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. Author manuscript; available in PMC: 2013 Jan 1.
Published in final edited form as: Paediatr Perinat Epidemiol. 2011 Sep 20;26(1):61–69. doi: 10.1111/j.1365-3016.2011.01234.x

Blood Lead Levels and Associated Socio-demographic Factors among Preschool Children in the South Eastern region of China

Jianghong Liu a,*, Yuexian Ai b,*, Linda McCauley c, Jennifer Pinto-Martin a, Chonghuai Yan d, Xiaoming Shen d, Herbert Needleman e
PMCID: PMC3241210  NIHMSID: NIHMS317248  PMID: 22150709

Summary

Children are especially vulnerable to lead toxicity, and exposure to lead has been linked to poor school performance and delinquency in children and adolescents. Even low-level lead exposure (blood lead level [BLL] lower than 10 µg/dL) can cause intelligence deficit. In China, BLLs in children decreased slightly after the phase-out of lead in gasoline, but few studies have examined the socio-demographic factors associated with BLL above 10 µg/dL. In thus study, we sought to examine the hypothesis that socio-demographic factors predict BLLs. We measured BLLs of 1,344 preschool children (3–5 years old) from the China Jintan Child Cohort Study. Children’s socio-demographics and health statuses, as well as parental socio-demographics, were collected through questionnaire. Multiple regression models were used to explore the association between socio-demographic factors and log-transformed BLLs as well as the relationship between socio-demographic factors and the risk of BLL above 10 µg/dL. We found the median BLL to be 6.2 µg/dL (range: 1.8–32.0 µg/dL), and 8% of children had BLLs above 10 µg/dL. Boys had a higher median BLL (6.4 µg/dL) than girls and were more likely to have BLL above 10 µg/dL (OR=1.77, 95% CI 1.14 –2.74). BLLs increased as children aged, with a median BLL of 6.6 µg/dL among 5-year old children. Children with sibling(s) had higher average BLL and prevalence of BLL above 10 µg/dL than those without sibling(s). Living in a crowded neighborhood was also associated with increased BLLs. Mother’s lower education, father’s occupation (as professional worker), and parental smoking at home were associated with increased BLLs. This study shows that children in this area still have relatively high BLLs after the phase-out of leaded gasoline. Both children’s and parental factors and community condition are associated with increased BLLs. Future efforts are needed to identify other exposure sources and develop targeted prevention strategies.

Keywords: Blood lead level, Children, Socio-demographic factors

Introduction

Lead exposure is a child health concern around the world, especially in developing countries. Children’s developing neurological systems are vulnerable to lead toxicity, as it can cause intellectual dysfunction1,2 and increase negative behavior outcomes. 3–6 Even low-level lead exposure can cause intelligence deficits. 7–9 As of yet, no threshold for lead toxicity has been established. A point of concern for levels at or above 10µg/dL was established in 1991 by the US Centers for Disease Control and Prevention, and it was adopted by the Ministry of Health of China in 2006. While average blood lead levels (BLLs) and the prevalence of BLLs above 10 µg/dL have declined, it has been argued that even the level of 10µg/dL is too high and should be lowered further. 7, 10–12

In China, reductions in both mean BLLs and the prevalence of BLL above 10 µg/dL have been reported since the ban of leaded gasoline in 2000.13,14 Still, from 2001–2007, the mean BLL of children in China remained high at 8.1 µg/dL, and 23.9% of children had BLLs above 10 µg/dL.15 This suggests that other important sources contribute to children’s lead exposure in China. While a number of studies have measured BLLs in urban children, limited data exists for children who live in rural and suburban areas and may be exposed to high levels of lead discharged from small-scale industrial facilities due to poor hazard controls.16 Indeed, after the phase-out of leaded gasoline, the prevalence of BLLs over 10 µg/dL was found to be higher among children living in suburban areas than that of children living in urban areas.15 This could be due to industries shifting from urban to suburban areas, as well as poorer environmental and socioeconomic levels in suburban areas.15

