FROM NUMBERS TO NEEDLES: TRUST IN FEDERAL STATISTICAL AGENCIES AND VACCINE HESITANCY AMONG US ADULTS

By: Nathaniel J. Maxey

Advisor: Jemar R. Bather, PhD

Introduction

Introduction

Research Question

• Among US adults, how is trust in federal statistics including confidence in the US Census Bureau and other federal statistical agencies associated with hesitancy to receive COVID 19 and influenza vaccines after accounting for key sociodemographic factors?

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• We hypothesized that lower trust in federal statistical agencies would be significantly associated with increased odds of vaccine hesitancy.

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Methods

Methods

Methods

Results �

Results �

Results �

Results �

Results �

Results �

Results �

Results �

Trust in Government Data Really Mattered

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• Even after accounting for age, gender identity, race, Hispanic ethnicity, educational attainment, employment status, marital status, number of children, and health insurance coverage

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• People who did not trust federal statistics were:

�→ 2.6 times more likely to skip the COVID vaccine�→ Almost 2 times more likely to skip the flu shot

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• People with very little confidence in the Census Bureau or other federal agencies were also much more likely to be vaccine hesitant.

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Takeaway

Key Takeaway: Trust Matters for Vaccination

• People who don’t trust federal statisitcs or agencies like the U.S. Census Bureau were significantly more likely to skip the COVID-19 and flu vaccine — even after accounting for age, race, and other factors.

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Why This Matters

• Trust in government data isn’t just about numbers — it shapes real health decisions.
• Building public trust is essential for future vaccine campaigns and public health efforts — especially in a time of misinformation and political division

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Conclusions

Strengths

• Nationally representative data from the U.S. Census Bureau’s Household Pulse Survey
• Broad coverage of social, economic, mental health, social isolation and sociodemographic factors
• Recent data (October 15–29, 2024) provides updated insights beyond studies using 2020–2021 data

Limitations

• Self-reported vaccination status may introduce information bias and misclassification Cross-sectional design prevents causal inference and limits understanding of how trust and behavior evolve over time

Future Research

• Conduct longitudinal studies to assess causal links between institutional trust and vaccine uptake
• Incorporate objective measures of health behavior (e.g. electronic records) to reduce information bias
• Evaluate targeted trust-building interventions in marginalized populations
• Explore the role of trust in statistical agencies, federal statistics and the U.S. Census Bureau in shaping public health behaviors

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Questions?

References

OpenAI. (2025). Image of global immunization efforts saving lives [AI-generated image]. ChatGPT. https://chat.openai.com/

World Health Organization. (n.d.). Global immunization efforts have saved at least 154 million lives over the past 50 years. Retrieved April 10, 2025, from https://www.who.int/news/item/24-04-2024-global-immunization-efforts-have-saved-at-least-154-million-lives-over-the-past-50-years

Levenson, M., Rosenbluth, T., & Mandavilli, A. (2025, February 26). Unvaccinated child dies of measles in Texas outbreak. The New York Times. https://www.nytimes.com/2025/02/26/us/texas-measles-outbreak-death.html

Texas Department of State Health Services. (n.d.). Measles outbreak in Texas. Retrieved April 10, 2025, from https://dshs.texas.gov/measles

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Acknowledgments

Acknowledgements

Thank you, Dr. Bather, for your invaluable guidance and support:

• Your expertise in survey methodology and statistical modeling has greatly strengthened this work
• Your thoughtful feedback on model specifications and interpretation kept this analysis on track
• Your encouragement and willingness to discuss ideas made all the difference
• I am deeply grateful for your mentorship and time.

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