[Association between timing of introducing complementary foods and anemia of infants aged 6-11 months in four provinces of China].

OBJECTIVE: To analyze the anemia status of infants aged 6-11 months in Beijing, Shanxi, Jiangxi and Zhejiang of China, and to explore the association between timing of introducing complementary foods and hemoglobin level, anemia of infants aged 6-11 months. METHODS: Data was from National Nutrition and Health Systematic Survey for 0-18 Years Old Children in China. A total of 1404 infants aged 6-11 months from Beijing, Shanxi, Jiangxi and Zhejiang were enrolled in this study. Demographic characteristics, socioeconomic status, birth status and complementary feeding information were collected through questionnaire survey. HemoCue Hb201+ hemoglobin analyzer was used to measure hemoglobin value. The exposure variables in this study were timing of introducing complementary foods(≤5 months, 6 months and ≥7 months), and the outcome variables were hemoglobin level and anemia rate. The association between timing of introducing complementary foods and hemoglobin level was analyzed by using multivariate linear regression model, and the association between timing of introducing complementary foods and anemia rate was analyzed by using multivariate Logistic regression model. RESULTS: The hemoglobin levels of infants aged 6-11 months were(114.8±11.0)g/L, (115.5±10.5)g/L in urban areas and(114.1±11.5) g/L in rural areas. The anemia rate was 28.2%, 24.0% in urban areas and 32.9% in rural areas. The hemoglobin levels of infants introducing complementary foods at ≤5 months, 6 months and ≥7 months were(114.0±11.1), (115.2±10.9) and(114.5±10.7) g/L, respectively. After adjusting for potential confounding factors, there was no significant difference in hemoglobin level between the ≤5 months group and 6 months group(F=2.37, P=0.124), and no significant difference between the ≥ 7 months group and the 6 months group(F=0.09, P=0.770). The anemia rate of infants introducing complementary foods at ≤5 months, 6 months and ≥7 months were 32.3%, 27.9% and 22.7%, respectively. After adjusting for potential confounding factors, there was no significant difference in anemia rate between the ≤5 months group and 6 months group(OR=1.26(95%CI 0.86-1.83)), and no significant difference between the ≥7 months group and the 6 months group(OR=0.65(95%CI 0.35-1.20)). CONCLUSION: Anemia remains a serious problem for infants aged 6-11 months in Beijing, Shanxi, Jiangxi and Zhejiang. Timing of introducing complementary foods may not be related with hemoglobin level and anemia rate of infants aged 6-11 months.

Application of a novel hybrid algorithm of Bayesian network in the study of hyperlipidemia related factors: a cross-sectional study.

BACKGROUND: This article aims to understand the prevalence of hyperlipidemia and its related factors in Shanxi Province. On the basis of multivariate Logistic regression analysis to find out the influencing factors closely related to hyperlipidemia, the complex network connection between various variables was presented through Bayesian networks(BNs). METHODS: Logistic regression was used to screen for hyperlipidemia-related variables, and then the complex network connection between various variables was presented through BNs. Since some drawbacks stand out in the Max-Min Hill-Climbing (MMHC) hybrid algorithm, extra hybrid algorithms are proposed to construct the BN structure: MMPC-Tabu, Fast.iamb-Tabu and Inter.iamb-Tabu. To assess their performance, we made a comparison between these three hybrid algorithms with the widely used MMHC hybrid algorithm on randomly generated datasets. Afterwards, the optimized BN was determined to explore to study related factors for hyperlipidemia. We also make a comparison between the BN model with logistic regression model. RESULTS: The BN constructed by Inter.iamb-Tabu hybrid algorithm had the best fitting degree to the benchmark networks, and was used to construct the BN model of hyperlipidemia. Multivariate logistic regression analysis suggested that gender, smoking, central obesity, daily average salt intake, daily average oil intake, diabetes mellitus, hypertension and physical activity were associated with hyperlipidemia. BNs model of hyperlipidemia further showed that gender, BMI, and physical activity were directly related to the occurrence of hyperlipidemia, hyperlipidemia was directly related to the occurrence of diabetes mellitus and hypertension; the average daily salt intake, daily average oil consumption, smoking, and central obesity were indirectly related to hyperlipidemia. CONCLUSIONS: The BN of hyperlipidemia constructed by the Inter.iamb-Tabu hybrid algorithm is more reasonable, and allows for the overall linking effect between factors and diseases, revealing the direct and indirect factors associated with hyperlipidemia and correlation between related variables, which can provide a new approach to the study of chronic diseases and their associated factors.

Prevalence of hyperlipidemia in Shanxi Province, China and application of Bayesian networks to analyse its related factors.

This study aimed to obtain the prevalence of hyperlipidemia and its related factors in Shanxi Province, China using multivariate logistic regression analysis and tabu search-based Bayesian networks (BNs). A multi-stage stratified random sampling method was adopted to obtain samples among the general population aged 18 years or above. The prevalence of hyperlipidemia in Shanxi Province was 42.6%. Multivariate logistic regression analysis indicated that gender, age, region, occupation, vegetable intake level, physical activity, body mass index, central obesity, hypertension, and diabetes mellitus are associated with hyperlipidemia. BNs were used to find connections between those related factors and hyperlipidemia, which were established by a complex network structure. The results showed that BNs can not only be used to find out the correlative factors of hyperlipidemia but also to analyse how these factors affect hyperlipidemia and their interrelationships, which is consistent with practical theory, is superior to logistic regression and has better application prospects.