• Pei-Pei Zhang and Chun Chang.
• Department of Social Medicine & Health Education Medicine, School of Public Health, Peking University, Beijing 100191, China.
• Zhonghua Yu Fang Yi Xue Za Zhi. 2012 Sep 1; 46 (9): 822-4.

ObjectiveTo measure, evaluate health literacy and discover its relative factors among residents of three cities in China.MethodsMultiple cluster sampling was employed and 3300 respondents were surveyed by self-designed questionnaires in Beijing, Datong and Shenzhen city during May to September in 2011. Information on demographic characteristics, health knowledge and health literacy was collected. Respondents' health literacy scores were statistically reported and evaluated referring to education level. To explore relative factors of health literacy, multiple linear regression model with score of health literacy as dependent variable, respondents' demographic characteristics and health knowledge as independent variables was built by multiple linear regression analysis.ResultsQuestionnaires were conducted among 3300 residents and resulted in 90.9% (3000/3300) qualified sample return. Respondents were (31.6 ± 12.0) (15 - 65) years old, who got (19.92 ± 5.17) (2 - 28) scores in the health literacy test with an average correct rate of 71.1%. The proportion of subjects with low (< 20.5 grades), medium (20.5 - 24.5 grades), and high (> 24.5 grades) level of health literacy were 46.6% (1398/3000), 33.1% (993/3000) and 20.3% (609/3000) respectively. The multiple linear regression model showed that positive correlation factors of health literacy included health knowledge (β = 0.28), education level (β = 0.28), income (β = 0.14), gender (β = 0.05), nationality (β = 0.05), registered permanent residence (β = 0.05) (all P values < 0.05) and the negative correlated factors included age (β = -0.28), occupation (β = -0.05), respectively (all P values < 0.05).ConclusionOver 50% residents in the three studied cities had medium and above health literacy. The positive correlated factors of health literacy included health knowledge, education level, income, gender, nationality, registered permanent residence and the negative correlated factors included age and occupation.

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