Ruori

Tämä sivu on tietokide alatyypiltään arviointi. Sivutunniste: Op_fi5889
Moderaattori:Jouni (katso kaikki) Sivun edistymistä ei ole arvioitu. Arvostuksen määrää ei ole arvioitu (ks. peer review).
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Ruori on VN-TEAS-hanke, jossa arvioidaan erilaisia ruokaan liittyviä riskitekijöitä, niiden vähentämispotentiaalia ja niiden terveys- ja talousvaikutuksia.

Rajaus

Kysymys

Millaista tautitaakkaa Suomessa aiheuttavat Ruori-altisteet (tyydyttynyt rasva, vähäiset vihannekset, vähäiset hedelmät, liiallnen suola, dioksiinit, lyijy, toksoplasma, norovirus ja legionella?

Aikataulu ja käyttäjät

• Hanke alkoi 2018 ja loppuu 30.6.2019.
• Toteuttajina ovat Ruokavirasto, THL ja Helsingin yliopisto.
• Seuraavat skenaariot ovat tarkastelussa:

Vastaus

Vastaus kirjoitetaan tähän pian.

Perustelut

Data

Mitä kaikkea kuuluu vähäiseen hedelmien tai vihannesten syöntiin?

• Vähähedelmäinen ruokavalio: hedelmien kulutus alle 3 annosta päivässä (310 g yhteensä) (sisältää tuoreet, pakastetut, keitetyt, säilötyt ja kuivatut hedelmät mutta ei sisällä hedelmämehuja tai suolaan tai etikkaan säilöttyjä hedelmiä) http://www.healthdata.org/terms-defined. Diet low in fruits: Consumption of less than 3 servings (310 g total) of fruits per day (includes fresh, frozen, cooked, canned, or dried fruit but excludes fruit juices and salted or pickled fruits).
• Vähävihanneksinen ruokavalio: vihannesten kulutus alle 4 annosta (400 g yhteensä) (sisältää tuoreet, pakastetut, keitetyt, säilötyt ja kuivatut vihannekset mukaan lukien palkokasvit mutta ei sisällä suolaan tai etikkaan säilöttyjä vihanneksia eikä pähkinöitä, siemeniä tai tärkkelyspitoisia vihanneksia kuten perunaa tai maissia). Diet low in vegetables: Consumption of less than 4 servings (400 g total) of vegetables per day (includes fresh, frozen, cooked, canned, or dried vegetables including legumes but excluding salted or pickled, juices, nuts and seeds, and starchy vegetables such as potatoes or corn).

Luken tilastoista löytyy tietoja kalansyönnistä Suomessa. Järvikalaa ei ole eritelty, mutta muut kuin viljellyt ja merilajit ovat yhteensä 2.6 kg/a henkeä kohti. https://stat.luke.fi/en/fish-consumption-2017_en

Voiko DALYt muuntaa euroiksi, ja miten se tehdään?

• Drake ehdottaa globaalin arvon päättämistä DALYn hinnaksi, samaan tapaan kuin 1,25 dollarin alittava päivätulo on määritelty absoluuttiseksi köyhyydeksi. Tällöin kaikki tuota hintaa kustannustehokkaammat toimet kannattaisi tehdä joko kansallisin, tai jos se ei jostain syystä onnistu, kansainvälisin toimin. Hän ei kuitenkaan ehdota suuruutta tälle hinnalle.
• Brent on analysoinut implisiittisiä hintoja DALYlle Global Fund for AIDS, Tuberculosis, and Malaria -säätiön rahoituspäätöksistä. DALYn hinta näyttää olevan 6300 USD kaikille taudeille keskimäärin, ja 11900 USD erityisesti HIV/AIDSille. Globaalit luvut ovat toki pienemmät kuin mitä rikkaissa länsimaissa katsottaisiin aiheelliseksi käyttää.
• Erilaisista arvioinneista löytyy vaihtelevia lukuja yhden DALYn rahalliselle hinnalle. Esimerkiksi IOMin Shecan-projekti käytti arvoa 50393 €/menetetty elinvuosi, ja IGCB(N)-meluarviointiryhmä käyttää arvoa 60000 GBP/QALY (laatupainotettu elinvuosi) mutta samalla toteaa, että eri arvioinneissa arvot voivat vaihdella välillä 29000 - 130000 GBP/QALY.
• Berryn ja Flindellin mukaan Isossa-Britanniassa käytäntö on muodostunut sellaiseksi, että lääkkeet tai muut lääketieteelliset toimenpiteet saavat kansallisessa terveysjärjestelmässä helposti puollon, jos ne tuottavat yhden terveen elinvuoden alle 20000 GBP:n kustannuksilla. Tyypillisesti toteutetaan hankkeita tasolla 30000 GBP/QALY, mutta hankkeilta hinnaltaan yli 50000 GBP/QALY vaaditaan erityisiä perusteluja.
• Toisaalta Hammitt todistelee, että hinta per tilastollinen elämä (value per statistical life, VSL) ja hinta per DALY muuttuvat epälineaarisesti suhteessa toisiinsa, eikä näin ollen olisi mahdollista käyttää hyvinvointimuutoksen mittarina vakiolla kerrottua DALY-arvoa, ainakaan taloudellisen hyvinvointiteorian (economic welfare theory) puitteissa.

Pitoisuusanalyysien kustannukset (poistettu).

Trikiinin valvontakustannukset: Tämän artikkelin mukaan trikiinin DALYt ovat vain luokkaa 100 DALY/miljardi ihmistä, joten valvonta ei ole mielekästä.