Studies in developed countries have shown that BLLs above 10 µg/dL were associated with age, gender, race/ethnicity, use of ethnic remedies, cosmetics, or goods, immigrant and refugee status, income level, age of housing, location of residence, parental occupation, and exposure to tobacco smoke.17,18 However, few studies in China have examined the determinants of BLLs after the phase-out of leaded gasoline. The majority of these studies in China have focused on gender and age differences, showing that BLLs were higher in boys and increased with age.15,19 However, investigations in other countries have shown the opposite trend, where BLL was highest among toddlers and then declined with age.20 We hypothesize that in addition to gender and age, other socioeconomic factors such as parental characteristics and community conditions predict BLL among young children in China as well. In the present study, we investigated the associations between socio-demographic factors and increased BBLs among preschool children from The China Jintan Child Cohort Study. 21

Methods

Subjects

All subjects were from The China Jintan Child Cohort Study, which consists of 1,656 preschool children accounting for 24.3% of all children in the 3–5 age range in Jintan, Jiangsu, China. Study design, purpose, subject enrollment, and laboratory and health measures have been described previously.21 Four pre-schools (Jianshe, Huacheng, Xuebu, and Huashan) were chosen to be representative of the geographic, social and economic profile of the whole city (Figure 1). Jianshe, Huacheng, and Huashan were chosen to represent urban, suburban, and rural areas, respectively. Xuebu was selected because of lead pollution from a cement factory in this mining area.22 Between Fall 2004 and Spring 2005, all children (ages 3–5 years) attending the four preschools were invited to participate in this study. Signed consent forms for children’s participations were obtained from parents. Institutional review board approval was obtained from the University of Pennsylvania and the ethical committee for research at Jintan Hospital in China.

Figure 1.

Figure 1

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Preschools selected in the China Jintan Child Cohort Study

BLL measurement

Blood specimens of 1,344 children were collected by trained pediatric nurses using a strict research protocol to avoid lead contamination and were frozen and shipped to the Research Center for Environmental Medicine of Children at Shanghai Jaotong University for lead and micronutrient (iron, copper, zinc, calcium, and magnesium) analyses. We were not able to successfully collect blood samples from other children due to a variety of reasons, but there were no differences in demographics between children with and without blood samples. BLLs were measured by a graphite furnace atomic absorption spectrophotometer (AA100 - Perkin-Elmer Company). This laboratory has participated successfully in a CDC-administered quality-control program (Blood Lead Proficiency Testing Program) for the measurement of lead in whole blood. The detailed analytic procedures and performances have been described previously.23,24 Analysis of each specimen was conducted using a replication procedure, and the mean of the repeated measurements was taken as the final measure. Blood lead reference materials for quality control (QC) were provided by Kaulson Laboratories, New Jersey. QC samples were inserted blindly among the study samples (one QC sample in every 10 study samples). The limit of detection (LOD) was 1.8 µg/dL and a half of LOD was imputed for samples under LOD.

Socio-demographic Information

Socio-demographic information obtained from the questionnaire include sex, age, parental educations and occupations (unemployed, general labor, skilled labor, professional worker), neighborhood condition such as whether living in a crowed neighborhood, person directly raising the child, having sibling(s) or not, parental smoking at home, and the mother’s age when the child was born. Parents filled out the socio-demographic questionnaire during their meeting at the pre-schools, which happened at the end of the school year when blood samples were collected. While this questionnaire was self-administered, research assistants were on-site to assist any parents in filling out the forms.

Data analysis

Means with standard deviations (SD) and medians with minimum and maximum values were calculated to describe the distributions of BLLs. Since BLLs were not normally distributed, we used one way nonparametric analysis of variance (Wilcoxon or Kruskal-Wallis test) to compare BLLs. The multivariable linear regression model was fitted to identify independent predictors of BLLs (log-transformed). Multivariable logistic regression analysis was also performed to explore the associations between these factors and BLL above 10 µg/dL (≥10 µg/dL vs. <10 µg/dL) and above 5µg/dL (≥5 µg/dL vs. <5 µg/dL). Blood micronutrient levels and health states were controlled as potential confounders. All analyses were done in SAS 9.2 (SAS Institute, Cary, NC).

