RESEARCH PAPER
Structural Equation Modelling in the exploration and analysis of intrauterine environmental exposures with infant health effects
 
More details
Hide details
1
Faculty of Medicine, School of Public Health, University of Chile, Santiago, Chile
 
2
Epidemiology Branch Chief, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Maryland, USA
 
3
Faculty of Health Science, Population Health Research Institute, McMaster University, Hamilton, Canada
 
 
Corresponding author
Macarena Alejandra Valdés Salgado   

School of Public Health. Faculty of Medicine. University of Chile, 939 Av. Independencia, 8380453, Santiago, Chile
 
 
Ann Agric Environ Med. 2019;26(4):617-622
 
KEYWORDS
TOPICS
ABSTRACT
Introduction:
In epidemiology, generalized linear models are the main statistical methods used to explore associations. However, the use of other methods such as Structural Equation Modelling (SEM) is gradually increasing.

Objective:
The aim of the study was to illustrate the use of SEM in the assessment of salivary cortisol concentration in infants as a biomarker of perinatal exposure to inorganic arsenic.

Material and methods:
This was a cohort study of pregnant women recruited from public health care centres in Arica, Chile, in 2013. Socio-demographic information and urine samples to assess inorganic arsenic were collected during the second trimester of pregnancy. Saliva samples were collected to assess cortisol in infants between 18–24 months of age. Four linear regression models (LRMs) and two SEMs were run to estimate the effect of prenatal exposure to inorganic arsenic on cortisol concentration in infants.

Results:
According to LRMs and SEMs, prenatal exposure to inorganic arsenic and salivary cortisol were not associated. However, the association between maternal cortisol and cortisol in infants was statistically significant in all models; for each increase in standard deviation of the covariate Ln(maternal cortisol), the outcome Ln(cortisol in infant) increased by 0.49 units of variance in both SEMs.

Conclusions:
LRMs and SEMs were useful to assess the effect of prenatal exposure to inorganic arsenic on cortisol in infants. However, SEM allowed the adjustment of estimations by an estimated latent that obtained the information about income, occupation, education and ethnicity in a more comprehensive way than achieved by LRM.

 
REFERENCES (26)
1.
VanderWeele TJ. Invited commentary: structural equation models and epidemiologic analysis. Am J Epidemiol. 2012; 176(7): 608–12.
 
2.
Anderson JG. Structural Equation Models in the Social and Behavioral Sciences: Model Building. Child Development. 1987; 58(1): 49–64.
 
3.
Violato C, Hecker KG. How to use structural equation modeling in medical education research: a brief guide. Teach Learn Med. 2007;19(4):362–71.
 
4.
Sánchez BN, Budtz-Jørgensen E, Ryan LM, Hu H. Structural Equation Models. Journal of the American Statistical Association. 2005; 100(472): 1443–55.
 
5.
Kline RB. Principles and Practice of Structural Equaiton Modelling. Little TD, editor. United States of America: The Guilford Press; 2011.
 
6.
Bollen K. Structural Equations with Latent Variables. United States of America: Willey; 1989.
 
7.
Valdés M. Prenatal exposure to low-level inorganic arsenic concentrations associated with salivary cortisol in infants from Arica, Chile. Doctoral thesis Santiago, Chile: University of Chile; 2017.
 
8.
Tsuji JS, Perez V, Garry MR, Alexander DD. Association of low-level arsenic exposure in drinking water with cardiovascular disease: a systematic review and risk assessment. Toxicology. 2014; 323: 78–94.
 
9.
Tsuji JS, Garry MR, Perez V, Chang ET. Low-level arsenic exposure and developmental neurotoxicity in children: A systematic review and risk assessment. Toxicology. 2015; 337: 91–107.
 
10.
Farzan SF, Chen Y, Rees JR, Zens MS, Karagas MR. Risk of death from cardiovascular disease associated with low-level arsenic exposure among long-term smokers in a US population-based study. Toxicol Applied Pharmacol. 2015; 287(2): 93–7.
 
