Difference between revisions of "KTL Sarcoma study"

Revision as of 11:02, 12 January 2009

Material and methods

Study population

The majority of sarcoma patients in southern Finland are treated by the multidisciplinary sarcoma group of Helsinki University Central Hospital, with the remaining cases in the University Hospitals of Kuopio, Turku, or Tampere. All patients referred to these hospitals for operative treatment of STS between June 1997 (August 1996 in Helsinki) and December 1999 and more than 15 years of age were eligible as cases. The diagnoses were verified histologically for all except 7 patients. Sarcomas connected with known familial or genetic conditions, as well as sarcomas arising in visceral organs and bone, were excluded. Also other malignancies than STS, as well as nonmalignant tumors, were rejected. Some patients were operated twice during the study period; the second sample was not processed.

All patients who were operated due to an appendicitis diagnosis in a study hospital and who were more than 15 years of age were eligible as controls. They were collected from the same catchment area as the STS patients by dividing it into 15 areas (mainly according to former Finnish health care districts). One hospital performing appendectomy operations was recruited to the study from each area (in Helsinki, 2 hospitals). These were the university, central, or district hospitals of Helsinki, Hyvinka¨a¨, Ha¨meenlinna, Joensuu, Jyva¨skyla¨, Kotka, Kuopio, Lahti, Lappeenranta, Pori, Seina¨joki, Tampere, Turku and Vaasa, and the municipality hospitals in Espoo (Jorvi Hospital) and Helsinki (Maria Hospital). Informed consent was obtained from all patients in writing before the operation. The study was approved by the ethics committees of the National Public Health Institute and the hospitals involved.

The total number of patients recruited during the fieldwork was 972. One case was deleted due to missing address information, 1 case and 2 controls due to missing age information, and 3 cases and 11 controls since their fat samples were too small for dioxin analysis. As a result, we had 954 patients (148 cases and 806 controls) available for matching. The age range was 17.0 –91.1 years for cases and 15.0–88.7 years for controls. Based on National Cancer Registry data, we caught 70%, 9%, 17% and 26% of STS patients in Helsinki, Turku, Tampere and Kuopio University hospital regions, respectively, during the study period (calendar years 1997–1999). In Helsinki, all patients treated surgically with correct diagnosis were caught and agreed to participate; those not caught were either treated nonsurgically or misdiagnosed. Based on hospital discharge registry data, we estimate that about onefourth of appendicitis patients were caught in average during the most active collection period, but differences between hospitals were large.

The cases and controls were individually matched for area and age at the end of the fieldwork. This was done to ensure that there are enough controls from small areas and old age groups in the final data set, as it was not possible to analyze all recruited patients for dioxin. Area was defined based on the area of residence using the 15 areas described above. The age was determined at the day of operation. Maximum allowed difference in age between cases and controls was ± 3 years if case was < 38.0 years old, and ± 6 years if case was >= 38.0 years old. The control closest by age was matched to the case. Cases with fewer controls had a priority over cases with more controls. The number of controls per case was limited to 3. For 110 cases, 227 matching controls could be found in the pool. Thirty-nine cases had 1 control, 25 cases had 2 and 46 cases had 3 controls; for 38 cases, no control matching both age and area could be found.

Exposure assessment

From the matched 337 patients, concentrations of the 17 toxic polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) were measured from a subcutaneous fat sample obtained during an appendectomy or sarcoma operation. Measurements were done by gas chromatography-mass spectrometry30 at the Laboratory of Chemistry, which is an accredited testing laboratory (T077) for the analysis of dioxins in human samples (current standard: EN ISO/ IEC 17025) and has successfully participated in WHO/Euro intercalibrations. The concentrations were summed up after the value of each congener was multiplied by its relative toxic potency (toxic equivalency factor, TEF). The TEF values according to WHO31 were used, resulting in toxic equivalent concentrations (WHOTEq). Fat samples were analyzed during and after the collection period. Samples from STS patients were always analyzed in a batch containing also samples from appendicitis patients. All analytical work was performed blind so that the chemistry laboratory did not know the diagnosis of the patient. Quality assurance of analysis was performed with 2 separate means: 2 preformulated pools of human fat with different concentrations of dioxins [10.6 (n = 35) and 40.2 (n = 33) ng/kg (WHO-TEq in fat)] were always run with each lot of samples, and 36 individual fat samples with WHO-TEqs ranging from 6.9 to 116 ng/kg fat were analyzed as duplicates. The coefficients of variation for WHO-TEq in preformulated pools were 5.1% and 5.7%, respectively, and in duplicate analysis, 6.2%.

A detailed questionnaire about socioeconomic and lifestyle factors and chemical exposures was given to the patients in the hospital. If the patient was found not to have received the questionnaire in the hospital or if the patient did not return it, a new copy was sent to the patient’s home address. Of the matched subjects, 84 cases (76%) and 185 controls (81%) have also questionnaire information.

