Discussion of statistical challenges in environmental health with an examination of four cases where these challenges were solved, enabling new insights to be obtained.

1. Applications of Contemporary
Statistical Approaches in
Environmental Health
B. Rey de Castro, Sc.D.
before the
CDC Emergency Response & Air Toxicants Branch
Atlanta, GA
April 28, 2011

2. Non-Independent Data

3. Normal & Non-Normal Data

4. Environmental Health Data

5. Missing Data

6. Filling In Missing Data

7. Statistical Characterization of an
MCF-7 Cell Culture Assay

8. MCF-7 Cell Culture Assay
• Cell culture assay for estrogenic potency
– E-SCREEN
• MCF-7 cell number increases with dose of
17 -estradiol, xenoestrogens

9. MCF-7 Assay Data
• 17 -Estradiol
• 396 observed cell counts
1998 17 -Estradiol E-SCREEN Data
– 11 plates 12 wells/plate 11 Plates 3 counts/well
1000000
800000
Cells per Well
600000
400000
200000
0
CNTL 1e-13 1e-12 1e-11 1e-10 1e-9
Log10 Dose [M]

10. Estrogenic PCB Data
250000 250000 250000
PCB17 PCB49 PCB66
200000 200000 200000
150000 150000 150000
100000 100000 100000
Cells per Well
50000 50000 50000
0 0 0
CNTL 2.5e-6 5.0e-6 7.5e-61.0e-5 2.5e-5 CNTL 1.0e-6 2.5e-6 5.0e-6 7.5e-6 1.0e-5 CNTL 2.5e-6 5.0e-6 7.5e-6 1.0e-5 2.5e-5
250000 250000
PCB74 PCB128
200000 200000
150000 150000
100000 100000
50000 50000
0 0
CNTL 2.5e-6 5.0e-6 7.5e-6 1.0e-5 2.5e-5 CNTL 2.5e-6 5.0e-6 7.5e-6 1.0e-5 2.5e-5
Log10 Dose [M]

11. MCF-7 Cell Culture Assay
• Dependent data – MCF-7 cell number
• Fixed effect – dose
– 5 estradiol dose levels
– 1 control dose

12. 12-Well Plate
• Random effects, or variance components
– Plate
– Plate Dose interaction
– Well within (Plate Dose)
CD FBS 5% 1E-11M E2
1E-9M E2 1E-12M E2
1E-10M E2 1E-13M E2

13. Generalized Linear Mixed Effects Model
g (Yijkm) ijkm d0 di Pj ( PD)ij W ( PD)k (ij)
where Yijkm = cell number
d0 = mean cell number of no-dose control (intercept; i = 0)
di = fixed effect of ith dose (i = 1, 2, … , 5)
Pj = random effect of jth plate (j = 1, 2, … , 21), P ~ N(0, P2)
(PD)ij = joint random effect of ith dose with jth plate, , PD ~ N(0, PD2)
W(PD)k(ij) = random effect of kth well (k = 1, 2) nested within the ith dose
and jth plate, , W ~ N(0, W2)
In addition, the error term from Y = + is as follows:
m(ijk) = random error of the mth count (m = 1, 2, 3)

14. Findings
• MCF-7 assay data
– Gamma error distribution & reciprocal link
– COV = 3.1 %
– All variance components significant

15. Estrogenic PCB Data
250000 250000 250000
PCB17 PCB49 PCB66
200000 200000 200000
150000 150000 150000
100000 100000 100000
Cells per Well
50000 50000 50000
0 0 0
CNTL 2.5e-6 5.0e-6 7.5e-61.0e-5 2.5e-5 CNTL 1.0e-6 2.5e-6 5.0e-6 7.5e-6 1.0e-5 CNTL 2.5e-6 5.0e-6 7.5e-6 1.0e-5 2.5e-5
250000 250000
PCB74 PCB128
200000 200000
150000 150000
100000 100000
50000 50000
0 0
CNTL 2.5e-6 5.0e-6 7.5e-6 1.0e-5 2.5e-5 CNTL 2.5e-6 5.0e-6 7.5e-6 1.0e-5 2.5e-5
Log10 Dose [M]

