Dynamic simulation periods are specified in Time's definition. This is usually a list of numbers or labels, typically in some unit of time (days, weeks, months, etc.). Use the “Dynamic()” function in your variables to perform dynamic simulation. 0 1 1 2 0 0 mtad 17. Janta 2003 15:22 jtue 16. helta 2009 18:09 48,24 1,557,82,641,356,17 1,646,6 Arial, 13 0,Model Who_mortality,2,2,0,1,C:\temp\WHO_mortality_data-2.ANA - Sex: male or female. ['Male','Female'] 168,256,1 48,12 1,104,114,416,303,0,MIDM Op_en2780 ['Variable','ICD-10 codes','Cause groupings','Sex','All ages','< 1','1-4','5-14','15-24','25-34','35-44','45-54','55-64','65-74','75+','Age not specified'] 168,88,1 48,12 1,232,242,416,303,0,MIDM ['Variable','ICD-10 codes','Cause groupings','Sex','All ages','< 1','1-4','5-14','15-24','25-34','35-44','45-54','55-64','65-74','75+','Age not specified'] sequence(1,392) 168,64,1 48,12 1,72,82,416,303,0,MIDM [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392] WHO data kopioitu WHO_Cause of Death.xls tiedostosta, joka on samassa hakemistossa tämän mallin kanssa. Kuolleisuusdata on kopioitu WHO_Cause of Death.xls tiedostoon WHO:n sivuilta 23.1.2003 osoitteesta: http://www3.who.int/whosis/ (Sivulta linkki: Cause of death statistics -> Table 1 -> Finland - 1996) Table(Var1,Item2)( 'Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Deaths/100000 py','Number','Deaths/100000 py','Number','Deaths/100000 py','Number','Deaths/100000 py','Number','Deaths/100000 py','Number','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py','Number','Number','Deaths/100000 py','Deaths/100000 py', 'AAA',0,0,0,'A00-B99',0,0,0,'A01',0,0,0,'A00, A02-A09',0,0,0,'A15-A16',0,0,0,'A17-A19',0,0,0,'A37',0,0,0,'A39',0,0,0,'A35',0,0,0,'A40-A41',0,0,0,'A20-A32, A36, A38, A42-49',0,0,0,'B05',0,0,0,'B20-B24',0,0,0,'A70-A74, A80-B34, B05, B20-B24',0,0,0,'B50-B54',0,0,0,'A75-A79, B55-B57, B60, B64',0,0,0,'A50-A64',0,0,0,'A65-A69, B35-B49, B58, B59, B65-B99',0,0,0,'C00-C97',0,0,0,'C00-C14',0,0,0,'C15',0,0,0,'C16',0,0,0,'C18',0,0,0,'C19-C21',0,0,0,'C22',0,0,0,'C32',0,0,0,'C33-C34',0,0,0,'C50',0,0,0,'C53',0,'C54-C55',0,'C61',0,'C67',0,0,0,'C17, C23-C31, C37-C49, C51, C52, C56-C60, C62-C66, C68-C80, C97',0,0,0,'C91-C95',0,0,0,'C81-C90, C96',0,0,0,'D00-D48',0,0,0,'E10-E14',0,0,0,'E00-E07, E15-E34, E65-E68, E70-E88',0,0,0,'E41-E46',0,0,0,'E40, E50-E64',0,0,0,'D50-D64',0,0,0,'D65-D89',0,0,0,'F01-F99',0,0,0,'G00, G03',0,0,0,'G35',0,0,0,'G40-G41',0,0,0,'G04-G31, G36-G37, G43-H95',0,0,0,'I00-I99',0,0,0,'I00-I02',0,0,0,'I05-I09',0,0,0,'I10-I13',0,0,0,'I21, I22',0,0,0,'I20, I24, I25',0,0,0,'I26-I51',0,0,0,'I60-I69',0,0,0,'I70',0,0,0,'I71-I78',0,0,0,'I80-I82',0,0,0,'I83-I99',0,0,0,'J00-J06',0,0,0,'J20-J21',0,0,0,'J12-J18',0,0,0,'J10-J11',0,0,0,'J40-J46',0,0,0,'J22, J30-J39, J47-J98',0,0,0,'K25-K27',0,0,0,'K35-K38',0,0,0,'K40-K46,K56',0,0,0,'K70,K73-K74,K76',0,0,0,'K00-K22, K28-K31, K50-K55, K57-K66, K71, K72, K75, K80-K92',0,0,0,'N00-N07, N13-N19',0,0,0,'N10-N12',0,0,0,'N40',0,'N20-N39, N41-N98',0,0,0,'O00-O07',0,'O20, O46, O67, O72',0,'O13-O16, O21',0,'O85-O92, A34',0,'O10-O12, O22-O75, O95-O97',0,'O98-O99',0,'L00-L98',0,0,0,'M00-M99',0,0,0,'Q03,Q05',0,0,0,'Q20-Q28',0,0,0,'Q00-Q02, Q04, Q06-Q18, Q30-Q99',0,0,0,'P10-P15',0,0,0,'P00-P08, P20-P96, A33',0,0,0,'R54',0,0,0,'R00-R53, R55-R99',0,0,0,'V01-X59, Y40-Y86, Y88',0,0,0,'V02-V04, V09, V12-V14, V19-V79, V86-V89',0,0,0,'V01, V05-V06, V10, V11, V15-V18, V80-V85, V90-V99',0,0,0,'X40-X49',0,0,0,'W00-W19',0,0,0,'X00-X09',0,0,0,'W65-W74',0,0,0,'W24-W31',0,0,0,'W32-W34',0,0,0,'W20-W23, W35-W64, W75-W99, X10-X39, X50-X59, Y85, Y86',0,0,0,'Y40-Y84, Y88',0,0,0,'X60-X84',0,0,0,'X85-Y09',0,0,0,'Y10-Y36, Y87, Y89',0,0,0, 'All causes',0,0,0,'Infectious and parasitic diseases',0,0,0,'Typhoid and paratyphoid fever',0,0,0,'Other intestinal infectious diseases',0,0,0,'Tuberculosis of respiratory system',0,0,0,'Tuberculosis, other forms',0,0,0,'Whooping cough',0,0,0,'Meningococcal infection',0,0,0,'Tetanus',0,0,0,'Septicaemia',0,0,0,'Other bacterial diseases',0,0,0,'Measles',0,0,0,'HIV disease',0,0,0,'Other viral diseases',0,0,0,'Malaria',0,0,0,'Other arthropod-borne diseases',0,0,0,'Sexually transmitted diseases',0,0,0,'Other infectious and parasitic diseases',0,0,0,'Malignant neoplasms',0,0,0,'Malignant neoplasm of lip, oral cavity and pharynx',0,0,0,'Malignant neoplasm of oesophagus',0,0,0,'Malignant neoplasm of stomach',0,0,0,'Malignant neoplasm of colon',0,0,0,'Malignant neoplasm of rectum, rectosigmoid junction and anus',0,0,0,'Malignant neoplasm of liver',0,0,0,'Malignant neoplasm of larynx',0,0,0,'Malignant neoplasm of trachea, bronchus and lung',0,0,0,'Malignant neoplasm of breast',0,0,0,'Malignant neoplasm of cervix uteri',0,'Malignant neoplasm of uterus, other and unspecified',0,'Malignant neoplasm of prostate',0,'Malignant neoplasm of bladder',0,0,0,'Malignant neoplasm of other sites',0,0,0,'Leukaemia',0,0,0,'Other malignant neoplasms of lymphoid and haematopoietic and related tissue',0,0,0,'Benign neoplasm, other and unspecified neoplasm',0,0,0,'Diabetes mellitus',0,0,0,'Other endocrine and metabolic diseases',0,0,0,'Malnutrition',0,0,0,'Other nutritional deficiencies',0,0,0,'Anaemias',0,0,0,'Other diseases of blood and blood-forming organs',0,0,0,'Mental disorders',0,0,0,'Meningitis',0,0,0,'Multiple sclerosis',0,0,0,'Epilepsy',0,0,0,'Other diseases of the nervous system and sense organs',0,0,0,'Diseases of the circulatory system',0,0,0,'Acute rheumatic fever',0,0,0,'Chronic rheumatic heart disease',0,0,0,'Hypertensive disease',0,0,0,'Acute myocardial infarction',0,0,0,'Other ischaemic heart diseases',0,0,0,'Diseases of pulmonary circulation and other forms of heart disease',0,0,0,'Cerebrovascular disease',0,0,0,'Atherosclerosis',0,0,0,'Embolism, thrombosis and other diseases of arteries, arterioles and capillaries',0,0,0,'Phlebitis, thrombophlebitis, venous embolism and thrombosis',0,0,0,'Other diseases of the circulatory system',0,0,0,'Acute upper respiratory infection',0,0,0,'Acute bronchitis and bronchiolitis',0,0,0,'Pneumonia',0,0,0,'Influenza',0,0,0,'Bronchitis, chronic and unspecified, emphysema and asthma',0,0,0,'Other diseases of the respiratory system',0,0,0,'Ulcer of stomach and duodenum',0,0,0,'Appendicitis',0,0,0,'Hernia of abdominal cavity and intestinal obstruction',0,0,0,'Chronic liver disease and cirrhosis',0,0,0,'Other diseases of the digestive system',0,0,0,'Nephritis, nephrotic syndrome and nephrosis',0,0,0,'Infections of kidney',0,0,0,'Hyperplasia of prostate',0,'Other diseases of the genitourinary system',0,0,0,'Abortion',0,'Haemorrhage of pregnancy and childbirth',0,'Toxaemia of pregnancy',0,'Complications of the puerperium',0,'Other direct obstetric causes',0,'Indirect obstetric causes',0,'Diseases of skin and subcutaneous tissue',0,0,0,'Diseases of the musculoskeletal system and connective tissue',0,0,0,'Spina bifida and hydrocephalus',0,0,0,'Congenital anomalies of the circulatory system',0,0,0,'Other congenital anomalies',0,0,0,'Birth trauma',0,0,0,'Other conditions originating in the perinatal period',0,0,0,'Senility',0,0,0,'Signs, symptoms and other ill-defined conditions',0,0,0,'Accidents and adverse effects',0,0,0,'Motor vehicle traffic accidents',0,0,0,'Other transport accidents',0,0,0,'Accidental poisoning',0,0,0,'Accidental falls',0,0,0,'Accidents caused by fire and flames',0,0,0,'Accidental drowning and submersion',0,0,0,'Accidents caused by machinery and by cutting and piercing instruments',0,0,0,'Accidents caused by firearm missile',0,0,0,'All other accidents, including late effects',0,0,0,'Drugs, medicaments causing adverse effects in therapeutic use',0,0,0,'Suicide and self- inflicted injury',0,0,0,'Homicide and injury purposely inflicted by other persons',0,0,0,'Other external causes',0,0,0, 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) 168,32,1 48,24 2,239,1,476,597 2,47,25,1209,720,0,MIDM 2,96,21,1156,479,0,MIDM 65535,52427,65534 [Var1,Item2] [Var1,Item2] - ICD-10-based diagnostic classification. 103 different diagnoses, including all causes. This index is NOT exclusive but is probably mutually exhaustive in respect to causes of death. var a:= who_data[var1='Cause groupings']; var b:= a[item2=unique(a,Item2)]; subset(b) 296,64,1 48,12 2,419,124,476,415 1,104,114,512,303,0,MIDM ['All causes',0,'Infectious and parasitic diseases','Typhoid and paratyphoid fever','Other intestinal infectious diseases','Tuberculosis of respiratory system','Tuberculosis, other forms','Whooping cough','Meningococcal infection','Tetanus','Septicaemia','Other bacterial diseases','Measles','HIV disease','Other viral diseases','Malaria','Other arthropod-borne diseases','Sexually transmitted diseases','Other infectious and parasitic diseases','Malignant neoplasms','Malignant neoplasm of lip, oral cavity and pharynx','Malignant neoplasm of oesophagus','Malignant neoplasm of stomach','Malignant neoplasm of colon','Malignant neoplasm of rectum, rectosigmoid junction and anus','Malignant neoplasm of liver','Malignant neoplasm of larynx','Malignant neoplasm of trachea, bronchus and lung','Malignant neoplasm of breast','Malignant neoplasm of cervix uteri','Malignant neoplasm of uterus, other and unspecified','Malignant neoplasm of prostate','Malignant neoplasm of bladder','Malignant neoplasm of other sites','Leukaemia','Other malignant neoplasms of lymphoid and haematopoietic and related tissue','Benign neoplasm, other and unspecified neoplasm','Diabetes mellitus','Other endocrine and metabolic diseases','Malnutrition','Other nutritional deficiencies','Anaemias','Other diseases of blood and blood-forming organs','Mental disorders','Meningitis','Multiple sclerosis','Epilepsy','Other diseases of the nervous system and sense organs','Diseases of the circulatory system','Acute rheumatic fever','Chronic rheumatic heart disease','Hypertensive disease','Acute myocardial infarction','Other ischaemic heart diseases','Diseases of pulmonary circulation and other forms of heart disease','Cerebrovascular disease','Atherosclerosis','Embolism, thrombosis and other diseases of arteries, arterioles and capillaries','Phlebitis, thrombophlebitis, venous embolism and thrombosis','Other diseases of the circulatory system','Acute upper respiratory infection','Acute bronchitis and bronchiolitis','Pneumonia','Influenza','Bronchitis, chronic and unspecified, emphysema and asthma','Other diseases of the respiratory system','Ulcer of stomach and duodenum','Appendicitis','Hernia of abdominal cavity and intestinal obstruction','Chronic liver disease and cirrhosis','Other diseases of the digestive system','Nephritis, nephrotic syndrome and nephrosis','Infections of kidney','Hyperplasia of prostate','Other diseases of the genitourinary system','Abortion','Haemorrhage of pregnancy and childbirth','Toxaemia of pregnancy','Complications of the puerperium','Other direct obstetric causes','Indirect obstetric causes','Diseases of skin and subcutaneous tissue','Diseases of the musculoskeletal system and connective tissue','Spina bifida and hydrocephalus','Congenital anomalies of the circulatory system','Other congenital anomalies','Birth trauma','Other conditions originating in the perinatal period','Senility','Signs, symptoms and other ill-defined conditions','Accidents and adverse effects','Motor vehicle traffic accidents','Other transport accidents','Accidental poisoning','Accidental falls','Accidents caused by fire and flames','Accidental drowning and submersion','Accidents caused by machinery and by cutting and piercing instruments','Accidents caused by firearm missile','All other accidents, including late effects','Drugs, medicaments causing adverse effects in therapeutic use','Suicide and self- inflicted injury','Homicide and injury purposely inflicted by other persons','Other external causes'] Op_en2779 index i:= who_data[var1='Cause groupings']; var a:= who_data[@item2=@i]; a[i=Diagnosis1, var1='ICD-10 codes'] 296,32,1 48,24 2,269,234,476,224 2,184,110,867,359,0,MIDM [Var1,Sys_localindex('I')] - Numbers of cases or incidence, i.e. numbers per 100000 person-years. ['Number','Number/100000 person-years'] 168,232,1 48,12 1,312,322,416,303,0,MIDM Op_en2784 a Five-year age groups with 1, 1-4, 75+, and not-specified groups. ['All ages','< 1','1-4','5-14','15-24','25-34','35-44','45-54','55-64','65-74','75+','Age not specified'] 56,192,1 48,12 2,102,90,476,346 1,264,274,416,303,0,MIDM Op_en2781 ['All ages','< 1','1-4','5-14','15-24','25-34','35-44','45-54','55-64','65-74','75+','Age not specified'] using a:= slice(who_data,var1,Age_groups) do a 168,136,1 48,24 1,110,170 2,38,30,721,505,0,MIDM [Item2,Age_group1] [Vars,1,Age_group1,1,Item2,1] Table(Age_group1,vars)( 1,3,4,5, 1,3,4,6, 1,3,4,7, 1,3,4,8, 1,3,4,9, 1,3,4,10, 1,3,4,11, 1,3,4,12, 1,3,4,13, 1,3,4,14, 1,3,4,15, 1,3,4,16 ) ['Variable','Cause groupings','Sex','Value'] 56,136,1 48,24 1,110,278 1,575,134,478,303,0,MIDM 1,339,222,718,318,0,MIDM [Vars,Age_group1] [Vars,Age_group1] ['Variable','Cause groupings','Sex','Value'] # or 1/100000 py WHO mortality data for different countries var a:= d_age[vars='Cause groupings']; var b:= 0; var d:= 0; var x:= 1; while x<=size(Item2) do ( var c:= a[Item2=x]; b:= if c=0 then b else c; d:= if Item2=x then b else d; x:= x+1); a:= if vars='Cause groupings' then d else d_age; a:= mdtable(a,Item2,vars,[Units1,diagnosis1,sex1]); a:= a[@sex1=@sex]; if country1='Finland' then a else a 168,200,1 48,24 2,650,273,476,415 2,83,123,992,591,0,MIDM 65535,52427,52427 [Age_group1,Diagnosis1] [Units1,1,Country1,1,Sex,1,Diagnosis1,1,Age_group1,1] Op_en2778 ['Variable','Cause groupings','Sex','Value'] 56,168,1 48,12 ['Variable','Cause groupings','Sex','Value'] <a href="http://en.opasnet.org/w/Image:Opasnet_base_connection.ANA">Wiki description</a> HP_Omistaja 9. maata 2008 10:42 jtue 3. helta 2009 14:17 48,24 472,40,0 48,29 1,0,0,1,1,1,0,0,0,0 1,772,97,462,377,17 2,102,90,476,224 Arial, 15 100,1,1,1,1,9,2970,2100,1,0 jtue 1. jouta 2008 10:57 48,24 192,80,1 48,24 1,9,11,636,495,17 100,1,1,0,1,9,2970,2100,15,0 jtue 18. heita 2008 10:14 48,24 496,296,1 48,24 1,672,61,564,502,17 100,1,1,1,1,9,2970,2100,15,0 This library contains functions to make various instances of concatenation more convenient. Concat3 thru Concat10 are generalizations of the built-in Concat function which concatenate from 3 to 10 arrays in a single call (while the built-in Concat concatenates two arrays). ConcatRows concatenates all the rows of a single array. David Kendall & Lonnie Chrisman Mon, Jan 26, 2004 8:49 AM Lonnie Wed, Sep 05, 2007 3:23 PM 48,24 456,176,1 68,20 1,0,0,1,1,1,0,0,0,0 1,50,200,488,454,23 (A1, A2, A3: ArrayType; I1, I2, I3, J: IndexType ) Concatenates three arrays, A1, A2, and A3. I1, I2, and I3 are the indexes that are joined; J is the index of the new array; J usually is the concatenation of I1, I2, and I3 Index I12 := Concat(I1,I2); Concat( Concat( A1,A2,I1,I2,I12 ), A3, I12, I3, J ) 88,64,1 48,26 2,56,56,986,596 A1,A2,A3,I1,I2,I3,J (A1, A2, A3, A4: ArrayType; I1, I2, I3, I4, J: IndexType ) Concatenates four arrays, A1, A2, A3, and A4. I1, I2, I3, and I4 are the indexes that are joined; J is the index of the new array; J usually is the concatenation of I1, I2, I3, and I4. Index I12 := Concat(I1,I2); Index I123:= Concat(I12, I3); Concat( Concat( Concat( A1,A2,I1,I2,I12 ), A3, I12, I3, I123), A4, I123, I4, J); 192,64,1 48,24 2,30,30,986,596 A1,A2,A3,A4,I1,I2,I3,I4,J 0 (A1, A2, A3, A4, A5, A6, A7, A8, A9: ArrayType; I1, I2, I3, I4, I5, I6, I7, I8, I9, J: IndexType) Concatenates nine arrays, A1, ..., A9. I1, ..., I9 are the indexes joined; J is the index of the new array; J usually is the concatenation of I1, ..., I9. Index I12 := Concat(I1,I2); Index I123 := Concat(I12, I3); Index I1234 := Concat(I123, I4); Index I12345 := Concat(I1234, I5); Index I123456 := Concat(I12345, I6); Index I1234567 := Concat(I123456, I7); Index I12345678 := Concat(I1234567, I8); Concat( Concat( Concat( Concat( Concat( Concat( Concat( Concat( A1,A2,I1,I2,I12 ), A3, I12, I3, I123), A4, I123, I4, I1234), A5, I1234, I5, I12345), A6, I12345, I6, I123456), A7, I123456, I7, I1234567), A8, I1234567, I8, I12345678), A9, I12345678, I9, J); 88,232,1 48,24 2,27,120,469,638 A1,A2,A3,A4,A5,A6,A7,A8,A9,I1,I2,I3,I4,I5,I6,I7,I8,I9,J 0 (A1, A2, A3, A4, A5: ArrayType; I1, I2, I3, I4, I5, J: IndexType ) Concatenates five arrays, A1, ..., A5. I1, ..., I5 are the indexes joined; J is the index of the new array; J usually is the concatenation of I1, ..., I5. Index I12 := Concat(I1,I2); Index I123:= Concat(I12, I3); Index I1234 := Concat(I123, I4); Concat( Concat( Concat( Concat( A1,A2,I1,I2,I12 ), A3, I12, I3, I123), A4, I123, I4, I1234), A5, I1234, I5, J); 88,120,1 48,24 2,160,160,986,596 A1,A2,A3,A4,A5,I1,I2,I3,I4,I5,J (A1, A2, A3, A4, A5, A6: ArrayType; I1, I2, I3, I4, I5, I6, J: IndexType ) Concatenates six arrays, A1, ..., A6. I1, ..., I6 are the indexes joined; J is the index of the new array; J usually is the concatenation of I1, ..., I6. Index I12 := Concat(I1,I2); Index I123:= Concat(I12, I3); Index I1234 := Concat(I123, I4); Index I12345 := Concat(I1234, I5); Concat( Concat( Concat( Concat( Concat( A1,A2,I1,I2,I12 ), A3, I12, I3, I123), A4, I123, I4, I1234), A5, I1234, I5, I12345), A6, I12345, I6, J); 192,120,1 48,24 2,644,94,602,712 A1,A2,A3,A4,A5,A6,I1,I2,I3,I4,I5,I6,J 0 (A1, A2, A3, A4, A5, A6, A7: ArrayType; I1, I2, I3, I4, I5, I6, I7, J: IndexType ) Concatenates seven arrays, A1, ..., A7. I1, ..., I7 are the indexes joined; J is the index of the new array; J usually is the concatenation of I1, ..., I7. Index I12 := Concat(I1,I2); Index I123:= Concat(I12, I3); Index I1234 := Concat(I123, I4); Index I12345 := Concat(I1234, I5); Index I123456 := Concat(I12345, I6); Concat( Concat( Concat( Concat( Concat( Concat( A1,A2,I1,I2,I12 ), A3, I12, I3, I123), A4, I123, I4, I1234), A5, I1234, I5, I12345), A6, I12345, I6, I123456), A7, I123456, I7, J); 88,176,1 48,24 2,580,98,551,565 A1,A2,A3,A4,A5,A6,A7,I1,I2,I3,I4,I5,I6,I7,J (A1, A2, A3, A4, A5, A6, A7, A8: ArrayType; I1, I2, I3, I4, I5, I6, I7, I8, J: IndexType ) Concatenates eight arrays, A1, ..., A8. I1, ..., I8 are the indexes joined; J is the index of the new array; J usually is the concatenation of I1, ..., I8. Index I12 := Concat(I1,I2); Index I123:= Concat(I12, I3); Index I1234 := Concat(I123, I4); Index I12345 := Concat(I1234, I5); Index I123456 := Concat(I12345, I6); Index I1234567 := Concat(I123456, I7); Concat( Concat( Concat( Concat( Concat( Concat( Concat( A1,A2,I1,I2,I12 ), A3, I12, I3, I123), A4, I123, I4, I1234), A5, I1234, I5, I12345), A6, I12345, I6, I123456), A7, I123456, I7, I1234567), A8, I1234567, I8, J); 192,176,1 48,24 2,12,98,561,737 A1,A2,A3,A4,A5,A6,A7,A8,I1,I2,I3,I4,I5,I6,I7,I8,J 0 (A1, A2, A3, A4, A5, A6, A7, A8, A9, A10: ArrayType; I1, I2, I3, I4, I5, I6, I7, I8, I9, I10, J: IndexType) Concatenates ten arrays, A1, ..., A10. I1, ..., I10 are the indexes joined; J is the index of the new array; J usually is the concatenation of I1, ..., I10. Index I12 := Concat(I1,I2); Index I123 := Concat(I12, I3); Index I1234 := Concat(I123, I4); Index I12345 := Concat(I1234, I5); Index I123456 := Concat(I12345, I6); Index I1234567 := Concat(I123456, I7); Index I12345678 := Concat(I1234567, I8); Index I123456789 := Concat(I12345678, I9); Concat( Concat( Concat( Concat( Concat( Concat( Concat( Concat( Concat( A1,A2,I1,I2,I12 ), A3, I12, I3, I123), A4, I123, I4, I1234), A5, I1234, I5, I12345), A6, I12345, I6, I123456), A7, I123456, I7, I1234567), A8, I1234567, I8, I12345678), A9, I12345678, I9, I123456789), A10, I123456789, I10, J); 192,232,1 48,24 2,542,93,632,744 A1,A2,A3,A4,A5,A6,A7,A8,A9,A10,I1,I2,I3,I4,I5,I6,I7,I8,I9,I10,J 0 (A : ArrayType ; RowIndex,ColIndex,ResultIndex : IndexType) Takes an array, A indexed by RowIndex & ColIndex, and concatenates each row, henceforth flattening the array by one dimension. The result is indexed by ResultIndex, which must be an index with size(RowIndex) * size(ColIndex) elements. index L := [ identifier of RowIndex, identifier of ColIndex, "val"]; slice(Mdarraytotable(A,ResultIndex,L),L,3) 320,64,1 64,24 2,499,85,478,348 A,RowIndex,ColIndex,ResultIndex Lonnie Thu, Sep 11, 1997 2:15 PM Lonnie Tue, Feb 05, 2008 10:03 AM 48,24 440,128,1 52,20 1,1,1,1,1,1,0,0,0,0 1,20,272,499,462,17 Arial, 13 (A:ArrayType;I:IndexType;L:IndexType;row:IndexType;dbTableName) Generates the SQL "INSERT INTO" statement for one line of table A. A is a 2-D table indexed by rows I and columns L. L's domain serves as the column names in the database table. dbTableName is the name of the table in the database. The result begins with two semi-colons, since it will be used with an SQL statement preceeding it. 29.8.2008 Jouni Tuomisto I added the parameter IGNORE because it ignores rows that would cause duplicate-key violations. This way, there is no need to check for e.g. existing locations of new indices. 