Trudging Through Murky Associations: Potential Impacts of Confounding in Pesticide Epidemiologic Studies

Transcription

1

2 Trudging Through Murky Associations: Potential Impacts of Confounding in Pesticide Epidemiologic Studies Rick Reiss Ap2, 2017

3 Introduction Confounding: A confounder is a third variable that can make it appear (often incorrectly) that an observed exposure is associated with an outcome. In other words, a confounder is an unobserved exposure associated with the exposure of interest and is a potential cause of the outcome of interest

4 Criteria for a Confounding Factor (Rothman and Greenland) A confounding factor must be a risk factor for the disease 4 A confounding factor must be associated with the exposure under study in the source population (the population at risk from which the cases are derived) A confounding must not be affected by the exposure or the disease. In particular, it cannot be an intermediate step in the causal path between exposure and disease

5 Confounding Example 5

6 6 Residual Confounding Residual confounding is the distortion that remains after controlling for confounding in the design and/or analysis of a study

7 Potential Effects of Confounding with Small Effect Sizes Fewell et al. in the American Journal of Epidemiology (2007): 7 With plausible assumptions, effect sizes of the magnitude frequently reported in observational epidemiologic studies can be generated by residual and/or unmeasured confounding alone.

8 8 Low Dose Pesticide Epidemiology Studies Series of studies that report associations between markers of OP exposure in pregnant women and subsequent neurodevelopmental outcomes in offspring CCCEH cohort: Uses cord blood chlorpyrifos as an exposure metric Other studies: Generally use dialkylphosphate (DAP) levels in urine Many, but not all, OPs metabolize to DAPs, in part Surprising, though inconsistent, associations with some neurodevelopmental endpoints

9 9 What do studies attempt to control for? HOME score (Home Observation for Measurement of Environment) Maternal education Maternal intelligence Body mass index Urinary creatinine Other contaminants (e.g., blood lead)

10 Challenges with interpreting OP Studies: Heterogeneity of Results 10

11 Challenges with interpreting OP Studies: Heterogeneity of Results 11

12 12 Consistency of Chlorpyrifos Associations Focus of regulators is on CCCEH study However, CHAMACOS study has cord blood chlorpyrifos data and TCPy data No analysis has been reported for cord blood chlorpyrifos No associations for TCPy No associations for DEPs

13 13 What Could Explain Heterogeneity in Studies? Different responses in different populations Confounding Biases Exposure misclassification Outcome misclassification

14 How Do We Evaluate Confounding in Pesticide Epidemiologic Studies Challenging without the raw data NHANES provides some data that can be used for analysis 14 Some possible areas for closer evaluation Other environmental contaminants Both diet/nutritional status and residential pesticide use may be associated with other factors that affect health, thereby potentially resulting in confounding bias PON1, an enzyme that detoxifies some OPs, may influence health in a myriad of ways

15 Several Studies Associate Other Environmental Contaminants with Neurodevelopmental Outcomes 15 CHAMACOS studies includes published associations with: OPs through DAPs Polybrominated diphenyl ethers (flame retardants) DDT Manganese

16 PBDEs: Both prenatal and childhood PBDE exposures were associated with poorer attention, fine motor coordination, and cognition. 16

17 DDT: In girls, but not boys, prenatal DDE levels were inversely associated with Full Scale IQ and Processing Speed at age 7 years. 17

18 Manganese: associated with poorer behavioral outcomes in schoolage boys and girls and better motor function, memory, and/or cognitive abilities in schoolage boys. 18

19 19 Using NHANES Data to Examine Confounding NHANES is a population-based dataset that includes numerous demographic and environmental contaminant measurements It includes data on dialkylphosphates (DAPs), allowing a correlation analysis with other variables

20 NHANES Analysis: Body Mass Index and DAPs: Adults 20

21 NHANES Analysis: Body Mass Index and DAPs: Children 21

22 NHANES Analysis: Blood Lead Levels and DAPs: Children 22

23 NHANES Analysis: Blood Lead Levels and 1-Napthol (PDBE): Children 23

24 24 What does this all mean? Confounding potentially effects the results of the low OP studies Knowing exactly what is going on may be impossible, but is certainly so without the raw data Caution is warranted in interpreting studies with small effect sizes given potential confounding or bias A consistency analysis becomes very important