Disease risk

This page is a knowledge crystal of subtype variable. The page identifier is Op_en5917
Moderator:Nobody (see all) Click here to sign up.
This page is a stub. You may improve it into a full page.
Upload data {{#opasnet_base_link:Op_en5917}}

Question

What are incidence or prevalence rates of different diseases in Finland?

Answer

+ Show code- Hide code library(OpasnetUtils) # A package with Opasnet functionalities library(xtable) # A package with table formats library(ggplot2) # A package with fancy graph formats objects.latest("Op_en5917", "initiate") # Get the latest ovariables from code calculations on page Op_en5917. diseaseRisk <- EvalOutput(diseaseRisk) oprint(summary(diseaseRisk)) ggplot(diseaseRisk@output, # Show a bar chart aes(x = Disease, y = diseaseRiskResult, fill = Response.metric)) + geom_bar(position="dodge") + theme_grey(base_size = 24) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + scale_y_log10() # limits = c(1, 1000000))

----#: . Code seems to work, but gives out the yellow errorbox. --Heta 13:02, 27 August 2013 (EEST) (type: truth; paradigms: science: comment)

Rationale

Data

• Burden of disease as DALY in Finland ⇤--#: . Inactivate and merge? --Jouni (talk) 16:03, 13 April 2017 (UTC) (type: truth; paradigms: science: attack)

Data from op_fi:Seturi/tautiriski

Population column defines the relevant population subgroup other than age and sex subgroups, which are defined in their respective columns. The numbers of cases are divided by the size of the population (total population even in cases where the cases are from a subgroup only. The population size is estimated at 5375276 in 2010, and this is used as denominator in the incidence calculation.

Disease risk: Difference between revisions(# /100000py, DALY)

Obs	Response	Population	Age	Sex	Unit	Incidence	Actual cases	Description
1	Lung cancer morbidity	Whole population			cases /100000py	42.342	2276	Year 2010 cancer register
2	Cardiopulmonary mortality	Whole population			cases /100000py	443.512	23840
3	Total mortality	Whole population			cases /100000py	940.752	50568	2011 data, Tilastokeskus
4	Bladder cancer morbidity	Whole population			cases /100000py	16.706	898	Year 2010 cancer register
5	Myocardial infarction morbidity	Whole population			cases /100000py	463.195	24898
6	Ischaemic heart disease mortality	Whole population			cases /100000py	221.142	11887
7	Coronary heart disease mortality	Whole population			cases /100000py	221.142	11887	This is a duplicate of the previous row to match more ERFs
8	CHD arrythmia mortality	Whole population			cases /100000py	221.142	11887	This is a duplicate of the previous row to match more ERFs
9	Asthma morbidity	Whole population			cases /100000py	270.888	14561
10	Asthma morbidity	Whole population	>21		cases /100000py	176.103	9466
11	Asthma morbidity	Whole population	<14		cases /100000py	81.224	4366
12	Pulmonary infection morbidity	Whole population	<2		DALY	35
13	Otitis media	Whole population	<3		DALY	48
14	Lower respiratory symptoms morbidity	Whole population			cases /100000py	8985.585	483000
15	Upper respiratory symptoms morbidity	Whole population			cases /100000py	17279.969	928846
16	Ischaemic heart disease/myocardial infarction morbidity	Whole population	35-64	Male	cases /100000py	86.6	4655	35-64v miesten 1. infarktin insidenssi 354/100000 1991-4. Aromaa ym Suomalaisten terveys s155
17	CHD	Whole population	35-64	Male	cases /100000py	86.6	4655	35-64v miesten 1. infarktin insidenssi 354/100000 1991-4. Aromaa ym Suomalaisten terveys s155. This is a duplicate of the previous row to match more ERFs
18	Nasal cancer morbidity	Whole population	20-64		cases /100000py	0.651	35	Age boundaries are assumed without original data
19	COPD mortality	Whole population	15-64		cases /100000py	20.966	1127
20	Asthma morbidity2	Whole population	15-64	Male	cases /100000py	58.044	3120
21	Asthma morbidity2	Whole population	15-64	Female	cases /100000py	78.954	4244
22	Cancer morbidity	Whole population			cases /100000py	202.408	10880
23	Cancer lifetime probability	Whole population			probability	1		This is used with absolute risk functions such as CSF to get rid of the parameter relative to background
24	All skin cancers and basalioma morbidity	Whole population			cases /100000py	163.285	8777
25	Skin cancer morbidity	Whole population			cases /100000py	37.3	2005
26	Melanoma morbidity	Whole population			cases /100000py	16.855	906
27	Carcinoma spinocellulare morbidity	Whole population			cases /100000py	20.445	1099
28	Basalioma morbidity	Whole population			cases /100000py	125.984	6772
29	Leukemia morbidity	Whole population			cases /100000py	10.027	539	Year 2010, cancer register

