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					National Cancer Institute


                                    Michael Alavanja, Dr.P.H.
                                         Captain, USPHS
                                       Senior Investigator,
                              Division of Cancer Epidemiology and
                                          Genetics, NCI

                            2007 North American Pesticide Applicator
                                         Certification &
                                  Safety Education Workshop
                                       August 20-23, 2007
                                        Portland, Maine
 Agricultural Health Study
   on Cancer Findings.
Session I: General Strategy

   Tuesday, August 21
   Breakout Session #1
    Do pesticides cause cancer ?


   Few strong and consistent associations linking a single
    chemical to a single cancer.

 Animal/laboratory studies show most pesticides in current
  use to be non-genotoxic.

 Exposure assessment in previous epidemiologic studies was
  general weak, they were based on interviews and could
  suffer from case recall bias.

 Studies of pesticide manufactures are generally too small to
  give meaningful results for cancer

 Exposures among the general population in developed
  countries are relative low and effect hard to measure.

 In summary: Neither animal studies nor human studies give a
  compelling case for an association.
                Background

World-wide occupational exposures to pesticides
exceed 1.8 billion people (World Bank estimate).

 Everyone in the USA has some indirect exposure
  to pesticides (NHANES).

 Agricultural Insecticides as a group labeled as
  probable (group 2A) human carcinogens by
  IARC.

 Only arsenic and dioxin are listed as human
  carcinogens by IARC.

 Vital public health need to identify human
  carcinogens on the market!
                      Background

   The Occupational & Environmental Epidemiology Branch,
    NCI has a history of ecological and case-control of
    farmers starting in the 1970’s.

   A common critique- exposure assessment was weak.

   I proposed the idea for a prospective cohort study of
    pesticide applicators in 1989- 1990.

   In 1991 an extramural advisory group recommended the
    OES conduct the AHS.

   The Agricultural Heath Study entered the field in
    December 12, 1993.

   Other federal partners joined the team in 1994 (EPA), 1995
    (NIEHS) and NIOSH (1997).
                    Design AHS
               (www.aghealth.org)
 Prospective cohort study of 89,658 pesticide
  applicators & spouses (IA and NC).

 82% of target population enrolled 1993-1997.

 Little loss to follow-up (<2%).

 Cancer incidence and mortality updated annually.

 Comprehensive exposure assessment information on
  82 pesticides collected at three points in time.

 Questionnaire exposure assessment evaluated with
  field measurements of pesticides.

 Buccal cells collected on >35,000 study subjects.
      Disease Etiology In the AHS
Central Research Objectives:

  1. Characterize exposures to the highest
  degree ever achieved in large cohort study.

  2. Identify pesticides and other agricultural
  exposures that increase the risk of cancer .

  3. Identify the mode of action of agents
  causing disease.
       Types of Pesticide Exposure


 Acute exposure events. High exposure dose, short
  time period (minutes or hours).

 Chronic exposure. Low exposure dose, long time
  period (hundreds or thousands of days in a lifetime).
               Agricultural Health Study
               Pesticide Exposure Estimates




Calculating Cumulative Exposure Index:



Cumulative Exposure = Intensity * Duration
Where:
Intensity = Exposure scores obtained from algorithms
Duration = Days/years * Years/life-time = days/life-time

From: Dosemeci et al. Ann Occup Hyg 46:245-260, 2002.
              CONCENTRATIONS IN POST-APPLICATION
                URINE - GEOMETRIC MEAN (ug/L)
       70               No Gloves
                        Cab                                      No
       60                                                      Gloves


       50   No Gloves
            No Cab
       40
ug/L




       30                                                               Gloves
                                        Gloves
                               Gloves   Cab
       20                      No Cab
                                                 No      Cab
                                                 Cab
       10

       0
                    Boom Spray                   In-Furrow     Hand Spray
    Questionnaire Evaluation: Monitoring Visits



Study Subjects     IA       NC        Total


   Pesticide
    Applicators
    (dermal,       84       23         107
  inhalation and
      urine)


Spouses (urine)    38       11         49



Children (urine)   9        3          12
        Questionnaire Evaluated with Field
    Measurements of 2,4-D and Other Pesticides

                 Technician
              observations MLA          Questionnaire

Day 1   }            Day 2          Day 3&4


             Mix Load Apply (MLA).
             1. Hand wipes after MLA    Collect full first morning void
             2. Dermal patches
             3. Air measurements