16. Increased sensitivity for
detecting
weakly estrogenic
environmental pollutants

17. Ambient Black Carbon From
Traffic in an Urban
Neighborhood

18. Baltimore Traffic Study
• Observe dynamics of ambient traffic-related
pollutants at a location embedded within an
urban residential neighborhood with high
vehicular volume

19. Baltimore Traffic Study
• 2nd floor row house on commuter street
• Real-time sampling
• Near-simultaneous indoor/outdoor sampling

20. Baltimore Traffic Study
• Black carbon, PM, particle-bound
PAH, CO, O3, NOx, VOCs
• Vehicle counts
• Meteorology

21. Missing Data
Observational
Interval N Missing
Black Carbon [ng/m3] .................................. 5 103,975 1,145
Vehicle Count [100s].................................... 5 93,482 11,638
Dew Point [5 C] ........................................... 30 15,801 1,719
Temperature [5 C] ........................................ 30 15,801 1,719
BWI Atm. Pressure [10 hPa] ........................ 60 8,758 2
BWI Precipitation [5 mm] ............................ 60 8,756 4
BWI Wind Speed [m/s] ................................ 60 8,760 0
AERMET Mixing Height [100 m] ................ 60 8,760 0

22. Impute Missing Data
• Regression prediction + N(0, 2)
– Add pseudorandom variation
– Minimize bias
• 2 estimated from regression

23. Meteorology Imputation
• External, simultaneous reference data
• BTS imputations estimated from BWI
observations
• R2s = 0.98

24. Traffic Imputation
• Internal, non-simultaneous reference data
• Imputations estimated from own street’s data
• Season, day-of-week, rush hour, time-of-day
• Calvert R2 = 0.52, St. Paul R2 = 0.65

27. Optimal Time Series Model

29. Findings
• Neighborhood-level exposure to black carbon
from mobile sources was 65.82 ng/m3 per 100
vehicles
• Background exposure to black carbon without
traffic was estimated to be 899.06 ng/m3

30. Findings
• Winds from the SW-S-SE quarter were
associated with the greatest increases in
black carbon
• Implicates atmospheric processes in
transporting black carbon from
– Baltimore’s central business district
– Interstate highways
– Regional and inter-regional sources

31. Longest
and highest resolution
time series analysis
I have ever seen

32. Without the
autocorrelation term,
the statistical relationship
between traffic
and black carbon is reversed

33. Microenvironment Exposure
Weights Can Be Obtained from a
Straightforward Statistical Model of
Time-Location Data

34. Time-Location Data
• Basis for estimating total exposure
– Amount of time in each microenvironment
– Concentration in each microenvironment
• Structured diaries

35. Time-Weighted Exposure
• ith time interval
• jth microenvironment
N M
Exposure twa = ∑ ∑ timeWeight ij × concentration ij
i j

36. Generalized Logit Model
• Regression framework
P
P[Y = j]
log = α j + ∑ β jp X p j = 2, 3, ...,K
P[Y = 1] p=1

37. Time-Weighted Exposure
time ij
timeWeight ij =
time total
subjects ij
=
subjects total
= p ij
P
α j + ∑ β jpX p
ℯ p= 1
P
(j > 1)
= K α k + ∑ β kpX p
1+ ∑ ℯ p= 1
k=2

38. Outcome: Microenvironments
• Indoor-home
• Indoor-school
• Indoor-other
• Commuting
• Outdoors

39. Subjects
• 95 children Sex
• 7 to 11-years-old Age Male Female
7 years 8 9
8 8 12
9 6 9
10 11 13
11 8 11

40. Time-Location
• 12 months
– June 1995 – May 1996
• 4 days
– Thursday - Monday
• 30-minute intervals
– 0600 to 2030

41. Data
• N = 171,000
• 1,800 longitudinal observations/subject
• Missing observations
– Imputation

42. Generalized Logit Model
indoor home
time of day sex
indoor school
Pr indoor other = β 0j + day of week + age
month nonwhite
commuting
lags 1 - 6 televisions
outdoor