Malliparametrit

Malliparametrit(various)

Obs	Response	Exposure_agent	Type	Subgroup	Unit	Result	Description
1	CHD death		BoD	Age:Female 25-69	DALY	9876 (9103 - 10784)	Z:\Projects\RUORI\tautitaakka\Rasvat\IHD_data_IHME.csv
2	CHD death		BoD	Age:Female 70+	DALY	42750 (41007 - 45909)	Z:\Projects\RUORI\tautitaakka\Rasvat\IHD_data_IHME.csv
3	CHD death		BoD	Age:Male 25-69	DALY	48851 (54035 - 46123)	Z:\Projects\RUORI\tautitaakka\Rasvat\IHD_data_IHME.csv
4	CHD death		BoD	Age:Male 70+	DALY	48150 (46327 - 51255)	Z:\Projects\RUORI\tautitaakka\Rasvat\IHD_data_IHME.csv
5	Diet high in sodium		BoD	Age:Total population	DALY	27670 (1310 - 66420)	IHME GBD2017
6	Diet low in fruits		BoD	Age:Total population	DALY	36050 (20570 - 54800)	IHME GBD2017 http://ghdx.healthdata.org/gbd-results-tool?params=gbd-api-2017-permalink/da3bde44e863adb438c5fb47a89942fb
7	Diet low in vegetables		BoD	Age:Total population	DALY	28440 (14190 - 45960)	IHME GBD2017
8	Liver cancer		BoD	Age:Age 25-64	DALY	2499 (2018 - 3115)	From IHME (2014)
9	Liver cancer		BoD	Age:Age 65-74	DALY	2745 (2245 - 3311)	From IHME (2014)
10	dummy		case burden	Age:Female 25-69	DALY /case	0	Needed for case_burden to cover all Ages
11	dummy		case burden	Age:Female 70+	DALY /case	0	Needed for case_burden to cover all Ages
12	dummy		case burden	Age:Male 25-69	DALY /case	0	Needed for case_burden to cover all Ages
13	dummy		case burden	Age:Male 70+	DALY /case	0	Needed for case_burden to cover all Ages
14	dummy		case burden	Age:Age 25-64	DALY /case	0	Needed for case_burden to cover all Ages
15	dummy		case burden	Age:Age 65-74	DALY /case	0	Needed for case_burden to cover all Ages
16	IQ loss		case burden	Age:Age 1	DALY /IQ	0.11 (0.06 - 0.16)	Arja used 0.013 but here we use Goherr value instead
17	Listeria		case burden	Age:Total population	DALY/case	10 (5 - 13.3)	WHO 2015 report, European values (Table A8.2)
18	Noro virus		case burden	Age:Total population	DALY/case	0.0015 - 0.0025	WHO 2015 report, European values (Table A8.2)
19	Toxoplasma gondii		case burden	Age:Age 0 (congenital)	DALY/case	7	WHO 2015 report, European values (Table A8.2)
20	Toxoplasma gondii		case burden	Age:Age 1+ (acquired)	DALY/case	0.05	WHO 2015 report, European values (Table A8.2)
21		Aflatoxin	exposure	Age:Age 25-64	ng /kg /d	0.85 - 1.14	Finravinto 2012
22		Aflatoxin	exposure	Age:Age 65-74	ng /kg /d	0.5 - 0.67	Finravinto 2012
23		Lead	exposure	Age:Age 1	ug /l	27.9	Measured as blood concentration. RASKURI, Z:\Projects\RUORI\tautitaakka\Lyijy\Lyijy_tautitaakkadata.xlsx
24		Saturated fat	exposure	Age:Age 25-69	E%	13.1 (12.9 - 13.4)	Finland, 2010 situation from Wang et al. Supplementary
25		Saturated fat	exposure	Age:Age 70+	E%	13.2 (12.8 - 13.6)	Finland, 2010 situation from Wang et al. Supplementary
26		Aflatoxin	frexposed	Age:Age 25-64	fraction	1	dummy variable
27		Aflatoxin	frexposed	Age:Age 65-74	fraction	1	dummy variable
28		Lead	frexposed	Age:Age 1	fraction	0.0657	Population exposed to lead over threshold: 3126. RASKURI, Z:\Projects\RUORI\tautitaakka\Lyijy\Lyijy_tautitaakkadata.xlsx
29	IQ loss		incidence	Age:Age 1	IQ /100000py	596000	On average, a population has ca. 6 IQ points per person below 100: mean(abs(rnorm(10000, 100,15)-100))/2
30	Listeria		incidence	Age:Total population	# /100000py	1.22	Tartuntatautirekisteri 2016: 66 kpl. WHO 2015 report, European values (Table A8.2): 0.2 (0.2 - 0.3)
31	Liver cancer		incidence	Age:Age 25-64	# /100000py	4.06	Finnish Cancer Registry, average 2011-2015
32	Liver cancer		incidence	Age:Age 65-74	# /100000py	26.16	Finnish Cancer Registry, average 2011-2015
33	Noro virus		incidence	Age:Total population	# /100000py	1652 (630 - 3294)	WHO 2015 report, European values (Table A8.2)
34	Toxoplasma gondii		incidence	Age:Age 0 (congenital)	# /100000py	0.3 (0.2 - 0.7)	WHO 2015 report, European values (Table A8.2)
35	Toxoplasma gondii		incidence	Age:Age 1+ (acquired)	# /100000py	119 (77 - 188)	WHO 2015 report, European values (Table A8.2)
36	Diet low in fruits	Fruits	PAF	Age:Total population	fraction	1	dummy variable
37	Listeria	Listeria	PAF	Age:Total population	fraction	1	dummy variable
38	Noro virus	Noro virus	PAF	Age:Total population	fraction	1	dummy variable
39	CHD death	Saturated fat	PAF	Age:Age 25-69	fraction	0.064 (0.050 - 0.078)	Finland, 2010 situation from Wang et al. Supplementary
40	CHD death	Saturated fat	PAF	Age:Age 70+	fraction	0.048 (0.033 - 0.063)	Finland, 2010 situation from Wang et al. Supplementary
41	Diet high in sodium	Sodium	PAF	Age:Total population	fraction	1	dummy variable
42	Toxoplasma gondii	Toxoplasma gondii	PAF	Age:Age 0 (congenital)	fraction	1	dummy variable
43	Toxoplasma gondii	Toxoplasma gondii	PAF	Age:Age 1+ (acquired)	fraction	1	dummy variable
44	Diet low in vegetables	Vegetables	PAF	Age:Total population	fraction	1	dummy variable
45			population	Age:Age 1		47577	Statistics Finland, 2018 http://stat.fi/til/synt/index.html
46			population	Age:Age 25-64	#	2835089	Statistics Finland?
47			population	Age:Age 65-74	#	615487	Statistics Finland?
48			population	Age:Total population	#	5471753	Statistics Finland?
49			population	Age:Age 0 (congenital)	#	47577	Statistics Finland?
50			population	Age:Age 1+ (acquired)	#	5471753	Statistics Finland?
51	Hepatitis		prevalence	Hepatitis:Hepatitis B-	fraction	0.005	TerveSuomi
52	Hepatitis		prevalence	Hepatitis:Hepatitis B+	fraction	0.995	TerveSuomi
53		Saturated fat	scenario exposure	Age:Male 18-24	E%	14.9	Finravinto 2017. Supplementary table 7.12. Average daily intake of saturated fats by gender and age.
54		Saturated fat	scenario exposure	Age:Male 25-44	E%	15.2	Finravinto 2017. Supplementary table 7.12. Average daily intake of saturated fats by gender and age.
55		Saturated fat	scenario exposure	Age:Male 45-64	E%	15.3	Finravinto 2017. Supplementary table 7.12. Average daily intake of saturated fats by gender and age.
56		Saturated fat	scenario exposure	Age:Male 65-74	E%	14.4	Finravinto 2017. Supplementary table 7.12. Average daily intake of saturated fats by gender and age.
57		Saturated fat	scenario exposure	Age:Female 18-24	E%	13.8	Finravinto 2017. Supplementary table 7.12. Average daily intake of saturated fats by gender and age.
58		Saturated fat	scenario exposure	Age:Female 25-44	E%	14.8	Finravinto 2017. Supplementary table 7.12. Average daily intake of saturated fats by gender and age.
59		Saturated fat	scenario exposure	Age:Female 45-64	E%	14.3	Finravinto 2017. Supplementary table 7.12. Average daily intake of saturated fats by gender and age.
60		Saturated fat	scenario exposure	Age:Female 65-74	E%	14.0	Finravinto 2017. Supplementary table 7.12. Average daily intake of saturated fats by gender and age.
61	IQ loss	Lead	threshold	Age:Age 1	ug /l	24	Lanphear et al 2005 https://doi.org/10.1289/ehp.7688 CHECK THRESHOLD
62	Liver cancer	Aflatoxin	UR	Hepatitis:Hepatitis B-	# /(ng /kg /d /100000py)	0.01 (0.002 - 0.03)	WHO Is this per year or per lifetime?
63	Liver cancer	Aflatoxin	UR	Hepatitis:Hepatitis B+	# /(ng /kg /d /100000py)	0.3 (0.01 - 0.5)	WHO Is this per year or per lifetime?
64	IQ loss	Lead	UR	Age:Age 1	IQ l /ug	0.039	Lanphear et al 2005 https://doi.org/10.1289/ehp.7688 CHECK THRESHOLD

