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In total, we included 12,241 individuals, comprised of 5,684 males (46.4%) and 6,557 females (53.6%). Only 14.1% male adults and 12.4% female adults were in the high-education group. In our study, 31.8% males and 34.7% females were in the low-income group (Table 1).
Characteristics Males Females Total Cases % Cases % Age (years) 18–34 1,050 18.5 1,311 20.0 2,361 35–49 2,090 36.8 2,416 36.8 4,506 50–64 2,544 44.8 2,830 43.2 5,374 Education Low 1,033 18.2 1,870 28.6 2,903 Medium 3,838 67.7 3,854 59.0 7,692 High 798 14.1 814 12.4 1,612 Region North 2,179 38.3 2,526 38.5 4,705 South 3,505 61.7 4,031 61.5 7,536 Area City 1,171 20.6 1,401 21.4 2,572 Suburb 952 16.7 1,096 16.7 2,048 County 1,033 18.2 1,168 17.8 2,201 Village 2,528 44.5 2,,892 44.1 5,420 Income (1,000 yuan/per capita) Low 1,806 31.8 2,272 34.7 4,078 Medium 1,924 33.8 2,157 32.9 4,081 High 1,954 34.4 2,128 32.4 4,082 Table 1. Characteristics of adults aged 18–64 years from 15 provinces of China, CHNS 2015
Table 2 presents the dietary intakes of thiamine, riboflavin, and niacin and the prevalence of inadequacy by age, education, area of residence, income, and region in both sexes. The prevalence of inadequate intake of thiamine and riboflavin was statistically significantly higher among males (77.4%, and 85.5%, respectively) than females (75.8% and 81.7%, respectively). Age, education, income, and area of residence were associated with the adjusted median intake of thiamine, riboflavin, and niacin among males and females. Region was associated with the adjusted median intake of thiamine among females, but not among males.
Variables n Thiamine (mg/d) Riboflavin (mg/d) Niacin (mg/d) Mean (95% CI) Median (95% CI) < EAR% Mean (95% CI) Median (95% CI) < EAR% Mean (95% CI) Median (95% CI) < EAR% Males Total 5,684 0.96 (0.95, 0.96) 0.88 (0.87, 0.89) 77.4 0.86 (0.85, 0.86) 0.78 (0.78, 0.79) 85.5 17.11 (16.98, 17.23) 15.83 (15.70,15.99) 25.6 Age (years) 18–34 1,050 0.95 (0.93, 0.97) 0.87 (0.85, 0.90) a 77.7 0.85 (0.84, 0.86) 0.78 (0.76, 0.79)a 86.4 17.44 (17.14, 17.74) 16.36 (16.00, 16.64)a 23.0 35–49 2,090 0.98 (0.97, 1.00) 0.90 (0.89,0.92) a 75.7 0.87 (0.86, 0.88) 0.80 (0.79, 0.81) a 84.8 17.65 (17.44, 17.87) 16.27 (16.01, 16.52) a 24.1 50–64 2,544 0.94 (0.93, 0.95) 0.86 (0.84, 0.87) a 78.7 0.85 (0.84, 0.86) 0.77 (0.77, 0.78) a 85.7 16.53 (16.33, 16.72) 15.34 (15.12, 15.53) a 28.0 Education Low 1,033 0.92 (0.90, 0.94) 0.85 (0.83, 0.87) a 77.7 0.79 (0.78, 0.80) 0.72 (0.71, 0.74) a 90.8 16.54 (16.23,16.85) 15.38 (15.00, 15.68) a 27.2 Medium 3,838 0.97 (0.96, 0.98) 0.88 (0.87, 0.90) a 77.3 0.86 (0.85, 0.87) 0.78 (0.77, 0.79) a 85.0 17.04 (16.88,17.20) 15.76 (15.60, 15.94) a 26.3 High 798 0.95 (0.93, 0.97) 0.87 (0.86, 0.90) a 77.6 0.94 (0.92, 0.95) 0.87 (0.86, 0.89) a 80.7 18.19 (17.87,18.52) 16.72 (16.49, 17.16) a 20.3 Region North 2,179 0.96 (0.95, 0.97) 0.87 (0.86, 0.89) 76.6 0.84 (0.83, 0.85) 0.76 (0.75, 0.77) a 86.7 15.02 (14.82,15.22) 13.54 (13.33, 13.76) a 39.4 South 3,505 0.95 (0.94, 0.96) 0.87 (0.87, 0.89) 77.9 0.87 (0.86, 0.88) 0.80 (0.79, 0.81) a 84.8 18.41 (18.25,18.56) 17.10 (16.91, 17.26) a 17.0 Area City 1,171 0.97 (0.95, 0.99) 