Statistical analyses

Conditional logistic regression analysis was performed with SAS PHREG procedure. Odds ratios were estimated for each quintile of WHO-TEq, the sum of the toxic congeners and the most relevant individual congeners, i.e., 2378-TCDD, 2378-TCDF, 12378-PeCDD, 23478-PeCDF and 123678-HxCDD (abbreviations: T, tetra; Pe, penta; Hx, hexa; Hp, hepta; O, octa; CDD, chlorinated dibenzo-p-dioxin; CDF, chlorinated dibenzofuran). In the other congener-specific analyses, exposures were treated as continuous variables and odds ratios were calculated for an increase of an interquartile range of the exposure.

All analyses were adjusted for sex. Several variables collected with the questionnaire were used as confounders in the analysis one by one. Nonbinary variables were analyzed as quartiles. Radiation therapy given to an STS patient was considered as diseaserelated and ignored in the analyses if the link to the disease was stated in the questionnaire or if the therapy had been given within 1 year before the operation. The analysis with the largest number of missing values was that with education years with 63 cases and 112 controls, but otherwise there were at least 70 cases and 125 controls in the analyses.

Fish consumption was studied in detail. Specific questions about the frequency of fish consumption were asked: 1 about total fish consumption, and 10 about specific types of fish or fish species. Four fish types contributed most to the total fish consumption. They were assumed to have high (Baltic herring, Baltic salmon) or low (predatory fish from lakes, rainbow trout) dioxin concentration based on previous results.^[1] The consumption frequencies (times per month) were calculated for high- and low-dioxin fish separately based on these 4 fish types. Exposure to the following chemicals was asked as a binary variable: solvents, solvent-based paints, formaldehyde, insecticides, fungicides/herbicides, wood preservatives, strong detergents, heavy metals, other chemicals.

Results

• The original data
• KTL Sarcoma study statistical analyses.txt
• Log file about the statistical analyses: Part 1, Part2
• Compilation of the results of statistical analyses

Variable information

The variable information was originally documented in Log file about the statistical analyses: Part 1, but unfortunately mostly in Finnish.