Annos-vasteet on tässä vain näytillä, ja oikeat käyttöön tulevat luvut ovat sivulla op_en:ERFs of environmental pollutants.

Decisions(-)

Obs	Decision	Option	Variable	Cell	Change	Result	Description
1	Adjust	BAU	incidence		Multiply	0.00001	1/100000 py --> 1 py
2	Adjust	BAU	PAF		Identity	1	Scaled from Wang to Finravinto 2017: mean(15.2,15.3,14.8,14.0)/13.1
3	Adjust	BAU	PAF	Exposure_agent:Saturated fat	Multiply	1.132	Healthy people. Data from TerveSuomi
4	Hepatitis	Hepatitis B-	BoD		Multiply	0.995	Hepatitis B patients. Data from TerveSuomi
5	Hepatitis	Hepatitis B+	BoD		Multiply	0.005	1/100000py --> 1/py
6	Adjust	BAU	ERF		Identity	1	For completion
7	Adjust	BAU	ERF	Response:Liver cancer	Multiply	0.00001	For completion

CollapseMarginals(-)

Obs	Variable	Index	Probs	Function	Dummy	Description
1	BoD	incidenceSource,disabilityweightSource,populationSource,BoDSource		sum	1	Remove redundant
2	PAF	Unit, Exposure, Scaling,Exposcen, ER_function, ERFchoiceSource, exposureSource, bgexposureSource, BWSource, doseSource, thresholdSource, ERFSource, RRSource, frexposedSource, incidenceSource, InpPAFSource		sum	1	Remove redundant
3	case_burden	case_burdenSource		sum	1	Fill missing Ages
4	BoDattr	PAFSource, Hepatitis		sum	1	Remove redundant