0.90 (0.88, 0.93) a 76.5 0.97 (0.96, 0.98) 0.90 (0.88, 0.91) a 78.6 18.07 (17.79,18.33) 16.53 (16.21, 16.81) a 20.5 Suburb 952 0.96 (0.94, 0.98) 0.86 (0.84, 0.88) a 78.7 0.87 (0.86, 0.88) 0.79 (0.77, 0.81) a 86.2 17.89 (17.57,18.21) 16.44 (16.21, 16.78) a 22.8 County 1,033 0.94 (0.92, 0.96) 0.88 (0.85, 0.90) a 76.8 0.84 (0.83, 0.85) 0.76 (0.74, 0.77) a 86.3 16.88 (16.58,17.18) 15.78 (15.49, 16.13) a 27.9 Village 2,528 0.95 (0.94, 0.96) 0.87 (0.86, 0.89) a 77.6 0.81 (0.80, 0.82) 0.76 (0.74, 0.77) a 88.1 16.47 (16.27,16.67) 15.36 (15.14, 15.51) a 28.1 Income (1,000 yuan/per capita) Low 1,806 0.95 (0.94, 0.97) 0.87 (0.86, 0.89) a 76.8 0.83 (0.82, 0.84) 0.75 (0.73, 0.76) a 87.7 16.72 (16.48,16.94) 15.52 (15.31, 15.70) a 27.8 Medium 1,924 0.94 (0.93, 0.95) 0.86 (0.85, 0.88) a 79.2 0.83 (0.82, 0.84) 0.75 (0.74, 0.77) a 87.9 16.53 (16.32,16.76) 15.29 (15.05, 15.50) a 28.6 High 1,954 0.98 (0.96, 0.99) 0.89 (0.88, 0.91) a 76.2 0.91 (0.90, 0.92) 0.84 (0.83, 0.85) a 81.1 18.03 (17.81,18.24) 16.68 (16.52, 17.03) a 20.6 Females Total 6,557 0.82 (0.81, 0.82) 0.75 (0.74, 0.76) 75.8 0.76 (0.75, 0.76) 0.69 (0.68, 0.79) 81.7 14.25 (14.14, 14.35) 13.13 (13.02, 13.24) 26.5 Age (years) 18–34 1,311 0.81 (0.79, 0.82) 0.75 (0.73, 0.76) a 77.4 0.76 (0.75, 0.77) 0.69 (0.67, 0.71) a 81.5 14.43 (14.19, 14.67) 13.24 (13.06, 13.59) a 24.6 35–49 2,416 0.84 (0.83, 0.85) 0.76 (0.75, 0.77) a 74.5 0.76 (0.76, 0.77) 0.69 (0.69, 0.71) a 80.7 14.60 (14.43, 14.78) 13.42 (13.23, 13.58) a 24.8 50–64 2,830 0.80 (0.79, 0.81) 0.74 (0.73, 0.75) a 76.3 0.75 (0.74, 0.75) 0.67 (0.67, 0.69) a 82.7 13.86 (13.70, 14.02) 12.81 (12.63, 12.99) a 28.8 Education Low 1,870 0.80 (0.79, 0.81) 0.73 (0.72, 0.75) a 77.3 0.69 (0.68, 0.70) 0.63 (0.62, 0.64) a 87.4 13.77 (13.58, 13.97) 12.80 (12.60, 13.02) a 28.1 Medium 3,854 0.83 (0.82, 0.83) 0.75 (0.75, 0.77) a 74.9 0.77 (0.76, 0.77) 0.70 (0.69, 0.70) a 80.8 14.31 (14.18, 14.46) 13.13 (12.96, 13.27) a 26.9 High 814 0.81 (0.80, 0.83) 0.74 (0.73, 0.77) a 77.2 0.84 (0.83, 0.86) 0.77 (0.75, 0.79) a 73.5 15.01 (14.72, 15.29) 13.76 (13.44, 14.02) a 21.0 Region North 2,526 0.81 (0.80, 0.82) 0.73 (0.73, 0.75) a 76.3 0.73 (0.72, 0.74) 0.65 (0.65, 0.66) a 84.2 12.36 (12.20, 12.51) 11.10 (10.97, 11.25) a 40.3 South 4,031 0.82 (0.81, 0.83) 0.76 (0.75, 0.77) a 75.6 0.77 (0.76, 0.77) 0.71 (0.70, 0.71) a 80.2 15.43 (15.31, 15.56) 14.18 (14.03, 14.31) a 17.9 Area City 1,401 0.84 (0.83, 0.85) 0.77 (0.76,0.79)a 73.4 0.87 (0.86, 0.89) 0.80 (0.79, 0.81) a 70.5 15.08 (14.86, 15.30) 13.91 (13.66, 14.09) a 22.6 Suburb 1,096 0.82 (0.81, 0.84) 0.74 (0.73,0.76) a 77.2 0.77 (0.75, 0.79) 0.69 (0.68, 0.70) a 81.6 14.75 (14.47, 15.02) 13.50 (13.18, 13.80) a 23.4 County 1,168 0.81 (0.79, 0.82) 0.75 (0.73,0.76) a 77.1 0.75 (0.74, 0.76) 0.66 (0.65, 0.68) a 82.5 14.10 (13.86, 14.33) 12.92 (12.66, 13.24) a 26.4 Village 2,892 0.81 (0.80, 0.82) 0.74 (0.72,0.75) a 76.0 0.69 (0.69, 0.70) 0.63 (0.62, 0.64) a 86.9 13.72 (13.56, 13.88) 12.92 (12.66, 13.24) a 29.6 Income (1,000 yuan/per capita) Low 2,272 0.82 (0.81, 0.84) 0.76 (0.74,0.77)a 75.3 0.73 (0.72, 0.74) 0.66 (0.65, 0.67) a 83.7 14.17 (13.98, 14.36) 12.94 (12.76, 13.16) a 28.0 Medium 2,157 0.80 (0.79, 0.81) 0.74 (0.73,0.75) a 76.8 0.72 (0.72, 0.74) 0.66 (0.65, 0.67) a 84.6 13.79 (13.61, 13.96) 12.76 (12.52, 12.95) a 29.5 High 2,128 0.82 (0.81, 0.99) 0.75 (0.74,0.76) a 75.5 0.81 (0.80, 0.82) 0.73 (0.73, 0.74) a 76.7 14.80 (14.63, 14.98) 13.68 (13.46, 13.88) a 21.8 Note. EAR: Estimated Average Requirements. The EAR were used as cutoffs to estimate the prevalence of inadequate intakes of three B-vitamins. Values were adjusted means (95% CI) and adjusted medians (95% CI). The adjusted medians of each B-vitamin intake across subgroups of age (18–34, 35–49, or 50–64), education (low, ≤ 6 years of education; middle, 7–12 years of education; high, ≥ 13 years of education), region (north or south), area (city, suburb, country, or village), and income (tertiles of annual household income) in each gender were compared by analysis or Wilcoxon rank-sum test (P < 0.05).
aSignificantly different from other groups by an overall test (P < 0.05).Table 2. Dietary intake and prevalence of inadequate intake (% of the population below the EAR) of thiamine, riboflavin, and niacin among adults aged 18–64 years, CHNS 2015
Table 3 presents the factors affecting inadequate intake of thiamine, riboflavin, and niacin in adults. Among males and females, no risk factors predicted inadequate thiamine intake in the final model. Controlling for all variables in the final model, significant factors for inadequate intake of riboflavin among males were area of residence and among females were income, region, and area of residence. The results showed a 2.7% reduction in the prevalence of inadequate intake of riboflavin for females in the high-income group compared with the low-income group. Females living in the southern region were associated with a 2.0% reduced prevalence of inadequate intake of riboflavin compared with those in northern region. Among males and females, living in a city was associated with a 6.6%–13.5% reduction in the prevalence of inadequate riboflavin intake compared with living in a village.
Variables Riboflavin Niacin PRb (95% CI) P PRb (95% CI) P Males Education Lowa 1 1 Medium 0.947 (0.877, 1.021) 0.155 0.946 (0.826, 1.083) 0.418 High 0.930 (0.829, 1.044) 0.218 0.766 (0.619, 0.947) 0.014 Income Lowa 1 1 Medium 1.004 (0.990, 1.018) 0.564 0.988 (0.875,1.115) 0.841 High 0.984 (0.967, 1.002) 0.085 0.780 (0.681,0.894) < 0.001 Region Northa 1 1 South 0.994 (0.982, 1.006) 0.354 0.433 (0.390,0.481) < 0.001 Area Villagea 1 1 County 0.996 (0.980, 1.013) 0.641 1.068 (0.925, 1.233) 0.368 Suburb 0.987 (0.969, 1.005) 0.168 0.841 (0.721, 0.981) 0.027 City 0.934 (0.910, 0.958) < 0.001 0.834 (0.712, 0.978) 0.025 Females Education Lowa 1 1 Medium 0.951 (0.890, 1.016) 0.134 0.973 (0.899, 1.052) 0.372 High 0.901 (0.804, 1.010) 0.073 0.788 (0.680, 0.914) < 0.001 Income Lowa 1 1 Medium 0.999 (0.983, 1.015) 0.884 0.966 (0.894, 1.043) 0.372 High 0.973 (0.953, 0.993) 0.010 0.769 (0.700, 0.846) < 0.001 Region Northa 1 1 South 0.980 (0.966, 0.994) < 0.001 0.478 (0.443, 0.515) < 0.001 Area Villagea 1 1 County 0.963 (0.942, 0.985) 0.001 0.999 (0.907, 1.101) 0.990 Suburb 0.964 (0.943, 0.986) 0.001 0.853 (0.768, 0.948) 0.003 City 0.865 (0.839, 0.892) < 0.001 0.891 (0.801, 0.992) 0.036 Note. aReference category.
