Disease risk

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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

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	Trait	Population	Age	Sex	Response metric	Unit	Incidence	Actual cases	Description
1	Lung cancer	Whole population			Morbidity	cases /100000py	42.342	2276	Year 2010 cancer register
2	Cardiopulmonary	Whole population			Mortality	cases /100000py	443.512	23840
3	Total mortality	Whole population			Mortality	cases /100000py	940.752	50568	2011 data, Tilastokeskus
4	Bladder cancer	Whole population			Morbidity	cases /100000py	16.706	898	Year 2010 cancer register
5	Myocardial infarction	Whole population			Morbidity	cases /100000py	463.195	24898
6	Ischaemic heart disease	Whole population			Mortality	cases /100000py	221.142	11887
7	CHD	Whole population			Mortality	cases /100000py	221.142	11887	This is a duplicate of the previous row to match more ERFs
8	CHD	Whole population			Arrythmia mortality	cases /100000py	221.142	11887	This is a duplicate of the previous row to match more ERFs
9	Asthma	Whole population			Morbidity	cases /100000py	270.888	14561
10	Asthma	Whole population	>21		Morbidity	cases /100000py	176.103	9466
11	Asthma	Whole population	<14		Morbidity	cases /100000py	81.224	4366
12	Pulmonary infections	Whole population	<2		Morbidity	DALY	35
13	Otitis media	Whole population	<3		Morbidity	DALY	48
14	Lower respiratory symptoms	Whole population			Morbidity	cases /100000py	8985.585	483000
15	Upper respiratory symptoms	Whole population			Morbidity	cases /100000py	17279.969	928846
16	Ischaemic heart disease/myocardial infarction	Whole population	35-64	M	Morbidity	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	M	Morbidity	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	Whole population	20-64		Morbidity	cases /100000py	0.651	35	Age boundaries are assumed without original data
19	COPD	Whole population	15-64		Mortality	cases /100000py	20.966	1127
20	Asthma	Whole population	15-64	M	Morbidity	cases /100000py	58.044	3120
21	Asthma	Whole population	15-64	F	Morbidity	cases /100000py	78.954	4244
22	Cancer	Whole population			Morbidity	cases /100000py	202.408	10880
23	Cancer	Whole population			Lifetime probability	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	Whole population			Morbidity	cases /100000py	163.285	8777
25	Skin cancer	Whole population			Morbidity	cases /100000py	37.3	2005
26	Melanoma	Whole population			Morbidity	cases /100000py	16.855	906
27	Carcinoma spinocellulare	Whole population			Morbidity	cases /100000py	20.445	1099
28	Basalioma	Whole population			Morbidity	cases /100000py	125.984	6772
29	Leukemia	Whole population			Morbidity	cases /100000py	10.027	539	Year 2010, cancer register

Calculations

+ Show code- Hide code 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")

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