6.1.2009 Jouni Tuomisto I changed the A[I=row] to A[@I=@row] because the original function does not work correctly, if there are non-unique rows in the index. (';;INSERT IGNORE INTO ' & dbTableName & '(' & JoinText(L,L,',') & ') VALUES (' & Vallist(A[@I=@row],L)) & ') ' 184,32,1 52,24 2,591,203,487,469 A,I,L,row,dbTableName (V:ArrayType;I:IndexType) Takes a list of values, and returns a string which the concatenation of each value, separated by commas, and with each value quoted. JoinText( '''' & V & '''', I, ',') 72,32,0 52,24 2,642,360,476,224 V,I 1,F,4,14,0,0 (Tabl:ArrayType;RowIndex:IndexType;LabelIndex:IndexType;dbTableName) Returns the SQL that will write the table to the database table. This can be used as the second argument to DBWrite. This SQL statement replaces the entire contents of an existing table with the new data. 'DELETE FROM '& Dbtablename & JoinText(Insertrecsql(Tabl, Rowindex, Labelindex, Rowindex, Dbtablename),RowIndex) 328,32,1 88,24 2,728,341,510,476 Tabl,RowIndex,LabelIndex,dbTableName (Tabl:ArrayType;RowIndex:IndexType;LabelIndex:IndexType;dbTableName) Returns the SQL that will write the table to the database table. This can be used as the second argument to DBWrite. This SQL statement replaces the entire contents of an existing table with the new data. JoinText(Insertrecsql(Tabl, Rowindex, Labelindex, Rowindex, Dbtablename),RowIndex) 328,88,1 88,24 2,559,127,510,476 Tabl,RowIndex,LabelIndex,dbTableName [] Makes a table to be written to the Loc table. index j:= ['id','Obj_id_i','Location','Description']; Locations2[.j=j] 320,200,1 48,16 2,711,325,476,224 2,349,49,679,278,0,MIDM 65535,45873,39321 [Sys_localindex('J'),Sys_localindex('I')] 2,D,4,2,0,0,4,0,$,0,"ABBREV",0 100,1,1,1,1,9,2970,2100,15,0 [] (table:texttype) Brings the largest id number from the table defined in the parameter. index i:= DBquery(odbc,' SELECT MAX(id) AS id FROM '&table&' '); index j:= dblabels(i); max(max(DBTable(i, j ),i),j) 440,72,1 48,12 2,102,90,476,331 39325,65535,39321 table List of such tables in Opasnet Base that are being written to by this module. ['Obj','Res','Loc','Locres','Sett','Item','Sam'] 200,64,1 48,13 2,15,594,158,227,0,MIDM ['Obj','Res','Loc','Locres','Sett','Item','Sam'] The largest id values for the selected Opasnet Base tables. The table is updated by pressing the R_cardinals button. Table(Table1)( 203,16.253K,879,64.864K,47,138,882.612K ) 200,32,1 48,24 2,634,394,476,332 2,193,270,416,303,0,MIDM 2,472,313,416,303,0,MIDM 39325,65535,39321 2,I,4,2,0,0,4,0,$,0,"ABBREV",0 Makes a list of all locations in all results in all variables. The list is as long as is needed for the Locres table. A subset is taken then for the Res table. 1) Initialises local variables, and slices variables from Object1. 2)-4) Does the process for each variable one at a time. 2) Only the deterministic information about variables are considered (therefore mean). Makes a 2D table of the locres info. 3) Makes a table with fields required by the Locres and Res tables. 4) Reduces one dimension by expanding the length from the length of Res to that of Locres. 5) Makes i the row index. var output=0; var e:= Cardinals[table1='Res']; var f:= Cardinals[table1='Locres']; var o:= if Objects1[.j='Typ_id'] = 1 or Objects1[.j='Typ_id'] = 10 then 1 else 0; index j:= ['id', 'Location', 'Res_id', 'Loc_id', 'Vident', 'Obj_id_v', 'Obj_id_r', 'Mean', 'N']; index k:= subset(o); o:= objects1[Object_all=k]; var temp:= 0; var x:= 1; while x<= size(k) do ( var c:= slice(o,k,x); var a:= mean(sample(evaluate(c[.j='identifier']))); index h:= indexnames(a); index L:= concat(h,['Value']); index res_id:= (1..size(a))+e; index locres_id= (1..size(a)*size(h))+f; e:= e+size(res_id); f:= f+size(locres_id); a:= mdarraytotable(a,res_id,L); var mean1:= a[L='Value']; a:= a[L=h]&''; var g:= if Loc.j='Obj_id_i' then Loc&'+'&Loc[.j='Location'] else Loc; var p:= h; a:= array(j,[ 0, a, res_id, findid(findid((Ident of p), Obj, 'Ident')&'+'&a, g, 'Obj_id_i'), c[.j='Ident'], findid(c[.j='Ident'], Obj, 'Ident'), findid(objects1[.j='Ident', Object_all='Run'], Obj, 'Ident'), mean1, if c[.j='Probabilistic?']=0 then 0 else samplesize]); a:= concatrows(a,h,res_id,locres_id); a:= if j='id' then locres_id else a; output:= if x= 1 then a else for y:= j do ( concat(output[j=y],a[j=y]) ); x:= x+1); index i:= 1..size(output)/size(j); for y:= j do (slice(output[j=y],i)) 200,248,1 48,16 2,618,8,526,659 2,34,59,777,552,0,MIDM [Formnode Inp_locres1] [Sys_localindex('I'),Sys_localindex('J')] 2,I,4,2,0,0,4,0,$,0,"ABBREV",0 [] [Undefined] [Sex,2,Units1,1,Country1,1,Diagnosis1,1,Age_group1,1] (in, table; cond:texttype) This function gets an id from a table. in: the property for which the id is needed. In MUST be unique in cond. table: the table from where the id is brought. The table MUST have .j as the column index, .i as the row index, and a column named 'id'. cond: the name of the field that is compared with in. Cond must be text. var id:= if (in&' ') = (table[.j=cond]&' ') then table[.j='id'] else 0; sum(id, table.i)&'' 440,48,1 48,12 2,636,101,494,398 in,table,cond Slices fields that are needed in the Locres table from Inp_locres. index j:= ['id','Res_id','Loc_id']; inp_locres[.j=j] 320,248,1 52,16 2,790,83,476,224 2,632,155,416,303,0,MIDM 65535,45873,39321 [Sys_localindex('J'),Sys_localindex('I')] [] Slices the fields that are needed in the Res table. Removes duplicate rows. index j:= ['id','Obj_id_v','Obj_id_r','Mean','N']; var a:= inp_locres[.j=j]; a:= if j='id' then inp_locres[.j='Res_id'] else a; index i:= unique(a,a.i); a[.i=i] 320,280,1 48,16 2,807,62,476,224 2,723,178,416,303,0,MIDM 65535,45873,39321 [Sys_localindex('J'),Sys_localindex('I')] [] Names of different wikis used. Table(Self)( 'Op_en','Op_fi','Heande','En','Fi','Erac','Beneris','Intarese','Piltti','Kantiva','Bioher','Heimtsa') [1,2,3,4,5,8,9,10,11,13,14,15] 56,64,1 48,12 65535,52427,65534 [Object2,Self] Types of different objects that may exist in Analytica or Opasnet Base. Types that have the same number are treated equally in these systems. Table(Self)( 'Variable','Dimension','Method','Model','Class','Index','Nugget','Encyclopedia article','Run','Chance','Decision','Objective','Constant','Determ','Module','Library','Form') [1,2,3,4,5,6,7,8,9,1,10,1,1,1,4,4,4] 56,32,1 48,20 2,674,34,416,606,0,MIDM 2,193,235,416,390,0,MIDM 65535,52427,65534 Makes a list of sets for the Sett table. There are three major kinds of sets: Indices belonging to an assessment, variables belonging to an assessment, and variables belonging to a run. Indices belonging to a dimension are NOT created with this node. index i:= ['Assessment','Assessment','Run']; index j:= ['id','Obj_id','Sty_id']; array(j,[ (Cardinals[table1='Sett']+@i)&'', findid(Objects1[Object_all=i, .j='Ident'], Obj, 'Ident'), array(i,[3,4,9])]) 320,64,1 48,16 2,740,132,495,444 2,661,16,416,340,0,MIDM [Formnode Write_sett1] 65535,45873,39321 [Sys_localindex('J'),Sys_localindex('I')] 2,D,4,2,0,0,4,0,$,0,"ABBREV",0 100,1,1,1,1,9,2970,2100,15,0 [] Selects relevant information for the Obj table from Objects1 node. index j:= ['id','Ident','Name','Unit','Typ_id','Page','Wik_id']; index i:= 1..size(Object_all); Objects1[.j=j, @Object_all=@i] 320,32,1 48,16 2,289,70,909,492,0,MIDM 65535,45873,39321 [Sys_localindex('J'),Sys_localindex('I')] 2,D,4,2,0,0,4,0,$,0,"ABBREV",0 [] [] Makes a list of items of sets into the Item table. This node does NOT handle indices of a dimensions, but they must be described elsewhere. For types of sets, see Write_sett. index j:= ['id','Sett_id','Obj_id','Fail']; index k:= types(1); index L:= types(6); var c:= if sett.j='Obj_id' then sett&'+'&sett[.j='Sty_id'] else sett; c:= findid(write_sett[.j='Obj_id']&'+'&write_sett[.j='Sty_id'], c, 'Obj_id'); var a:= array(j,k, [0, slice(c,1), k, 0]); var b:= array(j,L,[0, slice(c,2), L, 0]); index m:= 1..(size(k)+size(L)); a:= concat(a,b,k,l,m); b:= array(j,k, [0, slice(c,3), k, 0]); index i:= 1..(size(m)+size(k)); a:= concat(a,b,m,k,i); if j='id' then cardinals[table1='Item']+@i else a; 320,104,1 48,16 2,80,84,476,473 2,921,13,345,638,0,MIDM [Formnode Write_item2] 65535,45873,39321 [Sys_localindex('J'),Sys_localindex('I')] 2,D,4,2,0,0,4,0,$,0,"ABBREV",0 100,1,1,1,1,9,2970,2100,15,0 [] [Self,1,Sys_localindex('J'),1,Sys_localindex('K'),1] If the result is not a number, then the actual result text can be written into the Description field of the Descr table. Makes a list of text values to be written into the Descr table. index j:= ['id','Description']; index i:= subset(sample1[.j='Description']); sample1[.j=j, .i=i] 320,320,1 48,16 2,674,46,476,224 2,670,328,416,303,0,MIDM 65535,45873,39321 [Sys_localindex('J'),Sys_localindex('I')] 2,I,4,2,0,0,4,0,$,0,"ABBREV",0 [] Makes a list of objects that contains some additional information to be written into the Inf table. index j:= ['id','Begin','End','Who','Url']; var a:= Objects1; var b:= findid(a[.j='Ident'], Obj, 'Ident'); a:= a[.j=j]; a:= if a = null or j='id' or a='' then 0 else a; a:= if sum(a,j) = 0 then 0 else 1; index i:= subset(a); a:= Objects1[Object_all=i, .j=j]; a:= if j='id' then b[Object_all=i] else a; if a=null or a=0 then '' else a 320,136,1 48,16 2,94,102,476,340 2,55,45,483,478,0,MIDM [Formnode Write_inf1] 65535,45873,39321 [Sys_localindex('J'),Sys_localindex('I')] 2,D,4,2,0,0,4,0,$,0,"ABBREV",0 [] (type) Finds the objects that are of the object type "type" (the only parameter of this function). Based on the information in Objects1. var a:= if Objects1[.j='Typ_id']=type then 1 else 0; Objects1[Object_all=subset(a),.j='id'] 440,24,1 48,12 2,551,191,476,344 type Additional information about the run and the assessment. Table(Self,Info)( 'Op_en2778','WHO mortality data','Jouni','2009-2-3', 'Op_en2695','Testrun 2','Jouni',0 ) ['Assessment','Run'] 56,104,1 48,13 2,576,173,476,392 2,339,445,416,303,0,MIDM 2,664,117,416,303,0,MIDM [Formnode Run_info1] 52425,39321,65535 [Self,Info] [Self,Info] Op_en2694 Makes a table about object information. 1) Finds information for other parameters based on objects. 2) Adds info from nodes Run_info, Probabilistic_, and Index_info. 3) Makes the final adjustements based on information described above. There is no need to search for existing objects, because the Ident is unique. Thus, all attempts of duplicate additions just are ignored. Null values are ignored in the write procedure. This causes mistmatch between column and values. Therefore, null is replaced by ''. var a:= Object_all; var d:= findintext(Object_types,Class of a); d:= sum(if d=0 then 0 else indexvalue(object_types),object_types); var f:= {findid(Object4,Obj,'Ident'); f:= if f>0 then f else} Cardinals[table1='Obj']+@Object_all; Index j:= ['identifier', 'id','Ident','Name','Unit','Typ_id','Page','Wik_id', 'Who','Begin','Url','Probabilistic?','Description node']; a:= array(j,[ Object_all, f, Ident of a, Title of a, Units of a, if Object_all = 'Run' then 9 else d, '', '', '', '', 0, 0, 0]); var b:= if j='Probabilistic?' then probabilistic_[objects_excl_indices=Object_all] else null; a:= if b=null then a else b; b:= index_info[Add_info=j, Indices=Object_all]; a:= if b=null then a else b; b:= run_info[info=j, run_info=Object_all]; b:= if j= 'Begin' and Object_all='Run' and b=0 then datepart(today(),'Y')&'-'&datepart(today(),'M')&'-'&datepart(today(),'D') else b; a:= if b=null then a else b; b:= if Object_all ='Run' and j='Name' then a&': Analytica '&Analyticaedition&', ('&Analyticaplatform&'), Version: '&Analyticaversion&', Samplesize: '&samplesize else null; a:= if b=null then a else b; b:= findintext(wikis,a[j='Ident']); b:= if b=0 then 0 else b+textlength(Wikis); var c:= sum(if b=0 then 0 else @wikis,wikis); b:= sum(b,wikis); b:= if b = 0 then 0 else selecttext(a[j='Ident'],b); a:= if j='Page' then b else a; a:= if j='Wik_id' then c else a if a = null then '' else a 200,136,1 48,16 2,21,24,581,722 2,24,452,1217,247,0,MIDM [Formnode Objects] [Sys_localindex('J'),Object_all] 2,I,4,2,0,0,4,0,$,0,"ABBREV",0 ['','','','','','','','','','','','','','','','','','','','','','',''] [Indices,6,Add_info,2,Object_all,1,Sys_localindex('IOBJ'),1] This makes a list of all indices (including decision nodes) that are used by the variables in Object1. index a:= indexnames(evaluate(Objects_excl_indices)); a:= if a='Object1' or a='Objects_excl_indices' then 0 else 1; subset(a) 56,272,1 48,13 2,102,90,476,464 2,32,349,416,303,0,MIDM [Objects_excl_indices] ['Sex','Country1','Diagnosis2','Age_group2','Year4','Health_outcome1'] Additional information for each index and decision node. Description node is the name of a node containing information about the locations of the index. It must be indexed by the index. Table(Add_info,Indices)( 0,0,'Descr_diagnosis',0,0,'Va2' ) 56,216,1 48,20 2,140,217,476,224 2,605,351,664,303,0,MIDM 2,506,220,684,303,0,MIDM [Formnode Index_info1] 52425,39321,65535 [Add_info,Indices] [Add_info,Indices] Additional pieces of information about indices. Currently, the only piece is a description node. ['Description node'] 56,248,1 48,12 ['Description node'] Contains 1 for all variables that are stored as samples from probability distributions, and 0 for deterministic variables. Table(Objects_excl_indices)( 0) 56,168,1 48,22 2,197,26,416,371,0,MIDM 2,17,23,416,364,0,MIDM [Formnode Probabilistic_1] 52425,39321,65535 ['Ident','Name','Who','Begin'] 56,128,1 48,12 ['Ident','Name','Who','Begin'] List of variables, indices, assessment, and run to be stored into the Opasnet Base. concat(concat(objects_excl_indices,Indices),indexvalue(Run_info))&'' 200,160,1 48,13 1,1,1,1,1,1,0,0,0,0 2,49,109,558,527 2,200,210,773,264,0,MIDM [Self,Info] ['H1898','H1899','H1900','H1901','H1902','H1903','H1904','H1905','H1906','H1907','H1908','H1909','H1910','H1911','H1912','Pollutant1','Salmon1','H1899','Cause_of_death3','H1898'] The usage of local variables: a: the temporary variable that is being edited. e: cardinal of the Res table. f: cardinal of the Sam table. j: output column headings. i: output row numbers. 1) Several local variables are initiated. Variables and Decisions are included. 2)-5) The process is done for each variable one at a time (this is indexed by x). 3) Several within-loop local variables are initiated. 4) The variable is given index runn which is equal to run if probabilistic and [0] if not. The array is flattened first to 2-D, the value only is kept. 5) Variables are concatenated to each other. 6) Index i is made the index of the implicit index. NOTE! This node MUST be formatted to Integer, otherwise Sam_id will be stored in a wrong format. var output=0; var e:= Cardinals[table1='Res']; var f:= Cardinals[table1='Sam']; index j:= ['id','Res_id','Sample','Result','Description']; var o:= if Objects1[.j='Typ_id'] = 1 or Objects1[.j='Typ_id'] = 10 then 1 else 0; index k:= subset(o); o:= objects1[Object_all=k]; var x:= 1; while x<= size(k) do ( var c:= slice(o,k,x); var a:= c[.j='identifier']; a:= sample(evaluate(a)); index h:= indexnames(max(a,run)); index L:= concat(h,['Value']); index runn:= if c[.j='Probabilistic?']=1 then copyindex(run) else [0]; index res_id:= (1..size(max(a,run)))+e; index sam_id:= (1..size(res_id)*size(runn))+f; e:= e+size(res_id); f:= f+size(sam_id); a:= if c[.j='Probabilistic?']=1 then a[run=runn] else (if runn=0 then a else a); a:= mdarraytotable(a,res_id,L)[.L='Value']; a:= array(j,[0, res_id&'', runn&'', (if istext(a) then 0 else a) , (if istext(a) then a else 0)]); a:= concatrows(a,res_id,runn,sam_id); a:= if j='id' then sam_id&'' else a; output:= if x= 1 then a else for y:= j do ( concat(output[j=y],a[j=y]) ); x:= x+1); index i:= 1..size(output)/size(j); for y:= j do (slice(output[j=y],i)) 200,320,1 48,16 2,19,11,585,772 2,133,242,753,337,0,MIDM [Formnode Sample2] [Sys_localindex('J'),Sys_localindex('I')] 2,I,4,2,0,0,4,0,$,0,"ABBREV",0 Makes a list of all locations of all indices used in variables listed in Object1. 1) Initialises local variables. 2) Takes one index at a time, calculates the values and concatenates them to the previous values. 3) All parameters are lumped into a single array, with some fields calculated based on others. var a:= if objects1[.j='Typ_id']= 6 or objects1[.j='Typ_id']= 10 then 1 else 0; index k:= subset(a); a:= objects1[Object_all=k]; var b:= [0]; var c:= [0]; var e:= [0]; var f:= [0]; var temp:= 0; var x:= 1; while x<=size(k) do ( var d:= evaluate(slice(k,x)); b:= concat(b,d); c:= concat(c,(if d=0 then slice(k,x) else slice(k,x))); e:= concat(e,1..size(d)); var g:= evaluate(a[@k=x, .j='Description node']); temp:= if x=@k then a else temp; g:= {if size(g) = size(d) then g else} (if d=0 then g else g); f:= concat(f, g); x:= x+1); index i:= 1..size(b)-1; c:= slice(c,i+1); index j:= ['id','Obj_id_i', 'Ind_identifier', 'Location', 'Roww', 'Description']; array(j,[cardinals[table1='Loc']+i, findid(Ident of c,Obj,'Ident'), c, slice(b,i+1)&'', slice(e,i+1), slice(f,i+1)]); 200,200,1 48,16 2,40,23,521,628 2,52,153,1196,401,0,MIDM [Formnode Locations1] [Sys_localindex('J'),Sys_localindex('I')] ['','','','','','','','','',''] [Self,1,Sys_localindex('I'),1,Sys_localindex('J'),1] index j:= ['id','Res_id','Descr_id','Sample','Result']; var write_sam:= sample1[.j=j]; write_sam:= if write_sam=null then '0' else write_sam; {dbwrite(odbc_write, appendtablesql(write_sam,write_sam.i, write_sam.j,'Sam '))} 320,352,1 48,12 2,102,90,476,333 2,642,255,552,303,0,MIDM 65535,45873,39321 [Sys_localindex('J'),Sys_localindex('I')] 2,D,4,2,0,0,4,0,$,0,"ABBREV",0 [] [] (var, table) dbwrite(odbc_write, appendtablesql(var,var.i, var.j, table&' ')) 440,96,1 48,12 2,687,61,476,224 var,table 384,280,1 48,12 2,781,100,476,721 This module saves model results into the Opasnet Base. You need a password for that. Note that the necessary variable, index, dimension, and run information will be asked. You must fill in all tables before the process is completed. 