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# US childhood hepatitis B vaccine policy changes modelled to increase chronic infections
- URL: https://www.coios.me/us-childhood-hepatitis-b-vaccine-policy-changes-modelled-to/
- Published: 2026-10-23T13:08:54.000Z
- Updated: 2026-10-23T16:59:55.000Z
- Description: A Markov model of a 3.6 million US birth cohort projects 83% more childhood chronic hepatitis B infections under shared decision making.
- Author: Daniel Ryan
- Tags: Data, models and systems, Value in health, Infection and pandemics, United States, #peer-reviewed, #new, #item, #source-2026-10, #score-68

The study modelled proposed changes to the US childhood hepatitis B vaccination schedule. The cohort was a single birth cohort of 3.6 million US-born infants. A Markov cohort state transition model ran over 20 years. It used maternal HBV prevalence of 0.4%, test sensitivity of 99.5% and age-stratified risk of chronic infection. Universal birth-dose vaccination gave 255 childhood chronic infections (95% UI 130 to 530). Deferred two-month vaccination raised this by 19% to 303\. Shared decision making raised it by 83% to 467 (95% UI 209 to 1032). Susceptible people at year 20 rose from 232,000 to 474,000 and 1,806,000 respectively. The main limitation is uncertainty in the risk of horizontal HBV infection.

*Why it is interesting: Quantifies the infection burden attached to a specific vaccination schedule change. Also reports how many children would enter adulthood susceptible to hepatitis B each year.*

Source

[Annals of Internal Medicine, 20 October 2026](https://doi.org/10.7326/annals-26-02135?ref=coios.me)

Paper

Projected Effect of 2026 Federal Changes to the Childhood Immunization Schedule on Chronic Hepatitis B Virus Infections in the United States

Authors

Mohareb AM, Ganapathi L, Dugdale CM, Rajbhandari R, Ciaranello A, Neilan AM et al.

DOI

10.7326/annals-26-02135

Type

Peer-reviewed article

Design

Markov cohort state-transition model of perinatal and childhood HBV infection, one US birth cohort (n=3.6 million), 20-year horizon, probabilistic sensitivity analysis

Verdict

New finding

Driver

[Value in health](https://www.coios.me/d-health-economics/)

Driver

[Infection and pandemics](https://www.coios.me/d-infectious-disease/)