Laskenta

Arviointimalli

• Malliajo 29.5.2019 [3]
• Malliajo 31.5.2019 [4]

+ Näytä koodi- Piilota koodi library(OpasnetUtils) library(ggplot2) openv.setN(0) DecisionTableParser(opbase.data("Op_fi5889",subset="Decisions")) dat <- opbase.data("Op_fi5889", subset="Malliparametrit")[-1] CTable <- opbase.data("Op_fi5889",subset="CollapseMarginals") for(i in 1:ncol(CTable)) {CTable[[i]] <- as.character(CTable[[i]])} CollapseTableParser(CTable) ColBoD2$probs<-NULL # Fix CollapseTableParser! NA and "" in probs should work too. #' create_subgroups adjusts the data table for ovariables #' @param dat data.frame #' @param type type of data that is used. Must match content in column Type #' @param drop columns to remove #' @return data.frame #' Only works for one subgroup per cell. Should be generalised to match several subgroups separated by ; #' @param data.frame that contains columns Subgroup and - if type is used - Type #' @param type Selects given type from column Type #' @param drop column names to drop prepare <- function(dat, type=NULL, drop=NULL) { out <- dat if(!is.null(type)) out <- out[out$Type %in% type , ] if(!is.null(drop)) out <- out[!colnames(out) %in% drop] out$Subgroup <- as.character(out$Subgroup) tmp <- ifelse(out$Subgroup=="" | is.na(out$Subgroup), " : ", out$Subgroup) tmp <- lapply(strsplit(tmp,";"), function(x) {strsplit(x, ":")[[1]]}) tmp <- data.frame( Sg_type = sapply(tmp, function(x) {x[[1]]}), Subgroup = sapply(tmp, function(x) {x[[2]]}), stringsAsFactors = FALSE ) for(i in setdiff(unique(tmp$Sg_type)," ")) { out[[i]] <- ifelse(tmp$Sg_type==i, tmp$Subgroup,NA) } out$Subgroup <- NULL return(out) } ERFchoice <- Ovariable( "ERFchoice", dependencies = data.frame(Name="exposure"), formula = function(...) { out <- cbind(unique(exposure@output["Exposure_agent"]),Result=1) return(out) } ) dec <- opbase.data("Op_fi5889", subset="Decisions")[-1] population <- Ovariable("population", data = prepare(dat,"population",c("Type","Exposure_agent","Response"))) exposure <- Ovariable("exposure", data = prepare(dat, "exposure", c("Type","Response"))) frexposed <- Ovariable("frexposed", data=prepare(dat, "frexposed", c("Type","Response","Unit"))) incidence <- Ovariable("incidence", data = prepare(dat,"incidence",c("Type","Exposure_agent","Unit"))) disabilityweight <- Ovariable( "disabilityweight", ddata = "Op_en7748", subset = "DALYs of responses" ) colnames(disabilityweight@data)[match( c("Resp","disabilityweightResult"), colnames(disabilityweight@data))] <- c("Response","Result") disabilityweight@data <- orbind( disabilityweight@data, prepare(dat,"disability weight * duration",c("Type","Exposure_agent","Unit")) ) PAF <- Ovariable( "PAF", data = prepare(dat,"PAF","Type"), dependencies=data.frame(Name=c("PAF1","PAF2")), formula = function(...) { return(unkeep(PAF1,sources=TRUE,prevresults = TRUE))#OpasnetUtils::combine(PAF1, PAF2))# ,c("Subgroup","Adjust") } ) duration <- 1 #objects.latest("Op_en7422", code_name="BoD") # [[Burden of disease]] BoD2 <- Ovariable( "BoD2", dependencies=data.frame(Name=c("incidence","disabilityweight","population","duration")), formula=function(...) { out <- incidence * population * duration * disabilityweight return(out) } ) BoDattr <- Ovariable( "BoDattr", dependencies = data.frame(Name=c("BoD2","PAF")), formula = function(...) { out <- BoD2 * PAF return(out) } ) ########################################## # This is code Op_en2261/PAF1 on page [[Health impact assessment]]. library(OpasnetUtils) ### NOTE! The function does not work if the resultnames are not doseResult, ERFResult and thresholdResult. PAF1 <- Ovariable( "PAF1", # This calculates the burden of disease for background-independent endpoints. dependencies = data.frame( Name = c( # "population", # Population divided into subgroups as necessary "dose", # Exposure to the pollutants "ERF", # Other ERFs than those that are relative to background. "threshold", # exposure level below which the agent has no impact. "incidence", # background incidence of the disease. It cancels out if response not dependent on this "frexposed", # fraction of population that is exposed "sumExposcen", # function that calculates difference between exposure scenarios "mc2d", # Function to run two-dimensional Monte Carlo "mc2dparam" # Parameters for mc2d function. Default: don't run. ), Ident = c( # "Op_en2261/population", # [[Health impact assessment]] "Op_en2261/dose", # [[Health impact assessment]] "Op_en2031/initiate", # [[Exposure-response function]] "Op_en2031/initiate", # [[Exposure-response function]] "Op_en2261/incidence", # [[Health impact assessment]] "Op_en2261/frexposed", # [[Health impact assessment]] "Op_en2261/sumExposcen",# [[Health impact assessment]] "Op_en7805/mc2d", # [[Two-dimensional Monte Carlo]] "Op_en7805/mc2dparam" # [[Two-dimensional Monte Carlo]] ) ), formula = function(...) { out <- Ovariable() exps <- unique(dose$Exposure_agent) tmp <- ERF[ERF$ER_function %in% c("UR", "CSF", "ERS") , ] if(!is.null(exps)) tmp <- tmp[tmp$Exposure_agent %in% exps , ] if(nrow(tmp@output)>0) { tmp2 <- dose - threshold result(tmp2) <- pmax(0,result(tmp2)) out <- tmp2 * tmp * frexposed / incidence # Actual equation } # Step estimates: value is 1 below threshold and above ERF, and 0 in between. tmp <- ERF[ERF$ER_function %in% c("Step", "ADI", "TDI", "RDI", "NOAEL") , ] if(!is.null(exps)) tmp <- tmp[tmp$Exposure_agent %in% exps , ] if(nrow(tmp@output)>0) { tmp <- 1 - (dose >= threshold) * (dose <= tmp) tmp <- tmp * frexposed / incidence if(nrow(out@output)==0) out <- tmp else out <- OpasnetUtils::combine(out, tmp) } out <- oapply(out, NULL, sum, c("Exposure_agent", "Exposure", "ER_function", "Scaling")) out <- mc2d(out) # Run two-dimensional Monte Carlo if needed return(sumExposcen(out)) } ) #objects.store(PAF1) cat("Ovariable casesabs stored. page: Op_en2261, code_name: casesabs.\n") ################################################## # This is code Op_en2261/casesrr on page [[Health impact assessment]]. library(OpasnetUtils) PAF2 <- Ovariable( "PAF2", # This calculates the burden of disease for endpoints using RR. dependencies = data.frame( Name = c( # "population", # Population divided into subgroups as necessary "RR", # Relative risks for the given exposure "frexposed", # fraction of exposed population "sumExposcen" # function that calculates difference between exposure scenarios ), Ident = c( # "Op_en2261/population", # [[Health impact assessment]] "Op_en2261/RR", # [[Health impact assessment]] "Op_en2261/frexposed", # [[Health impact assessment]] "Op_en2261/sumExposcen" # [[Health impact assessment]] ) ), formula = function(...) { out <- frexposed * (RR - 1) # RR diluted to get PAF instead of AF r <- result(out) result(out) <- (r > 0) * (r / (r + 1)) + (r <= 1) * r # Negative if RR < 1 return(sumExposcen(out)) } ) #objects.store(PAF2) cat("Ovariable PAF2 stored. page: Op_en2261, code_name: casesrr.\n") ############################################### # This is code Op_en2031/initiate on page [[Exposure-response function]] library(OpasnetUtils) ERF <- Ovariable( "ERF", dependencies = data.frame(Name = c( "ERF_env", # [[ERF of environmental pollutants]] "ERF_omega3", # [[ERF_of_omega-3_fatty_acids]] "ERF_mehg", # [[ERF_of_methylmercury]] "ERF_diox", # [[ERF_of_dioxin]] "ERF_vit", # [[ERF_of_dioxin]] "ERFchoice" # an ovariable for choosing case-specific Responses, Exposure_units or ER_functions ), Ident = c( "Op_en5827/initiate", "Op_en5830/initiate", "Op_en5825/initiate", "Op_en5823/initiate", "Op_en6866/initiate", "Op_en2031/ERFchoice" )), formula = function(...) { out <- OpasnetUtils::combine( ERF_env, ERF_omega3, ERF_mehg, ERF_diox, ERF_vit ) # out@output$Age[is.na(out@output$Age)] <- "" out@marginal[colnames(out@output) == "Exposure_unit"] <- FALSE if("Iter" %in% colnames(out@output)) { out@output <- fillna(out@output, marginals = "Iter")} # To make sure that deterministic ERFs work properly. out <- unkeep(out * ERFchoice, cols=c("ERFchoiceSource", "ERFchoiceResult")) #technical nuisance removed out@output <- prepare(out@output) return(out) } ) threshold <- Ovariable( "threshold", dependencies = data.frame(Name = c( "threshold_env", # [[ERF of environmental pollutants]] "threshold_omega3", # [[ERF_of_omega-3_fatty_acids]] "threshold_mehg", # [[ERF_of_methylmercury]] "threshold_diox", # [[ERF_of_dioxin]] "threshold_vit" # [[ERFs of vitamins]] ), Ident = c( "Op_en5827/initiate", "Op_en5830/initiate", "Op_en5825/initiate", "Op_en5823/initiate", "Op_en6866/initiate" )), formula = function(...) { out <- OpasnetUtils::combine( threshold_env, threshold_omega3, threshold_mehg, threshold_diox, threshold_vit ) # out@output$Age[is.na(out@output$Age)] <- "" out@output <- prepare(out@output) return(out) } ) #objects.store(ERF, threshold) cat("Ovariables ERF, threshold stored. Page: Op_en2031, code: initiate.\n") ############################## # population <- EvalOutput(population, verbose=TRUE) BoD2 <- EvalOutput(BoD2, forceEvaluation=TRUE, verbose=TRUE) BoDattr <- EvalOutput(BoDattr, verbose=TRUE) oprint(ls()) oprint(summary(exposure)) oprint(summary(dose)) oprint(summary(ERF)) oprint(summary(incidence)) oprint(summary(frexposed)) oprint(summary(PAF1)) oprint(summary(PAF2),digits=7) oprint(summary(BoD2),digits=7) oprint(summary(BoDattr),digits=7) oprint(exposure@output) oprint(dose@output) oprint(ERF@output) oprint(incidence@output) oprint(frexposed@output) oprint(PAF1@output) oprint(PAF2@output) oprint(PAF@output) oprint(PAF@data) oprint(BoD2@output,digits=7) oprint(BoDattr@output,digits=7) ggplot(ERF@output, aes(x=Response, weight=ERFResult))+geom_bar()+coord_flip() ggplot(BoD2@output, aes(x=Response, weight=BoD2Result))+geom_bar()+coord_flip() ggplot(BoDattr@output, aes(x=Response, weight=BoDattrResult))+geom_bar()+coord_flip()