Results

Figure 2 shows the distribution of BLL. The median BLL was 6.2 µg/dL (range 1.8–32.0 µg/dL), with 7.8% of children having BLL above 10 µg/dL and 72.2% of children having BLL above 5 µg/dL. In univariable analysis (Table 1), statistically significant differences in BLLs were found between subgroups for all factors except for father’s occupation (p=0.26) and person directly raising the child (p=0.96). The median BLL in boys was 10% higher than that in girls. Median BLLs increased with increasing age of the children. Children with sibling(s) had higher median BLLs (6.7 µg/dL) than those without (6.1 µg/dL). Parental factors associated with increased BLLs include mother’s age (<25 years), lower education levels, mother’s occupation (unemployed or general labor woker), and smoking at home. Those living in houses or attending schools in rural area had the highest median BLLs. Living in crowed neighborhood was also associated with higher BLLs.

Figure 2.

Figure 2

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The distribution of blood lead levels among preschool children from China Jintan Child Cohort Study

Table 1.

Blood lead levels in pre-school children by social-demographic characteristics in Jintan, China

Characteristics N (%) Median
(min.,max.)		 Mean(SD) P value* % 5–10 µg/dL % ≥10µg/dL
Total 1344 (100) 6.2 (1.8, 32.0) 6.4 (2.6) 865 (64.4) 105 (7.8)
Sex Boys 740 (55.1) 6.4 (1.8,32.0) 6.7 (2.8) <.0001 490 (66.2) 72 (9.7)
Girls 604 (44.9) 5.9 (1.8,18.1) 6.0 (2.3) 375 (62.1) 33 (5.5)
Age (years) 5 596 (44.4) 6.6 (2.3, 24.0) 7.1(2.4) <.0001 450 (75.5) 60 (10.1)
4 438 (32.6) 6.1 (1.8, 32.0) 6.4 (2.8) 275 (62.8) 34 (7.8)
3 308 (23.0) 4.9 (1.8,17.0) 5.2 (2.3) 138 (44.8) 11 (3.6)
Mother’s age when the child was born <25 380 (30.1) 6.4 (1.8,24.6) 6.8 (2.6) <0.0001 261 (68.7) 37 (9.7)
25–29 723 (57.3) 5.9 (1.8,32.0) 6.2 (2.6) 442 (61.1) 52 (7.2)
30- 159 (12.6) 6.2 (1.8,18.1) 6.3 (2.8) 107 (67.3) 9 (5.7)
Father’s highest degree Primary school 40 (3.1) 7.1 (2.6,14.4) 7.3 (2.9) <0.002 25 (62.5) 7 (17.5)
Middle school 448 (35.2) 6.3 (1.8,32.0) 6.7 (2.8) 302 (67.4) 41 (9.2)