DALY/a thousands WHO 2002 EU-27 OECD, 22 Population, millions Trachea, bronchus & lung cancers Cardio-vascular diseases Trachea, bronchus & lung cancers Cardio-vascular diseases

DALY in Europe WHO 2002(-)

Obs	Country	EU-27	OECD22	Population	Trachea bronchus & lung cancers DALY/a thousands	Cardio-vascular diseases DALY/a thousands	Trachea bronchus & lung cancers DALY/a/100 000	Cardio-vascular diseases DALY/a/100 000	(sub)urban 2009-11 PM2.5 mean μg/m3	Description
1	Austria	1	1	8.4	27	189	321	2244	18.9
2	Belgium	1	1	11.0	55	209	499	1898	17.6
3	Bulgaria	1	0	7.3	32	526	435	7157	31.4
4	Cyprus	1	0	0.9	1	21	117	2467	21.3
5	Czech Republic	1	1	10.5	52	352	495	3352	23.0
6	Denmark	1	1	5.6	26	114	461	2021	14.0
7	Estonia	1	0	1.3	5	65	373	4852	19.5
8	Finland	1	1	5.4	14	122	260	2266	7.3
9	France	1	1	65.2	243	857	373	1315	17.9
10	Germany	1	1	81.8	354	2070	433	2530	16.8
11	Greece	1	1	10.8	49	341	453	3153	19.4
12	Hungary	1	1	10.0	76	410	762	4113	22.6
13	Ireland	1	1	4.5	12	77	265	1703	10.8
14	Italy	1	1	60.7	238	1222	392	2013	21.5
15	Latvia	1	0	2.1	10	130	486	6318	15.1
16	Lithuania	1	0	3.0	13	138	429	4555	12.7
17	Luxembourg	1	1	0.5	2	9	381	1715	15.9
18	Malta	1	0	0.4	1	8	240	1920	13.6
19	Netherlands	1	1	16.7	73	307	437	1839	17.0
20	Norway	0	1	5.0	15	87	303	1756
21	Poland	1	1	38.5	215	1226	558	3181	28.2
22	Portugal	1	1	10.6	29	263	275	2491	8.6
23	Romania	1	0	21.4	92	1094	430	5118	18.1
24	Slovakia	1	1	5.4	19	175	352	3242	25.0
25	Slovenia	1	0	2.1	9	51	439	2485	21.5
26	Spain	1	1	46.2	155	670	336	1450	12.5
27	Sweden	1	1	9.4	23	187	244	1981	8.0
28	Switzerland	0	1	7.9	25	116	316	1465
29	Turkey	0	1	74.8	115	2082	154	2785
30	United Kingdom	1	1	62.8	230	1297	366	2065	13.3
31	All of the above			590.2	2210	14415	374	2442	17.7
32	EU27 Total			502.6	2055	12130	409	2413	17.7
33	OECD Europe total			553.1	2052	12447	371	2250	17.7

Calculations

+ Show code- Hide code # This is code Op_en5917/initiate on page [[Burden of disease]] library(OpasnetUtils) d <- opbase.data("Op_en5917") d$Obs <- NULL colnames(d) <- gsub(" ", "_", colnames(d)) d$Result[d$Unit == "cases /100000py"] <- d$Result[d$Unit == "cases /100000py"] / 100000 # Units to cases/py levels(d$Unit)[levels(d$Unit) == "cases /100000py"] <- "cases /py" disincidence <- Ovariable("disincidence", data = d, save = TRUE) cat("Ovariable disincidence saved. page_indent = Op_en5917, code_name = initiate\n")

InpBoD is an ovariable to calculate total burden of disease for selected endpoints based on IHME data.