Tautitaakkakuvia

+ Näytä koodi- Piilota koodi # Tämä on koodi Op_fi5889 sivulla [[Ruori]] library(OpasnetUtils) library(tidyverse) library(treemap) library(plotly) #objects.latest("Op_en6007", code_name = "hnh2035") # [[OpasnetUtils/Drafts]] pushIndicatorGraph ##### Finnish translations transl <- as_tibble(opbase.data("Op_fi3944", subset="Tautiluokittelu")) %>% # [[Tautitaakka Suomessa]] mutate(Id=as.integer(as.character(Id))) BS <- 24 palet <- c( '#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f', '#ff7f00','#cab2d6','#6a3d9a','#ffff99','#b15928', # First 12 colours from Colorbrewer Paired. 'cyan2','cyan4','plum1','plum4', 'darkslategray4','darkslategray1','firebrick3' ) ###################### Graphs for the Tautitaakka auttaa hahmottamaan ... article # DALYs by causes (risks not included) dc <- as_tibble(opasnet.csv("2/2d/IHME_Fin_Risks_by_Cause.zip", wiki="opasnet_en", unzip="IHME-GBD_2017_DATA-8ce9adcf-1.csv",sep=",",dec=".",header=TRUE)) %>% left_join(transl[transl$Type=="Cause",], by=c("cause_id"="Id")) %>% mutate(cause_name = Result) %>% # transl combines some entries from cause_name to Name (in Finnish: Result) # "Neglected tropical diseases and malaria", # "HIV/AIDS and sexually transmitted infections", # "Enteric infections" >> "Other infectious diseases" filter(measure_name=="DALYs (Disability-Adjusted Life Years)" & metric_name=="Number" & year == 2017) %>% group_by(cause_name) %>% summarise(value=sum(val)) %>% mutate(cause_name = factor(cause_name, levels=cause_name[order(value)])) %>% mutate(valy = value/1000 + ifelse(value<250000, 20, -40)) # unit kDALY/a plot_cause <- ggplot(dc2, aes(x=cause_name,weight=value/1000,fill=cause_name, label=round(value/1000)))+ geom_bar(position="stack")+geom_text(aes(y=valy))+coord_flip()+ guides(fill=FALSE)+ scale_fill_manual(values = rev(palet))+ labs( title="Tautitaakka Suomessa syittäin 2017", x="Tauti tai haitta", y="Tautitaakka (tuhatta DALYa vuodessa)") plot_cause # ggsave("Tautitaakka Suomessa 2017.svg", width=8, height=5) # Png conversion: 300 pixels/inch, font size 4 times larger ############## DALYs by causes and risks dcr <- as_tibble( opasnet.csv("2/2d/IHME_Fin_Risks_by_Cause.zip", wiki="opasnet_en", unzip="IHME-GBD_2017_DATA-8c9ca17f-1.csv",sep=",",dec=".",header=TRUE) ) %>% left_join(transl[transl$Type=="Cause",], by=c("cause_id"="Id")) %>% filter(measure_name=="DALYs (Disability-Adjusted Life Years)" & metric_name=="Number" & year == 2017) %>% # mutate(cause_name = factor(Result, levels=dc2$cause_name)) %>% # transl combines some entries from cause_name to Name (in Finnish: Result) left_join(transl[transl$Type=="Risk",], by=c("rei_id"="Id")) dcr <- dcr %>% mutate( cause_name=factor(Result.x,levels=levels(dc$cause_name)), rei_name=factor(Result.y, levels = aggregate(dcr["val"],dcr["Result.y"],sum) %>% transmute(Result.y[order(val)]) %>% pull())) %>% group_by(rei_name, cause_name, Luokka.y) %>% summarise(val = sum(val)) # mutate(rei_name = factor(rei_name, levels= dcr2$rei_name)) # transl combines some entries from cause_name to Name (in Finnish: Result) # Strange discrepancy in two sources from IHME: especially in cardiovascular diseases. # Reason not known, use dcr anyway; because errors not found. # ihme <- read.csv("C:/Users/jtue/AppData/Local/Temp/download(1).csv") # ihme <- ihme[ihme$Measure=="DALYs",] # sum(ihme$Value) # [1] 911630.9 # sum(dcr$val) # [1] 1205234 # View( # merge( # aggregate(ihme["Value"], by=ihme["Cause.of.death.or.injury"], FUN=sum), # aggregate(dcr["val"], by=dcr["cause_name"], FUN=sum), # by.x=c("Cause.of.death.or.injury"), by.y=c("cause_name") # ) # ) plot_risk <- ggplot(dcr, aes(x=rei_name, weight=val/1000, fill=cause_name))+geom_bar()+coord_flip()+ scale_fill_manual(values = rev(palet)[-8])+ # Skin diseases missing, so remove that colour guides(fill = guide_legend(reverse=TRUE, title=NULL, keyheight=0.8))+ theme_gray(base_size=11)+ theme(legend.position=c(0.83,0.29)) + labs( title = "Tautitaakka Suomessa 2017 tunnettujen riskitekijöiden