43. Time-of-Day: Weekday
100%
Outdoor
• June 90%
Commuting
• Thursday
Percent Children [%]
80%
Indoor
Other
70%
Indoor School
60% Indoor Home
50%
40%
00
00
00
00
0
0
0
0
0
0
0
0
0
0
0
:0
:0
:0
:0
:0
:0
:0
:0
:0
:0
:0
6:
7:
8:
9:
10
11
12
13
14
15
16
17
18
19
20
Time-of-Day

44. Time-of-Day: Weekend
100%
Outdoor
• June 90% Commuting
Indoor Other
• Saturday
Percent-Children [%]
80%
Indoor
School
70%
Indoor Home
60%
50%
40%
00
00
00
00
0
0
0
0
0
0
0
0
0
0
0
:0
:0
:0
:0
:0
:0
:0
:0
:0
:0
:0
6:
7:
8:
9:
10
11
12
13
14
15
16
17
18
19
20
Time-of-Day

45. Directly provides
weights for
estimating total
time-weighted exposure

46. Acrolein and Adult Asthma in a
Nationally Representative
Sample of the United States
WORK IN PROGRESS

47. Acrolein
• Air toxic
• Aldehyde
• Respiratory irritant
• Hard to measure in ambient air
– Improved methods becoming available

48. Acrolein
• Industrial uses
• Tobacco smoke
• Mobile sources
• Indoor air pollutant

49. 2005 TRI: Acrolein

50. NATA 2005
• US EPA National-Scale Air Toxics Assessment
• Sources
– Point
– Non-point
– Mobile
• On-road
• Off-road
• Secondary formation and decay

51. NATA 2005
• National Emissions Inventory
• Air monitoring data
• Atmospheric dispersion modeling
• Modeled exposure estimates
– Every United States census tract
• No indoor sources assessed

52. NATA 2005
• Acrolein
– Responsible for 75% respiratory non-cancer health
effects nationwide

54. NHIS 2000 - 2009
• National Health Interview Survey
• Representative
– United States
– Non-institutionalized
– Civilian
• Cross-sectional prevalence

55. NHIS 2000 - 2009
• Adults 18 years-old and over
• Self-reported asthma attack in previous 12
months

56. NATA & NHIS
• NHIS subjects geographically linked to
NATA acrolein exposure estimates
• Census tract
– Survey subject residences
– Area exposure estimates
• Individual-level analysis

57. Preliminary Findings
• At highest quintile of acrolein exposure
– >0.055 g/m3
• pOR 1.11 [1.00:1.23]
• Controlling for
smoking, sex, age, education, race, poverty,
insurance, access to care, urban/rural
residence, survey year

58. Feasible to conduct
national epidemiologic
analysis for air toxic
with little measured data

59. First epidemiologic
evaluation of acrolein

60. Experimental Laboratory

61. Traffic Air Pollution

62. Multinomial Time-Location

63. Probabalistic National Survey

64. Biomarkers in Human Tissue

65. Toxicology

66. Gene Expression

67. Exposure Assessment

68. 7 days x 24 hr/day @900 LPM
PM2.5 SAEC 1i Microarray 1i
SAEC 2i Microarray 2i Summer
HVCI
SAEC 3i Microarray 3i
PUF
Organic
Extract
SAEC 1c Microarray 1c
Clean PUF
Control
Organic SAEC 2c Microarray 2c
Extract Field Blank
SAEC 3c Microarray 3c

69. B. Rey de Castro, Sc.D.
rey.decastro@comcast.net
+1 410 929 3583
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