472,76,-1 136,68 Note! You can insert several variables at the same time. Each variable MUST have at least one index. 472,176,-1 136,32 Fill in the data below in this order. 168,228,-5 160,220 1,0,0,1,0,1,0,,0, 0 156,44,1 140,12 1,0,0,1,0,0,0,110,0,1 52425,39321,65535 Username 0 156,68,1 140,12 1,0,0,1,0,0,0,110,0,1 52425,39321,65535 Password 0 156,93,1 140,13 1,0,0,1,0,0,0,72,0,1 52425,39321,65535 Objects_excl_indices ['WHO_mortality_data1'] 496,240,1 48,24 2,958,152,321,481 2,328,338,416,361,0,MIDM [Formnode Object2] 52425,39321,65535 ['WHO_mortality_data1'] 0 156,164,1 140,12 1,0,0,1,0,0,0,72,0,1 52425,39321,65535 Run_info 224,236,4 48,12 224,188,1 48,12 384,232,1 48,12 384,256,1 48,12 2,102,90,473,465 224,333,3 48,13 224,429,1 48,13 Check all the nodes before running buttons (from top to bottom)! 60,274,-1 44,74 1 200,381,1 96,13 1,0,0,1,0,0,0,72,0,1 Inp_locres 1 200,285,1 96,13 1,0,0,1,0,0,0,72,0,1 65535,45873,39321 Write_sett 1 200,357,1 96,13 1,0,0,1,0,0,0,72,0,1 65535,45873,39321 Write_item 1 200,261,1 96,13 1,0,0,1,0,0,0,72,0,1 65535,45873,39321 Write_inf 1 200,212,1 96,12 1,0,0,1,0,0,0,72,0,1 Objects1 1 200,404,1 96,12 1,0,0,1,0,0,0,72,0,1 Sample1 1 200,309,1 96,13 1,0,0,1,0,0,0,72,0,1 Locations2 ktluser 29. Decta 2008 21:51 48,24 496,352,1 48,24 1,279,39,902,527,17 92,1,1,0,2,9,2970,2100,15,0 192,56,1 48,24 39325,65535,39321 Cardinals__all_table 320,120,1 48,76 Objects__ 192,184,1 48,67 65535,45873,39321 Obj__id_ident_name_u 192,328,1 48,40 65535,45873,39321 Sett__id_obj_id_typ_ 56,329,1 48,49 65535,45873,39321 Obj__id_ident_name_u 56,186,1 48,58 65535,45873,39321 Obj__id_ident_name_u 320,336,1 48,52 65535,45873,39321 Loc__id_obj_id_d_loc 456,336,1 48,92 Inp_locres__locres_i 592,424,1 48,40 65535,45873,39321 Inp_locres__locres_i 592,312,1 48,58 65535,45873,39321 Sample__id_res_id_sa 592,120,1 48,52 65535,45873,39321 Sample__id_res_id_sa 592,216,1 48,31 65535,45873,39321 The arrows only show sequential dependencies. This means that e.g. Cardinals is a parent to many other nodes as well, but the critical values in Cardinals only change before Objects is defined, and there is no need to update Cardinals during the writing process. Orange nodes are actual Tables in Opasnet Base. Green nodes are SQL queries from Opasnet Base. Blue nodes are computed in Analytica. 752,168,-1 112,152 Objects__ 456,121,1 48,58 R Objects 192,176,-1 56,80 1,0,0,1,0,1,0,,0, R Structure 256,332,-1 120,68 1,0,0,1,0,1,0,,0, R Cardinals 192,48,-1 56,40 1,0,0,1,0,1,0,,0, 0 156,140,1 140,12 1,0,0,1,0,0,0,72,0,1 52425,39321,65535 Index_info 0 156,116,1 140,12 1,0,0,1,0,0,0,72,0,1 52425,39321,65535 Probabilistic_ ktluser 3. Augta 2008 18:31 jtue 9. lokta 2008 14:01 48,24 192,32,1 48,24 1,1,1,1,1,1,0,0,0,0 1,792,124,477,366,17 Arial, 15 (vident:text, runident:optional) Reads the mean data about the vident variable from the Opasnet Base. Uses the runident run if specified; otherwise uses the newest run of that variable. PARAMETERS: * Vident: the Ident of the variable in the Opasnet Base. * Runident: the Ident of the run from which the results will be brought. If omitted, the newest result will be brought. if isnotspecified(runident) then runident:= identfind(newestrun(vident)); var a:= ' SELECT Var.Ident as Vident, Var.Name as Vname, Var.Unit as Vunit, Res.id, Ind.Ident as Iident, Location, Mean, N, Run.Name as Rname, Run.ident AS Runident FROM Obj as Var, Res, Locres, Loc, Obj as Ind, Obj as Run WHERE Res.Obj_id_r = Run.id AND Res.Obj_id_v = Var.id AND Locres.Res_id = Res.id AND Locres.Loc_id = Loc.id AND Loc.Obj_id_i = Ind.id AND Var.Ident = '&chr(39)&vident&chr(39)&' AND Run.ident = '&chr(39)&runident&chr(39) ; index i:= DBquery(Odbc,a); index j:= dblabels(i); dbtable(i,j) 56,88,1 48,12 2,585,25,516,589 39325,65535,39321 vident,runident (vident:text) This function checks for the newest result (according to run_id) of the variable. The function is used if the user does not define the run_id as an optional parameter in functions Read_mean and Read_sample. PARAMETERS: * Vident: the Ident of the variable in the Opasnet Base. index i:= DBquery(Odbc,' SELECT Obj_id_r FROM Res, Obj as Var WHERE Var.id = Res.Obj_id_v AND Var.Ident = "'&vident&'" GROUP BY Var.id, Obj_id_r '); index j:= dblabels(i); max(max(dbtable(i,j),i),j) 56,16,1 48,12 2,678,59,476,566 39325,65535,39321 vident (vident:text, runident:optional) Reads the sample data about the vident variable from the Opasnet Base. Uses the runident run if specified; otherwise uses the newest run of that variable. PARAMETERS: * Vident: the name of the variable in the Opasnet Base. * Runident: the Ident of the run from which the results will be brought. If omitted, the newest result will be brought. if isnotspecified(runident) then runident:= identfind(newestrun(vident)); var a:= ' SELECT Temp.id, Sample, Result, Description FROM (SELECT Res.id, Sam.id AS Sam_id, Sample, Result, Obj_id_r FROM Res, Sam, Obj AS Run, Obj AS Var WHERE Var.Ident = '&chr(39)&vident&chr(39)&' AND Res.Obj_id_v = Var.id AND Res.Obj_id_r = Run.id AND Run.Ident = '&chr(39)&Runident&chr(39)&' AND Sam.Res_id = Res.id) AS Temp LEFT JOIN Descr ON Temp.Sam_id = Descr.id '; index i:= DBquery(Odbc,a); index j:= dblabels(i); dbtable(i,j) 56,120,1 48,22 2,700,47,516,612 39325,65535,39321 vident,runident 'Op_en1912' 168,88,1 48,31 [Formnode Enter_variable1] 52425,39321,65535 0 288,24,1 176,13 1,0,0,1,0,0,0,170,0,1 52425,39321,65535 Enter_variable newestrun(Enter_variable) 288,64,1 48,12 read_mean(Enter_variable) 288,112,1 48,12 2,56,66,1205,308,0,MIDM [Sys_localindex('J'),Sys_localindex('I')] (a,inde) The input table a must have a structure that is also used as input for MDTable function. The function removes one column with location information and makes a dimension (index) with the locations in the column. Inde is the (local) index that will be added. Note that unlike MDTable function, this can use local indices in the output. if size(a.m)= 1 then a else ( a:= if inde = a[@.m=1] then a else 0; index m:= slice(a.m,(2..size(a.m))); a:= a[.m=m]) 56,176,1 48,12 2,283,62,476,224 a,inde (a) Makes a multi-dimensional array with the same structure as the original variable that was stored into the Opasnet Base. However, the indices do not have original names. They are named In1, In2,... The contents of the array are the res.ids of the variable. The input parameter must be a 2D table with the structure that comes from the Read_mean function. 1) Slices the necessary columns from the input table and converts that to a 2D table that has the same structure as is used for input to the function MDTable. 2) Defines the local indices (up to 10), and changes a location column to a dimension one at a time until all columns have been changed. index k:= ['Iident','Location','id']; a:= a[.j=k]; index L:= a[@k=1]&'+'&a[@k=3]; index m:= concat(a[.i=unique(a[@k=1],a.i), @k=1],['Result']); index n:= a[.i=unique(a[@k=3],a.i), @k=3]; a:= a[@.i=@L]; a:= a[L=m&'+'&n, @k=2]; a:= if m='Result' then n else a; index in1:= a[n=unique(a[@m=1],n),@m=1]; index in2:= a[n=unique(a[@m=2],n),@m=2]; index in3:= a[n=unique(a[@m=3],n),@m=3]; index In4:= a[n=unique(a[@m=4],n),@m=4]; index In5:= a[n=unique(a[@m=5],n),@m=5]; index in6:= a[n=unique(a[@m=6],n),@m=6]; index in7:= a[n=unique(a[@m=7],n),@m=7]; index in8:= a[n=unique(a[@m=8],n),@m=8]; index in9:= a[n=unique(a[@m=9],n),@m=9]; index in10:= a[n=unique(a[@m=10],n),@m=10]; a:= makeind(a, in1); a:= makeind(a, in2); a:= makeind(a, in3); a:= makeind(a, in4); a:= makeind(a, in5); a:= makeind(a, in6); a:= makeind(a, in7); a:= makeind(a, in8); a:= makeind(a, in9); a:= makeind(a, in10); sum(sum(a,a.m),a.n) 56,152,1 48,12 2,669,44,476,545 a get_mean(Enter_variable) 288,136,1 48,12 2,547,35,416,622,0,MIDM [Sys_localindex('IN2'),Sys_localindex('IN3')] [Sys_localindex('IN1'),1,Sys_localindex('IN4'),1,Sys_localindex('IN5'),1,Sys_localindex('IN3'),1,Sys_localindex('IN2'),1] (vident:text, runident:optional) Gives the mean result of a (multidimensional) variable stored in the Opasnet Base. The procedure is simple because it utilises the variable structure (with res_ids) derived by the get_res_id function. var a:= {testmean} read_mean(vident, runident); index o:= a[.j='id']; var output:= a[@.i=@o, .j='Mean']; a:= get_res_id(a); output[o=a] 56,200,1 48,12 2,665,82,476,428 vident,runident (vident:text, runident:optional) Gives the sample result of a (multidimensional) variable stored in the Opasnet Base. The procedure is simple because it utilises the variable structure (with res_ids) derived by the get_res_id function. Note that if the Analytica samplesize is smaller than the samplesize stored in the Opasnet Base, the extra samples will be discarded. If the samplesize is larger, the remaining rows will be null. 1) Brings the data into the right structure. 2) Chooses whether the actual result is numerical (in the Result column) or text (in the Description column). var a:= read_sample(vident, runident); var b:= get_res_id(read_mean(vident,runident)); index k:= a[.j='id']&'+'&a[.j='Sample']; index runn:= min(a[.j='Sample'])..max(a[.j='Sample']); a:= a[@.i=@k]; a:= a[k=b&'+'&runn]; a:= if max(runn)=0 then a[@runn=1] else a[@runn=@run]; var c:= if a[.j='Description']='' then 0 else 1; c:= sum(sum(sum(sum(sum(sum(sum(sum(sum(sum(c)))))))))); if c=0 then a[.j='Result'] else a[.j='Description'] 56,224,1 48,12 2,641,28,476,556 vident,runident get_sample(Enter_variable) 288,160,1 48,12 2,226,324,416,303,0,MEAN [Sys_localindex('IN5'),Sys_localindex('IN3')] [Sys_localindex('IN1'),1,Sys_localindex('IN2'),1,Sys_localindex('IN4'),1,Sys_localindex('IN3'),1,Sys_localindex('J'),1,Sys_localindex('IN5'),1] (runid) Finds the Ident for the run (or another object) that has the id runid. index i:= DBquery(Odbc,' SELECT ident FROM Obj WHERE Obj.id = "'&runid&'" '); index j:= dblabels(i); var a:= dbtable(i,j); a[@i=1, @j=1] 56,64,1 48,12 2,732,65,516,589 39325,65535,39321 runid Describes the runs of the defined variable. This should be made a function. var_run_info(Enter_variable) 288,88,1 48,12 2,136,146,1111,285,0,MIDM [Sys_localindex('J'),Sys_localindex('I')] (vident:text) This function checks for the newest result (according to run_id) of the variable. The function is used if the user does not define the run_id as an optional parameter in functions Read_mean and Read_sample. PARAMETERS: * Vident: the Ident of the variable in the Opasnet Base. var a:= ' SELECT Var.Ident, Var.Name, Var.Unit, Run.Ident AS Runident, Inf.Begin, Inf.Who, Run.Name as Method FROM Obj as Var, Obj as Run, Res, Inf WHERE Var.Ident = '&chr(39)&vident&chr(39)&' AND Var.id = Res.Obj_id_v AND Run.id = Res.Obj_id_r AND Run.id = Inf.id GROUP BY Var.id, Run.id '; index i:= DBquery(Odbc,a); index j:= dblabels(i); dbtable(i,j) 56,40,1 48,13 2,678,59,476,566 39325,65535,39321 vident Instructions for uploading results to the Opasnet Base: * Make sure that you have created an object page in the Opasnet wiki for each object you want to upload. (This applies also to assessments and runs!) * Create a user-defined attribute Ident if it does not exist. * Use the wiki identifier as the Ident for the object in Analytica. * Use the wiki page name as the Title for the object. * Make sure that you have defined the Units. * If an object with the same Ident (Analytica identifier) already exists, metadata about that object will NOT be updated. However, the actual results will be uploaded normally. 220,220,-1 212,108 ktluser 8. Decta 2008 3:01 48,24 64,32,1 48,24 1,206,202,495,296,17 'Driver={MySQL ODBC 5.1 Driver};Server=10.66.10.102;Database=resultdb;User='&username&'; Password='&password&';Option=3' 168,200,1 48,12 1,1,0,1,1,1,0,,0, 2,102,90,495,302 2,168,178,833,303,0,MIDM [] '' 168,152,0 48,12 1,1,1,1,1,1,0,0,0,0 [Formnode Username1] 52425,39321,65535 '' 168,176,0 48,12 1,1,1,1,1,1,0,0,0,0 [Formnode Password1] 52425,39321,65535 Contains the parameters for the open database connectivity (ODBC). 'Driver={MySQL ODBC 5.1 Driver};Server=10.66.10.102;Database=resultdb;User=result_reader; Password=ora4ever;Option=3' 168,128,1 48,12 1,1,0,1,1,1,0,,0, 2,102,90,476,224 index i:= copyindex(D_i); index j:= copyindex(D_j); Dim1[d_i=i, d_j=j] 400,160,1 48,13 1,1,0,1,1,1,0,0,0,0 2,89,98,476,224 2,635,328,556,489,0,MIDM 19661,54073,65535 [D_i,D_j] [Sys_localindex('J'),Sys_localindex('I')] index i:= copyindex(I_i); index j:= copyindex(I_j); Ind1[I_i=i, I_j=j] 400,184,1 48,13 1,1,0,1,1,1,0,0,0,0 2,380,47,476,296 2,298,10,649,655,0,MIDM 19661,54073,65535 [Sys_localindex('J'),Sys_localindex('I')] index i:= copyindex(L_i); index j:= copyindex(L_j); Loc1[L_i=i, L_j=j] 400,96,1 48,13 1,1,0,1,1,1,0,0,0,0 2,370,45,476,445 2,120,301,1147,516,0,MIDM 19661,54073,65535 [Sys_localindex('J'),Sys_localindex('I')] This node checks the variables listed in Var_for_rdb and makes an index of those that are NOT found in the result database. This is then used as an index in Inp_var for adding variable information. index i:= copyindex(O_i); index j:= copyindex(O_j); Obj2[O_i=i, O_j=j] 400,48,1 48,13 1,1,0,1,1,1,0,0,0,0 2,378,21,493,501 2,69,355,889,421,0,MIDM 19661,54073,65535 [Sys_localindex('J'),Sys_localindex('I')] ['H1991'] [Self,1,Sys_localindex('I'),1,Sys_localindex('J'),1] Standard versions 400,112,-1 72,100 1,0,0,1,0,1,0,,0, [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22] 168,24,1 48,12 [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22] ['id','Ident','Name'] 168,48,1 48,12 ['id','Ident','Name'] [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34] 168,72,1 48,12 [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34] ['id','Iident','Iname','Did','Dident','Dname'] 168,96,1 48,12 ['id','Iident','Iname','Did','Dident','Dname'] [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660] 56,120,1 48,12 [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660] ['id','Obj_id_i','Location','Roww','Locn','Num','Description','id','Ident','Name','Unit','Typ_id','Page','Wik_id'] 56,144,1 48,12 ['id','Obj_id_i','Location','Roww','Locn','Num','Description','id','Ident','Name','Unit','Typ_id','Page','Wik_id'] [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130] 56,24,1 48,13 [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130] ['id','Ident','Name','Unit','Typ_id','Page','Wik_id'] 56,48,1 48,13 ['id','Ident','Name','Unit','Typ_id','Page','Wik_id'] This node checks the variables listed in Var_for_rdb and makes an index of those that are NOT found in the result database. This is then used as an index in Inp_var for adding variable information. index i:= copyindex(S_i); index j:= copyindex(S_j); Sett1[S_i=i, S_j=j] 400,72,1 48,13 1,1,0,1,1,1,0,0,0,0 2,378,21,493,501 2,227,134,319,515,0,MIDM 19661,54073,65535 [Sys_localindex('J'),Sys_localindex('I')] ['H1991'] [Self,1,Sys_localindex('I'),1,Sys_localindex('J'),1] This node checks the variables listed in Var_for_rdb and makes an index of those that are NOT found in the result database. This is then used as an index in Inp_var for adding variable information. index i:= copyindex(It_i); index j:= copyindex(It_j); Item1[it_i=i, it_j=j] 400,120,1 48,13 1,1,0,1,1,1,0,0,0,0 2,378,21,493,501 2,298,216,382,519,0,MIDM 19661,54073,65535 [Sys_localindex('J'),Sys_localindex('I')] ['H1991'] [Self,1,Sys_localindex('I'),1,Sys_localindex('J'),1] [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35] 56,168,1 48,13 [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35] ['id','Sett_id','Obj_id','Fail'] 56,192,1 48,13 ['id','Sett_id','Obj_id','Fail'] [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28] 56,72,1 48,13 [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28] ['id','Obj_id','Sty_id'] 56,96,1 48,13 ['id','Obj_id','Sty_id'] Table(D_i,D_j)( 43,'Vehicle_type','Vehicle type', 45,'Transport_mode','Transport mode', 46,'Cost_type','Cost type', 47,'Composite_fraction','Composite fraction', 51,'Food_source','The method for food production', 52,'Feed_pollutant','Decision about fish feed', 53,'Salmon_recomm','Decision about samon consumption recommendation', 32,'0','No dimension has been identified', 54,'Parameter','Statistical and other parameters of a variable', 42,'Environ_compartment','Environmental compartment', 41,'Emission_source','Emission source', 36,'Pollutant','Pollutant', 34,'Health_impact','Health impact', 33,'Decision','Possible range of decisions for a single decision-maker', 35,'Time','Time', 40,'Period','Period', 48,'Age','Age', 37,'Spatial_location','Spatial location', 38,'Length','Length', 49,'Municipality_fin','Municipalities in Finland', 44,'Person_or_group','Person or group', 39,'Non_health_impact','Non-health impact' ) 280,160,1 48,13 1,1,1,1,1,1,0,0,0,0 2,89,98,476,224 2,604,56,556,489,0,MIDM 39325,65535,39321 [D_i,D_j] [D_j,D_i] Table(I_i,I_j)( 55,'Salmon_decision','',33,'Decision','Possible range of decisions for a single decision-maker', 80,'Reg_poll','',33,'Decision','Possible range of decisions for a single decision-maker', 81,'Recommendation1','',33,'Decision','Possible range of decisions for a single decision-maker', 83,'H1899','',33,'Decision','Possible range of decisions for a single decision-maker', 84,'H1898','',33,'Decision','Possible range of decisions for a single decision-maker', 56,'Hma_area','',37,'Spatial_location','Spatial location', 57,'Hma_region','',37,'Spatial_location','Spatial location', 58,'Hma_zone','',37,'Spatial_location','Spatial location', 88,'Condb_location1','',37,'Spatial_location','Spatial location', 93,'Op_en2672','',37,'Spatial_location','Spatial location', 59,'Year_1','',35,'Time','Time', 61,'Year_2','',35,'Time','Time', 82,'Year3','',35,'Time','Time', 60,'Op_en2665','Cause of death 1',34,'Health_impact','Health impact', 62,'Cause_of_death_2','',34,'Health_impact','Health impact', 85,'Cause_of_death3','',34,'Health_impact','Health impact', 63,'Length_1','',38,'Length','Length', 70,'Output_1','',39,'Non_health_impact','Non-health impact', 65,'Period_1','',40,'Period','Period', 86,'Run','',32,'0','No dimension has been identified', 71,'Vehicle_noch','',43,'Vehicle_type','Vehicle type', 92,'Vehicle_1','',43,'Vehicle_type','Vehicle type', 72,'Stakeholder_1','',44,'Person_or_group','Person or group', 73,'Mode1','',45,'Transport_mode','Transport mode', 74,'Cost_structure_1','',46,'Cost_type','Cost type', 75,'Comp_fr_1','',47,'Composite_fraction','Composite fraction', 76,'Age1','',48,'Age','Age', 77,'Municipality_fin1','',49,'Municipality_fin','Municipalities in Finland', 79,'Salmon1','',51,'Food_source','The method for food production', 78,'Pollutant1','',36,'Pollutant','Pollutant', 89,'Condb_agent1','',36,'Pollutant','Pollutant', 91,'Condb_agent2','',36,'Pollutant','Pollutant', 87,'Condb_compartment1','',42,'Environ_compartment','Environmental compartment', 90,'Condb_param1','',54,'Parameter','Statistical and other parameters of a variable' ) 280,184,1 48,13 1,1,1,1,1,1,0,0,0,0 2,380,47,476,296 2,232,242,874,303,0,MIDM 2,12,22,876,493,0,MIDM 39325,65535,39321 [I_j,I_i] [I_j,I_i] Table(L_i,L_j)( 1,1,'Business as usual',0,0,0,'',1,'Op_en1901','Net health effects due to the consumption of salmon','avoided cases/a',1,1901,1, 2,1,'Recommend restrictions to salmon consumption',0,0,0,'',2,'Op_en1901','Net health effects due to the consumption of salmon','avoided cases/a',1,1901,1, 3,1,'Stricter limits for fish