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).
Lisää dataa {{#opasnet_base_link:Op_fi5889}}

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	CHD death	Saturated fat	PAF	Age:Age 25-69	fraction	0.064 (0.050 - 0.078)	Finland, 2010 situation from Wang et al. Supplementary
6	CHD death	Saturated fat	PAF	Age:Age 70+	fraction	0.048 (0.033 - 0.063)	Finland, 2010 situation from Wang et al. Supplementary
7		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.
8		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.
9		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.
10		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.
11		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.
12		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.
13		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.
14		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.
15		Saturated fat	exposure	Age:Age 25-69	E%	13.1 (12.9 - 13.4)	Finland, 2010 situation from Wang et al. Supplementary
16		Saturated fat	exposure	Age:Age 70+	E%	13.2 (12.8 - 13.6)	Finland, 2010 situation from Wang et al. Supplementary
17	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?
18	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?
19		Aflatoxin	exposure	Age:Age 25-64	ng /kg /d	0.85 - 1.14	Finravinto 2012
20		Aflatoxin	exposure	Age:Age 65-74	ng /kg /d	0.5 - 0.67	Finravinto 2012
21	Hepatitis		prevalence	Hepatitis:Hepatitis B-	fraction	0.005	TerveSuomi
22	Hepatitis		prevalence	Hepatitis:Hepatitis B+	fraction	0.995	TerveSuomi
23	Liver cancer		incidence	Age:Age 25-64	# /100000py	4.06	Finnish Cancer Registry, average 2011-2015
24	Liver cancer		incidence	Age:Age 65-74	# /100000py	26.16	Finnish Cancer Registry, average 2011-2015
25	Toxoplasma gondii		incidence	Age:Age 0 (congenital)	# /100000py	0.3 (0.2 - 0.7)	WHO 2015 report, European values (Table A8.2)
26	Toxoplasma gondii		incidence	Age:Age 1+ (acquired)	# /100000py	119 (77 - 188)	WHO 2015 report, European values (Table A8.2)
27	Toxoplasma gondii		disability weight * duration	Age:Age 0 (congenital)	DALY/case	7	WHO 2015 report, European values (Table A8.2)
28	Toxoplasma gondii		disability weight * duration	Age:Age 1+ (acquired)	DALY/case	0.05	WHO 2015 report, European values (Table A8.2)
29	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)
30	Listeria		disability weight * duration	Age:Total population	DALY/case	10 (5 - 13.3)	WHO 2015 report, European values (Table A8.2)
31	Noro virus		incidence	Age:Total population	# /100000py	1652 (630 - 3294)	WHO 2015 report, European values (Table A8.2)
32	Noro virus		disability weight * duration	Age:Total population	DALY/case	0.0015 - 0.0025	WHO 2015 report, European values (Table A8.2)
33	Toxoplasma gondii	Toxoplasma gondii	PAF	Age:Total population	fraction	1	dummy variable
34	Listeria	Listeria	PAF	Age:Total population	fraction	1	dummy variable
35	Noro virus	Noro virus	PAF	Age:Total population	fraction	1	dummy variable
36		Lead	exposure	Age:Age 1	ug /l	27.9	Measured as blood concentration. RASKURI, Z:\Projects\RUORI\tautitaakka\Lyijy\Lyijy_tautitaakkadata.xlsx
37	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
38	IQ loss	Lead	threshold	Age:Age 1	ug /l	24	Lanphear et al 2005 https://doi.org/10.1289/ehp.7688 CHECK THRESHOLD
39		Lead	frexposed	Age:Age 1	fraction	0.0657	Population exposed to lead over threshold: 3126. RASKURI, Z:\Projects\RUORI\tautitaakka\Lyijy\Lyijy_tautitaakkadata.xlsx
40		Aflatoxin	frexposed	Age:Age 25-64	fraction	1	dummy variable
41		Aflatoxin	frexposed	Age:Age 65-74	fraction	1	dummy variable
42	IQ loss		disability weight * duration	Age:Age 1	DALY /IQ	0.11 (0.06 - 0.16)	Arja used 0.013 but here we use Goherr value instead
43			population	Age:Age 1		47577	Statistics Finland, 2018 http://stat.fi/til/synt/index.html
44			population	Age:Age 25-64	#	2835089	Statistics Finland?
45			population	Age:Age 65-74	#	615487	Statistics Finland?
46			population	Age:Total population	#	5471753	Statistics Finland?
47			population	Age:Age 0 (congenital)	#	47577	Statistics Finland?
48			population	Age:Age 1+ (acquired)	#	5471753	Statistics Finland?
49	Diet low in fruits	Fruits	PAF	Age:Total population	fraction	1	dummy variable
50	Diet low in vegetables	Vegetables	PAF	Age:Total population	fraction	1	dummy variable
51	Diet high in sodium	Sodium	PAF	Age:Total population	fraction	1	dummy variable
52	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
53	Diet low in vegetables		BoD	Age:Total population	DALY	28440 (14190 - 45960)	IHME GBD2017
54	Diet high in sodium		BoD	Age:Total population	DALY	27670 (1310 - 66420)	IHME GBD2017
55	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
56	Liver cancer		BoD	Age:Age 25-64	DALY	2499 (2018 - 3115)	From IHME (2014)
57	Liver cancer		BoD	Age:Age 65-74	DALY	2745 (2245 - 3311)	From IHME (2014)

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	Exposure_agent:Saturated fat	Multiply	1.132	Scaled from Wang to Finravinto 2017: mean(15.2,15.3,14.8,14.0)/13.1
3	Hepatitis	Hepatitis B-	BoD2		Multiply	0.995	Healthy people. Data from TerveSuomi
4	Hepatitis	Hepatitis B+	BoD2		Multiply	0.005	Hepatitis B patients. Data from TerveSuomi
5	Adjust	BAU	ERF	Response:Liver cancer	Multiply	0.00001	1/100000py --> 1/py
6	Adjust	BAU	ERF		Identity	1	For completion
7	Adjust	BAU	PAF		Identity	1	For completion

CollapseMarginals(-)

Obs	Variable	Index	Probs	Function	Dummy	Description
1	BoD2	incidenceSource,disabilityweightSource,populationSource,BoD2Source		sum	1	Remove redundant
2	PAF	Unit,ERFchoiceSource,exposureSource,bgexposureSource,doseSource,thresholdSource,ERFSource,frexposedSource,incidenceSource,PAF1Source,InpPAFSource,PAFSource		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