Role of Statisticians in Follow-Up of A-Bomb Survivors

Donald A. Pierce

Oregon Health & Sciences Univ.

Retired from Radiation Effects Res. Fndn.

Slides for talk, related things, at www.science.oregonstate.edu/~piercedo

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• Some brief history of ABCC/RERF, including role of statisticians

• General nature of the radiation-cancer dose response, including age-time variation

(Note: Is primary source of quantitative information on radiation effects in humans -- medicine, workplace, environment)

• Why the continued research remains important after more than 50 years

My Talk Today

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• Bombs August 1945, “Joint Commission” of Occupation, October 1945

• Pres. Truman directive to NAS 1946, Atomic Bomb Casualty Commission (ABCC)

• Motivations: leukemia, cancer, acute effects, inherited effects, others

• By 1950 Depts of Genetics, OBGYN, PEDS, Internal Med, Radiology, Pathology, Biochem/Micro, Biometrics

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• Large-scale clinical and pathology programs: examinations and autopsies

• Enormous efforts interviewing survivors within 2 km for “shielding histories”

• More than 1500 employees at peak, now about 250 with 40 scientists

• Americans: Around 10-15 recently, with far more at peak (largely physicians – military and jointly with Yale)

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• Francis Committee (Jablon, Moore) 1955 profound effect establishing sound epidemiological study

• Fixed study cohort of around 100,000 that could be followed up (most importantly no addition of “cases only”; also for F1 and in-utero)

• Became bi-national Radiation Effects Research Foundation (RERF) 1975

• Recurrent low ebbs, particularly in the late 1970’s

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• Statisticians played increasingly major role from around 1950

• Gil Beebe, Seymour Jablon were the NAS contract officers during about 1955-85

• Charles Land (OSU 1970-75) was in Hiroshima about 6 years, is still involved

• Many other US statisticians were there for 2 years or so in that era

• Several Japanese statisticians highly involved, but …

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• In 1978 Jablon set up a major contract with UW Biostats (low ebb thing)

• Ross Prentice, Art Peterson, others, were there in 1980-81

• They recruited Dale Preston and me in 1981 – Preston stayed until 2004 and I was there for 16 years during 81-04. Other OSU connections include students Ken Kopecky and Bob Delongchamp

• By 1987 we had a Stats Dept of 15 that would have done well in a US university

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• Thanks to Beebe, Jablon & Land, by 1975 stat methods were state of art in testing for effects (Mantel-Haenszel methods)

• These methods did not lend themselves to estimation, so Preston and I took this on

• Relative risk regression notions had just become available; requiring adaptation for large study, suitable form of interactions, multiple “time scales”

• By 1986 we had this ready for use, with widely-used and general interactive software developed by Preston (Epicure)

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• Possibilities richer than most applications, due to size of study and small chance of confounding (can estimate RR’s of 1.1)

• Largely because the dose-distance gradient was very steep, so those with large and small doses differ little otherwise

• Also, the participation and follow-up rates were essentially 100% (interesting point)

• Finally, there is such a long-term strong interest, promoting continued efforts

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• To proceed, we need some perspective on radiation dose Gray

• 1 Gy to major organs causes severe illness, although seldom fatal

• A CT scan, although usually localized, is about 0.01 Gy ; GI series about half of that

• Occupational limits are about 0.02 Gy/yr, although cumulatively further limited

• Thus 0.10 Gy is a fairly large dose of considerable interest

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General SummaryDose Gy Mean

DistancePersonsFollowed

CA Cases1958-98

Est ExcessCases

< 0.005 3680 60,800 9,600 3

.005 – 0.1 1990 27,800 4,400 80

0.1 – 0.2 1630 5,500 970 75

0.2 – 0.5 1500 5,900 1,100 180

0.5 – 1 1280 3,170 690 210

1 – 2 1110 1,650 460 44

>2 900 564 185 61

Tot excl < .0005 row 44,584 7,805 650

First row is a sample of distal survivors 5-10 km --- thus analyses are done totally within cohort

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ERR is factor increasing baseline rates, here sex-averaged: F:M ratio is 6:4 (offsets baseline ratio) ---- EAR is absolute risk

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•ERR is factor increasing baseline rates, here sex averaged and at age 70

•At 1 Gy rates are increased by about 50% over normal levels

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• Why such long follow-up, and such extensive analysis, is needed

• Lifelong effect for cancer was not expected

• Even when this became apparent the age-decline in RR was confused with effect of exposure age

• Understanding of such things is only emerging with continued follow-up and analysis

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ERR / 100 mSv (Sex avg)

0%

10%

20%

30%

35 45 55 65 75 85

Age (at risk)

Agex 5

Agex 15

Agex 30

Agex 55

ERR / 100mSv (Sex avg)

0%

10%

20%

30%

35 45 55 65 75 85Age (at risk)

Agex 5

Agex 15

Agex 30

Agex 55

• The left panel here shows the view of things until the late 1990s (still widely held) and the right panel shows our current understanding of the same data

• We now have a reasonable understanding of why the age-declining ERR should be expected

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• Simply, cancer is caused by accumulation of somatic mutations, and

• The radiogenic mutations persist for all remaining lifetime, but become relatively less important as more accumulate

• For any mutational exposure (including smoking) with age-cumulative dose D(a) it is plausible and explains well the data that

0

0

{ ( )}( )

( )

r a D aRR a

r a

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• Continued follow-up and analysis is needed to clarify the effect of exposure age --- one of the most important remaining issues

• On another issue, some would like to believe that for small radiation doses, e.g. 0.05 Gy, there is no cancer risk at all

• But careful analysis based on the 30,000 survivors in the low-dose range shows that this is implausible

• Statisticians also have clarified the (modest) effect of random errors in dose estimates

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• Virtually no other data really bear strongly on the quantitative needs for radiation protection

• Less explicable effect on non-cancer mortality, much smaller ERR

• Possible that this is only for large doses, due to killing large proportions of marrow cells with immunological effects

• Virtually no evidence of inherited effects, where mechanisms seem mainly limited to gonadal mutations

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• The needs and opportunities at RERF, along with the “Golden Age” of biostatistics, made all this incredibly attractive

• My OSU career spanned 25 years and was very good for me, forming the basis for that “second career”

• Am really grateful for what both places have meant for me and my family

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SOME REFERENCES

Preston, D.L., Shimizu, Y., Pierce, D.A., Suyama, A. and Mabuchi, K. (2003b). Studies of mortality of atomic bomb survivors, Report 13: Solid cancer and noncancer mortality 1950 –1997. Radiation Research 160, 381-407.

Pierce, D.A. and Vaeth, M (2003e). Age-time patterns of cancer to be anticipated from exposure to general mutagens. Biostatistics 4, 231-248.

Pierce, D.A. (2002). Age-time patterns of radiogenic cancer risk: their nature and likely explanations. Journal of Radiological Protection 22, A147-A154.

Pierce, D.A., Stram, D.O., Vaeth, M., and Schafer, D.W. (1992b). The errors-in-variables problem: considerations provided by radiation dose-response analyses of the A-bomb survivor data. J. Amer. Statist. Assn. 87, 351-359.

Pierce, D.A. and Preston, D.L. (2000a). Radiation-related cancer risks at low doses among atomic bomb survivors. Radiation Research 154, 178-186.