feed pollutants',0,0,0,'',3,'Op_en1901','Net health effects due to the consumption of salmon','avoided cases/a',1,1901,1, 4,1,'Restrictions to salmon consumption AND stricter fish feed limits',0,0,0,'',4,'Op_en1901','Net health effects due to the consumption of salmon','avoided cases/a',1,1901,1, 26,2,'All causes',0,0,0,'',26,'Op_en2693','Testvariable','kg',1,2693,1, 197,6,'>= 5 km',0,0,0,'',197,'Ppmconc_bustraffic','PM2.5 concentration from bus traffic in Helsinki in 2020','ug/m3',1,0,0, 196,6,'< 5 km',0,0,0,'',196,'Ppmconc_bustraffic','PM2.5 concentration from bus traffic in Helsinki in 2020','ug/m3',1,0,0, 8,3,'2020',0,0,0,'',8,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1, 7,3,'1997',0,0,0,'',7,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1, 10,2,'Cardiopulmonary',0,0,0,'',10,'Op_en2693','Testvariable','kg',1,2693,1, 11,2,'Lung cancer',0,0,0,'',11,'Op_en2693','Testvariable','kg',1,2693,1, 12,2,'All others',0,0,0,'',12,'Op_en2693','Testvariable','kg',1,2693,1, 27,5,'Downtown',0,0,0,'',27,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 28,5,'Centre',0,0,0,'',28,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 29,5,'Suburb',0,0,0,'',29,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 30,5,'Länsi-Espoo',0,0,0,'',30,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 31,5,'Pohjois-Espoo',0,0,0,'',31,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 32,5,'Etelä-Espoo',0,0,0,'',32,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 33,5,'Keski-Espoo',0,0,0,'',33,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 34,5,'Länsi-Vantaa',0,0,0,'',34,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 35,5,'Keski-Vantaa',0,0,0,'',35,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 36,5,'Pohjois-Vantaa',0,0,0,'',36,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 37,5,'Itä-Vantaa',0,0,0,'',37,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 38,5,'Kanta-Helsinki',0,0,0,'',38,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 39,5,'Länsi-Helsinki',0,0,0,'',39,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 40,5,'Vanha-Helsinki',0,0,0,'',40,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 41,5,'Konalanseutu',0,0,0,'',41,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 42,5,'Pakilanseutu',0,0,0,'',42,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 43,5,'Malminseutu',0,0,0,'',43,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 44,5,'Itä-Helsinki',0,0,0,'',44,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 45,5,'1001',0,0,0,'',45,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 46,5,'1002',0,0,0,'',46,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 47,5,'1003',0,0,0,'',47,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 48,5,'1004',0,0,0,'',48,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 49,5,'1005',0,0,0,'',49,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 50,5,'1006',0,0,0,'',50,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 51,5,'1007',0,0,0,'',51,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 52,5,'1008',0,0,0,'',52,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 53,5,'1009',0,0,0,'',53,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 54,5,'1010',0,0,0,'',54,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 55,5,'1011',0,0,0,'',55,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 56,5,'1012',0,0,0,'',56,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 57,5,'1013',0,0,0,'',57,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 58,5,'1014',0,0,0,'',58,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 59,5,'1015',0,0,0,'',59,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 60,5,'1016',0,0,0,'',60,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 61,5,'1017',0,0,0,'',61,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 62,5,'1018',0,0,0,'',62,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 63,5,'1019',0,0,0,'',63,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 64,5,'1020',0,0,0,'',64,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 65,5,'1021',0,0,0,'',65,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 66,5,'1022',0,0,0,'',66,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 67,5,'1023',0,0,0,'',67,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 68,5,'1024',0,0,0,'',68,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 69,5,'1025',0,0,0,'',69,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 70,5,'1026',0,0,0,'',70,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 71,5,'1027',0,0,0,'',71,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 72,5,'1028',0,0,0,'',72,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 73,5,'1029',0,0,0,'',73,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 74,5,'1030',0,0,0,'',74,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 75,5,'1031',0,0,0,'',75,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 76,5,'1032',0,0,0,'',76,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 77,5,'1033',0,0,0,'',77,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 78,5,'1034',0,0,0,'',78,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 79,5,'1035',0,0,0,'',79,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 80,5,'1036',0,0,0,'',80,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 81,5,'1037',0,0,0,'',81,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 82,5,'1038',0,0,0,'',82,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 83,5,'1039',0,0,0,'',83,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 84,5,'1040',0,0,0,'',84,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 85,5,'1041',0,0,0,'',85,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 86,5,'1042',0,0,0,'',86,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 87,5,'1043',0,0,0,'',87,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 88,5,'1044',0,0,0,'',88,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 89,5,'1045',0,0,0,'',89,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 90,5,'1046',0,0,0,'',90,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 91,5,'1047',0,0,0,'',91,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 92,5,'1048',0,0,0,'',92,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 93,5,'1049',0,0,0,'',93,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 94,5,'1050',0,0,0,'',94,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 95,5,'1051',0,0,0,'',95,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 96,5,'1052',0,0,0,'',96,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 97,5,'1053',0,0,0,'',97,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 98,5,'1054',0,0,0,'',98,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 99,5,'1055',0,0,0,'',99,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 100,5,'1056',0,0,0,'',100,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 101,5,'1057',0,0,0,'',101,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 102,5,'1058',0,0,0,'',102,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 103,5,'1059',0,0,0,'',103,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 104,5,'1060',0,0,0,'',104,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 105,5,'1061',0,0,0,'',105,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 106,5,'1062',0,0,0,'',106,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 107,5,'1063',0,0,0,'',107,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 108,5,'1064',0,0,0,'',108,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 109,5,'1065',0,0,0,'',109,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 110,5,'1066',0,0,0,'',110,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 111,5,'1067',0,0,0,'',111,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 112,5,'1068',0,0,0,'',112,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 113,5,'1069',0,0,0,'',113,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 114,5,'1070',0,0,0,'',114,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 115,5,'1071',0,0,0,'',115,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 116,5,'1072',0,0,0,'',116,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 117,5,'1073',0,0,0,'',117,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 118,5,'1074',0,0,0,'',118,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 119,5,'1075',0,0,0,'',119,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 120,5,'1076',0,0,0,'',120,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 121,5,'1077',0,0,0,'',121,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 122,5,'1078',0,0,0,'',122,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 123,5,'1079',0,0,0,'',123,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 124,5,'1080',0,0,0,'',124,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 125,5,'1081',0,0,0,'',125,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 126,5,'1082',0,0,0,'',126,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 127,5,'1083',0,0,0,'',127,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 128,5,'1084',0,0,0,'',128,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 129,5,'1085',0,0,0,'',129,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 130,5,'1086',0,0,0,'',130,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 131,5,'1087',0,0,0,'',131,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 132,5,'1088',0,0,0,'',132,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 133,5,'1089',0,0,0,'',133,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 134,5,'1090',0,0,0,'',134,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 135,5,'1091',0,0,0,'',135,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 136,5,'1092',0,0,0,'',136,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 137,5,'1093',0,0,0,'',137,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 138,5,'1094',0,0,0,'',138,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 139,5,'1095',0,0,0,'',139,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 140,5,'1096',0,0,0,'',140,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 141,5,'1097',0,0,0,'',141,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 142,5,'1098',0,0,0,'',142,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 143,5,'1099',0,0,0,'',143,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 144,5,'1100',0,0,0,'',144,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 145,5,'1101',0,0,0,'',145,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 146,5,'1102',0,0,0,'',146,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 147,5,'1103',0,0,0,'',147,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 148,5,'1104',0,0,0,'',148,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 149,5,'1105',0,0,0,'',149,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 150,5,'1106',0,0,0,'',150,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 151,5,'1107',0,0,0,'',151,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 152,5,'1108',0,0,0,'',152,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 153,5,'1109',0,0,0,'',153,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 154,5,'1110',0,0,0,'',154,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 155,5,'1111',0,0,0,'',155,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 156,5,'1112',0,0,0,'',156,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 157,5,'1113',0,0,0,'',157,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 158,5,'1114',0,0,0,'',158,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 159,5,'1115',0,0,0,'',159,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 160,5,'1116',0,0,0,'',160,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 161,5,'1117',0,0,0,'',161,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 162,5,'1118',0,0,0,'',162,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 163,5,'1119',0,0,0,'',163,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 164,5,'1120',0,0,0,'',164,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 165,5,'1121',0,0,0,'',165,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 166,5,'1122',0,0,0,'',166,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 167,5,'1123',0,0,0,'',167,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 168,5,'1124',0,0,0,'',168,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 169,5,'1125',0,0,0,'',169,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 170,5,'1126',0,0,0,'',170,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 171,5,'1127',0,0,0,'',171,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 172,5,'1128',0,0,0,'',172,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 173,5,'1129',0,0,0,'',173,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 174,5,'1130',0,0,0,'',174,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 175,35,'2000',0,0,0,'',175,'Time','Time','s or date',2,2497,1, 176,3,'2001',0,0,0,'',176,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1, 177,3,'2002',0,0,0,'',177,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1, 178,3,'2003',0,0,0,'',178,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1, 179,3,'2004',0,0,0,'',179,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1, 180,3,'2005',0,0,0,'',180,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1, 181,3,'2006',0,0,0,'',181,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1, 182,3,'2007',0,0,0,'',182,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1, 183,3,'2008',0,0,0,'',183,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1, 184,3,'2009',0,0,0,'',184,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1, 185,3,'2010',0,0,0,'',185,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1, 186,3,'2011',0,0,0,'',186,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1, 187,3,'2012',0,0,0,'',187,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1, 188,3,'2013',0,0,0,'',188,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1, 189,3,'2014',0,0,0,'',189,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1, 190,3,'2015',0,0,0,'',190,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1, 191,3,'2016',0,0,0,'',191,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1, 192,3,'2017',0,0,0,'',192,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1, 193,3,'2018',0,0,0,'',193,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1, 194,3,'2019',0,0,0,'',194,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1, 418,1,'BAU3',0,0,0,'',418,'Op_en1901','Net health effects due to the consumption of salmon','avoided cases/a',1,1901,1, 198,8,' 6.00-20.00',0,0,0,'',198,'Comptraf_scenoutput','Composite traffic v.1 scenario outputs','various',1,0,0, 199,8,'20.00-24.00',0,0,0,'',199,'Comptraf_scenoutput','Composite traffic v.1 scenario outputs','various',1,0,0, 200,8,' 0.00- 6.00',0,0,0,'',200,'Comptraf_scenoutput','Composite traffic v.1 scenario outputs','various',1,0,0, 364,7,'Trips',0,0,0,'',364,'Op_en2202','Concentration-response to PM2.5','m3/ug',1,2202,1, 365,7,'Trips by vehicle',0,0,0,'',365,'Op_en2202','Concentration-response to PM2.5','m3/ug',1,2202,1, 366,7,'Vehicle km',0,0,0,'',366,'Op_en2202','Concentration-response to PM2.5','m3/ug',1,2202,1, 367,7,'Parking lot',0,0,0,'',367,'Op_en2202','Concentration-response to PM2.5','m3/ug',1,2202,1, 368,7,'Link intensity',0,0,0,'',368,'Op_en2202','Concentration-response to PM2.5','m3/ug',1,2202,1, 369,7,'Vehicles',0,0,0,'',369,'Op_en2202','Concentration-response to PM2.5','m3/ug',1,2202,1, 370,7,'Waiting',0,0,0,'',370,'Op_en2202','Concentration-response to PM2.5','m3/ug',1,2202,1, 371,11,'Bus no change',0,0,0,'',371,'Fig_5b_subsidies','Subsidies needed to obtain the composite fraction objective','e/day',1,0,0, 372,11,'Bus one change',0,0,0,'',372,'Fig_5b_subsidies','Subsidies needed to obtain the composite fraction objective','e/day',1,0,0, 373,11,'Cab no change',0,0,0,'',373,'Fig_5b_subsidies','Subsidies needed to obtain the composite fraction objective','e/day',1,0,0, 374,11,'Cab one change',0,0,0,'',374,'Fig_5b_subsidies','Subsidies needed to obtain the composite fraction objective','e/day',1,0,0, 375,11,'Cab non-full',0,0,0,'',375,'Fig_5b_subsidies','Subsidies needed to obtain the composite fraction objective','e/day',1,0,0, 376,11,'Car',0,0,0,'',376,'Fig_5b_subsidies','Subsidies needed to obtain the composite fraction objective','e/day',1,0,0, 377,11,'No-change',0,0,0,'',377,'Fig_5b_subsidies','Subsidies needed to obtain the composite fraction objective','e/day',1,0,0, 378,12,'Passenger',0,0,0,'',378,'Fig_5c_expanding','Societal costs at different levels of guarantee','e/day',1,0,0, 379,12,'Society',0,0,0,'',379,'Fig_5c_expanding','Societal costs at different levels of guarantee','e/day',1,0,0, 380,13,'Car',0,0,0,'',380,'Bw1','Human body weight in Harjavalta','kg',1,2475,1, 381,13,'Composite',0,0,0,'',381,'Bw1','Human body weight in Harjavalta','kg',1,2475,1, 382,14,'Vehicle',0,0,0,'',382,'Testvariable2','Another variable for testing','kg',1,0,0, 383,14,'Driver',0,0,0,'',383,'Testvariable2','Another variable for testing','kg',1,0,0, 384,14,'Driving',0,0,0,'',384,'Testvariable2','Another variable for testing','kg',1,0,0, 385,14,'Parking',0,0,0,'',385,'Testvariable2','Another variable for testing','kg',1,0,0, 386,14,'Parking land',0,0,0,'',386,'Testvariable2','Another variable for testing','kg',1,0,0, 387,14,'Emissions',0,0,0,'',387,'Testvariable2','Another variable for testing','kg',1,0,0, 388,14,'Time',0,0,0,'',388,'Testvariable2','Another variable for testing','kg',1,0,0, 389,14,'Accidents',0,0,0,'',389,'Testvariable2','Another variable for testing','kg',1,0,0, 390,14,'Ticket',0,0,0,'',390,'Testvariable2','Another variable for testing','kg',1,0,0, 391,15,'0',0,0,0,'',391,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0, 392,15,'0.02',0,0,0,'',392,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0, 393,15,'0.05',0,0,0,'',393,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0, 394,15,'0.1',0,0,0,'',394,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0, 395,15,'0.25',0,0,0,'',395,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0, 396,15,'0.4',0,0,0,'',396,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0, 397,15,'0.45',0,0,0,'',397,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0, 398,15,'0.5',0,0,0,'',398,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0, 399,15,'0.55',0,0,0,'',399,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0, 400,15,'0.65',0,0,0,'',400,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0, 401,15,'0.75',0,0,0,'',401,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0, 402,15,'0.9',0,0,0,'',402,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0, 403,15,'1',0,0,0,'',403,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0, 404,16,'18-65',0,0,0,'',404,'Op_en1900','Pollutant health risk due to the consumption of salmon','avoided cases/a',1,1900,1, 405,16,'3',0,0,0,'',405,'Op_en1900','Pollutant health risk due to the consumption of salmon','avoided cases/a',1,1900,1, 406,17,'Harjavalta',0,0,0,'',406,'Op_en1903','Persistent pollutant concentrations in salmon','µg/kg',1,1903,1, 407,36,'Dieldrin',0,0,0,'',407,'Pollutant','Pollutant','-',2,2493,1, 408,36,'Toxaphene',0,0,0,'',408,'Pollutant','Pollutant','-',2,2493,1, 409,36,'Dioxin',0,0,0,'',409,'Pollutant','Pollutant','-',2,2493,1, 