA, 326(7), 621-627. https://doi.org/10.1001/jama.2021.13304

Friedman, D. J., & Parrish, R. G. (2010). The population health record: Concepts, definition, design, and implementation. Journal of the American Medical Informatics Association, 17(4), 359-366. https://doi.org/10.1136/jamia.2009.001578

Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453. https://doi.org/10.1126/science.aax2342