mukaan", x="Riskitekijä", y="Tautitaakka (tuhatta DALYa vuodessa)") plot_risk # ggsave("Tautitaakka Suomessa 2017 riskitekijöittäin.svg", width=8, height=6) # Png conversion: 300 pixels/inch, font size 4 times larger ruori <- data.frame( Luokka.y = c(rep("Ympäristö",3),rep("Ravitsemus",4),rep("Ympäristö",3)), rei_name = c( "Aflatoksiini", "Lyijy", "Dioksiini", "Tyydyttyneet rasvat", "Suola", "Hedelmien liian vähäinen saanti", "Kasvisten liian vähäinen saanti", "Norovirus", "Listeria", "Toksoplasma" ), val = c( 8, 6, 22, 8190, 27062, 35314, 27778, 1, 660, 432 ), lower = c( 6, NA, NA, 5857, 1330, 20197, 13740, NA, 330, 270 ), upper = c( 13, NA, NA, 11125, 65542, 53635, 44716, NA, 880, 702 ) ) dcr$rei_name <- as.character(dcr$rei_name) # Subtract Ruori results from IHME background dcr$val[dcr$rei_name=="Ravitsemusriskit" & dcr$cause_name=="Sydän- ja verisuonitaudit"] <- dcr$val[dcr$rei_name=="Ravitsemusriskit" & dcr$cause_name=="Sydän- ja verisuonitaudit"] - sum(ruori$val[ruori$Luokka.y=="Ravitsemus"]) dcr$val[dcr$rei_name=="Muut ympäristöriskit" & dcr$cause_name=="Syöpä"] <- dcr$val[dcr$rei_name=="Muut ympäristöriskit" & dcr$cause_name=="Syöpä"] - sum(ruori$val[ruori$Luokka.y=="Ympäristö"]) dcr <- rbind(dcr[colnames(dcr)!="cause_name"],ruori[!colnames(ruori) %in% c("upper","lower")]) dcr$rei_name[dcr$rei_name=="Ravitsemusriskit"] <- "Muut ravitsemusriskit" pdf("Tautitaakka riskitekijöittäin.pdf", width=11.67, height=4.27) treemap(dcr, index=c("Luokka.y","rei_name"),vSize="val", title=paste0("Tunnettujen riskitekijöiden tautitaakka Suomessa 2017 (", round(sum(dcr$val),-3)," DALY)"), align.labels=list(c("center","top"),c("center","center")), aspRatio = 2, border.col = "gray") dev.off() tmp <- ruori %>% filter(rei_name %in% c("Aflatoksiini","Lyijy","Dioksiini","Norovirus","Listeria","Toksoplasma")) %>% rename(Altiste=rei_name) %>% mutate(Altiste = factor(Altiste, levels=ruori$rei_name[order(ruori$val)])) thlBarPlot(tmp, xvar=Altiste, yvar=val)+ coord_flip()+geom_errorbar(data=tmp, aes(ymin=lower, ymax=upper), width=0.25, size=0.7)+ labs( title="Ruorissa tutkittujen riskien tautitaakka", subtitle = "Tautitaakka mikrobiologisille ja kemiallisille epäpuhtauksille (DALYa vuodessa)") ggsave("Mikrobiologiset ja kemialliset riskit Ruorissa.svg", width=8, height=5) tmp <- ruori %>% filter(!rei_name %in% c("Aflatoksiini","Lyijy","Dioksiini","Norovirus","Listeria","Toksoplasma")) %>% rename(Altiste=rei_name) %>% mutate(Altiste = factor(Altiste, levels=ruori$rei_name[order(ruori$val)])) thlBarPlot(tmp, xvar=Altiste, yvar=val)+ coord_flip()+geom_errorbar(data=tmp, aes(ymin=lower, ymax=upper), width=0.25, size=0.7)+ labs( title="Ruorissa tutkittujen riskien tautitaakka", subtitle = "Tautitaakka ravitsemusriskeille (DALYa vuodessa)") ggsave("Ravitsemusriskit Ruorissa.svg", width=8, height=5) #################################### # Katso sivut [[:op_en:Goherr assessment]] ja [[:op_en:Health effects of Baltic herring and salmon: a benefit-risk assessment]] objects.get('155401096341') groups <- function(o) { o$Group <- paste(o$Gender, o$Ages) o$Group <- factor(o$Group, levels = c( "Female 18-45", "Male 18-45", "Female >45", "Male >45" )) return(o) } varit12 <- c( '#2f62ad', # THL-värit, koko väripaletti. Nro 2 '#7cd0d8', # 4 '#571259', # 9 '#5faf2c', # 11 '#bf4073', # 14 '#3b007f', # 18 '#16994a', # 20 '#cccc72', # 22 '#0e1e47', # 3 '#25a5a2', # 5 '#cc7acc', # 7 '#244911' # 12 ) #### Burden of disease # Figure 5. from Goherr assessment manuscript if(FALSE) tmp <- groups(BoD * info) else tmp <- groups(BoDRaw * info) tmp <- oapply( tmp[!grepl("TWI", tmp$Resp) & tmp$Background=="Yes",], INDEX=c("Resp","Group","Country","Cons.policy","Background"), mean )@output # levels(tmp$Group) # [1] "Female 18-45" "Male 18-45" "Female >45" "Male >45" levels(tmp$Group) <- c("Naiset 18-45","Miehet 18-45","Naiset >45","Miehet >45") #levels(tmp$Resp) #[1] "Stroke" "Heart (CHD)" "Cancer" "Child's IQ" "Infertility" #[6] "Tooth defect" "Dioxin TWI" "TWI 2018" "Vitamin D