Air Quality Criteria for Lead Volume II of II - (NEPIS)(EPA) - US ...

AX6-150
Table AX6-5.1 (cont’d). Effects of Lead on Blood Pressure and Hypertension
Reference, Study
Location, and
Period Study Description Pb Measurement Findings, Interpretation
Europe (cont’d)
Møller and
Kristensen (1992)
(cont’d)
Though this study was one of the few to use a longitudinal design, it did not
take advantage of that design feature in blood pressure modeling. Crosssectional
multiple regression modeling at each age loses valuable information
available in repeated measures modeling. Power to detect significant effects is
much higher in repeated measurement modeling than in cross-sectional
modeling. Analyzing only change in blood pressure loses information
regarding starting and ending blood pressure. Including change in blood Pb is
problematical due to the unknown history of Pb exposure prior to the start of
the study, the resultant bone Pb load as a result of past exposure, the unknown
Pb contribution of bone to blood, and the unknown relative contributions of
past exposure and present exposure to alteration in blood pressure. Modeling
other covariates as change is also questionable. BMI, to pick a covariate with
known and strong effects on blood pressure, may be high and relatively
constant over the study period or low and relatively constant over the study. In
both cases, the change in BMI will be small, but the high BMI will be
associated with higher blood pressure than will the low BMI. Thus, both cases
modeled as change in BMI should have the same effect on blood pressure
when the high BMI subject has expected higher blood pressure than the low
BMI subject. Using difference scores for the dependent and the exposure
variables also risks confounding secular trends in either or both of these
variables, for whatever reasons, with independent difference variable effect on
dependent difference variable effect.
The Cox proportional hazards model, however, is longitudinal in nature.
Failure to detect significant associations between Pb and cardiovascular
morbidity/mortality could have been due to the small sample size used for this
type of analysis. The blood pressure part of the study did not take mortality
into account during the study, which could have produced a progressively
increasing “healthy subject” effect. Since subjects taking antihypertensive
medications were included in analyses, an indicator variable should have been
used to account for them, whether or not their exclusion in preliminary testing
produced no apparent change in results. This paper contained a good
discussion of confounding variables. Incomplete reporting of results and
procedures. No model diagnostic tests were reported.