High school 409 (32.2) 6.1 (1.8,27.5) 6.4 (2.9) 248 (60.6) 37 (9.0)
College/University 375 (29.4) 5.9 (1.9,14.8) 6.0 (2.0) 239 (63.7) 14 (3.7)
Mother’s highest degree Primary school 145 (10.8) 6.9 (2.3,32.0) 7.1 (3.2) <.0001 101 (69.7) 17 (11.7)
Middle school 566 (42.1) 6.3 (1.8,24.6) 6.6 (2.7) 379 (67.0) 50 (8.8)
High school 377 (28.1) 6.0 (1.8,14.8) 6.1 (2.4) 224 (59.4) 27 (7.2)
College/University 256 (19.3) 5.9 (2.1,27.5) 6.1 (2.6) 161 (62.8) 11 (4.3)
Father’s occupation Unemployed/unskilled labor 156 (12.7) 6.3 (2.1,15.2) 6.7 (2.4) 0.260 113 (72.4) 12 (7.7)
Skilled labor 559 (45.5) 6.3 (1.8,17.0) 6.4 (2.5) 354 (63.3) 43 (7.7)
Professional workers 514 (41.8) 6.0 (1.9,32.0) 6.4 (3.0) 320 (62.3) 42 (8.2)
Mother’s occupation Unemployed/house wife 328 (26.5) 6.3 (1.9,14.4) 6.5 (2.2) 0.020 233 (71.0) 22 (6.7)
Labor/assistant/secretary 540 (43.6) 6.3 (1.8,32.0) 6.5 (2.9) 338 (62.6) 51 (9.4)
Professional workers 370 (29.9) 5.9 (1.9,27.5) 6.1 (2.6) 224 (60.5) 23 (6.2)
Having sibling(s) Yes 107 (9.2) 6.7 (1.8,16.3) 7.0 (2.8) 0.010 69 (64.5) 15 (14.0)
No 1055 (90.8) 6.1 (1.8,32.0) 6.4 (2.7) 673 (63.8) 75 (7.1)
Person directly raising the child Biological parents 1184 (93.3) 6.1 (1.8,32.0) 6.4 (2.7) 0.960 758 (64.0) 91 (7.7)
Others 86 (6.7) 6.0 (1.9,12.5) 6.4 (2.3) 54 (62.8) 8 (9.3)
Current living place Urban 933 (73.5) 6.0 (1.8,32.0) 6.2 (2.7) <0.001 574 (61.5) 61 (6.5)
Suburban 187 (14.7) 6.3 (1.9,17.0) 6.7 (2.5) 129 (69.0) 20 (10.7)
Rural 150 (11.8) 6.6 (2.3,18.1) 7.0 (2.6) 109 (72.7) 18 (12.0)
School location Suburban 525 (39.1) 6.0 (1.8,32.0) 6.3 (2.9) <0.005 315 (60.0) 43 (8.2)
Urban 534 (39.7) 6.1 (1.9,24.6) 6.3 (2.5) 345 (64.6) 31 (5.8)
Rural 285 (21.2) 6.4 (2.6,18.1) 6.9 (2.4) 205 (71.9) 31 (10.9)
Living in crowded neighborhood Yes 504 (46.3) 6.4 (1.8,24.6) 6.7 (2.6) <0.005 342 (67.9) 48 (9.5)
No 585 (53.7) 6.1 (1.8,17.0) 6.3 (2.4) 371 (63.4) 41 (7.0)
Parental smoking at home Yes 95 (11.2) 6.7(1.9,32.0) 7.4(4.2) <0.005 67 (70.5) 9(9.5)
No 752 (88.8) 5.9(1.8,24.6) 6.2(2.5) 453 (60.2) 57 (7.6)
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*

