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

AX6-152
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)
Staessen et al.
(1996a) (cont’d)
Multiple regression models were used to test the
association between natural log transformed
blood Pb (mean of baseline and follow up Pb)
and blood pressure (systolic and diastolic),
stratified by sex, then further stratified by use of
antihypertensive medications in men and
menopausal status in women. Age and agesquared
(calculated in quintiles) were forced into
the models, then remaining covariates were
stepwise added to the model. Though not
explicitly stated, natural log blood Pb (mean of
baseline and follow up) was likely forced in last.
Other candidate covariates were BMI,
hemoglobin or hematocrit, serum gammaglutamyltransferase
activity (an index of alcohol
use) and serum calcium, 24 h urinary sodium and
potassium excretion, energy expenditure,
exposure to heavy metals (at the workplace),
social class, smoking and drinking habits,
menstrual status in women, and use of
antihypertensive medications, oral contraceptives,
and hormone replacement therapy. In ambulatory
blood pressure models, differences between
baseline and follow up blood pressure models
were constructed in the same way. For the
difference models “concurrent variations in blood
Pb concentrations” were used, presumably
difference in baseline and blood Pb.
The study does not use the full power of repeated measurements in the
analyses. For problems encountered when collapsing repeated
measurements to difference measures, see Møller (1992) above. Stepwise
regressions are prone to capitalizing on chance results due to multiple
testing of the same data and almost always produce a different mix of
covariates when they are stratified. Thus, it was puzzling to find that where
information on the effects of stepwise covariate addition to models was
available in this article, that the same covariates were listed for both models
based on the stratification variable. There is excessive reliance on
fractionation of the data set due to multiple stratification, sometimes
reducing the number of subjects in a model to as few as 171. Even the
models using the most subjects had only 359 subjects. Low power to detect
significant effects cautions against any interpretation of non-significant
results. The time-integrated model used 10 baseline blood pressure
measurements and 5 follow up blood pressure measurements, thus
weighting the avg toward baseline blood pressure. The entry of the
biochemical correlate of alcohol use in most of the models suggests that Pb
effects and Pb-containing alcohol effects on blood pressure were confused,
especially given the European setting and the time period during which the
study was conducted. Control for use of hypertensive medication rarely
entered models and partial control for this variable was achieved only by
stratified analyses, further reducing power to detect significant effects in the
remaining subgroup. No justification was given for stratified analyses.
Incomplete information in statistical methods and results complicates
interpretation. It was uncertain if stepwise regression was used for logistic
models. No comparisons were performed to assess possible bias due to
subject attrition over the course of the study.