Difference between revisions of "Food intake in Finland"

Latest revision as of 15:45, 15 April 2016

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Question

What is intake of different food items (especially fish) in Finland in age groups 0-64 years?

Answer

Fish intake per age group
- Larger graph

+ Show code - Hide code

library(OpasnetUtils)
library(ggplot2)

objects.get("84Sg6BQDs7potVJr")

#oprint(head(foodFI))

fishlong <- c(
	"Atlantic salmon (Salmo salar)",
	"Baltic herring (Clupea harengus membras)",
	"Fish and other seafood(by species, these are examples - variation by country):",
	"Herring (Clupea harengus)",
	"Pike (Esox lucius)",
	"Rainbow trout (Onchorchys mykiss)" 
)

fishshort <- c(
	"Atlantic salmon",
	"Baltic herring", 
	"Fish and seafood", 
	"Herring", 
	"Pike", 
	"Rainbow trout"
)

# Why do we need to reorder the factor? It should be ordered already.

age =  c("6 mo", "7 mo", "8 mo", "9 mo", "7-11 mo", "10 mo", "11 mo", "1", "2", "3", "4-5", "6-9", "10-13", "14-17", 
	"18-24", "25-34", "35-44", "45-54", "55-64", "65-74")
foodFI$Age <- factor(foodFI$Age, age, ordered = TRUE)

#levels(foodFI$Food)[levels(foodFI$Food) %in% fishlong]
levels(foodFI$Food)[levels(foodFI$Food) %in% fishlong] <- fishshort

cat("Fish intake data based on results from Beneris project (child data mainly from DIPP study.)\n")

#head(foodFI[foodFI$Food %in% fishshort , ])

ggplot(foodFI[foodFI$Parameter == "Fractile0.5" & foodFI$Food == fishshort , ], aes(x = Age, y = Result)) + #
	geom_point(aes(colour = Food)) + 
	theme_grey(base_size = 24) +
	theme(axis.text.x = element_text(angle = 90, hjust = 1))

Rationale

Data

The data contains:

a) Main food groups (ingredient level, classification used in EPIC)

b) Nutrients

c) Nutrient supplements

d) Fish and other seafood by species

Age groups are: 0, 1, 2, 3, 4-5, 6-9, 10-13, 14-17, 18-24, 25-34, 35-44, 45-54, 55-64,

The data here is generated from the DIPP study, a Type 1 Diabetes Prediction and Prevention (DIPP) project in Finland, where the cohort of 5993 children (77 % of those invited) participated in the main study, and 117 randomly selected infants in the validation study. ^[1] ^[2] This was carried out in the university hospitals of Turku, Tampere and Oulu.^[3]

Food intake in Spain: [1] [2] [3] [4]
Food intake in Denmark: [5] [6]
Domestic fish consumption of the general population in Finland: Fish oil intake in Beneris [7]
Food consumption FINLAND.xls ^[4]. The Excel sheets are uploaded as tables (R data.frames): Main food categories, Fish categories
Food intake in subpopulations in Finland:
- Domestic fish consumption of the pregnant women in Finland
- Beneris_D18_subpopulation_intakes_Finland_th.xls ^[5]. The Excel sheets are uploaded as tables (R data.frames): Main food categories_children, Fish categories_children, Main food categories_adults, Fish categories_adults, Main food categories_pregnant, Fish categories_pregnant
- D14 Dietary patterns
- Beneris Food consumption pregnant women FINLAND 300507 th.xls The Excel sheets are uploaded as tables (R data.frames): Main food categories, Fish categories
Metals in Finnish human placentae
Food and contaminant intake in Ireland: [8] [9] [10]
Dioxin intakes in Finnish children

Unit

g/day AMONG THOSE WHO USE THIS PRODUCT

Calculations

+ Show code - Hide code

library(OpasnetUtils)
library(reshape2)
library(ggplot2)

importFoodTables <- function(
	dat = table, # Data sheet from Excel or elsewhere
	cols = c("mean", "SD", "Fractile0.05", "Fractile0.25", "Fractile0.5", "Fractile0.75", "Fractile0.95", "Userfraction"),
	sheet = "General population",
	obs, 
	sex = c("Male", "Female"),
	block, 
	age
) {
	out <- data.frame()
	for(i in 1:length(obs)) {	
		for(j in 1:length(block)) {
			temp <- dat[obs[[i]], block[[j]]]
			colnames(temp) <- cols
			temp <- data.frame(Population = sheet, Sex = sex[[i]], Age = age[[j]], Food  = dat[obs[[i]], 1], temp)
			temp[["Userfraction"]] <- as.numeric(gsub("%", "", temp[["Userfraction"]])) / 100
			temp <-  melt(temp, measure.vars = cols, variable.name = "Parameter", value.name = "Result")
			out <- rbind(out, temp)
		}
	}
	out$Result <- as.numeric(out$Result)
	out$Age <- factor(out$Age, age, ordered = TRUE)
	return(out)
}