+ Show code- Hide code # This is code Op_en5917/InpBoD on page [[Burden of disease]] library(OpasnetUtils) dat <- opasnet.csv( "/d/d3/IHME-GBD_2017_goherr_diseases_dk_ee_fi_se.zip", wiki="opasnet_en", unzip="IHME-GBD_2017_DATA-c3ad9a2c-1.csv", dec=".", sep=",", header=TRUE, quote="\"" ) dat <- as.data.frame(t(matrix(readClipboard(),nrow=10))) colnames(dat) <- c("Measure","Location","Sex","Age","Cause","Metric","Year","Val","Upper","Lower") #dat <- opbase.data("Op_en5917", subset = "IHME_Goherr")# [[Disease risk]] #dat <- dat[dat[[1]]=="DALYs (Disability-Adjusted Life Years)" , ] # Measure has a special character in name, so use position. dat$Location <- factor( dat$Location, levels=c("Denmark", "Estonia", "Finland", "Sweden"), labels=c("DK", "EE", "FI", "SE") ) dat <- dat[dat$Metric=="Number" & dat$Year=="2017" , ] dat <- dat[dat$Measure=="DALYs (Disability-Adjusted Life Years)" | dat$Cause=="All causes" , ] # YLL for this dat <- dat[!(dat$Measure=="DALYs (Disability-Adjusted Life Years)" & dat$Cause=="All causes") , ] dat <- dat[!dat$Age %in% c("All Ages","<1 year","1 to 4","5 to 9","10 to 14") , ] levels(dat$Age)[levels(dat$Age) %in% c("15 to 19","20 to 24","25 to 29","30 to 34", "35 to 39","40 to 44")] <- "18-45" levels(dat$Age)[levels(dat$Age) %in% c("45 to 49","50 to 54","55 to 59","60 to 64", "65 to 69","70 to 74","75 to 79","80 plus")] <- ">45" levels(dat$Cause)[levels(dat$Cause)== "All causes"] <- "All-cause mortality" #levels(dat$Cause)[levels(dat$Cause)== "Breast cancer"] levels(dat$Cause)[levels(dat$Cause)== "Caries of permanent teeth"] <- "Yes or no dental defect" #levels(dat$Cause)[levels(dat$Cause)== "Depressive disorders"] #levels(dat$Cause)[levels(dat$Cause)== "Dysthymia"] levels(dat$Cause)[levels(dat$Cause)== "Idiopathic developmental intellectual disability"] <- "Loss in child's IQ points" #levels(dat$Cause)[levels(dat$Cause)== "Intracerebral hemorrhage"] levels(dat$Cause)[levels(dat$Cause)== "Ischemic heart disease"] <- "CHD2 mortality" levels(dat$Cause)[levels(dat$Cause)== "Ischemic stroke"] <- "Stroke mortality" #levels(dat$Cause)[levels(dat$Cause)== "Liver cancer"] levels(dat$Cause)[levels(dat$Cause)== "Major depressive disorder"] <- "Depression" levels(dat$Cause)[levels(dat$Cause)== "Male infertility"] <- "Sperm concentration" levels(dat$Cause)[levels(dat$Cause)== "Neoplasms"] <- "Cancer morbidity" #levels(dat$Cause)[levels(dat$Cause)== "Stroke"] #levels(dat$Cause)[levels(dat$Cause)== "Subarachnoid hemorrhage"] colnames(dat)[colnames(dat)=="Cause"] <- "Response" colnames(dat)[colnames(dat)=="Location"] <- "Country" dat$Result <- gsub(",","",paste0(dat$Val, "1 (", dat$Lower, " - ", dat$Upper, "2)")) dat$Group <- paste(dat$Sex, dat$Age) InpBoD<- Ovariable( "InpBoD", data = dat[c("Country","Group","Response","Result")] ) objects.store(InpBoD) cat("Ovariable InpBoD stored.\n") InpBoD <- EvalOutput(InpBoD) oprint(summary(oapply(InpBoD, NULL,sum,"Group")))

See also

• OECD health data [1]

Related files

<mfanonymousfilelist></mfanonymousfilelist>