Peer-reviewed article
Inequities in Childhood Vaccination Coverage in Zhejiang, Province: Evidence from a Decomposition Analysis on Two-Round Surveys
Yu Hu, Hui Liang, Ying Wang, Yaping Chen ·
Summary
Written by AI, not yet AICS's view · claude-haiku-4-5-20251001This study examined inequality in childhood vaccination coverage in Zhejiang province, China, using survey data from 2014 and 2017 for children aged 24-35 months. Full primary immunization coverage increased from 80.6% to 85.2%, but socioeconomic inequality persisted. Mother's education level explained about 68% of the socioeconomic inequality in vaccination coverage, with other factors like birth order, ethnicity, and maternal employment also contributing. The study recommends focusing on poverty reduction and improving women's literacy to reduce vaccination inequity.
Key points
- Full primary immunization coverage rose from 80.6% in 2014 to 85.2% in 2017
- Socioeconomic inequality in vaccination coverage decreased but remained significant (concentration index fell from 0.12028 to 0.10129)
- Mother's education level explained 67-68% of socioeconomic inequality in vaccination coverage
- Other factors contributing to inequality included birth order, ethnic group, maternal age, employment status, residence, and immigration status
- Policy recommendations focus on poverty elimination and reducing women's illiteracy
Limitations
The study uses household income as a single measure of socioeconomic status and analyzes data from one province in China, which may not be generalizable to other regions. The decomposition analysis cannot establish causal relationships, only identify which factors statistically explain the measured inequality.
Abstract
Objective: The objectives of this study were to determine the degree and risk factors of the inequity in the childhood coverage of full primary immunization (FPI) in Zhejiang province. Method: We used data from two rounds of vaccination coverage surveys among children aged 24–35 months conducted in 2014 and 2017, respectively. The household income per month was used as an index of socioeconomic status for the inequality analysis. The concentration index (CI) was used to quantify the degree of inequality, and the decomposition approach was applied to quantify the contributions from demographic factors to inequality in the coverage of FPI. Results: The coverage rates of FPI were 80.6%, with a CI value of 0.12028 for the 2014 survey, while the coverage rates of FPI were 85.2%, with a CI value of 0.10129 for the 2017 survey. The results of decomposition analysis suggested that 68.2% and 67.1% of the socioeconomic inequality in the coverage of FPI could be explained by the mother’s education level for the 2014 and 2017 survey, respectively. Other risk factors including birth order, ethnic group, mother’s age, maternal employment status, residence, immigration status, GDP per-capita, and the percentage of the total health spending allocated to public health could also explain this inequality. Conclusion: The socioeconomic inequity in the coverage of FPI still remained, although this gap was reduced between 2014 and 2017. Policy recommendations for health interventions on reducing the inequality in the coverage of FPI should be focused on eliminating poverty and women’s illiteracy.