intake" levels(tmp$Resp) <- c("Aivohalvaus","Sepelvaltimotauti","Syöpä","Lapsen ÄO","Hedelmättömyys", "Hammasvaurio","","","D-vitamiini") fig22 <- ggplot(tmp[tmp$Country=="FI" & tmp$Cons.policy=="BAU",], aes(x=Group, weight=Result, fill=Resp))+ geom_bar()+facet_grid(Cons.policy ~ Country)+ theme_grey(base_size=BS-8)+ theme( legend.position = "bottom" # axis.text.x = element_text(angle = -90) )+ labs( title="Itämeren kalan aiheuttama tautitaakka Suomessa ryhmittäin", y = "Tautitaakka (DALY/a)", fill="", x="" )+ scale_fill_manual(values=varit12[-(7:8)]) fig22 ggsave("Itämeren kalan tautitaakka Suomessa.svg", width=8, height=5) ######### Figure 6. tmp <- groups(BoDRaw * info) #> levels(tmp$Resp) #[1] "Stroke" "Heart (CHD)" "Cancer" "Child's IQ" "Infertility" #[6] "Tooth defect" "Dioxin TWI" "TWI 2018" "Vitamin D intake" levels(tmp$Resp) <- c("Aivohalvaus","Sepelvaltimotauti","Syöpä","Lapsen ÄO","Hedelmättömyys", "Hammasvaurio","TWI 2001","TWI 2018","D-vitamiini") tmp@output <- rbind( cbind(tmp@output,Focusgroup = "Kaikki"), cbind(tmp@output[tmp$Group=="Female 18-45",],Focusgroup = "Nuoret naiset") ) tmp <- oapply( tmp[tmp$Cons.policy=="BAU" & tmp$Country=="FI",], INDEX=c("Resp","Background","Focusgroup","Country"), mean )@output tmp <- rbind( cbind( Objective="Kokonaisterveys, oletus", tmp[tmp$Background=="Yes" & !grepl("TWI",tmp$Resp),] ), cbind( Objective="Kokonaisterveys, taustatta", tmp[tmp$Background=="No" & !grepl("TWI",tmp$Resp),] ), cbind( Objective=tmp$Resp, tmp )[grepl("TWI",tmp$Resp),] ) sums <- aggregate(tmp["Result"], by=tmp[c("Objective","Focusgroup","Country")],sum) fig22b <- ggplot(tmp, aes(x=Objective, weight=Result))+ geom_bar(aes(fill=Resp))+facet_grid(Country ~ Focusgroup)+ theme_grey(base_size=BS-8)+ theme( legend.position = "bottom", axis.text.x = element_text(angle = -15, hjust=0.5) )+ geom_text(data=sums, size=6, aes(label=round(Result,-2),y=pmax(200,Result+200)))+ labs( title="Tautitaakka eri tavoitteiden näkökulmasta", y = "Tautitaakka (DALY/a)", fill="", x="" )+ scale_fill_manual(values=varit12) fig22b ggsave("Itämeren kalan tautitaakka eri näkökulmista.svg", width=10, height=7) ############################ Health care costs dat <- scrape.webtable( "https://en.wikipedia.org/wiki/List_of_countries_by_total_health_expenditure_per_capita", 6) colnames(dat) <- c("Country","Y2000","Y2005","Y2010","Y2015") dat$Y2015 <- as.numeric(gsub(",","",as.character(dat$Y2015))) dat$Country <- as.character(dat$Country) dat$Country[match(c( "Iran (Islamic Republic of)", "Lao People's Democratic Republic", "United Republic of Tanzania", "United States of America", "Venezuela (Bolivarian Republic of)", "Viet Nam" ),dat$Country)] <- c("Iran","Laos","Tanzania","United States","Venezuela","Vietnam") dat2 <- read.csv("C:/Users/jtue/AppData/Local/Temp/IHME-GBD_2017_DATA-96ba4b83-1.csv") levels(dat2$location_name)[match(c( "The Bahamas","The Gambia"),levels(dat2$location_name))] <- c("Bahamas","Gambia") dat3 <- merge(dat,dat2, by.x="Country",by.y="location_name", all=TRUE) dat3$label <- ifelse(dat3$Country %in% c("Finland","Sweden","United States","Poland"), dat3$Country, " ") tmp <- dat3[dat3$metric_name=="Rate" & dat3$cause_name=="All causes",] tmp$label[match(c("Finland","Sweden","United States","Poland"),tmp$label)] <- c("Suomi","Ruotsi", "USA","Puola") ggplot(tmp, aes(x=Y2015,y=val/100000, label=label, colour=label))+ geom_point()+geom_text()+ labs( title="Tautitaakka terveysmenojen funktiona maittain 2015", y="Tautitaakka per henkilö (DALY)", x="Terveydenhuoltomenot per henkilö (USD)" ) ggsave("Tautitaakka terveysmenojen funktiona maittain.svg", width=9, height=5) pl <- plot_ly(tmp, x=~Y2015, y=~val/100000,type="scatter", mode="markers", hovertext=~Country) %>% #, text=~label,color=~label) %>% layout( title="Tautitaakka terveysmenojen funktiona maittain 2015", xaxis=list(title="Terveydenhuoltomenot per henkilö (USD)"), yaxis=list(title="Tautitaakka per henkilö (DALY)") ) pl # pushIndicatorGraph(pl, 103)#, API_KEY= apikey)

Katso myös

• Ruori-hankkeen tautitaakka-arviot