Tähän on syytä kirjata myös muuttujaluettelo, koska sitä ei ole missään muualla kunnolla tehty.
      LEIKKAUS    leikkauspvm, SASin oma formaatti (päivää jostakin kiintopisteestä?)
      IKA         ikä vuosina leikkauspäivänä
      ALUENRO     aluenro	tutkimusalue (potilaan kotiosoitteen postinumeron perusteella)
			1	-
			2	Espoo
			3	Helsinki
			4	Hyvinkää
			5	Hämeenlinna
			6	Joensuu
			7	Jyväskylä
			8	Kotka
			9	Kuopio
			10	Lahti
			11	Lappeenranta
			12	Pori
			13	Seinäjoki
			14	Tampere
			15	Turku
			16	Vaasa
      PNRO        postinumero K3 (K#=kyselylomakkeen kysymys nro #)
      STRATUM     stratum eli tapauksen numero ilman S-etuliitettä
      STRATASE    strataset eli tässä analyysissä 9
      DGLUOKKA    diagnoosiluokka tapauksilla
			DG_id	DGnimi
			1	MFH
			2	Liposarcoma
			3	Leiomyosarcoma
			4	Angiosarcoma
			5	Chondrosarcoma
			6	Sarcoma synoviale
			7	Sarcoma Ewing
			8	Dermatofibrosarcoma
			9	Sarcoma alia
			10	Sarcoma NUD
			11	Ei tietoa
			12	Osteosarcoma extrasceletale
			21	Lipoma
			22	Tumor Desmoides
			23	Myxoma
			24	Muu benigni tuumori
			25	Melanoma
			26	Muu kuin tuumori
			27	Tuplanäyte
      SP          sukupuoli 1=mies, 2=nainen K6
      KOULUV      kouluvuodet K8
      PITUUS      pituus, cm K15
      PAINO       paino nyt, kg K16
      KALAA       kalansyönti K21
			1	Harvemmin kuin kerran kuukaudessa tai en lainkaan
			2	Kerran tai pari kuukaudessa
			3	Kerran viikossa
			4	Pari kertaa viikossa
			5	Lähes joka päivä
			6	Kerran päivässä tai useammin
      PETOKALA    K22 (muuttujaan _YRI_ISI asti)
			0	En lainkaan
			1	Harvemmin kuin kerran kuukaudessa
			2	Kerran tai pari kuukaudessa
			3	Kerran viikossa
			4	Pari kertaa viikossa
			5	Lähes joka päivä
      MUIKKUA     
      SIS_VESI    
      KIRJOLOH    
      SILAKKAA    
      IT_MEREN    
      MUUTA_IT    
      PAKASTEK    
      KALAS_IL    
      VALTAMER    
      _YRI_ISI    
      SADE        sädehoito K47
      VASTATTU    onko kysely palautettu? YES/NO
      SADEAIK     aikaisempi sädehoito (yhdistelty kyselyn ja sairaalan tiedoista)
      TCDF2378    kongeneerispesifinen pitoisuus ng/kg (absoluuttiyksiköt rasvassa)
      TCDD2378    
      PF12378     
      PF23478     
      PD12378     
      HF123478    
      HF123678    
      HF234678    
      HF123789    
      HD123478    
      HD123678    
      HD123789    
      F1234678    
      F1234789    
      D1234678    
      OCDF        
      OCDD        
      TOXSUM      edellä mainittujen 17 kongeneerin summa
      WHOTEQ      edellä mainittujen 17 kongeneerin WHO-TEF-kertoimella painotettu summa
      TUPAK       oletko koskaan tupakoinut 1=ei, 2=kyllä K30
      TSTATUS     tupakointi: 1= < 6 kk sitten, 2= > 6 kk sitten, 3=ei koskaan K30+K32
      PACKYEAR    askivuodet: poltetut vuodet K31*( eri valmisteiden summa kpl/pvä K33)/20
			NULL, jos K30=2 mutta K32=NULL
      KEMIK       monelleko kemikaaliryhmälle on altistunut? K42
		   Onko altistunut 1=en, 2=kyllä (K42 muuttujaan MUU2 asti)
      LIUOTTI     liuottimet
      MAALIT      liuotinpohjaiset maalit
      FORMALDE    formaldehydi
      HYONTEIS    hyönteismyrkyt
      KASVINSU    kasvinsuojeluaineet
      KYLLASTE    puunkyllästeet
      PESUAINE    voimakkaat pesuaineet
      METALLIT    raskasmetallit
      MUU1        muu
      MUU2        muu
			K42 kuvauksen perusteella 0=ei altistusta 4=pahin altistus
      HYONLUOK    hyönteismyrkyt
      KASVLUOK    kasvinsuojeluaineet
      KYLLLUOK    puunkyllästeet
      ALKO        kuinka usein alkoholia K35
			1	päivittäin
			2	muutaman kerran viikossa
			3	noin kerran viikossa
			4	pari kertaa kuukaudessa
			5	noin kerran kuukaudessa
			6	noin kerran parissa kuukaudessa
			7	3-4 kertaa vuodessa
			8	pari kertaa vuodessa
			9	kerran vuodessa tai harvemmin
			10	en koskaan
      ALKOUS      K35 ilmoitettuna kertaa/viikko (7, 3, 1, 0.5, 0.2, 0.1, 0.05, 0.03, 0.01, 0)
      ALKOKULU    alkoholin kulutus annosta/viikko. Lasketaan K36=pal ja K35 avulla
			alkokulu: IIf([pal] Is Null And [alko]>8,0,[alkous]*[alkomaar])
	alkomaar   K36 ilmoitettuna annosta/kerta: 0,1,2,3,5,8,12 annosta
      ASUKKAIT    potilaan kotikunnan asukasluku (vuonna 1996? Pitäisikö tarkistaa mistä tilasto on?)
      bmi         paino / pituus / pituus * 10000
      mkala       Nämä kolme kalamuuttujaa on saatu regressiomallilla sovittamalla luokittelu-
      mkaladio     muuttujat suoraan regressiomalliin. Yksiköt ovat siis epämääräisiä. Ehkä pitäisi
      mkalamuu     muuttaa ensin kalamuuttujat muotoon annosta/kk niin olisi mielekäs tulkinta?
      muu         if muu1 = 1 or muu2 = 1 then muu = 1 (muuten muu=0) HUOM! Tämä muu on laskettu
                  päin mäntyä: 1 tarkoittaa ei altistusta eli jos ei ole kahta altistusta, merkitään
                  altistuneeksi ja jos ei ole tietoa tai on kaksi altistusta, merkitään 
                  altistumattomaksi. ÄLÄ KÄYTÄ SIIS TÄTÄ MUUTTUJAA MISSÄÄN!
      smoking     3-tstatus; 0=ei ikinä tupakoinut, 1= > 6 kk sitten, 2= < 6 kk sitten
      never       smoking jaetaan 0->never=1, 1->former=1, 2->current=1
      former
      current
mkala = 1.180840432 +
     PETOKALA * 0.285437176 +
     MUIKKUA  * 0.120796063 +
     SIS_VESI * 0.081192631 +
     KIRJOLOH * 0.432524410 +
     SILAKKAA * 0.201481292 +
     IT_MEREN * 0.186820358 +
     MUUTA_IT *-0.096115539 +
     PAKASTEK * 0.105839909 +
     KALAS_IL *-0.059836406 +
     VALTAMER * 0.095303307 +
     _YRI_ISI * 0.033074683;
mkaladio = 1.180840432 +
     SILAKKAA * 0.201481292 +
     IT_MEREN * 0.186820358;
mkalamuu = 1.180840432 +
     PETOKALA * 0.285437176 +
     MUIKKUA  * 0.120796063 +
     SIS_VESI * 0.081192631 +
     KIRJOLOH * 0.432524410 +
     MUUTA_IT *-0.096115539 +
     PAKASTEK * 0.105839909 +
     KALAS_IL *-0.059836406 +
     VALTAMER * 0.095303307 +
     _YRI_ISI * 0.033074683;

References