410,36,'PCB',0,0,0,'',410,'Pollutant','Pollutant','-',2,2493,1, 411,42,'Farmed salmon',0,0,0,'',411,'Environ_compartment','Environmental compartment','-',2,2490,1, 412,42,'Wild salmon',0,0,0,'',412,'Environ_compartment','Environmental compartment','-',2,2490,1, 413,42,'Market salmon',0,0,0,'',413,'Environ_compartment','Environmental compartment','-',2,2490,1, 414,33,'BAU',0,0,0,'',414,'Decision','Possible range of decisions for a single decision-maker','-',2,2496,1, 415,33,'More actions',0,0,0,'',415,'Decision','Possible range of decisions for a single decision-maker','-',2,2496,1, 416,33,'BAU2',0,0,0,'',416,'Decision','Possible range of decisions for a single decision-maker','-',2,2496,1, 417,33,'Restrict farmed salmon use',0,0,0,'',417,'Decision','Possible range of decisions for a single decision-maker','-',2,2496,1, 419,1,'More actions',0,0,0,'',419,'Op_en1901','Net health effects due to the consumption of salmon','avoided cases/a',1,1901,1, 421,1,'Restrict farmed salmon use2',0,0,0,'',421,'Op_en1901','Net health effects due to the consumption of salmon','avoided cases/a',1,1901,1, 422,34,'Cardiovascular',0,0,0,'',422,'Health_impact','Health impact','',2,2495,1, 423,10,'Home indoor',0,0,0,'Abbreviation in the Concentration database: I',423,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0, 424,10,'(Home) outdoor',0,0,0,'Abbreviation in the Concentration database: O',424,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0, 425,10,'(Personal) Work',0,0,0,'Abbreviation in the Concentration database: W',425,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0, 426,10,'Personal',0,0,0,'Abbreviation in the Concentration database: P',426,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0, 427,10,'Drinking water',0,0,0,'Abbreviation in the Concentration database: DW',427,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0, 428,10,'Indoor dust',0,0,0,'Abbreviation in the Concentration database: ID',428,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0, 429,10,'Human',0,0,0,'Abbreviation in the Concentration database: H',429,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0, 430,10,'Soil',0,0,0,'Abbreviation in the Concentration database: S',430,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0, 431,10,'Beverage',0,0,0,'Abbreviation in the Concentration database: B',431,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0, 432,10,'Food',0,0,0,'Abbreviation in the Concentration database: F',432,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0, 433,10,'In-Vehicle',0,0,0,'Abbreviation in the Concentration database: IV',433,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0, 434,10,'School',0,0,0,'Abbreviation in the Concentration database: SC',434,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0, 435,5,'Athens',0,0,0,'Country: Greece. Abbreviation in the Concentration Database: A',435,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 436,5,'Antwerp',0,0,0,'Country: Belgium. Abbreviation in the Concentration Database: ANT',436,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 437,5,'Antioch-Pittsburg',0,0,0,'Country: USA. Abbreviation in the Concentration Database: AP',437,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 438,5,'Antioch-Pittsburg A-P',0,0,0,'Country: USA. Abbreviation in the Concentration Database: A-P',438,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 439,5,'Arizona',0,0,0,'Country: USA. Abbreviation in the Concentration Database: AZ',439,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 440,5,'Basel',0,0,0,'Country: Germany. Abbreviation in the Concentration Database: B',440,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 441,5,'Baltimore',0,0,0,'Country: USA. Abbreviation in the Concentration Database: BAL',441,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 442,5,'Bayonne',0,0,0,'Country: USA. Abbreviation in the Concentration Database: BAY',442,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 443,5,'Bayonne-Ellizabeth',0,0,0,'Country: USA. Abbreviation in the Concentration Database: BE',443,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 444,5,'Copenhagen',0,0,0,'Country: Denmark. Abbreviation in the Concentration Database: C',444,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 445,5,'California',0,0,0,'Country: USA. Abbreviation in the Concentration Database: CA',445,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 446,5,'Columbus',0,0,0,'Country: USA. Abbreviation in the Concentration Database: CO',446,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 447,5,'Daegu',0,0,0,'Country: South Korea. Abbreviation in the Concentration Database: D',447,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 448,5,'Devils Lake',0,0,0,'Country: USA. Abbreviation in the Concentration Database: DLA',448,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 449,5,'Dublin',0,0,0,'Country: Ireland. Abbreviation in the Concentration Database: DU',449,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 450,5,'Elizabeth',0,0,0,'Country: USA. Abbreviation in the Concentration Database: ELI',450,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 451,5,'EPA Region 5.',0,0,0,'Country: USA. Abbreviation in the Concentration Database: EPA5',451,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 452,5,'Flanders',0,0,0,'Country: Belgium. Abbreviation in the Concentration Database: FLA',452,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 453,5,'Florence',0,0,0,'Country: Italy. Abbreviation in the Concentration Database: FL',453,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 454,5,'Grenoble',0,0,0,'Country: France. Abbreviation in the Concentration Database: G',454,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 455,5,'Germany',0,0,0,'Country: Germany. Abbreviation in the Concentration Database: GE',455,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 456,5,'Genoa',0,0,0,'Country: Italy. Abbreviation in the Concentration Database: GEN',456,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 457,5,'Greensboro GNC',0,0,0,'Country: USA. Abbreviation in the Concentration Database: GNC',457,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 458,5,'Greensboro',0,0,0,'Country: USA. Abbreviation in the Concentration Database: GRB',458,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 460,5,'Helsinki',0,0,0,'Country: Finland. Abbreviation in the Concentration Database: H',460,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 461,5,'Hannover',0,0,0,'Country: Germany. Abbreviation in the Concentration Database: HA',461,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 462,5,'Ile de France',0,0,0,'Country: France. Abbreviation in the Concentration Database: IDF',462,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 463,5,'Los Angeles',0,0,0,'Country: USA. Abbreviation in the Concentration Database: LA',463,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 464,5,'Milan',0,0,0,'Country: Italy. Abbreviation in the Concentration Database: M',464,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 465,5,'Minneapolis',0,0,0,'Country: USA. Abbreviation in the Concentration Database: MP',465,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 466,5,'Minnesota',0,0,0,'Country: USA. Abbreviation in the Concentration Database: MS',466,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 467,5,'Murcia',0,0,0,'Country: Spain. Abbreviation in the Concentration Database: MU',467,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 468,5,'Mexico City',0,0,0,'Country: Mexico. Abbreviation in the Concentration Database: MXC',468,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 469,5,'Oxford',0,0,0,'Country: England. Abbreviation in the Concentration Database: O',469,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 470,5,'Prague',0,0,0,'Country: Czech. Abbreviation in the Concentration Database: P',470,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 471,5,'Padua',0,0,0,'Country: Italy. Abbreviation in the Concentration Database: PA',471,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 472,5,'Puebla',0,0,0,'Country: Mexico. Abbreviation in the Concentration Database: PB',472,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 473,5,'Rouen',0,0,0,'Country: France. Abbreviation in the Concentration Database: R',473,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 475,5,'Strasbourg',0,0,0,'Country: France. Abbreviation in the Concentration Database: STR',475,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 476,5,'Umbria region',0,0,0,'Country: Italy. Abbreviation in the Concentration Database: UMB',476,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 477,5,'United States',0,0,0,'Country: USA. Abbreviation in the Concentration Database: USA',477,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 478,5,'Valdez',0,0,0,'Country: USA. Abbreviation in the Concentration Database: VAL',478,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 479,5,'Woodland',0,0,0,'Country: USA. Abbreviation in the Concentration Database: WDL',479,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 480,4,'66-25-1',0,0,0,'hexanal',480,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 481,4,'71-36-3',0,0,0,'1-butanol',481,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 482,4,'71-43-2',0,0,0,'benzene',482,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 483,4,'78-83-1',0,0,0,'2-methyl-1-propanol',483,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 484,4,'79-00-5',0,0,0,'1,1,2-trichloroethane',484,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 485,4,'79-01-6',0,0,0,'trichloroethene',485,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 486,4,'80-56-8',0,0,0,'alfa-pinene',486,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 487,4,'91-20-3',0,0,0,'naphtalene',487,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 488,4,'95-47-6',0,0,0,'o-xylene',488,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 489,4,'95-63-6',0,0,0,'trimethylbenzenes',489,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 490,4,'100-41-4',0,0,0,'ethylbenzene',490,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 491,4,'100-42-5',0,0,0,'styrene',491,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 492,4,'100-52-7',0,0,0,'benzaldehyde',492,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 493,4,'103-65-1',0,0,0,'propylbenzene',493,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 494,4,'104-76-7',0,0,0,'2-ethylhexanol',494,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 495,4,'108-38-3',0,0,0,'m(&p)-xylene',495,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 496,4,'108-88-3',0,0,0,'toluene',496,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 497,4,'108-95-2',0,0,0,'phenol',497,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 498,4,'110-54-3',0,0,0,'hexane',498,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 499,4,'110-82-7',0,0,0,'cyclohexane',499,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 500,4,'111-76-2',0,0,0,'ethanol, 2-butoxy-',500,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 501,4,'111-84-2',0,0,0,'nonane',501,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 502,4,'111-87-5',0,0,0,'1-octanol',502,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 503,4,'124-13-0',0,0,0,'octanal',503,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 504,4,'124-18-5',0,0,0,'decane',504,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 505,4,'127-18-4',0,0,0,'tetrachloroethene',505,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 506,4,'138-86-3',0,0,0,'d-limonene',506,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 507,4,'872-50-4',0,0,0,'2-pyrrolidinone, 1-methyl-',507,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 508,4,'1120-21-4',0,0,0,'undecane',508,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 509,4,'13466-78-9',0,0,0,'3-caren',509,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 510,4,'TVOC',0,0,0,'Toluene based total VOC',510,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 511,4,'67-66-3',0,0,0,'chloroform',511,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 512,4,'106-46-7',0,0,0,'1,4-dichlorobenzene',512,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 514,4,'56-23-5',0,0,0,'carbon tetrachloride',514,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 515,4,'75-09-2',0,0,0,'methylene chloride',515,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 517,4,'127-91-3',0,0,0,'b-pinene',517,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 520,4,'142-82-5',0,0,0,'n-heptane',520,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 521,4,'111-65-9',0,0,0,'n-octane',521,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 525,4,'112-40-3',0,0,0,'n-dodecane',525,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 526,4,'629-50-5',0,0,0,'n-tridecane',526,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 527,4,'629-59-4',0,0,0,'n-tetradecane',527,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 528,4,'629-62-9',0,0,0,'n-pentadecane',528,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 529,4,'107-83-5',0,0,0,'2-methylpentane',529,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 530,4,'96-14-0',0,0,0,'3-methylpentane',530,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 531,4,'565-59-3',0,0,0,'2,3-dimethylpentane',531,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 532,4,'591-76-4',0,0,0,'2-methylhexane',532,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 533,4,'589-34-4',0,0,0,'3-methylhexane',533,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 534,4,'592-27-8',0,0,0,'2-methylheptane',534,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 535,4,'589-81-1',0,0,0,'3-methylheptane',535,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 536,4,'96-37-7',0,0,0,'methylcyclopentane',536,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 537,4,'108-87-2',0,0,0,'methylcyclohexane',537,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 538,4,'526-73-8',0,0,0,'1,2,3-trimethylbenzene',538,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 540,4,'108-67-8',0,0,0,'1,3,5 trimethylbenzene',540,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 541,4,'4994-16-5',0,0,0,'4-phenylcyclohexene',541,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 542,4,'1,1,1-trichloroethane',0,0,0,'1,1,1-trichloroethane',542,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 545,4,'141-78-6',0,0,0,'ethylacetate',545,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 546,4,'123-86-4',0,0,0,'n-butylacetate',546,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 547,4,'78-93-3',0,0,0,'methyl ethyl ketone',547,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 548,4,'106-35-4',0,0,0,'3-heptatone',548,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 549,4,'93-58-3',0,0,0,'methyl benzoate',549,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 552,4,'123-51-3',0,0,0,'iso-amyl alcohol^a',552,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 554,4,'67-63-0',0,0,0,'2-propanol^a',554,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 555,4,'1634-04-4',0,0,0,'t-butyl methylether',555,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 556,4,'7439-92-1',0,0,0,'lead',556,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 557,4,'7440-38-2',0,0,0,'arsenic',557,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 558,4,'7440-43-9',0,0,0,'cadmium',558,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 559,4,'7440-39-3',0,0,0,'barium',559,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 560,4,'7440-47-3',0,0,0,'chrome',560,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 561,4,'7440-50-8',0,0,0,'copper',561,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 562,4,'7439-96-5',0,0,0,'manganese',562,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 563,4,'7440-02-0',0,0,0,'nickel',563,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 564,4,'7782-49-2',0,0,0,'selenium',564,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 565,4,'7440-62-2',0,0,0,'vanadium',565,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 566,4,'7440-66-6',0,0,0,'zinc',566,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 567,4,'71-55-6',0,0,0,'1,1,1-trichloroethane',567,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 568,4,'7439-97-6',0,0,0,'mercury',568,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 570,4,'60-27-5',0,0,0,'creatinine',570,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 571,4,'7429-90-5',0,0,0,'aluminium',571,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 572,4,'7440-70-2',0,0,0,'calcium',572,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 573,4,'7439-95-4',0,0,0,'magnesium',573,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 574,4,'7723-14-0',0,0,0,'phosphorus',574,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 575,4,'7440-24-6',0,0,0,'strontium',575,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 576,4,'7439-89-6',0,0,0,'iron',576,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 577,4,'7440-09-7',0,0,0,'potassium',577,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 578,4,'7440-23-5',0,0,0,'sodium',578,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 579,4,'58-89-9',0,0,0,'lindane',579,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 580,4,'52645-53-1',0,0,0,'permenthrine',580,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 581,4,'107-13-1',0,0,0,'acrylonitrile',581,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 582,4,'79-06-1',0,0,0,'acrylamide',582,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 589,4,'611-14-3',0,0,0,'1-ethyl 2methyl benzene',589,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 592,4,'109-66-0',0,0,0,'n-pentane',592,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 593,4,'7785-26-4',0,0,0,'alpha-pinene',593,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 594,4,'5989-27-5',0,0,0,'d-limonene',594,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 596,4,'106-99-0',0,0,0,'butadiene',596,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 597,4,'74-84-0',0,0,0,'ethane',597,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 598,4,'74-85-1',0,0,0,'ethylene',598,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 599,4,'74-86-2',0,0,0,'acetylene',599,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 600,4,'107-06-2',0,0,0,'1,2-dichloroethane',600,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 601,4,'106-42-3',0,0,0,'p-xylene',601,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 603,4,'98-82-8',0,0,0,'isopropylbenzene',603,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 604,4,'110-86-1',0,0,0,'pyridine',604,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 606,4,'109-06-8',0,0,0,'2-picoline',606,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 608,4,'108-99-6',0,0,0,'3-picoline',608,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 609,4,'108-89-4',0,0,0,'4-picoline',609,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 610,4,'104-51-8',0,0,0,'n-butylbenzene',610,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 611,4,'536-78-7',0,0,0,'3-ethylpyridine',611,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 613,4,'25551-13-7',0,0,0,'trimethylbenzene',613,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 