# Food consumption FINLAND.xls / Main food categories. http://en.opasnet.org/en-opwiki/index.php/Special:R-tools?id=7YsAYvWNMy7wRqIR

objects.get("7YsAYvWNMy7wRqIR")

fooddata1 <- importFoodTables(
	obs = list(c(5:22, 25:33, 35:37), c(40:57, 60:68, 70:72)),
	block =  list(10:17, 26:33, 42:49, 74:81, 82:89, 90:97, 98:105),
	age =  c("1", "3", "6", "25-34", "35-44", "45-54", "55-64")
)

# Food consumption FINLAND.xls / Fish categories. http://en.opasnet.org/en-opwiki/index.php/Special:R-tools?id=E03tfupLAj1MNRur

objects.get("E03tfupLAj1MNRur")

fooddata2 <- importFoodTables(
	obs = list(4:16, 19:31),
	block =  list(3:10, 12:19, 21:28, 30:37, 39:46, 48:55, 57:64, c(65, 65:71), 73:80, 82:89, 91:98, 100:107, 109:116, 
	118:125, 127:134, 136:143, 151:158, 160:167, 169:176, 178:185),
	age =  c("6 mo", "7 mo", "8 mo", "9 mo", "10 mo", "11 mo", "1", "7-11 mo", "2", "3", "4-5", "6-9", "10-13", "14-17", 
	"18-24", "25-34", "35-44", "45-54", "55-64", "65-74")
)

foodFI <- rbind(fooddata1, fooddata2)

oprint(head(foodFI))

temp <- fooddata2
temp <- temp[temp$Parameter == "Fractile0.5" & 
	temp$Food %in% c(
		"Fish and other seafood(by species, these are examples - variation by country):", 
		"Baltic herring (Clupea harengus membras)", 
		"Herring (Clupea harengus)", 
		"Pike (Esox lucius)", 
		"Rainbow trout (Onchorchys mykiss)", 
		"Atlantic salmon (Salmo salar)"
	) , ]

oprint(head(temp))
ggplot(temp, aes(x = Age, y = Result)) + geom_point(aes(colour = Food)) + theme_grey(base_size = 24)

objects.put(foodFI)

cat("Object foodFI saved.\n")

References

↑ Virtanen SM, Aro A. Dietary factors in the aetiology of diabetes. Ann Med 1994;26:469-478. Virtanen et al 1994
↑ Virtanen SM, Knip M. Nutritional risk predictors of beta-cell autoimmunity and type 1 diabetes at a young age. Am J Clin Nutr 2003;78:1053-67. Virtanen et al 2003
↑ DIPP study
↑ File is also in N:\YTOS\Projects\BENERIS\WP2\Datat pyydetyssa muodossa\Food consumption_FINLAND.xls (original data accessible only for the THL))
↑ D18 Subpopulation intakes. The Excel data file is in N:\YMTO\PROJECTS\BENERIS\Admin\Deliverables\D18 Subpopulation intakes\Beneris_D18_subpopulation_intakes_Finland_th.xls.

[1] Virtanen SM, Aro A. Dietary factors in the aetiology of diabetes. Ann Med 1994;26:469-478. Virtanen et al 1994

[2] Virtanen SM, Knip M. Nutritional risk predictors of beta-cell autoimmunity and type 1 diabetes at a young age. Am J Clin Nutr 2003;78:1053-67. Virtanen et al 2003

[3] DIPP study

[4] File is also in N:\YTOS\Projects\BENERIS\WP2\Datat pyydetyssa muodossa\Food consumption_FINLAND.xls (original data accessible only for the THL))

[5] D18 Subpopulation intakes. The Excel data file is in N:\YMTO\PROJECTS\BENERIS\Admin\Deliverables\D18 Subpopulation intakes\Beneris_D18_subpopulation_intakes_Finland_th.xls.

[1]

[2]

[3]

[4]

[5]

@@ Line 4: / Line 4: @@
 [[Category:Beneris]]
 [[Category:Intake]]
+[[Category:Contains R code]]
 {{variable}}
@@ Line 11: / Line 12: @@
 ==Answer==
+* [http://en.opasnet.org/en-opwiki/index.php/Special:R-tools?id=crAJyAlr9taPQlIG Fish intake per age group]
+** [http://194.187.214.42/rtools_server/runs/crAJyAlr9taPQlIG_plot001.png Larger graph]
 <rcode graphics="1">
@@ Line 181: / Line 185: @@
 * [[Food intake in Spain]]
 * [[Food intake in Denmark]]
+* {{#l:Food consumption_FINLAND.xls}}
+* {{#l:Beneris_Food consumption pregnant women_FINLAND_300507_th.xls}}
+* {{#l:Beneris_Food consumption_SPAIN_130407_lr.xls}}
+* {{#l:Fish consumption and POPs_IRELAND.xls}}
+* {{#l:Food consumption_DK.xls}}
 ==References==
 <references/>

Difference between revisions of "Food intake in Finland"

Latest revision as of 15:45, 15 April 2016

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