Objective: Based on the characteristics of hierarchical data, a multilevel model was used to analysis possible influencing factors of urinary cadmium levels in one county population, and to discuss the advantages of multilevel model for processing hierarchical data in practical problems.
Methods: In May 2013, 1 460 participants aged 20 and above in 12 administrative villages in one county in central China were recruited by cluster sampling. Urinary cadmium level and its possible influencing factors were investigated, and cadmium level in farmland soil of survey area was also tested. A total of 1 410 participants completed the survey and met the inclusion criterion. 318 farmland samples in survey area were detected. According to the data, individuals were set as the level one unit, and the village was set as level two unit. the data were analyzed by MIXED procedure for hierarchical data of SAS 9.3 software. In the case of not considering the hierarchy of data, the general linear model was fitted by SAS 9.3 software, and the fitting results of the two models were compared.
Results: A total of 1 410 participants were included finally, the age was (55.2 ± 11.1) years. 645 (45.74%) were males and 765 (54.26%) were females. The amount of household per capita consumption of rice was (100.9 ± 40.3) kg/y. All 18.65% (262/1 410) of the participants had mining and mineral separation work experience. The urinary cadmium level was (9.39 ± 2.16) µg/g Cr. Most of the soil cadmium levels in villages were greater than tolerance value. The fitting results of general linear model suggested that whether doing mining and mineral separation work does not have significant difference (χ(2) = 1.05, P = 0.305). There was significant difference in the village soil cadmium levels, age, the amount of household per capita consumption of rice, and gender (χ(2) = 401.39, 34.9, 4.16 and 86.15, respectively, P < 0.01, <0.01, 0.041, <0.01, respectively). The fitting result of empty model showed the ICC was 0.435 5, the urinary cadmium had clustering at village level. The results of multilevel model showed that the explanatory variables of the village soil cadmium levels, age, the amount of household per capita consumption of rice and gender had significant difference (Wald χ(2) values 2.55, 6.34, 2.37 and 10.32, respectively, P = 0.029, <0.01, = 0.018 and <0.01), while whether doing mining and mineral separation work had no significant difference (χ(2) = 0.78, P = 0.438). To the fitting optimization index using for the comparison of models, the results of multilevel model were less than that of general linear model. The regression coefficient of level-2 explanatory variable (the village soil cadmium levels) was 0.84, which could explain the 35.26% of the total variance.
Conclusion: Multilevel model could analyze hierarchical data more reasonably than general linear model. Urinary cadmium levels is highly influenced by the village soil cadmium levels.