618,4,'1336-36-3',0,0,0,'PCBs',618,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 619,4,'3547-04-4',0,0,0,'DDE',619,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 620,4,'118-74-1',0,0,0,'HCB',620,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 621,4,'5315-79-7',0,0,0,'1-hydroxypyrene',621,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 623,4,'1330-20-7',0,0,0,'xylenes',623,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 624,4,'37210-16-5',0,0,0,'CO2',624,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 625,4,'630-08-0',0,0,0,'CO',625,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 626,4,'54-11-5',0,0,0,'nicotine',626,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 628,4,'3588-17-8',0,0,0,'trans,trans-Muconic acid',628,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 629,4,'50-32-8',0,0,0,'benzo(a)pyrene',629,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 631,4,'590-86-3',0,0,0,'isovaleraldehyde',631,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 632,4,'123-38-6',0,0,0,'propionaldehyde',632,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 633,4,'123-72-8',0,0,0,'n-butyraldehyde',633,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 634,4,'75-07-0',0,0,0,'acetaldehyde',634,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 636,4,'50-00-0',0,0,0,'formaldehyde',636,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 637,4,'110-62-3',0,0,0,'valeraldehyde',637,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 638,4,'4170-30-3',0,0,0,'crotonaldehyde',638,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 639,22,'n',0,0,0,'Number of observations',639,'Op_en1910','Total mortality in the Western Europe','cases/a',1,1910,1, 640,22,'n_lt_LOQ',0,0,0,'Number of observations below level of quantitation',640,'Op_en1910','Total mortality in the Western Europe','cases/a',1,1910,1, 641,22,'F0.10',0,0,0,'Fractile 0.1',641,'Op_en1910','Total mortality in the Western Europe','cases/a',1,1910,1, 642,22,'F0.50',0,0,0,'Fractile 0.5',642,'Op_en1910','Total mortality in the Western Europe','cases/a',1,1910,1, 643,22,'F0.90',0,0,0,'Fractile 0.9',643,'Op_en1910','Total mortality in the Western Europe','cases/a',1,1910,1, 644,22,'F0.95',0,0,0,'Fractile 0.95',644,'Op_en1910','Total mortality in the Western Europe','cases/a',1,1910,1, 645,22,'Mean',0,0,0,'Arithmetic mean',645,'Op_en1910','Total mortality in the Western Europe','cases/a',1,1910,1, 646,22,'GeoMean',0,0,0,'Geometric mean',646,'Op_en1910','Total mortality in the Western Europe','cases/a',1,1910,1, 647,5,'ang',0,0,0,'Anglian Water ',647,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 648,5,'bou',0,0,0,'Bristol Water ',648,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 649,5,'brw',0,0,0,'Bournemouth & West hants ',649,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 650,5,'caw',0,0,0,'Cambridge Water ',650,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 651,5,'cho',0,0,0,'Cholderton Water ',651,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 652,5,'dcc',0,0,0,'Dee Valley Water ',652,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 653,5,'eas',0,0,0,'Welsh Water ',653,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 654,5,'ess',0,0,0,'Essex and Suffolk Water ',654,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 655,5,'fol',0,0,0,'Folkestone & Dover Water ',655,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 656,5,'har',0,0,0,'Hartlepool Water ',656,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 657,5,'mik',0,0,0,'Mid Kent Water ',657,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 658,5,'nor',0,0,0,'Northumbrian Water ',658,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 659,5,'nww',0,0,0,'Portsmouth Water ',659,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 660,5,'por',0,0,0,'Sutton & East Surrey Water ',660,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 661,5,'sea',0,0,0,'South East Water ',661,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 662,5,'sev',0,0,0,'Southern Water ',662,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 663,5,'sos',0,0,0,'South Staffordshire Water ',663,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 664,5,'sou',0,0,0,'Severn Trent Water ',664,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 665,5,'sww',0,0,0,'South West Water ',665,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 666,5,'teh',0,0,0,'Tendring Hundred Water ',666,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 667,5,'tha',0,0,0,'Thames Water ',667,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 668,5,'thr',0,0,0,'Three Valleys Water ',668,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 669,5,'wes',0,0,0,'United Utilties (North West Water) ',669,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 670,5,'wrx',0,0,0,'Wessex Water ',670,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 671,5,'yor',0,0,0,'Yorkshire Water',671,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 672,25,'BAU',0,0,0,'0.00000000000000',672,'Op_en1898','Recommendation for consumption of farmed salmon','-',1,1898,1, 673,25,'Restrict farmed salmon use',0,0,0,'0.00000000000000',673,'Op_en1898','Recommendation for consumption of farmed salmon','-',1,1898,1, 674,26,'BAU',0,0,0,'0.00000000000000',674,'Op_en1899','Pollutant concentration limits for fish feed','-',1,1899,1, 675,26,'More actions',0,0,0,'0.00000000000000',675,'Op_en1899','Pollutant concentration limits for fish feed','-',1,1899,1, 676,130,'Dieldrin',0,0,0,'0.00000000000000',676,'Op_en2705','Pollutant','-',6,2705,1, 677,130,'Toxaphene',0,0,0,'0.00000000000000',677,'Op_en2705','Pollutant','-',6,2705,1, 678,130,'Dioxin',0,0,0,'0.00000000000000',678,'Op_en2705','Pollutant','-',6,2705,1, 679,130,'PCB',0,0,0,'0.00000000000000',679,'Op_en2705','Pollutant','-',6,2705,1, 680,131,'Farmed salmon',0,0,0,'0.00000000000000',680,'Op_en2706','Salmon type','-',6,2706,1, 681,131,'Wild salmon',0,0,0,'0.00000000000000',681,'Op_en2706','Salmon type','-',6,2706,1, 682,131,'Market salmon',0,0,0,'0.00000000000000',682,'Op_en2706','Salmon type','-',6,2706,1, 685,133,'Cardiovascular',0,0,0,'0.00000000000000',685,'Op_en2707','Cause of death3','ICD-10',6,2707,1, 688,135,'2000',0,0,0,'0.00000000000000',688,'Op_en2708','Year3','year',6,2708,1, 689,185,'Male',0,0,0,'0.00000000000000',689,'Op_en2780','Sex','-',6,2780,1, 690,185,'Female',0,0,0,'0.00000000000000',690,'Op_en2780','Sex','-',6,2780,1, 691,186,'All causes',0,0,0,'AAA',691,'Op_en2779','Diagnosis1','-',6,2779,1, 692,186,'Infectious and parasitic diseases',0,0,0,'A00-B99',692,'Op_en2779','Diagnosis1','-',6,2779,1, 693,186,'Typhoid and paratyphoid fever',0,0,0,'A01',693,'Op_en2779','Diagnosis1','-',6,2779,1, 694,186,'Other intestinal infectious diseases',0,0,0,'A00, A02-A09',694,'Op_en2779','Diagnosis1','-',6,2779,1, 695,186,'Tuberculosis of respiratory system',0,0,0,'A15-A16',695,'Op_en2779','Diagnosis1','-',6,2779,1, 696,186,'Tuberculosis, other forms',0,0,0,'A17-A19',696,'Op_en2779','Diagnosis1','-',6,2779,1, 697,186,'Whooping cough',0,0,0,'A37',697,'Op_en2779','Diagnosis1','-',6,2779,1, 698,186,'Meningococcal infection',0,0,0,'A39',698,'Op_en2779','Diagnosis1','-',6,2779,1, 699,186,'Tetanus',0,0,0,'A35',699,'Op_en2779','Diagnosis1','-',6,2779,1, 700,186,'Septicaemia',0,0,0,'A40-A41',700,'Op_en2779','Diagnosis1','-',6,2779,1, 701,186,'Other bacterial diseases',0,0,0,'A20-A32, A36, A38, A42-49',701,'Op_en2779','Diagnosis1','-',6,2779,1, 702,186,'Measles',0,0,0,'B05',702,'Op_en2779','Diagnosis1','-',6,2779,1, 703,186,'HIV disease',0,0,0,'B20-B24',703,'Op_en2779','Diagnosis1','-',6,2779,1, 704,186,'Other viral diseases',0,0,0,'A70-A74, A80-B34, B05, B20-B24',704,'Op_en2779','Diagnosis1','-',6,2779,1, 705,186,'Malaria',0,0,0,'B50-B54',705,'Op_en2779','Diagnosis1','-',6,2779,1, 706,186,'Other arthropod-borne diseases',0,0,0,'A75-A79, B55-B57, B60, B64',706,'Op_en2779','Diagnosis1','-',6,2779,1, 707,186,'Sexually transmitted diseases',0,0,0,'A50-A64',707,'Op_en2779','Diagnosis1','-',6,2779,1, 708,186,'Other infectious and parasitic diseases',0,0,0,'A65-A69, B35-B49, B58, B59, B65-B99',708,'Op_en2779','Diagnosis1','-',6,2779,1, 709,186,'Malignant neoplasms',0,0,0,'C00-C97',709,'Op_en2779','Diagnosis1','-',6,2779,1, 710,186,'Malignant neoplasm of lip, oral cavity and pharynx',0,0,0,'C00-C14',710,'Op_en2779','Diagnosis1','-',6,2779,1, 711,186,'Malignant neoplasm of oesophagus',0,0,0,'C15',711,'Op_en2779','Diagnosis1','-',6,2779,1, 712,186,'Malignant neoplasm of stomach',0,0,0,'C16',712,'Op_en2779','Diagnosis1','-',6,2779,1, 713,186,'Malignant neoplasm of colon',0,0,0,'C18',713,'Op_en2779','Diagnosis1','-',6,2779,1, 714,186,'Malignant neoplasm of rectum, rectosigmoid junction and anus',0,0,0,'C19-C21',714,'Op_en2779','Diagnosis1','-',6,2779,1, 715,186,'Malignant neoplasm of liver',0,0,0,'C22',715,'Op_en2779','Diagnosis1','-',6,2779,1, 716,186,'Malignant neoplasm of larynx',0,0,0,'C32',716,'Op_en2779','Diagnosis1','-',6,2779,1, 717,186,'Malignant neoplasm of trachea, bronchus and lung',0,0,0,'C33-C34',717,'Op_en2779','Diagnosis1','-',6,2779,1, 718,186,'Malignant neoplasm of breast',0,0,0,'C50',718,'Op_en2779','Diagnosis1','-',6,2779,1, 719,186,'Malignant neoplasm of cervix uteri',0,0,0,'C53',719,'Op_en2779','Diagnosis1','-',6,2779,1, 720,186,'Malignant neoplasm of uterus, other and unspecified',0,0,0,'C54-C55',720,'Op_en2779','Diagnosis1','-',6,2779,1, 721,186,'Malignant neoplasm of prostate',0,0,0,'C61',721,'Op_en2779','Diagnosis1','-',6,2779,1, 722,186,'Malignant neoplasm of bladder',0,0,0,'C67',722,'Op_en2779','Diagnosis1','-',6,2779,1, 723,186,'Malignant neoplasm of other sites',0,0,0,'C17, C23-C31, C37-C49, C51, C52, C56-C60, C62-C66, C68-C80, C97',723,'Op_en2779','Diagnosis1','-',6,2779,1, 724,186,'Leukaemia',0,0,0,'C91-C95',724,'Op_en2779','Diagnosis1','-',6,2779,1, 725,186,'Other malignant neoplasms of lymphoid and haematopoietic and related tissue',0,0,0,'C81-C90, C96',725,'Op_en2779','Diagnosis1','-',6,2779,1, 726,186,'Benign neoplasm, other and unspecified neoplasm',0,0,0,'D00-D48',726,'Op_en2779','Diagnosis1','-',6,2779,1, 727,186,'Diabetes mellitus',0,0,0,'E10-E14',727,'Op_en2779','Diagnosis1','-',6,2779,1, 728,186,'Other endocrine and metabolic diseases',0,0,0,'E00-E07, E15-E34, E65-E68, E70-E88',728,'Op_en2779','Diagnosis1','-',6,2779,1, 729,186,'Malnutrition',0,0,0,'E41-E46',729,'Op_en2779','Diagnosis1','-',6,2779,1, 730,186,'Other nutritional deficiencies',0,0,0,'E40, E50-E64',730,'Op_en2779','Diagnosis1','-',6,2779,1, 731,186,'Anaemias',0,0,0,'D50-D64',731,'Op_en2779','Diagnosis1','-',6,2779,1, 732,186,'Other diseases of blood and blood-forming organs',0,0,0,'D65-D89',732,'Op_en2779','Diagnosis1','-',6,2779,1, 733,186,'Mental disorders',0,0,0,'F01-F99',733,'Op_en2779','Diagnosis1','-',6,2779,1, 734,186,'Meningitis',0,0,0,'G00, G03',734,'Op_en2779','Diagnosis1','-',6,2779,1, 735,186,'Multiple sclerosis',0,0,0,'G35',735,'Op_en2779','Diagnosis1','-',6,2779,1, 736,186,'Epilepsy',0,0,0,'G40-G41',736,'Op_en2779','Diagnosis1','-',6,2779,1, 737,186,'Other diseases of the nervous system and sense organs',0,0,0,'G04-G31, G36-G37, G43-H95',737,'Op_en2779','Diagnosis1','-',6,2779,1, 738,186,'Diseases of the circulatory system',0,0,0,'I00-I99',738,'Op_en2779','Diagnosis1','-',6,2779,1, 739,186,'Acute rheumatic fever',0,0,0,'I00-I02',739,'Op_en2779','Diagnosis1','-',6,2779,1, 740,186,'Chronic rheumatic heart disease',0,0,0,'I05-I09',740,'Op_en2779','Diagnosis1','-',6,2779,1, 741,186,'Hypertensive disease',0,0,0,'I10-I13',741,'Op_en2779','Diagnosis1','-',6,2779,1, 742,186,'Acute myocardial infarction',0,0,0,'I21, I22',742,'Op_en2779','Diagnosis1','-',6,2779,1, 743,186,'Other ischaemic heart diseases',0,0,0,'I20, I24, I25',743,'Op_en2779','Diagnosis1','-',6,2779,1, 744,186,'Diseases of pulmonary circulation and other forms of heart disease',0,0,0,'I26-I51',744,'Op_en2779','Diagnosis1','-',6,2779,1, 745,186,'Cerebrovascular disease',0,0,0,'I60-I69',745,'Op_en2779','Diagnosis1','-',6,2779,1, 746,186,'Atherosclerosis',0,0,0,'I70',746,'Op_en2779','Diagnosis1','-',6,2779,1, 747,186,'Embolism, thrombosis and other diseases of arteries, arterioles and capillaries',0,0,0,'I71-I78',747,'Op_en2779','Diagnosis1','-',6,2779,1, 748,186,'Phlebitis, thrombophlebitis, venous embolism and thrombosis',0,0,0,'I80-I82',748,'Op_en2779','Diagnosis1','-',6,2779,1, 749,186,'Other diseases of the circulatory system',0,0,0,'I83-I99',749,'Op_en2779','Diagnosis1','-',6,2779,1, 750,186,'Acute upper respiratory infection',0,0,0,'J00-J06',750,'Op_en2779','Diagnosis1','-',6,2779,1, 751,186,'Acute bronchitis and bronchiolitis',0,0,0,'J20-J21',751,'Op_en2779','Diagnosis1','-',6,2779,1, 752,186,'Pneumonia',0,0,0,'J12-J18',752,'Op_en2779','Diagnosis1','-',6,2779,1, 753,186,'Influenza',0,0,0,'J10-J11',753,'Op_en2779','Diagnosis1','-',6,2779,1, 754,186,'Bronchitis, chronic and unspecified, emphysema and asthma',0,0,0,'J40-J46',754,'Op_en2779','Diagnosis1','-',6,2779,1, 755,186,'Other diseases of the respiratory system',0,0,0,'J22, J30-J39, J47-J98',755,'Op_en2779','Diagnosis1','-',6,2779,1, 756,186,'Ulcer of stomach and duodenum',0,0,0,'K25-K27',756,'Op_en2779','Diagnosis1','-',6,2779,1, 757,186,'Appendicitis',0,0,0,'K35-K38',757,'Op_en2779','Diagnosis1','-',6,2779,1, 758,186,'Hernia of abdominal cavity and intestinal obstruction',0,0,0,'K40-K46,K56',758,'Op_en2779','Diagnosis1','-',6,2779,1, 759,186,'Chronic liver disease and cirrhosis',0,0,0,'K70,K73-K74,K76',759,'Op_en2779','Diagnosis1','-',6,2779,1, 760,186,'Other diseases of the digestive system',0,0,0,'K00-K22, K28-K31, K50-K55, K57-K66, K71, K72, K75, K80-K92',760,'Op_en2779','Diagnosis1','-',6,2779,1, 761,186,'Nephritis, nephrotic syndrome and nephrosis',0,0,0,'N00-N07, N13-N19',761,'Op_en2779','Diagnosis1','-',6,2779,1, 762,186,'Infections of kidney',0,0,0,'N10-N12',762,'Op_en2779','Diagnosis1','-',6,2779,1, 763,186,'Hyperplasia of prostate',0,0,0,'N40',763,'Op_en2779','Diagnosis1','-',6,2779,1, 764,186,'Other diseases of the genitourinary system',0,0,0,'N20-N39, N41-N98',764,'Op_en2779','Diagnosis1','-',6,2779,1, 765,186,'Abortion',0,0,0,'O00-O07',765,'Op_en2779','Diagnosis1','-',6,2779,1, 766,186,'Haemorrhage of pregnancy and childbirth',0,0,0,'O20, O46, O67, O72',766,'Op_en2779','Diagnosis1','-',6,2779,1, 767,186,'Toxaemia of pregnancy',0,0,0,'O13-O16, O21',767,'Op_en2779','Diagnosis1','-',6,2779,1, 768,186,'Complications of the puerperium',0,0,0,'O85-O92, A34',768,'Op_en2779','Diagnosis1','-',6,2779,1, 769,186,'Other direct obstetric causes',0,0,0,'O10-O12, O22-O75, O95-O97',769,'Op_en2779','Diagnosis1','-',6,2779,1, 770,186,'Indirect obstetric causes',0,0,0,'O98-O99',770,'Op_en2779','Diagnosis1','-',6,2779,1, 771,186,'Diseases of skin and subcutaneous tissue',0,0,0,'L00-L98',771,'Op_en2779','Diagnosis1','-',6,2779,1, 772,186,'Diseases of the musculoskeletal system and connective tissue',0,0,0,'M00-M99',772,'Op_en2779','Diagnosis1','-',6,2779,1, 773,186,'Spina bifida and hydrocephalus',0,0,0,'Q03,Q05',773,'Op_en2779','Diagnosis1','-',6,2779,1, 774,186,'Congenital anomalies of the circulatory system',0,0,0,'Q20-Q28',774,'Op_en2779','Diagnosis1','-',6,2779,1, 775,186,'Other congenital anomalies',0,0,0,'Q00-Q02, Q04, Q06-Q18, Q30-Q99',775,'Op_en2779','Diagnosis1','-',6,2779,1, 776,186,'Birth trauma',0,0,0,'P10-P15',776,'Op_en2779','Diagnosis1','-',6,2779,1, 777,186,'Other conditions originating in the perinatal period',0,0,0,'P00-P08, P20-P96, A33',777,'Op_en2779','Diagnosis1','-',6,2779,1, 778,186,'Senility',0,0,0,'R54',778,'Op_en2779','Diagnosis1','-',6,2779,1, 779,186,'Signs, symptoms and other ill-defined conditions',0,0,0,'R00-R53, R55-R99',779,'Op_en2779','Diagnosis1','-',6,2779,1, 780,186,'Accidents and adverse effects',0,0,0,'V01-X59, Y40-Y86, Y88',780,'Op_en2779','Diagnosis1','-',6,2779,1, 781,186,'Motor vehicle traffic accidents',0,0,0,'V02-V04, V09, V12-V14, V19-V79, V86-V89',781,'Op_en2779','Diagnosis1','-',6,2779,1, 782,186,'Other transport accidents',0,0,0,'V01, V05-V06, V10, V11, V15-V18, V80-V85, V90-V99',782,'Op_en2779','Diagnosis1','-',6,2779,1, 783,186,'Accidental poisoning',0,0,0,'X40-X49',783,'Op_en2779','Diagnosis1','-',6,2779,1, 784,186,'Accidental falls',0,0,0,'W00-W19',784,'Op_en2779','Diagnosis1','-',6,2779,1, 785,186,'Accidents caused by fire and flames',0,0,0,'X00-X09',785,'Op_en2779','Diagnosis1','-',6,2779,1, 786,186,'Accidental drowning and submersion',0,0,0,'W65-W74',786,'Op_en2779','Diagnosis1','-',6,2779,1, 787,186,'Accidents caused by machinery and by cutting and piercing instruments',0,0,0,'W24-W31',787,'Op_en2779','Diagnosis1','-',6,2779,1, 788,186,'Accidents caused by firearm missile',0,0,0,'W32-W34',788,'Op_en2779','Diagnosis1','-',6,2779,1, 789,186,'All other accidents, including late effects',0,0,0,'W20-W23, W35-W64, W75-W99, X10-X39, X50-X59, Y85, Y86',789,'Op_en2779','Diagnosis1','-',6,2779,1, 790,186,'Drugs, medicaments causing adverse effects in therapeutic use',0,0,0,'Y40-Y84, Y88',790,'Op_en2779','Diagnosis1','-',6,2779,1, 791,186,'Suicide and self- inflicted injury',0,0,0,'X60-X84',791,'Op_en2779','Diagnosis1','-',6,2779,1, 792,186,'Homicide and injury purposely inflicted by other persons',0,0,0,'X85-Y09',792,'Op_en2779','Diagnosis1','-',6,2779,1, 793,186,'Other external causes',0,0,0,'Y10-Y36, Y87, Y89',793,'Op_en2779','Diagnosis1','-',6,2779,1, 794,187,'Number',0,0,0,'0.00000000000000',794,'Op_en2784','Units1','-',6,2784,1, 795,187,'Number/100000 person-years',0,0,0,'0.00000000000000',795,'Op_en2784','Units1','-',6,2784,1, 796,188,'All ages',0,0,0,'0.00000000000000',796,'Op_en2781','Age group1','a',6,2781,1, 797,188,'< 1',0,0,0,'0.00000000000000',797,'Op_en2781','Age group1','a',6,2781,1, 798,188,'1-4',0,0,0,'0.00000000000000',798,'Op_en2781','Age group1','a',6,2781,1, 799,188,'5-14',0,0,0,'0.00000000000000',799,'Op_en2781','Age group1','a',6,2781,1, 800,188,'15-24',0,0,0,'0.00000000000000',800,'Op_en2781','Age group1','a',6,2781,1, 801,188,'25-34',0,0,0,'0.00000000000000',801,'Op_en2781','Age group1','a',6,2781,1, 802,188,'35-44',0,0,0,'0.00000000000000',802,'Op_en2781','Age group1','a',6,2781,1, 803,188,'45-54',0,0,0,'0.00000000000000',803,'Op_en2781','Age group1','a',6,2781,1, 804,188,'55-64',0,0,0,'0.00000000000000',804,'Op_en2781','Age group1','a',6,2781,1, 805,188,'65-74',0,0,0,'0.00000000000000',805,'Op_en2781','Age group1','a',6,2781,1, 806,188,'75+',0,0,0,'0.00000000000000',806,'Op_en2781','Age group1','a',6,2781,1, 807,188,'Age not specified',0,0,0,'0.00000000000000',807,'Op_en2781','Age group1','a',6,2781,1, 808,189,'Finland',0,0,0,'0.00000000000000',808,'Op_en2785 ','Country1','-',6,2785,1, 809,193,'All',0,0,0,'0.00000000000000',809,'Op_en2812 ','Age2','a',6,2812,1, 810,193,'0-64',0,0,0,'0.00000000000000',810,'Op_en2812 ','Age2','a',6,2812,1, 811,193,'64+',0,0,0,'0.00000000000000',811,'Op_en2812 ','Age2','a',6,2812,1, 812,194,'Austria',0,0,0,'0.00000000000000',812,'Op_en2813','Country2','-',6,2813,1, 813,194,'Belgium',0,0,0,'0.00000000000000',813,'Op_en2813','Country2','-',6,2813,1, 814,194,'Bulgaria',0,0,0,'0.00000000000000',814,'Op_en2813','Country2','-',6,2813,1, 815,194,'Cyprus',0,0,0,'0.00000000000000',815,'Op_en2813','Country2','-',6,2813,1, 816,194,'Czech Republic',0,0,0,'0.00000000000000',816,'Op_en2813','Country2','-',6,2813,1, 