p values of one-way nonparametric analysis of variance (Wilcoxon or Kruskal-Wallis test)

In multivariable linear regression analysis (Table 2), children’s sex and age, mother’s age when the child was born, having sibling(s), living in a crowded neighborhood (defined as having a lot of neighbors), and parent’s smoking at home are the predictors of log-transformed BLLs. The estimated coefficient (beta) for boys was 0.096 units of log BLL, resulting in a 10% increase of BLL when transforming back to original scale. Compared with 3-year-old children, BLLs of 4- and 5 year-old children increased by 11% (beta=0.103 units of log BLL, P<0.0001) and 38% (beta=0.318 units of log BLL, P<0.0001), respectively. Mothers who were younger than <25 years old when the child was born had a 9% increase (beta=0.084 units of log BLL, P=0.02) when compared with those aged older than 30. Those children living in crowed neighborhood had an 8% increase compared with those living in less crowed neighborhood (beta=0.066 units of log BLL, P=0.01). Parental smoking at home increased children’s BLLs by 10%. Children of parents as profession workers tended to have higher BLLs.

Table 2.

Predictors of log-transformed blood lead levels (µg/dL) among children in Jintan, China

Characteristics Coefficient
(beta) estimate		 95% CI p-value
Sex Boys 0.096 [0.055, 0.137] <.0001
Girls Reference
Age (years) 5 0.318 [0.263, 0.373] <.0001
4 0.103 [0.054, 0.152]
3 Reference
Mother’s age when the child was born <25 0.084 [0.011, 0.157] 0.023
25–29 0.024 [−0.043, 0.091]
30- Reference
Father’s highest degree Primary school 0.028 [−0.115, 0.171] 0.879
Middle school 0.009 [−0.060, 0.078]
High school 0.007 [−0.054. 0.068]
College/University Reference
Mother’s highest degree Primary school 0.05 [−0.079, 0.179] 0.092
Middle school −0.013 [−0.093, 0.067]
High school −0.065 [−0.138, 0.008]
College/University Reference
Father’s occupation Professional workers 0.106 [−0.017, 0.229] 0.486
Skilled labor/driver 0.061 [−0.057, 0.179]
Unemployed/unskilled labor Reference
Mother’s occupation Professional workers −0.082 [−0.192, 0.028] 0.575
Skilled labor/driver −0.027 [−0.125, 0.071]
Unemployed/unskilled labor Reference
Having sibling(s) Yes 0.071 [−0.007, 0.149] 0.045
No Reference
Living in a crowded neighborhood Yes 0.066 [0.021, 0.111] 0.003
No Reference
Parental smoking at home Yes 0.093 [0.013, 0.173] 0.062
No Reference
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95% CI: 95% confidence interval.

As shown in Table 3, boys are more likely to have BLLs above 10 µg/dL than girls (OR=1.77, 95% CI 1.14 –2.74). Compared with 3-year old children, 4- and 5-year old children had twice the odds of having BLLs above 10 µg/dL. Both father’s education level and occupation were associated with a higher risk of BLL above 10 µg/dL among children. Compared with fathers who were college graduates, ORs ranged from 2.5 to 3.8 for fathers without a college education. Fathers as professional workers were more likely to have children with BLL above 10 µg/dL (OR=2.21, 95% CI 1.05 –4.66). Having sibling(s) was also associated with higher prevalence of BLL above 10 µg/dL (OR=2.18, 95% CI 1.12 –4.24). None of the mothers’ characteristics were associated with BLLs above 10 µg/dL among children. The findings were somewhat different when the model was fitted for BLL above 5 ug/dL. Living in a crowed neighborhood was associated with the prevalence of BLL above 5 ug/dL (OR=1.56, 95% CI 1.15–2.13), but neither father’s degree nor having sibling(s) were associated the risk.

Table 3.

Logistic regression analysis of BLL above 10 µg/dL and 5 µg/dL among preschool children in Jintan, China

Characteristics ≥ 10 µg/dL ≥ 5µg/dL
ORs and 95% CI p-value ORs and 95% CI p-value
Sex Boys 1.77 (1.14 –2.74) 0.011 1.55 [1.20, 2.01] 0.001
Girls Reference
Age (years) 5 2.28 (1.10 –4.70) 0.018 2.65 [1.91, 3.68] <0.0001
4 2.73 (1.36 –5.46) 6.20 [4.36, 8.81]
3 Reference
Mother’s age when the child was born <25 1.80 (0.78 –4.15) 0.545 1.28 [0.79, 2.07] 0.020
25–29 1.61 (0.72 –3.58) 0.80 [0.53, 1.26]
30- Reference
Father’s highest degree Primary school 3.81 (1.10 –13.19) 0.069 0.79 [0.29, 2.11] 0.886
Middle school 2.45 (1.10 –5.43) 0.96 [0.61, 1.48]
High school 2.81 (1.37 –5.78) 0.85 [0.59, 1.25]
College/University Reference
Mother’s highest degree Primary school 1.58 (0.46 –5.44) 0.858 1.07 [0.43, 2.64] 0.212
Middle school 1.28 (0.51 –3.22) 0.93 [0.57, 1.52]
High school 1.10 (0.46 –2.63) 0.69 [0.44, 1.07]
College/University Reference
Father’s occupation Skilled labor/driver 1.25 (0.62 –2.51) 0.086 1.49 [0.69, 3.21] 0.821
Professional workers 2.21 (1.05 –4.66) 1.86 [0.83, 4.21]
Unemployed/unskilled labor Reference
Mother’s occupation Skilled labor/driver 1.60 (0.92 –2.76) 0.380 0.71 [0.35, 1.41] 0.418
Professional workers 1.32 (0.63 –2.78) 0.50 [0.23, 1.07]
Unemployed/unskilled labor Reference
Having sibling Yes 2.18 (1.12 –4.24) 0.021 1.30 [0.76, 2.22] 0.341
No Reference
Living in a crowded neighborhood Yes 1.31 (0.83 –2.08) 0.250 1.56 [1.15, 2.13] 0.004
No Reference
Parent smoking at home Yes 1.03 (0.47 –2.23) 0.946 1.52 [0.86, 2.70] 0.145
No Reference
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ORs, odds rations; 95% CI, 95% confidence interval.