        3. Collect each void from MLA
        through next morning void.


  Collect full first morning void
Comparison of Questionnaire Based Intensity
   Scores and Field Measurements 2,4-D
               (Thomas et al., in review)


     Intensity Score        Urine
           Mean          Concentration
         (Range)            Ug/L
            low
                               13
            5.5
                           (2.5-170)
         (3.0-7.2)
          Medium
                               19
            9.4
                           (2.5-180)
        (8.4-11.2)
           High
                               52
           15.2
                           (1.6-970)
       (12.0-20.0)


                  N=68


             R=0.6 p<0.001
                  Conclusions:
        From Exposure Algorithm Assessment

 For 2,4-D applicators we observed a
  significant correlation between the
  questionnaire-based algorithm (intensity-
  factor) and post-application urine
  concentrations.

 Important additional determinants of
  exposure have been identified to refine the
  exposure algorithm.
  Evaluating the association
      between estimated
 exposures with health effects.

(Cancer Etiology Studies in the
              AHS)
                             Effect


   End point of a causal mechanism.

   Amount of change in a population’s disease
    frequency caused by a specific factor.

 Incident rate: Number of new cases of disease in a
  specified period of time.

 Absolute effect: I1 – I0

 Relative Effect: I1 / I0
              Confounding factors

   A confounding factor must be a risk factor for the
    disease.

   A confounding factor must be associated with the
    exposure under study in the source population (the
    population from which the cases are derived).

 A confounding factor must not be effected by the
  exposure or the disease. In particular, it cannot be
  an intermediate step in the causal path between the
  exposure and the disease.

 How do we control confounding? Collect
  quantitative information on the exposure to the
  confounder and add the term to the multivariate
  model: y=b0 + b1x1 + b2 x2
         Statistical Interaction-Effect
                  Modification

   An effect-modifier is an exposure or host factor that
    modulates the extent of the effect of the study
    variable on the disease under investigation.

   If a cohort is divided into two or more distinct
    categories defined by the level of an effect modifier
    the stratum-specific effect measures may or may not
    be equal. If they are equal there is no effect
    modification. If they are significantly different there
    is effect modification.


 How do measure effect modification? Collect
  quantitative information on the exposure thought to
  be an effect modifier and add the product term to
  the multivariate model: y=b0 + b1x1 + b2 x2 + b 3 x1 x2
 Typical Sequence of Cancer Etiology
     Studies in AHS [2003-2007]

   SIR analysis (generates general hypothesis) [n=1]

   Nested case-control study of specific cancers
    (generate specific hypotheses [n=6])

 1 ST COHORT ANALYSIS of specific pesticide
  (generates or refines specific hypotheses [n=21])

 2ND COHORT ANALYSIS:
  (Test Specific Hypotheses [n=1 in progress])

 Molecular epidemiology studies of cancer
  (Evaluates biological plausibility and mode of action
  [n=3 in progress])
       AHS Research Strategy:
    Mitigate False Positive Results

                                    Biological
           Initial    Replication Evidence in
           Findings   later in time Humans

          Exposure-   Exposure-
Iowa      Response    Response        YES


North    Exposure-    Exposure-
                                      YES
Carolina Response     Response

License   Exposure-   Exposure-
Type                                  YES
          Response    Response
Mitigate False-Positive Associations and
          Study Rare Diseases




   Agricultural Health Cohort
           Consortium.
           (NCI organized)
Regulatory Implications of AHS Findings



 International Agency for Research on
  Cancer.
    International recommendations



 United States Environmental Protection Agency
    Educating pesticide applicators
    Label instructions
    Limitations of use
    Banning use
  Thank you for listening: AHS Research
  Team

Michael Alavanja (PI)   Daehee Kang
Laura Beane-Freeman     Stella Koutros
Erin Bell               Charles Knott (NC field station)
Aaron Blair (Co-PI)     Won-Jin Lee
Matthew Bonner          Charles Lynch (Univ. IA)
Joseph Coble            Shannon Lynch
Brian Curwin (NIOSH)    Jay Lubin
Mustafa Dosemeci        Rajeev Mahajan
Anne Claire De Ross     Mark Purdue
Larry Engel             Jennifer Rusiecki
Richard Hayes           Claudine Samanic
Cynthia Hines (NIOSH)   Rashmi Singha
Jane Hoppin (NIEHS)     Dale Sandler (NIEHS)
Lifang Hou              Kent Thomas (EPA)
Ann Hsing               Our Many Extramural Collaborators

				
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