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Applications of Contemporary Statistical Approaches in Environmental Health April 28 2011

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

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Applications of Contemporary Statistical Approaches in Environmental Health April 28 2011

  1. 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. 2. Non-Independent Data
  3. 3. Normal & Non-Normal Data
  4. 4. Environmental Health Data
  5. 5. Missing Data
  6. 6. Filling In Missing Data
  7. 7. Statistical Characterization of an MCF-7 Cell Culture Assay
  8. 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. 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. 10. Estrogenic PCB Data 250000 250000 250000 PCB17 PCB49 PCB66 200000 200000 200000 150000 150000 150000 100000 100000 100000Cells 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. 11. MCF-7 Cell Culture Assay• Dependent data – MCF-7 cell number• Fixed effect – dose – 5 estradiol dose levels – 1 control dose
  12. 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. 13. Generalized Linear Mixed Effects Modelg (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. 14. Findings• MCF-7 assay data – Gamma error distribution & reciprocal link – COV = 3.1 % – All variance components significant
  15. 15. Estrogenic PCB Data 250000 250000 250000 PCB17 PCB49 PCB66 200000 200000 200000 150000 150000 150000 100000 100000 100000Cells 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. 16. Increased sensitivity for detecting weakly estrogenicenvironmental pollutants
  17. 17. Ambient Black Carbon From Traffic in an Urban Neighborhood
  18. 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. 19. Baltimore Traffic Study• 2nd floor row house on commuter street• Real-time sampling• Near-simultaneous indoor/outdoor sampling
  20. 20. Baltimore Traffic Study• Black carbon, PM, particle-bound PAH, CO, O3, NOx, VOCs• Vehicle counts• Meteorology
  21. 21. Missing Data Observational Interval N MissingBlack Carbon [ng/m3] .................................. 5 103,975 1,145Vehicle Count [100s].................................... 5 93,482 11,638Dew Point [5 C] ........................................... 30 15,801 1,719Temperature [5 C] ........................................ 30 15,801 1,719BWI Atm. Pressure [10 hPa] ........................ 60 8,758 2BWI Precipitation [5 mm] ............................ 60 8,756 4BWI Wind Speed [m/s] ................................ 60 8,760 0AERMET Mixing Height [100 m] ................ 60 8,760 0
  22. 22. Impute Missing Data• Regression prediction + N(0, 2) – Add pseudorandom variation – Minimize bias• 2 estimated from regression
  23. 23. Meteorology Imputation• External, simultaneous reference data• BTS imputations estimated from BWI observations• R2s = 0.98
  24. 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
  25. 25. Optimal Time Series Model
  26. 26. 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
  27. 27. 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
  28. 28. Longestand highest resolution time series analysis I have ever seen
  29. 29. Without the autocorrelation term, the statistical relationship between trafficand black carbon is reversed
  30. 30. Microenvironment Exposure Weights Can Be Obtained from aStraightforward Statistical Model of Time-Location Data
  31. 31. Time-Location Data• Basis for estimating total exposure – Amount of time in each microenvironment – Concentration in each microenvironment• Structured diaries
  32. 32. Time-Weighted Exposure• ith time interval• jth microenvironment N M Exposure twa = ∑ ∑ timeWeight ij × concentration ij i j
  33. 33. 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
  34. 34. 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
  35. 35. Outcome: Microenvironments• Indoor-home• Indoor-school• Indoor-other• Commuting• Outdoors
  36. 36. 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
  37. 37. Time-Location• 12 months – June 1995 – May 1996• 4 days – Thursday - Monday• 30-minute intervals – 0600 to 2030
  38. 38. Data• N = 171,000• 1,800 longitudinal observations/subject• Missing observations – Imputation
  39. 39. Generalized Logit Model indoor home time of day sex indoor schoolPr indoor other = β 0j + day of week + age month nonwhite commuting lags 1 - 6 televisions outdoor
  40. 40. 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
  41. 41. 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
  42. 42. Directly provides weights for estimating totaltime-weighted exposure
  43. 43. Acrolein and Adult Asthma in a Nationally Representative Sample of the United States WORK IN PROGRESS
  44. 44. Acrolein• Air toxic• Aldehyde• Respiratory irritant• Hard to measure in ambient air – Improved methods becoming available
  45. 45. Acrolein• Industrial uses• Tobacco smoke• Mobile sources• Indoor air pollutant
  46. 46. 2005 TRI: Acrolein
  47. 47. NATA 2005• US EPA National-Scale Air Toxics Assessment• Sources – Point – Non-point – Mobile • On-road • Off-road• Secondary formation and decay
  48. 48. NATA 2005• National Emissions Inventory• Air monitoring data• Atmospheric dispersion modeling• Modeled exposure estimates – Every United States census tract• No indoor sources assessed
  49. 49. NATA 2005• Acrolein – Responsible for 75% respiratory non-cancer health effects nationwide
  50. 50. NHIS 2000 - 2009• National Health Interview Survey• Representative – United States – Non-institutionalized – Civilian• Cross-sectional prevalence
  51. 51. NHIS 2000 - 2009• Adults 18 years-old and over• Self-reported asthma attack in previous 12 months
  52. 52. NATA & NHIS• NHIS subjects geographically linked to NATA acrolein exposure estimates• Census tract – Survey subject residences – Area exposure estimates• Individual-level analysis
  53. 53. 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
  54. 54. Feasible to conductnational epidemiologic analysis for air toxicwith little measured data
  55. 55. First epidemiologicevaluation of acrolein
  56. 56. Experimental Laboratory
  57. 57. Traffic Air Pollution
  58. 58. Multinomial Time-Location
  59. 59. Probabalistic National Survey
  60. 60. Biomarkers in Human Tissue
  61. 61. Toxicology
  62. 62. Gene Expression
  63. 63. Exposure Assessment
  64. 64. 7 days x 24 hr/day @900 LPMPM2.5 SAEC 1i Microarray 1i SAEC 2i Microarray 2i Summer HVCI SAEC 3i Microarray 3i PUF Organic Extract SAEC 1c Microarray 1cClean PUF Control Organic SAEC 2c Microarray 2c Extract Field Blank SAEC 3c Microarray 3c
  65. 65. B. Rey de Castro, Sc.D.rey.decastro@comcast.net +1 410 929 3583 www.slideshare.net

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