Discussion

Findings from this study indicate that despite the elimination of leaded gasoline, children in Jintan, China are still exposed to high levels of lead, with an average BLL greater than 6 µg/dL. This phenomenon has also been observed in other areas of China. Up to 6 years after the phase-out of leaded gasoline, mean BLLs among children aged 0–6 years from 14 Chinese cities during 2004–2006 varied around 5 µg/dL, with 7–10% of children having BLLs above 10 µg/dL.19 Besides leaded gasoline, other important sources of lead exposure among children in China might include industrial emissions, lead-contaminated paints (e.g. in houses, toys, and stationeries), parents’ take-home lead from work, coal combustion, food, and traditional medications.25, 26, 27 In our study, preschool boys and girls living in rural areas (Xuebu and Huashan) had higher mean BLLs and a higher rate of BLL above 10 µg/dL than children living in urban and suburban areas due to lead emission from a cement factory, the biggest employer in this community with about 800 employees. Diet has been postulated as another important contributor of lead exposure in this area, since lead emitted from the factory may have contaminated the locally grown food. Wang and colleagues (2009) found that the weekly lead intake per body weight in children living in this area was higher than that in adults and 30% of children’s weekly intake was greater than the provisional tolerable weekly intake (PTWI) of the Joint FAO/WHO Expert Committee on Food Additives.28 In the future, identifying the sources and quantifying their relative contributions to BLLs in these preschool children would help health agencies develop lead prevention strategies. Future investigations could include analysis of soil and dust samples of our study’s three locations.

In this study, we found that BLLs increased with the increasing age of children, and this is consistent with findings in other studies both in China and India.15, 29 This trend might be due to the fact that children are more mobile and thus are exposed to more sources of lead while they grow up. The increase in BLL may not continue after a certain age. Rabinowitz and colleagues (1985) found that BLLs increased up to age 5 and declined thereafter. Although we were not able to test this trend in the current analysis, the longitudinal design of this study will allow us to follow the temporal trend (with age increasing) in future analyses.30

The higher BLLs among boys might be explained by the differences in behaviors related to lead exposure. Compared to girls, boys spend more time doing outdoor activities and have more hand-to-mouth and object-to-mouth activities,31 which have been associated with higher BLLs.32 We did not collect information on physical activity and hand-to-mouth activity in our cohort, so the factors associated with our observed age and gender differences can only be postulated. Other factors such as physical activity and dietary intake may also influence the toxicokinetics of lead in the body and contribute to this difference. A recent study showed that the association of gender and BLL was modified by physical activity and race/ethnicity.33 For the first time in China, we were able to compare BLLs between children with and without sibling(s) and found that children with sibling(s) had higher BLLs than those without sibling(s). This is consistent with findings such as those by Jarosińska et al. (2004), who found that children in Poland with two or more siblings had higher BLLs than those without siblings,34 as well as a number of other studies that have found an association between the number of siblings and increased BLLs in children.35–37 This association may result from the fact that children with siblings have more activities (especially outdoor activities) and live in a more crowded house, which was shown as a predictor of BLL.