11.
Martinez-Finley EJ, Goggin SL, Labrecque MT, Allan AM. Reduced expression of MAPK/ERK genes in perinatal arsenic-exposed offspring induced by glucocorticoid receptor deficits. Neurotoxicol Teratol. 2011; 33(5): 530–7.
 
12.
Martinez EJ, Kolb BL, Bell A, Savage DD, Allan AM. Moderate perinatal arsenic exposure alters neuroendocrine markers associated with depression and increases depressive-like behaviors in adult mouse offspring. Neurotoxicology. 2008; 29(4): 647–55.
 
13.
Martinez-Finley EJ, Ali AM, Allan AM. Learning deficits in C57BL/6J mice following perinatal arsenic exposure: consequence of lower corticosterone receptor levels? Pharmacol Biochem Behav. 2009; 94(2): 271–7.
 
14.
Badrick E, Bobak M, Britton A, Kirschbaum C, Marmot M, Kumari M. The relationship between alcohol consumption and cortisol secretion in an aging cohort. J Clin Endocrinol Metabol. 2008; 93(3): 750–7.
 
15.
Eller NH, Netterstrom B Fau – Hansen AM, Hansen AM. Psychosocial factors at home and at work and levels of salivary cortisol. 2006; 0301–0511 (Print)).
 
16.
Kunz-Ebrecht SR, Kirschbaum C, Steptoe A. Work stress, socioeconomic status and neuroendocrine activation over the working day. Soc Sci Med. 2004; 58(8): 1523–30.
 
17.
Solar O, Irwin A. A conceptual framework for action on the social determinants of health. Social Determinants of Health Discussion Paper 2 (Policy and Practice). WHO Library Cataloguin-in- Publication Data. 2010.
 
18.
Schisterman EF, Cole SR, Platt RW. Overadjustment bias and unnecessary adjustment in epidemiologic studies. Epidemiol. 2009; 20(4): 488–95.
 
19.
Khoury JE, Gonzalez A, Levitan R, Masellis M, Basile V, Atkinson L. Maternal self-reported depressive symptoms and maternal cortisol levels interact to predict infant cortisol levels. (1097–0355 (Electronic)).
 
20.
Galobardes B, Shaw M, Lawlor DA, Lynch JW, Davey Smith G. Indicators of socioeconomic position (part 1). J Epidemiol Community Health. 2006; 60(1): 7–12.
 
21.
Galobardes B, Shaw M, Lawlor DA, Lynch JW, Davey Smith G. Indicators of socioeconomic position (part 2). J Epidemiol Community Health. 2006; 60(2): 95–101.
 
22.
Baja ES, Schwartz JD, Coull BA, Wellenius GA, Vokonas PS, Suh HH. Structural equation modeling of the inflammatory response to traffic air pollution. J Expo Sci Environ Epidemiol. 2013; 23(3): 268–74.
 
23.
Fontanella L, Ippoliti L, Valentini P. Environmental pollution analysis by dynamic structural equation models. Environmetrics. 2007; 18(3): 265–83.
 
24.
Fei DL, Koestler DC, Li Z, Giambelli C, Sanchez-Mejias A, Gosse JA, et al. Association between In Utero arsenic exposure, placental gene expression, and infant birth weight: a US birth cohort study. Environ Health. 2013; 12: 58.
 
25.
Kile ML, Cardenas A, Rodrigues E, Mazumdar M, Dobson C, Golam M, et al. Estimating Effects of Arsenic Exposure During Pregnancy on Perinatal Outcomes in a Bangladeshi Cohort. Epidemiology. 2016; 27(2): 173–81.
 
26.
Davis MA, Li Z, Gilbert-Diamond D, Mackenzie TA, Cottingham KL, Jackson BP, et al. Infant toenails as a biomarker of in utero arsenic exposure. J Expo Sci Environ Epidemiol. 2014; 24(5): 467–73.
 
eISSN:1898-2263
ISSN:1232-1966
Journals System - logo
Scroll to top