bAdjusted PR from log binomial regression.Table 3. Log binomial regression for the association of sociodemographic variables with the risk of inadequate intake of thiamine, riboflavin, and niacin among adults aged 18–64 years, CHNS 2015
Significant factors associated with inadequate niacin intake were education, income, region, and area of residence for both sexes in the final model. Among males and females, the high-education group was associated with a 21.2%–23.4% reduction in the prevalence of inadequate niacin intake compared with the low-education group. The results showed a 22.0%–23.1% reduction in the prevalence of inadequate niacin intake for both sexes in the high-income group compared with the low-income group. Among males and females, living in a city was associated with a 10.9%–16.6% reduction in the prevalence of inadequate of niacin intake compared with living in a village.
The lowest prevalence of inadequate riboflavin intake was observed among adults living in a city (PR: 0.934, 95% CI: 0.910, 0.958 for males; PR: 0.865, 95% CI: 0.839, 0.892 for females). The lowest prevalence of inadequate niacin intake was found among adults living in the southern region (PR: 0.433, 95% CI: 0.390, 0.481 for males; PR: 0.478, 95% CI: 0.443, 0.515 for females).
Table 4 presents the top 10 food sources of thiamine, riboflavin, and niacin intake in Chinese adults aged 18–64 years. The top three sources of thiamine, riboflavin, and niacin intake in both sexes are cereals, meat, and vegetables. The contribution of cereals to the intake of thiamine made up 41.2% for males and 40.8% for females, for riboflavin it was 23.9% for males and 22.5% for females, and for niacin, it was 39.4% for males and 38.7% for females.
Rank Thiamine Riboflavin Niacin Males Females Males Females Males Females Food group % Food group % Food group % Food group % Food group % Food group % 1 Cereals and products 41.2 Cereals and products 40.8 Cereals and products 23.9 Cereals and products 22.5 Cereals and products 39.4 Cereals and products 38.7 2 Meat and products 24.9 Meat and products 23.2 Meat and products 19.4 Meat and products 17.2 Meat and products 22.9 Meat and products 21.5 3 Vegetables and products 11.0 Vegetables and products 11.6 Vegetables and products 16.6 Vegetables and products 17.2 Vegetables and products 8.5 Vegetables and products 9.2 4 Legumes and product 4.7 Legumes and product 5.0 Eggs and products 9.4 Eggs and products 10.2 Poultry and product 6.9 Poultry and product 6.7 5 Tubers, starches, and products 3.0 Tubers, starches, and products 3.3 Legumes and product 4.7 Milk and products 4.8 Fish, shellfish, and mollusks 4.5 Fish, shellfish, and mollusks 4.4 6 Eggs and products 2.9 Eggs and products 3.2 Fungi and algae 3.9 Legumes and product 4.8 Fast foods 3.0 Fast foods 3.3 7 Fast foods 2.3 Fruit and products 2.8 Condiments 3.5 Fungi and algae 4.3 Condiments 2.6 Fungi and algae 2.9 8 Fruit and products 1.7 Fast foods 2.6 Poultry and product 3.3 Condiments 3.5 Nuts and seeds 2.5 Condiments 2.6 9 Liquor and alcoholic beverages 1.5 Nuts and seeds 1.2 Milk and products 3.3 Poultry and product 2.9 Fungi and algae 2.5 Tubers, starches, and products 2.5 10 Poultry and product 1.2 Poultry and product 1.1 Fish, shellfish, and mollusks 3.1 Fish, shellfish, and mollusks 2.9 Tubers, starches, and products 2.2 Nuts and seeds 2.4 Table 4. Top 10 food sources of thiamine, riboflavin, and niacin among adults aged 18–64 years, CHNS 2015
Sociodemographic Factors Associated with Dietary Intake of Thiamine, Riboflavin, and Niacin among Chinese Adults in 2015
doi: 10.3967/bes2020.087
- Received Date: 2019-10-24
- Accepted Date: 2020-04-17
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Key words:
- Thiamine /
- Riboflavin /
- Niacin /
- Vitamin B deficiency /
- Nutritional requirements /
- China
Abstract:
Citation: | LI Li, ZHANG Bing, WANG Hui Jun, OUYANG Yi Fei, HUANG Fei Fei, WANG Yun, ZHANG Ji Guo, SU Chang, DU Wen Wen, JIA Xiao Fang, JIANG Hong Ru, WANG Zhi Hong. Sociodemographic Factors Associated with Dietary Intake of Thiamine, Riboflavin, and Niacin among Chinese Adults in 2015[J]. Biomedical and Environmental Sciences, 2020, 33(9): 660-669. doi: 10.3967/bes2020.087 |