Several parental factors were associated with BLLs in this study. Children of factory workers are more likely to have BLLs above 10 µg/dL. In Jintan, the majority of factory jobs are in manufacturing industries (such as smelter and cement) with lead exposure. In this area, while less educated persons are more likely to do general labor jobs, more-educated persons are more likely to have factory jobs and become smelter workers and cement workers, who are exposed to higher levels of lead. Lead taken home by parents has been recognized as an important source of children’s exposure.17 Lead dust from parents’ surfaces and work clothing can be digested or inhaled by children at home. Lower education levels in fathers were also associated with increased BLLs as shown previously.38–40 The association between mother’s education and BLLs observed in univariable analysis disappeared in the multivariable analysis, indicating that education level may be a proxy for the combination of socioeconomic factors (e.g., occupation, home income, nutrition, location of residence) that more directly impact children’s lead exposure and toxicokinetics.

Parental smoking at home has been associated with higher BLLs in children,41–44 but a limited number of studies have been done to evaluate this association in China. This study, along with two previous studies,23,45 verified this association. Children could be exposed to lead by inhaling tobacco smoke and by digesting floor dust containing lead that may be partially from indoor smoking. Studies showed that indoor smoking is one of the predictors for lead level in floor dust, which is associated with higher BLLs.42,46 In China, 45% to 65% of children were exposed to environmental tobacco smoking, mainly in the home, and lead has been detected in all 15 randomly selected cigarette products in the Chinese market.47 In the United States, 25% of children aged 3–11 years live with one or more smokers in the household.48 Children with high cotinine levels were more likely to have BLLs above 10 µg/dL.44 These data suggest that the control of parent smoking might be especially important for children in China.

Findings in this study have several implications for government policy regarding lead prevention. First, after the implementation of lead-free gasoline and regulations on lead levels in paint and industrial emissions, children in urban and suburban areas still had relatively high BLLs, suggesting that a broader national policy is necessary to control lead exposures from other sources. In 2006, the Ministry of Health of China issued its first guide for the prevention of children’s lead poisoning through health education (knowledge, behavior, and nutrition) and blood lead level monitoring strategies. This guidance outlines comprehensive interventions including not smoking at home, no taking-home lead, no food with lead as additive. However, to our knowledge, there is no data showing how these recommendations have been implemented or the effectiveness of the prevention. Both children’s factors and parental factors were associated with BLLs, suggesting the need of both school-based and community-based lead prevention programs.

This study has several limitations. First, despite accounting for many factors associated with BLLs, our multivariable model explained a small portion of variance, suggesting that other important contributors have not been identified. Second, blood lead is a biomarker of the most recent exposure to lead due to the short half-life (about one month in blood). All associations were analyzed using one measurement which may not represent the children’s average exposure. Seasonal variations of BLLs have also been reported.29 Third, we were not able to collect information about family income, drinking water sources, and traditional medications which have been associated with BLLs in previous studies.17 Despite the association observed between father’s occupations and BLLs above 10 µg/dL, we were not able to exactly know father’s lead exposure status and some of them had multiple jobs which makes exposure assessment harder.

Acknowledgements

National Institute of Environment Health Sciences (NIEHS, K01-ES015 877; R01-ES018858) US; UPenn CEET P30 ES013508; The Wacker Foundation US; Jintan City Government; Jintan Hospital, China.

Thanks are extended to the participating children and their families from Jintan City, and to the Jintan Cohort Study Group. This group consists of the Jintan Maternal Child Health Center, Jintan Hospital (Dr Liping Zhang, Yue Xian Ai and Liudi Han), China Southern Eastern University (Dr Guiju Sun and Honglei Peng), Nanjing Brain Hospital (Dr Tunong Chen) and The Chinese University of Hong Kong (Patrick W.L. Leung). We are very grateful to the Jintan city government and the Jintan Hospital for their support and assistance.

List of Abbreviations

BLLs

blood lead levels

CDC

Centers for Disease Control and Prevention

QC

Quality Control

SD

Standard deviations

Footnotes

Competing Interests

The authors have declared that no competing interests exist.

Authors’ Contribution

JL conducted data collection, data analysis, data interpretation, and manuscript preparation. Ai Yuexian involved in research design and data collection. LM and JP conducted data interpretation and manuscript preparation. CY and XS conducted blood lead analysis. HN conducted data interpretation and manuscript preparation. All authors read and approved the final manuscript.

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