Food intake in Finland

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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. This was carried out in the university hospitals of Turku, Tampere and Oulu.

• 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 . 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 . 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")

See also

• Food intake in Finland
• Food intake in Spain
• Food intake in Denmark
• Food consumption_FINLAND
• Beneris_Food consumption pregnant women_FINLAND_300507_th
• Beneris_Food consumption_SPAIN_130407_lr
• Fish consumption and POPs_IRELAND
• Food consumption_DK