817,194,'Denmark',0,0,0,'0.00000000000000',817,'Op_en2813','Country2','-',6,2813,1, 818,194,'Estonia',0,0,0,'0.00000000000000',818,'Op_en2813','Country2','-',6,2813,1, 819,194,'Finland',0,0,0,'0.00000000000000',819,'Op_en2813','Country2','-',6,2813,1, 820,194,'France',0,0,0,'0.00000000000000',820,'Op_en2813','Country2','-',6,2813,1, 821,194,'Germany',0,0,0,'0.00000000000000',821,'Op_en2813','Country2','-',6,2813,1, 822,194,'Greece',0,0,0,'0.00000000000000',822,'Op_en2813','Country2','-',6,2813,1, 823,194,'Hungary',0,0,0,'0.00000000000000',823,'Op_en2813','Country2','-',6,2813,1, 824,194,'Ireland',0,0,0,'0.00000000000000',824,'Op_en2813','Country2','-',6,2813,1, 825,194,'Italy',0,0,0,'0.00000000000000',825,'Op_en2813','Country2','-',6,2813,1, 826,194,'Latvia',0,0,0,'0.00000000000000',826,'Op_en2813','Country2','-',6,2813,1, 827,194,'Luxembourg',0,0,0,'0.00000000000000',827,'Op_en2813','Country2','-',6,2813,1, 828,194,'Malta',0,0,0,'0.00000000000000',828,'Op_en2813','Country2','-',6,2813,1, 829,194,'Netherlands',0,0,0,'0.00000000000000',829,'Op_en2813','Country2','-',6,2813,1, 830,194,'Poland',0,0,0,'0.00000000000000',830,'Op_en2813','Country2','-',6,2813,1, 831,194,'Portugal',0,0,0,'0.00000000000000',831,'Op_en2813','Country2','-',6,2813,1, 832,194,'Romania',0,0,0,'0.00000000000000',832,'Op_en2813','Country2','-',6,2813,1, 833,194,'Slovakia',0,0,0,'0.00000000000000',833,'Op_en2813','Country2','-',6,2813,1, 834,194,'Slovenia',0,0,0,'0.00000000000000',834,'Op_en2813','Country2','-',6,2813,1, 835,194,'Spain',0,0,0,'0.00000000000000',835,'Op_en2813','Country2','-',6,2813,1, 836,194,'Sweden',0,0,0,'0.00000000000000',836,'Op_en2813','Country2','-',6,2813,1, 837,194,'United Kingdom',0,0,0,'0.00000000000000',837,'Op_en2813','Country2','-',6,2813,1, 838,194,'EU ',0,0,0,'0.00000000000000',838,'Op_en2813','Country2','-',6,2813,1, 839,195,'1970',0,0,0,'0.00000000000000',839,'Op_en2814','Year2','a',6,2814,1, 840,195,'1971',0,0,0,'0.00000000000000',840,'Op_en2814','Year2','a',6,2814,1, 841,195,'1972',0,0,0,'0.00000000000000',841,'Op_en2814','Year2','a',6,2814,1, 842,195,'1973',0,0,0,'0.00000000000000',842,'Op_en2814','Year2','a',6,2814,1, 843,195,'1974',0,0,0,'0.00000000000000',843,'Op_en2814','Year2','a',6,2814,1, 844,195,'1975',0,0,0,'0.00000000000000',844,'Op_en2814','Year2','a',6,2814,1, 845,195,'1976',0,0,0,'0.00000000000000',845,'Op_en2814','Year2','a',6,2814,1, 846,195,'1977',0,0,0,'0.00000000000000',846,'Op_en2814','Year2','a',6,2814,1, 847,195,'1978',0,0,0,'0.00000000000000',847,'Op_en2814','Year2','a',6,2814,1, 848,195,'1979',0,0,0,'0.00000000000000',848,'Op_en2814','Year2','a',6,2814,1, 849,195,'1980',0,0,0,'0.00000000000000',849,'Op_en2814','Year2','a',6,2814,1, 850,195,'1981',0,0,0,'0.00000000000000',850,'Op_en2814','Year2','a',6,2814,1, 851,195,'1982',0,0,0,'0.00000000000000',851,'Op_en2814','Year2','a',6,2814,1, 852,195,'1983',0,0,0,'0.00000000000000',852,'Op_en2814','Year2','a',6,2814,1, 853,195,'1984',0,0,0,'0.00000000000000',853,'Op_en2814','Year2','a',6,2814,1, 854,195,'1985',0,0,0,'0.00000000000000',854,'Op_en2814','Year2','a',6,2814,1, 855,195,'1986',0,0,0,'0.00000000000000',855,'Op_en2814','Year2','a',6,2814,1, 856,195,'1987',0,0,0,'0.00000000000000',856,'Op_en2814','Year2','a',6,2814,1, 857,195,'1988',0,0,0,'0.00000000000000',857,'Op_en2814','Year2','a',6,2814,1, 858,195,'1989',0,0,0,'0.00000000000000',858,'Op_en2814','Year2','a',6,2814,1, 859,195,'1990',0,0,0,'0.00000000000000',859,'Op_en2814','Year2','a',6,2814,1, 860,195,'1991',0,0,0,'0.00000000000000',860,'Op_en2814','Year2','a',6,2814,1, 861,195,'1992',0,0,0,'0.00000000000000',861,'Op_en2814','Year2','a',6,2814,1, 862,195,'1993',0,0,0,'0.00000000000000',862,'Op_en2814','Year2','a',6,2814,1, 863,195,'1994',0,0,0,'0.00000000000000',863,'Op_en2814','Year2','a',6,2814,1, 864,195,'1995',0,0,0,'0.00000000000000',864,'Op_en2814','Year2','a',6,2814,1, 865,195,'1996',0,0,0,'0.00000000000000',865,'Op_en2814','Year2','a',6,2814,1, 866,195,'1997',0,0,0,'0.00000000000000',866,'Op_en2814','Year2','a',6,2814,1, 867,195,'1998',0,0,0,'0.00000000000000',867,'Op_en2814','Year2','a',6,2814,1, 868,195,'1999',0,0,0,'0.00000000000000',868,'Op_en2814','Year2','a',6,2814,1, 869,195,'2000',0,0,0,'0.00000000000000',869,'Op_en2814','Year2','a',6,2814,1, 870,195,'2001',0,0,0,'0.00000000000000',870,'Op_en2814','Year2','a',6,2814,1, 871,195,'2002',0,0,0,'0.00000000000000',871,'Op_en2814','Year2','a',6,2814,1, 872,195,'2003',0,0,0,'0.00000000000000',872,'Op_en2814','Year2','a',6,2814,1, 873,195,'2004',0,0,0,'0.00000000000000',873,'Op_en2814','Year2','a',6,2814,1, 874,195,'2005',0,0,0,'0.00000000000000',874,'Op_en2814','Year2','a',6,2814,1, 875,195,'2006',0,0,0,'0.00000000000000',875,'Op_en2814','Year2','a',6,2814,1, 876,195,'2007',0,0,0,'0.00000000000000',876,'Op_en2814','Year2','a',6,2814,1, 877,196,'Male',0,0,0,'0.00000000000000',877,'Op_en2815 ','Sex2','-',6,2815,1, 878,196,'Female',0,0,0,'0.00000000000000',878,'Op_en2815 ','Sex2','-',6,2815,1, 879,196,'All',0,0,0,'0.00000000000000',879,'Op_en2815 ','Sex2','-',6,2815,1, 883,207,'All causes',0,0,0,'-’',883,'Op_en2835','Diagnosis2','ICD-10',6,2835,1, 884,207,'Infectious and parasitic diseases',0,0,0,'A00-B99’',884,'Op_en2835','Diagnosis2','ICD-10',6,2835,1, 885,207,'Typhoid and paratyphoid fever',0,0,0,'A01’',885,'Op_en2835','Diagnosis2','ICD-10',6,2835,1 ) 280,96,1 48,13 1,1,1,1,1,1,0,0,0,0 2,370,45,476,445 2,518,523,725,303,0,MIDM 2,404,34,750,516,0,MIDM 39325,65535,39321 [L_j,L_i] [L_j,L_i] This node checks the variables listed in Var_for_rdb and makes an index of those that are NOT found in the result database. This is then used as an index in Inp_var for adding variable information. Table(O_i,O_j)( 1,'Op_en1901','Net health effects due to the consumption of salmon','avoided cases/a',1,1901,1, 2,'Op_en2693','Testvariable','kg',1,2693,1, 3,'Op_en2201','The mortality due to PM 2.5 from buses','premature deaths',1,2201,1, 4,'Op_en2205','Bus engine technology','see wiki page',1,2205,1, 5,'Op_en2204','Primary PM2.5 emissions from bus traffic in Helsinki Metropolitan Area','kg/a',1,2204,1, 6,'Ppmconc_bustraffic','PM2.5 concentration from bus traffic in Helsinki in 2020','ug/m3',1,0,0, 7,'Op_en2202','Concentration-response to PM2.5','m3/ug',1,2202,1, 8,'Comptraf_scenoutput','Composite traffic v.1 scenario outputs','various',1,0,0, 9,'Fig_3_cost_by_source','Cost by source','e/trip',1,0,0, 10,'Fig_5a_societal_cost','Societal cost','e/day',1,0,0, 11,'Fig_5b_subsidies','Subsidies needed to obtain the composite fraction objective','e/day',1,0,0, 12,'Fig_5c_expanding','Societal costs at different levels of guarantee','e/day',1,0,0, 13,'Bw1','Human body weight in Harjavalta','kg',1,2475,1, 14,'Testvariable2','Another variable for testing','kg',1,0,0, 15,'Testvariable3','Testvariable 3: Another variable for testing','kg',1,0,0, 16,'Op_en1900','Pollutant health risk due to the consumption of salmon','avoided cases/a',1,1900,1, 17,'Op_en1903','Persistent pollutant concentrations in salmon','µg/kg',1,1903,1, 18,'Op_en1905','Exposure to persistent pollutants due to salmon in the population of the Western Europe','µg/kg/d',1,1905,1, 19,'Op_en1906','Dose-response function of persistent pollutants','(mg/kg/d)-1',1,1906,1, 20,'Op_en1907','Omega-3 content in salmon','g/g',1,1907,1, 21,'Op_en1908','Omega-3 intake due to salmon in the population of the Western Europe','g/d',1,1908,1, 22,'Op_en1910','Total mortality in the Western Europe','cases/a',1,1910,1, 23,'Op_en1911','Cardiovascular mortality in the Western Europe','cases/a',1,1911,1, 24,'Op_en1912','Cardiovascular effects of omega-3 in salmon in teh Western Europe','avoided cases/a',1,1912,1, 25,'Op_en1898','Recommendation for consumption of farmed salmon','-',1,1898,1, 26,'Op_en1899','Pollutant concentration limits for fish feed','-',1,1899,1, 27,'Op_en1902','Persistent pollutant concentrations in fish feed','fraction',1,1902,1, 28,'Op_en1904','Salmon intake in the population of the Western Europe','g/d',1,1904,1, 29,'Op_en1909','ERF of omega-3 fatty acids on cardiovascular effects','1/(g/d)',1,1909,1, 30,'Op_en2556','Personal exposures to volatile organic compounds in Germany','ug/m^3',1,2556,1, 31,'Op_en2406','Excess cases of iMetHb in England and Wales','number',1,2406,1, 33,'Decision','Possible range of decisions for a single decision-maker','-',2,2496,1, 34,'Health_impact','Health impact','',2,2495,1, 35,'Time','Time','s or date',2,2497,1, 36,'Pollutant','Pollutant','-',2,2493,1, 37,'Spatial_location','Spatial location',' km or °',2,2498,1, 38,'Length','Length','km',2,2498,1, 39,'Non_health_impact','Non-health impact','-',2,2500,1, 40,'Period','Period','s',2,2497,1, 41,'Emission_source','Emission source','-',2,2492,1, 42,'Environ_compartment','Environmental compartment','-',2,2490,1, 43,'Vehicle_type','Vehicle type','-',2,0,0, 44,'Person_or_group','Person or group','-',2,2499,1, 45,'Transport_mode','Transport mode','-',2,0,0, 46,'Cost_type','Cost type','-',2,0,0, 47,'Composite_fraction','Composite fraction','fraction',2,0,0, 48,'Age','Age','a',2,2497,1, 49,'Municipality_fin','Municipalities in Finland','-',2,2498,1, 51,'Food_source','The method for food production','-',2,0,0, 52,'Feed_pollutant','Decision about fish feed','-',2,0,0, 53,'Salmon_recomm','Decision about samon consumption recommendation','-',2,0,0, 32,'0','No dimension has been identified','-',2,0,0, 54,'Parameter','Statistical and other parameters of a variable','-',2,0,0, 55,'Salmon_decision','','',6,0,0, 56,'Hma_area','','',6,0,0, 57,'Hma_region','','',6,0,0, 58,'Hma_zone','','',6,0,0, 59,'Year_1','','',6,0,0, 60,'Op_en2665','Cause of death 1','ICD-10',6,2665,1, 61,'Year_2','','',6,0,0, 62,'Cause_of_death_2','','',6,0,0, 63,'Length_1','','',6,0,0, 70,'Output_1','','',6,0,0, 65,'Period_1','','',6,0,0, 86,'Run','','',6,0,0, 71,'Vehicle_noch','','',6,0,0, 72,'Stakeholder_1','','',6,0,0, 73,'Mode1','','',6,0,0, 74,'Cost_structure_1','','',6,0,0, 75,'Comp_fr_1','','',6,0,0, 76,'Age1','','',6,0,0, 77,'Municipality_fin1','','',6,0,0, 82,'Year3','','',6,0,0, 81,'Recommendation1','','',6,0,0, 80,'Reg_poll','','',6,0,0, 79,'Salmon1','','',6,0,0, 78,'Pollutant1','','',6,0,0, 83,'H1899','','',6,0,0, 84,'H1898','','',6,0,0, 85,'Cause_of_death3','','',6,0,0, 87,'Condb_compartment1','','',6,0,0, 88,'Condb_location1','','',6,0,0, 89,'Condb_agent1','','',6,0,0, 90,'Condb_param1','','',6,0,0, 91,'Condb_agent2','','',6,0,0, 92,'Vehicle_1','','',6,0,0, 93,'Op_en2672','','',6,0,0, 94,'94','Analytica','',9,0,0, 95,'95','Analytica 4.1.0.9','',9,0,0, 97,'97','Analytica 4.1.0.9, CompositeTraffic_1_0_6.ana v. 11:47, 1000 iterations','',9,0,0, 99,'99','Analytica 4.1.0.9, RDB connection.ANA, 100 iterations','',9,0,0, 100,'100','RDB connection.ANA v. 1.9.2008, Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 100','',9,0,0, 101,'101','RDB connection.ANA v. 2.9.2008. Test data only., Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 100','',9,0,0, 102,'102','RDB connection.ANA v. 3.9.2008 b. Test data only., Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 100','',9,0,0, 103,'103','Farmed salmon.ANA 10:36, 31 December 2007, RDB connection.ANA 13:58, 3 September 2008, Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 1000','',9,0,0, 104,'104','Farmed salmon.ANA 10:36, 31 December 2007, RDB connection.ANA 13:58, 3 September 2008, Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 1000','',9,0,0, 105,'105','Farmed salmon.ANA 10:36, 31 December 2007, RDB connection.ANA 13:58, 3 September 2008, Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 1000','',9,0,0, 106,'106','Farmed salmon.ANA 10:36, 31 December 2007, RDB connection.ANA 13:58, 3 September 2008, Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 10','',9,0,0, 107,'107','Farmed salmon.ANA 8.9.2008, RDB connection.ANA 8.9.2008, Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 10','',9,0,0, 108,'108','Farmed salmon.ANA 8.9.2008, RDB connection.ANA 8.9.2008, Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 1000','',9,0,0, 98,'98','Test','',9,0,0, 109,'109',' CompositeTraffic_1_0_6.ANA 16.9.2008, RDB connection.ANA 16.9.2008, Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 10','',9,0,0, 110,'110',' CompositeTraffic_1_0_6.ANA 16.9.2008, RDB connection.ANA 16.9.2008, Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 1000','',9,0,0, 111,'111','RDB connection.ANA 16.9.2008, Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 100','',9,0,0, 112,'112','RDB connection.ANA 9.10.2008, Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 100','',9,0,0, 113,'113','RDB connection.ANA 9.10.2008, Edition: Enterprise, Platform: Windows, Version: 40100, Samplesize: 10','',9,0,0, 114,'Op_en1896','Benefit-risk assessment on farmed salmon','',4,1896,1, 130,'Op_en2705','Pollutant','-',6,2705,1, 131,'Op_en2706','Salmon type','-',6,2706,1, 133,'Op_en2707','Cause of death3','ICD-10',6,2707,1, 135,'Op_en2708','Year3','year',6,2708,1, 137,'Op_en2694','Testrun 1: Analytica Enterprise, (Windows), Version: 40100, Samplesize: 10','',9,2694,1, 159,'Op_eni1896','Benefit-risk assessment of farmed salmon','',4,0,1, 160,'Op_eni2694','Testrun 1: Analytica Enterprise, (Windows), Version: 40100, Samplesize: 10','',9,0,1, 183,'Op_eni2695','Testrun 2: Analytica Enterprise, (Windows), Version: 40100, Samplesize: 1000','',9,0,1, 184,'Op_en2778','Mortality in Finland','# or 1/100000 py',1,2778,1, 185,'Op_en2780','Sex','-',6,2780,1, 186,'Op_en2779','Diagnosis1','-',6,2779,1, 187,'Op_en2784','Units1','-',6,2784,1, 188,'Op_en2781','Age group1','a',6,2781,1, 189,'Op_en2785 ','Country1','-',6,2785,1, 191,'Op_en2695','Testrun 2: Analytica Enterprise, (Windows), Version: 40100, Samplesize: 100','',9,2695,1, 192,'Op_en2811','Morbidity data for Europe','1/100000 a',1,2811,1, 193,'Op_en2812 ','Age2','a',6,2812,1, 194,'Op_en2813','Country2','-',6,2813,1, 195,'Op_en2814','Year2','a',6,2814,1, 196,'Op_en2815 ','Sex2','-',6,2815,1, 207,'Op_en2835','Diagnosis2','ICD-10',6,2835,1, 203,'203','Testrun 2: Analytica Enterprise, (Windows), Version: 40100, Samplesize: 100','',9,2817,1, 210,'Op_en2495','Health outcome1','-',6,2495,1 ) 280,48,1 48,13 1,1,1,1,1,1,0,0,0,0 2,378,21,493,501 2,152,162,1057,343,0,MIDM 2,573,21,700,421,0,MIDM 39325,65535,39321 [O_j,O_i] [O_j,O_i] ['H1991'] [Self,1,Sys_localindex('I'),1,Sys_localindex('J'),1] This node checks the variables listed in Var_for_rdb and makes an index of those that are NOT found in the result database. This is then used as an index in Inp_var for adding variable information. Table(S_i,S_j)( 1,33,1, 2,37,1, 5,35,1, 6,34,1, 9,38,1, 10,39,1, 11,40,1, 12,32,1, 13,43,1, 14,44,1, 15,45,1, 16,46,1, 17,47,1, 18,48,1, 19,49,1, 23,51,1, 24,36,1, 28,42,1, 31,54,1, 35,114,3, 38,137,9, 37,114,4, 39,159,3, 41,160,9, 44,183,9, 47,191,9, 46,184,4, 45,184,3 ) 280,72,1 48,13 1,1,1,1,1,1,0,0,0,0 2,378,21,493,501 2,529,143,700,421,0,MIDM 39325,65535,39321 [S_j,S_i] [S_i,S_j] ['H1991'] [Self,1,Sys_localindex('I'),1,Sys_localindex('J'),1] This node checks the variables listed in Var_for_rdb and makes an index of those that are NOT found in the result database. This is then used as an index in Inp_var for adding variable information. Table(It_i,It_j)( 1,1,55,0, 2,2,56,0, 3,2,57,0, 4,2,58,0, 5,5,59,0, 6,6,60,0, 7,5,61,0, 8,6,62,0, 9,9,63,0, 10,11,65,0, 11,10,70,0, 12,13,71,0, 13,14,72,0, 14,15,73,0, 15,16,74,0, 16,17,75,0, 17,18,76,0, 18,19,77,0, 19,24,78,0, 20,23,79,0, 21,1,80,0, 22,1,81,0, 23,5,82,0, 24,1,83,0, 25,1,84,0, 26,6,85,0, 27,12,86,0, 28,28,87,0, 29,2,88,0, 30,24,89,0, 31,31,90,0, 32,24,91,0, 33,13,92,0, 34,2,93,0, 35,35,28,0 ) 280,120,1 48,13 1,1,1,1,1,1,0,0,0,0 2,378,21,493,501 2,529,143,700,421,0,MIDM 39325,65535,39321 [It_j,It_i] [It_i,It_j] ['H1991'] [Self,1,Sys_localindex('I'),1,Sys_localindex('J'),1] DO NOT REMOVE THIS NODE. It is needed for computing the Objects node. ktluser 29. Decta 2008 21:51 48,24 168,224,1 52,12 1,11,11,550,300,17 - ['Finland'] 168,280,1 48,12 Op_en2785 ['M','F'] 296,216,1 48,12 1,104,114,416,303,0,MIDM ['Male','Female'] var a:= splittext(readtextfile('c:\temp\morticd10.csv'),chr(10)); index itemp:= 1..size(a); a:= slice(a,itemp); index jtemp:= 1..39; a {a:= for x:= itemp do ( array(jtemp, splittext(a[itemp=x],','))); index j:= a[@itemp=1]; index i:= 1..(size(itemp)-1); a[@jtemp=@j, @itemp=i+1]} 288,144,1 48,24 2,56,66,416,303,0,MIDM [Sys_localindex('I'),Sys_localindex('J')] 1..26 416,144,1 48,12 [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26] 1..206 416,168,1 48,12 [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206] http://www.who.int/whosis/database/mort/table1.cfm Data: Mortality Finland 2006 Table(In1,In2)( 'Table 1:','Country:','Year:',' ','ICD Codes','-’','-’','-’','A00-B99’','A00-B99’','A01’','A01’','A00; A02-A09’','A00; A02-A09’','A15-A16’','A15-A16’','A17-A19’','A17-A19’','A37’','A37’','A39’','A39’','A35’','A35’','A40-A41’','A40-A41’','A20-A32; A36; A38; A42-49’','A20-A32; A36; A38; A42-49’','B05’','B05’','B20-B24’','B20-B24’','A70-A74; A80-B34; ¬B05; ¬B20-B24’','A70-A74; A80-B34; ¬B05; ¬B20-B24’','B50-B54’','B50-B54’','A75-A79; B55-B57; B60; B64’','A75-A79; B55-B57; B60; B64’','A50-A64’','A50-A64’','A65-A69; B35-B49; B58; B59; B65-B99’','A65-A69; B35-B49; B58; B59; B65-B99’','C00-C97’','C00-C97’','C00-C14’','C00-C14’','C15’','C15’','C16’','C16’','C18’','C18’','C19-C21’','C19-C21’','C22’','C22’','C32’','C32’','C33-C34’','C33-C34’','C50’','C50’','C53’','C54-C55’','C61’','C67’','C67’','C17; C23-C31; C37-C49; C51; C52; C56-C60; C62-C66; C68-C80; C97’','C17; C23-C31; C37-C49; C51; C52; C56-C60; C62-C66; C68-C80; C97’','C91-C95’','C91-C95’','C81-C90; C96’','C81-C90; C96’','D00-D48’','D00-D48’','E10-E14’','E10-E14’','E00-E07; E15-E34; E65-E68; E70-E88’','E00-E07; E15-E34; E65-E68; E70-E88’','E41-E46’','E41-E46’','E40; E50-E64’','E40; E50-E64’','D50-D64’','D50-D64’','D65-D89’','D65-D89’','F01-F99’','F01-F99’','G00; G03’','G00; G03’','G35’','G35’','G40-G41’','G40-G41’','G04-G31; G36-G37; G43-H95’','G04-G31; G36-G37; G43-H95’','I00-I99’','I00-I99’','I00-I02’','I00-I02’','I05-I09’','I05-I09’','I10-I13’','I10-I13’','I21; I22’','I21; I22’','I20; I24; I25’','I20; I24; I25’','I26-I51’','I26-I51’','I60-I69’','I60-I69’','I70’','I70’','I71-I78’','I71-I78’','I80-I82’','I80-I82’','I83-I99’','I83-I99’','J00-J06’','J00-J06’','J20-J21’','J20-J21’','J12-J18’','J12-J18’','J10-J11’','J10-J11’','J40-J46’','J40-J46’','J22; J30-J39; J47-J98’','J22; J30-J39; J47-J98’','K25-K27’','K25-K27’','K35-K38’','K35-K38’','K40-K46;K56’','K40-K46;K56’','K70;K73-K74;K76’','K70;K73-K74;K76’','K00-K22; K28-K31; K50-K55; K57-K66; K71; K72; K75; K80-K92’','K00-K22; K28-K31; K50-K55; K57-K66; K71; K72; K75; K80-K92’','N00-N07; N13-N19’','N00-N07; N13-N19’','N10-N12’','N10-N12’','N40’','N20-N39; N41-N98’','N20-N39; N41-N98’','O00-O07’','O20; O46; O67; O72’','O13-O16; O21’','O85-O92; A34’','O10-O12; O22-O75; O95-O97; minus (O46; O67;O72)’','O98-O99’','L00-L98’','L00-L98’','M00-M99’','M00-M99’','Q03;Q05’','Q03;Q05’','Q20-Q28’','Q20-Q28’','Q00-Q02; Q04; Q06-Q18; Q30-Q99’','Q00-Q02; Q04; Q06-Q18; Q30-Q99’','P10-P15’','P10-P15’','P00-P08; P20-P96; A33’','P00-P08; P20-P96; A33’','R54’','R54’','R00-R53; R55-R99’','R00-R53; R55-R99’','V01-X59; Y40-Y86; Y88’','V01-X59; Y40-Y86; Y88’','V02-V04; V09; V12-V14; V19-V79; V86-V89’','V02-V04; V09; V12-V14; V19-V79; V86-V89’','V01; V05-V06; V10; V11; V15-V18; V80-V85; V90-V99’','V01; V05-V06; V10; V11; V15-V18; V80-V85; V90-V99’','X40-X49’','X40-X49’','W00-W19’','W00-W19’','X00-X09’','X00-X09’','W65-W74’','W65-W74’','W24-W31’','W24-W31’','W32-W34’','W32-W34’','W20-W23; W35-W64; W75-W99; X10-X39; X50-X59; Y85; Y86’','W20-W23; W35-W64; W75-W99; X10-X39; X50-X59; Y85; Y86’','Y40-Y84; Y88’','Y40-Y84; Y88’','X60-X84’','X60-X84’','X85-Y09’','X85-Y09’','Y10-Y36; Y87; Y89’','Y10-Y36; Y87; Y89’',' ','Demographic Data',,, ' Numbers of registered deaths','Finland',2006,,'Cause Groupings','All causes','All causes','All causes','Infectious and parasitic diseases','Infectious and parasitic diseases','Typhoid and paratyphoid fever','Typhoid and paratyphoid fever','Other intestinal infectious diseases','Other intestinal infectious diseases','Tuberculosis of respiratory system','Tuberculosis of respiratory system','Tuberculosis other forms','Tuberculosis other forms','Whooping cough','Whooping cough','Meningococcal infection','Meningococcal infection','Tetanus','Tetanus','Septicaemia','Septicaemia','Other bacterial diseases','Other bacterial diseases','Measles','Measles','HIV disease','HIV disease','Other viral diseases','Other viral diseases','Malaria','Malaria','Other arthropod-borne diseases','Other arthropod-borne diseases','Sexually transmitted diseases','Sexually transmitted diseases','Other infectious and parasitic diseases','Other infectious and parasitic diseases','Malignant neoplasms','Malignant neoplasms','Malignant neoplasm of lip oral cavity and pharynx','Malignant neoplasm of lip oral cavity and pharynx','Malignant neoplasm of oesophagus','Malignant neoplasm of oesophagus','Malignant neoplasm of stomach','Malignant neoplasm of stomach','Malignant neoplasm of colon','Malignant neoplasm of colon','Malignant neoplasm of rectum rectosigmoid junction and anus','Malignant neoplasm of rectum rectosigmoid junction and anus','Malignant neoplasm of liver','Malignant neoplasm of liver','Malignant neoplasm of larynx','Malignant neoplasm of larynx','Malignant neoplasm of trachea bronchus and lung','Malignant neoplasm of trachea bronchus and lung','Malignant neoplasm of breast','Malignant neoplasm of breast','Malignant neoplasm of cervix uteri','Malignant neoplasm of uterus other and unspecified','Malignant neoplasm of prostate','Malignant neoplasm of bladder','Malignant neoplasm of bladder','Malignant neoplasm of other sites','Malignant neoplasm of other sites','Leukaemia','Leukaemia','Other malignant neoplasms of lymphoid and haematopoietic and related tissue','Other malignant neoplasms of lymphoid and haematopoietic and related tissue','Benign neoplasm other and unspecified neoplasm','Benign neoplasm other and unspecified neoplasm','Diabetes mellitus','Diabetes mellitus','Other endocrine and metabolic diseases','Other endocrine and metabolic diseases','Malnutrition','Malnutrition','Other nutritional deficiencies','Other nutritional deficiencies','Anaemias','Anaemias','Other diseases of blood and blood-forming organs','Other diseases of blood and blood-forming organs','Mental disorders','Mental disorders','Meningitis','Meningitis','Multiple sclerosis','Multiple sclerosis','Epilepsy','Epilepsy','Other diseases of the nervous system and sense organs','Other diseases of the nervous system and sense organs','Diseases of the circulatory system','Diseases of the circulatory system','Acute rheumatic fever','Acute rheumatic fever','Chronic rheumatic heart disease','Chronic rheumatic heart disease','Hypertensive disease','Hypertensive disease','Acute myocardial infarction','Acute myocardial infarction','Other ischaemic heart diseases','Other ischaemic heart diseases','Diseases of pulmonary circulation and other forms of heart disease','Diseases of pulmonary circulation and other forms of heart disease','Cerebrovascular disease','Cerebrovascular disease','Atherosclerosis','Atherosclerosis','Embolism thrombosis and other diseases of arteries arterioles and capillaries','Embolism thrombosis and other diseases of arteries arterioles and capillaries','Phlebitis thrombophlebitis venous embolism and thrombosis','Phlebitis thrombophlebitis venous embolism and thrombosis','Other diseases of the circulatory system','Other diseases of the circulatory system','Acute upper respiratory infection','Acute upper respiratory infection','Acute bronchitis and bronchiolitis','Acute bronchitis and bronchiolitis','Pneumonia','Pneumonia','Influenza','Influenza','Bronchitis chronic and unspecified emphysema and asthma','Bronchitis chronic and unspecified emphysema and asthma','Other diseases of the respiratory system','Other diseases of the respiratory system','Ulcer of stomach and duodenum','Ulcer of stomach and duodenum','Appendicitis','Appendicitis','Hernia of abdominal cavity and intestinal obstruction','Hernia of abdominal cavity and intestinal obstruction','Chronic liver disease and cirrhosis','Chronic liver disease and cirrhosis','Other diseases of the digestive system','Other diseases of the digestive system','Nephritis nephrotic syndrome and nephrosis','Nephritis nephrotic syndrome and nephrosis','Infections of kidney','Infections of kidney','Hyperplasia of prostate','Other diseases of the genitourinary system','Other diseases of the genitourinary system','Abortion','Haemorrhage of pregnancy and childbirth','Toxaemia of pregnancy','Complications of the puerperium','Other direct obstetric causes','Indirect obstetric causes','Diseases of skin and subcutaneous tissue','Diseases of skin and subcutaneous tissue','Diseases of the musculoskeletal system and connective tissue','Diseases of the musculoskeletal system and connective tissue','Spina bifida and hydrocephalus','Spina bifida and hydrocephalus','Congenital anomalies of the circulatory system','Congenital anomalies of the circulatory system','Other congenital anomalies','Other congenital anomalies','Birth trauma','Birth trauma','Other conditions originating in the perinatal period','Other conditions originating in the perinatal period','Senility','Senility','Signs symptoms and other ill-defined conditions','Signs symptoms and other ill-defined conditions','Accidents and adverse effects','Accidents and adverse effects','Motor vehicle traffic accidents','Motor vehicle traffic accidents','Other transport accidents','Other transport accidents','Accidental poisoning','Accidental poisoning','Accidental falls','Accidental falls','Accidents caused by fire and flames','Accidents caused by fire and flames','Accidental drowning and submersion','Accidental drowning and submersion','Accidents caused by machinery and by cutting and piercing instruments','Accidents caused by machinery and by cutting and piercing instruments','Accidents caused by firearm missile','Accidents caused by firearm missile','All other accidents including late effects','All other accidents including late effects','Drugs medicaments causing adverse effects in therapeutic use','Drugs medicaments causing adverse effects in therapeutic use','Suicide and self- inflicted injury','Suicide and self- inflicted injury','Homicide and injury purposely inflicted by other persons','Homicide and injury purposely inflicted by other persons','Other external causes','Other external causes',,'Live births',30.005K,28.835K, ,,,,'Sex','M','F','U','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','F','F','M','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','M','F','F','F','F','F','F','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F','M','F',,'Sex','M','F', ,,,,'All Ages',24.335K,23.771K,0,154,181,0,0,18,26,9,5,4,3,0,0,3,2,0,0,60,62,19,27,0,0,6,3,11,12,0,0,0,0,0,2,24,39,5738,5039,87,58,110,81,308,218,306,324,239,192,244,177,34,7,1464,541,4,856,57,140,814,162,65,1434,1809,178,147,354,367,115,154,241,232,48,63,2,0,4,2,5,19,19,14,831,1854,9,6,28,35,57,34,1185,1847,9576,10.404K,0,0,14,36,188,351,2876,2451,3189,3217,1011,1140,1785,2753,56,125,387,239,65,87,5,5,1,0,2,5,314,304,3,11,848,352,172,114,103,111,6,4,101,145,783,293,392,471,69,91,46,146,26,24,68,0,1,0,1,1,1,21,13,84,203,3,6,27,32,56,59,0,0,43,30,2,8,184,93,2069,1014,207,86,134,16,644,217,642,536,83,26,105,30,15,0,4,0,234,103,1,0,802,259,75,30,67,21,,'All Ages',2.578046M,2.688222M, ,,,,' ',943.9,884.3,,6,6.7,0,0,0.7,1,0.3,0.2,0.2,0.1,0,0,0.1,0.1,0,0,2.3,2.3,0.7,1,0,0,0.2,0.1,0.4,0.4,0,0,0,0,0,0.1,0.9,1.5,222.6,187.4,3.4,2.2,4.3,3,11.9,8.1,11.9,12.1,9.300000000000001,7.1,9.5,6.6,1.3,0.3,56.8,20.1,0.2,31.8,2.1,5.2,31.6,6.3,2.4,55.6,67.3,6.9,5.5,13.7,13.7,4.5,5.7,9.300000000000001,8.6,1.9,2.3,0.1,0,0.2,0.1,0.2,0.7,0.7,0.5,32.2,69,0.3,0.2,1.1,1.3,2.2,1.3,46,68.7,371.4,387,0,0,0.5,1.3,7.3,13.1,111.6,91.2,123.7,119.7,39.2,42.4,69.2,102.4,2.2,4.6,15,8.9,2.5,3.2,0.2,0.2,0,0,0.1,0.2,12.2,11.3,0.1,0.4,32.9,13.1,6.7,4.2,4,4.1,0.2,0.1,3.9,5.4,30.4,10.9,15.2,17.5,2.7,3.4,1.8,5.4,1,0.9,2.5,0,0,0,0,0,0,0.8,0.5,3.3,7.6,0.1,0.2,1,1.2,2.2,2.2,0,0,1.7,1.1,0.1,0.3,7.1,3.5,80.3,37.7,8,3.2,5.2,0.6,25,8.1,24.9,19.9,3.2,1,4.1,1.1,0.6,0,0.2,0,9.1,3.8,0,0,31.1,9.6,2.9,1.1,2.6,0.8,,,,, ,,,,'Under 1',97,71,0,2,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,2,1,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,12,5,19,20,0,0,43,29,0,0,11,4,2,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,1,,'Under 1',29.679K,28.585K, ,,,,' ',323.3,246.2,,6.7,3.5,0,0,3.3,0,0,0,0,0,0,0,0,0,0,0,3.3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.5,0,6.9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.5,0,3.5,0,0,0,3.5,0,0,10,6.9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.3,3.5,3.3,6.9,3.3,3.5,0,0,0,0,0,0,0,0,0,0,3.3,3.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.5,40,17.3,63.3,69.40000000000001,0,0,143.3,100.6,0,0,36.7,13.9,6.7,3.5,0,0,0,0,0,0,0,0,3.3,0,0,0,0,0,0,0,3.3,3.5,0,0,0,0,3.3,0,0,3.5,,,,, ,,,,'1 to 4',12,15,0,2,3,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,3,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,3,0,0,0,0,0,0,1,0,3,3,0,0,1,1,0,0,0,0,1,0,1,2,0,0,0,0,0,0,0,0,0,0,1,0,0,0,,'1 to 4',116.692K,111.585K, ,,,,' ',10.3,13.4,,1.7,2.7,0,0,0.9,0,0,0,0,0,0,0,0,0.9,0,0,0,0.9,0,0,0,0,0,0,0.9,0.9,0,0,0,0,0,0,0,0,2.6,1.8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.7,0.9,0.9,0.9,0,0,0,0,0,0,0.9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.8,0.9,2.7,0,0,0,0,0,0,0.9,0,2.6,2.7,0,0,0.9,0.9,0,0,0,0,0.9,0,0.9,1.8,0,0,0,0,0,0,0,0,0,0,0.9,0,0,0,,,,, ,,,,'5 to 14',43,31,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,8,8,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,4,4,4,0,0,1,0,0,0,5,4,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,2,3,4,1,0,0,0,0,0,0,0,0,0,0,3,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,2,4,1,0,0,0,0,0,0,2,0,8,6,2,3,2,1,0,0,1,0,0,0,0,1,0,0,0,0,3,1,0,0,3,1,0,1,1,0,,'5 to 14',315.017K,302.489K, ,,,,' ',13.7,10.2,,0.3,0.3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.3,0,0,0,0,0,0,0,0,0.3,2.5,2.6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.3,1.3,1.3,1.3,0,0,0.3,0,0,0,1.6,1.3,0,0,0,0,0,0.3,0,0,0.3,0,0,0,0,0,0,0.3,0.6,1,1.3,0.3,0,0,0,0,0,0,0,0,0,0,1,0,0.3,0.3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.3,0,0,0,0,0,0,0,0,0,0,0,0.3,0,0,0,0,0,0,0,0.3,0,0,0,0,0,0,0,0,0,0,0,0,0.3,0.7,1.3,0.3,0,0,0,0,0,0,0.6,0,2.5,2,0.6,1,0.6,0.3,0,0,0.3,0,0,0,0,0.3,0,0,0,0,1,0.3,0,0,1,0.3,0,0.3,0.3,0,,,,, ,,,,'15 to 24',317,81,0,2,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,12,13,0,0,0,0,0,0,0,0,0,0,1,3,0,0,0,0,0,0,0,0,0,0,0,5,4,4,5,2,1,1,0,1,0,2,5,0,0,0,0,0,0,0,0,2,1,0,0,0,0,2,3,7,1,10,7,0,0,0,0,0,0,0,0,0,0,7,1,2,3,0,0,1,1,0,2,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,2,2,0,0,0,0,0,0,5,2,143,21,61,13,11,1,44,5,6,1,5,0,6,0,2,0,1,0,7,1,0,0,108,19,10,1,8,3,,'15 to 24',335.416K,320.699K, ,,,,' ',94.5,25.3,,0.6,0,0,0,0,0,0,0,0,0,0,0,0.3,0,0,0,0.3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.6,4.1,0,0,0,0,0,0,0,0,0,0,0.3,0.9,0,0,0,0,0,0,0,0,0,0,0,1.5,1.2,1.2,1.6,0.6,0.3,0.3,0,0.3,0,0.6,1.6,0,0,0,0,0,0,0,0,0.6,0.3,0,0,0,0,0.6,0.9,2.1,0.3,3,2.2,0,0,0,0,0,0,0,0,0,0,2.1,0.3,0.6,0.9,0,0,0.3,0.3,0,0.6,0,0,0,0,0,0,0.3,0.3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.3,0,0,0,0,0,0,0,0,0,0,0,0.3,0.3,0,0.6,0.6,0,0,0,0,0,0,1.5,0.6,42.6,6.5,18.2,4.1,3.3,0.3,13.1,1.6,1.8,0.3,1.5,0,1.8,0,0.6,0,0.3,0,2.1,0.3,0,0,32.2,5.9,3,0.3,2.4,0.9,,,,, ,,,,'25 to 34',354,112,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,24,19,0,0,1,0,0,3,0,0,2,0,0,0,0,0,0,1,0,5,2,0,0,0,0,12,8,4,0,5,0,0,0,3,4,1,0,0,0,0,0,0,0,1,0,3,5,0,0,0,0,4,1,5,3,26,13,0,0,0,0,1,0,7,0,1,1,9,3,5,8,0,0,3,1,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,1,0,9,1,6,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,1,0,1,1,0,5,3,0,0,0,0,0,0,7,6,118,11,30,7,8,0,58,4,5,0,2,0,8,0,2,0,1,0,4,0,0,0,113,33,10,6,10,2,,'25 to 34',329.601K,313.759K, ,,,,' ',107.4,35.7,,0.9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.3,0,0,0,0,0,0.6,0,0,0,0,0,0,0,0,0,0,0,7.3,6.1,0,0,0.3,0,0,1,0,0,0.6,0,0,0,0,0,0,0.3,0,1.6,0.6,0,0,0,0,3.6,2.5,1.2,0,1.5,0,0,0,0.9,1.3,0.3,0,0,0,0,0,0,0,0.3,0,0.9,1.6,0,0,0,0,1.2,0.3,1.5,1,7.9,4.1,0,0,0,0,0.3,0,2.1,0,0.3,0.3,2.7,1,1.5,2.5,0,0,0.9,0.3,0,0,0,0,0,0,0,0,0.6,0.3,0,0,0,0,0,0,0,0,0,0,0.3,0,2.7,0.3,1.8,0.3,0,0,0,0,0,0,0,0,0,0,0,0,0.3,0,0,0.6,0.3,0,0.3,0.3,0,1.5,1,0,0,0,0,0,0,2.1,1.9,35.8,3.5,9.1,2.2,2.4,0,17.6,1.3,1.5,0,0.6,0,2.4,0,0.6,0,0.3,0,1.2,0,0,0,34.3,10.5,3,1.9,3,0.6,,,,, ,,,,'35 to 44',746,315,0,6,4,0,0,1,0,0,0,1,1,0,0,0,0,0,0,3,2,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,76,101,3,1,4,1,7,6,7,3,2,2,2,1,0,0,8,3,0,41,5,1,0,3,0,33,32,5,3,2,2,3,1,17,4,4,2,0,0,0,0,0,1,1,0,21,4,0,1,2,3,9,3,6,4,124,38,0,0,0,0,1,1,27,3,14,4,44,7,29,22,0,0,5,0,3,1,1,0,0,0,0,1,6,1,0,0,0,3,3,0,1,2,0,1,1,1,56,13,15,2,1,0,0,0,0,0,0,0,1,0,1,1,0,1,0,3,2,0,0,2,5,1,1,0,0,0,1,0,0,14,6,219,61,21,9,15,2,104,31,34,4,9,6,17,3,1,0,0,0,18,6,0,0,130,39,13,6,11,1,,'35 to 44',365.461K,353.529K, ,,,,' 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54',2049,915,0,12,7,0,0,0,1,1,0,0,0,0,0,1,1,0,0,6,2,1,0,0,0,2,1,0,1,0,0,0,0,0,0,1,1,312,346,10,6,8,2,13,19,13,21,21,7,15,7,3,0,63,34,1,122,6,5,8,6,1,119,96,11,8,21,12,3,5,37,15,4,3,0,0,0,0,1,0,3,2,49,16,2,1,7,10,12,7,29,13,546,158,0,0,0,1,19,5,131,13,149,20,139,39,80,66,2,0,18,3,8,10,0,1,0,0,0,0,17,4,0,0,19,12,3,1,13,2,1,0,7,2,242,72,56,16,2,2,2,1,0,2,0,0,0,0,0,0,0,3,0,3,2,2,0,4,4,5,8,0,0,0,0,0,0,38,11,403,117,26,9,24,3,194,68,73,18,23,3,19,8,5,0,0,0,39,8,0,0,179,65,20,7,11,6,,'45 to 54',386.717K,383.119K, ,,,,' 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64',4009,1732,0,16,8,0,0,0,0,0,0,2,1,0,0,0,0,0,0,9,4,0,0,0,0,0,1,4,0,0,0,0,0,0,0,1,2,1096,790,33,8,35,11,81,25,50,34,50,22,45,25,7,2,322,110,2,185,11,25,55,17,5,317,270,20,14,62,43,10,9,39,19,7,10,1,0,1,2,1,1,3,1,50,18,6,2,9,9,14,7,62,73,1366,339,0,0,2,1,33,9,429,64,486,79,166,55,189,103,0,0,49,13,10,15,2,0,1,0,0,0,30,12,0,0,87,26,18,8,31,7,1,1,13,10,320,109,80,26,7,1,3,2,0,2,1,0,0,0,0,0,0,7,1,14,15,1,3,5,2,10,7,0,0,0,0,0,0,43,17,494,138,24,16,33,2,171,70,142,19,18,8,28,7,4,0,2,0,72,16,0,0,132,50,16,3,13,5,,'55 to 64',355.493K,363.495K, ,,,,' 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74',5166,2804,0,33,28,0,0,4,4,2,2,0,0,0,0,1,0,0,0,12,10,9,5,0,0,1,0,1,2,0,0,0,0,0,0,3,5,1615,1142,30,9,32,15,79,52,76,70,61,47,77,37,11,2,453,147,1,187,8,30,201,41,13,408,418,47,25,98,82,29,24,63,42,8,11,0,0,2,0,1,2,2,2,94,43,1,1,6,8,7,5,212,173,2100,875,0,0,3,5,32,25,638,232,753,224,172,109,358,231,11,5,118,33,14,10,1,1,0,0,0,0,42,17,0,0,187,66,51,12,22,11,3,1,19,13,122,54,62,51,10,10,5,5,6,2,5,0,0,0,0,0,0,2,2,20,28,0,0,1,5,6,7,0,0,0,0,0,0,40,16,288,113,18,15,21,2,64,24,112,40,17,5,13,4,1,0,0,0,42,23,0,0,93,28,3,3,9,1,,'65 to 74',208.581K,248.032K, ,,,,' ',2476.7,1130.5,,15.8,11.3,0,0,1.9,1.6,1,0.8,0,0,0,0,0.5,0,0,0,5.8,4,4.3,2,0,0,0.5,0,0.5,0.8,0,0,0,0,0,0,1.4,2,774.3,460.4,14.4,3.6,15.3,6,37.9,21,36.4,28.2,29.2,18.9,36.9,14.9,5.3,0.8,217.2,59.3,0.5,75.40000000000001,3.2,12.1,96.40000000000001,19.7,5.2,195.6,168.5,22.5,10.1,47,33.1,13.9,9.699999999999999,30.2,16.9,3.8,4.4,0,0,1,0,0.5,0.8,1,0.8,45.1,17.3,0.5,0.4,2.9,3.2,3.4,2,101.6,69.7,1006.8,352.8,0,0,1.4,2,15.3,10.1,305.9,93.5,361,90.3,82.5,43.9,171.6,93.09999999999999,5.3,2,56.6,13.3,6.7,4,0.5,0.4,0,0,0,0,20.1,6.9,0,0,89.7,26.6,24.5,4.8,10.5,4.4,1.4,0.4,9.1,5.2,58.5,21.8,29.7,20.6,4.8,4,2.4,2,2.9,1,2,0,0,0,0,0,0,1,0.8,9.6,11.3,0,0,0.5,2,2.9,2.8,0,0,0,0,0,0,19.2,6.5,138.1,45.6,8.6,6,10.1,0.8,30.7,9.699999999999999,53.7,16.1,8.199999999999999,2,6.2,1.6,0.5,0,0,0,20.1,9.300000000000001,0,0,44.6,11.3,1.4,1.2,4.3,0.4,,,,, ,,,,'Over 75',11.542K,17.695K,0,77,129,0,0,11,21,6,3,1,1,0,0,0,0,0,0,27,43,9,22,0,0,0,0,4,8,0,0,0,0,0,2,19,29,2592,2616,11,34,30,52,128,113,160,196,103,114,104,104,13,3,618,246,0,316,25,79,550,95,46,534,975,82,86,164,227,68,114,81,148,13,26,1,0,1,0,2,14,9,9,611,1767,0,1,4,5,8,6,861,1574,5399,8972,0,0,9,29,102,311,1644,2139,1786,2889,470,925,1121,2319,43,120,193,188,30,49,1,3,0,0,2,4,216,268,3,11,554,245,97,93,36,89,1,1,59,119,34,44,172,374,49,78,36,138,20,18,60,0,0,0,0,0,0,8,10,42,155,0,0,0,7,3,7,0,0,0,0,2,8,23,31,391,543,25,14,19,4,9,15,269,454,7,4,13,5,0,0,0,0,48,47,1,0,44,24,1,3,4,2,,'Over 75',135.396K,262.936K, ,,,,' ',8524.6,6729.8,,56.9,49.1,0,0,8.1,8,4.4,1.1,0.7,0.4,0,0,0,0,0,0,19.9,16.4,6.6,8.4,0,0,0,0,3,3,0,0,0,0,0,0.8,14,11,1914.4,994.9,8.1,12.9,22.2,19.8,94.5,43,118.2,74.5,76.09999999999999,43.4,76.8,39.6,9.6,1.1,456.4,93.59999999999999,0,120.2,9.5,30,406.2,70.2,17.5,394.4,370.8,60.6,32.7,121.1,86.3,50.2,43.4,59.8,56.3,9.6,9.9,0.7,0,0.7,0,1.5,5.3,6.6,3.4,451.3,672,0,0.4,3,1.9,5.9,2.3,635.9,598.6,3987.6,3412.2,0,0,6.6,11,75.3,118.3,1214.2,813.5,1319.1,1098.7,347.1,351.8,827.9,882,31.8,45.6,142.5,71.5,22.2,18.6,0.7,1.1,0,0,1.5,1.5,159.5,101.9,2.2,4.2,409.2,93.2,71.59999999999999,35.4,26.6,33.8,0.7,0.4,43.6,45.3,25.1,16.7,127,142.2,36.2,29.7,26.6,52.5,14.8,13.3,22.8,0,0,0,0,0,0,5.9,3.8,31,58.9,0,0,0,2.7,2.2,2.7,0,0,0,0,1.5,3,17,11.8,288.8,206.5,18.5,5.3,14,1.5,6.6,5.7,198.7,172.7,5.2,1.5,9.6,1.9,0,0,0,0,35.5,17.9,0.7,0,32.5,9.1,0.7,1.1,3,0.8,,,,, ,,,,'Age not specified',0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,'Age not specified',0,0 ) 416,112,1 48,24 2,110,29,1145,580,0,MIDM 2,22,41,740,402,0,MIDM 65535,52427,65534 [In1,In2] [In1,In2] # or 1/100000 py var a:= mortality_finland; index temp:= a[@in1=2]&'+'&a[@in1=3]; a:= a[@in2=@temp]; a:= a[temp=Diagnosis2&'+'&Sex1]; a:= a[@sex1=@sex]; var b:= array(Age_group2,sequence(4,24,2)); var c:= array(Age_group2,sequence(5,25,2)); a:= array(Health_outcome1,[a[@in1=b], a[@in1=c]]); if Country1=0 and Year4=0 then a else a 536,112,1 48,24 2,20,114,832,548,0,MIDM [Diagnosis2,Age_group2] [7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202] [Health_outcome1,1,Sex,2,Country1,1,Year4,1,Age_group2,1,Diagnosis2,1] Op_en2778 ICD-10 var a:= Mortality_finland[@in1=2, in2=6..202]; unique(a,a) 536,144,1 48,12 2,120,130,416,303,0,MIDM [Self,Sex] Op_en2835 a Five-year age groups with 1, 1-4, 75+, and not-specified groups. ['All Ages','Under 1','1 to 4','5 to 14','15 to 24','25 to 34','35 to 44','45 to 54','55 to 64','65 to 74','Over 75'] 536,168,1 48,12 2,102,90,476,346 1,264,274,416,303,0,MIDM Op_en2781 a [2006] 536,192,1 48,12 Op_en2708 - ['# deaths','Mortality'] 536,224,1 48,20 Op_en2495 ['# deaths','Mortality'] index temp:= mortality_finland[@in1=2]; var a:= mortality_finland[@in1=1, @in2=@temp]; a:= a[temp=diagnosis2] 416,224,1 48,24 2,200,210,787,582,0,MIDM Table(Health_outcome1)( 'Unit: number of cases','Unit: 1/100000 person-years') 288,280,1 48,24